Cover crops play a critical role in providing agroecological services such as improving soil health, reducing erosion and nitrogen loss, and suppressing weeds, which are closely tied to their performance such as accumulated biomass. This study evaluated the Active Canopy Sensor (ACS) -214, an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor, for estimating winter cover crop biomass across 13 U.S. states from 2020 to 2024. We assessed 11 species from three functional groups – grasses (n = 797), legumes (n = 264), and brassicas (n = 181) – using Random Forest (RF) models and four cross-validation strategies. The ACS-214 showed moderate to strong prediction accuracy for grasses (R2 = 0.51 – 0.64) and legumes (R2 = 0.44 – 0.76), though performance declined in leave-one-region-out analyses (R2 = 0.06 – 0.46), indicating limited spatial generalizability. Brassica models had low prediction accuracy for all models (R2 < 0.30), likely due to flowering and patchy growth. Biomass prediction breakpoints were observed at ∼3000 kg ha−1 for legumes and ∼4000 kg ha−1 for grasses. We also evaluated the effectiveness of using ACS-214 data to train Sentinel-2 satellite imagery for estimating grass cover crop biomass using withheld, out of bag data from 2023 to 2024. Sentinel-2 RF models trained with ACS-214 data showed good agreement with field-sampled (R2 = 0.58 – 0.61) and ACS-214-estimated biomass (R2 = 0.70). While Sentinel-2 offers scalability, the ACS-214 enables finer-resolution biomass mapping and better accounts for within-field variability, making it an effective tool for localized management and monitoring. These findings support the integration of proximal and satellite sensing approaches to enhance cover crop biomass estimation and agroecological assessment.
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Special conditions identified from U.S. Geological Survey gaging records and basin characteristics included regulation and diversion, urbanization, indeterminate drainage areas, drainage areas less than the minimum used in regional analyses, glacial lake outburst floods, other outburst floods, and snowmelt floods. For peak flows that occurred during calendar years 1980–2019, an atmospheric river dataset was used to identify atmospheric river presence or absence on the dates peak flows occurred. Extreme floods (defined as peak flows exceeding the 1-percent annual exceedance probability flood magnitude or an empirical measure of relative magnitude using Creager’s coefficient C) were identified and associated with flood-generating mechanisms using the other inventoried special conditions and other information.
The gaging record contained glacial lake outburst floods at 15 streamgages and other types of outburst floods at 10 streamgages. Non-outburst peak flows in Alaska resulted from a mixture of rainfall and melt-based flood-generating mechanisms in all but the most rain-dominated seasonal flow regime. Melt-based flood-generating mechanisms included snowmelt, high-elevation snow and ice melt, or rain-on-snow events. Atmospheric rivers were common in Alaska and conterminous basins in Canada, occurring in that region on 67 percent of the days in the calendar year 1980–2019 period. Atmospheric rivers were more common on the days of peak flows and even more common on the days of non-outburst extreme floods. The percentage of days when an atmospheric river was present increased to 78 percent for the days of peak flows in that period and to 83 percent for the days of non-outburst extreme floods in that period. Of 149 extreme floods in the gaging record, 38 were generated by outburst floods. Of the non-outburst extreme floods, 72 percent were generated by rainfall and 26 percent were generated by melt-based processes or a combination of rainfall and melt-based processes. Flood-generating mechanisms could not be determined for the final 2 percent of the non-outburst extreme floods because the month and day of the peak flows were unknown and no other information was available. Secondary factors strongly associated with extreme floods included antecedent rain and streamflow conditions and warm storm conditions that produced rain instead of snow or generated snowmelt.
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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska, 99508
While aquatic environmental DNA (eDNA) methods have reached relative maturity, terrestrial eDNA methods are nascent and have yet to reach widespread use. Field-ready applications require eDNA survey methods where samples are easy to collect by inexperienced practitioners, easy to transport between the field and lab, and easy to process thereafter. Here, we demonstrate methods that satisfy these requirements and show strong potential for characterizing diverse terrestrial eDNA samples collected from flower and leaf surfaces.
We used novel methods to collect and process 236 flower eDNA samples and 21 leaf surface eDNA samples, obtaining 2,228 Arthropoda eDNA detections spanning 175 families using amplicon sequencing of two genetic markers.
Detected taxa were diverse and included numerous groups of conservation concern, such as bees (Hymenoptera; Anthophila, 32 genera spanning 5 families) and Lepidoptera (209 genera from 21 families). Data reveal strong associations between insect community richness and remotely sensed measures of forest habitat, providing a quantitative perspective of relevance to insect conservation.
It is increasingly clear that a variety of organisms readily disperse eDNA throughout the environment, supporting the notion that eDNA will be a powerful tool for characterizing species distributions and monitoring at-risk species. However, we conclude that researchers seeking to characterize fine-scale habitat associations or plant-pollinator interactions using eDNA will need to carefully design studies with appropriate field controls, such as the leaf surface eDNA samples collected here.
This report synthesizes current (2024) science-based knowledge related to the impacts of climate change on big sagebrush vegetation in Western North America. This effort was conducted through the U.S. Geological Survey working with the Bureau of Land Management as part of multiple science syntheses to aid management agencies developing environmental impacts assessments in response to human-related or caused events. This report reviews the potential impacts climate change may have on sagebrush vegetation and related management decisions. The body of the synthesis introduces the diverse impacts of climate change across the region by first focusing directly on what climate change may entail in terms of altered temperature and precipitation patterns. The report then discusses how these changes could likely affect individual plant species based on experimental results and scale the impacts to species distributions and community composition. The synthesis section ends by surveying efforts to model potential future changes in habitat. The report goes on to link the synthesis conclusions to individual land uses or land management decisions, such as forage resources, restoration or fuel treatment. Finally, the report provides a section that discusses the pros and cons of available datasets that model the potential future of vegetation in the sagebrush region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255045","collaboration":"Prepared in cooperation with Yale School of the Environment and Bureau of Land Management","usgsCitation":"Carpenter, S.M., Holdrege, M.C., Schlaepfer, D.R., Phillips, J., Griffin, P., Lauenroth, W.K., and Bradford, J.B., 2025, Climate change impacts on plant communities in the sagebrush region—A science synthesis to inform Bureau of Land Management resource management: U.S. Geological Survey Scientific Investigations Report 2025–5045, 60 p., https://doi.org/10.3133/sir20255045.","productDescription":"x, 60 p.","onlineOnly":"Y","ipdsId":"IP-158152","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":492288,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5045/images"},{"id":492163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5045/sir20255045.pdf","text":"Report","size":"54.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5045"},{"id":492162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5045/coverthb.jpg"},{"id":492435,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255045/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5045"},{"id":492289,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5045/sir20255045.xml"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.47085655229651,\n 49.03087841335443\n ],\n [\n -109.36707098761016,\n 48.97533905613594\n ],\n [\n -108.59984126360919,\n 47.10350873556769\n ],\n [\n -111.17120403969128,\n 48.93205699878433\n ],\n [\n -110.72899206743233,\n 47.28774464917828\n ],\n [\n -114.81019931620497,\n 44.92767854204746\n ],\n [\n -118.26252607142581,\n 47.40161992530679\n ],\n [\n -120.96059041198399,\n 48.913826471813934\n ],\n [\n -121.51113648480029,\n 48.583049845013676\n ],\n [\n -122.22592814966379,\n 42.19558595467558\n ],\n [\n -120.23956000538604,\n 38.86950616483054\n ],\n [\n -115.16424378294235,\n 34.49668753215643\n ],\n [\n -114.66904039390542,\n 35.774420444698194\n ],\n [\n -111.91445748122553,\n 36.57404002586604\n ],\n [\n -111.08039807202803,\n 38.090459720027894\n ],\n [\n -109.66040807077158,\n 34.09264795999532\n ],\n [\n -105.49956098365757,\n 33.35306735179675\n ],\n [\n -105.1942616934904,\n 36.86164728405714\n ],\n [\n -104.46325968085759,\n 41.6756079905559\n ],\n [\n -103.67824727595544,\n 42.2158730516426\n ],\n [\n -104.8704368129265,\n 44.69923426792971\n ],\n [\n -103.37233643926561,\n 45.25198814864976\n ],\n [\n -102.99032903885458,\n 48.723152452506895\n ],\n [\n -103.77002889466604,\n 48.730618780629214\n ],\n [\n -105.59751217809955,\n 47.36393384939501\n ],\n [\n -105.88781115813256,\n 48.97431691272439\n ],\n [\n -108.47085655229651,\n 49.03087841335443\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Researchers and policymakers increasingly recognize the contribution of aquatic food systems, such as fisheries, to food security and nutrition. Yet governing fisheries for nutrition objectives is complicated by the multiple overlapping processes that shape availability and access to nutrients over time, including fishing sustainability, climate change, trade dynamics, and consumer preferences. Anticipating the effect of governance interventions to sustain or enhance nutritional benefits from fisheries entails accounting for these multiple interacting influences. We develop an analytical approach to link available data on aquatic foods production, nutrition, distribution, and potential climate impacts to evaluate the nutrition implications of fishery management and post-harvest allocation interventions. We demonstrate this approach using national and publicly available datasets for five case study countries: Peru, Chile, Indonesia, Sierra Leone, and Malawi. As examples, we evaluate the potential to enhance domestic supply of key nutrients to nutritionally-vulnerable populations by (a) dynamically adjusting fishing effort in response to climate impacts on fish stocks, and (b) retaining aquatic foods currently diverted via trade or foreign fishing. The results indicate substantial differences across countries in terms of anticipated climate change effects, with potential for substantially increased nutrition yield in Chile and Peru under adaptive management, vs more modest yield increases in Indonesia. The impacts of post-harvest allocation policies related to foreign fishing, exports, fishing sector, and subnational trade also vary, with exports weighing heavily on nutrient availability in Sierra Leone. This methodological approach represents a step toward operationalizing calls to manage fisheries as part of national food and nutrient supplies, in light of climate change risks.
","language":"English","publisher":"Purpose-Led Publishing","doi":"10.1088/2976-601x/add164","usgsCitation":"Bennett, A., Mason, J.G., Battista, W., Free, C.M., Gephart, J.A., Kleisner, K.M., Rice, E.D., Robinson, K.F., and Virdin, J., 2025, An analytical approach to explore prospects and limits of nutrition-sensitive fisheries governance under climate change: Environmental Research: Food Systems, v. 2, 035003, 25 p., https://doi.org/10.1088/2976-601x/add164.","productDescription":"035003, 25 p.","ipdsId":"IP-166669","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":495048,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2976-601x/add164","text":"Publisher Index Page"},{"id":494537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2025-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Bennett, Abigail","contributorId":360346,"corporation":false,"usgs":false,"family":"Bennett","given":"Abigail","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":946987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mason, Julia G.","contributorId":360348,"corporation":false,"usgs":false,"family":"Mason","given":"Julia","middleInitial":"G.","affiliations":[{"id":15310,"text":"Environmental Defense Fund","active":true,"usgs":false}],"preferred":false,"id":946988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Battista, Willow","contributorId":360351,"corporation":false,"usgs":false,"family":"Battista","given":"Willow","affiliations":[{"id":15310,"text":"Environmental Defense Fund","active":true,"usgs":false}],"preferred":false,"id":946989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Free, Christopher M.","contributorId":360354,"corporation":false,"usgs":false,"family":"Free","given":"Christopher","middleInitial":"M.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":946990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gephart, Jessica A.","contributorId":360357,"corporation":false,"usgs":false,"family":"Gephart","given":"Jessica","middleInitial":"A.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":946991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kleisner, Kristin M.","contributorId":360360,"corporation":false,"usgs":false,"family":"Kleisner","given":"Kristin","middleInitial":"M.","affiliations":[{"id":15310,"text":"Environmental Defense Fund","active":true,"usgs":false}],"preferred":false,"id":946992,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rice, Emma D.","contributorId":360362,"corporation":false,"usgs":false,"family":"Rice","given":"Emma","middleInitial":"D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":946993,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Robinson, Kelly Filer 0000-0001-8109-9492","orcid":"https://orcid.org/0000-0001-8109-9492","contributorId":340631,"corporation":false,"usgs":true,"family":"Robinson","given":"Kelly","email":"","middleInitial":"Filer","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946994,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Virdin, John","contributorId":360366,"corporation":false,"usgs":false,"family":"Virdin","given":"John","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":946995,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70269490,"text":"70269490 - 2025 - Grand Canyon landslide-dam and paleolake triggered by the Meteor Crater impact at 56 ka","interactions":[],"lastModifiedDate":"2025-11-18T17:01:10.597342","indexId":"70269490","displayToPublicDate":"2025-07-15T09:03:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Grand Canyon landslide-dam and paleolake triggered by the Meteor Crater impact at 56 ka","docAbstract":"This paper hypothesizes that the Meteor Crater impact in Arizona, USA, 56,000 years ago triggered landslides in Grand Canyon that dammed the Colorado River and formed Nankoweap paleolake. This is compatible with shock and earthquake physics for the impact that infer a M5.4 seismic event, attenuated to an effective magnitude of M3.5 at Grand Canyon. Results that support the hypothesis include radiocarbon dating of driftwood and luminescence dating of associated slack-water lake sediments that are preserved in caves up to 60 m above the modern Colorado River. Radiocarbon ages from two locations, including Stanton’s Cave, date the driftwood as 55.25 ± 2.44 ka (n = 4). Sediments associated with the driftwood gave a luminescence age of 56.00 ± 6.39 ka (n = 2). These six Grand Canyon dates, and three published ages for the Meteor Crater impact, show statistically indistinguishable results that support the hypothesis for a geologically instantaneous series of events with a mean age of 55.60 ± 1.30 ka. This work highlights the value of radiocarbon dating near the limits of the technique, integration of multiple dating methods, and seismic and landslide hazards associated with meteorite impacts in regions of extreme topography like Grand Canyon.
The geographic information system (GIS) community in Vermont has a long history of interdisciplinary and cooperative projects that have facilitated the leveraging of geospatial technology on myriad data acquisitions across the State. High-resolution elevation data are proving to be a resource of great economic value in dealing with many important issues in Vermont. Vermont attained statewide coverage of quality level 2 coverage of topographic light detection and ranging (lidar) data in 2019. Having access to elevation data that are exponentially more accurate than what was previously available is enabling GIS professionals to better support and empower decision makers in economically important efforts such as environmental protection, public safety, watershed management and water quality, geology, transportation planning, forest and wildlife management, local planning, and flood plain management. In addition, developing a consistent and seamless statewide topographic framework supplants the traditionally time consuming and costly approach of extensive field data collection by requiring less time and money, therefore adding economic benefits. Critical applications that meet the State’s management needs depend on 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 Vermont. The status of available and in-progress 3DEP baseline lidar data in Vermont 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 $1.64 million in new benefits annually to the State. The top eight Vermont business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Somatic growth rate is a fundamental trait that influences metabolism, lifespan and reproductive maturity and is critical for understanding population dynamics and informing management actions. Brown Treesnakes (Boiga irregularis) introduced to Guam are highly invasive and can reproduce year-round without discrete cohorts. We compared snake size trajectories described by the conventionally used von Bertalanffy growth function versus the Gompertz model. Using quantile regression with a regularized effect for individual snakes we modeled growth rates of 270 marked, wild snakes as a function of size. The Gompertz model explained more of the variation in growth and rendered more realistic predictions of asymptotic sizes than did the von Bertalanffy model. With the Gompertz model, growth rates were 1.05–1.16× faster in males than in females. Females reached asymptotic sizes at shorter snout-vent lengths than males. Growth rate was positively correlated with amount of precipitation, and modeling wet-dry seasonality on Guam as a sinusoidal function identified a growth peak in September—October. Effects of seasonality and precipitation, however, were minor compared to individual and sex related differences in size-adjusted growth rates. We estimated that the 50th (and 5th, 95th) growth-rate percentile males in our study population become sexually mature at an age of 33 (∞, 15) months, while females mature at 41 (∞, 18) months, where ∞ indicates that the slowest growing snakes never reach maturity. However, 50% of the snakes mature at a size below the median, and age at maturity may be as low as 10.4 (males) and 13.7 (females) months for average-sized hatchlings that grow fast. Our results have implications for the timing of management options for this species and our approach can be broadly applied to animals where repeated growth data are obtained and age is unknown.
","language":"English","doi":"10.1002/ece3.71695","usgsCitation":"Lardner, B., Cade, B.S., Savidge, J.A., Rodda, G.H., Reed, R., and Yackel Adams, A.A., 2025, Growth rate variation in Brown Treesnakes (Boiga irregularis): An invasive species of conservation concern: Ecology and Evolution, v. 15, no. 7, e71695, 13 p., https://doi.org/10.1002/ece3.71695.","productDescription":"e71695, 13 p.","ipdsId":"IP-129633","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":492872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71695","text":"Publisher Index Page"},{"id":492628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.97370767089484,\n 13.602893910874045\n ],\n [\n 144.85437262594462,\n 13.66982861291649\n ],\n [\n 144.74819953448088,\n 13.502237780603025\n ],\n [\n 144.60561170503658,\n 13.462933343165659\n ],\n [\n 144.6200898538703,\n 13.238449316687053\n ],\n [\n 144.76311641509852,\n 13.23674118706542\n ],\n [\n 144.7977762259491,\n 13.410418069709003\n ],\n [\n 144.9383897623706,\n 13.516040821003134\n ],\n [\n 144.97370767089484,\n 13.602893910874045\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Lardner, Bjorn","contributorId":225066,"corporation":false,"usgs":false,"family":"Lardner","given":"Bjorn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":943626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":943627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":943628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodda, Gordon H. 0000-0002-6696-7308 roddag@usgs.gov","orcid":"https://orcid.org/0000-0002-6696-7308","contributorId":210066,"corporation":false,"usgs":true,"family":"Rodda","given":"Gordon","email":"roddag@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":943629,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":267796,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":943630,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":943631,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271920,"text":"70271920 - 2025 - DNA metabarcoding and video camera collars yield different inferences about the summer diet of an arctic ungulate","interactions":[],"lastModifiedDate":"2025-09-24T15:46:38.131894","indexId":"70271920","displayToPublicDate":"2025-07-12T10:42:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"DNA metabarcoding and video camera collars yield different inferences about the summer diet of an arctic ungulate","docAbstract":"The diets of wild ungulates are a foundational component of their ecology, influencing their behavior, body condition, and demography. With changing environmental conditions, there is a significant need to identify important forage items for ungulates, but this has often proved challenging. Declines in several barren-ground caribou herds across the North American Arctic have raised concerns about the influence of climate change on caribou forage conditions. Shifts in plant phenology, biomass, quality, and composition may be influencing caribou diets and subsequently affecting their body condition and demographic rates. Although forage is a primary driver of barren-ground caribou behavior and population dynamics, there is limited recent information about the specific foods they consume, and uncertainty about appropriate methods for identifying those foods. Investigators are increasingly using fecal DNA metabarcoding and video camera collars to assess ungulate diets, but comparative studies of these approaches are lacking. To examine the summer diets of barren-ground caribou, we used both fecal metabarcoding and video camera collars to identify forage used by the Porcupine caribou herd, which spans the Alaska–Yukon border. In 2021, we sampled the diets of adult females by collecting fecal samples and observing collar videos during 4 sampling occasions. We found that caribou consumed very specific forage items, and those items varied markedly across the growing season. Caribou predominantly consumed graminoids and lichens during early summer, and shrubs and forbs later in the season. Metabarcoding and video data provided significantly different estimates of diet for all taxonomic levels we evaluated, and inferences from the two approaches were often disparate. Metabarcoding failed to detect some items frequently consumed in videos, such as lichens, and indicated high use of other items rarely consumed, such as mosses. We found that video data provided greater taxonomic diversity and resolution for vascular plants and lichens, and more closely aligned with past research and biological expectations than data from fecal metabarcoding. Additional research is needed to be able to use these methods to identify the biomass of different forage items consumed.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70319","usgsCitation":"Johnson, H.E., Coulombe, G., Adams, L., Arnison, C., Barboza, P., Kienzler, M., Leacock, W., and Suitor, M.J., 2025, DNA metabarcoding and video camera collars yield different inferences about the summer diet of an arctic ungulate: Ecosphere, v. 16, no. 7, e70319, 19 p., https://doi.org/10.1002/ecs2.70319.","productDescription":"e70319, 19 p.","ipdsId":"IP-162922","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":496161,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70319","text":"Publisher Index Page"},{"id":496019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.7923723105851,\n 70.35287221669569\n ],\n [\n -144.7923723105851,\n 67.05263325896493\n ],\n [\n -135.98153547631983,\n 67.05263325896493\n ],\n [\n -135.98153547631983,\n 70.35287221669569\n ],\n [\n -144.7923723105851,\n 70.35287221669569\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coulombe, Gabrielle Lys 0000-0002-6272-9302","orcid":"https://orcid.org/0000-0002-6272-9302","contributorId":350671,"corporation":false,"usgs":true,"family":"Coulombe","given":"Gabrielle Lys","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":949385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Layne G. 0000-0001-6212-2896 ladams@usgs.gov","orcid":"https://orcid.org/0000-0001-6212-2896","contributorId":2776,"corporation":false,"usgs":true,"family":"Adams","given":"Layne G.","email":"ladams@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":949386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arnison, Colleen","contributorId":350672,"corporation":false,"usgs":false,"family":"Arnison","given":"Colleen","affiliations":[],"preferred":false,"id":949387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barboza, Perry","contributorId":190361,"corporation":false,"usgs":false,"family":"Barboza","given":"Perry","affiliations":[],"preferred":false,"id":949388,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kienzler, Martin","contributorId":350675,"corporation":false,"usgs":false,"family":"Kienzler","given":"Martin","affiliations":[],"preferred":false,"id":949389,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Leacock, William","contributorId":192123,"corporation":false,"usgs":false,"family":"Leacock","given":"William","affiliations":[],"preferred":false,"id":949390,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Suitor, Michael J.","contributorId":264206,"corporation":false,"usgs":false,"family":"Suitor","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":33063,"text":"Yukon Department of Environment","active":true,"usgs":false}],"preferred":false,"id":949391,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268982,"text":"70268982 - 2025 - A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints","interactions":[],"lastModifiedDate":"2025-07-14T14:07:33.918712","indexId":"70268982","displayToPublicDate":"2025-07-11T09:00:47","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints","docAbstract":"Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2025.133824","usgsCitation":"Khand, K., Senay, G.B., Friedrichs, M., Yi, K., Fisher, J., Wang, L., Suvočarev, K., Ahmadi, A., Chu, H., Good, S., Mallick, K., Missik, J., Nelson, J., Reed, D., Wang, T., and Xiao, X., 2025, A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints: Journal of Hydrology, v. 662, 133824, 14 p., https://doi.org/10.1016/j.jhydrol.2025.133824.","productDescription":"133824, 14 p.","ipdsId":"IP-168356","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":492485,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2025.133824","text":"Publisher Index Page"},{"id":492197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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We analysed necropsy data from 2,179 pythons captured between 2006 and 2022, revealing nine cases of coelomic foreign bodies, primarily consisting of bird beaks, which presumably entered the coelom following gastrointestinal perforations during prey consumption. Despite the presence of foreign bodies, most examined pythons exhibited no obvious health issues. These findings indicate that the consumption of prey with sharp morphological features, such as wading birds, may not impede the pythons’ survival or health significantly, thereby underscoring their adaptability as apex predators in the Greater Everglades Ecosystem.
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An operational ETa using the Visible Infrared Imaging Radiometer Suite (VIIRS) and global weather datasets was developed through the Simplified Surface Energy Balance Model (SSEBop) model. An operational framework is established with the Famine Early Warning System Network (https://earlywarning.usgs.gov/fews) to generate and update global 1 km ETa at dekadal (∼10 day), monthly, and yearly time scales since February 2012. Modeled ETa at monthly and annual time scales was evaluated using 67 eddy covariance (EC) flux tower stations around the world and water balance-based ETa based on 810 United States eight-digit Hydrologic Unit Code (HUC8) and 18 Global Runoff Data Center (GRDC) basins. The correlation coefficient (r=0.68–0.94) shows relatively strong and consistent performance across the three datasets, capturing the spatiotemporal variability in HUC8 and GRDC basins and EC tower sites reliably. The bias (3%–15%) and root mean square error (RMSE: 13%–34%) showed relatively large errors and high variability among the three datasets. The evaluation results indicate the usefulness of the VIIRS ETa for drought monitoring and early warning applications without further adjustments, while bias-correction and calibration procedures may be required before using the VIIRS ETa data for localized water budget assessments. Availability of gridded actual ETa data from a combination of flux towers and basin-scale ETa is desired to establish bias-correction procedures to improve the absolute accuracy of remote-sensing ETa such as the SSEBop VIIRS operational products.
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Researchers and managers benefit from understanding how to interpret and ensure the accuracy of fChl data collected by in situ fluorometers. Although fChl values produced by different manufacturers are often in agreement with discrete laboratory-derived Chlorophyll a (Chl a) concentration measurements, there are instances in which results significantly differ. Further, when measuring fChl side by side, different fluorometers may report values that differ significantly from each other, despite passing calibration checks prior to deployment. We compared environmental conditions and phytoplankton species composition associated with instances in which fChl measurements from three different fluorometers (EXO2 Total Algae Smart Sensor, YSI Inc./Xylem Inc., Yellow Springs, Ohio; FluoroProbe III, bbe Moldaenke GmbH, Kiel, Germany; WETStar, Sea-Bird Scientific, Bellevue, Washington) were significantly different from laboratory-derived Chl a concentrations. Results indicated that elevated primary productivity, as indicated by high pH, dissolved oxygen, and the ratio of Chl a to phaeophytin, were correlated with underestimated fChl values recorded by each sensor. After removing outliers, we determined unique correction guidance for each of the three sensors and demonstrated that after applying correction formulae, fChl measurements produced by each sensor became directly comparable.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lom3.10705","usgsCitation":"Richardson, E.T., Kraus, T.E., Sturgeon, C.L., O’Donnell, K., and Bergamaschi, B.A., 2025, Identifying conditions associated with outliers produced by three different chlorophyll fluorometers: A comparison of instrumentation and development of correction formulae: Limnology & Oceanography: Methods, v. 23, no. 9, p. 673-687, https://doi.org/10.1002/lom3.10705.","productDescription":"15 p.","startPage":"673","endPage":"687","ipdsId":"IP-168613","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":493096,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":493321,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lom3.10705","text":"Publisher Index Page"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.1569041318973,\n 38.781445775867496\n ],\n [\n -122.591638749212,\n 38.781445775867496\n ],\n [\n -122.41414092861007,\n 37.110093240321405\n ],\n [\n -121.1569041318973,\n 37.53901515220369\n ],\n [\n -121.1569041318973,\n 38.781445775867496\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Emily T. 0000-0003-2696-8266","orcid":"https://orcid.org/0000-0003-2696-8266","contributorId":304430,"corporation":false,"usgs":true,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Tamara E. 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C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturgeon, Crystal Lee 0000-0002-1799-9127","orcid":"https://orcid.org/0000-0002-1799-9127","contributorId":302710,"corporation":false,"usgs":true,"family":"Sturgeon","given":"Crystal","email":"","middleInitial":"Lee","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Katy 0000-0003-2323-8970 kodonnell@usgs.gov","orcid":"https://orcid.org/0000-0003-2323-8970","contributorId":5640,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Katy","email":"kodonnell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944256,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944257,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268887,"text":"tm5B13 - 2025 - Determination of per- and polyfluoroalkyl substances in water by direct injection of matrix-modified centrifuge supernatant and liquid chromatography/tandem mass spectrometry with isotope dilution","interactions":[],"lastModifiedDate":"2026-02-03T14:25:36.603546","indexId":"tm5B13","displayToPublicDate":"2025-07-09T17:20:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-B13","displayTitle":"Determination of Per- and Polyfluoroalkyl Substances in Water by Direct Injection of Matrix-Modified Centrifuge Supernatant and Liquid Chromatography/Tandem Mass Spectrometry with Isotope Dilution","title":"Determination of per- and polyfluoroalkyl substances in water by direct injection of matrix-modified centrifuge supernatant and liquid chromatography/tandem mass spectrometry with isotope dilution","docAbstract":"A direct-injection liquid chromatography/tandem mass spectrometry method was developed to determine 34 per- and polyfluoroalkyl substances (PFAS), including selected branched isomers, in centrifuge supernatant of matrix-modified (amended with approximately 50 percent methanol) water samples. The method has been validated in reagent water, surface water, groundwater, and wastewater effluent. Other water types (for example, drinking water, untreated wastewater, and landfill leachate) have been analyzed by the method but not systematically validated. Recovery of isotope-dilution standards, added to each sample, may be used to assess method performance in nonvalidated matrices on a sample-by-sample basis.
Using this method, PFAS concentrations were determined in the range of 2–2,000 nanograms per liter in water samples. This range can be extended by diluting concentrated samples. At circumneutral pH, most compounds are present in the environment in their ionized form, and data are reported as such (for example, perfluorooctanoic acid is referred to as “perfluorooctanoate” [PFOA], perfluorooctane sulfonic acid is referred to as “perfluorooctane sulfonate” [PFOS]).
Sample preparation procedures were designed without the use of filtration and with minimum sample handling steps to mitigate procedural losses of target compounds due to sorption to surfaces. Further, isotope-dilution quantification allowed for the correction of bias that may result from procedural losses, matrix-induced signal suppression or enhancement, and other factors.
Validation experiments to characterize bias and variability, method detection level, and holding time were done in four distinct water matrices—reagent water, surface water, treated wastewater effluent, and groundwater—at multiple concentration levels. Mean PFAS recoveries met data quality objectives of bias and variability studies in all four validation matrices except for two compounds with low and variable recovery in the reagent water matrix only. Isotope-dilution standards, treated as surrogate compounds, were analyzed in more than 1,500 customer-submitted environmental samples with aggregate recovery of 102.5±6.5 percent (mean±standard deviation). Maximum holding times for all target compounds in the four validation matrices were 28 days for refrigerated samples and 90 days for frozen samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm5B13","collaboration":"Strategic Laboratory Science Branch and National Water Quality Laboratory","usgsCitation":"Gray, J.L., Kanagy, L.K., Kanagy, C.J., and Anderson, C.A., 2025, Determination of per- and polyfluoroalkyl substances in water by direct injection of matrix-modified centrifuge supernatant and liquid chromatography/tandem mass spectrometry with isotope dilution: U.S. Geological Survey Techniques and Methods, book 5, chap. B13, 121 p., https://doi.org/10.3133/tm5B13.","productDescription":"Report: xii, 121 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-144091","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":491919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/b13/coverthb.jpg"},{"id":491984,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/05/b13/images"},{"id":491920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/b13/tm5B13.pdf","text":"Report","size":"5.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 5-B13"},{"id":491921,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P3YPXG","text":"USGS data release","linkHelpText":"Concentrations of per- and polyfluoroalkyl substances (PFAS) from validation experiments and custom sample analysis by U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL) Laboratory Code 9660, December 2020 to March 2022"},{"id":491985,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/05/b13/tm5B13.xml"},{"id":492165,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm5B13/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 5-B13"}],"contact":"Chief, National Water Quality Laboratory
U.S. Geological Survey
Box 25046, Mail Stop 407
Denver, CO 80225-0585
Structure contours and other geologic information from numerous published geologic maps were digitized and compiled into a digital dataset showing the configuration of a single stratigraphic datum, the base of the Dakota Sandstone and its equivalents across the Colorado Plateau. The principal maps compiled in digital form are a series of 1:250,000-scale 1 degree (°) × 2° quadrangle maps published by the U.S. Geological Survey, augmented by other geologic maps published at various map scales. The compiled digital dataset contains geologic map polygons of the Dakota Sandstone and regional stratigraphic equivalents, the location of faults and fold axes, structure contour lines that define the altitude of the base of the unit and bedding orientation data computed from the structure contour lines. This report provides the scientific rationale for compilation of these data and describes the compilation methodology for each of the data elements. This report provides an extended description of the data compilation in a companion U.S. Geological Survey digital data release of spatial data and attributes associated with the contoured surface and associated geologic data layers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/dr1213","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"Sweetkind, D.S., 2025, Methodology for compilation of previously published contour data showing the altitude of the base of Dakota Sandstone on the Colorado Plateau, Arizona, Colorado, New Mexico, and Utah: U.S. Geological Survey Data Report 1213, 22 p., https://doi.org/10.3133 dr1213.","productDescription":"Report: vi, 22 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-158512","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":494152,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118688.htm","linkFileType":{"id":5,"text":"html"}},{"id":491771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1213/coverthb.jpg"},{"id":491772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1213/dr1213.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1213"},{"id":491983,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1213/dr1213.xml"},{"id":491982,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1213/images"},{"id":491773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P961QY86","text":"USGS data release","linkHelpText":"Digital Data from Previously Published Contour Data Showing the Altitude of the Base of Dakota Sandstone on the Colorado Plateau, Arizona, Colorado, New Mexico, and Utah"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113,\n 40\n ],\n [\n -113,\n 35\n ],\n [\n -107,\n 35\n ],\n [\n -107,\n 40\n ],\n [\n -113,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
Recent and ongoing collections of high-resolution elevation data in Louisiana are providing information that supports improved critical public safety modeling and enables the State to strengthen its efforts to fight the effects of land subsidence and sea-level rise. The availability of current and accurate three-dimensional (3D) elevation data supports numerous business activities, including flood risk management, infrastructure and construction management, coastal zone management, wildlife and habitat management, recreation, agriculture and precision farming, urban and regional planning, water supply and quality assessment, and natural resources conservation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed 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 Louisiana. The status of available and in-progress 3DEP baseline lidar data in Louisiana is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $6.96 million in new benefits annually to the State. The top 10 Louisiana business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253034","usgsCitation":"Cretini, C., 2025, The 3D Elevation Program—Supporting Louisiana's economy: U.S. Geological Survey Fact Sheet 2025–3034, 2 p., https://doi.org/10.3133/fs20253034.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160425","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":491723,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3034/images/"},{"id":491722,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3034/fs20253034.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2025-3034 XML"},{"id":491721,"rank":3,"type":{"id":39,"text":"HTML 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"nation\":\"USA \"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"White-tailed deer (Odocoileus virginianus; hereafter, deer) have been widely studied regarding their breeding ecology and responses to hunting pressures. However, variations in defining the breeding season—its duration and timing—across studies have created uncertainty about whether regional differences in deer breeding ecology stem from ecological factors or methodological inconsistencies. This study aims to clarify the peak breeding season timing and the movement patterns of males during this period, particularly in relation to hunting seasons. Understanding how age and the timing of hunting seasons impact movement and breeding behaviors is important for wildlife managers, as these factors can affect harvest success. This study took place in southwest Wisconsin, using GPS data collected from 188 collared male deer between 15 October and 1 December from 2017 to 2020. Based on generalized linear mixed models, 2-year-old males exhibited higher hourly movement rates than other ages, and the opening weekend of the firearm hunting season had no significant effect on movement rates. In contrast, the variance in daily movement rate differed significantly between yearlings and older ages, with males 3 years and older displaying the highest variance. This suggests that older males may alternate more frequently between high-movement mate searching and lower-movement mate tending, potentially enhancing reproductive success. Similarly, 2-year-old males had larger daily ranges than both older and younger ages. Changepoint analysis of daily movement rates determined that the peak breeding season occurred between 23 October and 12 November, with little variation among ages and alternative metrics. Our findings indicate that male movement rates and ranges can reflect deer reproductive efforts and vary by age, which has important implications for reproductive success and disease transmission risk.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71589","usgsCitation":"Hunsaker, M., Gilbertson, M., Storm, D., and Turner, W.C., 2025, The breeding season and movement ecology of male white‐tailed deer in southwest Wisconsin: Ecology and Evolution, v. 15, no. 7, e71589, 13 p., https://doi.org/10.1002/ece3.71589.","productDescription":"e71589, 13 p.","ipdsId":"IP-165270","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493315,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71589","text":"Publisher Index Page"},{"id":492997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.8,\n 43.25\n ],\n [\n -90.8,\n 42.95\n ],\n [\n -89.6,\n 42.95\n ],\n [\n -89.6,\n 43.25\n ],\n [\n -90.8,\n 43.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Hunsaker, Matthew","contributorId":358692,"corporation":false,"usgs":false,"family":"Hunsaker","given":"Matthew","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":944110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilbertson, Marie L.J.","contributorId":358694,"corporation":false,"usgs":false,"family":"Gilbertson","given":"Marie L.J.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":944111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storm, Daniel J.","contributorId":358697,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":944112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":944113,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268814,"text":"ofr20251034 - 2025 - Preparation and analysis methods for fish tissue collected from Lake Koocanusa, Montana","interactions":[],"lastModifiedDate":"2026-02-03T14:19:50.914046","indexId":"ofr20251034","displayToPublicDate":"2025-07-08T12:33:51","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1034","displayTitle":"Preparation and Analysis Methods for Fish Tissue Collected from Lake Koocanusa, Montana","title":"Preparation and analysis methods for fish tissue collected from Lake Koocanusa, Montana","docAbstract":"Lake Koocanusa, a reservoir, receives mine wastes from metallurgical coal mines in the Elk River Valley of British Columbia, Canada. Selenium and other elements discharged by the mines into the waters of the United States can pose unknown risks to aquatic life. The U.S. Geological Survey Wyoming-Montana Water Science Center can collaborate with Montana Fish, Wildlife and Parks and other State and Federal agencies to design studies and to collect fish tissues to help fill this knowledge gap. This report describes the processes, techniques, and methods used to collect and analyze fish tissue collected from Lake Koocanusa; and procedures used to review and manage data, including quality assurance and quality control procedures used by the U.S. Geological Survey Wyoming-Montana Water Science Center and supporting analytical laboratories. These fish tissue collections began in 2021.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251034","usgsCitation":"Schmidt, T.S., Bussell, A.M., Moloney, M.A., Dunnigan, J.L., Selch, T.M., Brandt, J.E., Stricker, C.A., Stewart, A.R., Kocen, V.A., Cleveland, D., Blazer, V.S., Janssen, S.E., Ogorek, J.M., Dunn, M., McBride, T.L., Adams, K.B., Colman, B.P., Young, M., and Christensen, J., 2025, Preparation and analysis methods for fish tissue collected from Lake Koocanusa, Montana: U.S. Geological Survey Open-File Report 2025–1034, 16 p., https://doi.org/10.3133/ofr20251034.","productDescription":"vii, 16 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-153220","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":491708,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251034/full"},{"id":491707,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1034/images/"},{"id":491706,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1034/ofr20251034.XML"},{"id":491705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1034/ofr20251034.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025–1034"},{"id":491704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1034/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.29104936748958,\n 50.23265651730222\n ],\n [\n -117.29104936748958,\n 48.55695721902586\n ],\n [\n -114.96658880253837,\n 48.55695721902586\n ],\n [\n -114.96658880253837,\n 50.23265651730222\n ],\n [\n -117.29104936748958,\n 50.23265651730222\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
As of October 2024, the U.S. Geological Survey (USGS) operated 588 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells selected to monitor the effects of climate variability, such as droughts, on groundwater levels nationwide. The CRN includes nearly 500 locations with real-time data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip254","usgsCitation":"Fine, J.M., and Caldwell, R.R., 2025, U.S. Geological Survey Groundwater Climate Response Network, 2024: U.S. Geological Survey General Information Product 254, 1 p., https://doi.org/10.3133/gip254.","productDescription":"1 p.","ipdsId":"IP-175295","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":491829,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/254/gip254.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 254"},{"id":491828,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/254/coverthb.jpg"}],"contact":"National Groundwater Networks Coordinator
Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Soil moisture is a key link between hydrologic and ecologic processes in desert shrublands. Understanding how soil moisture is spatially distributed in desert shrublands provides valuable insights into how shrubs use and impact limiting water resources, and how shrublands may respond to future meteorological and climate change. Our goals were to determine how soil moisture is partitioned between soil volumes under canopies and in the bare soil interspaces across multiple desert shrublands, and to evaluate the roles of physical soil properties, shrub-type characteristics, meteorology, and measurement resolution in influencing and observing variation in soil moisture partitioning. Utilizing two long-term soil moisture datasets (monthly resolution, 30 years, whole soil profile measurements; and 30 min resolution, 10 years, 10–30 cm measurements), we compared soil moisture partitioning across nine northern Chihuahuan Desert shrubland sites (three sites dominated by creosotebush [Larrea tridentata], three by honey mesquite [Prosopis glandulosa], and three by tarbush [Flourensia cernua]) in the Jornada Basin, southern New Mexico, USA. Over 30 years, monthly, whole profile data showed that soil moisture in mesquite shrublands was consistently higher in bare soil interspaces compared to under canopies, whereas soil moisture under and between shrubs was more similar in creosotebush and tarbush shrublands. Physical soil properties were linked as explanatory variables of long-term soil moisture partitioning (monthly whole profile dataset), whereas 30-minute data showed that shorter-term periods of higher precipitation promoted greater near surface soil moisture (10–30 cm) in bare soil interspaces that was not captured at monthly time steps. Thus, although the long-term average partitioning of soil moisture in these shrublands is strongly controlled by soil physical properties, soil moisture partitioning varies at shorter timescales (daily to weekly) in response to precipitation events. Moreover, shrub-type characteristics influenced soil moisture partitioning, with dense and tall mesquite shrubs having lower under canopy soil moisture than tarbush, and root architecture potentially influencing partitioning across creosotebush sites. These results illustrate diversity in soil moisture partitioning both between and within shrublands of the northern Chihuahuan Desert, and elucidate how physical soil properties, shrub-type characteristics, and meteorological variation interact to shape their soil moisture dynamics.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-025-00987-4","usgsCitation":"Pinos, J., Hammond, K., Duniway, M.C., Anderson, J.P., Hanan, N.P., and Petrie, M., 2025, Soil moisture partitioning between under canopy and interspace environments in shrublands of the northern Chihuahuan Desert: Ecosystems, v. 28, 41, 21 p., https://doi.org/10.1007/s10021-025-00987-4.","productDescription":"41, 21 p.","ipdsId":"IP-172365","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":491794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"northern Chihuahuan Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 32.8\n ],\n [\n -107,\n 32.45\n ],\n [\n -106.5,\n 32.45\n ],\n [\n -106.5,\n 32.8\n ],\n [\n -107,\n 32.8\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2025-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Pinos, Juan","contributorId":357729,"corporation":false,"usgs":false,"family":"Pinos","given":"Juan","affiliations":[{"id":85544,"text":"School of Life Sciences, University of Nevada Las Vegas, Las Vegas, Nevada 89154, USA","active":true,"usgs":false}],"preferred":false,"id":942368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hammond, Keegan","contributorId":357730,"corporation":false,"usgs":false,"family":"Hammond","given":"Keegan","affiliations":[{"id":85544,"text":"School of Life Sciences, University of Nevada Las Vegas, Las Vegas, Nevada 89154, USA","active":true,"usgs":false}],"preferred":false,"id":942369,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942370,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, John P.","contributorId":206326,"corporation":false,"usgs":false,"family":"Anderson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":37311,"text":"Jornada Experimental Range Department, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":942371,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanan, Niall P.","contributorId":208283,"corporation":false,"usgs":false,"family":"Hanan","given":"Niall","email":"","middleInitial":"P.","affiliations":[{"id":37773,"text":"Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":942372,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Petrie, Matthew D.","contributorId":206328,"corporation":false,"usgs":false,"family":"Petrie","given":"Matthew D.","affiliations":[{"id":37312,"text":"Department of Plant & Environmental Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":942373,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270914,"text":"70270914 - 2025 - Land application of drill waste: A scope analysis","interactions":[],"lastModifiedDate":"2025-08-27T15:41:07.70669","indexId":"70270914","displayToPublicDate":"2025-07-07T08:35:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22191,"text":"Journal of the Air & Waste Management Association","active":true,"publicationSubtype":{"id":10}},"title":"Land application of drill waste: A scope analysis","docAbstract":"Drilling fluid waste land application, a process where drilling wastes are spread and tilled into the land surface, has become common in some petroleum-producing states, however, the potential benefits and risks of this practice are not well studied. Drilling fluids can be water- or oil-based and can have high concentrations of total soluble salts and total petroleum hydrocarbons. Comprehensive chemical characterization of these fluids is not well documented in the literature, and the extent of land application is largely unknown. We hypothesized that the land application of drill waste would fluctuate over time due to economic factors. To begin to understand the extent of historical and potential future land application, we analyzed data from over 5,800 drilling fluid land application permits collected by the Oklahoma Corporation Commission for years 2000, 2005, 2010, and 2015–2020. During the years studied, drilling fluid wastes were applied to more than 250,000 acres in Oklahoma, with over 54,000 thousand barrels (Mbbl) of liquids and nearly 21,000 Mbbl of solids applied. Land application is widespread (occurring in 59/77 counties), however recent drilling activity, land availability, and the economics of transportation have created conditions favorable for land application specifically in the Anadarko Basin. Land application can co-occur with sensitive areas, such as important groundwater and surface-water drinking sources and agricultural fields used for subsistence or feed crop production. Our approach for quantifying the extent of land application, along with further chemical characterization studies, can aid operators and land managers who are considering this practice in assessing the associated benefits and risks.
Highly pathogenic avian influenza virus (HPAIV) has caused extensive mortalities in wild birds with a disproportionate impact on raptors since 2021. The population-level impact of HPAIV can be informed by telemetry studies that track large samples of initially healthy, wild birds. We leveraged movement data from 71 rough-legged hawks (Buteo lagopus) across all major North American migratory bird flyways concurrent with the 2022–2023 HPAIV outbreak and identified a total of 29 mortalities, of which 11 were confirmed, and an additional ~9 were estimated to have been caused by HPAIV. We estimated a 28% HPAIV cause-specific mortality rate among rough-legged hawks during a single year concurrent with the HPAIV outbreak in North America. Additionally, the overall mortality rate during the HPAIV outbreak (47%) was significantly higher than baseline annual mortality rates (3%–17%) suggesting that HPAIV-caused deaths were additive above baseline mortality levels. HPAIV mortalities were concentrated within the Central and Atlantic flyways during prebreeding migration and peaked in April 2022 when large-scale HPAIV mortalities were reported in other wild birds throughout North America. HPAIV exposure was most likely caused by scavenging or preying on infected waterfowl, as rough-legged hawks are known to opportunistically scavenge during the nonbreeding season. We utilized movement data to identify a continental-scale HPAIV cause-specific mortality event in rough-legged hawks that has the potential to exacerbate ongoing population declines. Our study highlights the usefulness of monitoring movement data to pinpoint sources of mortality that can help better understand the drivers of population change, even if studies are focused on other research questions.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71715","usgsCitation":"Paprocki, N., Kidd, J., Conway, C.J., 2025, Increased mortality rates caused by highly pathogenic avian influenza virus in a migratory raptor: Ecology and Evolution, v. 15, no. 7, e71715, 9 p., https://doi.org/10.1002/ece3.71715.","productDescription":"e71715, 9 p.","ipdsId":"IP-175046","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500825,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71715","text":"Publisher Index Page"},{"id":500337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.48107169920122,\n 33.854684262885186\n ],\n [\n -80.01853686335615,\n 38.18351676111723\n ],\n [\n -70.38701390108191,\n 42.41835122143618\n ],\n [\n -51.60484236017351,\n 47.93996656277548\n ],\n [\n -62.142196774650486,\n 67.55774528768225\n ],\n [\n -81.32631368778695,\n 76.1647300166936\n ],\n [\n -126.79642681009375,\n 75.05830106703817\n ],\n [\n -164.01601967620257,\n 69.95744509464026\n ],\n [\n -167.8253912694451,\n 65.37165740136732\n ],\n [\n -166.51043144858755,\n 62.0157119861629\n ],\n [\n -130.6425851599529,\n 60.724801406221076\n ],\n [\n -124.22486207042786,\n 47.55468424281966\n ],\n [\n -122.6165496747429,\n 38.85674316643377\n ],\n [\n -100.48107169920122,\n 33.854684262885186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Paprocki, Neil","contributorId":355054,"corporation":false,"usgs":false,"family":"Paprocki","given":"Neil","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":956002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kidd, Jeff W","contributorId":243473,"corporation":false,"usgs":false,"family":"Kidd","given":"Jeff W","affiliations":[],"preferred":false,"id":956003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956004,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268872,"text":"70268872 - 2025 - Disease-driven collapse of the native Kauaʻi avifauna and the rise of introduced bird species","interactions":[],"lastModifiedDate":"2025-07-09T15:23:24.011957","indexId":"70268872","displayToPublicDate":"2025-07-05T10:18:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Disease-driven collapse of the native Kauaʻi avifauna and the rise of introduced bird species","docAbstract":"Hawaii hosts one of Earth’s most unique and threatened avifaunas. Upslope migration of mosquito-vectored avian malaria on Kauaʻi (maximum elevation 1,598 m) has likely caused its rapid loss of avifaunal diversity; only 8 of 13 historic forest bird species remain. We update the status and trends of Kauaʻi forest bird populations since the original (1981) surveys using the latest (2023) survey data and distance sampling. We fit detection functions to species-specific count data and stratified estimates across the Interior (since 1981) and Exterior (since 2000) survey areas, and between low (900–1,100 m), medium (1,100–1,300 m) and high (> 1,300 m) elevation bands (since 2000). Log-linear trends of ʻakekeʻe (Loxops caeruleirostris), ʻanianiau (Magumma parva), ʻiʻiwi (Drepanis coccinea), and Kauaʻi ʻamakihi (Chlorodrepanis stejnegeri) steeply declined across the timeseries, with extinction of ʻakekeʻe and ʻiʻiwi expected before 2050. Undetected in 2023, ʻakikiki (Oreomystis bairdi) were excluded from analysis. ʻApapane (Himatione sanguinea), Kauaʻi ʻelepaio (Chasiempis sclateri), Chinese hwamei (Garrulax canorus), and white-rumped shama (Copsychus malabaricus) were stable overall. Northern cardinal (Cardinalis cardinalis) steadily declined, whereas Japanese bush warbler (Horornis diphone) and warbling white-eye (Zosterops japonicus) exponentially increased. Taxonomic and functional diversity did not vary greatly across our timeseries, while the proportion of introduced species in the Exterior increased from 34 to 59%. However, introduced species do not replace the losses of ecological functions from native species, whose populations are likely declining from avian malaria. Future monitoring can be used to evaluate forest bird population responses to mosquito suppression using the Incompatible Insect Technique.
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