Black Carp Mylopharyngodon piceus were imported into the United States in the 1970s and 1980s for use in aquaculture; escape occurred and reported wild captures increased. Lacking species-specific capture methods, we assessed fisheries dependent incidental Black Carp catches for a common method, hoop nets, by kernel density analysis to identify an area of increased reporting and compare frequency of reports for water temperature, river stage, and capture date to identify seasonality. We then used fisheries independent effort to identify co-occurrence of species via non-metric multi-dimensional scaling and fit Black Carp catch and environmental covariates by generalized linear models to assess site-specific environmental covariates facilitating capture. The best approximating distribution was refitted for predictions and inference. The greatest density of fisheries dependent hoop net captures (39 %) was near the confluence of the Missouri and Mississippi rivers, primarily from July-September. Captures were characterized by median water temperature 26.7°C, river stage 5.02 m, and 223 day-of-year (DOY; mid-August). Ordination of fisheries independent catch identified similarity in environmental covariates of Smallmouth Buffalo Ictiobus bubalus and Black Carp. The probability of capturing ≥ 1 Black Carp increased with DOY, decreased with increasing current velocity, and increased with depth. Most captures occurred in outside bends (87 %) or side channels (12 %). Probability of Black Carp capture was low but increased in summer and early fall when stage is lower, facilitating reduced current velocity and access to deeper areas. Results may be validated beyond this river segment to test if site-specific hydrology or habitat characteristics facilitated increased commercial and biologist capture and for replication.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2025.107368","usgsCitation":"Kroboth, P., Colvin, M.E., and Broaddus, C., 2025, Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus: Fisheries Research, v. 285, 107368, 12 p., https://doi.org/10.1016/j.fishres.2025.107368.","productDescription":"107368, 12 p.","ipdsId":"IP-167531","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":487576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2025.107368","text":"Publisher Index Page"},{"id":485444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Missouri","otherGeospatial":"MIssissippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.22145042868262,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.90746978465282\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"285","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroboth, Patrick 0000-0002-9447-4818","orcid":"https://orcid.org/0000-0002-9447-4818","contributorId":216578,"corporation":false,"usgs":true,"family":"Kroboth","given":"Patrick","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colvin, Michael E. 0000-0002-6581-4764","orcid":"https://orcid.org/0000-0002-6581-4764","contributorId":331490,"corporation":false,"usgs":true,"family":"Colvin","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broaddus, Courtney 0000-0003-3851-3584","orcid":"https://orcid.org/0000-0003-3851-3584","contributorId":354595,"corporation":false,"usgs":true,"family":"Broaddus","given":"Courtney","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935822,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","interactions":[{"subject":{"id":70257569,"text":"70257569 - 2024 - Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington","indexId":"70257569","publicationYear":"2024","noYear":false,"title":"Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington"},"predicate":"SUPERSEDED_BY","object":{"id":70265982,"text":"sir20255029 - 2025 - Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","indexId":"sir20255029","publicationYear":"2025","noYear":false,"title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington"},"id":1}],"lastModifiedDate":"2025-08-07T21:05:21.759632","indexId":"sir20255029","displayToPublicDate":"2025-04-23T07:58:02","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":"2025-5029","displayTitle":"Spatial Stream Network Modeling of Water Temperature within the White River Basin, Mount Rainier National Park, Washington","title":"Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington","docAbstract":"Water temperature is a primary control on the occurrence and distribution of fish and other ectothermic aquatic species. In the Pacific Northwest, cold-water species such as Pacific salmon (Oncorhynchus spp.) and bull trout (Salvelinus confluentus) have specific temperature requirements during different life stages that must be met to ensure the viability of their populations. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support a number of cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer baseflow during August and September, and the climatic, hydrologic, and physical processes regulating it are not well constrained. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and 7-day average daily maximum water temperature for August and September for the White River Basin. The SSN models were calibrated using water temperature measurements collected in 2010 through 2020. The extent of the models included the White River and its tributaries upstream from its confluence with Silver Creek in Mount Rainier National Park, Washington. SSN models incorporated covariates hypothesized to represent the climatic, hydrologic, and physical processes that influence water temperature. SSN models were fit to the measured data and compared to generalized linear models that lacked spatial autocovariance structures. Statistically significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow-water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. Additionally, models of mean August and September water temperature had better predictive performance than those of 7-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of the mainstem White River compared to cooler water temperatures in the high-elevation headwater streams. The proportion of ice cover emerged as an inversely related significant covariate to both mean August and September water temperature because streams that receive glacial meltwater are colder than non-glaciated streams. Water temperatures of the upper White River increased downstream and are attributed to warming of water temperature from accumulated solar radiation and inflow of non-glaciated tributaries. Estimated water temperatures for the upper White River model are 3–4 degrees Celsius (°C) warmer for tributaries, but 1–2 °C cooler for the mainstem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale NorWeST model are attributed to the fact that the upper White River SSN included water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream from the Mount Rainer National Park boundary were used in the regional scale model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255029","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Gendaszek, A.S., Leach, A.C., and Jaeger, K.L., 2025, Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington (ver. 1.1, May 2025): U.S. Geological Survey\nScientific Investigations Report 2025–5029, 17 p., https://doi.org/10.3133/sir20255029. [Supersedes preprint https://doi.org/10.31223/X5712P.]","productDescription":"Report: vi, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-168299","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":484931,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/6542802dd34ee4b6e05bd2cb","text":"USGS data release","description":"USGS data release","linkHelpText":"Stream Temperature Models of White River Watershed, Mount Rainier National Park, Washington"},{"id":484872,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.XML"},{"id":484871,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5029/images"},{"id":484870,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255029/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5029"},{"id":484869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5029/sir20255029.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5029"},{"id":484868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5029/coverthb2.jpg"},{"id":486241,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5029/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":493767,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118576.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park, upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.75,\n 47\n ],\n [\n -121.75,\n 46.8333\n ],\n [\n -121.5,\n 46.8333\n ],\n [\n -121.5,\n 47\n ],\n [\n -121.75,\n 47\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: April 23.2025; Version 1.1: May 20, 2025","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Previous efforts to characterize tsunami threats to people have focused primarily on individual scenarios in specific areas but have not recognized multiple scenarios across an entire country. This study addresses this gap by quantifying population exposure and evacuation potential in the United States to 102 earthquake-related, tsunami-hazard zones, including 92 local scenarios, 8 distant scenarios, and 2 probabilistic products. Geospatial path-distance modeling quantified evacuation potential and the influence of departure delays. We focused on residents to support other national, multi-hazard risk analyses. Millions of residents are in distant-tsunami zones, and hundreds of thousands of residents are in local-tsunami zones. In 41 scenarios, there is at least one resident that may have insufficient time to evacuate before wave arrival. Tens of thousands of residents may have insufficient time to evacuate from local tsunamis that impact the U.S. Pacific Northwest or Puerto Rican coastlines. The largest improvements in evacuation potential may come from reducing departure delays in some areas but may involve vertical-evacuation structures or changing land use in other areas.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2025.105511","usgsCitation":"Wood, N.J., Peters, J., Sheehan, A., and Bausch, D., 2025, National population exposure and evacuation potential in the United States to earthquake-generated tsunami threats: International Journal of Disaster Risk Reduction, v. 123, 105511, 18 p., https://doi.org/10.1016/j.ijdrr.2025.105511.","productDescription":"105511, 18 p.","ipdsId":"IP-176718","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":485209,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":934864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peters, Jeff 0000-0003-4312-0590 jpeters@usgs.gov","orcid":"https://orcid.org/0000-0003-4312-0590","contributorId":4711,"corporation":false,"usgs":true,"family":"Peters","given":"Jeff","email":"jpeters@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":934865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sheehan, Anne 0009-0005-0636-6892","orcid":"https://orcid.org/0009-0005-0636-6892","contributorId":358952,"corporation":false,"usgs":false,"family":"Sheehan","given":"Anne","affiliations":[{"id":30786,"text":"FEMA","active":true,"usgs":false}],"preferred":false,"id":934866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bausch, Doug","contributorId":195191,"corporation":false,"usgs":false,"family":"Bausch","given":"Doug","email":"","affiliations":[{"id":34169,"text":"Pacific Disaster Center","active":true,"usgs":false}],"preferred":false,"id":934867,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273768,"text":"70273768 - 2025 - Seasonal movements and demographics of the endangered White River Spinedace to inform restoration and translocation","interactions":[],"lastModifiedDate":"2026-01-28T16:54:16.957569","indexId":"70273768","displayToPublicDate":"2025-04-22T09:46:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal movements and demographics of the endangered White River Spinedace to inform restoration and translocation","docAbstract":"Objective
Translocation is a tool being explored to restart extirpated populations or facilitate new populations of endangered spring-dependent fish populations. Our objective was to provide information on habitat requirements for endangered White River Spinedace Lepidomeda albivallis during all seasons of the year and the population demographics that are necessary to plan conservation translocations of this species
Methods
We tagged and released White River Spinedace with passive integrated transponders during four twice-a-year events. Fish were subsequently recaptured or detected on six passive antennas placed throughout the Flag Springs Complex, Nevada. We evaluated movement data to understand seasonal habitat use patterns, used a Barker model to estimate monthly survival rates, adjusted counts to account for capture probability and estimate abundance, and applied reverse-time mark–recapture models to estimate recruitment to 70 mm total length.
Results
White River Spinedace were more active but used similar habitats during spawning seasons than during nonspawning seasons. Median life expectancy was about 5 months after tagging, and only 1% of adult White River Spinedace survived 3–4 years posttagging. The estimated population size in the Flag Springs Complex during our sampling period (November 2020 to June 2022) was fewer than a thousand White River Spinedace, and this estimate has been steady or slightly increasing.
Conclusions
Complex spring habitats with water temperatures ranging about 13°C to 21°C that are free from piscivorous fish are appropriate for White River Spinedace. The White River Spinedace population at Flag Springs is small but stable or increasing in size.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/tafafs/vnaf007","usgsCitation":"Burdick, S.M., Harter, J.F., Beckstrand, M., Paul-Wilson, R.K., Hayes, B., Perry, R.W., and Smith, C.D., 2025, Seasonal movements and demographics of the endangered White River Spinedace to inform restoration and translocation: Transactions of the American Fisheries Society, v. 154, no. 3, p. 246-261, https://doi.org/10.1093/tafafs/vnaf007.","productDescription":"16 p.","startPage":"246","endPage":"261","ipdsId":"IP-165644","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harter, James F.","contributorId":365736,"corporation":false,"usgs":false,"family":"Harter","given":"James","middleInitial":"F.","affiliations":[{"id":87201,"text":"United States Fish and Wildlife Service, Las Vegas, Nevada","active":true,"usgs":false}],"preferred":false,"id":954696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beckstrand, Mark","contributorId":365737,"corporation":false,"usgs":false,"family":"Beckstrand","given":"Mark","affiliations":[{"id":87202,"text":"Nevada Department of Wildlife, Eli, Nevada","active":true,"usgs":false}],"preferred":false,"id":954697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul-Wilson, Rachael Katelyn 0000-0002-8213-1084","orcid":"https://orcid.org/0000-0002-8213-1084","contributorId":298894,"corporation":false,"usgs":true,"family":"Paul-Wilson","given":"Rachael","email":"","middleInitial":"Katelyn","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Brian S. 0000-0001-8229-4070","orcid":"https://orcid.org/0000-0001-8229-4070","contributorId":37022,"corporation":false,"usgs":true,"family":"Hayes","given":"Brian S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":954699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Russell W. 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":214553,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Collin D. 0000-0003-4184-5686 cdsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-4184-5686","contributorId":3111,"corporation":false,"usgs":true,"family":"Smith","given":"Collin","email":"cdsmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954701,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270100,"text":"70270100 - 2025 - Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?","interactions":[],"lastModifiedDate":"2025-08-11T15:39:28.971692","indexId":"70270100","displayToPublicDate":"2025-04-22T08:35:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5781,"text":"The Depositional Record","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?","docAbstract":"Emblematic of global coral-reef ecosystem decline, the coral ecosystem-engineer Acropora palmata is now rare throughout much of the western Atlantic. Understanding when and where this foundation species occurred during the past can provide information about the environmental limits defining its distribution through space and time. In this paper, the present, historical and newly dated geological records of A. palmata are compared to reveal novel insights into the environmental constraints on its occurrence in Dry Tortugas National Park, a subtropical reef system at the south-western terminus of the Florida reef tract. Although past geological investigation found little evidence of the species in the park, a single, moderately sized A. palmata reef existed throughout historical times (1881 Common Era [CE] to present day; ‘historical population’, termed herein). Over the last 140 years, repeated population declines occurred with little to no recovery, culminating in the extirpation of A. palmata from the area during the 2023–2024 CE global coral bleaching event. Reported here for the first time is a significant record of Late Holocene A. palmata populations that existed from ca 4500 to 375 years before present (‘Late Holocene population,’ termed herein) in three broadly distributed areas of the shallow Dry Tortugas platform. This discovery challenges previous assumptions regarding the species' limited contribution to reef development in the area by providing data that extend the known spatial and stratigraphic extent of Holocene populations in this location. It is posited that, although the Late Holocene climate largely suppressed regional reef development, the new records provide evidence for centennial-scale periods of more favourable and stable climate that allowed for short-term expansions of A. palmata populations in the Dry Tortugas. In conclusion, the species' prospects for future success in this and other subtropical location
","language":"English","publisher":"Wiley","doi":"10.1002/dep2.70005","usgsCitation":"Stathakopoulos, A., Toth, L., Modys, P.A., Johnson, S.A., and Kuffner, I.B., 2025, Discovery of late Holocene-aged Acropora palmata reefs in Dry Tortugas National Park, Florida, USA: The past as a key to the future?: The Depositional Record, v. 11, no. 3, p. 808-828, https://doi.org/10.1002/dep2.70005.","productDescription":"21 p.","startPage":"808","endPage":"828","ipdsId":"IP-169190","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494189,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/dep2.70005","text":"Publisher Index Page"},{"id":493935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.9661549521622,\n 24.68040740481777\n ],\n [\n -82.9661549521622,\n 24.595463709079198\n ],\n [\n -82.8127098632192,\n 24.595463709079198\n ],\n [\n -82.8127098632192,\n 24.68040740481777\n ],\n [\n -82.9661549521622,\n 24.68040740481777\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Modys, Peter Alexander Bacon 0000-0002-2948-5983","orcid":"https://orcid.org/0000-0002-2948-5983","contributorId":336719,"corporation":false,"usgs":true,"family":"Modys","given":"Peter","email":"","middleInitial":"Alexander Bacon","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945454,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70265960,"text":"70265960 - 2025 - Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California","interactions":[],"lastModifiedDate":"2025-04-23T13:18:13.267716","indexId":"70265960","displayToPublicDate":"2025-04-21T11:18:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California","docAbstract":"This study evaluates the potential water availability in Barka Slough and the effects of changing hydrological conditions on the aquatic habitat of five protected species. Barka Slough is a historically perennial wetland at the downstream western end of the San Antonio Creek Valley watershed (SACVW). A previously published hydrologic model of the SACVW for 1948–2018 was extended to include 2019–2021 and then modified to simulate the future years of 2022–2051. Two models simulating the future years of 2022–2051 were constructed, each with different climate inputs: (1) a repeated historical climate and (2) a 2070-centered Drier Extreme Warming climate (2070 DEW). The model with the 2070 DEW climate had warmer temperatures and an increase in average annual precipitation driven by larger, albeit more infrequent, precipitation events than the model with the historical climate. Simulated groundwater pumpage resulted in cumulative groundwater storage depletion and groundwater-level decline in Barka Slough in both future models. The simulations indicate that Barka Slough may transition from a perennial to an ephemeral wetland. Streamflow, stream disconnection, and depth to groundwater are key habitat metrics for federally listed species in Barka Slough. Future seasonal conditions for each metric are more likely to affect federally listed species’ habitats under 2070 DEW climatic conditions. Future seasonal streamflow volume may negatively impact unarmored threespine stickleback (Gasterosteus aculeatus williamsoni) and tidewater goby (Eucyclogobis newberryi) habitats. Future seasonal stream disconnection may negatively impact the unarmored threespine stickleback habitat. Future groundwater-level decline may negatively impact Gambel’s watercress (Nasturtium gambelii) and La Graciosa thistle (Cirsium scariosum var. loncholepis) habitats and could influence the ability to use Barka Slough as a restoration or reintroduction site for these species. Results from this study can be used to inform water management decisions to sustain future groundwater availability in the SACVW.
","language":"English","publisher":"MDPI","doi":"10.3390/w17081238","usgsCitation":"Cromwell, G., Culling, D., Young, M.J., and Larsen, J., 2025, Simulated effects of future water availability and protected species habitat in a perennial wetland, Santa Barbara County, California: Water, v. 17, no. 8, 1238, 29 p., https://doi.org/10.3390/w17081238.","productDescription":"1238, 29 p.","ipdsId":"IP-168161","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":488483,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17081238","text":"Publisher Index Page"},{"id":484844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.5333,\n 34.85\n ],\n [\n -120.5333,\n 34.6833\n ],\n [\n -120.1,\n 34.6833\n ],\n [\n -120.1,\n 34.85\n ],\n [\n -120.5333,\n 34.85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Cromwell, Geoffrey 0000-0001-8481-405X gcromwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-405X","contributorId":5920,"corporation":false,"usgs":true,"family":"Cromwell","given":"Geoffrey","email":"gcromwell@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culling, Daniel Philip 0000-0002-6585-0650","orcid":"https://orcid.org/0000-0002-6585-0650","contributorId":299662,"corporation":false,"usgs":true,"family":"Culling","given":"Daniel Philip","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934168,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266049,"text":"70266049 - 2025 - Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries","interactions":[],"lastModifiedDate":"2025-04-24T16:03:22.35888","indexId":"70266049","displayToPublicDate":"2025-04-21T11:01:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21212,"text":"Global Sustainability","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries","title":"Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries","docAbstract":"Fisheries encompass humans and fish, but fisheries researchers rarely model human–nature interactions over space and time. I filled this information gap for dolphinfish (Coryphaena hippurus), a popular, widely distributed species that supports industrial, artisanal, recreational, and subsistence fisheries. Dolphinfish human–nature interactions showed a long-term up-and-down pattern in 1950–2019. Recent declines in catch mirror decreases in abundance and size that have been observed in parts of the species’ range. This research provides a robust perspective on the recreational, economic, cultural, and nutritional significance of dolphinfish while creating an approach for evaluating human–nature interactions in fisheries worldwide.","language":"English","publisher":"Cambridge University Press","doi":"10.1017/sus.2025.3","usgsCitation":"Carlson, A.K., 2025, Mahi-mahi metacouplings: Quantifying human–nature interactions in dolphinfish (Coryphaena hippurus) fisheries: Global Sustainability, https://doi.org/10.1017/sus.2025.3.","ipdsId":"IP-167357","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/sus.2025.3","text":"Publisher Index Page"},{"id":484991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Andrew Kenneth 0000-0002-6681-0853","orcid":"https://orcid.org/0000-0002-6681-0853","contributorId":340581,"corporation":false,"usgs":true,"family":"Carlson","given":"Andrew","email":"","middleInitial":"Kenneth","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934452,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70273070,"text":"70273070 - 2025 - Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus)","interactions":[],"lastModifiedDate":"2025-12-15T14:45:22.18155","indexId":"70273070","displayToPublicDate":"2025-04-21T08:24:54","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus)","title":"Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus)","docAbstract":"Biological invasions pose significant risks to ecosystems and native species. Wild pigs (Sus scrofa) are a highly detrimental invasive species in North America, directly and indirectly affecting native species. Co-occurrence of wild pigs and native species may lead to interspecific interactions that alter ecological communities. Accordingly, we investigated spatial and temporal factors influencing detection and occupancy of Eurasian Wild Pig and Nine-banded Armadillo (Dasypus novemcinctus) before examining interspecific effects. We analyzed camera-trap data collected from August to September 2021 using a hierarchical modeling framework to estimate detection and occupancy of both species individually (single-species analyses) and concurrently (conditional co-occurrence analyses). We observed higher Wild Pig detection rates and space use in late summer and in areas with greater riparian cover, respectively. Armadillo detection increased linearly throughout our sampling season and in response to precipitation. Moreover, armadillo detection was 3.5 to 5.1× higher at sites used by wild pigs, regardless of whether wild pigs were detected during a survey period. Occupancy of armadillo was best explained by a quadratic trend in site elevation but did not depend on the presence of wild pigs. Our results indicate that wild pigs may influence armadillo detection (or site-use intensity), but not occupancy, therefore revealing nuanced interspecific interactions. Between species, we observed high overlap in diel activity but significantly different activity peaks, with armadillos being strictly nocturnal and wild pigs being crepuscular but with more cathemeral activity, suggesting that fine-scale temporal partitioning may have occurred. Our results provide insights into the influence of a large-bodied and destructive invasive species (Wild Pig) on a smaller, ecologically important native species (Nine-banded Armadillo).
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyaf023","usgsCitation":"Broadway, M.S., Todaro, H.M., Koeck, M.M., Dotterweich, C.N., Cain, S.A., Chitwood, M., and Lonsinger, R.C., 2025, Interspecific effects of invasive wild pigs (Sus scrofa) on native nine-banded armadillos (Dasypus novemcinctus): Journal of Mammalogy, v. 106, no. 4, p. 976-988, https://doi.org/10.1093/jmammal/gyaf023.","productDescription":"13 p.","startPage":"976","endPage":"988","ipdsId":"IP-163684","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":497716,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyaf023","text":"Publisher Index Page"},{"id":497469,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"James Collins Wildlife Management Area, Sans Bois Wildlife Management Area, southeast Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.05973004796076,\n 35.30322610651754\n ],\n [\n -98.05973004796076,\n 33.72739259313137\n ],\n [\n -94.40186391242315,\n 33.72739259313137\n ],\n [\n -94.40186391242315,\n 35.30322610651754\n ],\n [\n -98.05973004796076,\n 35.30322610651754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"106","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Broadway, Matthew S.","contributorId":364085,"corporation":false,"usgs":false,"family":"Broadway","given":"Matthew","middleInitial":"S.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Todaro, Holly M.","contributorId":364088,"corporation":false,"usgs":false,"family":"Todaro","given":"Holly","middleInitial":"M.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koeck, Molly M.","contributorId":364091,"corporation":false,"usgs":false,"family":"Koeck","given":"Molly","middleInitial":"M.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dotterweich, Courtney N.","contributorId":364094,"corporation":false,"usgs":false,"family":"Dotterweich","given":"Courtney","middleInitial":"N.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952225,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cain, Sarah A.","contributorId":364097,"corporation":false,"usgs":false,"family":"Cain","given":"Sarah","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952226,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chitwood, M. Colter","contributorId":364100,"corporation":false,"usgs":false,"family":"Chitwood","given":"M. Colter","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":952227,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":952228,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266494,"text":"70266494 - 2025 - Object detection-assisted workflow facilitates cryptic snake monitoring","interactions":[],"lastModifiedDate":"2025-11-18T16:44:06.826627","indexId":"70266494","displayToPublicDate":"2025-04-20T08:58:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Object detection-assisted workflow facilitates cryptic snake monitoring","docAbstract":"Camera traps are an important tool used to study rare and cryptic animals, including snakes. Time-lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time-lapse photography require labor-intensive classification, limiting their use in large-scale studies. While many artificial intelligence-based object detection models are effective at identifying mammals in images, their ability to detect snakes is unproven. Here, we used camera data to evaluate the efficacy of an object detection model to rapidly and accurately detect snakes. We classified images manually to the species level and compared this with a hybrid review workflow where the model removed blank images followed by a manual review. Using a ≥0.05 model confidence threshold, our hybrid review workflow correctly identified 94.5% of blank images, completed image classification 6× faster, and detected large (>66 cm) snakes as well as manual review. Conversely, the hybrid review method often failed to detect all instances of a snake in a string of images and detected fewer small (<66 cm) snakes than manual review. However, most relevant ecological information requires only a single detection in a sequence of images, and study design changes could likely improve the detection of smaller snakes. Our findings suggest that an object detection-assisted hybrid workflow can greatly reduce time spent manually classifying data-heavy time-lapse snake studies and facilitate ecological monitoring for large snakes.
","language":"English","publisher":"Zoological Society of London","doi":"10.1002/rse2.70009","usgsCitation":"Miller, S., Kirkland, M., Hart, K., and McCleery, R.A., 2025, Object detection-assisted workflow facilitates cryptic snake monitoring: Remote Sensing in Ecology and Conservation, v. 11, no. 5, p. 606-617, https://doi.org/10.1002/rse2.70009.","productDescription":"12 p.","startPage":"606","endPage":"617","ipdsId":"IP-171959","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488156,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.70009","text":"Publisher Index Page"},{"id":485551,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.3162930521356,\n 25.76295442989739\n ],\n [\n -80.73353749057956,\n 25.76295442989739\n ],\n [\n -80.73353749057956,\n 25.272501320110464\n ],\n [\n -80.3162930521356,\n 25.272501320110464\n ],\n [\n -80.3162930521356,\n 25.76295442989739\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Storm","contributorId":354750,"corporation":false,"usgs":false,"family":"Miller","given":"Storm","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":936283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kirkland, Michael","contributorId":301069,"corporation":false,"usgs":false,"family":"Kirkland","given":"Michael","email":"","affiliations":[{"id":36603,"text":"SFWMD","active":true,"usgs":false}],"preferred":false,"id":936284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":936285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCleery, Robert A.","contributorId":139849,"corporation":false,"usgs":false,"family":"McCleery","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":936286,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270754,"text":"70270754 - 2025 - Habitat and predator influences on the spatial ecology of nine-banded armadillos","interactions":[],"lastModifiedDate":"2025-08-22T17:13:03.576077","indexId":"70270754","displayToPublicDate":"2025-04-19T10:02:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"Habitat and predator influences on the spatial ecology of nine-banded armadillos","docAbstract":"Mesopredator suppression has implications for community structure, biodiversity, and ecosystem function, but mesopredators with physical defenses may not avoid apex predators. We investigated nine-banded armadillos (Dasypus novemcinctus) in southwestern Oklahoma (USA) to evaluate if a species with physical defenses was influenced by a dominant predator, the coyote (Canis latrans). We sampled nine-banded armadillos and coyotes with motion-activated cameras. We used single-species and conditional two-species occupancy models to assess the influences of environmental factors and coyotes on nine-banded armadillo occurrence and site-use intensity (i.e., detection). We used camera-based detections to characterize the diel activity of each species and their overlap. Nine-banded armadillo occupancy was greater at sites closer to cover, with lower slopes, and further from water, whereas coyote space use was greater at higher elevations; both species were positively associated with recent burns. Nine-banded armadillo occurrence was not influenced by coyotes, but site-use intensity was suppressed by the presence of coyotes. Nine-banded armadillos (strictly nocturnal) and coyotes (predominantly nocturnal) had a high overlap in summer diel activity. Nine-banded armadillos are ecosystem engineers but are often considered a threat to species of concern and/or a nuisance. Thus, understanding the role of interspecific interactions on nine-banded armadillos has important implications for conservation and management.
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Changes in land use and agricultural practices, such as subsurface agricultural drainage, may be contributing to changes in streamflow characteristics. Subsurface agricultural drainage, also known as tile drainage, is the practice of installing drains in the subsurface of agricultural fields to improve productivity. Because of the complex interactions between subsurface drainage systems, precipitation, local soil conditions, and land management practices, it is difficult to determine how subsurface agricultural drainage affects downstream flow. Previously developed subsurface agricultural drainage conceptual models under dry, saturated, and winter conditions are summarized, and current literature on the effects of subsurface agricultural drainage on downstream flows, focusing on peak flow, non-event flow, and total flow to develop frameworks for discussing these systems is compiled.
The effects that subsurface drainage has on hydrologic systems are expected to vary by site and are seasonally based on system design, soil type, moisture conditions, precipitation characteristics, and land conditions. Subsurface drainage can affect the magnitude of peak flow by converting surface runoff from a storm event to subsurface runoff. By increasing hydrologic connectivity of a catchment, subsurface drainage can increase non-event flow or the flow between two storm events, typically dependent on lateral flow through the subsurface and groundwater. Theoretically, by diverting water from groundwater recharge or by reducing water available for evapotranspiration, subsurface drainage may increase the total volume of flow. Precipitation changes may increase infiltration, excess overland flow, and flood risk regardless of the presence or absence of subsurface drainage.
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U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Gulf ribbed mussels (Geukensia granosissima) act as ecosystem engineers and reside within the marsh platform of saltmarshes across the northern Gulf of Mexico. With climate models projecting increasing temperatures, and more frequent and extreme heat events, these mussels face increasing temperature-related risks. Marsh surface and subsurface (5-cm depth) temperature was measured continuously in the summer of 2022 in south Louisiana Gulf ribbed mussel habitat at nine stations. Marsh surface maximum temperatures were 5°C higher and more variable than recorded water temperatures, exceeding 38°C for periods of up to 3 h which generally coincided with low tides and peak solar radiation. Marsh subsurface temperatures were cooler with a lower mean and maximum temperature compared with the marsh surface, but higher than adjacent water. In two laboratory experiments the acclimated and acute thermal tolerance of wild mussels collected from the saltmarsh where temperatures were recorded, were explored. G. granosissima survived more than 40 days of continuous exposure in the laboratory to mean daily temperature values recorded for the marsh and subsurface microhabitats (28°C–34°C) but their calculated median lethal time (LT50) ranged from 35 to 56 days (36°C), to less than 3 days (40°C). Mussels acclimated to temperatures similar to long-term average water temperatures (28°C–32°C) and then exposed to maximum daily temperatures acutely experienced LT50 of less than 6 days (38°C), <1 day (40°C), and of less than 5 h (42°C). For G. granosissima both their thermal tolerance and behavioral response likely contribute to their survival in the face of extreme heat events, and their resulting distribution across the marsh surface and subsurface. Overall, results indicate that ribbed mussels in coastal Louisiana may rely on their ability to migrate vertically and bury in the marsh to avoid extreme heat exposure (temperature, duration) that may be lethal. The ability of Gulf ribbed mussels to endure short-term thermal extremes may ultimately determine the mussels' use as a tool in marsh stabilization and coastal restoration.
","language":"English","publisher":"BioOne","doi":"10.2983/035.044.0105","usgsCitation":"Liner, S.R., Roberts, B.J., Coxe, N., Lavaud, R., La Peyre, J.F., and La Peyre, M., 2025, Vulnerability of gulf ribbed mussels to marsh surface maximum temperatures: Journal of Shellfish Research, v. 44, no. 1, p. 45-53, https://doi.org/10.2983/035.044.0105.","productDescription":"9 p.","startPage":"45","endPage":"53","ipdsId":"IP-164252","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","city":"Cocodrie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.81142673123271,\n 29.357597141906496\n ],\n [\n -90.81142673123271,\n 29.191048554283725\n ],\n [\n -90.47329864138207,\n 29.191048554283725\n ],\n [\n -90.47329864138207,\n 29.357597141906496\n ],\n [\n -90.81142673123271,\n 29.357597141906496\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liner, Skylar R.","contributorId":360165,"corporation":false,"usgs":false,"family":"Liner","given":"Skylar","middleInitial":"R.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":946868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Brian J.","contributorId":360168,"corporation":false,"usgs":false,"family":"Roberts","given":"Brian","middleInitial":"J.","affiliations":[{"id":16627,"text":"Louisiana Universities Marine Consortium (LUMCON)","active":true,"usgs":false}],"preferred":false,"id":946869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coxe, Nicholas","contributorId":341331,"corporation":false,"usgs":false,"family":"Coxe","given":"Nicholas","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":946870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lavaud, Romain","contributorId":341281,"corporation":false,"usgs":false,"family":"Lavaud","given":"Romain","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":946871,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Jerome F.","contributorId":360171,"corporation":false,"usgs":false,"family":"La Peyre","given":"Jerome","middleInitial":"F.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":946872,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946873,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267879,"text":"70267879 - 2025 - Stressor-driven changes in freshwater biological indicators inform spatial management strategies using expert knowledge, observational data, and hierarchical models","interactions":[],"lastModifiedDate":"2025-06-09T14:25:22.071785","indexId":"70267879","displayToPublicDate":"2025-04-17T07:56:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Stressor-driven changes in freshwater biological indicators inform spatial management strategies using expert knowledge, observational data, and hierarchical models","docAbstract":"Stream ecosystems face continuous pressures from multiple anthropogenic stressors that reshape biological communities and impact ecosystem health and services. Managers can encounter challenges in stewarding ecosystems threatened by multiple stressors, in part because most multiple stressor studies are experimental and, while valuable, offer limited management relevance in targeting these stressors on the landscape. Recent advances in causal inference coupled with large biomonitoring data sets could further understanding of observational stressor-response relationships, aiding management. In this study, we use bioassessment data in the Chesapeake Bay watershed in the mid-Atlantic region of the United States to identify how water quality and physical habitat stressors influence key benthic macroinvertebrate response metrics, considering hierarchical relationships using Bayesian networks. Results suggest water temperature and specific conductivity were prevalent stressors in a mountainous region (northern Appalachians), whereas in an agriculturally dominated region (southern Appalachians) physical habitat alterations were the predominant stressor. In mixed-land use regions (Piedmont & Coastal Plains), specific conductivity was a key stressor, but habitat heterogeneity was important for macroinvertebrate metrics. To illustrate how these stressor-response relationships can be used to guide management decisions, we applied the resist-accept-direct (RAD) framework to develop a portfolio of management options based on predicted changes in macroinvertebrate metrics in response to physical habitat and water quality stressors. For example, accepting changes in areas with co-occurring stressors may be the most feasible option, whereas directing changes through stream restoration or water quality improvements may be effective in areas with single stressor groups. By leveraging observational bioassessment data and causal inference to identify key stressor-response relationships, this research supports decision making by building a simple, strategic management portfolio.
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0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":939258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fanelli, Rosemary M. 0000-0002-0874-1925 rfanelli@usgs.gov","orcid":"https://orcid.org/0000-0002-0874-1925","contributorId":199822,"corporation":false,"usgs":true,"family":"Fanelli","given":"Rosemary","email":"rfanelli@usgs.gov","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pond, Greg","contributorId":238186,"corporation":false,"usgs":false,"family":"Pond","given":"Greg","affiliations":[{"id":13529,"text":"US Environmental 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0000-0002-6277-1260","orcid":"https://orcid.org/0000-0002-6277-1260","contributorId":304097,"corporation":false,"usgs":true,"family":"Woods","given":"Taylor","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":939262,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":939263,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70265913,"text":"70265913 - 2025 - Gaps in water quality modeling of hydrologic systems","interactions":[],"lastModifiedDate":"2025-04-21T13:16:19.082806","indexId":"70265913","displayToPublicDate":"2025-04-16T09:41:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Gaps in water quality modeling of hydrologic systems","docAbstract":"This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.
","language":"English","publisher":"MDPI","doi":"10.3390/w17081200","usgsCitation":"Lucas, L., Brown, C., Robertson, D., Baker, N.T., Johnson, Z., Green, C., Cho, J., Erickson, M., Gellis, A.C., Jasmann, J.R., Knowles, N., Prein, A., and Stackelberg, P.E., 2025, Gaps in water quality modeling of hydrologic systems: Water, v. 17, no. 8, 1200, 98 p., https://doi.org/10.3390/w17081200.","productDescription":"1200, 98 p.","ipdsId":"IP-157684","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488460,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17081200","text":"Publisher Index Page"},{"id":484764,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":933941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baker, Nancy T. 0000-0002-7979-5744","orcid":"https://orcid.org/0000-0002-7979-5744","contributorId":222870,"corporation":false,"usgs":true,"family":"Baker","given":"Nancy","email":"","middleInitial":"T.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":933945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":933946,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cho, Jong 0000-0001-5514-6056","orcid":"https://orcid.org/0000-0001-5514-6056","contributorId":291384,"corporation":false,"usgs":true,"family":"Cho","given":"Jong","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":933947,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933948,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933949,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jasmann, Jeramy Roland 0000-0002-5251-6987","orcid":"https://orcid.org/0000-0002-5251-6987","contributorId":238713,"corporation":false,"usgs":true,"family":"Jasmann","given":"Jeramy","email":"","middleInitial":"Roland","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":933950,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knowles, Noah 0000-0001-5652-1049","orcid":"https://orcid.org/0000-0001-5652-1049","contributorId":206338,"corporation":false,"usgs":true,"family":"Knowles","given":"Noah","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":933951,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Prein, Andreas","contributorId":352146,"corporation":false,"usgs":false,"family":"Prein","given":"Andreas","affiliations":[{"id":24610,"text":"NCAR","active":true,"usgs":false}],"preferred":false,"id":933952,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":933953,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70266843,"text":"70266843 - 2025 - Does the Lost Jim lava flow (Alaska) really preserve evidence of interaction with permafrost?","interactions":[],"lastModifiedDate":"2025-05-13T16:35:06.894023","indexId":"70266843","displayToPublicDate":"2025-04-16T09:29:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Does the Lost Jim lava flow (Alaska) really preserve evidence of interaction with permafrost?","docAbstract":"The basaltic Lost Jim lava flow, the youngest member of the Imuruk Lake volcanic field, Alaska, is reported to have interacted with underlying permafrost by thawing it and forming cavities into which the lava flow collapsed, forming pits and other depressions on the lava flow's surface. Our field observations contradict this hypothesis. The Lost Jim lava flow exhibits surface features typical of an inflated pāhoehoe flow, and we propose instead that most of the pits are unambiguously the result of flow inflation (i.e., lava-rise pits). These pits are found on elevated, relatively level surfaces, and their inner walls preserve features like rotated surface slabs and fine-scale flow banding on exposed crack surfaces, both of which are hallmarks of lava flow inflation. While collapse pits do exist on the Lost Jim lava flow, they are morphologically distinct and formed by crustal failure into drained lava tubes.
Satellite images of the Lost Jim lava flow show similarities in the size and distribution of pits within other young pāhoehoe lava flows scattered across the globe. The small diameter of many of the pits (<10 m), compared to flow thickness (≥10 m), also argues against collapse—numerical modeling shows that the relatively high tensile strength of a coherent lava flow would have prevented its collapse into cavities similar in diameter to the lava flow's thickness. Finally, the pits are found scattered across the Lost Jim lava flow, including in locations where the lava flow rests directly on bedrock, which consists of older lava flows. Segregated ice lenses and soil expansion—necessary components for thermokarst formation when thawed—do not exist in such locations. Altogether, these factors show that the Lost Jim lava flow is an inflated lava flow, and permafrost played no significant role during or after its emplacement.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2025.108347","usgsCitation":"Orr, T., Coombs, W., Rader, E., and Larsen, J., 2025, Does the Lost Jim lava flow (Alaska) really preserve evidence of interaction with permafrost?: Journal of Volcanology and Geothermal Research, v. 464, 108347, 12 p., https://doi.org/10.1016/j.jvolgeores.2025.108347.","productDescription":"108347, 12 p.","ipdsId":"IP-156249","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488269,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2025.108347","text":"Publisher Index Page"},{"id":485838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lost Jim lava flow","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.99392503271278,\n 65.97766449153795\n ],\n [\n -163.99392503271278,\n 65.7063086880865\n ],\n [\n -162.3815948765932,\n 65.7063086880865\n ],\n [\n -162.3815948765932,\n 65.97766449153795\n ],\n [\n -163.99392503271278,\n 65.97766449153795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"464","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Orr, Tim R. 0000-0003-1157-7588","orcid":"https://orcid.org/0000-0003-1157-7588","contributorId":26365,"corporation":false,"usgs":true,"family":"Orr","given":"Tim R.","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":936886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coombs, William M. 0000-0003-2099-1676","orcid":"https://orcid.org/0000-0003-2099-1676","contributorId":355121,"corporation":false,"usgs":false,"family":"Coombs","given":"William M.","affiliations":[{"id":35079,"text":"Durham University, Durham, UK","active":true,"usgs":false}],"preferred":false,"id":936887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rader, Erika 0000-0001-8205-3461","orcid":"https://orcid.org/0000-0001-8205-3461","contributorId":331813,"corporation":false,"usgs":false,"family":"Rader","given":"Erika","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":936888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Jessica 0000-0003-1171-129X","orcid":"https://orcid.org/0000-0003-1171-129X","contributorId":242808,"corporation":false,"usgs":false,"family":"Larsen","given":"Jessica","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936889,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270107,"text":"70270107 - 2025 - What is the lowest latitude of discrete aurorae during superstorms?","interactions":[],"lastModifiedDate":"2025-08-11T15:47:09.174324","indexId":"70270107","displayToPublicDate":"2025-04-16T08:41:37","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"What is the lowest latitude of discrete aurorae during superstorms?","docAbstract":"From a survey of published accounts of visual sightings of aurorae, a compilation is presented of the lowest identified geomagnetic latitude at which discrete aurorae were seen at local zenith during magnetic storms having intensities with maximum −Dst > 200 nT. The compilation includes data for the superstorms of 2 September 1859, 4 February 1872, and 15 May 1921. A statistical model is developed representing the equatorward boundary of discrete aurorae versus storm intensity. The model indicates that a once-per-century storm would likely induce discrete aurorae at zenith down to a geomagnetic latitude of 34°. Insofar as aurorae can be taken as a proxy for electrojet currents, such a storm would expose many nighttime electric-power systems, in the contiguous United States or Europe, to high levels of geomagnetic disturbance. A Carrington-class storm would induce discrete aurorae down to 24°. These exposures are much greater than those indicated in recent numerical simulations of extreme magnetic storms. Using the model to infer storm intensity from reports of low-latitude aurorae, a storm on 28 August 1859, likely had maximum −Dst = 673 nT. That this storm occurred just a few days before the Carrington storm of 2 September (maximum −Dst = 964 nT) deserves attention. A storm that occurred on 17 September 1770 is estimated to have had maximum −Dst = 928 nT. The vision of Ezekiel could have been inspired by aurorae from a storm with maximum −Dst = 550 nT.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024SW004286","usgsCitation":"Love, J.J., Mann, I., Qvick, T., and Mursula, K., 2025, What is the lowest latitude of discrete aurorae during superstorms?: Space Weather, v. 23, no. 4, e2024SW004286, 22 p., https://doi.org/10.1029/2024SW004286.","productDescription":"e2024SW004286, 22 p.","ipdsId":"IP-173082","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024sw004286","text":"Publisher Index Page"},{"id":493936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mann, Ian R.","contributorId":359451,"corporation":false,"usgs":false,"family":"Mann","given":"Ian R.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":945474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qvick, Timo","contributorId":359452,"corporation":false,"usgs":false,"family":"Qvick","given":"Timo","affiliations":[{"id":82926,"text":"University of Oulu","active":true,"usgs":false}],"preferred":false,"id":945475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mursula, Kalevi","contributorId":344048,"corporation":false,"usgs":false,"family":"Mursula","given":"Kalevi","affiliations":[{"id":82280,"text":"Space Climate Group, Space Physics and Astronomy Research Unit, University of Oulu, PO Box 3000, 90014 Oulu, Finland","active":true,"usgs":false}],"preferred":false,"id":945476,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267815,"text":"70267815 - 2025 - Lithium from magma to mine in an early Yellowstone hotspot caldera","interactions":[],"lastModifiedDate":"2025-07-10T14:50:14.042773","indexId":"70267815","displayToPublicDate":"2025-04-16T08:37:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Lithium from magma to mine in an early Yellowstone hotspot caldera","docAbstract":"Renewable energy technologies rely on the extraction of metals not historically in high demand, such as lithium (Li), for which ore deposit models are incompletely understood. One of the world’s largest Li deposits is hosted in lake sediments of the 16.4 Ma McDermitt caldera, which formed during the early stages of Yellowstone hotspot volcanism in the western United States. Eruptive and posteruptive mobility of Li are major challenges in elucidating deposit formation. Melt inclusions preserved in quartz crystals provide a means to assess pre-eruptive magmatic Li contents. Concentrations of Li determined by ion microprobe for melt inclusions in a McDermitt rhyolite lava are 400−1350 ppm, compared to 20−70 ppm Li in matrix rhyolite glasses. Synthesis with melt inclusion data for eight additional calderas demonstrates a recurrence of Li-rich rhyolitic magmas (200−2000 ppm Li) in the western part of the Yellowstone hotspot track. However, unlike the multicyclic caldera complexes with overlapping fault networks that may have compromised Li retention, the McDermitt caldera remained a closed hydrologic system throughout its evolution. Modeling indicates 100 km3 of resurgent magma could yield 25−150 Mt Li in a magmatic fluid and supports accumulation of Li-rich magmatic fluid in a closed intracaldera lake, followed by evaporative concentration and sequestration of Li within clay minerals to generate the McDermitt deposit.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G53140.1","usgsCitation":"Watts, K., 2025, Lithium from magma to mine in an early Yellowstone hotspot caldera: Geology, v. 53, no. 7, p. 592-596, https://doi.org/10.1130/G53140.1.","productDescription":"5 p.","startPage":"592","endPage":"596","ipdsId":"IP-167363","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":489475,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490666,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g53140.1","text":"Publisher Index Page"}],"country":"United States","state":"Idaho, Nevada, Oregon, Wyoming","otherGeospatial":"Yellowstone hotspot caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.21830246150877,\n 45.126578896874065\n ],\n [\n -119.21693278725452,\n 45.126578896874065\n ],\n [\n -119.21693278725452,\n 41.23242701033587\n ],\n [\n -114.17059390138817,\n 40.859473447854995\n ],\n [\n -114.02540645163282,\n 42.00890055289802\n ],\n [\n -110.44161856797778,\n 41.981517173869975\n ],\n [\n -110.21830246150877,\n 45.126578896874065\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Watts, Kathryn E. 0000-0002-6110-7499","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":204344,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":939006,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70266349,"text":"70266349 - 2025 - The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset","interactions":[],"lastModifiedDate":"2025-05-07T13:11:44.831779","indexId":"70266349","displayToPublicDate":"2025-04-16T08:13:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset","docAbstract":"Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 reflectance data showed that the between-sensor VI difference is below 4.5% for most of the indices. These results suggest that the harmonization is robust and the HLS V2.0 data are adequate for quantitative terrestrial applications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2025.114723","usgsCitation":"Ju, J., Zhou, Q., Freitag, B., Roy, D., Zhang, H., Sridhar, M., Mandel, J., Arab, S., Schmidt, G.L., Crawford, C., Gascon, F., Strobl, P., Masek, J.G., and Neigh, C., 2025, The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset: Remote Sensing of Environment, v. 324, 114723, 17 p., https://doi.org/10.1016/j.rse.2025.114723.","productDescription":"114723, 17 p.","ipdsId":"IP-178601","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488127,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2025.114723","text":"Publisher Index Page"},{"id":485453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"324","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ju, Junchang","contributorId":354466,"corporation":false,"usgs":false,"family":"Ju","given":"Junchang","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":935736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhou, Qiang","contributorId":354468,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":935737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freitag, Brian","contributorId":354470,"corporation":false,"usgs":false,"family":"Freitag","given":"Brian","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roy, David P.","contributorId":294404,"corporation":false,"usgs":false,"family":"Roy","given":"David P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":935739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Hankui","contributorId":354472,"corporation":false,"usgs":false,"family":"Zhang","given":"Hankui","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":935740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sridhar, Madhu","contributorId":350383,"corporation":false,"usgs":false,"family":"Sridhar","given":"Madhu","affiliations":[{"id":83729,"text":"University of Alabama Huntsville","active":true,"usgs":false}],"preferred":false,"id":935741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mandel, John","contributorId":354474,"corporation":false,"usgs":false,"family":"Mandel","given":"John","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935742,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":354476,"corporation":false,"usgs":true,"family":"Arab","given":"Saeed","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":935743,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schmidt, Gail L. 0000-0002-9684-8158 gschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":3475,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","email":"gschmidt@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":935744,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":935745,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gascon, Ferran","contributorId":173965,"corporation":false,"usgs":false,"family":"Gascon","given":"Ferran","email":"","affiliations":[{"id":27013,"text":"European Space Agency, Belgium","active":true,"usgs":false}],"preferred":false,"id":935746,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Strobl, Peter A.","contributorId":354478,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter A.","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":935747,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Masek, Jeffrey G.","contributorId":197725,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":935748,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Neigh, Christopher S.R.","contributorId":354481,"corporation":false,"usgs":false,"family":"Neigh","given":"Christopher S.R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":935749,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70265700,"text":"sir20255003 - 2025 - Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","interactions":[],"lastModifiedDate":"2025-08-07T20:57:16.247704","indexId":"sir20255003","displayToPublicDate":"2025-04-16T07:09:29","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":"2025-5003","displayTitle":"Estimation of Baseflow and Flooding Characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","title":"Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah","docAbstract":"An improved understanding of hydrologic responses to changing climatic conditions is needed to better inform water management practices. East Canyon Creek, a perennial, snowmelt-dominated stream in the Wasatch Mountains of northern Utah, is subjected to increasing development and demands on water in the Snyderville Basin and adjacent areas. In this study, streamflow and specific conductance measured at three U.S. Geological Survey streamgages on East Canyon Creek were used to estimate daily baseflow for water years 2011–22. Trends in these estimates and correlations with climate data from two Natural Resource Conservation Service snow telemetry (SNOTEL) stations within the Snyderville Basin above East Canyon Reservoir, were quantified and reported. Peak annual streamflow also was assessed for flood potential on the study reach of East Canyon Creek. The hydrograph separations showed consistent baseflow indices among all sites, with a larger baseflow component during the fall–spring period (September–April; baseflow indices approximately equal to [≈] 0.751–0.835) and smaller component during the summer period (May–August; baseflow indices ≈ 0.428–0.532). In-stream specific conductance during spring (February–April) was influenced by road salt application, limiting the utility of the hydrograph separation approach. Annual streamflow and climate data were evaluated for trends using the nonparametric Mann–Kendall test, with inconclusive results. Related tests for trends, the Seasonal and Regional Kendall tests, were used to evaluate data at monthly timesteps and indicated a decreasing trend in total streamflow and baseflow at all streamgages. The rank-based Kendall’s tau test for correlation was used to measure the ordinal association with climatic data at co-located SNOTEL stations. Total streamflow and baseflow were strongly correlated with precipitation and snow-water equivalent. By incorporating a predictive regression model, the nonparametric Theil–Sen line, these correlations could support the development of streamflow forecast models using climate data from SNOTEL stations. Such models would provide water managers with tools to help make proactive decisions, such as reservoir or water reclamation releases and curtailment of withdrawals, in response to regional drought or varying snowpack and spring runoff in a given year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255003","collaboration":"Prepared in cooperation with Snyderville Basin Water Reclamation District","usgsCitation":"Root, J.C., and Rumsey, C.A., 2025, Estimation of baseflow and flooding characteristics for East Canyon Creek, Summit and Morgan Counties, Utah: U.S. Geological Survey Scientific Investigations Report 2025–5003, 29 p., https://doi.org/10.3133/sir20255003.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-162488","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":493759,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118539.htm","linkFileType":{"id":5,"text":"html"}},{"id":484540,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14SJDMX","text":"USGS data release","description":"Root, J.C., 2025, Baseflow estimation and trend and correlation analysis results for East Canyon Creek, Summit and Morgan Counties, Utah, 2010–2022: U.S. Geological Survey data release, https://doi.org/10.5066/P14SJDMX.","linkHelpText":"Baseflow estimation and trend and correlation analysis results for East Canyon Creek, Summit and Morgan Counties, Utah, 2010–2022"},{"id":484539,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5003/images"},{"id":484538,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5003/sir20255003.XML","description":"SIR 2025-5003 XML"},{"id":484537,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255003/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5003 HTML"},{"id":484536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5003/sir20255003.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5003 PDF"},{"id":484535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5003/coverthb.jpg"}],"country":"United States","state":"Utah","county":"Morgan County, Summit County","otherGeospatial":"East Canyon Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.85739630382633,\n 41.2514958778022\n ],\n [\n -111.85739630382633,\n 40.5798335667547\n ],\n [\n -110.91729451616551,\n 40.5798335667547\n ],\n [\n -110.91729451616551,\n 41.2514958778022\n ],\n [\n -111.85739630382633,\n 41.2514958778022\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
Many open-vent arc volcanoes display two modes in their continuous gas emissions, one with a characteristic CO2/ ST ratio typical of periods of quiescent degassing and another punctuated by high CO2/ ST gas emitted in the weeks before eruption, a recently recognized eruption precursor. In this study we explore the origin of the two modes of degassing revealed by time-series gas data at Turrialba volcano (Costa Rica) in the context of new melt inclusion (MI) data. To reconstruct the c[CO2] of undegassed magma, we developed a rapid-quench piston-cylinder assembly to rehomogenize the vapor bubble commonly contained in MIs. We focus on olivine-hosted MIs from a mafic scoria sample erupted from Turrialba in 1864–1866. The reconstructed CO2 contents in MIs decrease from ∼4000 to <1000 ppmw as S contents decrease from 3500 to <1000 ppmw. The highest reconstructed S and CO2 in the MIs resulted in an initial magmatic CO2/ ST ratio (molar) of 0.83. Informed by the MI data, we modeled the decompression degassing of Turrialba magma and vapor composition using the Sulfur_X and EVo models. Instead of being controlled by initial magmatic CO2/ST ratio as suggested by previous studies, we find that the quiescent gas emitted from Turrialba during 2014–2018 (CO2/ ST = 2.3 ± 0.8, molar) appears to reflectequilibrium with magmas stored at 4–8 km (Sulfur_X) or 2 km (EVo) depth, when H2O is degassing extensively from the magma. A magma storage region at 4–8 km is also supported by seismic tomography. The second gas mode is noted by spikes in CO2/ ST ∼ 7.9 ± 2 in the weeks prior to eruption. This gas reflects equilibrium with a magma at 12–18 km (Sulfur_X) or 4–8 km (EVo), where the ascending magma is saturated with a CO2-rich vapor. Thus, there are two important trans crustal depths beneath the volcano: one where the rate of H2O loss from the magma and thus magma viscosity increases, and one at greater depths where high CO2/ST vapor forms and may facilitate dike propagation. We interpret the shallower, H2O-loss region as the main site of magma stalling and storage, where quiescent gas is generated continuously. We interpret the greater depth (12–18 km) as the source of the precursory gas that precedes eruption, and where the mafic melt lastly equilibrated with a mush zone before ascending and triggering eruption weeks later. This hypothesis is ripe for testing at other volcanoes that exhibit two modes in gas geochemistry.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2025.119349","usgsCitation":"Ding, S., Plank, T., de Moor, J., Moussallam, Y., Brounce, M., and Kelly, P.J., 2025, Volcanic gases reflect magma stalling and launching depths: Earth and Planetary Science Letters, v. 660, 119349, 13 p., https://doi.org/10.1016/j.epsl.2025.119349.","productDescription":"119349, 13 p.","ipdsId":"IP-160372","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":484641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Turrialba volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.92,\n 10.0833\n ],\n [\n -83.92,\n 9.9167\n ],\n [\n -83.667,\n 9.9167\n ],\n [\n -83.667,\n 10.0833\n ],\n [\n -83.92,\n 10.0833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"660","noUsgsAuthors":false,"publicationDate":"2025-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Ding, Shuo","contributorId":353454,"corporation":false,"usgs":false,"family":"Ding","given":"Shuo","affiliations":[{"id":84404,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA","active":true,"usgs":false}],"preferred":false,"id":933573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plank, Terry","contributorId":353455,"corporation":false,"usgs":false,"family":"Plank","given":"Terry","affiliations":[{"id":84404,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA","active":true,"usgs":false}],"preferred":false,"id":933574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Moor, J. Maarten","contributorId":353456,"corporation":false,"usgs":false,"family":"de Moor","given":"J. Maarten","affiliations":[{"id":38348,"text":"Observatorio Vulcanológico y Sismológico de Costa Rica, Universidad Nacional, Heredia, Costa Rica","active":true,"usgs":false}],"preferred":false,"id":933575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moussallam, Yves","contributorId":353457,"corporation":false,"usgs":false,"family":"Moussallam","given":"Yves","affiliations":[{"id":84404,"text":"Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA","active":true,"usgs":false}],"preferred":false,"id":933576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brounce, Maryjo","contributorId":353458,"corporation":false,"usgs":false,"family":"Brounce","given":"Maryjo","affiliations":[{"id":84406,"text":"Earth & Planetary Sciences Department, University of California, Riverside","active":true,"usgs":false}],"preferred":false,"id":933577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":933578,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272696,"text":"70272696 - 2025 - Assessing legacy nitrogen in groundwater using numerical models of the Long Island aquifer system, New York","interactions":[],"lastModifiedDate":"2025-12-04T15:01:52.627998","indexId":"70272696","displayToPublicDate":"2025-04-15T08:56:58","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Assessing legacy nitrogen in groundwater using numerical models of the Long Island aquifer system, New York","docAbstract":"Nitrogen transported along groundwater flow paths in coastal aquifers can contribute substantially to nitrogen loading into surface water receptors, particularly in hydrologic systems dominated by groundwater discharge. Nitrogen entrained in the aquifer is a function of land use and associated nitrogen sources at the time of groundwater recharge, which may differ considerably from present-day sources. Legacy nitrogen can result in substantial discrepancies between observed present-day nitrogen loading to surface water receptors and loading estimated from present-day sources. Additionally, legacy nitrogen can continue to discharge into surface waters after nitrogen mitigation actions have been undertaken. Here, we use a numerical modeling framework to compare three methods of estimating time-varying historical nitrogen loads to four water bodies (receptors) on eastern Long Island, New York. The methods span a range of data requirements and process complexity, from instantaneous receptor loads calculated from steady-state groundwater contributing areas, to transient loads estimated by explicitly simulating legacy groundwater nitrogen transport over a century with large changes in nitrogen sources and hydrologic conditions. The effects of legacy nitrogen on estimated receptor loads varied temporally and spatially within the study area. Depending on antecedent nitrogen inputs and hydrologic conditions, historical annual nitrogen loads estimated from transient simulations accounting for legacy nitrogen can be quite similar (<10% difference) or substantially different (±100%) from those estimated from simpler instantaneous methods. Continued input of present-day nitrogen sources using methods that account for legacy nitrogen results in asymptotic increases in receptor nitrogen loads over time, indicating that simulated present-day receptor nitrogen loads are not in equilibrium with present-day inputs. For these receptors in disequilibrium, models simulating transient groundwater nitrogen transport could be used to account for legacy nitrogen lag times to help resource managers evaluate the potential effectiveness of proposed nitrogen mitigation actions.
","language":"English","publisher":"EarthArXiv","doi":"10.31223/X56Q8J","usgsCitation":"Jahn, K., and Walter, D.A., 2025, Assessing legacy nitrogen in groundwater using numerical models of the Long Island aquifer system, New York: EarthArXiv, https://doi.org/10.31223/X56Q8J.","productDescription":"38 p.","ipdsId":"IP-170367","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":497047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jahn, Kalle 0000-0002-4976-0137","orcid":"https://orcid.org/0000-0002-4976-0137","contributorId":333053,"corporation":false,"usgs":true,"family":"Jahn","given":"Kalle","email":"","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walter, Donald A. 0000-0003-0879-4477 dawalter@usgs.gov","orcid":"https://orcid.org/0000-0003-0879-4477","contributorId":1101,"corporation":false,"usgs":true,"family":"Walter","given":"Donald","email":"dawalter@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951353,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270315,"text":"70270315 - 2025 - Multi-species telemetry quantifies current and future efficacy of a remote marine protected area","interactions":[],"lastModifiedDate":"2025-08-14T14:54:17.613606","indexId":"70270315","displayToPublicDate":"2025-04-15T07:47:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Multi-species telemetry quantifies current and future efficacy of a remote marine protected area","docAbstract":"Large-scale marine protected areas (LSMPAs; > 1000 km2) provide important refuge for large mobile species, but most do not encompass species' ranges. To better understand current and future LSMPA value, we concurrently tracked nine species (seabirds, cetaceans, pelagic fishes, manta rays, reef sharks) at Palmyra Atoll and Kingman Reef (PKMPA) in the U.S. Pacific Islands Heritage Marine National Monument. PKMPA and the U.S. Exclusive Economic Zone encompassed 39% and 54% of species movements (n = 83; tracking duration range: 0.5–350 days), respectively. Species distribution models indicated 73% of PKMPA contained highly suitable habitat. Under two projected future scenarios (SSP 1–2.6, “Sustainability”; SSP 3–7.0, “Rocky Road”), strong sea surface temperature gradients initially could cause abrupt oceanic change resulting in predicted habitat loss in 2040–2050, followed by an equilibrium response and regained habitat by 2090–2100. Current and future suitable habitats were available adjacent to PKMPA, suggesting that increased MPA size could enhance protection. Our three-tiered approach combining animal tracking with publicly available remote sensing data and future projected environmental scenarios could be used to design, study, and monitor protected areas throughout the world. Holistic approaches that encompass diverse species and habitat use can enhance assessments of protected area designs. Animal telemetry and remote sensing may be helpful for ascertaining the extent to which other MPAs protect large mobile species in the future.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.70138","usgsCitation":"Gilmour, M.E., Pollock, K., Adams, J., Block, B.A., Caselle, J.E., Filous, A., Friedlander, A.M., Game, E.T., Hazen, E.L., Hill, M., Holmes, N.D., Lafferty, K.D., Maxwell, S.M., McCauley, D.J., Schallert, R., Shaffer, S.A., Wolff, N.H., and Wegmann, A., 2025, Multi-species telemetry quantifies current and future efficacy of a remote marine protected area: Global Change Biology, v. 31, no. 4, e70138, 17 p., https://doi.org/10.1111/gcb.70138.","productDescription":"e70138, 17 p.","ipdsId":"IP-169295","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":494201,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.70138","text":"Publisher Index Page"},{"id":494095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Kingman Reef, Palmyra Atoll, U.S. Pacific Islands Heritage Marine National Monument.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -162.47031093286282,\n 6.4554137414790915\n ],\n [\n -162.47031093286282,\n 5.8142234034186515\n ],\n [\n -161.9746083876704,\n 5.8142234034186515\n ],\n [\n -161.9746083876704,\n 6.4554137414790915\n ],\n [\n -162.47031093286282,\n 6.4554137414790915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilmour, Morgan Elizabeth 0000-0002-2618-1095","orcid":"https://orcid.org/0000-0002-2618-1095","contributorId":289509,"corporation":false,"usgs":true,"family":"Gilmour","given":"Morgan","email":"","middleInitial":"Elizabeth","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":945997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollock, Kydd","contributorId":359650,"corporation":false,"usgs":false,"family":"Pollock","given":"Kydd","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":945998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":945999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Block, Barbara A.","contributorId":359653,"corporation":false,"usgs":false,"family":"Block","given":"Barbara","middleInitial":"A.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":946000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caselle, Jennifer E.","contributorId":359655,"corporation":false,"usgs":false,"family":"Caselle","given":"Jennifer","middleInitial":"E.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":946001,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Filous, Alexander","contributorId":272557,"corporation":false,"usgs":false,"family":"Filous","given":"Alexander","email":"","affiliations":[],"preferred":false,"id":946002,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Friedlander, Alan M.","contributorId":359658,"corporation":false,"usgs":false,"family":"Friedlander","given":"Alan","middleInitial":"M.","affiliations":[{"id":85893,"text":"National Geographic Society; Hawaiʻi Institute of Marine Biology","active":true,"usgs":false}],"preferred":false,"id":946003,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Game, Edward T.","contributorId":359659,"corporation":false,"usgs":false,"family":"Game","given":"Edward","middleInitial":"T.","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":946004,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hazen, Elliott L.","contributorId":359660,"corporation":false,"usgs":false,"family":"Hazen","given":"Elliott","middleInitial":"L.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":946005,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hill, Marie","contributorId":359661,"corporation":false,"usgs":false,"family":"Hill","given":"Marie","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":946006,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Holmes, Nick D.","contributorId":359662,"corporation":false,"usgs":false,"family":"Holmes","given":"Nick","middleInitial":"D.","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":946007,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":946008,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Maxwell, Sara M.","contributorId":359663,"corporation":false,"usgs":false,"family":"Maxwell","given":"Sara","middleInitial":"M.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":946009,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"McCauley, Douglas J.","contributorId":359664,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas","middleInitial":"J.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":946010,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schallert, Robert","contributorId":359665,"corporation":false,"usgs":false,"family":"Schallert","given":"Robert","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":946011,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Shaffer, Scott A.","contributorId":359666,"corporation":false,"usgs":false,"family":"Shaffer","given":"Scott","middleInitial":"A.","affiliations":[{"id":24620,"text":"San Jose State University","active":true,"usgs":false}],"preferred":false,"id":946012,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Wolff, Nicholas H.","contributorId":359667,"corporation":false,"usgs":false,"family":"Wolff","given":"Nicholas","middleInitial":"H.","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":946013,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Wegmann, Alex","contributorId":189488,"corporation":false,"usgs":false,"family":"Wegmann","given":"Alex","email":"","affiliations":[],"preferred":false,"id":946014,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70266481,"text":"70266481 - 2025 - Seismic moment and local magnitude scales in Ridgecrest, CA from the SCEC/USGS Community Stress Drop Validation Study","interactions":[],"lastModifiedDate":"2025-05-28T14:57:24.741473","indexId":"70266481","displayToPublicDate":"2025-04-15T07:40:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Seismic moment and local magnitude scales in Ridgecrest, CA from the SCEC/USGS Community Stress Drop Validation Study","docAbstract":"We illustrate the systematic difference between moment magnitude and local magnitude caused by underlying earthquake source physics, using seismic moments submitted to the Statewide California Earthquake Center/United States Geological Survey Community Stress Drop Validation Study 2019 Ridgecrest data set. While the relationship between seismic moment and moment magnitude (M or Mw) of log10(M0) ~ 1.5* M is uniformly valid for all earthquake sizes by definition (Hanks and Kanamori, 1979), the relationship between local magnitude ML and moment is itself magnitude dependent. For moderate events, ~3< M < ~6, M and ML are coincident; for earthquakes smaller than ~3, ML ~ 1.0 log10 M0 (Hanks and Boore, 1984). This is a physical consequence of the corner frequency fc becoming larger than the upper frequency of observation and implies that ML and M differ systematically by a factor of 1.5 for these small events. While this idea is not new, we propose a new, continuous relationship between local magnitude and moment, for magnitudes 2 to 6 which extrapolates to smaller and larger magnitudes, applicable to southern California specific to the Ridgecrest region. We make use of the plethora of seismic moments as submitted by many participants of the Community Stress Drop study, compared to the Southern California Seismic Network (SCSN) catalog magnitudes. Overall, the seismic moments in the Community Study recover moment magnitude well, so we use our new ML-M0 to convert ML to M, refining the SCSN operational MLr scale. This systematic difference of 50% in slope between local and moment magnitude at small magnitudes has implications for spectral stress drop estimates, earthquake ground motion modeling, as well as other magnitude scales and earthquake occurrence statistics.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240162","usgsCitation":"Baltay Sundstrom, A.S., and Abercrombie, R., 2025, Seismic moment and local magnitude scales in Ridgecrest, CA from the SCEC/USGS Community Stress Drop Validation Study: Bulletin of the Seismological Society of America, v. 115, no. 3, p. 1279-1293, https://doi.org/10.1785/0120240162.","productDescription":"15 p.","startPage":"1279","endPage":"1293","ipdsId":"IP-167974","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":485557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.78392771143658,\n 35.70994070706057\n ],\n [\n -117.78392771143658,\n 35.55296259649002\n ],\n [\n -117.5741750312894,\n 35.55296259649002\n ],\n [\n -117.5741750312894,\n 35.70994070706057\n ],\n [\n -117.78392771143658,\n 35.70994070706057\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":936193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abercrombie, Rachel E.","contributorId":293131,"corporation":false,"usgs":false,"family":"Abercrombie","given":"Rachel E.","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":936194,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70265508,"text":"sir20255005 - 2025 - Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","interactions":[],"lastModifiedDate":"2025-08-07T20:54:11.145385","indexId":"sir20255005","displayToPublicDate":"2025-04-14T12:55:00","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":"2025-5005","displayTitle":"Potential Water-Quality and Hydrology Stressors on Freshwater Mussels With Development of Environmental DNA Assays for Selected Mussels and Macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","title":"Potential water-quality and hydrology stressors on freshwater mussels with development of environmental DNA assays for selected mussels and macroinvertebrates in Big Darby Creek Basin, Ohio, 2020–22","docAbstract":"The richness and abundance of freshwater mussels in the Big Darby Creek Basin has declined in recent decades, according to survey results published by the Ohio Biological Survey. In October 2016, a major mussel die-off of undetermined cause reportedly affected over 50 miles of Big Darby Creek; however, fishes and other wildlife were not noticeably impacted. Pollution, habitat destruction, climate change, and hydrologic modification have all been theorized as potential reasons for the widespread declines in freshwater mussel populations in North America. To better understand potential stressors to mussels and other aquatic organisms in the Big Darby Creek Basin, the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority, evaluated water quality and temporal changes in hydrology at selected locations. In addition, environmental deoxyribonucleic acid (eDNA) quantitative polymerase chain reaction (qPCR) assays were developed to detect the presence of selected mussels and macroinvertebrates using stream water.
Time-weighted average concentrations of pesticides, organic wastewater compounds (OWCs), and polycyclic aromatic hydrocarbons (PAHs) were determined for selected locations within the Big Darby Creek Basin. Passive samplers designed to mimic the respiratory exposure of aquatic organisms and the bioconcentration of organic contaminants into their fatty tissues were deployed three times annually at three sites within the Big Darby Creek Basin in 2020 and 2021. Analyses were done for 204 pesticide compounds, 38 OWCs, and 33 PAHs. Of the 204 pesticide compounds, 70 were detected in at least one sample; 30 were detected in all samples. Herbicides and herbicide degradates were the pesticides most frequently detected and also had some of the highest concentrations of the pesticides detected in this study. Three herbicides (atrazine, ametryn, and metribuzin) were detected in at least 88 percent of samples and two fungicides (azoxystrobin and propiconazole) were detected in all samples. Of the 38 OWCs, 24 were detected in at least one sample; however, only one (N,N-diethyltoluamide [DEET]) was detected in all samples. Of the 33 PAHs, 29 were detected in at least one sample; 12 were detected in all samples.
A continuous water-quality monitor was operated seasonally on Big Darby Creek above Georgesville, Ohio, from 2020 to 2022. Dissolved oxygen concentrations generally followed a daily cycle, peaking in early evening and troughing around sunrise. There were occasional 24-hour swings in dissolved oxygen concentration that had a range exceeding 10 milligrams per liter. However, dissolved oxygen concentrations never fell below Ohio’s aquatic life criteria for warmwater habitats (outside of mixing zones) of 4.0 milligrams per liter as an instantaneous minimum and 5.0 milligrams per liter as a minimum 24-hour average. The Ohio water-quality criteria for temperatures are 29.4 degrees Celsius as an instantaneous maximum and 27.8 degrees Celsius as a 24-hour average maximum. In 2020, there were 10 days when the maximum instantaneous value for temperature was exceeded and 3 consecutive days when the maximum 24-hour average temperature was exceeded.
Streamflow time-series data from three gaging stations within the Big Darby Creek Basin were evaluated for trends in annual flow statistics and daily nonexceedance probabilities over time. In general, the evaluation of streamflow conditions at the Big Darby Creek gage (with 97 years of record) indicated that streamflow changed between water years 1922 and 2021. During that time span, flows in general increased, the number of high-flow pulses became more frequent, and low-flow pulses and extreme low-flow periods became less frequent. The only strong indication of trends over time in annual flow statistics for the relatively short records for the other two gages (on Little Darby Creek, with 25 years of record, and Hellbranch Run, with 29 years of record) was that as time went on, reversals between rising and falling periods became more frequent.
The U.S. Geological Survey Ohio Water Microbiology Laboratory developed eDNA qPCR assays to detect Epioblasma rangiana (northern riffleshell mussels), Chimarra obscura (a species of caddisfly), Maccaffertium pulchellum (a species of mayfly), and optimized a preexisting eDNA qPCR assay to detect for Ptychobranchus fasciolaris (kidneyshell mussels). The assays were validated by using environmental sampling methods. Assay sensitivity was established by determining the limits of detection and quantification. Water samples were collected at 12 sites in the Big Darby Creek Basin between 2020 and 2022 and analyzed for eDNA with the qPCR assays developed for this study.
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U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229