The Emmons Lake volcanic center is a spatially clustered group of stratovolcanoes and calderas in the southwestern part of the Alaska Peninsula, Alaska. The volcanic center is characterized by several ice- and snow-clad stratovolcanoes located within and along the margins of a nested-caldera complex that includes Emmons Lake. A shieldlike ancestral edifice (ancestral Mount Emmons) is truncated by the caldera complex and forms a broad volcanic platform around the center. The main stratovolcanoes of the Emmons Lake volcanic center are Pavlof Sister, Pavlof Volcano, Little Pavlof, Double Crater, Mount Hague, and Mount Emmons. Several small unnamed cinder cones and vents also are located within Emmons Lake volcanic center and on the east flank of Pavlof Volcano. Many of these cones and vents have been the source of the young lava flows that mantle the floor of the caldera. Pavlof Volcano, in the northeastern part of the Emmons Lake volcanic center, is one of the most historically (that is, the past about 300 years) active volcanoes in Alaska, and eruptions from Pavlof Volcano pose the greatest hazards to the region.
Volcanic rocks of the Emmons Lake volcanic center overlie continental and marine sedimentary rocks of chiefly Late Jurassic to early Tertiary age. The oldest rocks in the area are those of the Naknek Formation, consisting of volcaniclastic sandstone, siltstone, and conglomerate of Late Jurassic age. The southern part of the area includes rocks of the Belkofski Formation, a thick sequence of volcaniclastic sandstone, siltstone, and conglomerate of middle Tertiary age. Lava flows, volcanic breccia, and fluvial volcaniclastic rocks of late Miocene age, which unconformably overlie the Belkofski Formation south of the Emmons Lake volcanic center, are primarily exposed on the islands just south of the Alaska Peninsula.
The Emmons Lake volcanic center was affected multiple times by glaciation associated with the glacier expansion that characterized the Quaternary. Glaciation has played a key role in shaping the present-day landscape, and much of the eruptive history of the Emmons Lake volcanic center has involved interactions with glacier ice. Thus, a brief review of the Quaternary glacial history of the area is provided to establish the physical context for Emmons Lake volcanic center eruptive activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3519","usgsCitation":"Miller, T.P., Waythomas, C.F., Mangan, M.T., Trusdell, F.A., and Calvert, A.T., 2026, Geologic map of the Emmons Lake volcanic center, Alaska: U.S. Geological Survey Scientific Investigations Map 3519, 1 sheet, scale 1:100,000, pamphlet 59 p., https://doi.org/10.3133/sim3519.","productDescription":"Pamphlet: x, 59 p.; 1 Sheet: 49.75 x 31.44 inches; 3 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098480","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":501635,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QN3Y6J","text":"USGS data release","linkHelpText":"Whole-rock compositions of volcanic rocks and deposits in the Emmons Lake volcanic center, Alaska"},{"id":501634,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EN4EF","text":"USGS data release","linkHelpText":"Thin-section data for volcanic rocks and deposits in the Emmons Lake volcanic center, Alaska"},{"id":501604,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3519/sim3519_sheet.pdf","text":"Sheet","size":"19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3519 Sheet"},{"id":501603,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3519/sim3519_pamphlet.pdf","text":"Pamphlet","size":"57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3519 Pamphlet"},{"id":501602,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3519/coverthb.jpg"},{"id":501633,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HUP9CA","text":"USGS data release","linkHelpText":"Geospatial database of the geologic map of the Emmons Lake volcanic center, Alaska"}],"scale":"100000","country":"United States","state":"Alaska","otherGeospatial":"Emmons Lake volcanic center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -162.5,\n 55.75\n ],\n [\n -162.5,\n 55\n ],\n [\n -161.5833,\n 55\n ],\n [\n -161.5833,\n 55.75\n ],\n [\n -162.5,\n 55.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
Igneous activity, including shallow intrusions and volcanism, has the potential to disrupt underground critical infrastructure. Notably, future underground infrastructure projects like high-level radioactive waste repositories must be sited in areas of extremely low disruption probability by igneous activity. In Germany, according to the Repository Site Selection Act of 2017 (Standortauswahlgesetz, or StandAG), areas in which Quaternary volcanism is either present or future volcanic activity is expected within the next 1 million years (m.y.) must be excluded from the site selection process. Although the locations of regions with Quaternary volcanism are reasonably well known in Germany, forecasting potential igneous activity at intraplate volcanic fields is challenging, as many processes and their interactions control the spatial distribution of volcanic centers. Here, a semi-quantitative, multi-criteria approach is proposed for a regional evaluation of the relative potential of future igneous activity in Germany. A variety of geoscientific indicators are used, including seismic anomalies in Earth’s mantle, gravity data, tectonic activity, sutures, ground motion, earthquakes, mantle degassing centers, and geochronological data of volcanic rocks. The indicators describe the sequence of processes from potential melt generation in Earth’s mantle, through ascent and accumulation of melt within the lithosphere, to eruption at Earth’s surface. In total, 15 out of 30 proposed geoscientific indicators are selected and quantified using 20 total assigned parameters. Defined threshold values are used to spatially delimit relevant parameter properties to focus on areas with higher potential of future magmatic activity. To consider uncertainties of parameters and their underlying processes, which are usually more spatially extensive below ground, buffer zones are defined in which values of relevance decrease with increasing distance from the initial lateral shape of a parameter. Normalized parameters are combined into an index, whose spatial value distribution is used to differentiate the relative potential of future igneous activity (within the next 1 m.y.). The sensitivity of the results is shown by varying the weighting factors for the relevant parameters in country-wide index maps. Thereby, profiles illustrate the distribution of the resulting index values and respective index fractions of various parameters. Different index maps for the relative potential of future igneous activity are presented and can be used for hazard assessments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1890C","usgsCitation":"Bartels, A., Rummel, L., and May, F., 2026, Forecasting igneous activity in Germany—A multi-criteria approach, chap. C of Poland, M.P., Ort, M.H., Stovall, W.K., Vaughan, R.G., Connor, C.B., and Rumpf, M.E., eds., Distributed volcanism—Characteristics, processes, and hazards: U.S. Geological Survey Professional Paper 1890, 42 p., https://doi.org/10.3133/pp1890C.","productDescription":"iv, 42 p.","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-154535","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":500698,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1890/c/images"},{"id":500696,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/pp1890C/full","linkFileType":{"id":5,"text":"html"},"description":"Professional Paper 1890-C HTML"},{"id":500695,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1890/c/pp1890C.pdf","text":"Report","size":"22.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1890-C PDF"},{"id":500694,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1890/c/coverthb.jpg"},{"id":500697,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1890/c/pp1890C.XML","linkFileType":{"id":8,"text":"xml"},"description":"Professional Paper 1890-C XML"}],"country":"Germany","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[9.92191,54.9831],[9.93958,54.59664],[10.95011,54.36361],[10.93947,54.00869],[11.95625,54.19649],[12.51844,54.47037],[13.64747,54.07551],[14.11969,53.75703],[14.35332,53.24817],[14.07452,52.98126],[14.4376,52.62485],[14.68503,52.08995],[14.6071,51.74519],[15.017,51.10667],[14.57072,51.00234],[14.30701,51.11727],[14.05623,50.92692],[13.33813,50.73323],[12.96684,50.48408],[12.24011,50.26634],[12.41519,49.96912],[12.52102,49.54742],[13.03133,49.30707],[13.59595,48.87717],[13.24336,48.41611],[12.8841,48.28915],[13.02585,47.63758],[12.93263,47.46765],[12.62076,47.67239],[12.14136,47.70308],[11.42641,47.52377],[10.5445,47.5664],[10.40208,47.30249],[9.89607,47.5802],[9.59423,47.52506],[8.52261,47.83083],[8.3173,47.61358],[7.46676,47.62058],[7.59368,48.33302],[8.09928,49.01778],[6.65823,49.20196],[6.18632,49.4638],[6.24275,49.90223],[6.04307,50.12805],[6.15666,50.80372],[5.98866,51.85162],[6.5894,51.85203],[6.84287,52.22844],[7.09205,53.14404],[6.90514,53.48216],[7.10042,53.69393],[7.93624,53.7483],[8.12171,53.52779],[8.80073,54.02079],[8.57212,54.39565],[8.52623,54.96274],[9.28205,54.83087],[9.92191,54.9831]]]},\"properties\":{\"name\":\"Germany\"}}]}","contact":"Director, Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
1. Rising timber demand is transforming forest structure globally, profoundly affecting biodiversity and climate resilience. Logging-driven fragmentation is potentially a major driver of biodiversity loss in production landscapes, yet its interactions with escalating climate stressors remain poorly understood.
2. We combine two decades of Landsat-derived habitat metrics with 29,000 surveys of the marbled murrelet (Brachyramphus marmoratus)—an iconic Pacific Northwest old-forest specialist seabird affecting management of >10 million hectares. Controlling for habitat amount and detection probability, increasing landscape-scale forest edge amount sharply reduces murrelet occupancy, with impacts worsening under unfavourable climate-driven ocean conditions.
3. Comparing alternative landscape-scale timber harvest strategies, spatially concentrated logging consistently supports higher murrelet populations than fragmented approaches producing equivalent wood volumes, with benefits amplified under adverse ocean conditions. However, historical harvesting policies in the Pacific Northwest have instead driven severe habitat fragmentation, which we show is eroding the value of core set-aside forests on federal and conservation lands and ultimately rendering murrelets more vulnerable to climate change.
4. Synthesis and applications: We map key opportunities to boost populations by reducing edginess around remaining nesting habitat and investigate these opportunities' spatial distribution across land ownership and timber productivity gradients. Concentrating logging could be critical for mitigating fragmentation and climate threats for murrelets and potentially other forest-dependent species amid rising timber demand.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.70317","usgsCitation":"Cerullo, G., Gannon, D., Bailey Guerrero, J.A., Conklin, E., Kohlberg, A., Nelson, K., Rivers, J.W., Valente, J., Yang, Z., and Betts, M.G., 2026, Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird: Journal of Applied Ecology, v. 63, no. 2, e70317, 15 p., https://doi.org/10.1111/1365-2664.70317.","productDescription":"e70317, 15 p.","ipdsId":"IP-181232","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502091,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.70317","text":"Publisher Index Page"},{"id":502015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Pacific Northwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -126.62346834330545,\n 49.423390089555795\n ],\n [\n -125.05731376141374,\n 37.49095069699514\n ],\n [\n -119.79362574221338,\n 38.47443712695113\n ],\n [\n -119.85861425224797,\n 41.78784262090305\n ],\n [\n -116.86760646031209,\n 41.957392910252224\n ],\n [\n -117.25973955716492,\n 49.423390089555795\n ],\n [\n -126.62346834330545,\n 49.423390089555795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"63","issue":"2","noUsgsAuthors":false,"publicationDate":"2026-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Cerullo, Gianluca","contributorId":369152,"corporation":false,"usgs":false,"family":"Cerullo","given":"Gianluca","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gannon, Dusty","contributorId":369153,"corporation":false,"usgs":false,"family":"Gannon","given":"Dusty","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bailey Guerrero, Jennifer A.","contributorId":369154,"corporation":false,"usgs":false,"family":"Bailey Guerrero","given":"Jennifer","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conklin, Emily","contributorId":369155,"corporation":false,"usgs":false,"family":"Conklin","given":"Emily","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kohlberg, Anna Bloch","contributorId":369156,"corporation":false,"usgs":false,"family":"Kohlberg","given":"Anna Bloch","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958548,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Kim","contributorId":92810,"corporation":false,"usgs":false,"family":"Nelson","given":"Kim","affiliations":[],"preferred":false,"id":958549,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rivers, James W.","contributorId":369162,"corporation":false,"usgs":false,"family":"Rivers","given":"James","middleInitial":"W.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958550,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958551,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yang, Zhiqiang","contributorId":219468,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","affiliations":[{"id":27560,"text":"PNNL","active":true,"usgs":false}],"preferred":false,"id":958552,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":" Betts, Matthew G.","contributorId":369163,"corporation":false,"usgs":false,"family":" Betts","given":"Matthew","middleInitial":"G.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958553,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70274559,"text":"70274559 - 2026 - Stepovers and beyond: Structural control of The Geysers geothermal system and the broader Clear Lake region","interactions":[],"lastModifiedDate":"2026-03-30T16:05:54.109213","indexId":"70274559","displayToPublicDate":"2026-02-09T10:47:36","publicationYear":"2026","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Stepovers and beyond: Structural control of The Geysers geothermal system and the broader Clear Lake region","docAbstract":"Fault geometry exerts a first-order control on geothermal systems by governing stress localization, fracture development, and permeability, yet in complex fault networks or broader shear zones, the relative influence of individual geometric features is often difficult to resolve. In the northern California Coast Ranges, The Geysers geothermal field is commonly interpreted to occur within a releasing stepover, although no single, clearly defined stepover is identified in published studies. To investigate the structural controls on The Geysers and the broader Clear Lake region, a two-dimensional elastic boundary element model is developed to evaluate spatial patterns of dilational strain associated with progressively more complete fault geometries. Model results show that dilation in the region is not controlled by a single structure but instead reflects the combined effects of multiple interacting fault elements. Three primary controls are identified: (1) opposing bends in the regional strike-slip fault system, including a releasing bend along the Maacama fault; (2) the southern fault tip of the Collayomi fault, which generates a prominent dilational lobe beneath the southern Geysers; and (3) a releasing stepover between the Collayomi fault and the Geyser Peak–Mercuryville–Big Sulphur Creek fault system, inferred to collectively behave as a right-lateral shear zone bounding the western margin of The Geysers. Predicted dilational strain magnitudes are sufficient to localize permeability between faults. These results highlight that incorporating complete fault networks and bedrock geological mapping can enhance geothermal assessments and provide a transferable framework for evaluating structurally controlled permeability in tectonically active regions.
","conferenceTitle":"51st Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 9-11, 2026","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford","usgsCitation":"Melosh, B.L., 2026, Stepovers and beyond: Structural control of The Geysers geothermal system and the broader Clear Lake region, 51st Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 9-11, 2026, 10 p.","productDescription":"10 p.","ipdsId":"IP-185865","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":501817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501816,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=38346"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake region, Geysers geothermal system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.08882992251972,\n 39.16306777840023\n ],\n [\n -123.08882992251972,\n 38.698271344676584\n ],\n [\n -122.51130712624908,\n 38.698271344676584\n ],\n [\n -122.51130712624908,\n 39.16306777840023\n ],\n [\n -123.08882992251972,\n 39.16306777840023\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2026-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Melosh, Benjamin L. 0000-0002-8017-7193","orcid":"https://orcid.org/0000-0002-8017-7193","contributorId":217215,"corporation":false,"usgs":true,"family":"Melosh","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":958309,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70274099,"text":"70274099 - 2026 - ENSO and PDO drive shoreline position anomalies in the U.S. Pacific Northwest","interactions":[],"lastModifiedDate":"2026-02-25T15:35:31.584463","indexId":"70274099","displayToPublicDate":"2026-01-09T08:26:28","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10942,"text":"PNAS Nexus","active":true,"publicationSubtype":{"id":10}},"title":"ENSO and PDO drive shoreline position anomalies in the U.S. Pacific Northwest","docAbstract":"Sandy beaches act as buffers against various coastal hazards but are vulnerable to episodic (seasonal) and chronic (interannual) erosion. Understanding the variation of shoreline position, a key metric in coastal morphology, over a spectrum of time scales is therefore crucial in assessing hazard vulnerability. Long-standing research has investigated the role of El Niño-Southern Oscillation (ENSO), the dominant mode of climate variability in the Pacific Basin, in seasonal shoreline variability. Yet, ENSO’s chronic influence—and that of another Pacific climate mode, the Pacific Decadal Oscillation (PDO)—on shoreline anomalies remains poorly understood. Here, we examine the variability of sandy beaches in the US Pacific Northwest, a ∼750 km long coastal region on the US West Coast. We leverage 40 years (1984–2024) of shoreline data from publicly available Earth-observing (Landsat) satellite imagery at a high spatial resolution (>10,000 shore-normal transects at 50-m alongshore spacing) and employ Convergent Cross Mapping (CCM), a methodology for inferring causality in dynamical systems. We discover that strong El Niño years are signified by erosion (75.1% of transects), and strong La Niña years exhibit accretional behavior (73.4% of transects). Furthermore, we establish, for the first time, that both ENSO and PDO exert a statistically significant control on interannual shoreline variability, particularly on the alongshore component (in 95 and 100% of littoral cells, respectively), with water level fluctuations playing a critical role. This effort advances our understanding of the seasonal-to-interannual interactions between Pacific Basin climate variability and the PNW’s coastal morphodynamics, with implications for sediment management and coastal adaptation.
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U.S. Geological Survey (USGS) Mission—The USGS national mission is to monitor, analyze, and predict the current and evolving dynamics of complex human and natural Earth-system interactions and to deliver actionable information at scales and timeframes relevant to decision makers. Consistent with the national mission, the USGS in Alaska provides timely and objective scientific information to help address issues and inform management decisions across five interconnected focus areas:
The USGS in Alaska consists of approximately 350 scientists and support staff working in 3 Alaska-based science centers. USGS science activities are also initiated by the Cooperative Research Unit and USGS centers outside Alaska. In the last 5 years, USGS research in Alaska has produced many scientific benefits resulting from more than 900 publications. Publications relevant to Alaska can be conveniently searched by keyword through the USGS Publications Warehouse at https://pubs.usgs.gov/.
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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The ability to accurately map erosion, flooding, and habitat loss in coastal environments is crucial for formulating national strategies aimed at preventing and mitigating the impacts of natural disasters. A fundamental component of this process is the implementation of coastal morphodynamics monitoring through Structure-from-Motion (SfM) techniques, utilizing high-resolution 2D/3D data obtained from aerial photogrammetry. To assess morphodynamic changes over a three-year period (2022 – 2024), several SfM-based photogrammetric studies were conducted, each year, in the Romanian sector of the Danube-Black Sea coastal zone, specifically at the mouth of one of the Danube River distributaries (Sf Gheorghe branch) into the Black Sea, and along the left bank, near Sf Gheorghe locality, located within the Danube Delta Biosphere Reserve (DDBR). The essential equipment for aerial photogrammetry comprises Unmanned Aerial Vehicles (UAVs) and Global Navigation Satellite Systems (GNSS). In this study, the UAV used was a DJI Mavic 3T (Enterprise/Thermal) drone, complemented by two Trimble R12i and R4 GNSS systems, as well as approximately 10 Ground Control Points (GCPs). Data acquisition and processing were carried out using specialized photogrammetric software (Agisoft Metashape) along with various GIS tools (e.g., Blue Marble Geographics Global Mapper and ESRI ArcMap). The photogrammetric products generated for the study, as detailed in this paper, include Digital Elevation Models (DEMs), Digital Terrain Models (DTMs), orthomosaics (orthophotos), and others. At Sfântu Gheorghe beach, a comparison between 2023 and 2024 photogrammetric surveys revealed that the left bank of the Sf. Gheorghe Arm, at the river mouth into the Black Sea, suffered from a twist (erosion) of up to 64 metres. Additionally, on the selected perimetre (total area of 31,910 square meters ) from the beach and dune zone of Sf. Gheorghe, an area of up to 16,202 square meters was eroded between 2023 and 2024. This contrasts with the period between 2022 and 2023, during which deposition predominated. Erosion at the Danube mouths and the adjacent Black Sea coastline is driven by a complex interaction of natural and anthropogenic factors. Natural processes, including subsidence, sea-level rise, and episodic extreme storm events, contribute significantly to coastal dynamics. Meanwhile, human-induced factors, such as upstream hydrotechnical works that limits sediment transport, cutting of navigation canals, as well as the exacerbating effects of climate change, further accelerate erosion. The recent Structure-from-Motion (SfM) surveys provide essential quantitative data, enabling a detailed analysis of both short-term and long-term morphodynamic changes influenced by seasonal variations and extreme hydrometeorological events in this highly dynamic coastal system.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of Inżynieria Mineralna WMCEES 2025","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Polish Mineral Engineering Society","doi":"10.29227/IM-2025-02-03-15","usgsCitation":"Dragos, A.G., Iordache, G., Dutu, F., Palaseanu-Lovejoy, M., Pitea, F., Stanciu, I., and Stanica, A., 2025, Assessment of coastal and fluvial morphodynamic changes using Structure-for-Motion: A case study of the Sfȃntu Gheorghe Mouth (Danube Delta, Romania), in Proceedings of Inżynieria Mineralna WMCEES 2025, v. 3, no. 2, 9 p., https://doi.org/10.29227/IM-2025-02-03-15.","productDescription":"9 p.","ipdsId":"IP-183130","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":498701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.29227/im-2025-02-03-15","text":"Publisher Index Page"},{"id":498610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Romania","otherGeospatial":"Sfȃntu Gheorghe Mouth (Danube Delta)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 29.533907040308605,\n 44.932418076703044\n ],\n [\n 29.533907040308605,\n 44.86262917846662\n ],\n [\n 29.630386502266845,\n 44.86262917846662\n ],\n [\n 29.630386502266845,\n 44.932418076703044\n ],\n [\n 29.533907040308605,\n 44.932418076703044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Dragos, Andrei Gabriel 0000-0003-3268-8615","orcid":"https://orcid.org/0000-0003-3268-8615","contributorId":365121,"corporation":false,"usgs":false,"family":"Dragos","given":"Andrei","middleInitial":"Gabriel","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iordache, Gabriel 0000-0002-8335-5995","orcid":"https://orcid.org/0000-0002-8335-5995","contributorId":365122,"corporation":false,"usgs":false,"family":"Iordache","given":"Gabriel","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dutu, Florin 0000-0002-5393-3125","orcid":"https://orcid.org/0000-0002-5393-3125","contributorId":365123,"corporation":false,"usgs":false,"family":"Dutu","given":"Florin","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":305576,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","affiliations":[],"preferred":true,"id":953728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pitea, Florin 0009-0007-0206-5886","orcid":"https://orcid.org/0009-0007-0206-5886","contributorId":365124,"corporation":false,"usgs":false,"family":"Pitea","given":"Florin","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953729,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanciu, Irina 0000-0003-1842-6619","orcid":"https://orcid.org/0000-0003-1842-6619","contributorId":365125,"corporation":false,"usgs":false,"family":"Stanciu","given":"Irina","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953730,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stanica, Adrian 0000-0001-5983-6302","orcid":"https://orcid.org/0000-0001-5983-6302","contributorId":351791,"corporation":false,"usgs":false,"family":"Stanica","given":"Adrian","affiliations":[{"id":84044,"text":"GeoEcoMar National Research institute, Romania","active":true,"usgs":false}],"preferred":false,"id":953731,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273139,"text":"70273139 - 2025 - Assessing flood water infiltration and storage in a restored floodplain","interactions":[],"lastModifiedDate":"2025-12-16T15:30:48.761523","indexId":"70273139","displayToPublicDate":"2025-10-05T09:20:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23098,"text":"Hydological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Assessing flood water infiltration and storage in a restored floodplain","docAbstract":"In urban areas, floodplain restoration is gaining prominence as a strategy for restoring the natural functions of floodplain ecosystems and reducing flood risk. This has spurred research into potential interactions between floodwaters, the hyporheic zone, and the floodplain aquifer. An urban restored stream in Wisconsin, USA, was used as a case study to examine four methods to estimate floodplain infiltration and storage during overbank floods. We characterised flood-related infiltration over a 4-year period from 2018 through 2021 by simultaneously and continuously measuring groundwater levels and vertical temperature profiles with stream water levels linked to high-resolution flood inundation maps. High-resolution topographic data helped to quantify surface floodplain storage and the unsaturated soil volume relative to flood stage. Infiltration estimates from the simple methods align well with those from the more complex methods; however, the complex methods provide additional insights about the factors influencing infiltration. Results from all methods indicate that the volume of water that vertically infiltrates during floods is likely small relative to the total volume of the flood, with 0.08%–0.52% of flood water infiltrating into the floodplain, on average. Spatially variable vertical hydraulic gradients, driven by flood depth, groundwater level, and permeability, imply heterogeneous patterns of infiltration across the floodplain. Gradients favourable for infiltration typically occurred during the onset of flooding but, over the study period, were mostly (98% of the time) favourable for groundwater discharge to the channel (non-flood periods). These findings highlight the importance of considering surface-groundwater dynamics, floodplain soils, and unsaturated floodplain volume in defining the benefits of floodplain infiltration for flood attenuation.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70281","usgsCitation":"Corson-Dosch, N., Fitzpatrick, F., Juckem, P., Blount, J.D., and Ha, W.S., 2025, Assessing flood water infiltration and storage in a restored floodplain: Hydological Processes, v. 39, no. 10, e70281, 18 p., https://doi.org/10.1002/hyp.70281.","productDescription":"e70281, 18 p.","ipdsId":"IP-141807","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70281","text":"Publisher Index Page"},{"id":497570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Underwood Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.047778,\n 43.047222\n ],\n [\n -88.047778,\n 43.0375\n ],\n [\n -88.043333,\n 43.0375\n ],\n [\n -88.043333,\n 43.047222\n ],\n [\n -88.047778,\n 43.047222\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Corson-Dosch, Nicholas 0000-0002-6776-6241","orcid":"https://orcid.org/0000-0002-6776-6241","contributorId":202630,"corporation":false,"usgs":true,"family":"Corson-Dosch","given":"Nicholas","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952428,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209588,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952429,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Juckem, Paul 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":214445,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blount, James D. 0000-0002-0006-3947 jblount@usgs.gov","orcid":"https://orcid.org/0000-0002-0006-3947","contributorId":200231,"corporation":false,"usgs":true,"family":"Blount","given":"James","email":"jblount@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952431,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ha, Wonsook S. 0000-0002-7252-698X","orcid":"https://orcid.org/0000-0002-7252-698X","contributorId":266139,"corporation":false,"usgs":true,"family":"Ha","given":"Wonsook","email":"","middleInitial":"S.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":952432,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272174,"text":"70272174 - 2025 - Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain","interactions":[],"lastModifiedDate":"2025-11-18T15:48:07.770771","indexId":"70272174","displayToPublicDate":"2025-09-25T09:44:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain","docAbstract":"Floodplain forests are being transformed by multiple pressures, prompting widespread management and restoration efforts. It is uncertain how disturbances, including hydrologic change, and management actions will interact to influence the ecology of these threatened forests.
This study examined the effects of alternative management and hydrologic regimes on forest succession at an Upper Mississippi River floodplain site with a restoration project in planning.
We used the spatially explicit forest landscape model, LANDIS-II, to simulate forest succession for 100 years under four hydrogeomorphic management scenarios, three forest management scenarios, and two scenarios of future hydrologic conditions. We evaluated changes in forest biomass and composition over time and assessed the relative importance of management actions and hydrologic change on succession.
Forest aboveground biomass decreased in all management-hydrology scenarios, especially in the wetter hydrological scenario. Intensified hydrogeomorphic and forest management scenarios reduced the magnitude and extent of biomass declines; however, they were unable to prevent overall declines in biomass or cause large shifts in tree species composition. Silver maple (Acer saccharinum) was projected to decrease in biomass, while increases in biomass were projected for several late-successional species including swamp white oak (Quercus bicolor). Among the factors influencing variation in biomass, forest management had the largest influence in the first 50 years of our simulations, but hydrological regime became the most important factor by the end of the century.
Our simulations indicate that management actions could play an important role in the conservation of floodplain forests, but their effectiveness will likely be limited if recent upward trends in flooding conditions in this system continue in the future. Thus, our results highlight both the potential benefits and limitations of management actions in the face of hydrologic change.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-025-02144-7","usgsCitation":"Trumper, M., De Jager, N.R., Van Appledorn, M., and Meier, A.R., 2025, Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain: Landscape Ecology, v. 40, 186, 21 p., https://doi.org/10.1007/s10980-025-02144-7.","productDescription":"186, 21 p.","ipdsId":"IP-173189","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":496735,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-025-02144-7","text":"Publisher Index Page"},{"id":496589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","otherGeospatial":"Reno Bottoms study area, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.39940329445804,\n 43.68624998546463\n ],\n [\n -91.39940329445804,\n 43.44291861394157\n ],\n [\n -91.13016274447915,\n 43.44291861394157\n ],\n [\n -91.13016274447915,\n 43.68624998546463\n ],\n [\n -91.39940329445804,\n 43.68624998546463\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2025-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Trumper, Matthew L. 0000-0002-9881-7742","orcid":"https://orcid.org/0000-0002-9881-7742","contributorId":357508,"corporation":false,"usgs":true,"family":"Trumper","given":"Matthew","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meier, Andrew R.","contributorId":362320,"corporation":false,"usgs":false,"family":"Meier","given":"Andrew","middleInitial":"R.","affiliations":[{"id":16919,"text":"U.S. Army Corps of Engineers, St. Paul District","active":true,"usgs":false}],"preferred":false,"id":950316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274071,"text":"70274071 - 2025 - Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities","interactions":[],"lastModifiedDate":"2026-02-23T15:34:30.951807","indexId":"70274071","displayToPublicDate":"2025-09-20T09:23:24","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"title":"Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities","docAbstract":"There is increasing awareness of the global diversity of tidal forested wetlands (TFWs) and their significance in the provision of ecosystem services. These ecosystems, including mangrove forests, tidal freshwater forested wetlands, supratidal forests and transitional forests together span multiple climatic zones, geomorphic settings, and inundation and salinity regimes. We utilise case studies across five continents to demonstrate the state of knowledge among TFWs. Intertidal mangroves are the best-defined of the TFWs thanks to decades of research on their geomorphology, hydrology and ecology across their broad distribution. Non-mangrove forest settings, however, demonstrate more diverse hydrological, biochemical and vegetation conditions. In many cases, non-mangrove forests are situated at upper intertidal or supratidal elevations, where surface waters and groundwater are subject to interactions between tides freshwater inputs. Salinity datasets show variations ranging from tidal freshwater forested wetlands and ‘low-salinity mangroves’ to mesohaline or marine salinities, often with high temporal variability. While the floristic composition of non-mangrove forests vary among biogeographic regions, locally dominant TFW species are commonly distributed beyond the tidal niche into non-tidal wetland and upland forests. This presents challenges for traditional remote sensing approaches to ecosystem mapping, which are mostly lacking for non-mangrove forests. Geomorphic approaches and developments in machine learning offer opportunities to address this.
","language":"English","publisher":"Earth ArXiv","doi":"10.31223/X5ZF2B","usgsCitation":"Kelleway, J.J., Noe, G.E., Krauss, K., Brophy, L., Conner, W.H., Duberstein, J.A., Friess, D.A., Gedan, K., White, E., Adame, M.F., Adams, J.B., Carvalho, R.C., Freddie, A., Ikenna, I.N., Ocasio, E.R., Owers, C.J., Sasmito, S., Swales, A., Stewart-Sinclair, P., Ward, R.D., Zabarte-Maeztu, I., 2025, Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities, preprint posted September 20, 2025, https://doi.org/10.31223/X5ZF2B.","productDescription":"85 p.","ipdsId":"IP-185904","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":500404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelleway, J. J.","contributorId":366945,"corporation":false,"usgs":false,"family":"Kelleway","given":"J.","middleInitial":"J.","affiliations":[{"id":37474,"text":"University of Wollongong","active":true,"usgs":false}],"preferred":false,"id":956418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":956419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":210857,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":956420,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brophy, L.S.","contributorId":366946,"corporation":false,"usgs":false,"family":"Brophy","given":"L.S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":956421,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conner, W. H.","contributorId":366947,"corporation":false,"usgs":false,"family":"Conner","given":"W.","middleInitial":"H.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":956422,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duberstein, J. A.","contributorId":366948,"corporation":false,"usgs":false,"family":"Duberstein","given":"J.","middleInitial":"A.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":956423,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Friess, D. 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F.","contributorId":366952,"corporation":false,"usgs":false,"family":"Adame","given":"M.","middleInitial":"F.","affiliations":[{"id":7117,"text":"Griffith University","active":true,"usgs":false}],"preferred":false,"id":956427,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Adams, J. B.","contributorId":366953,"corporation":false,"usgs":false,"family":"Adams","given":"J.","middleInitial":"B.","affiliations":[{"id":68971,"text":"Nelson Mandela University","active":true,"usgs":false}],"preferred":false,"id":956428,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Carvalho, R. 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R.","contributorId":366959,"corporation":false,"usgs":false,"family":"Ocasio","given":"E.","middleInitial":"R.","affiliations":[{"id":68971,"text":"Nelson Mandela University","active":true,"usgs":false}],"preferred":false,"id":956432,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Owers, C. J.","contributorId":366960,"corporation":false,"usgs":false,"family":"Owers","given":"C.","middleInitial":"J.","affiliations":[{"id":87511,"text":"University of Newcastle","active":true,"usgs":false}],"preferred":false,"id":956433,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sasmito, S.","contributorId":366961,"corporation":false,"usgs":false,"family":"Sasmito","given":"S.","affiliations":[{"id":40403,"text":"James Cook University","active":true,"usgs":false}],"preferred":false,"id":956434,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Swales, A.","contributorId":366962,"corporation":false,"usgs":false,"family":"Swales","given":"A.","affiliations":[{"id":40175,"text":"National Institute of Water and Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":956435,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Stewart-Sinclair, P.","contributorId":366963,"corporation":false,"usgs":false,"family":"Stewart-Sinclair","given":"P.","affiliations":[{"id":40175,"text":"National Institute of Water and Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":956436,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Ward, R. D.","contributorId":366964,"corporation":false,"usgs":false,"family":"Ward","given":"R.","middleInitial":"D.","affiliations":[{"id":35299,"text":"Queen Mary University of London","active":true,"usgs":false}],"preferred":false,"id":956437,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Zabarte-Maeztu, I.","contributorId":366965,"corporation":false,"usgs":false,"family":"Zabarte-Maeztu","given":"I.","affiliations":[{"id":87514,"text":"National Institute of Water and Atmospheric Research,","active":true,"usgs":false}],"preferred":false,"id":956438,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70269818,"text":"ofr20251044 - 2025 - Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","interactions":[{"subject":{"id":70269818,"text":"ofr20251044 - 2025 - Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","indexId":"ofr20251044","publicationYear":"2025","noYear":false,"displayTitle":"Insights and Strategic Opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","title":"Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop"},"predicate":"IS_ADDENDUM_TO","object":{"id":70226853,"text":"cir1490 - 2021 - Integrated science for the study of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in the environment—A strategic science vision for the U.S. Geological Survey","indexId":"cir1490","publicationYear":"2021","noYear":false,"title":"Integrated science for the study of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in the environment—A strategic science vision for the U.S. Geological Survey"},"id":1}],"lastModifiedDate":"2026-02-03T15:00:44.018949","indexId":"ofr20251044","displayToPublicDate":"2025-08-11T13:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1044","displayTitle":"Insights and Strategic Opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","title":"Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop","docAbstract":"In 2021, the U.S. Geological Survey (USGS) published Circular 1490 titled, “Integrated Science for the Study of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Environment: A Strategic Science Vision for the U.S. Geological Survey” (Tokranov and others, 2021). Circular 1490 was created to be a resource for USGS scientists prioritizing and planning research related to per- and polyfluoroalkyl substances (PFAS) and to be a guide for developing partnerships with other scientists, State and Federal agencies, and stakeholders engaged in PFAS research and management and mitigation of the environmental and human-health effects of PFAS. This USGS PFAS Strategic Science Vision document was intended to be the foundation for a “living strategic vision,” periodically providing updates on the state of USGS PFAS research, emerging PFAS data gaps and needs, and progress on interagency and stakeholder PFAS partnerships and priorities. To meet this objective, the USGS planned to host an Interagency and Stakeholder PFAS Workshop every 2–3 years.
During September 10–12, 2024, the USGS hosted the first Interagency and Stakeholder PFAS Workshop in Reston, Virginia. The Workshop brought together experts from other Federal agencies (U.S. Environmental Protection Agency, National Institute of Environmental Health Sciences, Food and Drug Administration, Department of Defense [Air Force, Army]), State agencies (Washington Fish and Wildlife, Virginia Department of Transportation), and academia (Harvard University, University of Maryland) to address key challenges relating to the measurement and modeling of PFAS and the implications for environmental health. Participants engaged in in-depth discussions centered around six pivotal topics related to PFAS: (1) sampling protocols, methods and interpretation; (2) environmental sources, source apportionment, and occurrence; (3) environmental fate and transport; (4) human and wildlife exposure routes and risk; (5) bioconcentration, bioaccumulation, and biomagnification; and (6) ecotoxicology and effects. Each topic had three breakout sessions.
A recurrent theme of workshop discussions was how data on a nationwide scale for PFAS occurrence in various environmental matrices, including air, water, food crops, biota, soil, and streambed sediment could help to advance scientific understanding. Participants noted significant geospatial data gaps, particularly in the midwestern and southern United States and the Pacific Northwest. PFAS data collection tends to be more robust along the eastern seaboard and in California.
Participants stressed how enhancing the integration of large and small datasets across various agencies could help to support national scale understanding of PFAS. To address these gaps, attendees suggested leveraging datasets from Federal entities like the USGS and the U.S. Department of Defense, State agencies, and municipal utility services to develop predictive contaminant detection and transport models. Improved coordination between water quality programs and USGS research could help to facilitate access to valuable data, leading to comprehensive databases that inform PFAS point (wastewater treatment plants and landfills) and nonpoint (runoff from land, atmospheric deposition, food packaging) sources, environmental transport mechanisms, environmental detection and concentrations, potential exposure routes, and health effects on different biota, including humans. A specific request was made to develop a map demarking the depth of modern (1953 or later) groundwater, which is susceptible to surface-derived anthropogenic (that is, human-made) contamination, based on tritium-age dating. Emphasis was placed on incorporation of hydrology, groundwater flow paths, groundwater–surface water interactions, and landscape factors in predictive statistical models as a step to improve contaminant source identification and tracking.
Molecular fingerprinting approaches garnered attention as techniques to link specific PFAS mixtures detected in a sample to environmental sources and levels in biota (Dávila-Santiago and others, 2022). Integrating data from abiotic (that is, water, soil, and air) and biotic (that is, living organisms) systems identified as a research opportunity. For example, understanding the composition of soils and sediments, which include a mixture of mineral, plant, and animal components, could advance understanding of exposure pathways.
The discussions highlighted opportunities to explore and understand the potential redistribution and biotic exposures of PFAS from biosolid and wastewater treatment plant effluent land application practices, in addition to atmospheric releases and discharges from landfill and wastewater treatment plants. Participants identified research gaps surrounding how these sources may contribute to contamination and may affect surrounding ecosystems, including a better definition of anthropogenic background concentrations.
Moving forward, the collection of co-occurrence data was noted as a means to improve understanding of complex mixtures and to leverage companion modeling efforts focused on areas with high and low contamination levels to identify areas of concern and unaffected resources. Participants emphasized how centralized USGS databases and the establishment of sample-metadata archives can help to ensure that samples are preserved and accessible for future research.
In conclusion, the workshop participants identified opportunities to bridge data gaps and improve measurement techniques, modeling frameworks, databases, and communication, to enhance the understanding of PFAS and their effects on environmental and human health. Upon completion of the workshop, participants indicated an interest in developing strategic data collection, modeling, and analytical approaches to address these challenges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251044","programNote":"Environmental Health Program","usgsCitation":"Iwanowicz, D.D., Beisner, K.R., Bradley, P.M., Bright, P.R., Brown, J.B., Churchill, C.J., Gordon, S.E., Karouna, N.K., Kolpin, D.W., Lambert, R.B., Pulster, E.L., Shively, R.S., Smalling, K., Steevens, J.A., and Tokranov, A.K., 2025, Insights and strategic opportunities from the USGS 2024 Per- and Polyfluoroalkyl Substances (PFAS) Interagency Workshop—Addendum I of Circular 1490: U.S. Geological Survey Open-File Report 2025–1044, 10 p., https://doi.org/10.3133/ofr20251044.","productDescription":"iii, 10 p.","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-177608","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":493438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1044/coverthb.jpg"},{"id":493439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1044/ofr20251044.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1044 PDF"},{"id":493440,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1044 HTML"},{"id":493442,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1044/images/"},{"id":493441,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1044/ofr20251044.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1044 XML"}],"contact":"Director, Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, Virginia 20192
Landsliding in river valleys poses unique risks for cascading hazards and can damage infrastructure and cause fatalities. In postglacial valleys, many landslides are posited to occur in relation to lateral river erosion, but the dynamics of fluvial-hillslope interactions are not well understood. Here, we investigate a section of the Nooksack River in western Washington State where the channel is flanked by landslide-prone glacial terraces similar to those that failed in the 2014 State Route 530 “Oso” landslide. We map 216 landslides through time across 17 aerial imagery data sets (1933–2022) and analyze them in relation to river meandering and curvature. We observe dynamic feedbacks between lateral river meandering and valley-adjacent landsliding. Terrace lateral retreat rates of up to 25 m/year owing to combined fluvial erosion and slope failure occur on pinned, outer meander bends immediately downstream from peaks in river curvature (>0.0075 1/m); these locations are predisposed to both shallow and deep-seated landslides. Deep-seated landslides extending 17%–32% of the active valley width into the floodplain can displace the river away from the floodplain margin and change the channel planform. River-displacing landslides relocate meanders up- or downstream, thereby conditioning the location of subsequent landslides. This conceptual model of coupled landslide-driven meander displacement and valley-adjacent landsliding is exemplified across western Washington river systems. The distance between up- and downstream valley-adjacent landsliding scales with valley width, meander wavelength, and terrace height. Our results can advance our understanding of the river-hillslope interface in landscape evolution and can be used to inform hazard management in river corridors.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JF008249","usgsCitation":"Ahrendt, S., Mirus, B., LaHusen, S.R., and Perkins, J.P., 2025, Dynamic feedbacks between river meandering and landsliding in northwestern Washington glacial terraces: JGR Earth Surface, v. 130, no. 8, e2024JF008249, 29 p., https://doi.org/10.1029/2024JF008249.","productDescription":"e2024JF008249, 29 p.","ipdsId":"IP-171862","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":494447,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jf008249","text":"Publisher Index Page"},{"id":494093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.28,\n 48.835\n ],\n [\n -122.28,\n 48.82255\n ],\n [\n -122.25,\n 48.8225\n ],\n [\n -122.25,\n 48.835\n ],\n [\n -122.28,\n 48.835\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahrendt, Shelby Marie 0000-0002-3678-5087","orcid":"https://orcid.org/0000-0002-3678-5087","contributorId":358942,"corporation":false,"usgs":true,"family":"Ahrendt","given":"Shelby Marie","affiliations":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"preferred":true,"id":945951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":169597,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaHusen, Sean Richard 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":294677,"corporation":false,"usgs":true,"family":"LaHusen","given":"Sean","email":"","middleInitial":"Richard","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":945953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Jonathan Patrick 0000-0001-9039-1153","orcid":"https://orcid.org/0000-0001-9039-1153","contributorId":359616,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","middleInitial":"Patrick","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":945954,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274005,"text":"70274005 - 2025 - Using integrated step-selection analyses to map high-risk electrocution areas for a highly mobile species","interactions":[],"lastModifiedDate":"2026-02-20T16:11:51.850785","indexId":"70274005","displayToPublicDate":"2025-07-21T10:05:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Using integrated step-selection analyses to map high-risk electrocution areas for a highly mobile species","docAbstract":"Knowledge of animal-movement patterns is a crucial component in identifying areas with high potential for human–wildlife conflict and in prioritizing associated management actions. Electrical energy infrastructure is a major source of mortality for animals worldwide, with millions of birds colliding with or being electrocuted by power lines and power-pole infrastructure each year. Movement, habitat use, and the spatial distribution of electrocution risk can vary with age, but studies of younger age classes are often hampered because these groups are difficult to observe and lack well-defined home ranges. To identify movement patterns and high-use areas of bald eagles in Arizona, USA, we analyzed global positioning system (GPS) telemetry data collected from 13 immature bald eagles (Haliaeetus leucocephalus) across Arizona between 2017 and 2023. We built multi-scale, integrated step-selection functions that evaluated eagle responses to a suite of environmental covariates. We then used these models to simulate eagle movement and predict habitat use within and surrounding Maricopa County, which contains both the Phoenix Metropolitan Area and the plurality of bald eagle breeding areas in Arizona. We provide a use case for how these simulated movements could be used by resource managers to identify high-risk areas for electrocution. Eagles avoided urban areas and selected steeper slopes, more pronounced ridges, and areas with greater water and wetland land cover. Predicted habitat use by bald eagles was greatest near waterbodies and along ridges and steep slopes, and indicated where power infrastructure may pose greater electrocution risk. We show how integrated step-selection analyses and movement path simulation may be used for subadult animals lacking stable home ranges to predict high-use areas and identify locations with greater potential for negative human–wildlife interactions.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70061","usgsCitation":"Cappello, C. ., Jacobson, K.V., Driscoll, J.T., McCarty, K.M., Bauder, J.M., 2025, Using integrated step-selection analyses to map high-risk electrocution areas for a highly mobile species: Journal of Wildlife Management, v. 89, no. 7, e70061, 19 p., https://doi.org/10.1002/jwmg.70061.","productDescription":"e70061, 19 p.","ipdsId":"IP-178672","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.5,\n 34.1\n ],\n [\n -113.5,\n 32.5\n ],\n [\n -111,\n 32.5\n ],\n [\n -111,\n 34.1\n ],\n [\n -113.5,\n 34.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Cappello, Caroline D.","contributorId":366625,"corporation":false,"usgs":false,"family":"Cappello","given":"Caroline","middleInitial":" D.","affiliations":[{"id":81133,"text":"Arizona Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":956103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobson, Kenneth V.","contributorId":366626,"corporation":false,"usgs":false,"family":"Jacobson","given":"Kenneth","middleInitial":"V.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":956104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, James T.","contributorId":366627,"corporation":false,"usgs":false,"family":"Driscoll","given":"James","middleInitial":"T.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":956105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCarty, Kyle M.","contributorId":366629,"corporation":false,"usgs":false,"family":"McCarty","given":"Kyle","middleInitial":"M.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":956106,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956107,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267590,"text":"ofr20251028 - 2025 - Preliminary field report of landslide hazards following Hurricane Helene","interactions":[],"lastModifiedDate":"2025-08-14T19:21:57.113137","indexId":"ofr20251028","displayToPublicDate":"2025-06-09T10:45:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1028","displayTitle":"Preliminary Field Report of Landslide Hazards Following Hurricane Helene","title":"Preliminary field report of landslide hazards following Hurricane Helene","docAbstract":"This report reflects our knowledge regarding the widespread landslide activity associated with Hurricane Helene observed during the U.S. Geological Survey’s (USGS) mission assignment to North Carolina in October 2024. The material in this report was originally prepared for the Federal Emergency Management Agency under mission assignment DR-4827-NC. The data and commentary in this report are reflective of a report provided to the Federal Emergency Management Agency (FEMA) on October 18, 2024, as well as information provided in briefings at the Buncombe County Emergency Operations Center. The report has been modified for public dissemination.
This assessment was based on systematic visual examination and mapping of landslide locations from aerial and satellite imagery, visual and photographic observations from low-level helicopter overflights and conversations with local landslide experts from the North Carolina Geological Survey and Appalachian Landslide Consultants PLLC, and more than 50 years of combined landslide hazard professional experience of the mission-assigned field team. No systematic field investigations were done by the USGS.
While responding to the event, the USGS did not identify any landslides that posed an immediate major threat to recovery personnel in parts of nine counties in North Carolina (Avery, Buncombe, Henderson, McDowell, Mitchell, Polk, Rutherford, Watauga, and Yancey); however, threats from renewed landslide activity may remain heightened in localized areas for months or even years. Known areas of the most abundant landslide occurrence include Bat Cave, Lake Lure, Chimney Rock, Swannanoa, Black Mountain, Fairview, steep areas in Asheville, and the Blue Ridge Parkway. The USGS shared detailed locations of known landslides with the Emergency Operations Centers. The thousands of landslide scars on hillsides and landslide deposits on flatter ground may present some threat to recovery activities. Soil and rocks will continue to erode from newly exposed landslide scars and may pose a threat to people and infrastructure who are immediately nearby. In general, the steeper and taller the landslide scar, the greater the potential threat. This threat is heightened during periods of rainfall and increases with the duration and intensity of rainstorms. Very heavy rainfall, or repeated rainfall events during short periods, could also initiate new landslides on steep slopes. Excavation of landslide deposits, particularly excavation of those deposits directly adjacent to steep slopes, may also pose a threat to nearby people and equipment.
An interagency collaborative mapping effort led by the USGS that informed this assessment identified 1,155 landslide locations by the October 2024 briefings, but that number increased to 2,217 in a final reviewed version of the locations published in January 2025. Locations were mapped from satellite imagery, fixed-wing and helicopter surveys, media and social media, and field reports in the 3 weeks following the passage of the remnants of Hurricane Helene. USGS products outlined in this report are publicly available and include geotagged photographs from aerial reconnaissance, hazard models, an interactive view of mapped landslide locations, and landslide safety and education resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20251028","programNote":"Landslide Hazards Program","usgsCitation":"Allstadt, K.E., McBride, S.K., Godt, J.W., Slaughter, S.L., Baxstrom, K.W., Sobieszczyk, S., and Stull, A., 2025, Preliminary field report of landslide hazards following Hurricane Helene: U.S. Geological Survey Open-File Report 2025–1028, 15 p., https://doi.org/10.3133/ofr20251028.","productDescription":"Report vi, 15 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-175853","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":494134,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118637.htm","linkFileType":{"id":5,"text":"html"}},{"id":490259,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251028/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1028"},{"id":488392,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1028/ofr20251028.xml"},{"id":488391,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1028/images"},{"id":486657,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1C5W3PQ","text":"USGS data release","description":"USGS data release for OFR 2025-1028","linkHelpText":"Oblique Aerial Photographs from October 13 and 17, 2024, of Landslides and Flooding Caused by Hurricane Helene (ver 1.1, March 2025)"},{"id":486656,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1028/ofr20251028.pdf","text":"Report","size":"7.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1028"},{"id":486655,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1028/coverthb.jpg"}],"country":"United States","state":"North Carolina, South Carolina, Tennessee, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81,\n 36.667\n ],\n [\n -83.25,\n 36.667\n ],\n [\n -83.25,\n 35\n ],\n [\n -81,\n 35\n ],\n [\n -81,\n 36.667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Background
Severe fire weather is becoming more common throughout the western United States. Changing conditions demand a better understanding of how prescribed fire treatments perform under extreme burning conditions, including the interactive influence of the age of treatments, vegetation, and fire weather. The Carr Fire of July 2018 burned nearly the entire land area of Whiskeytown National Recreation Area (NRA) under extreme fuel moisture and temperature conditions. Prior to the Carr Fire and since 1997, staff at Whiskeytown NRA treated 23% of the 15,756-ha NRA using large-scale prescribed fire (underburn) treatments ranging in size from 40 to 400 hectares.
Methods
We used simultaneous autoregressive (SAR) models to describe the effects of landscape-scale fuel treatments on wildfire severity under extreme burning conditions and across diverse biophysical settings at Whiskeytown NRA. Because vegetation type and structure are known drivers of fire severity in diverse ecosystems such as at Whiskeytown NRA, we also considered three different sources of vegetation structure data, including a 2006 physiognomic-floristic classification, a 2011 lidar-based forest structure classification, and a 2016 Landfire map of existing vegetation physiognomy-subclass.
Results
The greatest effect on 2018 Carr Fire severity was time since treatment of underburn treatments, but treatment effectiveness on fire severity dissipated rapidly—showing notable effectiveness within 5 years of underburning but virtually no effectiveness beyond 10 years post-treatment. Additional factors related to severity included vegetation structure type, topographic position index, aspect, slope, temperature, and wind gust speed. Model variance explained and model parameters, including the effect of underburn treatments, were similar regardless of the source of vegetation structure data.
Conclusions
Our results show that large-scale underburning treatments can reduce wildfire severity even under extreme fire weather conditions but suggest that frequent maintenance intervals are required to maintain treatment effectiveness ahead of severe wildfire events.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-025-00377-0","usgsCitation":"Beckman, J.J., van Mantgem, P.J., Wright, M., and Engber, E., 2025, Recent large-scale prescribed fire treatments reduced Carr Fire severity at Whiskeytown National Recreation Area: Fire Ecology, v. 21, 35, 20 p., https://doi.org/10.1186/s42408-025-00377-0.","productDescription":"35, 20 p.","ipdsId":"IP-165481","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498683,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-025-00377-0","text":"Publisher Index Page"},{"id":498548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Whiskeytown National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.77239214268279,\n 40.681212821117555\n ],\n [\n -122.77239214268279,\n 40.474214754578185\n ],\n [\n -122.47725029049735,\n 40.474214754578185\n ],\n [\n -122.47725029049735,\n 40.681212821117555\n ],\n [\n -122.77239214268279,\n 40.681212821117555\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2025-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Beckman, Jill J.","contributorId":364982,"corporation":false,"usgs":false,"family":"Beckman","given":"Jill","middleInitial":"J.","affiliations":[{"id":87020,"text":"Northern Arizona University; Former USGS","active":true,"usgs":false}],"preferred":false,"id":953552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422 pvanmantgem@usgs.gov","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":222994,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip","email":"pvanmantgem@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Micah C. 0000-0002-5324-1110","orcid":"https://orcid.org/0000-0002-5324-1110","contributorId":229071,"corporation":false,"usgs":true,"family":"Wright","given":"Micah","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engber, Eamon","contributorId":202777,"corporation":false,"usgs":false,"family":"Engber","given":"Eamon","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":953555,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267230,"text":"sir20235064G - 2025 - Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70267230,"text":"sir20235064G - 2025 - Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064G","publicationYear":"2025","noYear":false,"chapter":"G","displayTitle":"Peak Streamflow Trends in Montana and Northern Wyoming and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2026-01-26T19:13:21.257304","indexId":"sir20235064G","displayToPublicDate":"2025-05-19T13:20:42","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":"2023-5064","chapter":"G","displayTitle":"Peak Streamflow Trends in Montana and Northern Wyoming and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","docAbstract":"Frequency analysis on annual peak streamflow (hereinafter, peak flow) is essential to water-resources management applications, including critical structure design (for example, bridges and culverts) and floodplain mapping. Nonstationarity is a statistical property of a peak-flow series such that the distributional properties (the mean, variance, or skew) change either gradually (monotonic trend) or abruptly (shift, step change or change point) through time. Not incorporating or accounting for observed nonstationarity into peak-flow frequency analysis might result in a poor representation of the true probability of large floods and thus misrepresent the actual flood risks to life and property. This report summarizes how hydroclimatic variability might affect the temporal and spatial distributions of peak-flow data in the State of Montana (and northern Wyoming) and is part of a larger study to document peak-flow nonstationarity and hydroclimatic changes across a nine-State region consisting of Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flows. This study was completed in cooperation with the Montana Department of Natural Resources and Conservation.
The purpose of this report is to characterize temporal and spatial patterns of nonstationarity in peak flows and hydroclimatology in Montana and northern Wyoming. In this evaluation, peak-flow, daily streamflow, and model-simulated gridded climatic data were examined for monotonic trends, change points, and other statistical properties that might indicate changing climatic and environmental conditions. This report includes background information on the study area, the history of U.S. Geological Survey peak-flow data collection and frequency analysis in Montana, and the review of research relating to hydroclimatic variability and change in Montana. This study might help provide a framework for addressing potential nonstationarity issues in peak-flow frequency updates that commonly are completed by the U.S. Geological Survey in cooperation with other agencies throughout the Nation.
The analytical structure of this study includes analyses of monotonic trends and change points in numerous hydroclimatic variables in assigned 30-, 50-, 75-, and 100-year analysis periods. For Montana and part of Wyoming, the 30-, 50-, 75, and 100-year analyses included 157, 70, 48, and 12 streamgages, respectively. For those streamgages, nonstationarities were analyzed in the following variables: (1) climatic variables, including annual and seasonal (winter, spring, summer, and fall) temperature and precipitation; (2) daily streamflow variables, including the annual center of volume duration, annual center of volume median, and peaks over threshold with a mean of four events per year; and (3) annual peak-flow variables, including peak-flow timing and magnitude. A likelihood approach was used to express statistical confidence and assign the nonstationarity results as likely upward or downward (highest statistical confidence), somewhat likely upward or downward (less statistical confidence), or about as likely as not (little statistical confidence; hereinafter, neutral). For the nonstationarity analyses of the climatic, daily streamflow, and peak-flow variables, the results are presented in detail and discussed with respect to statewide patterns and geographic variability. For each of the 30-, 50-, and 75-year analyses, peak-flow change-point and monotonic trend analyses were compiled for streamgages classified with likely downward or likely upward trends. For those streamgages, the associated basin characteristics and nonstationarity results for peak-flow timing, daily streamflow, and climatic variables were investigated and statistically compared to discern associations among other variables that might contribute to the peak-flow nonstationarity results.
The 50- and 75-year peak-flow nonstationarities identified in this study are mostly downward, in association with mostly upward temperature and potential evapotranspiration:precipitation monotonic trends. For the 50-, 75-, and 100-year analyses, the peak-flow change points are predominantly downward and are concentrated in the 1970s and 1980s, which indicates general consistency among the longer trend periods. These findings are in association with substantial research documenting globally rising temperature and atmospheric greenhouse gas concentrations that might be largely attributed to anthropogenic activities. Anthropogenic effects might represent long-term (on the order of several decades to more than a century) climate changes that might happen within highly variable natural climate fluctuations. Several paleo studies in the north-central United States have indicated that hydroclimatic extremes (that is, low- and high-streamflow conditions) before European settlement have been outside of extremes since the 1900s. Depending on the interactions of anthropogenic effects and natural climate variability, extreme high-streamflow conditions might occur in the future, even in the presence of long-term downward peak-flow trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064G","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., Barth, N.A., Sando, R., and Chase, K.J., 2025, Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020, chap. G of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 129 p., https://doi.org/10.3133/sir20235064G.","productDescription":"Report: x, 129 p.; Data Release; Dataset","numberOfPages":"144","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-159092","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":486055,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.pdf","text":"Report","size":"19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5064-G"},{"id":486054,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/coverthb.jpg"},{"id":486056,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.XML"},{"id":486057,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/images/"},{"id":486059,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064G/full"},{"id":499038,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118575.htm","linkFileType":{"id":5,"text":"html"}},{"id":486073,"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":486058,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R71WWZ","text":"USGS data release","linkHelpText":"Peak streamflow data, climate data, and results from investigating hydroclimatic trends and climate change effects on peak streamflow in the Central United States, 1921–2020"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.16658108866669,\n 49\n ],\n [\n -116.16658108866669,\n 43\n ],\n [\n -104.0598539498924,\n 43\n ],\n [\n -104.0598539498924,\n 49\n ],\n [\n -116.16658108866669,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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3162 Bozeman Avenue
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Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
The Long Island Sound watershed is home to nearly 9 million people in parts of Connecticut, Massachusetts, New Hampshire, New York, Rhode Island, Vermont, and Canada. Government agencies, nonprofits, and Tribal Nations have overseen numerous projects to monitor and protect the water resources of this watershed and the sound. Although there is an abundance of data, there is no easy way to search them or a central place to manage this information. To help, the U.S. Geological Survey, the U.S. Environmental Protection Agency, and the Long Island Sound Study have created an interactive map to help users find and understand data about the water resources of the Long Island Sound and its watershed.
","publicationDate":"2025-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Stagnitta, Timothy J. 0000-0001-8903-428X","orcid":"https://orcid.org/0000-0001-8903-428X","contributorId":304230,"corporation":false,"usgs":true,"family":"Stagnitta","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":true,"id":936961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Groseclose, Gina N. 0000-0003-2546-7099","orcid":"https://orcid.org/0000-0003-2546-7099","contributorId":329473,"corporation":false,"usgs":true,"family":"Groseclose","given":"Gina N.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":936962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wavra, Harper N. 0000-0001-5688-902X","orcid":"https://orcid.org/0000-0001-5688-902X","contributorId":292171,"corporation":false,"usgs":true,"family":"Wavra","given":"Harper","email":"","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":936963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Shawn C. 0000-0001-6324-1061 scfisher@usgs.gov","orcid":"https://orcid.org/0000-0001-6324-1061","contributorId":4843,"corporation":false,"usgs":true,"family":"Fisher","given":"Shawn","email":"scfisher@usgs.gov","middleInitial":"C.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":936964,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267209,"text":"70267209 - 2025 - Effects of climate change on midwestern ecosystems: Appalachian – Interior – Northeast Mesic Forest","interactions":[],"lastModifiedDate":"2026-03-17T14:22:08.438751","indexId":"70267209","displayToPublicDate":"2025-05-01T09:13:46","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Effects of climate change on midwestern ecosystems: Appalachian – Interior – Northeast Mesic Forest","docAbstract":"The Appalachian-Interior-Northeast Mesic Forest ecosystem, historically buffered by cool, moist conditions, may experience significant stress under future climate change, particularly due to intensifying droughts and milder winters in the midwestern United States. Droughts are expected to intensify in frequency and severity, depleting soil moisture, increasing tree mortality, and reshaping species composition. Increasing aridity and disrupted hydrologic cycles will likely accelerate soil erosion, deplete nutrients, and heighten wildfire risk. Meanwhile, milder winters may reduce snowpack insulation, increase freeze-thaw cycles, and alter growing seasons, potentially amplifying cold stress, disrupting phenology, and contributing to shifts in habitat structure and community composition. While easing winter severity may temporarily boost plant productivity and facilitate species migration into and throughout the Midwest, it can also increase the risk of frost damage for early-leafing trees and disrupt ecological relationships, such as plant-pollinator interactions.
Together, these stressors may drive fundamental shifts in habitat structure and community composition, favoring drought-, fire-, and cold-tolerant species, while historically dominant, moisture-dependent species decline. Species with limited drought resistance, such as those with shallow roots or low water-use efficiency, may be especially vulnerable, while drought-adapted taxa could gain a competitive advantage. This shift could trigger a departure from over a century of mesophication in the Appalachian-Interior-Northeast Mesic Forest, which has favored shade-loving, moisture-dependent species in fire-suppressed landscapes. As a result, these forests may be particularly ill-equipped to withstand the novel environmental conditions imposed by intensifying droughts and milder winters. The Appalachian-Northeast Mesic Forest habitat group, dominated by eastern hemlock (Tsuga canadensis) and eastern white pine (Pinus strobus), is likely particularly vulnerable, as both dominant species are projected to decline due to increasing drought stress and shifting competitive dynamics. In the North-Central Beech - Maple - Basswood Forest, the Driftless Area of Wisconsin, Minnesota, and Iowa may be more vulnerable than more eastern portions of the habitat due to its already drier conditions, with climate change expected to push these communities beyond favorable conditions.
Species interactions, including invasive species, pests, and herbivory, are also likely to be reshaped by climate change, compounding stress on habitat groups throughout the Appalachian-Interior-Northeast Mesic Forest. Warmer winters and increased disturbance may facilitate the expansion of invasive species, which outcompete native vegetation and alter ecosystem dynamics. At the same time, pests and pathogens are likely to become more destructive, as milder winters enhance their survival and spread and drought weakens tree defenses. Additionally, rising white-tailed deer (Odocoileus virginianus) populations, supported by warmer winters, may shift forest regeneration patterns by selectively browsing on sensitive seedlings and saplings, limiting the recruitment of historically dominant tree species while favoring browse-resistant plants. Collectively, these pressures can drive significant and ongoing ecological transformation in the Appalachian-Interior-Northeast Mesic Forest, highlighting the need for adaptive management strategies to sustain biodiversity and ecosystem function.
","language":"English","publisher":"Midwest Climate Adaptation Science Center","usgsCitation":"Ratcliffe, H., Charton, K., Siddons, T., Lyons, M.P., and LeDee, O.E., 2025, Effects of climate change on midwestern ecosystems: Appalachian – Interior – Northeast Mesic Forest, 97 p.","productDescription":"97 p.","ipdsId":"IP-177855","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":486042,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://mwcasc.umn.edu/research-publications"},{"id":501211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, 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,{"id":70274641,"text":"70274641 - 2025 - Pluvial and potential compound flooding in a coupled coastal modeling framework: New York City during post-tropical Cyclone Ida (2021)","interactions":[],"lastModifiedDate":"2026-04-02T16:06:57.228299","indexId":"70274641","displayToPublicDate":"2025-04-23T11:03:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Pluvial and potential compound flooding in a coupled coastal modeling framework: New York City during post-tropical Cyclone Ida (2021)","docAbstract":"Many coastal urban areas are prone to extreme pluvial flooding due to limitations in stormwater system capacity, with the additional potential for flooding compounded by storm surge, tides, and waves. Understanding and simulating these processes can improve prediction and flood risk management. Here, we adapt the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling framework (COAWST) to simulate pluvial flooding from post-tropical Cyclone Ida (2021) in the Jamaica Bay watershed of New York City (NYC). We modify the model to capture the volumetric effects of rainfall and parameterize soil infiltration and a stormwater conveyance system as the drainage rate. We generate a spatially continuous flood map of Ida with a root-mean-square error (RMSE) of 20 cm when compared to high-water marks, useful for understanding Ida's impacts and subsequent mitigation planning. Results show that over 23 km2 and 4621 buildings were flooded deeper than 0.3 m during Ida. Sensitivity analyses are used to study the broader risk from events like Ida (pluvial flooding) as well as potential compound (pluvial–coastal) flooding. Spatial shifting of the storm track within a typical 12 h forecast uncertainty reveals a worst-case scenario that increases this flooded area to 62 km2 (5907 buildings). Shifting Ida's rainfall to coincide with high tide increases this flooded area by 1 km2, a relatively small change due to the lack of significant storm surge. The application of COAWST to this storm event addresses a broader goal of developing the capability to model compound pluvial–coastal flooding by simultaneously representing coastal storm processes such as rain, tide, waves, erosion, and atmosphere–wave–ocean interactions. The sensitivity analysis results underscore the need for detailed flood risk assessments, showing that Ida, already NYC's worst rain event, could have been even more devastating with slight shifts in the storm track.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-29-2043-2025","usgsCitation":"Kasaei, S., Orton, P.M., Ralston, D.K., and Warner, J., 2025, Pluvial and potential compound flooding in a coupled coastal modeling framework: New York City during post-tropical Cyclone Ida (2021): Hydrology and Earth System Sciences, v. 29, no. 8, p. 2043-2058, https://doi.org/10.5194/hess-29-2043-2025.","productDescription":"16 p.","startPage":"2043","endPage":"2058","ipdsId":"IP-168323","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":502086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-29-2043-2025","text":"Publisher Index Page"},{"id":502009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","city":"New York City","otherGeospatial":"Jamaica Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.01902277171598,\n 40.81541595710692\n ],\n [\n -74.01902277171598,\n 40.5619948325492\n ],\n [\n -73.61280152477734,\n 40.5619948325492\n ],\n [\n -73.61280152477734,\n 40.81541595710692\n ],\n [\n -74.01902277171598,\n 40.81541595710692\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasaei, Shima","contributorId":369142,"corporation":false,"usgs":false,"family":"Kasaei","given":"Shima","affiliations":[{"id":28243,"text":"Stevens Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":958528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Orton, Phillip M.","contributorId":369143,"corporation":false,"usgs":false,"family":"Orton","given":"Phillip","middleInitial":"M.","affiliations":[{"id":28243,"text":"Stevens Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":958529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ralston, David K.","contributorId":369144,"corporation":false,"usgs":false,"family":"Ralston","given":"David","middleInitial":"K.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":958530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":958531,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265829,"text":"sir20255023 - 2025 - A framework for understanding the effects of subsurface agricultural drainage on downstream flows","interactions":[],"lastModifiedDate":"2025-04-18T14:23:34.614404","indexId":"sir20255023","displayToPublicDate":"2025-04-17T15:29:38","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":"2025-5023","displayTitle":"A Framework for Understanding the Effects of Subsurface Agricultural Drainage on Downstream Flows","title":"A framework for understanding the effects of subsurface agricultural drainage on downstream flows","docAbstract":"Understanding controls on streamflow volume and magnitude is important to water resource management applications, such as critical water and transportation structure design and floodplain mapping. Changes in land use and agricultural practices, such as subsurface agricultural drainage, may be contributing to changes in streamflow characteristics. Subsurface agricultural drainage, also known as tile drainage, is the practice of installing drains in the subsurface of agricultural fields to improve productivity. Because of the complex interactions between subsurface drainage systems, precipitation, local soil conditions, and land management practices, it is difficult to determine how subsurface agricultural drainage affects downstream flow. Previously developed subsurface agricultural drainage conceptual models under dry, saturated, and winter conditions are summarized, and current literature on the effects of subsurface agricultural drainage on downstream flows, focusing on peak flow, non-event flow, and total flow to develop frameworks for discussing these systems is compiled.
The effects that subsurface drainage has on hydrologic systems are expected to vary by site and are seasonally based on system design, soil type, moisture conditions, precipitation characteristics, and land conditions. Subsurface drainage can affect the magnitude of peak flow by converting surface runoff from a storm event to subsurface runoff. By increasing hydrologic connectivity of a catchment, subsurface drainage can increase non-event flow or the flow between two storm events, typically dependent on lateral flow through the subsurface and groundwater. Theoretically, by diverting water from groundwater recharge or by reducing water available for evapotranspiration, subsurface drainage may increase the total volume of flow. Precipitation changes may increase infiltration, excess overland flow, and flood risk regardless of the presence or absence of subsurface drainage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255023","collaboration":"Prepared in cooperation with Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Podzorski, H.L., and Ryberg, K.R., 2025, A framework for understanding the effects of subsurface agricultural drainage on downstream flows: U.S. Geological Survey Scientific Investigations Report 2025–5023, 24 p., https://doi.org/10.3133/sir20255023.","productDescription":"vi, 24 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161597","costCenters":[{"id":34685,"text":"Dakota Water Science 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U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Molecular characterization of sedimentary organic matter (SOM), termed macerals, is a common goal when seeking to understand petroleum generation as well as other geologic processes in deep time. However, unambiguous measurement of discrete macerals is challenging due to the small size of organic particles in sedimentary rocks, the proximity of different organic matter types to one another, mineral-organic matter interactions, and maceral mixing that occurs during SOM isolation prior to ex situ analysis. The recent advent of infrared spectrometers capable of nanometer-scale resolution and the application of these technologies to geologic samples has enabled advances in rapid, in situ molecular characterization of SOM allowing for insights into paleoenvironmental processes, such as organic matter productivity and preservation, among others. Here we employ one such technology, optical photothermal infrared (OPTIR) spectroscopy, to map SOM functional group distributions at 500-nm resolution in a sample from the Lower Cretaceous Sunniland Limestone of the South Florida Basin. Examined fields of view include occurrences of amorphous organic matter (AOM), inertinite, micrinite, solid bitumen, telalginite, and vitrinite. OPTIR data from these macerals are compared against traditional organic petrographic data from the same organic grains including fluorescence intensity and white light reflectance as well as against cathodoluminescence response, an emerging organic petrographic approach. Maceral oxygen content (using carbonyl functional group abundance as a proxy) is observed to vary widely between maceral types but correlates strongly with fluorescence and cathodoluminescence intensity as well as against reflectance. These findings highlight the important role that oxygen content plays in determining the optical properties of SOM and further demonstrate the ability of OPTIR to discriminate subtle molecular differences between SOM types.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2025.104990","usgsCitation":"Jubb, A., Hackley, P.C., McAleer, R.J., and Qu, J., 2025, Nanometer-scale relationships between sedimentary organic matter molecular composition, fluorescence, cathodoluminescence, and reflectance: The importance of oxygen content at low thermal maturities: Organic Geochemistry, v. 204, 104990, 7 p., https://doi.org/10.1016/j.orggeochem.2025.104990.","productDescription":"104990, 7 p.","ipdsId":"IP-173289","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":488599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2025.104990","text":"Publisher Index Page"},{"id":484251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"204","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":932125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":932126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":932127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qu, Jing","contributorId":242671,"corporation":false,"usgs":false,"family":"Qu","given":"Jing","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":932128,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263339,"text":"70263339 - 2025 - Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning","interactions":[],"lastModifiedDate":"2025-02-06T15:53:28.669956","indexId":"70263339","displayToPublicDate":"2025-01-29T09:49:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning","docAbstract":"Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.
","language":"English","publisher":"MDPI","doi":"10.3390/rs17030457","usgsCitation":"Shastry, A.R., Cerovski-Darriau, C., Coltin, B., and Stock, J.D., 2025, Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning: Remote Sensing, v. 17, no. 3, 457, 11 p., https://doi.org/10.3390/rs17030457.","productDescription":"457, 11 p.","ipdsId":"IP-153917","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":487628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17030457","text":"Publisher Index Page"},{"id":481746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.41245311245856,\n 35.20197552578807\n ],\n [\n -117.96299074904582,\n 36.06858120494961\n ],\n [\n -118.86530937366612,\n 37.63884023254646\n ],\n [\n -119.85965473348287,\n 38.80651233617289\n ],\n [\n -120.17114612624695,\n 40.23030133169971\n ],\n [\n -120.73602418336918,\n 40.662012753561754\n ],\n [\n -122.36739903137283,\n 40.400491599532984\n ],\n [\n -120.74692405664294,\n 38.0147515126105\n ],\n [\n -119.36615838296214,\n 35.979191454701876\n ],\n [\n -118.41245311245856,\n 35.20197552578807\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Shastry, Apoorva Ramesh 0000-0002-3996-4857","orcid":"https://orcid.org/0000-0002-3996-4857","contributorId":317867,"corporation":false,"usgs":true,"family":"Shastry","given":"Apoorva","email":"","middleInitial":"Ramesh","affiliations":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true}],"preferred":true,"id":926515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cerovski-Darriau, Corina 0000-0002-0543-0902","orcid":"https://orcid.org/0000-0002-0543-0902","contributorId":221159,"corporation":false,"usgs":true,"family":"Cerovski-Darriau","given":"Corina","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coltin, Brian","contributorId":350636,"corporation":false,"usgs":false,"family":"Coltin","given":"Brian","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":926517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stock, Jonathan D. 0000-0001-8565-3577 jstock@usgs.gov","orcid":"https://orcid.org/0000-0001-8565-3577","contributorId":3648,"corporation":false,"usgs":true,"family":"Stock","given":"Jonathan","email":"jstock@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":926518,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70262522,"text":"70262522 - 2025 - From subsidies to stressors: Shifting ecological baselines alter biological responses to nutrients in highly modified agricultural streams","interactions":[],"lastModifiedDate":"2025-01-22T14:45:54.773366","indexId":"70262522","displayToPublicDate":"2025-01-17T09:57:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"From subsidies to stressors: Shifting ecological baselines alter biological responses to nutrients in highly modified agricultural streams","docAbstract":"Subsidy–stress gradients offer a useful framework for understanding ecological responses to perturbation and may help inform ecological metrics in highly modified systems. Historic, region-wide shifts from bottomland hardwood forest to row crop agriculture can cause positively skewed impact gradients in alluvial plain ecoregions, resulting in tolerant organisms that typically exhibit a subsidy response (increased abundance in response to environmental stressors) shifting to a stress response (declining abundance at higher concentrations). As a result, observed biological tolerance in modified ecosystems may differ from less modified regions, creating significant challenges for detecting biological responses to restoration efforts. Using the agriculturally dominated Mississippi Alluvial Plain (MAP) ecoregion in Mississippi, USA, as a case study, we tested the hypothesis that macroinvertebrate taxa that typically display a subsidy response to nutrient enrichment in less modified ecoregions (i.e., nutrient-tolerance) shift to a stress response to increasing nutrients in highly modified watersheds with elevated baseline nutrient conditions (i.e., nutrient intolerance). The abundance and diversity of MAP-specific intolerant taxa identified with threshold indicator taxa analysis were either unresponsive or exhibited a subsidy response to increasing nutrients in less modified ecoregions in Mississippi with less land alteration and lower nutrient concentrations, but declined at higher concentrations, providing evidence for a stress response to elevated nutrients in the MAP. Additionally, MAP-specific tolerant and intolerant taxa richness responded to increased nutrients predictably and consistently across space and time within the MAP. However, in MAP streams, elevated specific conductance was predicted to dampen the response of tolerant and intolerant taxa richness to increasing nutrient concentrations, highlighting the importance of considering multistressor interactions when interpreting biological data. Lastly, we demonstrate the efficacy of this approach with sediment bacterial communities characterized with amplicon sequencing, which lack sufficient life history characteristics necessary for the development of multimetric indices. Both macroinvertebrate and bacterial communities responded similarly to increasing nutrient concentrations, suggesting DNA-based approaches may provide an efficient biological assessment tool for monitoring water quality improvements in highly modified watersheds.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.3086","usgsCitation":"Devilbiss, S., Taylor, J., and Hicks, M.B., 2025, From subsidies to stressors: Shifting ecological baselines alter biological responses to nutrients in highly modified agricultural streams: Ecological Applications, v. 35, no. 1, e3086, 21 p., https://doi.org/10.1002/eap.3086.","productDescription":"e3086, 21 p.","ipdsId":"IP-159539","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":481027,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.3086","text":"Publisher Index Page"},{"id":480830,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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