While there have been recent advancements in synthetic aperture radar (SAR)-based snow water equivalent (SWE) retrievals, obtaining accurate estimates of SWE requires knowledge of the amount of liquid water content (LWC) in the snowpack given its strong impact on radar velocity. Recent studies have utilised Sentinel-1 SAR to identify snowmelt runoff onset in complex, high-elevation terrain based on a seasonal minimum backscatter time-series; however, detailed investigations into the snowpack state before and after snowmelt runoff onset are lacking. In this study, we integrated repeat field measurements at five sites, SNOw TELemetry (SNOTEL) station data (n = 260) from across the Western United States, and paired Sentinel-1 SAR estimates of snowmelt runoff onset to (1) assess the snowpack state prior to and after Sentinel-1 SAR-derived runoff onset estimates, and (2) evaluate Sentinel-1 SAR estimates of runoff onset with SNOTEL-derived estimates of melt output via soil moisture ‘pulses’. We found that on the date of minimum backscatter, the snowpack was isothermal at three of the five field sites, and snow pit-measured LWC was increasing at all field sites relative to previous survey dates. SNOTEL soil moisture pulses preceded Sentinel-1 SAR estimates of snowmelt runoff onset by a median of 3 days (standard deviation = ±25.3 days) and post-dated peak SWE by a median of 3 days (standard deviation = ±18.2 days). Snow density and the number of positive degree days on soil moisture pulse date increased with latitude and longitude and decreased with elevation. Although satellite-based estimates of snowmelt runoff onset provide a promising approach for improving spaceborne retrievals of SWE, local climatological conditions exert significant influence on meltwater runoff onset signal clarity for both in situ and satellite-based estimates.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70341","usgsCitation":"Detre, A., McGrath, D., Gagliano, E., Bonnell, R.R., Webb, R., Marshall, H., and Shean, D., 2025, Sentinel-1 SAR estimates of snowmelt onset coincide with SNOTEL soil moisture pulses across the western U.S.: Hydrological Processes, v. 39, no. 12, e70341, 18 p., https://doi.org/10.1002/hyp.70341.","productDescription":"e70341, 18 p.","ipdsId":"IP-180871","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":503768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70341","text":"Publisher Index Page"},{"id":503550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.75873923043854,\n 48.828742030010915\n ],\n [\n -125.75873923043854,\n 34.45259906675574\n ],\n [\n -104.65537215897085,\n 34.45259906675574\n ],\n [\n -104.65537215897085,\n 48.828742030010915\n ],\n [\n -125.75873923043854,\n 48.828742030010915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Detre, Ally 0009-0004-6977-9283","orcid":"https://orcid.org/0009-0004-6977-9283","contributorId":370422,"corporation":false,"usgs":false,"family":"Detre","given":"Ally","affiliations":[{"id":79077,"text":"Department of Geosciences, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":960331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGrath, Daniel 0000-0002-9462-6842","orcid":"https://orcid.org/0000-0002-9462-6842","contributorId":221142,"corporation":false,"usgs":false,"family":"McGrath","given":"Daniel","affiliations":[{"id":40333,"text":"Department of Geosciences, Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":960332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gagliano, Eric","contributorId":370423,"corporation":false,"usgs":false,"family":"Gagliano","given":"Eric","affiliations":[{"id":12994,"text":"Department of Civil and Environmental Engineering, University of Washington","active":true,"usgs":false}],"preferred":false,"id":960333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnell, Randall Ray 0000-0002-8812-351X","orcid":"https://orcid.org/0000-0002-8812-351X","contributorId":365098,"corporation":false,"usgs":true,"family":"Bonnell","given":"Randall","middleInitial":"Ray","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":960334,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webb, Ryan","contributorId":330966,"corporation":false,"usgs":false,"family":"Webb","given":"Ryan","email":"","affiliations":[{"id":79081,"text":"Department of Civil and Architectural Engineering and Construction Management, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":960335,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marshall, Hans-Peter","contributorId":365100,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":960336,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shean, David","contributorId":299742,"corporation":false,"usgs":false,"family":"Shean","given":"David","affiliations":[],"preferred":false,"id":960337,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274577,"text":"70274577 - 2025 - From fences to roads: Changes in barrier behaviour of Mongolian gazelle across different types of linear infrastructure in Mongolia","interactions":[],"lastModifiedDate":"2026-04-06T15:48:16.485239","indexId":"70274577","displayToPublicDate":"2025-11-26T14:51:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"From fences to roads: Changes in barrier behaviour of Mongolian gazelle across different types of linear infrastructure in Mongolia","docAbstract":"Poorly designed linear infrastructure can reduce habitat connectivity and be major barriers for migratory wildlife. An important start at effective mitigation is understanding how individuals respond when barriers are encountered. This can be done via comparison of fine- and broad-scale behavioural responses to various anthropogenic barrier types. We classified fine-scale responses of 62 Mongolian gazelles (Procapra gutturosa) across different barrier types, seasons and times of day. We also investigated interactions at a broader scale by measuring the length of linear infrastructure traced, interaction duration and crossing success rate. We learned that gazelle behaviour varied according to barrier permeability, and that fences were major obstacles. Gazelles exhibited similar bouncing behaviour when confronted with paved roads as with fences, suggesting paved roads can act as semi-permeable barriers during high traffic volume. Broad-scale movement patterns revealed gazelles travelled considerable distances along fences—averaging 40.2 km, and up to 211.6 km—before moving away or crossing. Long-distance tracing movements can help identify areas with the strongest barrier effect and guide mitigation measures for current and future linear infrastructure. Designing infrastructure and implementing conservation strategies for ungulates in steppe ecosystems will benefit from taking into account behavioural responses at both fine and broad scales.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2025.2093","usgsCitation":"Sévêque, A., Mendgen, P., Freeman, I., Bayarbaatar, B., Kauffman, M.J., Olsen, K., Usukhjargal, D., Uuganbayar, G., Xu, W., Mueller, T., and Dejid, N., 2025, From fences to roads: Changes in barrier behaviour of Mongolian gazelle across different types of linear infrastructure in Mongolia: Proceedings of the Royal Society B: Biological Sciences, v. 292, no. 2059, 20252093, https://doi.org/10.1098/rspb.2025.2093.","productDescription":"20252093","ipdsId":"IP-180143","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":501972,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mongolia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 86.14815830860528,\n 52.011926051186464\n ],\n [\n 86.14815830860528,\n 41.82159331743961\n ],\n [\n 119.95403757641532,\n 41.82159331743961\n ],\n [\n 119.95403757641532,\n 52.011926051186464\n ],\n [\n 86.14815830860528,\n 52.011926051186464\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"292","issue":"2059","noUsgsAuthors":false,"publicationDate":"2025-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Sévêque, Anthony","contributorId":369043,"corporation":false,"usgs":false,"family":"Sévêque","given":"Anthony","affiliations":[{"id":27439,"text":"Senckenberg Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":958345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mendgen, Philipp","contributorId":339036,"corporation":false,"usgs":false,"family":"Mendgen","given":"Philipp","email":"","affiliations":[{"id":81238,"text":"Goethe University","active":true,"usgs":false}],"preferred":false,"id":958346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Ian","contributorId":335506,"corporation":false,"usgs":false,"family":"Freeman","given":"Ian","email":"","affiliations":[],"preferred":false,"id":958347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bayarbaatar, Buuveibaatar","contributorId":287848,"corporation":false,"usgs":false,"family":"Bayarbaatar","given":"Buuveibaatar","affiliations":[{"id":61638,"text":"Wildlife Conservation Society, Mongolia Program, Ulaanbaatar, Mongolia","active":true,"usgs":false}],"preferred":false,"id":958348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":958349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olsen, Kirk","contributorId":369044,"corporation":false,"usgs":false,"family":"Olsen","given":"Kirk","affiliations":[{"id":87706,"text":"Wildlife Conservation Society, Mongolia Program","active":true,"usgs":false}],"preferred":false,"id":958350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Usukhjargal, Dorj","contributorId":287888,"corporation":false,"usgs":false,"family":"Usukhjargal","given":"Dorj","email":"","affiliations":[{"id":61645,"text":"Hustai National Park Trust, Mongolia","active":true,"usgs":false}],"preferred":false,"id":958351,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Uuganbayar, Ganbold","contributorId":287889,"corporation":false,"usgs":false,"family":"Uuganbayar","given":"Ganbold","email":"","affiliations":[{"id":61645,"text":"Hustai National Park Trust, Mongolia","active":true,"usgs":false}],"preferred":false,"id":958352,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Xu, Wenjing","contributorId":285005,"corporation":false,"usgs":false,"family":"Xu","given":"Wenjing","email":"","affiliations":[],"preferred":false,"id":958353,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mueller, Thomas","contributorId":337677,"corporation":false,"usgs":false,"family":"Mueller","given":"Thomas","affiliations":[{"id":81034,"text":"Goethe-University Frankfurt and Senckenberg Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":958354,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dejid, Nandintsetseg","contributorId":287854,"corporation":false,"usgs":false,"family":"Dejid","given":"Nandintsetseg","affiliations":[{"id":61644,"text":"Senckenberg Biodiversity and Climate Research Centre, Frankfurt (Main), Germany","active":true,"usgs":false}],"preferred":false,"id":958355,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70272619,"text":"ofr20251054 - 2025 - Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques","interactions":[],"lastModifiedDate":"2026-02-03T16:40:03.569804","indexId":"ofr20251054","displayToPublicDate":"2025-11-26T12:05:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1054","displayTitle":"Comparisons of Shoreline Positions from Satellite-Derived and Traditional Field- and Remote-Sensing Techniques","title":"Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques","docAbstract":"Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251054","programNote":"Coastal and Marine Hazards and Resources Program","usgsCitation":"O’Neill, A.C., Batiste, S.F., Buscombe, D.D., Burgess, J., Doran, K.S., Gibbs, A.E., Henderson, R.E., Heslin, J.L., Janda, C.N., Lundine, M.A., Terrano, J.F., Warrick, J.A., and Weber, K.M., 2025, Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques: U.S. Geological Survey Open-File Report 2025–1054, 41 p., https://doi.org/10.3133/ofr20251054.","productDescription":"viii, 41 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-177965","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":496871,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251054/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1054 HTML"},{"id":496869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1054/coverthb.jpg"},{"id":496873,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1054/images/"},{"id":496872,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1054/ofr20251054.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1054 XML"},{"id":496870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1054/ofr20251054.pdf","text":"Report","size":"8.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1054 PDF"}],"country":"United States","state":"Alaska, Florida, Massachusetts, Washington","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.23262232723678,\n 42.09708901142952\n ],\n [\n -70.23262232723678,\n 41.7296953201367\n ],\n [\n -69.88869105489118,\n 41.7296953201367\n ],\n [\n -69.88869105489118,\n 42.09708901142952\n ],\n [\n -70.23262232723678,\n 42.09708901142952\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.8586476385848,\n 27.855837758503966\n ],\n [\n -82.8586476385848,\n 27.73142586709062\n ],\n [\n -82.70953873940795,\n 27.73142586709062\n ],\n [\n -82.70953873940795,\n 27.855837758503966\n ],\n [\n -82.8586476385848,\n 27.855837758503966\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.6354762817767,\n 48.15958852423637\n ],\n [\n -123.6354762817767,\n 48.07700409405399\n ],\n [\n -123.49731286568142,\n 48.07700409405399\n ],\n [\n -123.49731286568142,\n 48.15958852423637\n ],\n [\n -123.6354762817767,\n 48.15958852423637\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -143.75227424150037,\n 70.14885784620938\n ],\n [\n -143.75227424150037,\n 70.08259373043822\n ],\n [\n -143.56394337243916,\n 70.08259373043822\n ],\n [\n -143.56394337243916,\n 70.14885784620938\n ],\n [\n -143.75227424150037,\n 70.14885784620938\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.26663872487647,\n 18.348796644288143\n ],\n [\n -67.26663872487647,\n 18.327492880601937\n ],\n [\n -67.24442657044659,\n 18.327492880601937\n ],\n [\n -67.24442657044659,\n 18.348796644288143\n ],\n [\n -67.26663872487647,\n 18.348796644288143\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission Street
Santa Cruz, California 95060
Sudden habitat loss associated with environmental disturbance can trigger animals to move from affected to undisturbed areas, where increases in local density may occur. Although pathogen transmission is strongly related to local density, how crowding after habitat loss affects infection dynamics in wild populations remains unclear. Here we conceptualize the Disturbance-Density-Disease hypothesis, which posits that disturbance-induced habitat loss results in increased pathogen prevalence via increases in local density at adjacent, undisturbed patches. We then used data from before, during, and after flooding disturbance to test this hypothesis in boreal toads Anaxyrus boreas boreas co-occurring with the pathogenic fungus Batrachochytrium dendrobatidis (Bd). We collected Bd samples from captured individuals during a 5-year (2015–2019) mark-recapture study of boreal toads (n = 1295) that breed in beaver ponds in western Wyoming, USA. During spring of 2017, an extreme flooding event destroyed several beaver dams, resulting in the loss of breeding habitat. We compared host density and pathogen prevalence pre- and post-disturbance at sites affected versus unaffected by flooding. At affected sites, population density and Bd prevalence increased at adjacent, undisturbed ponds following the sudden loss of habitat. Moreover, neither host density nor Bd prevalence increased at control sites in areas unaffected by flooding. Taken together, our results support hypothesized links between disturbance, adjacent increases in density, and subsequent increases in pathogen prevalence. Our study contributes to a growing body of ecological research leveraging natural experiments to extract insights from extreme disturbance events. By doing so, we demonstrate an important consequence of disturbance beyond proximate habitat loss and introduce a clear conceptual approach (the Disturbance-Density-Disease hypothesis) to understanding how pathogen transmission can be affected by disturbance via alterations to local density.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.70265","usgsCitation":"Barrile, G.M., Chalfoun, A.D., Walters, A.W., Merkle, J.A., 2025, Density as a mechanism linking habitat disturbance to increased pathogen prevalence: Evidence from a natural experiment: Ecology, v. 106, no. 11, e70265, 15 p., https://doi.org/10.1002/ecy.70265.","productDescription":"e70265, 15 p.","ipdsId":"IP-171436","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bridger-Teton National Forest, northern Wyoming Range, Wind River Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.9701288311253,\n 44.01466129417253\n ],\n [\n -110.9701288311253,\n 41.8640098196185\n ],\n [\n -108.35885871498692,\n 41.8640098196185\n ],\n [\n -108.35885871498692,\n 44.01466129417253\n ],\n [\n -110.9701288311253,\n 44.01466129417253\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"106","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":366853,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel","middleInitial":"M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":956316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merkle, Jerod A.","contributorId":366854,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","middleInitial":"A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":956319,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272658,"text":"70272658 - 2025 - The acoustic-Doppler current profiler (ADCP): A comprehensive tool for river-reach hydromorphodynamics","interactions":[],"lastModifiedDate":"2025-12-03T17:19:25.259086","indexId":"70272658","displayToPublicDate":"2025-11-26T11:01:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"The acoustic-Doppler current profiler (ADCP): A comprehensive tool for river-reach hydromorphodynamics","docAbstract":"This paper introduces the use of acoustic Doppler current profiler (ADCP) measurements as input for the Acoustic Mapping Velocimetry (AMV) method, a technique for characterizing the dynamics of riverine bedforms. The performance of this new approach, ADCP-AMV, is compared with input from a multibeam echosounder through a field study conducted on the Mississippi River (USA). A virtual ADCP tool has been created to support the ADCP-AMV measurements with optimal data density predictions. To the authors’ knowledge, this is the first time ADCP measurements have been used in conjunction with the AMV dune-tracking method. Subsequently, the paper discusses the coupling of ADCP-AMV measurements with ancillary data extracted from the ADCP. These ancillary data are processed using previously developed protocols to characterize hydrodynamics and the suspended sediment distribution in the water column. This paper emphasizes the capability of ADCPs to characterize open-channel river hydromorphodynamic parameters with high spatiotemporal resolution. Recommendations to accurately and efficiently acquire these multi-variable measurements and derived datasets are discussed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2025.105180","usgsCitation":"Fleit, G., Muste, M., Baranya, S., Kim, D., Whaling, A., McAlpin, T., and You, H., 2025, The acoustic-Doppler current profiler (ADCP): A comprehensive tool for river-reach hydromorphodynamics: Advances in Water Resources, v. 206, 105180, 15 p., https://doi.org/10.1016/j.advwatres.2025.105180.","productDescription":"105180, 15 p.","ipdsId":"IP-177812","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":497092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.advwatres.2025.105180","text":"Publisher Index Page"},{"id":497018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","city":"Memphis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.08,\n 35.13\n ],\n [\n -90.08,\n 35.12\n ],\n [\n -90.07,\n 35.12\n ],\n [\n -90.07,\n 35.13\n ],\n [\n -90.08,\n 35.13\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"206","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fleit, Gábor","contributorId":363187,"corporation":false,"usgs":false,"family":"Fleit","given":"Gábor","affiliations":[{"id":86640,"text":"Research fellow at Budapest University of Technology and Economics","active":true,"usgs":false}],"preferred":false,"id":951233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muste, Marian 0000-0002-5975-462X","orcid":"https://orcid.org/0000-0002-5975-462X","contributorId":192136,"corporation":false,"usgs":false,"family":"Muste","given":"Marian","email":"","affiliations":[],"preferred":false,"id":951234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baranya, Sándor","contributorId":363188,"corporation":false,"usgs":false,"family":"Baranya","given":"Sándor","affiliations":[{"id":86642,"text":"Professor (Associate) at Budapest University of Technology and Economics","active":true,"usgs":false}],"preferred":false,"id":951235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, Dongsu","contributorId":363189,"corporation":false,"usgs":false,"family":"Kim","given":"Dongsu","affiliations":[{"id":86643,"text":"Professor at Dankook University","active":true,"usgs":false}],"preferred":false,"id":951236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whaling, Amanda 0000-0003-1375-8323","orcid":"https://orcid.org/0000-0003-1375-8323","contributorId":213953,"corporation":false,"usgs":true,"family":"Whaling","given":"Amanda","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McAlpin, Tate","contributorId":363190,"corporation":false,"usgs":false,"family":"McAlpin","given":"Tate","affiliations":[{"id":86644,"text":"Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA.","active":true,"usgs":false}],"preferred":false,"id":951238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"You, Hojun","contributorId":363191,"corporation":false,"usgs":false,"family":"You","given":"Hojun","affiliations":[{"id":86646,"text":"Senior Researcher at K-water Research Institute","active":true,"usgs":false}],"preferred":false,"id":951239,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70272640,"text":"70272640 - 2025 - Potential thiamine deficiency of phytoplankton across a productivity gradient and seasons in Ohio lakes","interactions":[],"lastModifiedDate":"2025-12-02T16:25:40.80763","indexId":"70272640","displayToPublicDate":"2025-11-26T10:21:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Potential thiamine deficiency of phytoplankton across a productivity gradient and seasons in Ohio lakes","docAbstract":"The depth level at which porphyry Cu–forming magmas fractionated and exsolved mineralizing fluids is actively debated. In the classic model, extensive magma fractionation occurs in large, upper crustal magma chambers, and concomitant fluid exsolution leads to forceful expulsion of residual magmas in the form of porphyry dikes, stocks, and breccia pipes, which subsequently serve as pathways for the mineralizing fluids. In contrast, some recent studies highlighting the role of deep crustal magma fractionation in the production of fertile magmas essentially deny the existence of upper crustal magma chambers at the time of mineralization. To address this, we conducted a detailed thermobarometric investigation of 13 intermediate to felsic, porphyritic intrusive rocks related to porphyry-skarn Cu mineralization at Santa Rita and Hanover-Fierro, New Mexico, United States, representing two premineralization magmas (61–60 Ma), seven synmineralization magmas (60–58 Ma), and four late- to postmineralization magmas (58–57 Ma).
For each sample, the pressure of last magma crystallization before final magma ascent to the current exposure level was reconstructed based on Al-in-hornblende barometry of small hornblende inclusions trapped within quartz phenocrysts and through titanium-in-quartz (TitaniQ) thermobarometry of the host quartz phenocrysts themselves. Since quartz is one of the last crystallizing magmatic minerals, and no significant phenocryst growth could have occurred in small dikes and stocks after final magma emplacement, quartz phenocrysts and their contained hornblende inclusions record the depth of last magma crystallization before final magma ascent. When present, hornblende phenocrysts and hornblende inclusions within other major phenocrysts were also analyzed. Both quartz and hornblende barometers return consistent average pressures of 3.2 ± 0.4 kbar for the entire suite of pre- to postmineralization magmas, corresponding to depths of 11 to 14 km. The synmineralization magmas return even more consistent average pressures of 3.1 ± 0.2 kbar, corresponding to a depth of 12 ± 1 km.
The volume of the mineralizing porphyry dikes and stocks at the emplacement level is far too small to have provided all the fluids and metals required to form the observed ore deposits. Therefore, the majority of the ore-forming fluids must have originated from the magmas that crystallized at 12 ± 1 km depth. The ore deposits, conversely, formed at ~5-km paleodepth. This implies that most of the mineralizing fluids traveled an average vertical distance of ~7 km from their magmatic source to the eventual site of ore precipitation. The relatively unaltered nature and low veining degree of deeper parts of mineralized porphyry dikes and stocks suggest that the fluid transport through these intrusive bodies occurred mostly at near-solidus conditions by means of fluid percolation along grain boundaries.
In summary, our results suggest that (1) a large, upper crustal pluton exists ~7 km beneath the Santa Rita and Hanover-Fierro deposits; (2) abundant phenocryst crystallization occurred at this depth level; and (3) this pluton was the main source for the exsolution of ore-forming fluids. However, the investigated rocks have elevated whole-rock Sr/Y ratios, indicating magma fractionation at deep crustal levels. As a result, our preferred model is a combination of the two end-member models introduced above, with most magma fractionation having occurred in the deep crust and with residual, intermediate to felsic melts having ascended and accumulated at 11 to 14 km paleodepth, where they continued to crystallize with comparatively little crystal-liquid separation, before some of these magmas ascended further to shallow levels and quenched to porphyries.
","language":"English","publisher":"Society of Economic Geology","doi":"10.5382/econgeo.5197","usgsCitation":"Audétat, A., Chang, J., and Gaynor, S.P., 2025, Depth of magma crystallization and fluid exsolution beneath the porphyry-skarn Cu deposits at Santa Rita and Hanover-Fierro, New Mexico, USA: Economic Geology, v. 120, no. 7, p. 1679-1699, https://doi.org/10.5382/econgeo.5197.","productDescription":"21 p.","startPage":"1679","endPage":"1699","ipdsId":"IP-174016","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":496982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Hanover-Fierro deposit, Santa Rita mine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108,\n 32.875\n ],\n [\n -108.1667,\n 32.875\n ],\n [\n -108.1667,\n 32.75\n ],\n [\n -108,\n 32.75\n ],\n [\n -108,\n 32.875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"120","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Audétat, Andreas","contributorId":363151,"corporation":false,"usgs":false,"family":"Audétat","given":"Andreas","affiliations":[{"id":83309,"text":"Bavarian Geoinstitute, University of Bayreuth","active":true,"usgs":false}],"preferred":false,"id":951173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chang, Jia","contributorId":363152,"corporation":false,"usgs":false,"family":"Chang","given":"Jia","affiliations":[{"id":83309,"text":"Bavarian Geoinstitute, University of Bayreuth","active":true,"usgs":false}],"preferred":false,"id":951174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaynor, Sean Patrick 0000-0002-8353-511X","orcid":"https://orcid.org/0000-0002-8353-511X","contributorId":346264,"corporation":false,"usgs":true,"family":"Gaynor","given":"Sean","email":"","middleInitial":"Patrick","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951175,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272618,"text":"sir20255099 - 2025 - Temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa, during the Mississippi River Basin Healthy Watersheds Initiative (2010–23)","interactions":[],"lastModifiedDate":"2026-02-03T16:39:07.258008","indexId":"sir20255099","displayToPublicDate":"2025-11-26T08:25:00","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":"2025-5099","displayTitle":"Temporal Changes in Nutrient Concentrations in the Lower Grand River and Selected Drainage Basins, Missouri and Iowa, During the Mississippi River Basin Healthy Watersheds Initiative (2010–23)","title":"Temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa, during the Mississippi River Basin Healthy Watersheds Initiative (2010–23)","docAbstract":"This report describes a cooperative study by the U.S. Geological Survey and Missouri Department of Natural Resources that evaluated temporal changes in total nitrogen (TN) and total phosphorus (TP) concentrations in the Lower Grand River hydrologic unit. The study focused on trends since 2010, when the basin was designated as a priority drainage basin of the Mississippi River Basin Healthy Watersheds Initiative (MRBI). At three local drainage basins within the Lower Grand hydrological unit (MRBI sites), stream nutrient trends were evaluated using flow-adjusted (FA) TN and TP concentrations for water years 2011 through 2023. FATN concentration trends were not statistically significant for any MRBI site. One site (site 2) showed a statistically significant increasing trend in FATP concentration, indicating a possible increase in phosphorus sources in parts of the basin. Overall, streamflow variability appeared to be the dominant factor affecting nutrient concentrations at MRBI sites. At five regional drainage basins, including the Grand River and nearby rivers with data from 1994 through 2023 (long-term sites), annual flow-normalized (FN) TN and TP concentrations were evaluated for trends before (water years 2000–10) and during (water years 2010–23) the MRBI. For water years 2010 through 2023, annual FNTN and FNTP concentrations decreased in the Grand River, as well as in the Nodaway and Chariton Rivers, which were not targeted by the MRBI. The Grand River (site 9) reversed from increasing to decreasing FNTP concentrations after 2010, with a 26-percent reduction. Annual FNTN and FNTP concentrations also decreased at the Missouri River sites. While nutrient reductions in the Grand River may reflect the effects of implemented conservation practices, similar trends in nearby, nontargeted rivers and the absence of strong decreasing trends at MRBI sites suggest that broader regional factors, instead of or in addition to MRBI efforts, may have contributed to nutrient reductions in the Grand River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255099","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kamrath, B.J.W., Lauderback, C.N., and Murphy, J.C., 2025, Temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa, during the Mississippi River Basin Healthy Watersheds Initiative (2010–23): U.S. Geological Survey Scientific Investigations Report 2025–5099, 19 p., https://doi.org/10.3133/sir20255099.","productDescription":"Report: vii, 19 p.; 5 Linked Tables; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-167198","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497801,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118990.htm"},{"id":496854,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":496853,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13FQ2YN","text":"USGS data release","linkHelpText":"Archive of the load estimation models used in the analyses of temporal changes in nutrient concentrations in the Lower Grand River and selected drainage basins, Missouri and Iowa (2010–23)"},{"id":496855,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255099/full"},{"id":496848,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5099/coverthb.jpg"},{"id":496852,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2025/5099/downloads/","text":"Tables 1.1 to 1.5","linkFileType":{"id":3,"text":"xlsx"}},{"id":496851,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5099/images/"},{"id":496849,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5099/sir20255099.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5099"},{"id":496850,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5099/sir20255099.XML"}],"country":"United States","state":"Iowa, Missouri","otherGeospatial":"Lower Grand River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.5,\n 41.5\n ],\n [\n -95.5,\n 38.5\n ],\n [\n -91.5,\n 38.5\n ],\n [\n -91.5,\n 41.5\n ],\n [\n -95.5,\n 41.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, estimated total nitrogen and total phosphorus concentrations at three local and five regional monitoring sites in Missouri. Temporal changes in total nitrogen and total phosphorus were quantified to evaluate whether instream nutrient concentrations have changed at local or regional scales. At the local scale sites, total phosphorus concentrations substantially increased at one site, which indicated a possible increase in phosphorus sources in the Lower Grand River hydrologic unit, while total nitrogen concentrations did not change substantially. At the regional site, annual total nitrogen and total phosphorus concentrations generally decreased. The regional decline in stream nutrients paired with the lack of nutrient reduction at the local sites indicated that nutrient reductions in the Grand River may have been driven by regional changes in nutrient export, instead of or in addition to conservation practices implemented as part of the Mississippi River Basin Healthy Watersheds Initiative.
","publicationDate":"2025-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Kamrath, Brock J.W. 0000-0001-7118-0537","orcid":"https://orcid.org/0000-0001-7118-0537","contributorId":347859,"corporation":false,"usgs":true,"family":"Kamrath","given":"Brock","middleInitial":"J.W.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lauderback, Courtney N. 0000-0002-6975-0331","orcid":"https://orcid.org/0000-0002-6975-0331","contributorId":363041,"corporation":false,"usgs":true,"family":"Lauderback","given":"Courtney","middleInitial":"N.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950959,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272103,"text":"sir20255062 - 2025 - An evaluation of the effects of different deicing salt application rates on three watersheds in Essex County, New York","interactions":[],"lastModifiedDate":"2026-02-03T16:38:12.480343","indexId":"sir20255062","displayToPublicDate":"2025-11-25T15:50:00","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":"2025-5062","displayTitle":"An Evaluation of the Effects of Different Deicing Salt Application Rates on Three Watersheds in Essex County, New York","title":"An evaluation of the effects of different deicing salt application rates on three watersheds in Essex County, New York","docAbstract":"The U.S. Geological Survey, in cooperation with the New York State Department of Transportation, evaluated the effects of different deicing salt application rates on surface water, groundwater, and highway runoff quality near State highways in northern New York. Three reaches of State highways were tested with different deicing treatments between October 2019 and November 2022: a salt-sand mixture (Treatment A), a salt mixture applied at a lower rate (Treatment B), and a control mixture consistent with typical deicing salt amounts and application rates. Data on pavement conditions and the quality of surface water, highway runoff, and groundwater were collected. Surface electromagnetic data were also collected. Surface-water and groundwater quality downgradient from the State highways were compared with water quality at upgradient locations. The percentage of snow or ice coverage was used to evaluate the effectiveness of the salt applications.
This report provides an overview of the transport of deicing salt. The Treatment B watershed had deicing mixture applied more frequently than other highway reaches, which caused it to have the highest annual total chloride application. Despite differences in chloride application, flow-weighted mean chloride concentrations in highway runoff were comparable across treatments. Chloride concentrations were elevated in surface water and groundwater downgradient from highways relative to chloride concentrations upgradient from highways. A chloride mass balance, calculated for one treatment watershed, indicated that groundwater affected by legacy deicing practices may be contributing additional chloride to surface water. Spatial patterns from electromagnetic surveys show a shallow saline plume alongside the highway in that area.
Differences in winter severity and pavement-surface conditions drove deicing salt applications in the treatment areas. This study found that several factors affect chloride loads in the watersheds, including variable winter conditions, adaptive snow and ice management, legacy management practices, and area-specific aquifer and groundwater conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255062","collaboration":"Prepared in cooperation with the New York State Department of Transportation","usgsCitation":"Gutchess, K., Scavotto, N., Dondero, A., Woda, J., Terry, N., Smith, K., and Williams, J., 2025, An evaluation of the effects of different deicing salt application rates on three watersheds in Essex County, New York: U.S. Geological Survey Scientific Investigations Report 2025–5062, 31 p., https://doi.org/10.3133/sir20255062.","productDescription":"Report: viii, 31 p.; 2 Data Releases","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160931","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497798,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118989.htm"},{"id":496533,"rank":7,"type":{"id":30,"text":"Data 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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 1280–8349
Observations of soil moisture have been found to improve predictions of fire danger as soil moisture impacts both fuel load and fuel moisture. A relative soil moisture process was developed and integrated into the USGS Wildland Fire Potential Index data products. The process uses soil moisture estimates from the CYGNSS GNSS-R constellation and compares current to historical values which then modifies the fuel moisture term in the WFPI equations. Comparisons of WFPI with the soil moisture enhanced version over known large fire events indicate a potential improvement in predicting large fire probability. The goal of this process is to identify areas prone to large fire activity that can be used to target additional CYGNSS observations to assist with wildfire monitoring.
","conferenceTitle":"IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) 2025","conferenceDate":"August 3-8, 2025","conferenceLocation":"Brisbane, Australia","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS55030.2025.11242770","usgsCitation":"Nelson, K., Roy-SIngh, S., Ravindra, V., Moghaddam, M., Kannan, A., and Melebari, A., 2025, Integration of GNSS-R derived soil moisture into the USGS Wildland Fire Potential Index, IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) 2025, Brisbane, Australia, August 3-8, 2025, p. 3477-3480, https://doi.org/10.1109/IGARSS55030.2025.11242770.","productDescription":"4 p.","startPage":"3477","endPage":"3480","ipdsId":"IP-174843","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":504916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":954024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy-SIngh, Sreeja","contributorId":365272,"corporation":false,"usgs":false,"family":"Roy-SIngh","given":"Sreeja","affiliations":[{"id":84643,"text":"Bay Area Environmental Research Institute","active":true,"usgs":false}],"preferred":false,"id":954025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ravindra, Vinay","contributorId":340220,"corporation":false,"usgs":false,"family":"Ravindra","given":"Vinay","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":954026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moghaddam, Mahta","contributorId":267922,"corporation":false,"usgs":false,"family":"Moghaddam","given":"Mahta","email":"","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":954027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kannan, Archana","contributorId":365273,"corporation":false,"usgs":false,"family":"Kannan","given":"Archana","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":954028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Melebari, Amer","contributorId":365274,"corporation":false,"usgs":false,"family":"Melebari","given":"Amer","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":954029,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275128,"text":"70275128 - 2025 - Grass-Cast Southwest: A seasonal rangeland productivity forecast for the southwestern United States","interactions":[],"lastModifiedDate":"2026-04-16T15:10:19.361216","indexId":"70275128","displayToPublicDate":"2025-11-25T10:04:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":24004,"text":"Cambridge Prisms: Drylands","active":true,"publicationSubtype":{"id":10}},"title":"Grass-Cast Southwest: A seasonal rangeland productivity forecast for the southwestern United States","docAbstract":"Here, we present a first assessment of the US Department of Agriculture’s (USDA) “Grass-Cast Southwest,” which is a forecasting tool for rangeland aboveground net primary productivity (ANPP) for the southwest region of the United States. Our results show that ANPP forecasts in early April were relatively close to the observation-based ANPP estimates in late May for all years evaluated (R = 0.6–0.9). The relatively high predictability of spring rangeland productivity in this region is likely because it is strongly driven by antecedent winter/early spring precipitation. Conversely, the first summer forecasts produced in June did not consistently predict the final observation-based ANPP estimates in late August (R = −0.5–0.7), likely because summer rangeland productivity in this region is highly dependent on variable, less predictable precipitation from the North American Monsoon (NAM). Antecedent El Niño Southern Oscillation (ENSO) indices could be used to improve Grass-Cast Southwest performance in both the spring and summer. The ENSOJFM (January–March) index was significantly positively correlated with rangeland productivity during the spring season, whereas ENSOMAM (March–May) was significantly negatively correlated with rangeland productivity during the summer season.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/dry.2025.10013","usgsCitation":"Aguilar-Cubilla, E., Hartman, M.D., Parton, W.J., Reed, S., Derner, J.D., Schulte, D.K., Gornish, E., Moore, D.J., Elias, E., Peck, D.E., Fuchs, B.A., and Smith, W.K., 2025, Grass-Cast Southwest: A seasonal rangeland productivity forecast for the southwestern United States: Cambridge Prisms: Drylands, v. 2, e16, 11 p., https://doi.org/10.1017/dry.2025.10013.","productDescription":"e16, 11 p.","ipdsId":"IP-183979","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":502980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/dry.2025.10013","text":"Publisher Index Page"},{"id":502925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New 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D.","contributorId":370055,"corporation":false,"usgs":false,"family":"Derner","given":"Justin","middleInitial":"D.","affiliations":[{"id":87939,"text":"Rangeland Resources and Systems Research Unit, United States Department of Agriculture -Agricultural Research Service, Cheyenne, WY, 82009, USA","active":true,"usgs":false}],"preferred":false,"id":959589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schulte, Darin K.","contributorId":370056,"corporation":false,"usgs":false,"family":"Schulte","given":"Darin","middleInitial":"K.","affiliations":[{"id":25437,"text":"Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA","active":true,"usgs":false}],"preferred":false,"id":959590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gornish, Elise S.","contributorId":270204,"corporation":false,"usgs":false,"family":"Gornish","given":"Elise S.","affiliations":[{"id":56108,"text":"University of Arizona School of Natural Resources and the Environment, Tucson, Arizona, 85721","active":true,"usgs":false}],"preferred":false,"id":959591,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moore, David J.P.","contributorId":370058,"corporation":false,"usgs":false,"family":"Moore","given":"David","middleInitial":"J.P.","affiliations":[{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false}],"preferred":false,"id":959592,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elias, Emile","contributorId":194484,"corporation":false,"usgs":false,"family":"Elias","given":"Emile","affiliations":[],"preferred":false,"id":959593,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Peck, Dannele E.","contributorId":370060,"corporation":false,"usgs":false,"family":"Peck","given":"Dannele","middleInitial":"E.","affiliations":[{"id":87942,"text":"United States Department of Agriculture Northern Plains Climate Hub, Fort Collins, CO, 80526, USA","active":true,"usgs":false}],"preferred":false,"id":959594,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fuchs, Brian A.","contributorId":370061,"corporation":false,"usgs":false,"family":"Fuchs","given":"Brian","middleInitial":"A.","affiliations":[{"id":87943,"text":"National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, NE 68583, USA","active":true,"usgs":false}],"preferred":false,"id":959595,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smith, William K.","contributorId":370062,"corporation":false,"usgs":false,"family":"Smith","given":"William","middleInitial":"K.","affiliations":[{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false}],"preferred":false,"id":959596,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70272654,"text":"70272654 - 2025 - Evaluating the central–marginal hypothesis: Introgression and genetic variation at the trailing edge of Quercus bicolor","interactions":[],"lastModifiedDate":"2026-01-05T17:01:51.876178","indexId":"70272654","displayToPublicDate":"2025-11-25T09:50:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluating the central–marginal hypothesis: Introgression and genetic variation at the trailing edge of Quercus bicolor","title":"Evaluating the central–marginal hypothesis: Introgression and genetic variation at the trailing edge of Quercus bicolor","docAbstract":"The central–marginal hypothesis (CMH) predicts reduced genetic diversity and increased differentiation in range-edge populations due to ecological marginality and limited gene flow. Deviations from this pattern, however, can result from historical demographic processes, variation in reproductive strategies or interspecific hybridization. The genus Quercus, known for hybridization and long-distance pollination, offers an excellent model to examine the spatial patterns of genetic diversity, structure and introgression across species distributions. Here, we investigate these dynamics in Quercus bicolor Willd., a widespread eastern North American oak. Using RADseq, we genotyped 142 individuals from 12 sites at the fragmented trailing range edge and nine sites from the range core. To detect introgression, we incorporated reference data from six sympatric white oak species. We reveal extensive introgression, particularly from Q. lyrata Walt., in nearly all southern edge populations, but none in core populations despite sympatry with closely related congeners. Southern populations also showed increased genetic structure and differentiation, but not reduced diversity or increased inbreeding, even when only examining non-admixed individuals. Regression analyses reveal relationships between introgressed ancestry and heterozygosity, inbreeding and differentiation, indicating that introgression may buffer range-edge populations against genetic erosion by introducing novel alleles. Hindcast, current and forecast ecological niche models demonstrate temporally changing degrees of overlap between the geographic range of Q. lyrata and Q. bicolor and suggest higher hybridization potential in the future. These findings offer mixed support for the CMH while underscoring the evolutionary relevance of introgression in shaping genetic landscapes at range margins with significant implications for conservation.
","language":"English","publisher":"Wiley","doi":"10.1111/mec.70185","usgsCitation":"Jesse B. Parker, Sean Hoban, Thompson, L., and Scott E. Schlarbaum, 2025, Evaluating the central–marginal hypothesis: Introgression and genetic variation at the trailing edge of Quercus bicolor: Molecular Ecology, v. 34, no. 24, e70185, 19 p., https://doi.org/10.1111/mec.70185.","productDescription":"e70185, 19 p.","ipdsId":"IP-182841","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":497082,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/mec.70185","text":"Publisher Index Page"},{"id":496986,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.7397874284601,\n 47.69010833548356\n ],\n [\n -96.50515645608941,\n 38.374728664216455\n ],\n [\n -94.46111669832068,\n 35.024323679813264\n ],\n [\n -75.54658793458438,\n 34.796796242513224\n ],\n [\n -73.9846649987043,\n 39.20135867583498\n ],\n [\n -69.38877471060589,\n 41.55065832321056\n ],\n [\n -66.75532196682867,\n 44.51664342888958\n ],\n [\n -66.71186067192609,\n 47.40639899204692\n ],\n [\n -76.96565391885454,\n 44.77224521173778\n ],\n [\n -85.96379061474377,\n 47.170718633297994\n ],\n [\n -96.7397874284601,\n 47.69010833548356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"24","noUsgsAuthors":false,"publicationDate":"2025-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Jesse B. Parker","contributorId":363175,"corporation":false,"usgs":false,"family":"Jesse B. Parker","affiliations":[{"id":63836,"text":"University of Tennessee, Knoxville","active":true,"usgs":false}],"preferred":false,"id":951194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sean Hoban","contributorId":363177,"corporation":false,"usgs":false,"family":"Sean Hoban","affiliations":[{"id":86637,"text":"Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":951195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":951196,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott E. Schlarbaum","contributorId":363179,"corporation":false,"usgs":false,"family":"Scott E. Schlarbaum","affiliations":[{"id":63836,"text":"University of Tennessee, Knoxville","active":true,"usgs":false}],"preferred":false,"id":951197,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273031,"text":"70273031 - 2025 - Spatial occupancy patterns of the endangered northern long‐eared bat in New England","interactions":[],"lastModifiedDate":"2025-12-12T16:38:07.189122","indexId":"70273031","displayToPublicDate":"2025-11-25T09:30:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Spatial occupancy patterns of the endangered northern long‐eared bat in New England","docAbstract":"Aim
White-nose syndrome has caused severe declines in eastern North American cave bats, leading to the federal listing of the northern long-eared bat (Myotis septentrionalis) as endangered in the United States and Canada. This has heightened the importance of long-term monitoring to inform species status assessments. We employed a combination of long-term repeated and single-season acoustic survey data to assess the regional presence, spatial distribution, occupancy, and detection probability of northern long-eared bats.
Location
New England, United States.
Methods
We analysed acoustic data from 2357 detector sites, aggregated by year, using Bayesian single-species occupancy models. We investigated the influence of habitat characteristics, climatic variables, and year (2015–2022) on occupancy and the effects of weather conditions and survey month (May to August) on detection probability. Spatial random effects were included to address residual spatial autocorrelation, with a 1-km resolution chosen based on significant positive autocorrelation observed in a non-spatial model.
Results
Occupancy was highest on steep, forested hillsides with minimal anthropogenic development, higher in warmer regions, particularly along coastlines and on offshore islands, and declined across survey years. Including a 1-km spatial random effect reduced residual autocorrelation and suggests northern long-eared bats utilise resources at small to medium landscape scales. Detection probability was highest earlier in the maternity season, but declined when monthly precipitation or temperature exceeded average conditions.
Conclusions
Conservation efforts that focus on steep, forested hillsides in warmer regions with low anthropogenic development could be beneficial. Our analysis supports the use of spatial random effects at a 1-km2 scale, highlighting the importance of survey designs that capture ecological variation at species-specific resolutions. Additionally, early-season acoustic surveys conducted during favourable weather conditions may improve monitoring effectiveness. Acoustic sampling and spatial occupancy modelling offer powerful tools for monitoring remnant populations of northern long-eared bats and guiding conservation practices.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.70122","usgsCitation":"De La Cruz, J.L., Deeley, S.M., Hunter, E.A., and Ford, W., 2025, Spatial occupancy patterns of the endangered northern long‐eared bat in New England: Diversity and Distributions, v. 31, no. 11, e70122, 14 p., https://doi.org/10.1111/ddi.70122.","productDescription":"e70122, 14 p.","ipdsId":"IP-173151","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497707,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.70122","text":"Publisher Index Page"},{"id":497482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont","otherGeospatial":"New England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.0467630667936,\n 47.51205906310295\n ],\n [\n -69.45031396213979,\n 47.35719130879998\n ],\n [\n -70.61580060874599,\n 45.750869986415296\n ],\n [\n -71.56018342332794,\n 45.25079633685677\n ],\n [\n -73.36997254667905,\n 44.93837726441758\n ],\n [\n -73.64279304233246,\n 41.439981251049005\n ],\n [\n -69.6894535963497,\n 41.37398252890503\n ],\n [\n -67.03254188469603,\n 44.57837534082219\n ],\n [\n -68.0467630667936,\n 47.51205906310295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"De La Cruz, Jesse L","contributorId":363941,"corporation":false,"usgs":false,"family":"De La Cruz","given":"Jesse","middleInitial":"L","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":952119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deeley, Sabrina M.","contributorId":363943,"corporation":false,"usgs":false,"family":"Deeley","given":"Sabrina","middleInitial":"M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":952120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":952122,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70276660,"text":"70276660 - 2025 - Improved ground-truthing and benthic habitat characterization with machine learning models and 3D photogrammetry","interactions":[],"lastModifiedDate":"2026-06-15T13:55:02.747366","indexId":"70276660","displayToPublicDate":"2025-11-25T08:50:12","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Improved ground-truthing and benthic habitat characterization with machine learning models and 3D photogrammetry","docAbstract":"Benthic mapping relies on a range of methods to ground truth remotely sensed data to map physical and biotic features. Traditional methods are labor-intensive and time-consuming with limited scalability. Autonomous underwater vehicles (AUVs) and remote operated vehicles (ROVs) can collect high volumes of quality data but require automated processing routines and pose other logistic challenges. This paper explores the automated use of multiple machine learning (ML) models to detect and delineate benthic habitat components from structure-from-motion (SfM) orthomosaics. Overlapping AUV photos were processed into high-resolution, seamless orthomosaics for a portion of the Lake Michigan lakebed. ML models were trained to delineate rocks and live mussels and subsequently applied to the SfM orthomosaics. Results demonstrate that a robust pipeline with SfM and ML models is an effective approach to efficiently, accurately, and comprehensively map benthic habitats.
","conferenceTitle":"Oceans 2025","conferenceDate":"September 29-October 2, 2025","conferenceLocation":"Chicago, IL","language":"English","publisher":"IEEE","doi":"10.23919/OCEANS59106.2025.11245182","usgsCitation":"Wernette, P., Esselman, P., Geglio, A., Galloway, A., Moradi, S., and Pierce, J., 2025, Improved ground-truthing and benthic habitat characterization with machine learning models and 3D photogrammetry, Oceans 2025, v. 2025, Chicago, IL, September 29-October 2, 2025, 11245182, 5 p., https://doi.org/10.23919/OCEANS59106.2025.11245182.","productDescription":"11245182, 5 p.","ipdsId":"IP-182925","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":505570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","volume":"2025","noUsgsAuthors":false,"publicationDate":"2025-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Wernette, Phillipe 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":372346,"corporation":false,"usgs":false,"family":"Wernette","given":"Phillipe","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":963007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":963008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Geglio, Anthony J. 0009-0000-7930-5656","orcid":"https://orcid.org/0009-0000-7930-5656","contributorId":357593,"corporation":false,"usgs":false,"family":"Geglio","given":"Anthony J.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":963009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galloway, Angus 0000-0003-0343-9951","orcid":"https://orcid.org/0000-0003-0343-9951","contributorId":372350,"corporation":false,"usgs":false,"family":"Galloway","given":"Angus","affiliations":[{"id":88304,"text":"Engineering Technologies Canada, Ltd.","active":true,"usgs":false}],"preferred":false,"id":963010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moradi, Shadi 0000-0002-9120-8952","orcid":"https://orcid.org/0000-0002-9120-8952","contributorId":342265,"corporation":false,"usgs":false,"family":"Moradi","given":"Shadi","email":"","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":963011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pierce, Jordan","contributorId":372351,"corporation":false,"usgs":false,"family":"Pierce","given":"Jordan","affiliations":[{"id":83025,"text":"CSS Inc.","active":true,"usgs":false}],"preferred":false,"id":963012,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272800,"text":"70272800 - 2025 - Detection of viral, bacterial, and protozoan pathogens and microbial source tracking markers in paired large- and small-volume water samples","interactions":[],"lastModifiedDate":"2025-12-09T15:04:46.203031","indexId":"70272800","displayToPublicDate":"2025-11-25T07:58:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19118,"text":"ES&T Water","active":true,"publicationSubtype":{"id":10}},"title":"Detection of viral, bacterial, and protozoan pathogens and microbial source tracking markers in paired large- and small-volume water samples","docAbstract":"When sampling for waterborne microbes, researchers may need to diverge from recommended sample volumes due to logistical constraints, novel targets, or challenging matrices, with little guidance about the potential impact on results. In field studies, we measured bacteria, viruses, and protozoa (15 quantitative polymerase chain reaction assays) in paired large- and small-volume samples to evaluate method performance and relevant factors. Concordance between methods was low. Large-volume ultrafiltration yielded more detections than small-volume sampling, especially for pathogens in groundwater. Greater microbial concentrations were associated with more frequent detections in small-volume samples and greater concordance between paired samples. Large-volume samples appeared to be more susceptible to diminished sensitivity from complex sample matrices. In laboratory studies, recovery of microbes was poorer for large- than small-volume methods, although large-volume methods more reliably detected low-concentration targets. Large-volume samples were less stable than small-volume samples during storage. Overall, large-volume sampling was superior for detecting pathogens but may underestimate concentrations; small-volume sampling was more prone to false negatives but was adequate when concentrations were relatively high, like we observed for microbial source tracking in surface waters.
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Traditional audio-visual surveys have been the standard method for detecting murrelets in forests. Given that murrelets can be extremely cryptic in forest environments, audio-visual surveys are difficult, time consuming, and expensive to conduct. Passive acoustic monitoring (PAM) may be a useful and cost-effective alternative. Using a paired murrelet survey design, we compared detection probabilities across 88 survey points in forests of Oregon and Washington. We conducted 2–4 early morning audio-visual surveys and 13–53-day PAM surveys from 14 June through 5 August 2022 and 2023, when murrelets typically make the most frequent inland flights during the nesting season. We found the average detection probability for murrelets on any given day was higher for an audio-visual survey visit (0.40, SD = 0.17) than for a PAM survey day (0.19, SD = 0.10). However, the cumulative detection probability for PAM surveys was 0.98 (95% CI = 0.96–1.0) after 19 survey days, whereas the same cumulative detection probability would require 9 audio-visual survey visits. Using simulations to compare the cost-effectiveness of both methods, we found audio-visual surveys cost more when compared with PAM surveys in 61–91% of scenarios because they require specialized personnel training and additional pre-dawn visits. Further, PAM per survey costs decreased as the number of survey days per point increased and over time after initial costs were incurred, particularly when autonomous recording units were reused for multiple years. While audio-visual surveys are the standard method for murrelet surveys in forest habitats, PAM represents a more cost-effective approach to determine the presence of murrelets.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1640","usgsCitation":"Thomas, A.D., Duarte, A., Gostin, M.J., Peterson, J.T., and Lesmeister, D.B., 2025, Passive acoustic monitoring is a cost-effective approach to conduct inland surveys for marbled murrelets: Wildlife Society Bulletin, v. 49, no. 4, e1640, 21 p., https://doi.org/10.1002/wsb.1640.","productDescription":"e1640, 21 p.","ipdsId":"IP-176313","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":505484,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1640","text":"Publisher Index Page"},{"id":505248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.468941266289,\n 48.97312233463978\n ],\n [\n -117.05956481478819,\n 49.44066172891584\n ],\n [\n -116.90084276070849,\n 42.179337468611294\n ],\n [\n -125.19228376858035,\n 41.72311163735378\n ],\n [\n -126.468941266289,\n 48.97312233463978\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Alaina D.","contributorId":371924,"corporation":false,"usgs":false,"family":"Thomas","given":"Alaina","middleInitial":"D.","affiliations":[{"id":39530,"text":"U.S.D.A. 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The equations were developed using generalized least-squares regression and flood-frequency and drainage-basin characteristics from 156 streamgages. Flood-frequency analyses were completed using data through the 2023 water year. The drainage-basin characteristics used as explanatory variables in the regression equations are drainage area, percentage of wetland area, and basin-wide mean of the average annual precipitation. The average standard errors of prediction used to estimate flood discharges at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP with these equations are 34.9, 37.1, 38.2, 41.6, 43.8, 46.0, 49.1, and 53.2 percent, respectively.
Flood discharges at selected AEPs for streamgages were computed using the Expected Moments Algorithm. Techniques used to adjust an AEP discharge computed from a streamgage record with results from the regression equations and to estimate flood discharge at a selected AEP for an ungaged site upstream or downstream from a streamgage using a drainage-area adjustment are both described. The final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is an internet-based application that provides automated regression-equation solutions for user-selected sites on streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255088","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Olson, S.A., 2025, Estimating flood discharges at selected annual exceedance probabilities for unregulated, rural \nstreams in Vermont, 2023: U.S. Geological Survey Scientific Investigations Report 2025–5088, 22 p., https://doi.org/10.3133/sir20255088.","productDescription":"Report: vii, 22 p.; Data Release; Appendix","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-175471","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":496788,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5088/images"},{"id":496787,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5088/sir20255088.XML","description":"SIR 2025-5088 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Surface water quantity and quality is important for arid and semi-arid regions where many people, including underserved and Indigenous communities, rely on a scarce resource for drinking water, irrigation, livestock and ceremonial uses. The southwestern United States, and specifically the Four Corners Region (Colorado, Arizona, New Mexico and Utah), is an example of this situation. Elevated concentrations of metals including aluminium, arsenic and lead were identified in previous studies and this study in the San Juan River from below the Navajo Dam, through the Navajo Nation to Mexican Hat, Utah. An interdisciplinary team applied approaches and principles of geology, geochemistry, geomorphology, hydrology and statistics to gain a better understanding of the tributaries supplying the source(s) of metals to the San Juan River. This introductory paper provides an overview of the ‘Metal geochemical fingerprinting to identify sub-watershed source contributions to surface water at a regional arid watershed scale, Four Corners Region, USA’ thematic collection. An overview of sampling sites, techniques and potential sources of metals is provided. Approaches used in this study could be applied to investigations in similar systems globally.
","language":"English","publisher":"Geological Society of London","doi":"10.1144/geochem2024-027","usgsCitation":"Blake, J., Austin, S.A., Johnson, F., Brown, J., Chavarria, S., Mixon, R., Van Zante, C., Wilkins, K., Whiting, M.R., Ferguson, C.L., Shephard, Z., Bosch, K., Austring, T.J., Ratigan, Z., Shomour, A.A., and Yager, D., 2025, Tracking the sources of metals to the San Juan River, Four Corners Region, USA: An introduction to the thematic issue: Geochemistry: Exploration, Environment, Analysis, v. 25, no. 4, geochem2024-027, 16 p., https://doi.org/10.1144/geochem2024-027.","productDescription":"geochem2024-027, 16 p.","ipdsId":"IP-163791","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Four Corners region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.73849819939772,\n 37.90517872002948\n ],\n [\n -111.80435454605075,\n 37.90517872002948\n ],\n [\n -111.80435454605075,\n 35.07498855685233\n ],\n [\n -107.73849819939772,\n 35.07498855685233\n ],\n [\n -107.73849819939772,\n 37.90517872002948\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Blake, Johanna 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":217272,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Austin, Stephen 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0000-0001-8792-1010","orcid":"https://orcid.org/0000-0001-8792-1010","contributorId":222578,"corporation":false,"usgs":true,"family":"Chavarria","given":"Shaleene","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958098,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mixon, Rachel Lynn 0000-0001-9863-6784","orcid":"https://orcid.org/0000-0001-9863-6784","contributorId":328595,"corporation":false,"usgs":true,"family":"Mixon","given":"Rachel Lynn","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958099,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van Zante, C.A. 0000-0003-0266-9827","orcid":"https://orcid.org/0000-0003-0266-9827","contributorId":334817,"corporation":false,"usgs":true,"family":"Van Zante","given":"C.A.","email":"","affiliations":[{"id":472,"text":"New Mexico Water Science 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0000-0002-3874-4609","orcid":"https://orcid.org/0000-0002-3874-4609","contributorId":369065,"corporation":false,"usgs":true,"family":"Bosch","given":"K.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958105,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Austring, Tristan Joel 0000-0002-5790-5498","orcid":"https://orcid.org/0000-0002-5790-5498","contributorId":338725,"corporation":false,"usgs":true,"family":"Austring","given":"Tristan","email":"","middleInitial":"Joel","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958106,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ratigan, Zoreya (Zev) Eden 0009-0005-1075-8266","orcid":"https://orcid.org/0009-0005-1075-8266","contributorId":334365,"corporation":false,"usgs":true,"family":"Ratigan","given":"Zoreya (Zev) Eden","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958107,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Shomour, Anani Atahnibaa 0009-0005-2626-392X","orcid":"https://orcid.org/0009-0005-2626-392X","contributorId":368918,"corporation":false,"usgs":true,"family":"Shomour","given":"Anani","middleInitial":"Atahnibaa","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958108,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Yager, Douglas 0000-0001-5074-4022","orcid":"https://orcid.org/0000-0001-5074-4022","contributorId":305726,"corporation":false,"usgs":false,"family":"Yager","given":"Douglas","affiliations":[],"preferred":false,"id":958109,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70274120,"text":"70274120 - 2025 - Late Quaternary pollen record from southwest Seward Peninsula, western Alaska, and the vegetation history of central Beringia","interactions":[],"lastModifiedDate":"2026-02-26T16:45:16.585601","indexId":"70274120","displayToPublicDate":"2025-11-24T09:40:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":899,"text":"Arctic, Antarctic, and Alpine Research","active":true,"publicationSubtype":{"id":10}},"title":"Late Quaternary pollen record from southwest Seward Peninsula, western Alaska, and the vegetation history of central Beringia","docAbstract":"Pollen analysis of samples from a coastal exposure near Teller, southwestern Seward Peninsula, Alaska, provides a record of vegetation and climate spanning the Last Glacial Maximum (LGM) through the Holocene. The site is near the center of the former Bering Land Bridge (BLB). The oldest pollen-bearing sediment unit is a loess deposit of LGM age, with pollen assemblages that closely resemble LGM assemblages from other key sites in central Beringia spanning 16° of latitude. These fossil assemblages represent vegetation composed primarily of grasses, sedges, Artemisia, willows, and forbs and are interpreted to represent steppe–tundra, associated with dry climates and summer temperatures cooler than at present. LGM mosses did not accumulate insulating layers of peat; the summer active soil layer was deeper than at present. Permafrost with ice wedges and loess deposition were widespread. A regional transition from steppe–tundra vegetation to a dwarf shrub–sedge–moss mesic-to-wetland vegetation began in central Beringia with the onset of Bølling–Allerød (B-A) warming at 14,700 cal yr BP. Warming events of the B-A and early Holocene resulted in widespread development of thermokarst terrain on the BLB and on ice-rich terrain in Western Alaska. Mesic climates and vegetation developed on the BLB during the marine transgression and because of B-A and early Holocene warming. Early Holocene warming allowed some boreal forest species such as alders to begin colonizing Western Alaska from the interior.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230430.2025.2575555","usgsCitation":"Ager, T.A., 2025, Late Quaternary pollen record from southwest Seward Peninsula, western Alaska, and the vegetation history of central Beringia: Arctic, Antarctic, and Alpine Research, v. 57, no. 1, 2575555, 35 p., https://doi.org/10.1080/15230430.2025.2575555.","productDescription":"2575555, 35 p.","ipdsId":"IP-173345","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":500611,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15230430.2025.2575555","text":"Publisher Index Page"},{"id":500550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Seward Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -169.32417977560934,\n 66.76862458219912\n ],\n [\n -169.32417977560934,\n 64.13111893464969\n ],\n [\n -161.8068024698362,\n 64.13111893464969\n ],\n [\n -161.8068024698362,\n 66.76862458219912\n ],\n [\n -169.32417977560934,\n 66.76862458219912\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"57","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-11-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ager, Thomas A. 0000-0002-5029-7581","orcid":"https://orcid.org/0000-0002-5029-7581","contributorId":220219,"corporation":false,"usgs":false,"family":"Ager","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":12545,"text":"USGS retired","active":true,"usgs":false}],"preferred":false,"id":956596,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70272820,"text":"70272820 - 2025 - A monitoring framework to assess forest bird population response to landscape scale mosquito suppression using the Incompatible Insect Technique","interactions":[],"lastModifiedDate":"2025-12-10T15:52:46.387704","indexId":"70272820","displayToPublicDate":"2025-11-24T09:39:50","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":6053,"text":"Hawaii Cooperative Studies Unit Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"HCSU-119","title":"A monitoring framework to assess forest bird population response to landscape scale mosquito suppression using the Incompatible Insect Technique","docAbstract":"The Birds, Not Mosquitoes Monitoring and Support Science Working Group detailed methods for monitoring the population response of Hawaiian forest birds during implementation of the Incompatible Insect Technique (IIT) on the islands of Maui and Kauaʻi. The group prioritized methods for measuring the influence of mosquito suppression on populations within IIT treatment and control areas and identified focal species for IIT efficacy monitoring in birds. Three primary metrics were established to assess the impact of IIT on vulnerable species: population demography, density, and geographic range. Each metric can be evaluated using multiple methods. This report reviews those methods, with emphasis on approaches supported by pre-IIT baseline data and compatible with a before-after control-impact (BACI) study design for evaluating population responses over time. Focal avian species were selected based on population size estimates, fecundity, and disease susceptibility. We identified ʻākohekohe (Palmeria dolei), ʻiʻiwi (Drepanis coccinea), Maui ʻalauahio (Paroreomyza montana), Hawaiʻi ʻamakihi (Chlorodrepanis virens), Kauaʻi ʻamakihi (Chlorodrepanis stejnegeri), Kauaʻi ʻelepaio (Chasiempis sclateri), and ʻanianiau (Magumma parva) as focal species for monitoring population level response to disease suppression.
Populations of kiwikiu (Pseudonestor xanthophrys), ʻakikiki (Oreomystis bairdi), akekeʻe (Loxops caeruleirostris), and the ʻiʻiwi population on Kauaʻi may be too small (e.g., <100 individuals) to effectively monitor, and it is unlikely that sufficient data can be collected from these birds to show IIT efficacy in a relatively short time frame (i.e., 5–10 years). Despite the logistical challenges to IIT implementation, there is potential to maintain disease-free status in individual populations of birds. Indeed, the continued existence of these critically endangered species in the wild within or near IIT treatment areas could be considered an accomplishment of IIT, given the current predictions for their extinction in the wild within 5–10 years. Demographic monitoring methods, including territory mapping, nest monitoring, mist-netting, and mark-recapture studies, provide direct evidence of survivorship and reproductive output.
When combined with disease surveillance, these approaches could provide the most robust evidence of increased survivorship and productivity resulting from avian malaria suppression via IIT. However, demographic studies require several years of monitoring to achieve statistically robust BACI comparisons of survivorship and are more difficult to implement relative to other approaches. Given that these field efforts are labor-intensive and heavily reliant on personnel availability and funding, demographic monitoring could be conducted when adequate resources permit.
On both Maui and Kauaʻi, passive acoustic monitoring (PAM) was identified as a priority method for monitoring the range, occupancy, and relative abundance of focal species. Autonomous recording units (ARUs) can record bird vocalizations in remote areas for several months.
Innovative machine learning techniques permit rapid and semi-autonomous identification of most endemic honeycreepers on each island, maximizing sampling efficiencies and minimizing data processing costs. We predict mosquito suppression could support expansion of focal species into areas where disease transmission is currently excluding these species and expect acoustic monitoring data of focal species to reflect these spatial patterns. Additionally, the relative occupancy and call densities can be monitored temporally and spatially to assess the efficacy of IIT for supporting positive growth in vulnerable bird species. It is not yet clear if PAM is more effective than other methods, such as distance sampling, for detecting trends in the densities of rare species. However, the increased detections resulting from the larger sample size per observation point using ARUs will likely improve accuracy in detecting changes in species’ ranges. Collection of during and after treatment data within the BACI design could help to provide critical information to track avian population response, recovery, and potential range expansion related to IIT efforts. Point-transect distance sampling (point-counts) was prioritized as a method for monitoring population densities of focal species. Extensive historical sampling across focal species’ ranges provides a robust baseline for detecting change. These counts provide updated population densities and can be used to assess the distribution of focal species within IIT treatment areas.
However, detecting subtle population changes with traditional distance sampling requires intensive spatial and temporal effort and may be less effective for rare species. To improve resolution, density surface modeling can integrate multiple data sources (e.g., point-counts, PAM, spot-mapping, and resightings) to estimate species-specific densities at finer spatial scales, including within and outside IIT treatment areas. This integrated modeling approach allows for detailed comparisons and may reveal early signs of recovery, including recolonization of formerly occupied sites. A coordinated monitoring strategy can allow managers to evaluate the success of mosquito suppression as a conservation intervention and support adaptive management in the face of emerging challenges.
","language":"English","publisher":"University of Hawai‘i at Hilo","usgsCitation":"Judge, S., Warren, C.C., Navine, A.K., Camp, R.J., Crampton, L.H., Mounce, H.L., Vetter, J., Smith, L., Hart, P.J., Bellinger, M.R., and McClure, K.M., 2025, A monitoring framework to assess forest bird population response to landscape scale mosquito suppression using the Incompatible Insect Technique: Hawaii Cooperative Studies Unit Technical Report HCSU-119, iv, 40 p.","productDescription":"iv, 40 p.","ipdsId":"IP-179519","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":497301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":497294,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/5402"}],"country":"United States","state":"Hawaii","otherGeospatial":"Alaka'i Plateau, Haleakalā National Park , Waikamoi region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.1522358062181,\n 20.73823881977907\n ],\n [\n -156.1522358062181,\n 20.64621454011673\n ],\n [\n -156.0266571558431,\n 20.64621454011673\n ],\n [\n -156.0266571558431,\n 20.73823881977907\n ],\n [\n -156.1522358062181,\n 20.73823881977907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.24921043067422,\n 20.86997751409396\n ],\n [\n -156.24921043067422,\n 20.764987165934627\n ],\n [\n -156.1306083719868,\n 20.764987165934627\n ],\n [\n -156.1306083719868,\n 20.86997751409396\n ],\n [\n -156.24921043067422,\n 20.86997751409396\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.60125365576934,\n 22.147325602222963\n ],\n [\n -159.60125365576934,\n 22.042746120889333\n ],\n [\n -159.48161313138613,\n 22.042746120889333\n ],\n [\n -159.48161313138613,\n 22.147325602222963\n ],\n [\n -159.60125365576934,\n 22.147325602222963\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Judge, Seth 0000-0003-3832-3246","orcid":"https://orcid.org/0000-0003-3832-3246","contributorId":189965,"corporation":false,"usgs":false,"family":"Judge","given":"Seth","email":"","affiliations":[],"preferred":false,"id":951876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warren, Christopher C","contributorId":264665,"corporation":false,"usgs":false,"family":"Warren","given":"Christopher","email":"","middleInitial":"C","affiliations":[{"id":54533,"text":"Maui Forest Bird Recovery Project, Pacific Cooperative Studies Unit, University of Hawai‘i at Manoa","active":true,"usgs":false}],"preferred":false,"id":951877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Navine, Amanda K","contributorId":333575,"corporation":false,"usgs":false,"family":"Navine","given":"Amanda","email":"","middleInitial":"K","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":951878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":951879,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crampton, Lisa H. 0000-0002-5420-4338","orcid":"https://orcid.org/0000-0002-5420-4338","contributorId":359942,"corporation":false,"usgs":false,"family":"Crampton","given":"Lisa","middleInitial":"H.","affiliations":[{"id":85948,"text":"Kauaʻi Forest Bird Recovery Project","active":true,"usgs":false}],"preferred":false,"id":951880,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mounce, Hanna L","contributorId":363605,"corporation":false,"usgs":false,"family":"Mounce","given":"Hanna","middleInitial":"L","affiliations":[{"id":13352,"text":"Maui Forest Bird Recovery Project","active":true,"usgs":false}],"preferred":false,"id":951881,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vetter, John","contributorId":291840,"corporation":false,"usgs":false,"family":"Vetter","given":"John","affiliations":[{"id":55513,"text":"USFWS - Pacific Islands Fish and Wildlife Office","active":true,"usgs":false}],"preferred":false,"id":951882,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Lauren K. 0000-0003-1783-715X","orcid":"https://orcid.org/0000-0003-1783-715X","contributorId":353538,"corporation":false,"usgs":false,"family":"Smith","given":"Lauren K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":951883,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":951884,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bellinger, Mona Renee 0000-0001-5274-9572","orcid":"https://orcid.org/0000-0001-5274-9572","contributorId":301018,"corporation":false,"usgs":true,"family":"Bellinger","given":"Mona","email":"","middleInitial":"Renee","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":951885,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McClure, Katherine Maria 0000-0001-8595-7677","orcid":"https://orcid.org/0000-0001-8595-7677","contributorId":332279,"corporation":false,"usgs":true,"family":"McClure","given":"Katherine","email":"","middleInitial":"Maria","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":951886,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70273153,"text":"70273153 - 2025 - Groundwater structures fish growth and production across a riverscape","interactions":[],"lastModifiedDate":"2025-12-17T15:07:26.397254","indexId":"70273153","displayToPublicDate":"2025-11-23T08:59:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater structures fish growth and production across a riverscape","docAbstract":"Occupancy models have become increasingly popular for species monitoring and assessment, in part, because detection/non-detection data are readily obtained using a variety of methods. Multispecies occupancy models (MSOMs) can yield more accurate parameter estimates than single-species models (SSOMs) with less data through their hierarchical structure, making MSOMs an attractive option when species are hard to detect or when data collection is constrained, leading to sparse datasets. Such constraints may arise from limited sampling resources, but also occur in rare species monitoring or where preliminary results are desired to inform adaptive management. Further, experimental habitat treatments often impose spatial constraints on sampling based on the scale of their implementation. Whether a MSOM outperforms SSOMs depends on the volume of data, characteristics of the ecological community, research goals of a study and how these factors align with modeling assumptions. We performed a simulation study of hypothetical pollinator communities under varying sampling intensities for scenarios in which experimental habitat treatments produced different community-level effects. We fit occupancy models to simulated datasets and assessed model performance. At lower sampling intensities (< 20 spatial replicates and < 4 temporal replicates), MSOM community-level treatment effect estimates were biased. Even at twice this sampling intensity, SSOMs yielded more accurate species-specific effect estimates in treatment effect scenarios with high variance. In some cases, MSOMs can pull species in the tails of distributions too far toward the community mean effect, which risks incorrect conclusions concerning whether treatments help or harm individual species. When quantifying species-specific effects is the main objective, particularly for rarely observed species, SSOMs are more robust to outliers across a range of community response scenarios. Researchers can use this information to inform study design, guide simulation studies and decide whether the higher precision of MSOMs outweighs risks of improperly estimated effects for some species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72315","usgsCitation":"Cotterill, G.G., Keinath, D.A., and Graves, T., 2025, When do single-species occupancy models outperform multispecies models?: Ecology and Evolution, v. 15, no. 11, e72315, 14 p., https://doi.org/10.1002/ece3.72315.","productDescription":"e72315, 14 p.","ipdsId":"IP-178046","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":496936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72315","text":"Publisher Index Page"},{"id":496899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-11-23","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":951037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keinath, Douglas A.","contributorId":363056,"corporation":false,"usgs":false,"family":"Keinath","given":"Douglas","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":951038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":951039,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273831,"text":"70273831 - 2025 - Topographic, climatic, and age controls on the reworking of volcanic debris avalanche deposits","interactions":[],"lastModifiedDate":"2026-02-05T15:07:01.492317","indexId":"70273831","displayToPublicDate":"2025-11-23T08:01:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Topographic, climatic, and age controls on the reworking of volcanic debris avalanche deposits","docAbstract":"Volcanic debris avalanches have deposited as much as 1000 km3 of largely unconsolidated material on landscapes and remodeled existing drainage networks. The landscape disturbances created by these events pose severe, cascading downstream sedimentation hazards that can require long-term societal management, as demonstrated by decades of observations and ongoing interventions after the deposition of the 1980 debris avalanche of Mount St. Helens (United States). There, post-emplacement sediment yields caused by deposit erosion remain several times above estimated background yield and lakes impounded by the deposit still pose threats of downstream flooding. Despite the length and quality of measurements of the geomorphic evolution and consequent sediment release at Mount St. Helens, the long-term trajectory of drainage network evolution across, and the associated sediment release from, large volcanic debris avalanches remains uncertain. Observations and modeling at Mount St. Helens, however, indicate channel instability can persist many decades and may persist for centuries to millennia. We examined potential influences on the erosion and preservation of volcanic debris avalanche deposits (VDADs) by mapping valley networks developed on 89 VDADs selected from volcanic arcs across the world and spanning a variety of topographic settings and climate regimes. Using the best available topographic data (1 m lidar to 30 m radar-derived data depending on location) and aerial imagery, we estimated the areas of deposits that have been reworked relative to initial deposit footprints as a proxy for post-emplacement erosion. We found that a primary influence on reworking is the topographic confinement of the VDAD: confined, valley-filling deposits are systematically more reworked than unconfined deposits. There is no apparent relationship between deposit age and reworking for valley-filling deposits, indicating that drainage networks on deposits in confined topographic settings like at Mount St. Helens reform rapidly after emplacement. In contrast, our data indicate that the reworking of unconfined deposits has a monotonic positive relationship with age. This observation agrees with a conceptual model of channel formation at Mount Taranaki (New Zealand), which posits that an unconfined VDAD created a topographic high that initially (e.g., 2–8 ky for the Pungarehu formation at Taranaki) diverted erosion to the deposit margins. We found only a weak to moderate relationship between reworking and modern precipitation regimes, which may reflect differences between modern and paleo-precipitation conditions at many of our study sites. We also found no correlation between the size (surface area or volume) of deposits and the degree of reworking. The work presented here implies that downstream cascading sediment hazards from landscape-resetting processes like VDADs (such as thick, extensive pyroclastic flow deposits) depend on the relief and organization of the surrounding landscape.
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The number of large-scale solar photovoltaic facilities increased approximately tenfold between 2012 and 2021, with an associated 25-fold increase in cumulative installed capacity. With ambitious decarbonization and renewable energy deployment goals at both the federal and state levels, deployments of large-scale solar photovoltaic facilities will continue apace. This growth is likely to be complex with ripples of impacts felt throughout different aspects of society, and thus accurate solar land use metrics allowing more accurate predictions are of value to policymakers, planners, and other stakeholders in the future photovoltaic build-out. In this paper, we leverage data from the newly released US Large-Scale Solar Photovoltaic Database to examine recent trends in large-scale solar photovoltaic land use. We analyze the relationships between solar array capacity density (W/acre) and a range of facility attributes to better understand the future land requirements of solar capacity expansion over the coming years. Installed capacity was the single strongest determinant of solar array area. However, we found substantial variation in capacity density across facility attributes, including mount type, latitude, urbanicity, time, and prior land use.
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