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Seven potential sources of water, consisting of free-flowing discharge from abandoned coal mines at six locations and one abandoned flooded underground coal mine air shaft, were sampled for chemical analysis to assess the quality of the groundwater emanating from the seven mine sources. The six free-flowing mine discharge sources were also assessed for discharge by current-meter measurements on two separate occasions. The U.S. Geological Survey assessed these seven sources to provide information to the City of Gary, West Virginia (W. Va.), and the City of Gary’s consulting engineer with groundwater-quality and flow data to allow them to assess the seven sites as potential alternate sources of water for the City of Gary to augment its existing supply.

For the six sites where discharge could be measured, discharge ranged from a minimum of 0.082 cubic feet per second (ft3/s) to a maximum of 3.685 ft3/s. Of the six sites measured, only two, Harmon Branch at Thorpe, W. Va. (USGS site 372201081303501) and the abandoned public-supply water wells near Havaco, W. Va. (USGS site 372358081344601), had discharge in excess of 1.00 ft3/s. Discharge from the abandoned public supply wells was 3.685 ft3/s on September 20, 2023, and 2.888 ft3/s on October 16, 2023, and discharge from Harmon Branch at Thorpe, W. Va., was 1.049 ft3/s on September 22, 2023, and 1.038 ft3/s on October 17, 2023. Discharge in the abandoned underground mine air shaft (USGS site 372224081340901) could not be assessed, but the air shaft drains an abandoned mine that likely contains water stored in approximately 1.7 square miles (mi2) of abandoned underground coal mines in the Pocahontas No. 3 coal seam, and possibly an additional 0.9 mi2 of leakage from the overlying Pocahontas No. 4 coal seam. Discharge for the six sites measured for the study was measured during a period between September 20 and October 18, 2023, and corresponded to the 12th to the 15th percentile of flow-duration statistics for the Tug Fork downstream of Elkhorn Creek at Welch, W. Va. streamgage (USGS site 03212750).

Water-quality data for the seven sites sampled overall were acceptable with respect to drinking water standards. Of the 203 constituents analyzed, only a few failed to meet applicable U.S. Environmental Protection Agency (EPA) drinking water standards. Iron exceeded the 300 micrograms per liter (μg/L) secondary maximum contaminant level (SMCL) at only 1 of the 7 sites (14.3 percent) sampled. Iron concentrations ranged from a minimum of less than (<) 5.00 μg/L to a maximum of 724 μg/L with a median concentration of 7.62 μg/L. Manganese exceeded the 50.0 μg/L SMCL at 2 of the 7 sites (28.6 percent) sampled. Manganese concentrations ranged from a minimum of 1.93 μg/L to a maximum of 271 μg/L with a median concentration of 4.03 μg/L. No sites sampled exceeded the arsenic maximum contaminant level (MCL) of 10 μg/L. Arsenic concentrations ranged from a minimum of <0.100 μg/L to a maximum of 2.35 μg/L with a median arsenic concentration of 0.200 μg/L. None of the seven sites sampled for selenium for this study exceeded the EPA MCL of 50.0 μg/L. Selenium concentrations ranged from a minimum of <0.050 μg/L to a maximum of 5.26 μg/L with a median concentration of 3.21 μg/L.

All seven sites were sampled for volatile organic compounds (VOCs), semivolatile organic compounds (SVOCs), and polychlorinated biphenyls (PCBs), but most had concentrations below the detection limit. Of the 10 PCB compounds analyzed for the seven sites sampled, none contained detectable concentrations of PCBs or Aroclor compounds. Of the 44 SVOCs analyzed at each of the seven sites sampled, only 1 SVOC, acenaphthene, was detected, at a concentration of 0.02 μg/L. Of the 96 VOCs analyzed, from each of the seven sites sampled, only two were found at detectable concentrations. Trichloromethane was detected only at 1 of the 7 (14.3 percent) sites sampled at a concentration of 0.027 μg/L, and benzene was detected at the same site and 3 additional sites (4 of the 7 sites or 57.1 percent of the sites sampled) at concentrations of 0.028, 0.029, 0.021, and 0.035 μg/L, but none exceeded the EPA MCL for benzene of 5.00 μg/L.

Total coliform bacteria are ubiquitous in the environment, and their presence only suggests the potential for contamination by near-surface processes. Escherichia coli (E. coli) bacteria are derived from either human or animal fecal material and can be an indicator of potential contamination by pathogenic bacteria or viruses. Total coliform bacteria were detected at all 7 sites sampled at concentrations ranging from 17.5 to greater than (>) 2,420 most probable number per 100 mL (MPN/100 mL) of sample, with a median total coliform concentration of 1,553 MPN/100 mL. Escherichia coli bacteria were detected at 4 of the 7 sites sampled at concentrations ranging from <1 to 11.9 MPN/100 mL, with a median E. coli concentration of 5.1 MPN/100 mL.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251037","collaboration":"Prepared in cooperation with the City of Gary, West Virginia","usgsCitation":"Kozar, M.D., and Austin, S.H., 2025, Reconnaissance of potential alternate water supply sources for the City of Gary, West Virginia: U.S. Geological Survey Open-File Report 2025–1037, 27 p., https://doi.org/10.3133/ofr20251037.","productDescription":"Report: viii, 27 p.; Appendix","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-176784","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":496467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1037/ofr20251037.pdf","text":"Report","size":"5.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1037 PDF"},{"id":496466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1037/coverthb.jpg"},{"id":497789,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118952.htm"},{"id":496471,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2025/1037/ofr20251037_app2.csv","text":"Appendix 2","size":"222 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Water-Quality Data Collected During the Study"},{"id":496470,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1037/ofr20251037.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1037 XML"},{"id":496469,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1037/images/"},{"id":496468,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251037/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1037 HTML"}],"country":"United States","state":"West Virginia","city":"Gary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.616667,\n 37.433333\n ],\n [\n -81.616667,\n 37.25\n ],\n [\n -81.45,\n 37.25\n ],\n [\n -81.45,\n 37.433333\n ],\n [\n -81.616667,\n 37.433333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228

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Introduction 

Massachusetts extends from the mountains of the Appalachian system in the west of the State to the sandy beaches and rocky shorelines of the Atlantic coast in the east. Inland topographic data support a wide range of important activities, including geologic mapping, transportation planning, forest and wildlife management, quantifying ecological services, water supply protection, commonwealth-wide infrastructure planning, local site planning, and flood-plain management. Nearshore bathymetry can be used to support coastal portions of the Commonwealth by addressing the combined threats of ocean warming, strong storm surge, and rising sea levels. The maintenance and (or) expansion of Massachusetts ports (for instance, Boston, New Bedford) and Cape Cod sediment management depends upon the accurate mapping of bathymetry and the frequent influx of sediment and redeposition. Critical applications that address the broad range of requirements depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Massachusetts. The status of available and in-progress 3DEP baseline lidar data in Massachusetts is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $1.23 million in new benefits annually to Massachusetts. The top 10 Massachusetts business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.

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\"}}]}","contact":"

Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2025-11-14","noUsgsAuthors":false,"publicationDate":"2025-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Walters, Dan","contributorId":291381,"corporation":false,"usgs":true,"family":"Walters","given":"Dan","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":949733,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70272087,"text":"70272087 - 2025 - Sensitive environmental DNA methods for low-risk surveillance of at-risk bumble bees","interactions":[{"subject":{"id":70274281,"text":"70274281 - 2025 - Sensitive environmental DNA methods for low-risk surveillance of at-risk bumble bees","indexId":"70274281","publicationYear":"2025","noYear":false,"title":"Sensitive environmental DNA methods for low-risk surveillance of at-risk bumble bees"},"predicate":"SUPERSEDED_BY","object":{"id":70272087,"text":"70272087 - 2025 - Sensitive environmental DNA methods for low-risk surveillance of at-risk bumble bees","indexId":"70272087","publicationYear":"2025","noYear":false,"title":"Sensitive environmental DNA methods for low-risk surveillance of at-risk bumble bees"},"id":1}],"lastModifiedDate":"2025-12-01T16:49:46.874038","indexId":"70272087","displayToPublicDate":"2025-11-13T07:54:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Sensitive environmental DNA methods for low-risk surveillance of at-risk bumble bees","docAbstract":"

Terrestrial environmental DNA (eDNA) techniques have been proposed as a means of sensitive, non-lethal pollinator monitoring. To date, however, no studies have provided evidence that eDNA methods can achieve detection sensitivity on par with traditional pollinator surveys. Using a large-scale dataset of eDNA and corresponding net surveys, we show that eDNA methods enable sensitive, species-level characterisation of whole bumble bee communities, including rare and critically endangered species such as the rusty patched bumble bee (RPBB; Bombus affinis). All species present in netting surveys were detected within eDNA surveys, apart from two rare species in the socially parasitic subgenus Psithyrus (cuckoo bumble bees). Further, for rare non-parasitic species, eDNA methods exhibited similar sensitivity relative to traditional netting. Compared with flower eDNA samples, sequenced leaf surface eDNA samples resulted in significantly lower rates of Bombus detection, and these detections were likely attributable to high rates of background eDNA on environmental surfaces, perhaps due to airborne eDNA or eDNA movement during rainfall events. Lastly, we found that eDNA-based frequency of detection across replicate surveys was strongly associated with net-based measures of abundance across site visits. We conclude that the COI-based metabarcoding method we present is cost-effective and highly scalable for quantitative characterisation of at-risk bumble bee communities, providing a new approach for improving our understanding of species habitat associations.

","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.70073","usgsCitation":"Richardson, R.T., Avalos, G., Garland, C.J., Trott, R., Hager, O., Hepner, M.J., Raines, C.D., and Goodell, K., 2025, Sensitive environmental DNA methods for low-risk surveillance of at-risk bumble bees: Molecular Ecology Resources, v. 26, no. 1, e70073, 10 p., https://doi.org/10.1111/1755-0998.70073.","productDescription":"e70073, 10 p.","ipdsId":"IP-177744","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":496707,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.70073","text":"Publisher Index Page"},{"id":496476,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Central Appalachian Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.8129124415969,\n 42.016862263060546\n ],\n [\n -82.1750155405594,\n 36.54899586680892\n ],\n [\n -80.0203510955439,\n 36.258277099718455\n ],\n [\n -75.31731755526653,\n 41.757585494571146\n ],\n [\n -76.8129124415969,\n 42.016862263060546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Rodney T.","contributorId":332908,"corporation":false,"usgs":false,"family":"Richardson","given":"Rodney","middleInitial":"T.","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":950022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avalos, Grace","contributorId":332902,"corporation":false,"usgs":false,"family":"Avalos","given":"Grace","email":"","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":950023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garland, Cameron J.","contributorId":360431,"corporation":false,"usgs":false,"family":"Garland","given":"Cameron","middleInitial":"J.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":950024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trott, Regina","contributorId":332903,"corporation":false,"usgs":false,"family":"Trott","given":"Regina","email":"","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":950025,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hager, Olivia","contributorId":360433,"corporation":false,"usgs":false,"family":"Hager","given":"Olivia","affiliations":[{"id":86002,"text":"University of Maryland Center for Environmental Science; MD Western EcoSystems Technology, Inc","active":true,"usgs":false}],"preferred":false,"id":950026,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hepner, Mark J.","contributorId":335438,"corporation":false,"usgs":false,"family":"Hepner","given":"Mark","middleInitial":"J.","affiliations":[{"id":80404,"text":"Metamophecology","active":true,"usgs":false}],"preferred":false,"id":950027,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raines, Clayton D. 0000-0002-0403-190X","orcid":"https://orcid.org/0000-0002-0403-190X","contributorId":296362,"corporation":false,"usgs":true,"family":"Raines","given":"Clayton","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":950028,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goodell, Karen","contributorId":332906,"corporation":false,"usgs":false,"family":"Goodell","given":"Karen","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":950029,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70272074,"text":"70272074 - 2025 - Land change, fire, and climate weaken carbon sink in the conterminous U.S.","interactions":[],"lastModifiedDate":"2025-11-14T16:23:59.95018","indexId":"70272074","displayToPublicDate":"2025-11-12T09:17:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Land change, fire, and climate weaken carbon sink in the conterminous U.S.","docAbstract":"The land carbon sink of the conterminous United States was evaluated using a bottom-up\nmodeling framework and 30-meter land change data from 1985 to 2020. This cross-scale,\ncross-landscape, and cross-system approach tracked fractional land cover changes and applied regional model calibration. Results show average terrestrial and aquatic carbon sinks of +110 ± 37 and +19 ± 0.5 teragrams of carbon per year, respectively. The terrestrial carbon sink, showing no clear trend, peaked in the 1990s, with more years as a carbon source since 2000, contradicting recent national and global studies. Land change had the largest impact (−70 ± 5.5 teragrams of carbon per year), exceeding impacts of climate (−33 ± 48 teragrams of carbon per year), wildfire (−7.7 ± 2.4 teragrams of carbon per year), and erosion transport (−1.9 ± 0.13 teragrams of carbon per year). The positive CO2 fertilization effect (+69 ± 12 teragrams of carbon per year) was insufficient to maintain the carbon sink strength. Our framework\nreveals key paths of carbon loss, with implications for carbon budget and energy policies in the United States and beyond.","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.adx7823","usgsCitation":"Liu, J., Sleeter, B., Zhu, Z., Cochrane, M.A., Zhou, Q., Wang, B., Domke, G., Selmants, P., Windham-Myers, L., Zhu, Q., Wilson, T., Byrd, K., Ward, E., Sohl, T., Hawbaker, T., Zhang, Z., Soulard, C., Wickland, K., and Striegl, R.G., 2025, Land change, fire, and climate weaken carbon sink in the conterminous U.S.: Science Advances, v. 11, no. 46, eadx7823, 16 p., https://doi.org/10.1126/sciadv.adx7823.","productDescription":"eadx7823, 16 p.","ipdsId":"IP-156977","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":496712,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adx7823","text":"Publisher Index 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Basin morphometry, climate and geology control how a hydrological network evolves over time, controlling the efficiency of weathering of elements from geological materials, and ultimately the input of sediment and dissolved constituents to river systems. Exceedances to the Navajo Nation surface water quality standards for trace metals have been reported in the San Juan River watershed. Because metals are transported adsorbed to fine-grain sediment, the identification of areas with elevated sources of trace metals and/or areas with increased erosion and sediment transport potential is an important first step in protecting water quality. Physical factors such as elevation, slope, relief and stream order were used to quantify morphometric parameters that effect the contribution of trace metals into the stream network. By correlating these parameters with water quality data that were collected from tributaries along the San Juan River, we identified statistically significant regressions between morphometric parameters and total Al, Pb, U, Fe and Mn in surface water. Positive correlations with trace metals include tributary drainage basin perimeter, pour point elevation and total number of streams, while negative correlations include stream length ratio, ruggedness number and longest basin axis. Stream reach measurements within geological units that contain known trace metal constituents reveal that Gallegos Canyon and Desert Creek are the most susceptible to sediment mobilization and transport, while other tributary drainage basins, such as Desert, Recapture and Salt creeks, are associated with naturally elevated concentrations of Al, As, Pb and U.

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The use of fiber‐optic sensing systems in seismology has exploded in the past decade. Despite an ever‐growing library of ground‐breaking studies, questions remain about the potential of fiber‐optic sensing technologies as tools for advancing if not revolutionizing earthquake‐hazards‐related research, monitoring, and early warning systems. A working group convened to explore these topics; we comprehensively examined the application of fiber optics in various aspects of earthquake hazards, encompassing earthquake source processes, crustal imaging, data archiving, and technological challenges. There is great potential for fiber‐optic systems to advance earthquake monitoring and understanding, but to fully unlock their capabilities requires continued progress in key areas of research and development, including instrument testing and validation, increased dynamic range for applications focused on larger earthquakes, and continued improvement in subsurface and source imaging methods. A key current stumbling block results from the lack of clear data archiving requirements, and we propose an initial strategy that balances data volume requirements with preserving key data for a broad range of future studies. In addition, we demonstrate the potential for fiber‐optic sensing to impact monitoring efforts by documenting the data completeness in a number of long‐term experiments. Finally, we outline the features of a instrument testing facility that would enable progress toward reliable and standardized distributed acoustic sensing data. Overcoming these current obstacles would facilitate progress in fiber‐optic sensing and unlock its potential application to a broad range of earthquake hazard problems.

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environmental DNA survey","interactions":[],"lastModifiedDate":"2025-11-19T15:47:46.625525","indexId":"70272218","displayToPublicDate":"2025-11-06T09:37:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Spatial distribution and relative biomass of bigheaded carps in Lake Balaton, Hungary estimated from an environmental DNA survey","docAbstract":"

Silver carp (Hypophthalmichthys nobilis), bighead carp (H. molitrix) and their hybrids, collectively known as bigheaded carps, have been introduced to Lake Balaton, Hungary. The current stock sizes are difficult to assess. We investigated environmental DNA (eDNA) techniques targeted for bigheaded carps, assessed the spatial distribution of eDNA in Lake Balaton, compared eDNA concentrations to environmental variables to assess potential habitat selection based on those variables, and provided an estimate of biomass of bigheaded carps relative to eDNA shedding rates per unit biomass observed in controlled experiments. Water samples were collected from 70 sites in an array across the lake. Biomass estimation was calculated using mean eDNA concentration obtained by quantitative PCR of the samples and previously determined eDNA shedding rates of bigheaded carps under controlled conditions in a laboratory. Concentration of eDNA was highly variable between sites, resulting in wide confidence intervals. Basins did not significantly differ in eDNA concentration, and there were no strong relationships between environmental variables and eDNA concentration, indications that bigheaded carps use the entire lake. The model provided an estimate of 4,830 metric tonnes (2,750–8,030 tonnes) of bigheaded carps in Lake Balaton, or 81.0 kg/ha. The eDNA method produced a value close to previous estimates by traditional means of total biomass of bigheaded carps in the lake, and like traditional methods, there was a broad confidence interval on the estimate of the mean. The results of the present study support the utility of aquatic eDNA analysis, and the need for further comparisons with fisheries methods and supporting data from laboratory studies.

","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0335950","usgsCitation":"Boross, N., Laszlo, A., Chapman, D.C., Boros, G., Vitál, Z., Tóth, V., Thompson, N., Klymus, K.E., and Richter, C.A., 2025, Spatial distribution and relative biomass of bigheaded carps in Lake Balaton, Hungary estimated from an environmental DNA survey: PLoS ONE, v. 20, no. 11, 0335950, 15 p., https://doi.org/10.1371/journal.pone.0335950.","productDescription":"0335950, 15 p.","ipdsId":"IP-178103","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":496746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0335950","text":"Publisher Index Page"},{"id":496639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Hungary","otherGeospatial":"Lake Balaton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 18.21350370081612,\n 47.10491295878799\n ],\n [\n 17.172489215123477,\n 47.10491295878799\n ],\n [\n 17.172489215123477,\n 46.62325119241811\n ],\n [\n 18.21350370081612,\n 46.62325119241811\n ],\n [\n 18.21350370081612,\n 47.10491295878799\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Boross, Nora","contributorId":362434,"corporation":false,"usgs":false,"family":"Boross","given":"Nora","affiliations":[{"id":86526,"text":"HUN-REN Balaton Limnological Research Institute, Hungary","active":true,"usgs":false}],"preferred":false,"id":950467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laszlo, Ardo","contributorId":362435,"corporation":false,"usgs":false,"family":"Laszlo","given":"Ardo","affiliations":[{"id":86527,"text":"Hungarian University of Agricultural and Life Science, Hungary","active":true,"usgs":false}],"preferred":false,"id":950468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapman, Duane C.","contributorId":362436,"corporation":false,"usgs":false,"family":"Chapman","given":"Duane","middleInitial":"C.","affiliations":[{"id":86529,"text":"(retired) USGS Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":950469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boros, Gergely","contributorId":295274,"corporation":false,"usgs":false,"family":"Boros","given":"Gergely","email":"","affiliations":[{"id":63813,"text":"Centre for Ecological Research, Balaton Limnological Institute","active":true,"usgs":false}],"preferred":false,"id":950470,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vitál, Zoltán","contributorId":352562,"corporation":false,"usgs":false,"family":"Vitál","given":"Zoltán","affiliations":[{"id":84260,"text":"Hungarian University of Agriculture and Life Sciences","active":true,"usgs":false}],"preferred":false,"id":950471,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tóth, Viktor","contributorId":362437,"corporation":false,"usgs":false,"family":"Tóth","given":"Viktor","affiliations":[{"id":86526,"text":"HUN-REN Balaton Limnological Research Institute, Hungary","active":true,"usgs":false}],"preferred":false,"id":950472,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":950473,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":950474,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":950475,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70272664,"text":"70272664 - 2025 - U.S. Geological Survey geomagnetic variometer data: Capitalizing on seismic infrastructure","interactions":[],"lastModifiedDate":"2025-12-03T16:50:50.794884","indexId":"70272664","displayToPublicDate":"2025-11-05T10:43:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"U.S. Geological Survey geomagnetic variometer data: Capitalizing on seismic infrastructure","docAbstract":"

The U.S. Geological Survey’s Geomagnetism Program is collaborating with the Earthquake Hazards Program and Global Seismographic Network Program to densify magnetic field observations. This collaboration focuses on the installation of magnetometers, or magnetic variometers, at existing seismic stations. Along with improving the density of space weather observations for hazard monitoring, these data can be used to correct colocated magnetic field induced noise in seismic data. Such corrections are especially useful during time periods of large magnetic storms where the magnetic field‐induced instrument noise can be of similar amplitude to earthquake ground‐motion records.

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Self-potential (SP) monitoring is increasingly used for subsurface flow characterization due to its sensitivity to hydrogeological and geochemical processes. However, SP inversion remains challenging due to its ill-posed nature, sparse data coverage, and strong transient noise. This study proposes a hybrid framework to image hyporheic exchange using a time-lapse SP data set monitored from a streamflow site in Oak Ridge, Tennessee. Dipole moment tomography grids generated from the physics-informed numerical inversion is first used to train a Vision Transformer (ViT) model that maps surface SP sequences to 2D source distributions. While the numerical method is more responsive to transient signals, the ViT model better captures persistent spatial structures. Their complementary outputs are jointly analyzed in the spatiotemporal domain to isolate dynamic hyporheic exchange zones and distinguish transient from steady state subsurface flow features. This approach integrates physical inversion and deep learning to enhance interpretability, generalization, and temporal awareness in SP analysis.

","language":"English","publisher":"Americal Geophysical Union","doi":"10.1029/2025GL118772","usgsCitation":"Yin, H., Ikard, S., Rucker, D.F., Brooks, S.C., Dai, Z., Carroll, K.C., 2025, Imaging hyporheic exchange by integrating deep learning and physics-informed inversion of time-lapse self-potential data: Geophysical Research Letters, v. 52, no. 21, e2025GL118772, 11 p., https://doi.org/10.1029/2025GL118772.","productDescription":"e2025GL118772, 11 p.","ipdsId":"IP-180027","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":500584,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl118772","text":"Publisher Index Page"},{"id":500417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"21","noUsgsAuthors":false,"publicationDate":"2025-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Yin, Huichao 0000-0001-6172-5580","orcid":"https://orcid.org/0000-0001-6172-5580","contributorId":366938,"corporation":false,"usgs":false,"family":"Yin","given":"Huichao","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":956406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":201775,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rucker, Dale F. 0000-0002-8930-2747","orcid":"https://orcid.org/0000-0002-8930-2747","contributorId":294463,"corporation":false,"usgs":false,"family":"Rucker","given":"Dale","email":"","middleInitial":"F.","affiliations":[{"id":63573,"text":"hydroGEOPHYSICS, Inc.","active":true,"usgs":false}],"preferred":false,"id":956408,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooks, Scott C. 0000-0002-8437-9788","orcid":"https://orcid.org/0000-0002-8437-9788","contributorId":294464,"corporation":false,"usgs":false,"family":"Brooks","given":"Scott","email":"","middleInitial":"C.","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":956409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dai, Zhenxue 0000-0002-0805-7621","orcid":"https://orcid.org/0000-0002-0805-7621","contributorId":366941,"corporation":false,"usgs":false,"family":"Dai","given":"Zhenxue","affiliations":[{"id":87510,"text":"Jilin University","active":true,"usgs":false}],"preferred":false,"id":956410,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carroll, Kenneth C. 0000-0003-2097-9589","orcid":"https://orcid.org/0000-0003-2097-9589","contributorId":247827,"corporation":false,"usgs":false,"family":"Carroll","given":"Kenneth","email":"","middleInitial":"C.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":956411,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272202,"text":"70272202 - 2025 - Missing data in ecology: Syntheses, clarifications, and considerations","interactions":[],"lastModifiedDate":"2025-11-19T16:17:18.846028","indexId":"70272202","displayToPublicDate":"2025-11-05T10:14:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Missing data in ecology: Syntheses, clarifications, and considerations","docAbstract":"

In ecology and related sciences, missing data are common and occur in a variety of different contexts. When missing data are not handled properly, subsequent statistical estimates tend to be biased, inefficient, and lack proper confidence interval coverage. Missing data are often grouped into three categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). We review each category and compare their benefits and drawbacks. We review several approaches to handling missing data including complete case analysis, imputation, inverse probability weighting, and data augmentation. We clarify what types of variables should accompany imputation methods and how those variables are influenced by the analysis methods. Additionally, we discuss missing data that lack a formal basis for measurement and hence are fundamentally different from MCAR, MAR, and MNAR missing data. Throughout, we introduce concepts and numeric examples using both simulated data and data from the United States Environmental Protection Agency's 2016 National Wetland Condition Assessment. We conclude by providing five considerations for ecologists and other scientists handling missing data.

","language":"English","publisher":"Elsevier","doi":"10.1002/ecm.70037","usgsCitation":"Dumelle, M., Trangucci, R., Nahlik, A.M., Olsen, A.R., Irvine, K., Blocksom, K.A., Ver Hoef, J., and Fuentes, C., 2025, Missing data in ecology: Syntheses, clarifications, and considerations: Ecological Monographs, v. 95, no. 4, e70037, 41 p., https://doi.org/10.1002/ecm.70037.","productDescription":"e70037, 41 p.","ipdsId":"IP-174769","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":496750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecm.70037","text":"Publisher Index Page"},{"id":496645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Dumelle, Michael 0000-0002-3393-5529","orcid":"https://orcid.org/0000-0002-3393-5529","contributorId":355601,"corporation":false,"usgs":false,"family":"Dumelle","given":"Michael","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":950423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trangucci, Rob","contributorId":362469,"corporation":false,"usgs":false,"family":"Trangucci","given":"Rob","affiliations":[],"preferred":false,"id":950544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nahlik, Amanda M. 0000-0003-0591-375X","orcid":"https://orcid.org/0000-0003-0591-375X","contributorId":272622,"corporation":false,"usgs":false,"family":"Nahlik","given":"Amanda","email":"","middleInitial":"M.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":950425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olsen, Anthony R","contributorId":362407,"corporation":false,"usgs":false,"family":"Olsen","given":"Anthony","middleInitial":"R","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":950424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":220632,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":950426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blocksom, Karen A. 0000-0003-4606-7430","orcid":"https://orcid.org/0000-0003-4606-7430","contributorId":329596,"corporation":false,"usgs":false,"family":"Blocksom","given":"Karen","email":"","middleInitial":"A.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":950427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ver Hoef, Jay","contributorId":177840,"corporation":false,"usgs":false,"family":"Ver Hoef","given":"Jay","affiliations":[],"preferred":false,"id":950428,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fuentes, Claudio","contributorId":245477,"corporation":false,"usgs":false,"family":"Fuentes","given":"Claudio","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":950429,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273444,"text":"70273444 - 2025 - Assessment of coastal and fluvial morphodynamic changes using Structure-for-Motion: A case study of the Sfȃntu Gheorghe Mouth (Danube Delta, Romania)","interactions":[],"lastModifiedDate":"2026-01-14T15:28:25.24061","indexId":"70273444","displayToPublicDate":"2025-11-05T09:22:13","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assessment of coastal and fluvial morphodynamic changes using Structure-for-Motion: A case study of the Sfȃntu Gheorghe Mouth (Danube Delta, Romania)","docAbstract":"

The ability to accurately map erosion, flooding, and habitat loss in coastal environments is crucial for formulating national strategies aimed at preventing and mitigating the impacts of natural disasters. A fundamental component of this process is the implementation of coastal morphodynamics monitoring through Structure-from-Motion (SfM) techniques, utilizing high-resolution 2D/3D data obtained from aerial photogrammetry. To assess morphodynamic changes over a three-year period (2022 – 2024), several SfM-based photogrammetric studies were conducted, each year, in the Romanian sector of the Danube-Black Sea coastal zone, specifically at the mouth of one of the Danube River distributaries (Sf Gheorghe branch) into the Black Sea, and along the left bank, near Sf Gheorghe locality, located within the Danube Delta Biosphere Reserve (DDBR). The essential equipment for aerial photogrammetry comprises Unmanned Aerial Vehicles (UAVs) and Global Navigation Satellite Systems (GNSS). In this study, the UAV used was a DJI Mavic 3T (Enterprise/Thermal) drone, complemented by two Trimble R12i and R4 GNSS systems, as well as approximately 10 Ground Control Points (GCPs). Data acquisition and processing were carried out using specialized photogrammetric software (Agisoft Metashape) along with various GIS tools (e.g., Blue Marble Geographics Global Mapper and ESRI ArcMap). The photogrammetric products generated for the study, as detailed in this paper, include Digital Elevation Models (DEMs), Digital Terrain Models (DTMs), orthomosaics (orthophotos), and others. At Sfântu Gheorghe beach, a comparison between 2023 and 2024 photogrammetric surveys revealed that the left bank of the Sf. Gheorghe Arm, at the river mouth into the Black Sea, suffered from a twist (erosion) of up to 64 metres. Additionally, on the selected perimetre (total area of 31,910 square meters ) from the beach and dune zone of Sf. Gheorghe, an area of up to 16,202 square meters was eroded between 2023 and 2024. This contrasts with the period between 2022 and 2023, during which deposition predominated. Erosion at the Danube mouths and the adjacent Black Sea coastline is driven by a complex interaction of natural and anthropogenic factors. Natural processes, including subsidence, sea-level rise, and episodic extreme storm events, contribute significantly to coastal dynamics. Meanwhile, human-induced factors, such as upstream hydrotechnical works that limits sediment transport, cutting of navigation canals, as well as the exacerbating effects of climate change, further accelerate erosion. The recent Structure-from-Motion (SfM) surveys provide essential quantitative data, enabling a detailed analysis of both short-term and long-term morphodynamic changes influenced by seasonal variations and extreme hydrometeorological events in this highly dynamic coastal system.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of Inżynieria Mineralna WMCEES 2025","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Polish Mineral Engineering Society","doi":"10.29227/IM-2025-02-03-15","usgsCitation":"Dragos, A.G., Iordache, G., Dutu, F., Palaseanu-Lovejoy, M., Pitea, F., Stanciu, I., and Stanica, A., 2025, Assessment of coastal and fluvial morphodynamic changes using Structure-for-Motion: A case study of the Sfȃntu Gheorghe Mouth (Danube Delta, Romania), in Proceedings of Inżynieria Mineralna WMCEES 2025, v. 3, no. 2, 9 p., https://doi.org/10.29227/IM-2025-02-03-15.","productDescription":"9 p.","ipdsId":"IP-183130","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":498701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.29227/im-2025-02-03-15","text":"Publisher Index Page"},{"id":498610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Romania","otherGeospatial":"Sfȃntu Gheorghe Mouth (Danube Delta)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 29.533907040308605,\n 44.932418076703044\n ],\n [\n 29.533907040308605,\n 44.86262917846662\n ],\n [\n 29.630386502266845,\n 44.86262917846662\n ],\n [\n 29.630386502266845,\n 44.932418076703044\n ],\n [\n 29.533907040308605,\n 44.932418076703044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Dragos, Andrei Gabriel 0000-0003-3268-8615","orcid":"https://orcid.org/0000-0003-3268-8615","contributorId":365121,"corporation":false,"usgs":false,"family":"Dragos","given":"Andrei","middleInitial":"Gabriel","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iordache, Gabriel 0000-0002-8335-5995","orcid":"https://orcid.org/0000-0002-8335-5995","contributorId":365122,"corporation":false,"usgs":false,"family":"Iordache","given":"Gabriel","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dutu, Florin 0000-0002-5393-3125","orcid":"https://orcid.org/0000-0002-5393-3125","contributorId":365123,"corporation":false,"usgs":false,"family":"Dutu","given":"Florin","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":305576,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","affiliations":[],"preferred":true,"id":953728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pitea, Florin 0009-0007-0206-5886","orcid":"https://orcid.org/0009-0007-0206-5886","contributorId":365124,"corporation":false,"usgs":false,"family":"Pitea","given":"Florin","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953729,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanciu, Irina 0000-0003-1842-6619","orcid":"https://orcid.org/0000-0003-1842-6619","contributorId":365125,"corporation":false,"usgs":false,"family":"Stanciu","given":"Irina","affiliations":[{"id":87048,"text":"National Research and Development Institute for Marine Geology and Geoecology, GeoEcoMar, Romania","active":true,"usgs":false}],"preferred":false,"id":953730,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stanica, Adrian 0000-0001-5983-6302","orcid":"https://orcid.org/0000-0001-5983-6302","contributorId":351791,"corporation":false,"usgs":false,"family":"Stanica","given":"Adrian","affiliations":[{"id":84044,"text":"GeoEcoMar National Research institute, Romania","active":true,"usgs":false}],"preferred":false,"id":953731,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273769,"text":"70273769 - 2025 - Gas bubble trauma progression and mortality in sculpin, threespine stickleback, and Northern pikeminnow","interactions":[],"lastModifiedDate":"2026-01-28T16:15:47.03889","indexId":"70273769","displayToPublicDate":"2025-11-04T09:02:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2900,"text":"Northwest Science","onlineIssn":"2161-9859","printIssn":"0029-344X","active":true,"publicationSubtype":{"id":10}},"title":"Gas bubble trauma progression and mortality in sculpin, threespine stickleback, and Northern pikeminnow","docAbstract":"

We examined the progression of gas bubble trauma (GBT) and associated mortality in sculpin (Cottus spp.), threespine stickleback (Gasterosteus aculeatus), and Northern pikeminnow (Ptychocheilus oregonensis) exposed to three levels of total dissolved gas (TDG; 120, 125, and 130% saturation) in laboratory experiments. Sculpin were most sensitive to elevated TDG followed by stickleback and then pikeminnow, which were least sensitive. This was evidenced by GBT and associated mortality progressing fastest in sculpin and slowest in pikeminnow. GBT incidence and severity increased through time at all TDG levels tested, but relationships between severity and exposure time were statistically weak or nonexistent. GBT mortality progressed more rapidly as TDG increased in all species. Regional criteria developed to rank GBT in salmonids did not fully capture the incidence and severity of GBT in the three nonsalmonids we examined. Rather, using criteria that considered all areas of the fish provided more accurate data. The lateral line, body, dorsal fin, and pectoral fins were common locations of GBT in sculpin whereas in stickleback and pikeminnow, GBT was most common on the head and body. The proximate cause of GBT-related death was bubbles in the gills and heart, but unlike in other species, bubbles in these organs appeared rapidly just before the point of death. Our findings provide some of the first information on TDG effects on these little-studied species.

","language":"English","publisher":"BioOne","doi":"10.3955/046.098.0301","usgsCitation":"Tiffan, K.F., and Liedtke, B.D., 2025, Gas bubble trauma progression and mortality in sculpin, threespine stickleback, and Northern pikeminnow: Northwest Science, v. 98, no. 3, p. 174-189, https://doi.org/10.3955/046.098.0301.","productDescription":"16 p.","startPage":"174","endPage":"189","ipdsId":"IP-169570","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Bonneville Dam, Columbia River, Ives Island, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.07792688551268,\n 49.060742133839824\n ],\n [\n -125.07792688551268,\n 41.99908206800043\n ],\n [\n -116.81687980720761,\n 41.99908206800043\n ],\n [\n -116.81687980720761,\n 49.060742133839824\n ],\n [\n -125.07792688551268,\n 49.060742133839824\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"98","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":220176,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liedtke, Brad D. 0000-0002-0458-7377","orcid":"https://orcid.org/0000-0002-0458-7377","contributorId":303795,"corporation":false,"usgs":true,"family":"Liedtke","given":"Brad","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954703,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70272789,"text":"70272789 - 2025 - Predicting secretive species distribution using Bayesian networks with and without expert elicitation: A case study incorporating double-blind peer review","interactions":[],"lastModifiedDate":"2025-12-09T15:25:42.910526","indexId":"70272789","displayToPublicDate":"2025-11-04T08:15:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Predicting secretive species distribution using Bayesian networks with and without expert elicitation: A case study incorporating double-blind peer review","docAbstract":"

1. Species that are secretive, imperilled and consequently data deficient often re-quire conservation action despite limited available information. In such scenarios, Bayesian networks (BNs) offer a versatile and intuitive approach for utilizing various information sources, including literature reviews, community science data sets and expert knowledge. Although it has been suggested that peer review be incorporated during expert elicitations in a BN modelling context, little information exists about how to implement this approach or about how models constructed using this approach perform.

2. We documented a double-blind peer review approach for expert elicitation in a BN modelling context. Further, we compared BN models that were generated by experts who engaged in this peer-review process (PRBNs) to those that were generated by a single expert whose knowledge was supplemented only by a literature review (LRBNs). These comparisons were based on the ability to predict the occurrence (via community science and satellite telemetry data) of a secretive and data deficient species, the King Rail (Rallus elegans), throughout a large region.

3. We found that the LRBNs tended to predict King Rail occurrence as well as, or better than, the PRBNs. The LRBNs that we evaluated provided more consistent predictions across our study area. However, preliminary data suggest that the PRBNs may better distinguish between locations of focal and non-focal species within smaller regions.

4. Practical implication. Our framework for utilizing double-blind peer review could serve as a useful guide and have practical implications for incorporating expert knowledge in BN models. Further, our model comparison case study suggests that, in some contexts, a single expert who uses a literature review to inform the creation of BN models may be able to accurately predict the occurrence of a secretive and data-deficient focal species. Taken together, this information could help ecologists decide when a double-blind peer review approach to expert elicitation is necessary and how to implement this approach in a BN modelling context.

","language":"English","publisher":"British Ecological Society","doi":"10.1002/2688-8319.70140","usgsCitation":"Brewer, D.E., Webb, E.B., Mini, A.E., and McKnight, S.K., 2025, Predicting secretive species distribution using Bayesian networks with and without expert elicitation: A case study incorporating double-blind peer review: Ecological Solutions and Evidence, v. 6, no. 4, e70140, 13 p., https://doi.org/10.1002/2688-8319.70140.","productDescription":"e70140, 13 p.","ipdsId":"IP-173106","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":497410,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2688-8319.70140","text":"Publisher Index Page"},{"id":497279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.1068107990959,\n 36.53488280878683\n ],\n [\n -91.61423177860152,\n 34.64999645355131\n ],\n [\n -91.32812729950412,\n 33.779728217016626\n ],\n [\n -92.23976277110854,\n 31.169028802843997\n ],\n [\n -90.1068107990959,\n 29.669842940257624\n ],\n [\n -89.51855955615441,\n 29.669842940257624\n ],\n [\n -91.11618518098236,\n 31.200696392028455\n ],\n [\n -90.47470081531254,\n 32.88931314596513\n ],\n [\n -89.51855955615441,\n 36.53488280878683\n ],\n [\n -90.1068107990959,\n 36.53488280878683\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-11-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Dustin E.","contributorId":363560,"corporation":false,"usgs":false,"family":"Brewer","given":"Dustin","middleInitial":"E.","affiliations":[{"id":16806,"text":"Missouri State University","active":true,"usgs":false}],"preferred":false,"id":951788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":951789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mini, Anne E.","contributorId":363561,"corporation":false,"usgs":false,"family":"Mini","given":"Anne","middleInitial":"E.","affiliations":[{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false}],"preferred":false,"id":951790,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKnight, S. Keith","contributorId":363562,"corporation":false,"usgs":false,"family":"McKnight","given":"S.","middleInitial":"Keith","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":951791,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272091,"text":"70272091 - 2025 - Changes in phosphorus concentration and flux from 2011 to 2023 in major U.S. tributaries to the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2026-01-05T16:49:14.640082","indexId":"70272091","displayToPublicDate":"2025-11-02T10:46:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Changes in phosphorus concentration and flux from 2011 to 2023 in major U.S. tributaries to the Laurentian Great Lakes","docAbstract":"

Reducing phosphorus (P) flux to the Great Lakes is critical for improving water quality and controlling eutrophication. We used 13 water years (2011–2023) of U.S. Geological Survey data from 24 major U.S. tributaries (representing 47% of the U.S. Great Lakes watershed area) to evaluate temporal changes in orthophosphate (PO4-P) and total P (TP) using Weighted Regressions on Time, Discharge, and Season. We assessed actual and flow-normalized P concentrations and fluxes. Between 2011 and 2023, P concentrations and fluxes declined in many tributaries, although the extent and significance of these declines varied. Decreases were more common and statistically likely for TP than PO4-P, and several high-loading watersheds had modest or non-significant changes. Flow-normalized PO4-P:TP flux ratios increased in over half the tributaries, suggesting that even where P reductions occurred, reductions in the more bioavailable P fraction were proportionally smaller. Actual P fluxes were strongly correlated with streamflow, and year-to-year variability in actual fluxes was, on average, three times greater than variability related to trends in flow-normalized fluxes. This underscores the role of hydrology in modulating P export and highlights how changing precipitation and runoff patterns can obscure or counteract management progress. Spring accounted for the largest share of annual P flux in most tributaries, though many showed declining spring contributions. Our basin-wide analysis reveals that while management efforts may have yielded progress in reducing TP in many watersheds, additional strategies would be needed to address PO4-P reductions and account for changing hydrology, especially in high-contributing watersheds.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2025.102669","usgsCitation":"Kincaid, D., Diebel, M.W., Bertke, E., Bonville, D.B., Koltun, G.F., Robertson, D., and Loken, L.C., 2025, Changes in phosphorus concentration and flux from 2011 to 2023 in major U.S. tributaries to the Laurentian Great Lakes: Journal of Great Lakes Research, v. 51, no. 6, 102669, 13 p., https://doi.org/10.1016/j.jglr.2025.102669.","productDescription":"102669, 13 p.","ipdsId":"IP-178201","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":496717,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2025.102669","text":"Publisher Index Page"},{"id":496502,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.60633559922388,\n 48.148551186404575\n ],\n [\n -88.2067643812503,\n 40.60216025077551\n ],\n [\n -83.23151190577836,\n 39.05980019478196\n ],\n [\n -80.80498690897306,\n 40.234426092092406\n ],\n [\n -80.13264358698466,\n 41.74623034694679\n ],\n [\n -75.68002709570973,\n 41.69292244621492\n ],\n [\n -75.72465463652733,\n 42.23052760530962\n ],\n [\n -74.88658728745591,\n 44.40491984342111\n ],\n [\n -79.06485288677936,\n 43.305925528873495\n ],\n [\n -78.99574717113413,\n 42.844586701072004\n ],\n [\n -81.75752883413966,\n 41.63784527164743\n ],\n [\n -82.97923399054226,\n 42.07649353088971\n ],\n [\n -82.35201917635362,\n 43.23951025772632\n ],\n [\n -82.4731703330764,\n 45.44772983144884\n ],\n [\n -86.77797304973124,\n 47.58223992997367\n ],\n [\n -92.60633559922388,\n 48.148551186404575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kincaid, Dustin William 0000-0003-1640-685X","orcid":"https://orcid.org/0000-0003-1640-685X","contributorId":353877,"corporation":false,"usgs":true,"family":"Kincaid","given":"Dustin William","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diebel, Matthew W. 0000-0002-5164-598X mdiebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5164-598X","contributorId":33762,"corporation":false,"usgs":true,"family":"Diebel","given":"Matthew","email":"mdiebel@usgs.gov","middleInitial":"W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bertke, Erin E. 0000-0003-3172-280X","orcid":"https://orcid.org/0000-0003-3172-280X","contributorId":330809,"corporation":false,"usgs":true,"family":"Bertke","given":"Erin E.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonville, Donald B. 0000-0003-4480-9381","orcid":"https://orcid.org/0000-0003-4480-9381","contributorId":248849,"corporation":false,"usgs":true,"family":"Bonville","given":"Donald","email":"","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":140048,"corporation":false,"usgs":true,"family":"Koltun","given":"G.","email":"gfkoltun@usgs.gov","middleInitial":"F.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":950041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loken, Luke C. 0000-0003-3194-1498 lloken@usgs.gov","orcid":"https://orcid.org/0000-0003-3194-1498","contributorId":195600,"corporation":false,"usgs":true,"family":"Loken","given":"Luke","email":"lloken@usgs.gov","middleInitial":"C.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":950042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273911,"text":"70273911 - 2025 - Analysis of trends in terrestrial vegetation at Mediterranean Coast Network Parks: Channel Islands National Park","interactions":[],"lastModifiedDate":"2026-02-17T17:24:13.996226","indexId":"70273911","displayToPublicDate":"2025-11-01T11:15:09","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18517,"text":"Science Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SR-2025/358","displayTitle":"Analysis of Trends in Terrestrial Vegetation at Mediterranean Coast Network Parks: Channel Islands National Park","title":"Analysis of trends in terrestrial vegetation at Mediterranean Coast Network Parks: Channel Islands National Park","docAbstract":"

The five islands comprising Channel Islands National Park (CHIS) experience natural gradients in temperature and moisture driven by ocean currents. Additionally, the islands were used as ranchlands and military land before becoming a national park, resulting in widespread erosion and vegetation change. As a result, CHIS spans gradients in climate as well as ranching duration and time since animal removal. Vegetation monitoring was initiated in 1984 on three islands (Anacapa, Santa Barbara, San Miguel), in 1990 on Santa Rosa Island, and in 1998 on Santa Cruz Island, with the goal of documenting the long-term response of island vegetation to ranch animal removal and climate fluctuations. Since that time, monitoring has documented the range of natural fluctuation in island environments over decades and provided insights into vegetation change in ecosystems unencumbered by ongoing development. Long-term vegetation monitoring at CHIS is therefore a rare example of an ecosystem experiment that demonstrates the results of management actions and serves as a baseline for land managers and scientists worldwide. 

Terrestrial vegetation data collected between 1984 and 2018 were modeled to estimate trends over time and to characterize relationships with covariates related to site characteristics, nonnative mammal removal programs, and water balance metrics. Data were analyzed for trends in vegetation cover, woody plant density, and plant community diversity grouped by life form and nativity across all islands and within individual islands, as well as for several individual species that dominate plant communities or present challenges to native plant recovery. In all, a total of 162 trend and covariate models were tested in this study, the details of which are provided in this report. Briefly, results reflect a decline in nonnative annual disturbance-thriving species with the reduction in animal grazing and trampling. Increasing trends were observed in native shrub density and native shrub recruitment density, as well as native shrub cover across all islands averaged together and on Santa Cruz Island. However, opposite trends were seen on the smaller islands of Santa Barbara and Anacapa, where increasing seabird activity may be damaging vegetation. Further results indicate the importance of soil moisture, relative humidity, fog, precipitation, site exposure, and solar radiation for vegetation patterns and trends. In many instances, there are apparent interacting effects of environmental variables with trends related to nonnative mammal removal and site location. Vegetation patterns in space and time emerge in the dataset as nuanced responses to interacting drivers. 

","language":"English","publisher":"National Park Service","doi":"10.36967/2315831","usgsCitation":"Starcevich, L.A., Murray, C., Lee, L.F., Williams, C.B., and McEachern, K., 2025, Analysis of trends in terrestrial vegetation at Mediterranean Coast Network Parks: Channel Islands National Park: Science Report NPS/SR-2025/358, xvi, 176 p., https://doi.org/10.36967/2315831.","productDescription":"xvi, 176 p.","ipdsId":"IP-144822","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":500096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calfornia","otherGeospatial":"Channel Islands National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.7325747,\n 34.3878669\n ],\n [\n -120.6273817,\n 33.7780062\n ],\n [\n -119.4370392,\n 32.9735463\n ],\n [\n -118.1857956,\n 32.6851079\n ],\n [\n -118.2300874,\n 33.4875961\n ],\n [\n -119.4591851,\n 34.2049123\n ],\n [\n -120.2398283,\n 34.3513079\n ],\n [\n -120.7325747,\n 34.3878669\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Starcevich, Leigh Ann","contributorId":366371,"corporation":false,"usgs":false,"family":"Starcevich","given":"Leigh","middleInitial":"Ann","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":955748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murray, Christopher","contributorId":340084,"corporation":false,"usgs":false,"family":"Murray","given":"Christopher","affiliations":[{"id":81451,"text":"School of Marine and Environmental Affairs and Washington Ocean Acidification Center, 7 University of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":955749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Lena F.S.","contributorId":366372,"corporation":false,"usgs":false,"family":"Lee","given":"Lena","middleInitial":"F.S.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":955750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Cameron B.","contributorId":366373,"corporation":false,"usgs":false,"family":"Williams","given":"Cameron","middleInitial":"B.","affiliations":[{"id":6993,"text":"Channel Islands National Park","active":true,"usgs":false}],"preferred":false,"id":955751,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McEachern, Kathryn 0000-0003-2631-8247 kathryn_mceachern@usgs.gov","orcid":"https://orcid.org/0000-0003-2631-8247","contributorId":146324,"corporation":false,"usgs":true,"family":"McEachern","given":"Kathryn","email":"kathryn_mceachern@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":955752,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273274,"text":"70273274 - 2025 - Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud","interactions":[],"lastModifiedDate":"2025-12-29T16:30:45.746731","indexId":"70273274","displayToPublicDate":"2025-11-01T10:22:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5987,"text":"Photogrammetric Engineering & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud","docAbstract":"

Accurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed and irrigated area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel based supervised random forest classifier, and pixel based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer's accuracies of 90.2% and 95.7%, respectively, and user's accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (68%) was classified as rainfed and 44.5 Mha (32%) was classified as irrigated. State level summaries highlight regional differences and their implications for national and global food and water security.

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Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":952988,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aneece, Itiya 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":211471,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":952989,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biggs, Trent","contributorId":208268,"corporation":false,"usgs":false,"family":"Biggs","given":"Trent","affiliations":[],"preferred":false,"id":952990,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murali Krishna Gumma","contributorId":364644,"corporation":false,"usgs":false,"family":"Murali Krishna Gumma","affiliations":[{"id":33518,"text":"ICRISAT","active":true,"usgs":false}],"preferred":false,"id":952991,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":952992,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCormick, Richard L","contributorId":364645,"corporation":false,"usgs":false,"family":"McCormick","given":"Richard","middleInitial":"L","affiliations":[],"preferred":false,"id":952993,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rohitha, Neelam","contributorId":364646,"corporation":false,"usgs":false,"family":"Rohitha","given":"Neelam","affiliations":[],"preferred":false,"id":952994,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Long, Emerson","contributorId":364647,"corporation":false,"usgs":false,"family":"Long","given":"Emerson","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":952995,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lawton, Jake","contributorId":364648,"corporation":false,"usgs":false,"family":"Lawton","given":"Jake","affiliations":[],"preferred":false,"id":952996,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70272681,"text":"70272681 - 2025 - Drone-based radiometric surveys provide high-resolution mine waste characterization","interactions":[],"lastModifiedDate":"2025-12-04T16:04:36.978233","indexId":"70272681","displayToPublicDate":"2025-11-01T09:57:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3568,"text":"The Leading Edge","active":true,"publicationSubtype":{"id":10}},"title":"Drone-based radiometric surveys provide high-resolution mine waste characterization","docAbstract":"

Airborne radiometric surveys use passive geophysical techniques to characterize geochemical variations at or near earth’s surface. These methods have been used for a variety of mapping applications, including mineral resource evaluation. However, detailed characterization of smaller geologic targets, including mine waste features, requires flying at lower altitudes and with tighter line spacing than is feasible with traditional aircraft. Here, a small uncrewed aircraft system (sUAS) equipped with a radiometric sensor was used to acquire high-resolution gamma-spectrometry over small mine waste features and a low-grade stockpile in southwestern New Mexico. The sUAS radiometric system mapped local variability within each survey area and revealed ~2–10 m wide zones where radioelements K, Th, and U may be elevated 2–10× the surrounding material. Additionally, the sUAS radiometric data revealed radioelement variability across survey sites, which correlated reasonably well with variability seen in geochemical samples at each survey site, even though samples collected from individual sites showed high local variability. The sUAS data characterized local heterogeneity within mine waste and other small geologic targets at scales of a few meters to tens of meters, which is not possible with traditional crewed aircraft, and with continuity of coverage that is not possible with ground surveys, thus filling a key gap in geophysical survey spatial resolution.

","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/tle44110889.1","usgsCitation":"Gustafson, C., Shah, A.K., Burgess, M.A., Adams, J., McLemore, V., and Owen, E.J., 2025, Drone-based radiometric surveys provide high-resolution mine waste characterization: The Leading Edge, v. 44, no. 11, p. 889-900, https://doi.org/10.1190/tle44110889.1.","productDescription":"12 p.","startPage":"889","endPage":"900","ipdsId":"IP-180021","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":497111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1190/tle44110889.1","text":"Publisher Index Page"},{"id":497057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Blackhawk mining district, Copper Flat Mine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109,\n 33.5\n ],\n [\n -109,\n 31.5\n ],\n [\n -108.22801231819798,\n 31.506679785521342\n ],\n [\n -108.22398985560156,\n 31.79511507151517\n ],\n [\n -106.9946669081999,\n 31.78156997939864\n ],\n [\n -107,\n 33.5\n ],\n [\n -109,\n 33.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gustafson, Chloe Danielle 0000-0001-8323-2568","orcid":"https://orcid.org/0000-0001-8323-2568","contributorId":346924,"corporation":false,"usgs":true,"family":"Gustafson","given":"Chloe Danielle","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgess, Matthew Alexander 0000-0003-3487-4972 mburgess@usgs.gov","orcid":"https://orcid.org/0000-0003-3487-4972","contributorId":225090,"corporation":false,"usgs":true,"family":"Burgess","given":"Matthew","email":"mburgess@usgs.gov","middleInitial":"Alexander","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":951322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Josip 0000-0001-8470-4141","orcid":"https://orcid.org/0000-0001-8470-4141","contributorId":217936,"corporation":false,"usgs":true,"family":"Adams","given":"Josip","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":951323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLemore, Virginia","contributorId":363225,"corporation":false,"usgs":false,"family":"McLemore","given":"Virginia","affiliations":[{"id":86657,"text":"New Mexico Bureau of Geology and Mineral Resources, New Mexico Institute of Mining and Technology","active":true,"usgs":false}],"preferred":false,"id":951324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Owen, Evan J.","contributorId":363226,"corporation":false,"usgs":false,"family":"Owen","given":"Evan","middleInitial":"J.","affiliations":[{"id":86659,"text":"Mining & Minerals Division, New Mexico Department of Energy, Minerals & Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":951325,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272061,"text":"70272061 - 2025 - The Mammoth magnetic anomaly, Pinal County, Arizona","interactions":[],"lastModifiedDate":"2025-11-14T15:32:54.822911","indexId":"70272061","displayToPublicDate":"2025-11-01T08:28:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3568,"text":"The Leading Edge","active":true,"publicationSubtype":{"id":10}},"title":"The Mammoth magnetic anomaly, Pinal County, Arizona","docAbstract":"

A high-resolution Earth Mapping Resources Initiative airborne geophysical survey was flown in the southwest North American porphyry copper province to improve bedrock geologic maps and to identify areas that have unrecognized critical mineral resource potential. During the review of the aeromagnetic data, a distinctly monopolar-shaped, negative magnetic anomaly was observed at a flight elevation of 200 m above the ground with a maximum amplitude of –9500 nT. We have named this the Mammoth magnetic anomaly (MMA) because it is centered 12 km northeast of the town of Mammoth, Arizona, USA. The total field anomaly (TFA) contour of –500 nT enclosing the MMA defines an elongate shape measuring 2.5 km long by 1 km wide that trends northwest–southeast. Given the striking nature of this negative, monopolar-shaped magnetic anomaly, we conducted a ground campaign in May 2025 to determine its authenticity and potential relationship to critical mineral endowment. The MMA was confirmed on the ground with a TFA approaching –46,000 nT. Total magnetic intensity (TMI) observations routinely fell below the 18,000 nT operating floor of an industry-standard cesium-vapor total field magnetometer, and extremely low TMI measurements were corroborated along coincident traverse lines using two high dynamic range, but lower sensitivity, smartphone vector magnetometers. The lowest TMI values recorded by both smartphone magnetometers were 1000 nT and confirmed with multiple adjacent and crossing lines. Field observations suggest that this magnetic feature is caused by strong remanent magnetization within fine-grained magnetite hosted within locally altered Pinal Schist.

","language":"English","publisher":"The Society of Exploration Geophysicists","doi":"10.1190/tle44110879.1","usgsCitation":"Walter, C.A., Scheirer, D.S., Beno, C., Borchardt, J.S., and Connell, D., 2025, The Mammoth magnetic anomaly, Pinal County, Arizona: The Leading Edge, v. 44, no. 11, p. 879-888, https://doi.org/10.1190/tle44110879.1.","productDescription":"10 p.","startPage":"879","endPage":"888","ipdsId":"IP-179841","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":496710,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1190/tle44110879.1","text":"Publisher Index Page"},{"id":496913,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13LYAKZ","text":"USGS data release","linkHelpText":"Ground Magnetic Observations of the Mammoth Magnetic Anomaly, Pinal County, Arizona, May 2025"},{"id":496912,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1GGHK8X","text":"USGS data release","linkHelpText":"Airborne magnetic and radiometric data acquired over parts of Cochise, Graham, Greenlee, Pima, Pinal, and Santa Cruz Counties, Arizona"},{"id":496486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","county":"Pinal County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.14609001840401,\n 33.405406351098335\n ],\n [\n -111.14609001840401,\n 31.33976116419477\n ],\n [\n -109.03721319402516,\n 31.33976116419477\n ],\n [\n -109.03721319402516,\n 33.405406351098335\n ],\n [\n -111.14609001840401,\n 33.405406351098335\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walter, Callum Andrew 0000-0001-7955-2016","orcid":"https://orcid.org/0000-0001-7955-2016","contributorId":360911,"corporation":false,"usgs":true,"family":"Walter","given":"Callum","middleInitial":"Andrew","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":949952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheirer, Daniel S. 0000-0001-8015-7072 dscheirer@usgs.gov","orcid":"https://orcid.org/0000-0001-8015-7072","contributorId":214825,"corporation":false,"usgs":true,"family":"Scheirer","given":"Daniel","email":"dscheirer@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":949953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beno, Carl Joseph 0000-0001-7611-1602","orcid":"https://orcid.org/0000-0001-7611-1602","contributorId":347444,"corporation":false,"usgs":true,"family":"Beno","given":"Carl Joseph","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":949954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Borchardt, Jackson Stone 0000-0001-6891-3314","orcid":"https://orcid.org/0000-0001-6891-3314","contributorId":346157,"corporation":false,"usgs":true,"family":"Borchardt","given":"Jackson","email":"","middleInitial":"Stone","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":949955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Connell, Dylan Mark 0000-0001-8678-2776","orcid":"https://orcid.org/0000-0001-8678-2776","contributorId":292570,"corporation":false,"usgs":true,"family":"Connell","given":"Dylan Mark","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":949956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272137,"text":"70272137 - 2025 - A spatiotemporal interrogation of hydrologic drought model performance for machine learning model interpretability","interactions":[],"lastModifiedDate":"2025-11-17T16:08:00.3984","indexId":"70272137","displayToPublicDate":"2025-10-30T10:01:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"A spatiotemporal interrogation of hydrologic drought model performance for machine learning model interpretability","docAbstract":"

The predictive accuracy of regional hydrologic models often varies across both time and space. Interpreting relationships between watershed characteristics, hydrologic regimes, and model performance can reveal potential areas for model improvement. In this study, we use machine learning to assess model performance of a regional hydrologic model to forecast the occurrence of streamflow drought. We demonstrate our methodology using a regional long short-term memory (LSTM) deep learning model developed by the U.S. Geological Survey (USGS) and data from 384 streamgages across the Colorado River Basin region. Performance was assessed by clustering catchments using: (a) physical and climatological catchment attributes, and (b) streamflow drought signatures time series. We examined the association of USGS LSTM model error measures with clusters generated by both approaches to interpret meaningful spatial and temporal information about LSTM model performance. Clustering static catchment attributes identified elevation, degree of streamflow regulation, baseflow contribution, catchment aridity, and drainage area as the most influential attributes to model performance. Clustering gages by their drought signatures revealed that catchments with significant seasonal peak runoff between January and June generally exhibited better model performance. Additionally, a Random Forest classifier was trained to successfully predict LSTM model performance (F1 score of 0.72) based on physical and climatological catchment attributes. Low degree of flow regulation was identified as a key indicator of better LSTM model performance. These findings point to the opportunities for improving the USGS LSTM model performance in future hydrologic drought prediction efforts across regional and CONUS scales.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR039077","usgsCitation":"Dadkhah, A., Hamshaw, S.D., van der Heijden, R., and Rizzo, D.M., 2025, A spatiotemporal interrogation of hydrologic drought model performance for machine learning model interpretability: Water Resources Research, v. 61, no. 11, e2024WR039077, 20 p., https://doi.org/10.1029/2024WR039077.","productDescription":"e2024WR039077, 20 p.","ipdsId":"IP-171117","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":496726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr039077","text":"Publisher Index Page"},{"id":496550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, South Dakota, Texas, Utah, Wyoming","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.82376678919411,\n 36.26233085914717\n ],\n [\n -115.85085818096155,\n 32.567720561211985\n ],\n [\n -110.51032589138043,\n 31.22468250332072\n ],\n [\n -108.12617557977711,\n 31.516111303152087\n ],\n [\n -107.07123697669522,\n 32.48455428990293\n ],\n [\n -102.78172590159346,\n 34.79341128012621\n ],\n [\n -102.32122175185708,\n 37.822265600751294\n ],\n [\n -104.54120424685271,\n 39.31008340398529\n ],\n [\n -105.01672465698343,\n 42.43285898166596\n ],\n [\n -102.61536956018404,\n 44.597711605623914\n ],\n [\n -103.68883267765926,\n 45.86117991057367\n ],\n [\n -104.94111657475693,\n 46.52791159365242\n ],\n [\n -111.34981535286528,\n 46.65300676975278\n ],\n [\n -113.2114048549673,\n 44.05547092656684\n ],\n [\n -115.54731167526421,\n 41.97871186707138\n ],\n [\n -119.35050975086381,\n 42.039351853495674\n ],\n [\n -119.8152614776348,\n 40.77295494694408\n ],\n [\n -119.24558792818462,\n 38.8937217445025\n ],\n [\n -116.82376678919411,\n 36.26233085914717\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Dadkhah, Ali 0000-0002-0861-4926","orcid":"https://orcid.org/0000-0002-0861-4926","contributorId":362194,"corporation":false,"usgs":false,"family":"Dadkhah","given":"Ali","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":950171,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":950172,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van der Heijden, Ryan 0000-0003-1320-9500","orcid":"https://orcid.org/0000-0003-1320-9500","contributorId":362195,"corporation":false,"usgs":false,"family":"van der Heijden","given":"Ryan","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":950173,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rizzo, Donna M.","contributorId":362196,"corporation":false,"usgs":false,"family":"Rizzo","given":"Donna","middleInitial":"M.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":950174,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273296,"text":"70273296 - 2025 - Comparative life history of mud turtles (genus: Kinosternon) from the North American deserts","interactions":[],"lastModifiedDate":"2026-01-05T15:03:45.59267","indexId":"70273296","displayToPublicDate":"2025-10-30T08:56:53","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Comparative life history of mud turtles (genus: Kinosternon) from the North American deserts","title":"Comparative life history of mud turtles (genus: Kinosternon) from the North American deserts","docAbstract":"

The warm deserts of North America are characterized by diverse environments that include the transition zone between tropical and temperate regions on the continent. This vast region includes the Sonoran and Chihuahuan deserts, which have different precipitation regimes and are composed of different floras and faunas, separated by the Cochise Filter Barrier. Inhabiting these deserts are 7 mud turtles (representing 4 separate clades within the genus Kinosternon), and we compared their basic ecology, life history, and estivation time to test for variation between deserts. We used phylogenetic comparative methods to correlate the life history traits with environmental variables (temperature and precipitation) to test for variation between deserts. Life history strategies (clutch size, egg size, and reproductive phenology) of mud turtles were similar across both deserts, with negative correlations of clutch size and age of maturity with both aridity and temperature variables. Maximum estivation time was correlated with the seasonality of each included locality. Overall, life history strategies were quite similar, with small local specializations to avoid high temperatures and periodic lack of water. From a population ecology perspective, populations showed varied sex ratios biased toward males or females, along with different population structure among populations and species. However, most published studies lacked data for hatchlings. Phylogenetic signal is high in traits related to body size, including sexual size dimorphism. Overall, mud turtles from the southwest deserts are adapted to regional seasonality and precipitation regimes, with minor adjustments to fit local conditions.

","language":"English","publisher":"BioOne","doi":"10.3398/064.085.0302","usgsCitation":"Macipríos, R., and Lovich, J.E., 2025, Comparative life history of mud turtles (genus: Kinosternon) from the North American deserts: Western North American Naturalist, v. 85, no. 3, p. 396-410, https://doi.org/10.3398/064.085.0302.","productDescription":"15 p.","startPage":"396","endPage":"410","ipdsId":"IP-165079","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.32669609969128,\n 42.18591387423689\n ],\n [\n -115.30100988851879,\n 42.18591387423689\n ],\n [\n -115.30100988851879,\n 24.48732899818424\n ],\n [\n -101.32669609969128,\n 24.48732899818424\n ],\n [\n -101.32669609969128,\n 42.18591387423689\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Macipríos, Rodrigo","contributorId":347546,"corporation":false,"usgs":false,"family":"Macipríos","given":"Rodrigo","affiliations":[{"id":83188,"text":"Escuela Nacional de Estudios Superiores, Unidad Morelia. Universidad Nacional Atónoma de México, Antigua Carretera a Páztcuaro, No. 8701, Col. Ex Hacienda San José la Huerta, Morelia, Michoacán, 58190, México","active":true,"usgs":false}],"preferred":false,"id":953270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953271,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70272693,"text":"70272693 - 2025 - Climatological effects on survival, recruitment, and possible extirpation of a Sierra Nevada anuran","interactions":[],"lastModifiedDate":"2025-12-04T16:39:23.520639","indexId":"70272693","displayToPublicDate":"2025-10-28T10:27:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Climatological effects on survival, recruitment, and possible extirpation of a Sierra Nevada anuran","docAbstract":"

The drivers of population dynamics are a primary interest of ecologists, and predicting the consequences of climate variability on wildlife populations benefits from an understanding of how weather causes variation in the vital rates of populations. Given recent and projected extremes in annual precipitation in the Sierra Nevada of California, USA, including two severe droughts, we sought to examine the role of snowpack and summer water availability on the population dynamics and potential extirpation of a meadow population of the U.S. Endangered Sierra Nevada yellow-legged frog (Rana sierrae) using a long-term capture-mark-recapture dataset. We found that snowpack and summer water availability affected both survival and recruitment probabilities. Although these variables only explained approximately 17 % of the annual variation in adult survival, they explained 81 % of the variation in recruitment into the adult population. Following two severe, extended droughts and a nearby wildfire, the population consisted of 20 or fewer individuals with >95 % certainty, and 10 or fewer individuals with 64 % certainty. If realized, increased precipitation volatility and extended droughts likely present an additional threat to some meadow populations of this endangered frog.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecochg.2025.100099","usgsCitation":"Halstead, B., Kleeman, P.M., Rose, J.P., Grasso, R.L., and Fellers, G.M., 2025, Climatological effects on survival, recruitment, and possible extirpation of a Sierra Nevada anuran: Climate Change Ecology, v. 10, 100099, 11 p., https://doi.org/10.1016/j.ecochg.2025.100099.","productDescription":"100099, 11 p.","ipdsId":"IP-161742","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":497114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecochg.2025.100099","text":"Publisher Index Page"},{"id":497060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Summit Meadow, Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.64672349244009,\n 37.675274534515\n ],\n [\n -119.65704609741877,\n 37.675274534515\n ],\n [\n -119.65704609741877,\n 37.668624663917555\n ],\n [\n -119.64672349244009,\n 37.668624663917555\n ],\n [\n -119.64672349244009,\n 37.675274534515\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":215986,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian","email":"bhalstead@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":951347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleeman, Patrick M. 0000-0001-6567-3239 pkleeman@usgs.gov","orcid":"https://orcid.org/0000-0001-6567-3239","contributorId":3948,"corporation":false,"usgs":true,"family":"Kleeman","given":"Patrick","email":"pkleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":951348,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":951349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grasso, Robert L.","contributorId":363246,"corporation":false,"usgs":false,"family":"Grasso","given":"Robert","middleInitial":"L.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":951350,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fellers, Gary M.","contributorId":209920,"corporation":false,"usgs":false,"family":"Fellers","given":"Gary","email":"","middleInitial":"M.","affiliations":[{"id":38025,"text":"9 Goldfinch Court, Novato, CA 94947; gary_fellers@worldnet.att.net","active":true,"usgs":false}],"preferred":false,"id":951351,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273509,"text":"70273509 - 2025 - Recovery of Delaware Bay horseshoe crabs following harvest reductions","interactions":[],"lastModifiedDate":"2026-01-21T15:03:39.197863","indexId":"70273509","displayToPublicDate":"2025-10-27T07:57:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20748,"text":"Marine and Coastal Fisheries: Dynamics, Management and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Recovery of Delaware Bay horseshoe crabs following harvest reductions","docAbstract":"

Objective

Horseshoe crabs Limulus polyphemus play a vital role in the Delaware Bay ecosystem. The migratory stopover of several shorebird species occurs during the horseshoe crab spawning season, and the eggs of horseshoe crabs provide an essential food source to fuel their northward migration to breeding areas. High commercial fishery use of horseshoe crabs as bait during the 1990s coincided with a decline in crabs and shorebirds, particularly the red knot Calidris canutus rufa, which has been listed as threatened under the U.S. Endangered Species Act since 2015. In response to the population decline of shorebirds, the Atlantic States Marine Fisheries Commission began reducing the harvest of horseshoe crabs in 2000 with a goal of rebuilding the population of horseshoe crabs and shorebirds that depend upon them. The objective of this analysis was to determine whether horseshoe crab harvest management in the Delaware Bay region has increased the abundance of the species in recent years.

Methods

We analyzed data from fisheries-independent trawl surveys of horseshoe crab relative abundance using a Bayesian hierarchical model to determine whether harvest management has resulted in the rebuilding of the horseshoe crab population to levels seen in 1990—a period before the overuse of horseshoe crabs and the decline in the population of red knots.

Results

Data from multiple surveys showed that the horseshoe crab population in Delaware Bay declined from the 1990s through approximately 2005, was relatively low and stable until 2010, and then increased through 2023, with a 0.38 probability of exceeding the 1990 level.

Conclusions

The results of this analysis support the effectiveness of management decisions related to horseshoe crabs in the Delaware Bay region. In response to harvest restrictions, the abundance of horseshoe crabs has neared levels observed in the early 1990s—a period prior to high commercial use and a decline in both horseshoe crabs and shorebirds that depend on them for food during annual migrations.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/mcfafs/vtaf040","usgsCitation":"Sweka, J., Anstead, K., Smith, D.R., Barry, L., Zimmerman, J., Doctor, S., Weedon, C., Gartland, J., Jiao, Y., Ferretti, F., and Hallerman, E.M., 2025, Recovery of Delaware Bay horseshoe crabs following harvest reductions: Marine and Coastal Fisheries: Dynamics, Management and Ecosystem Science, v. 17, no. 5, vtaf040, 11 p., https://doi.org/10.1093/mcfafs/vtaf040.","productDescription":"vtaf040, 11 p.","ipdsId":"IP-180039","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":498928,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/mcfafs/vtaf040","text":"Publisher Index Page"},{"id":498794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.73652508190773,\n 39.70362450005183\n ],\n [\n -75.73652508190773,\n 38.62515671907127\n ],\n [\n -74.71835783856952,\n 38.62515671907127\n ],\n [\n -74.71835783856952,\n 39.70362450005183\n ],\n [\n -75.73652508190773,\n 39.70362450005183\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Sweka, John A.","contributorId":348306,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":954088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anstead, Kristen 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Quality image input is fundamental to the quality of derived land cover products. Substantial time and effort are usually required to prepare images. Here, we present a novel and streamlined compositing algorithm that ingests Landsat Collection 2 Analysis Ready Data (ARD) and outputs cloud-free and gap-free composite imagery, which can be directly used for classification. This method leverages and improves the previous National Land Cover Database (NLCD) Virtual Median Value Point (VMVP) compositing method, the first part of the image preparation for NLCD 2019 operational production. The NLCD 2019 image preparation approach includes a second part, a residual cloud and cloud shadow detection and gap-filling method, to produce final cloud-free and gap-free composite imagery. The second part requires one clear reference image for each target year. Additional reference images are needed for producing reasonable observations for perennial ice/snow areas because Pixel QA (Quality Assessment) from ARD has difficulties differentiating ice/snow areas from clouds. Unlike the NLCD 2019 image preparation approach, our new compositing method, which is referred to as Automated VMVP (AVMVP), uses Landsat ARD as the only input and does not require reference images and extra steps. In this method, we developed new spectral filter criteria coupled with counts of clear observations using Pixel QA to identify potential cloud and cloud shadow observations on initially selected observations from the NLCD VMVP compositing algorithm. We also automate “gap-filling” using clear observations retrieved from a maximum of ±2 years around the target year when needed. Finally, a percentile-filtered compositing method was developed for the perennial ice/snow areas. All these steps are streamlined, pixel-based, and directly run on Landsat Collection 2 ARD. We have run successful tests on the conterminous United States (CONUS). Composite images derived from our innovative method were used to produce the CONUS Annual NLCD Collection 1 product suite that covers the period from 1985 to 2023.

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Context

Interspecific interactions shape ecological communities, influence community dynamics, and drive co-evolution. Despite their ecological significance, predation and competition remain understudied in plains spotted skunks (Spilogale interrupta), a species of conservation concern. Clarifying how predator management influences their occurrence is crucial for effective conservation.

Aims

We investigated how coyote (Canis latrans) management affects the occupancy of plains spotted skunks and whether interspecific interactions with domestic cats (Felis catus) and striped skunks (Mephitis mephitis) influence plains spotted skunk occurrence.

Methods

We analysed live-trap data from east-central South Dakota collected in spring of 2021 and 2022. The study area encompassed portions of counties that implemented disparate predator management regimes, including one with systematic annual coyote removal and another without. We used single-species occupancy models to estimate detection and occupancy probabilities for plains spotted skunks, domestic cats, and striped skunks, incorporating environmental factors, including the site-specific predator control regime. We then applied conditional two-species occupancy models to test whether cats and striped skunks influenced plains spotted skunk occurrence.

Key results

Plains spotted skunks had the lowest occupancy, followed by domestic cats, and striped skunks. Our findings showed significant associations between coyote removal and occupancy probabilities for each mesocarnivore species. Plains spotted skunks had higher occupancy in areas where coyotes were annually removed. Spotted skunk occurrence was not conditional on either domestic cat or striped skunk occurrence.

Conclusions

In our study system, cats appear to pose less predation risk to spotted skunks than do other predators, reducing the likelihood that cats significantly influence spotted skunk occupancy. Defensive behaviours and use of spatial refugia by plains spotted skunks may further mitigate predation risk. In addition, co-evolutionary pressures may have led to trait adaptations that facilitate the independent co-occurrence of plains spotted skunks and striped skunks.

Implications

Our findings highlighted the ecological consequences of predator management and the importance of considering predator–prey dynamics in conservation and management planning. Strategies aimed at conserving plains spotted skunks should integrate predator control measures while considering broader mesocarnivore community interactions.

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