Remote Pacific islands (RPI) are characterized by ecological isolation, diverse endemic species, and vulnerability to invasive organisms due to globalization-driven connectivity. Among these species, Aedes albopictus, a highly invasive vector of flaviviruses, has spread extensively across the RPI via human-mediated dispersal, posing significant health and economic burdens. While the population structure and the degree of gene flow between mosquito populations can inform the dispersal pathways critical for disease vector management, the population genetics of Ae. albopictus in Northern RPI remains understudied. The present work investigated the population structure and connectivity of Ae. albopictus populations from Guam, Hawaiian Islands, and the Republic of the Marshall Islands (RMI) to inform disease and vector-based biosecurity risks and develop targeted management strategies. This is the first assessment to develop and analyze whole genome sequences of Ae. albopictus for RPI, enabling more accurate estimates of differentiation, admixture, and ancestry. We found distinct genetic clustering between regions, distinct ancestry of populations across RPI, and potential invasions that originated from Hawaii and spread into the RMI, and invasions from North America that spread to Guam. These findings can inform biosecurity protocols to limit the invasion of Ae. albopictus and their associated diseases within Hawaii and around the Pacific. Given the significant degree of genetic differentiation, we found between islets, islands, and regions, the genome data from this study can be used to enable the development of locally confined geographically isolated gene drives. These drives may be used to prevent and control outbreaks of dengue, chikungunya, and Zika, diseases that have had devastating consequences in these remote island communities.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pntd.0013414","usgsCitation":"Seok, S., Vorsino, A.E., Collier, T.C., Hapairai, L., Jacobsen, C.M., Hasty, J.M., Romero-Weaver, A.L., Buckner, E.A., Lapointe, D., Leong, M., Braack, L., Tabuloc, C.A., Chiu, J.C., Raban, R., Akbari, O.S., and Lee, Y., 2025, Population genomics of Aedes albopictus across remote Pacific islands for genetic biocontrol considerations: PLoS Neglected Tropical Diseases, v. 19, no. 8, e0013414, 20 p., https://doi.org/10.1371/journal.pntd.0013414.","productDescription":"e0013414, 20 p.","ipdsId":"IP-179310","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":498291,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pntd.0013414","text":"Publisher Index 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Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic feedbacks between river meandering and landsliding in northwestern Washington glacial terraces","docAbstract":"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":70270156,"text":"70270156 - 2025 - Performance mapping and weighting for the evapotranspiration models of the OpenET ensemble","interactions":[],"lastModifiedDate":"2025-08-12T15:32:26.724967","indexId":"70270156","displayToPublicDate":"2025-08-09T08:15:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Performance mapping and weighting for the evapotranspiration models of the OpenET ensemble","docAbstract":"Evapotranspiration (ET) accounts for the majority of water available from precipitation in the terrestrial water cycle, and improvements to the accuracy, resolution, and coverage of ET data can enhance hydrologic models and assessments. The OpenET collaboration of six remotely sensed ET modeling teams has demonstrated that an ensemble approach to ET estimation generally provides improved accuracy relative to individual ensemble members. The performance of individual models has been shown to vary by land cover type and climate zone, but a thorough study of the variables that influence model performance differences has not yet been conducted. In this paper, we model the performance of OpenET models relative to flux tower data as a function of variables such as land cover type and precipitation. These performance models are used to map estimated OpenET model performance across the conterminous United States. We develop relative weights based on these modeled performance metrics and show that a performance-weighted ensemble improves accuracy relative to the current OpenET ensemble method to varying degrees. The monthly mean absolute error of the weighted ensemble is reduced relative to the current method by 8% in agricultural settings, by 23% in shrublands and mixed forests, and by 5% in grasslands and evergreen forests. We produce weight maps that can be used to generate performance-weighted ensemble values for OpenET data. The results can be used to inform model selection and provide insight about the controls on model performance that could lead to model refinement.
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However, quantitative understanding of its Quaternary origin, architecture, and hydrologic function remains incomplete. Here we develop a three-dimensional hydrostratigraphic model to characterize the deposition of clay and silt, fine-medium sands, and graveliferous sands using lithologic data from 75,000 boreholes compiled across the Lower Mississippi Valley and a geostatistical method—interval kriging. We find that cyclic glacial entrenchments, evidenced by remnants of pre-Wisconsinan postglacial sediments, alongside geodynamic activities shaped the MRVA basal configuration. Stratal weakening from faulting and salt diapirism enhanced glacial incision and thereby produced abrupt aquifer thickening. We demarcate the top of graveliferous sands as the regional marker of the Pleistocene-Holocene transition. The MRVA hydrostratigraphy reveals hydrologic function and geologic controls on groundwater storage and quality, advancing the assessment of aquifer sustainability under a changing climate, with implications for alluvial aquifers globally.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-025-02545-1","usgsCitation":"Song, Y., Tsai, F.T., Minsley, B.J., Wu, C., and Heggy, E., 2025, Quantitative subsurface characterization illuminates the origin of the Quaternary Mississippi River Valley alluvial aquifer: Communications Earth and Environment, v. 6, 646, 16 p., https://doi.org/10.1038/s43247-025-02545-1.","productDescription":"646, 16 p.","ipdsId":"IP-172339","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":497110,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-025-02545-1","text":"Publisher Index Page"},{"id":497056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River Valley alluvial aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94,\n 38\n ],\n [\n -94,\n 28.5\n ],\n [\n -88,\n 28.5\n ],\n [\n -88,\n 38\n ],\n [\n -94,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2025-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Yuqi","contributorId":363220,"corporation":false,"usgs":false,"family":"Song","given":"Yuqi","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":951315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tsai, Frank T.-C.","contributorId":305938,"corporation":false,"usgs":false,"family":"Tsai","given":"Frank","email":"","middleInitial":"T.-C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":951316,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951317,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Chenliang","contributorId":363221,"corporation":false,"usgs":false,"family":"Wu","given":"Chenliang","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":951318,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heggy, Essem","contributorId":363223,"corporation":false,"usgs":false,"family":"Heggy","given":"Essem","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":951319,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270110,"text":"70270110 - 2025 - Deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in fish communities of the Chesapeake Bay watershed, USA: A regional assessment and potential landscape drivers","interactions":[],"lastModifiedDate":"2025-08-11T15:28:06.323665","indexId":"70270110","displayToPublicDate":"2025-08-08T08:22:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in fish communities of the Chesapeake Bay watershed, USA: A regional assessment and potential landscape drivers","docAbstract":"Fish diseases in freshwater ecosystems pose significant ecological and socioeconomic challenges, yet monitoring them in wild populations is complex due to interactions between pathogens, hosts, and environmental conditions. We examine the prevalence and watershed-scale landscape drivers of external deformity, erosion, lesion, tumor, and parasite (DELT) anomalies in 57 riverine fish species using a large dataset (577,266 individuals collected 2008–2019) from the Chesapeake Bay watershed that originated from state and federal agencies. Overall, DELT prevalence was low (1.4%), but was higher in larger, longer-lived species, including Channel Catfish (Ictalurus punctatus) (18.9%), Rock Bass (Ambloplites rupestris) (7.6%), Smallmouth Bass (Micropterus dolomieu) (7.3%), Brown Bullhead (Ameiurus nebulosus) (5.6%), and Yellow Bullhead (Ameiurus natalis) (5.1%), signifying their potential as regional environmental health indicators. Spatial analysis indicated warmer temperatures increased the estimated probability of DELT occurrence, whereas higher precipitation often mitigated the probability of DELT occurrence. Conservation strategies (e.g., best management practices) had mixed effectiveness in reducing DELT occurrence probability across agricultural and urban landscapes. Across the landscape, various drivers, including harvested forest, impervious land, and pesticide use, influenced DELT occurrence probability differently across species. However, uncertainty remains partly due to low prevalence and variability in sampling methods across agencies. Despite low overall prevalence, DELT occurrence is a rapid fish health indicator. Future research could emphasize species-specific responses and longitudinal studies that incorporate life stages and health indicators. Understanding these intricate, multi-scale interactions is vital for effective monitoring, conservation, and adaptive management of freshwater ecosystems.
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2025 - Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition","interactions":[],"lastModifiedDate":"2026-03-27T17:34:41.462684","indexId":"70271124","displayToPublicDate":"2025-08-08T07:55:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14424,"text":"Applied Computing and Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition","docAbstract":"The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2025.100274","usgsCitation":"Goldman, M.A., Lederer, G.W., Rosera, J.M., Graham, G.E., Mishra, A., and Yepremyan, A., 2025, Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition: Applied Computing and Geosciences, v. 27, 100274, 15 p., https://doi.org/10.1016/j.acags.2025.100274.","productDescription":"100274, 15 p.","ipdsId":"IP-164764","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":501736,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FXSPT1","text":"Data Release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Training and validation data from the AI for Critical Mineral Assessment Competition (ver. 2.0, July 2025)"},{"id":495004,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":495070,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2025.100274","text":"Publisher Index Page"}],"volume":"27","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldman, Margaret A. 0000-0003-2232-6362 mgoldman@usgs.gov","orcid":"https://orcid.org/0000-0003-2232-6362","contributorId":176468,"corporation":false,"usgs":true,"family":"Goldman","given":"Margaret","email":"mgoldman@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":947494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lederer, Graham W. 0000-0002-9505-9923","orcid":"https://orcid.org/0000-0002-9505-9923","contributorId":202407,"corporation":false,"usgs":true,"family":"Lederer","given":"Graham","email":"","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":947495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosera, Joshua Mark 0000-0003-3807-5000","orcid":"https://orcid.org/0000-0003-3807-5000","contributorId":270284,"corporation":false,"usgs":true,"family":"Rosera","given":"Joshua","email":"","middleInitial":"Mark","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":947496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":947497,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mishra, Asitang","contributorId":301178,"corporation":false,"usgs":false,"family":"Mishra","given":"Asitang","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":947498,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yepremyan, Alice","contributorId":358951,"corporation":false,"usgs":false,"family":"Yepremyan","given":"Alice","affiliations":[{"id":85724,"text":"NASA - JPL","active":true,"usgs":false}],"preferred":false,"id":947499,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271344,"text":"70271344 - 2025 - Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network","interactions":[],"lastModifiedDate":"2025-09-08T14:58:50.690039","indexId":"70271344","displayToPublicDate":"2025-08-08T07:48:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network","docAbstract":"Smallmouth bass (Micropterus dolomieu) is an important recreational sportfish and destructive non-native species when introduced into freshwater habitats. There is therefore a need to understand the drivers of, and uncertainties in, smallmouth bass population dynamics for various management objectives. We combined long-term smallmouth bass catch-effort and early life history data from a non-native population in the Green River sub-basin of the upper Colorado River to develop a demographic model that links interannual variability in environmental conditions to recruitment in three river reaches. We used the model to quantify how hydrology, river temperature, and exploitation drive smallmouth bass population dynamics. Early life stages were influenced by timing of hatching and discharge. Dispersal of age-0 fish and density-dependent dynamics were identified as primary sources of uncertainty. Determining the true nature of density-dependent dynamics is important, as the impact of exploitation-based management actions is dependent on the strengths of any density-dependent feedbacks. Our model provides a framework to predict smallmouth bass population responses to future climate conditions, reservoir operations, and exploitation levels.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0183","usgsCitation":"Bruckerhoff, L.A., Yackulic, C., Eppehimer, D.E., Bestgen, K.R., Jones, M.T., and Michaud, C., 2025, Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-24, https://doi.org/10.1139/cjfas-2024-0183.","productDescription":"24 p.","startPage":"1","endPage":"24","ipdsId":"IP-166028","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.07791103370728,\n 38.47445328611764\n ],\n [\n -110.07791103370728,\n 38.06202716185106\n ],\n [\n -109.72266590720243,\n 38.06202716185106\n ],\n [\n -109.72266590720243,\n 38.47445328611764\n ],\n [\n -110.07791103370728,\n 38.47445328611764\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bruckerhoff, Lindsey A.","contributorId":361014,"corporation":false,"usgs":false,"family":"Bruckerhoff","given":"Lindsey","middleInitial":"A.","affiliations":[{"id":86151,"text":"Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, Ohio","active":true,"usgs":false}],"preferred":false,"id":948117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eppehimer, Drew Elliot 0000-0003-0076-1494","orcid":"https://orcid.org/0000-0003-0076-1494","contributorId":333633,"corporation":false,"usgs":true,"family":"Eppehimer","given":"Drew","email":"","middleInitial":"Elliot","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":948119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bestgen, Kevin R.","contributorId":361015,"corporation":false,"usgs":false,"family":"Bestgen","given":"Kevin","middleInitial":"R.","affiliations":[{"id":86153,"text":"Larval Fish Laboratory, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":948120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, M. Tildon","contributorId":361016,"corporation":false,"usgs":false,"family":"Jones","given":"M.","middleInitial":"Tildon","affiliations":[{"id":86154,"text":"U.S. Fish and Wildlife Service, Upper Colorado River Endangered Fish Recovery Program, Vernal","active":true,"usgs":false}],"preferred":false,"id":948121,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Michaud, Chris","contributorId":361017,"corporation":false,"usgs":false,"family":"Michaud","given":"Chris","affiliations":[{"id":86156,"text":"U.S. Fish and Wildlife Service, Upper Colorado River Endangered Fish Recovery Program, Lakewood, Colorado","active":true,"usgs":false}],"preferred":false,"id":948122,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271489,"text":"70271489 - 2025 - Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca)","interactions":[],"lastModifiedDate":"2025-12-01T16:36:48.962288","indexId":"70271489","displayToPublicDate":"2025-08-07T08:26:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca)","title":"Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca)","docAbstract":"Freshwater mussels are among the most sensitive species to a variety of chemicals in water exposures. However, few studies have been conducted to evaluate the effect of toxicants in sediments on mussels. Industrial discharges containing polyaromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and metals entered the Kanawha River surrounding Blaine Island, South Charleston, West Virginia, USA; a river which supports eight federally endangered mussel species. We collected sediment samples from a highly contaminated site, a nearby upstream site, and a further upstream reference site to assess the effects of contaminated sediment on the survival and growth of a unionid mussel (fatmucket, Lampsilis siliquoidea) and a commonly tested benthic organism (amphipod, Hyalella azteca) using standard 28-d sediment toxicity tests. We also determined mussel toxicity in a serial dilution of the highly contaminated sediment. Results showed that concentrations of PAHs, VOCs, and metals in the contaminated sediment were consistently greater than the other two sites. The mean survival of mussels and amphipods in the reference sediment was 100% and 95%, respectively, whereas the mean survival of both test species in the contaminated sediment was 0%. In the sediment dilution study, mean survival and biomass of mussels in the ≥6.25% treatment were significantly reduced relative to the control, with a 25% inhibition concentration of 4.1% for survival and 3.6% for biomass. We used sediment screening values and equilibrium partitioning sediment benchmarks to determine that nickel, mercury, and PAH mixture were likely responsible for the toxicity observed to mussels and amphipods and will provide critical data to identify and mitigate the sources of the mixture in contaminated sediment.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1093/etojnl/vgaf200","usgsCitation":"Ivey, C.D., Steevens, J.A., Wang, N., Patnode, K., Kunz, J.L., and Besser, J.M., 2025, Evaluation of the effects of sediments contaminated by industrial discharges to a unionid mussel (Fatmucket, Lampsilis siliquoidea) and a common test benthic organism (Amphipod, Hyalella azteca): Environmental Toxicology and Chemistry, v. 44, no. 11, p. 3202-3211, https://doi.org/10.1093/etojnl/vgaf200.","productDescription":"10 p.","startPage":"3202","endPage":"3211","ipdsId":"IP-168278","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":495715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Blaine Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.69768088630148,\n 38.375274297576794\n ],\n [\n -81.69768088630148,\n 38.36648034085218\n ],\n [\n -81.67359328193788,\n 38.36648034085218\n ],\n [\n -81.67359328193788,\n 38.375274297576794\n ],\n [\n -81.69768088630148,\n 38.375274297576794\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948950,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patnode, Kathleen","contributorId":361533,"corporation":false,"usgs":false,"family":"Patnode","given":"Kathleen","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":948951,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948952,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":948953,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270897,"text":"70270897 - 2025 - Mapping ecological states in the upper Colorado River basin: Implications for fire management","interactions":[],"lastModifiedDate":"2025-08-26T14:55:55.492663","indexId":"70270897","displayToPublicDate":"2025-08-07T07:47:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22185,"text":"Environmental Research: Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Mapping ecological states in the upper Colorado River basin: Implications for fire management","docAbstract":"Spatially explicit information on ecosystem dynamics that offers a mechanistic understanding of ecological processes can benefit environmental management. Broad-scale maps based on state-and-transition models provide valuable insight into transitions among ecological states resulting from specific drivers within areas sharing similar climatic and edaphic characteristics ecological sites (ES). We aimed to quantify ecological dynamics of two ES groups in the Upper Colorado River Basin from 1986 to 2022 through annual maps of ecological states and assess potential drivers of observed state change. This region comprises important sagebrush shrublands and pinyon-juniper woodlands affected by non-native annual grass invasion, wildfires, and drought-induced tree mortality. Using field-based and remote sensing data, we modeled vegetation states using random forest models and mapped the states annually from 1986 to 2022. To demonstrate the utility of the state maps for monitoring and management, we used this time series of maps to investigate the influences of fire and drought on state occurrence. Our findings revealed a statistically significant increase in states invaded by non-native annual species (Invaded state), which replaced Grassland and Shrubland states, while Shrubland states decreased significantly, transitioning to invaded and Woodland states. Invaded states had the highest likelihood of burning, followed by Woodlands. Drought was associated with increased area of Grassland and Bare states, but with decreased area of invaded and Shrubland states. These results indicate an accelerating fire cycle is potentially leading to ongoing regional environmental degradation. Despite increasing drought conditions during the study period, the invaded states continued to increase in area, indicating additional underlying mechanisms. Our reproducible, broad-scale, ecologically-driven state mapping process enhances understanding of how drought, fire, and invasion by non-native plants can transform semiarid landscapes of the western USA.
","language":"English","publisher":"IOPscience","doi":"10.1088/2752-664X/adf55f","usgsCitation":"Severson, J.P., Bishop, T.B., Knight, A.C., Nauman, T.W., McNellis, B.E., Villarreal, M.L., Reed, S.C., Young, K.E., Brunson, M., and Duniway, M.C., 2025, Mapping ecological states in the upper Colorado River basin: Implications for fire management: Environmental Research: Ecology, v. 4, no. 3, 035004, 21 p., https://doi.org/10.1088/2752-664X/adf55f.","productDescription":"035004, 21 p.","ipdsId":"IP-177340","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2752-664x/adf55f","text":"Publisher Index Page"},{"id":494895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.86831536697369,\n 43.42056013100884\n ],\n [\n -113.9651578898021,\n 35.88773718647083\n ],\n [\n -113.42515034117945,\n 35.250820583313256\n ],\n [\n -107.51033502507522,\n 34.06562974390198\n ],\n [\n -106.78782386602788,\n 39.68976401424576\n ],\n [\n -108.89167762624004,\n 42.868819570877264\n ],\n [\n -110.86831536697369,\n 43.42056013100884\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":947311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bishop, Tara B.","contributorId":360618,"corporation":false,"usgs":false,"family":"Bishop","given":"Tara","middleInitial":"B.","affiliations":[{"id":86058,"text":"Utah Valley University, Department of Earth Science, Orem, UT, USA","active":true,"usgs":false}],"preferred":false,"id":947312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis 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USA","active":true,"usgs":false}],"preferred":false,"id":947316,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":217604,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947317,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Young, Kristina E.","contributorId":360622,"corporation":false,"usgs":false,"family":"Young","given":"Kristina","middleInitial":"E.","affiliations":[{"id":86061,"text":"Agricultural Research Service, USDA Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":947318,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brunson, Mark","contributorId":178263,"corporation":false,"usgs":false,"family":"Brunson","given":"Mark","affiliations":[],"preferred":false,"id":947319,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947320,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70269795,"text":"sir20255070 - 2025 - Water-resources inventory and assessment at Katahdin Woods and Waters National Monument","interactions":[],"lastModifiedDate":"2026-02-03T14:51:22.993377","indexId":"sir20255070","displayToPublicDate":"2025-08-07T07:05:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5070","displayTitle":"Water-Resources Inventory and Assessment at Katahdin Woods and Waters National Monument","title":"Water-resources inventory and assessment at Katahdin Woods and Waters National Monument","docAbstract":"The U.S. Geological Survey, in cooperation with the National Park Service, prepared a water-resources inventory and assessment for Katahdin Woods and Waters National Monument (KAWW). This compilation includes published and publicly accessible hydrologic data and resource assessments of streams, rivers, ponds, lakes, wetlands, vernal pools, and groundwater in and near KAWW. It also includes reports and datasets summarizing attributes of KAWW’s hydrologic infrastructure, such as stream crossings, dams, wastewater discharge plants, groundwater monitoring wells, and U.S. Geological Survey streamflow-gaging stations. Descriptions of data and details of current limitations in available datasets are included. Wetland, groundwater, streamflow, and water-quality information are all limited. Hydrography data are available; however, there are limited ground-truth data. Accurate streamlines within KAWW were developed from light detection and ranging (lidar) as a part of this work. Hydrologic infrastructure information is available from multiple sources; however, differences exist among the datasets. Datasets are summarized in appendix 1.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255070","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Tudor, A.L., 2025, Water-resources inventory and assessment at Katahdin Woods and Waters National Monument: U.S. Geological Survey Scientific Investigations Report 2025–5070, 16 p., https://doi.org/10.3133/sir20255070.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172915","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":493343,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5070/coverthb.jpg"},{"id":493344,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5070/sir20255070.pdf","text":"Report","size":"3.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5070 PDF"},{"id":493345,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255070/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5070 HTML"},{"id":493346,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5070/sir20255070.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5070 XML"},{"id":493347,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5070/images/"},{"id":493348,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94QSSSP","text":"USGS data release","linkHelpText":"Lidar-derived hydrography of Katahdin Woods and Waters National Monument, Maine, 2023"},{"id":494169,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118733.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maine","otherGeospatial":"Katahdin Woods and Waters National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.72130102300684,\n 46.47955962684003\n ],\n [\n -69.77140212088534,\n 46.34053986409191\n ],\n [\n -68.86469443278118,\n 45.69306934376334\n ],\n [\n -68.55553399953126,\n 45.50754141763335\n ],\n [\n -68.4003426211154,\n 46.253578073434255\n ],\n [\n -68.8194818178918,\n 46.353192713029614\n ],\n [\n -69.3168268626853,\n 46.282299294777914\n ],\n [\n -69.72130102300684,\n 46.47955962684003\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Passive acoustic monitoring is a cost-effective, minimally invasive technology commonly used to study behavior and population dynamics of soniferous fish species. To understand the strengths and limitations of acoustic monitoring for this purpose at fish spawning aggregations (FSA) requires an assessment of the variability in aggregation-associated sounds (AAS) as a function of time, space, and proximity for spawning fishes of interest. Here, we evaluate temporal and spatial trends in the detection of AAS by Nassau Grouper (Epinephelus striatus) using an array of six hydrophones deployed across a large Nassau Grouper FSA at Little Cayman, Cayman Islands. We collected continuous data for nine days during a winter spawning season and subsequently used an automatic classifier to extract the embedded Nassau Grouper AAS. Using these data, we analyzed variability in spatiotemporal AAS detection rates across the array with a Bayesian mixed effects model. We found high variability in the detection of AAS across the spawning site, with positive correlations among neighboring hydrophone pairs trending toward negative correlations with distances exceeding 350 m. Indeed, temporal trends in AAS rates at the spawning site were approximately inverted at the two most distant hydrophones (~600 m). Across the hydrophone network, our model predicted strong positive effects of fish proximity, spawning behavior, and crepuscular periods on detected AAS. Our findings suggest hydrophone placement can strongly influence AAS detection rates and even basic temporal patterns in AAS across the spawning season. Given both the vagaries of movement and behavior of aggregating fish at spawning sites and the limits of AAS detection using standard monitoring tools, we suggest spawning site acoustic monitoring programs deploy hydrophone arrays of sufficient size to capture the site-wide trends in AAS rates if possible; this is particularly true if researchers hope to compare/contrast AAS rates between spawning sites or across seasons for the purpose of population assessment.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70081","usgsCitation":"Van Horn, C.J., Candelmo, A.C., Heppell, S.A., McCoy, C.R., Pattengill-Semmens, C.V., Waterhouse, L., Cherubin, L.M., Taylor, J., Michaels, W., Locascio, J., Ibrahim, A.K., and Semmens, B.X., 2025, Hydrophone placement yields high variability in detection of Epinephelus striatus calls at a spawning site.: Ecological Applications, v. 35, no. 5, e70081, 21 p., https://doi.org/10.1002/eap.70081.","productDescription":"e70081, 21 p.","ipdsId":"IP-170566","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494455,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70081","text":"Publisher Index Page"},{"id":494311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Little Cayman, Cayman Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.1384616529091,\n 19.741226725935803\n ],\n [\n -80.1384616529091,\n 19.647682249769503\n ],\n [\n -79.94337474038241,\n 19.647682249769503\n ],\n [\n -79.94337474038241,\n 19.741226725935803\n ],\n [\n -80.1384616529091,\n 19.741226725935803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Horn, Cameron J.","contributorId":359810,"corporation":false,"usgs":false,"family":"Van Horn","given":"Cameron","middleInitial":"J.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":946323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Candelmo, Alli C.","contributorId":359814,"corporation":false,"usgs":false,"family":"Candelmo","given":"Alli","middleInitial":"C.","affiliations":[{"id":13188,"text":"Reef Environmental Education Foundation (REEF)","active":true,"usgs":false}],"preferred":false,"id":946325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heppell, Scott A.","contributorId":359816,"corporation":false,"usgs":false,"family":"Heppell","given":"Scott","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":946326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCoy, Croy R.M.","contributorId":359818,"corporation":false,"usgs":false,"family":"McCoy","given":"Croy","middleInitial":"R.M.","affiliations":[{"id":85923,"text":"Department of Environment","active":true,"usgs":false}],"preferred":false,"id":946327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pattengill-Semmens, Christine V.","contributorId":359819,"corporation":false,"usgs":false,"family":"Pattengill-Semmens","given":"Christine","middleInitial":"V.","affiliations":[{"id":13188,"text":"Reef Environmental Education Foundation (REEF)","active":true,"usgs":false}],"preferred":false,"id":946328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waterhouse, Lynn 0000-0002-7455-7632","orcid":"https://orcid.org/0000-0002-7455-7632","contributorId":348524,"corporation":false,"usgs":true,"family":"Waterhouse","given":"Lynn","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":946329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cherubin, Laurent M.","contributorId":359820,"corporation":false,"usgs":false,"family":"Cherubin","given":"Laurent","middleInitial":"M.","affiliations":[{"id":65664,"text":"Harbor Branch Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":946330,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Taylor, J. Christopher","contributorId":359821,"corporation":false,"usgs":false,"family":"Taylor","given":"J. Christopher","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":946331,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Michaels, William","contributorId":359822,"corporation":false,"usgs":false,"family":"Michaels","given":"William","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":946332,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Locascio, James","contributorId":359823,"corporation":false,"usgs":false,"family":"Locascio","given":"James","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":946333,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ibrahim, Ali K.","contributorId":359812,"corporation":false,"usgs":false,"family":"Ibrahim","given":"Ali","middleInitial":"K.","affiliations":[{"id":65664,"text":"Harbor Branch Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":946324,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Semmens, Brice X.","contributorId":359824,"corporation":false,"usgs":false,"family":"Semmens","given":"Brice","middleInitial":"X.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":946334,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70269614,"text":"70269614 - 2025 - Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","interactions":[],"lastModifiedDate":"2025-08-06T15:04:23.764097","indexId":"70269614","displayToPublicDate":"2025-08-05T09:57:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands","docAbstract":"Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne-field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation (r = 0.85) than solely using elevation (r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70356","usgsCitation":"Silva, G., Roberts, D., Byrd, K.B., Chadwick, D., Walker, I., and King, J., 2025, Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands: Ecosphere, v. 16, no. 8, e70356, 22 p., https://doi.org/10.1002/ecs2.70356.","productDescription":"e70356, 22 p.","ipdsId":"IP-172039","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":494433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70356","text":"Publisher Index Page"},{"id":493643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.86741425943123,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.4236991476653\n ],\n [\n -119.88462619707985,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.406950669793815\n ],\n [\n -119.86741425943123,\n 34.4236991476653\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Silva, German","contributorId":358801,"corporation":false,"usgs":false,"family":"Silva","given":"German","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Dar","contributorId":358803,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":944181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chadwick, Dana","contributorId":358806,"corporation":false,"usgs":false,"family":"Chadwick","given":"Dana","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":944182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, Ian","contributorId":358809,"corporation":false,"usgs":false,"family":"Walker","given":"Ian","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, Jennifer","contributorId":358812,"corporation":false,"usgs":false,"family":"King","given":"Jennifer","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944184,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269975,"text":"70269975 - 2025 - Elk personality and anthropogenic food subsidy: Managing conflict and migration loss","interactions":[{"subject":{"id":70261672,"text":"70261672 - 2024 - Ungulate personality and the human shield contribute to long-distance migration loss","indexId":"70261672","publicationYear":"2024","noYear":false,"title":"Ungulate personality and the human shield contribute to long-distance migration loss"},"predicate":"SUPERSEDED_BY","object":{"id":70269975,"text":"70269975 - 2025 - Elk personality and anthropogenic food subsidy: Managing conflict and migration loss","indexId":"70269975","publicationYear":"2025","noYear":false,"title":"Elk personality and anthropogenic food subsidy: Managing conflict and migration loss"},"id":1}],"lastModifiedDate":"2025-08-07T14:14:57.928515","indexId":"70269975","displayToPublicDate":"2025-08-05T09:14:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Elk personality and anthropogenic food subsidy: Managing conflict and migration loss","docAbstract":"The continued decline of long-distance ungulate migrations threatens to decouple important ecological processes that increase biodiversity and wildlife abundance. Past research has focused on preserving migration paths where habitat fragmentation and loss disrupt movement corridors. However, shifting residency-migration trade-offs are the stronger driver of migration loss in some populations. Suburban residential developments may provide ungulates with anthropogenic food sources and refuge from predators, which can increase population growth among short-distance migrants relative to long-distance migrants. This trend can increase wildlife vehicle collisions and other human–wildlife conflicts while simultaneously reducing hunting opportunities. Yet, individual animals vary in their tolerance of human disturbance. We investigated how interindividual variation relative to conflict and human habituation influences elk migration and space use on shared winter range. We used a clustering algorithm applied to GPS collar data to identify elk use of anthropogenic food resources in suburban habitat. Cluster locations identified all known anthropogenic subsidy locations during the study period. Elk that used suburban anthropogenic food sources also migrated 60% shorter distances between summer and winter ranges than elk with no known use of these food subsidies. Elk use of protected wintering grounds was spatially structured such that conflict-prone, short-distance migrants disproportionately used areas with more human activity. Clustering algorithms applied to GPS collar data may allow managers to identify foci of concentrated use that generates human–wildlife conflict, and where prion deposition and environmental contamination facilitate the spread of chronic wasting disease, particularly in suburban areas with anthropogenic food subsidies. The apparent spatial structuring of shared winter range according to the conflict potential and migration strategy of individual elk may also permit managers to assess relative recruitment among cryptic population segments using different migration strategies and facilitate targeted, adaptive management actions. These associations between conflict, human habituation, and migration shed light on the urbanization of wildlife species, inform efforts to manage human–wildlife conflict and disease spread, and emphasize that a multipronged approach beyond maintaining habitat corridors may be necessary to conserve long-distance migrations for species that can become human-habituated.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ECS2.70344","usgsCitation":"Cotterill, G.G., Cole, E., Cross, P., Dewey, S., Wise, B., and Graves, T., 2025, Elk personality and anthropogenic food subsidy: Managing conflict and migration loss: Ecosphere, v. 16, no. 8, e70344, 12 p., https://doi.org/10.1002/ECS2.70344.","productDescription":"e70344, 12 p.","ipdsId":"IP-159240","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":494437,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70344","text":"Publisher Index Page"},{"id":493704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"National Elk Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111,\n 43.7\n ],\n [\n -111,\n 43.4\n ],\n [\n -110.5,\n 43.4\n ],\n [\n -110.5,\n 43.7\n ],\n [\n -111,\n 43.7\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Cotterill, Gavin G. 0000-0002-1408-778X","orcid":"https://orcid.org/0000-0002-1408-778X","contributorId":346534,"corporation":false,"usgs":true,"family":"Cotterill","given":"Gavin","middleInitial":"G.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":945107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cole, Eric K.","contributorId":302890,"corporation":false,"usgs":false,"family":"Cole","given":"Eric K.","affiliations":[{"id":65572,"text":"U.S. Fish and Wildlife Service, National Elk Refuge","active":true,"usgs":false}],"preferred":false,"id":945108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":945109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dewey, Sarah R.","contributorId":342391,"corporation":false,"usgs":false,"family":"Dewey","given":"Sarah R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":945110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wise, Ben L.","contributorId":359279,"corporation":false,"usgs":false,"family":"Wise","given":"Ben L.","affiliations":[{"id":83136,"text":"Wyoming Game & Fish Department","active":true,"usgs":false}],"preferred":false,"id":945111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":945112,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269632,"text":"fs20253038 - 2025 - Global maps of critical mineral production in 2023","interactions":[],"lastModifiedDate":"2026-02-03T14:45:36.109669","indexId":"fs20253038","displayToPublicDate":"2025-08-05T09:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3038","displayTitle":"Global Maps of Critical Mineral Production in 2023","title":"Global maps of critical mineral production in 2023","docAbstract":"The global production of many mineral commodities, especially critical minerals, is concentrated in a few countries that have mineral resources and the infrastructure necessary to mine and process those resources. For this reason, the type and amount of mineral production differ by country. For example, many countries produce such metallic ores as gold and silver, whereas only a few countries produce magnesium, niobium, platinum-group metals, and rare earths. The concentration of mining and processing in certain countries necessitates the existence of a global supply chain.
A mineral supply chain is the sequence of mining and processing of minerals and manufacturing of products. Mineral supply chains are global in scale, complex, and dynamic. Supply chain data can be used to understand how a country’s mineral resources and various economic, technical, and environmental factors affect the complexity of global supply chains.
This fact sheet summarizes the world’s leading countries (those accounting for 5 percent or more of a commodity’s global production in 2023) for production of select mineral commodities (mainly critical minerals) in the mining and processing stages. These countries and the minerals they produce are synthesized on global maps to communicate the status of, and potential risk to, mineral commodity supply chains from geographic production concentration. Trade data from United Nations Statistics Division (2025) is used to support assessments of the observed production data.
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U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
","tableOfContents":"The U.S. Geological Survey (USGS) has been a National Atmospheric Deposition Program (NADP) partner agency since 1981. NADP is composed of five atmospheric monitoring networks that verify Clean Air Act effectiveness and provide essential data to protect human health and preserve ecosystems for current and future generations. Stakeholders include land management agencies overseeing sensitive habitats (the National Park Service, Bureau of Land Management, U.S. Forest Service, and Tribes), Federal and State regulatory agencies, and the public.
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Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Insects are declining worldwide, yet gaps remain in our understanding of how declines are distributed across species within communities. Using three decades of butterfly monitoring data aggregated from the Midwestern United States, we found that no butterfly species increased in abundance from 1992 to 2023. 59 out of 136 species declined (annual mean trend: −1.2 to −6.9% per year) with losses distributed across all functional groups including residents, migrants, rare, and common species. Community composition changed such that abundance is now more even across species, driven by more severe losses in abundance—but not richness—of common species compared to rare species. These widespread declines are likely cascading across ecosystems. Conservation efforts that focus on entire communities could mitigate butterfly biodiversity loss.
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\"}}]}","volume":"122","issue":"33","noUsgsAuthors":false,"publicationDate":"2025-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Leuenberger, Wendy","contributorId":352549,"corporation":false,"usgs":false,"family":"Leuenberger","given":"Wendy","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":947588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doser, Jeffrey W.","contributorId":360759,"corporation":false,"usgs":false,"family":"Doser","given":"Jeffrey","middleInitial":"W.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":947589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Michael W.","contributorId":360761,"corporation":false,"usgs":false,"family":"Belitz","given":"Michael","middleInitial":"W.","affiliations":[{"id":6601,"text":"Michigan State 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rivers","interactions":[],"lastModifiedDate":"2026-02-20T22:23:05.443123","indexId":"70274016","displayToPublicDate":"2025-08-02T15:17:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Shrinking channels, growing threats: Habitat degradation from channel narrowing and invasive vegetation in three dryland rivers","docAbstract":"Water development and the proliferation of invasive riparian vegetation have led to widespread habitat loss and simplification of rivers in the western United States, contributing to the imperilment of native fishes. Here, we quantify channel narrowing and vegetation encroachment, which are conspicuous indicators of riverine habitat alteration, along ∼400 km of three dryland tributaries of the upper Colorado River. We conducted a comparative analysis of aerial photographs between the 1930s and 2010s/2020s time periods using visual interpretation and used Light Detection and Ranging (LiDAR) data along with Object-Based Image Analysis (OBIA) to quantify canopy cover of woody riparian species. All three rivers underwent substantial channel narrowing, coinciding with a general decrease in spring floods over time. However, the extent of narrowing varied among the rivers (78 %, 73 %, and 29 %) with greater narrowing corresponding to larger reductions in spring flows. In contrast, contemporary woody cover was similarly high among all three rivers (39 %, 41 %, and 36 %), and a woody vegetation analysis we conducted for one river indicated a substantial increase in vegetation along the active channel (4 %–74 %). These findings underscore a common pattern observed in rivers throughout the basin, where river channels often undergo narrowing and encroachment by invasive vegetation following dam construction and/or decreases in flows, ultimately leading to habitat simplification, with negative implications for native fishes and other riparian biota. Our findings also emphasize that, even in the presence of nonnative vegetation establishment, preserving or restoring large magnitude and long duration floods can help conserve diverse habitat in dryland rivers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2025.126714","usgsCitation":"Miller, B.J., McKinstry, M.C., Wilcock, P.R., Macfarlane, W.W., Bassett, S., Budy, P., Pennock, C.A., 2025, Shrinking channels, growing threats: Habitat degradation from channel narrowing and invasive vegetation in three dryland rivers: Journal of Environmental Management, v. 392, 126714, 12 p., https://doi.org/10.1016/j.jenvman.2025.126714.","productDescription":"126714, 12 p.","ipdsId":"IP-180680","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"San Juan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.20458933342701,\n 37.628183776979654\n ],\n [\n -111.20458933342701,\n 36.4333212502403\n ],\n [\n -107.18146321534954,\n 36.4333212502403\n ],\n [\n -107.18146321534954,\n 37.628183776979654\n ],\n [\n -111.20458933342701,\n 37.628183776979654\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"392","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Benjamin J. 0009-0009-8097-0763","orcid":"https://orcid.org/0009-0009-8097-0763","contributorId":366731,"corporation":false,"usgs":false,"family":"Miller","given":"Benjamin","middleInitial":"J.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":956170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKinstry, Mark C.","contributorId":366732,"corporation":false,"usgs":false,"family":"McKinstry","given":"Mark","middleInitial":"C.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":956171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilcock, Peter R.","contributorId":366733,"corporation":false,"usgs":false,"family":"Wilcock","given":"Peter","middleInitial":"R.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":956172,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Macfarlane, William W.","contributorId":366734,"corporation":false,"usgs":false,"family":"Macfarlane","given":"William","middleInitial":"W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":956173,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bassett, Steven 0000-0002-3826-3960","orcid":"https://orcid.org/0000-0002-3826-3960","contributorId":211628,"corporation":false,"usgs":false,"family":"Bassett","given":"Steven","affiliations":[{"id":38280,"text":"The Nature Conservancy, Minneapolis MN","active":true,"usgs":false}],"preferred":false,"id":956174,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Budy, Phaedra E. 0000-0002-9918-1678 pbudy@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":140028,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra","email":"pbudy@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - 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A total of 224 basin characteristics were developed for 213 unaltered streamgages (locations where the human effects on streamflow were limited), across the following categories: basin geometry, climate, land cover, soils, surficial geology, and other characteristics. The basins with unaltered streamgages were evaluated for potential redundancy, and streamgages in close proximity and with similar drainage areas were flagged and removed from the testing and cross-validation datasets to prevent data leaking from the training dataset to the testing dataset.
Random forest regression models were created by using basin characteristics as predictor variables and by developing a workflow to train, tune, and test the model. Models were developed to estimate the ungaged lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10). The top four basin characteristics used for the 7Q10 and 30Q10 models were drainage area, total stream length, perimeter of the basin, and length of the longest flow path. Results for the 7Q10 and 30Q10 models had coefficients of determination (R2) of 0.796 and 0.853, respectively. The output model results were bias-corrected for ungaged locations across New York and are available within the interactive StreamStats tool.
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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Pseudogymnoascus destructans (Pd), the causative agent of white-nose syndrome (WNS) in bats, has caused serious declines in bat populations across North America. We conducted WNS surveillance in five different park units in the North Coast and Cascades Network (NCCN) from 2016 to 2024, following the initial detection of Pd and WNS in Washington State in 2016. We captured and swabbed bats, swabbed roost materials, and collected guano and tested these samples for Pd DNA using qPCR. We confirmed WNS through histopathology of tissue samples and carcasses. We detected Pd at five locations in Mount Rainier National Park, starting in 2017. We confirmed WNS at four of these locations, with the first clinical signs detected in 2022. We detected Pd for the first time in Olympic and North Cascades National Parks in 2024. From these efforts, we generated information that can be compared to other datasets, helping us advance our knowledge of WNS/Pd epidemiology. We also conducted three field and laboratory-based experiments to inform early detection/rapid response (EDRR) planning. The first was a field experiment using non-infective Pd DNA to evaluate the rate of DNA degradation and the probability of detecting Pd DNA in the field. Experimental degradation rates for Pd DNA ranged from 1.6% to 8.2% and were lower in protected sites. The second was a laboratory-based experiment to understand Pd growth on four different substrates. We detected increasing levels of Pd in autoclaved guano and in plywood, suggesting these substrates may be environmental reservoirs. Pd remained stable in fresh guano but in soil it decreased, suggesting microbial interactions that may influence Pd growth in these substrates. We also collected wood shavings from a Pd positive bat box in June and August to evaluate viable Pd persistence in wood in a summer roost. Despite the characterization that Pd required cold conditions to persist, viable Pd was present in wood shavings collected during the summer season. Finally, we evaluated the National White-Nose Syndrome Decontamination Protocol through experiments. We found that ethanol was not effective as a sporicidal agent in any of the concentrations we tested and that a 1:10 dilution of bleach did not kill Pd spores, though higher concentrations did. These findings resulted in changes to the national protocol.
","language":"English","publisher":"National Park Service","doi":"10.36967/2314473","usgsCitation":"Chestnut, T., Urbina, J., Hansen, M.E., McCaffery, R.M., Rhea-Fournier, D.J., Allen, J., and Levi, T., 2025, White-nose syndrome surveillance and bat monitoring activities in North Coast and Cascades Network parks 2016–2024: Science Report NPS/SR-2025/340, x, 52 p., https://doi.org/10.36967/2314473.","productDescription":"x, 52 p.","ipdsId":"IP-175067","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":495308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"North Coast and Cascades Network","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.78943923322424,\n 49.02810935945061\n ],\n [\n -123.06047794055165,\n 48.97640128667916\n ],\n [\n -123.40077512551461,\n 48.836309180578326\n ],\n [\n -123.34080643131713,\n 48.277542765315246\n ],\n [\n -124.80221188220374,\n 48.434572735416\n ],\n [\n -124.57490062136836,\n 45.7863792724126\n ],\n [\n -119.78943923322424,\n 45.7863792724126\n ],\n [\n -119.78943923322424,\n 49.02810935945061\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chestnut, Tara","contributorId":264792,"corporation":false,"usgs":false,"family":"Chestnut","given":"Tara","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":948356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Urbina, Jenny","contributorId":361186,"corporation":false,"usgs":false,"family":"Urbina","given":"Jenny","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":948357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Michael Elizabeth 0009-0001-7148-4191","orcid":"https://orcid.org/0009-0001-7148-4191","contributorId":361187,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","middleInitial":"Elizabeth","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":948358,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCaffery, Rebecca M. 0000-0002-0396-0387","orcid":"https://orcid.org/0000-0002-0396-0387","contributorId":211539,"corporation":false,"usgs":true,"family":"McCaffery","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":948359,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rhea-Fournier, Dylan J.","contributorId":361191,"corporation":false,"usgs":false,"family":"Rhea-Fournier","given":"Dylan","middleInitial":"J.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":948360,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allen, Jennifer","contributorId":350828,"corporation":false,"usgs":false,"family":"Allen","given":"Jennifer","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":948361,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Levi, Taal","contributorId":191295,"corporation":false,"usgs":false,"family":"Levi","given":"Taal","email":"","affiliations":[],"preferred":false,"id":948362,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270863,"text":"70270863 - 2025 - Representing 3-dimensional fuels for physics-based fire behavior models: A general framework and case study in a type-converted post-fire shrubfield","interactions":[],"lastModifiedDate":"2025-08-26T15:57:07.631634","indexId":"70270863","displayToPublicDate":"2025-08-01T08:50:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Representing 3-dimensional fuels for physics-based fire behavior models: A general framework and case study in a type-converted post-fire shrubfield","docAbstract":"Background
Physics-based three-dimensional (3D) fire behavior models improve planning for prescribed fire application and wildfire mitigation, but require high spatial resolution 3D fuel models as inputs. While multiple methods and data sources for realistically representing 3D, heterogeneous fuels are available, no unifying framework exists to guide the use of these tools to create 3D fuel models across gradients of vegetation characteristics and data availability. Existing data and methods are most uncertain for mid-level fuels (e.g., shrubs and small trees), due to canopy obstruction of remotely sensed data and a relative lack of modeling efforts. Yet, mid-level fuels are especially important as potential ladder fuels and increasingly common as the dominant fuel in type-converted, post-fire, shrub-dominated landscapes.
Results
Here we introduce the Framework for Representing 3D Fuels (FR3D), a general framework for combining multiple data sources and methods to construct 3D fuel models for forested and unforested landscapes. We then demonstrate FR3D in a case study to build a 3D fuelbed model in a post-fire, shrub-dominated landscape using three new methods for deriving mid-level shrub fuels from: (1) Airborne Laser Scanning (ALS), (2) imputation of Terrestrial Laser Scanning (TLS), and (3) generative modeling of TLS. We compare the resulting fuel models and examine how they affected simulated 3D fire behavior using QUIC-Fire. While each method represented the broad landscape patterning of shrubs, differences in shrub loading, height, and cover highlighted advantages and drawbacks of the different methods. Modeled fire behavior was realistic for all fuel representation methods, but rate of spread and fine fuel consumption was sensitive to the different arrangements of shrubs.
Conclusions
The sensitivity of fire behavior to shrub modeling methods emphasizes the need for fuel models that faithfully represent local fuelbed characteristics and conditions, and highlights the value in testing a range of modeled fuels to understand the potential range of prescribed fire outcomes. FR3D and novel methods of modeling mid-level fuel provide a foundation for tool integration efforts and increased site-specificity of fuel representation for physics-based fire models.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-025-00383-2","usgsCitation":"Tutland, N., Wion, A.P., May, C.J., Hutchings, G.C., Nowak, H., Gattiker, J.R., Hiers, J.K., Linn, R.R., Pokswinski, S.M., and Margolis, E.Q., 2025, Representing 3-dimensional fuels for physics-based fire behavior models: A general framework and case study in a type-converted post-fire shrubfield: Fire Ecology, v. 21, 43, 18 p., https://doi.org/10.1186/s42408-025-00383-2.","productDescription":"43, 18 p.","ipdsId":"IP-176508","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":495062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-025-00383-2","text":"Publisher Index Page"},{"id":494909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Sanchez Canyon, Santa Fe National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.03368207720807,\n 36.27913419354171\n ],\n [\n -107.03368207720807,\n 35.6434439241678\n ],\n [\n -106.074466969809,\n 35.6434439241678\n ],\n [\n -106.074466969809,\n 36.27913419354171\n ],\n [\n -107.03368207720807,\n 36.27913419354171\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2025-08-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Tutland, Niko","contributorId":360588,"corporation":false,"usgs":false,"family":"Tutland","given":"Niko","affiliations":[{"id":86045,"text":"New Mexico Consortium","active":true,"usgs":false}],"preferred":false,"id":947236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wion, Andreas Paul 0000-0002-0701-2843","orcid":"https://orcid.org/0000-0002-0701-2843","contributorId":335166,"corporation":false,"usgs":true,"family":"Wion","given":"Andreas","email":"","middleInitial":"Paul","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":947237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Carolina Jasmine 0009-0005-1667-109X","orcid":"https://orcid.org/0009-0005-1667-109X","contributorId":360589,"corporation":false,"usgs":true,"family":"May","given":"Carolina","middleInitial":"Jasmine","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":947238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutchings, Grant C.","contributorId":360590,"corporation":false,"usgs":false,"family":"Hutchings","given":"Grant","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowak, Hope","contributorId":360591,"corporation":false,"usgs":false,"family":"Nowak","given":"Hope","affiliations":[{"id":7197,"text":"Unaffiliated","active":true,"usgs":false}],"preferred":false,"id":947240,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gattiker, James R.","contributorId":360592,"corporation":false,"usgs":false,"family":"Gattiker","given":"James","middleInitial":"R.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947241,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hiers, J. Kevin","contributorId":360593,"corporation":false,"usgs":false,"family":"Hiers","given":"J.","middleInitial":"Kevin","affiliations":[{"id":86048,"text":"Strategic Environmental Research and Development Program","active":true,"usgs":false}],"preferred":false,"id":947242,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Linn, Rodman R.","contributorId":360594,"corporation":false,"usgs":false,"family":"Linn","given":"Rodman","middleInitial":"R.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947243,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pokswinski, Scott M.","contributorId":360595,"corporation":false,"usgs":false,"family":"Pokswinski","given":"Scott","middleInitial":"M.","affiliations":[{"id":86045,"text":"New Mexico Consortium","active":true,"usgs":false}],"preferred":false,"id":947244,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":947245,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70274059,"text":"70274059 - 2025 - Evidence for marine-driven, cyclical fluctuations in burrow-nesting seabird habitat on the Oregon Coast","interactions":[],"lastModifiedDate":"2026-02-23T15:35:42.41776","indexId":"70274059","displayToPublicDate":"2025-07-31T08:28:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for marine-driven, cyclical fluctuations in burrow-nesting seabird habitat on the Oregon Coast","docAbstract":"Seabirds are among the most threatened birds globally, with the loss or deterioration of coastal breeding habitats posing a severe threat. Natural and anthropogenic disturbances substantially influence coastal ecosystems through erosion and vegetation loss, altering habitat for the wildlife species that depend on them. In addition to these disturbances, oceanographic processes may play an important role in shaping the vegetation at breeding habitats; however, there is limited information on how vegetative conditions for burrow nesting seabirds have changed over time, and whether these changes are related to specific oceanographic or climatic factors. The Oregon Coast National Wildlife Refuge Complex, USA (NWRC) is home to a diverse suite of 1.3 million nesting seabirds from 14 species, which provide valuable ecological, economic, and cultural services, including nutrient transfer to terrestrial habitats and ecotourism for local communities. Over the last 30 years, populations of several burrow nesting seabird species including tufted puffin (Fratercula cirrhata) and rhinoceros auklet (Cerorhinca monocerata), which breed on offshore islands, have sharply declined along the Oregon Coast. To better understand the potential factors driving these declines, we conducted a spatiotemporal analysis of an aspect of burrow nesting seabird habitat, vegetation cover, within the Oregon Coast NWRC. Specifically, we quantified vegetative cover on 16 islands from 1992 to 2022 using a combination of empirical data, historical aerial photography (1992–2005), and aerial photography from the National Agriculture Imagery Program (2005–2022). Results showed cyclical fluctuations in vegetation cover coast-wide, which were closely related to large scale oceanographic oscillations. Specifically, vegetation cover was negatively correlated with the winter Pacific Decadal Oscillation and positively correlated with the spring El Nino Southern Oscillation. We did not directly compare seabird population trends to vegetation trends; however, quantifying these long-term changes in vegetation at breeding habitats can contribute to our comprehensive understanding of the myriad factors influencing seabird population dynamics and conservation.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2025.1589794","usgsCitation":"Kusaka, C.M., Stephensen, S., Peterson, J.T., Davis, M.J., 2025, Evidence for marine-driven, cyclical fluctuations in burrow-nesting seabird habitat on the Oregon Coast: Frontiers in Ecology and Evolution, v. 13, 1589794, 17 p., https://doi.org/10.3389/fevo.2025.1589794.","productDescription":"1589794, 17 p.","ipdsId":"IP-172895","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500834,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2025.1589794","text":"Publisher Index Page"},{"id":500405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Islands National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.15366691965761,\n 46.41412674563094\n ],\n [\n -125.15366691965761,\n 41.994179108986174\n ],\n [\n -123.59879881574449,\n 41.994179108986174\n ],\n [\n -123.59879881574449,\n 46.41412674563094\n ],\n [\n -125.15366691965761,\n 46.41412674563094\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2025-08-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Kusaka, Carina M.","contributorId":366855,"corporation":false,"usgs":false,"family":"Kusaka","given":"Carina","middleInitial":"M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":956323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephensen, Shawn","contributorId":366856,"corporation":false,"usgs":false,"family":"Stephensen","given":"Shawn","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":956324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":956326,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269721,"text":"sir20255067 - 2025 - Hydrologic budgets and water availability of six bedrock aquifers in the Black Hills area, South Dakota and Wyoming, 1931–2022","interactions":[],"lastModifiedDate":"2026-02-03T14:40:19.630141","indexId":"sir20255067","displayToPublicDate":"2025-07-30T15:39:55","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5067","displayTitle":"Hydrologic Budgets and Water Availability of Six Bedrock Aquifers in the Black Hills Area, South Dakota and Wyoming, 1931–2022","title":"Hydrologic budgets and water availability of six bedrock aquifers in the Black Hills area, South Dakota and Wyoming, 1931–2022","docAbstract":"Population growth and recurring droughts in the Black Hills region raised interest in water resources and future availability. The Black Hills hydrology study (BHHS) was initiated in the early 1990s to address questions regarding water resources. Since completion of the BHHS in the early 2000s, the population of the Black Hills region increased by about 39 percent, which has renewed interest in water demand and availability in the Black Hills. The U.S. Geological Survey, in cooperation with the Western Dakota Regional Water System, completed a study to update hydrologic budgets from the BHHS for six of the most used aquifers in the Black Hills. Water availability was determined by comparing results from hydrologic budgets to modern well withdrawals (2003–22) and water rights information. Key updates to the BHHS budgets included adding available data from 1999 to 2022 and determining hydrologic budgets for six aquifers in nine smaller areas (called “subareas”).
Inflows for the hydrologic budget included recharge from precipitation and streamflow losses to aquifers. Total mean annual recharge for the six aquifers in the study area was estimated at 278,900 acre-feet, with 205,100 acre-feet from precipitation recharge and 73,800 acre-feet from streamflow recharge. Mean annual precipitation recharge for the Madison and Minnelusa aquifers together accounted for 76 percent of the total mean annual precipitation recharge, with the Madison aquifer contributing 57,000 acre-feet and the Minnelusa aquifer contributing 98,100 acre-feet. Outflow components estimated for the hydrologic budget include artesian springflow and well withdrawals. Total mean annual artesian springflow in the study area was estimated as 166,100 acre-feet for the combined Madison and Minnelusa aquifers. Mean total annual well withdrawals for 2003–22 in the study area were about 50,000 acre-feet. No increased well withdrawal patterns corresponding to population increases were observed between 2003 and 2022.
Water availability was determined by comparing total annual appropriations and mean and maximum annual well withdrawals for 2003–22 to mean annual recharge for 1931–2022 for each aquifer in subareas 1–9. Modern well withdrawals (mean and maximum for 2003–22) exceeded mean annual recharge for only the Deadwood and Inyan Kara aquifers in subareas 9 and 4, respectively. Additionally, total annual appropriations did not exceed mean annual recharge in most subareas, except most notably in subarea 4 (Rapid City area) where appropriations exceeded recharge for the Madison, Minnelusa, and Inyan Kara aquifers. Total annual appropriations also exceeded mean annual recharge for the Inyan Kara aquifer in subareas 3 and 5. In addition to recharge, water availability includes the water stored in pore spaces of aquifer materials. Estimates of total volume of recoverable water in storage were updated as part of this study to include the portion of aquifers in Wyoming, which were omitted during the BHHS. In total, the estimated total amount of recoverable water in storage in the study area was 356.9 million acre-feet for six major aquifers in the Black Hills area of South Dakota and Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255067","collaboration":"Prepared in cooperation with the Western Dakota Regional Water System","usgsCitation":"Medler, C.J., Anderson, T.M., and Eldridge, W.G., 2025, Hydrologic budgets and water availability of six bedrock aquifers in the Black Hills area, South Dakota and Wyoming, 1931–2022: U.S. Geological Survey Scientific Investigations Report 2025–5067, 87 p., https://doi.org/10.3133/sir20255067.","productDescription":"Report: ix, 87 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-169475","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":493206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QWKUKP","text":"USGS data release","linkHelpText":"Datasets used in constructing hydrologic budgets for six bedrock aquifers in the Black Hills area of South Dakota and Wyoming, 1931–2022"},{"id":493201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5067/coverthb.jpg"},{"id":493202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.pdf","text":"Report","size":"27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Sir 2025–5067"},{"id":493203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.XML"},{"id":493204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5067/images/"},{"id":493205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255067/full"}],"country":"United States","state":"South Dakota, Wyoming","otherGeospatial":"Black Hills area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.5,\n 44.75\n ],\n [\n -104.5,\n 43.25\n ],\n [\n -103,\n 43.25\n ],\n [\n -103,\n 44.75\n ],\n [\n -104.5,\n 44.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
The State of Tennessee has an area of approximately 42,100 square miles and includes six physiographic regions: Blue Ridge, Valley and Ridge, Appalachian Plateaus, Highland Rim, Nashville Basin, and the Gulf Coastal Plains. Up-to-date elevation data support key activities across the State, such as economic development, infrastructure and construction management, agriculture and precision farming, forest resources management, natural resources conservation, flood risk management, emergency management, and urban and regional planning. The State experiences frequent landslides affecting major roadways. High-resolution elevation data can help identify potential landslide-prone areas. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
The 3D Elevation Program (3DEP; refer to sidebar) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at qual-ity level 2 or better to meet the many needs of the Nation and Tennessee. The status of available and in-progress 3DEP baseline lidar data in Tennessee is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $6.32 million in new benefits annually to the State. The top 10 Tennessee business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"