Limitations of traditional insect sampling methods have motivated the development and optimisation of new non-lethal methods capable of quantifying diverse arthropod communities. Environmental DNA (eDNA) metabarcoding using arthropod-specific primers has recently been investigated as a novel way to characterise arthropod communities from the DNA they deposit on the surface of plants. This sampling method has had demonstrated success, but pollinators—especially bees—are oddly underrepresented in these studies. To evaluate this inconsistency, we investigated the limitations of eDNA metabarcoding for bees and other pollinators. We compared pollinator diversity derived from eDNA extracted from flowers and DNA extracted from pulverised bulk samples of insects collected from vane traps deployed at the same sites using three metabarcoding primers, two of which target arthropods generally (COI-Jusino and 16S-Marquina) and one that targets bumblebees (Bombus spp., COI-Milam). Across methods, we detected 77 insect families from 9 orders. The COI-Jusino marker amplified the highest taxonomic diversity compared to 16S-Marquina and COI-Milam. More amplicon sequence variants (ASVs) were recovered from vane traps (blue: 1357, yellow: 1542) than flowers (245), but only 23% of families and 13% of genera were shared among methods, indicating that flowers and blue and yellow vane traps may each sample different parts of the available arthropod community. Of 29 flower samples with known bee visitations, only 10 samples had bee detections from eDNA, and incomplete reference databases hindered assignment to species. Although our study provides additional evidence for the usefulness of eDNA metabarcoding for characterising arthropod communities, significant challenges remain when using eDNA metabarcoding methods to identify and quantify pollinator communities, especially bees.
","language":"English","doi":"10.1111/mec.70003","usgsCitation":"Jones, K., Pilliod, D.S., and Aunins, A.W., 2025, Metabarcoding analysis of arthropod pollinator diversity: A methodological comparison of eDNA derived from flowers and DNA derived from bulk samples of insects: Molecular Ecology, v. 34, no. 14, e70003, 17 p., https://doi.org/10.1111/mec.70003.","productDescription":"e70003, 17 p.","ipdsId":"IP-175536","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":492510,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/mec.70003","text":"Publisher Index Page"},{"id":492417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"14","noUsgsAuthors":false,"publicationDate":"2025-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Kara Suzanne 0000-0002-8168-0815","orcid":"https://orcid.org/0000-0002-8168-0815","contributorId":331477,"corporation":false,"usgs":true,"family":"Jones","given":"Kara Suzanne","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":943285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":943286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":943287,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269548,"text":"70269548 - 2025 - Anthropogenic activities have greatly altered mangroves over the last hundred years","interactions":[],"lastModifiedDate":"2026-02-10T13:48:58.794738","indexId":"70269548","displayToPublicDate":"2025-06-25T09:00:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Anthropogenic activities have greatly altered mangroves over the last hundred years","docAbstract":"Mangroves not only provide ecosystem and cultural services but also contribute to the mitigation of global warming. Mangrove dynamics and their environmental responses as re-constructed from the past can inform current mangrove conservation and restoration. However, our understanding of mangrove dynamics over the past century and the impact of human activities on these ecosystems remains limited. Using the quantified mangrove-derived organic carbon (MOC) contributions of seven sediment cores, we reconstructed the historical mangrove dynamics in Yingluo Bay and the Maowei Sea in tropical China dating back to 1900. The results indicated that the natural undisturbed mangroves in Yingluo Bay flourished in response to rising temperatures. In contrast, the significantly human-disturbed mangroves in Maowei Sea experienced a marked decline. Although both areas share similar natural conditions, intense anthropogenic disturbance reversed the natural potential for mangrove growth in the Maowei Sea. To explore the global prevalence of this phenomenon, we compiled data on mangrove pollen, δ13Corg, MOC, and mangrove area change from over 40 sites/regions worldwide, and re-constructed the natural and human-affected mangrove dynamics over the past century. Our findings indicated that, owing to the globally rising temperatures, natural undisturbed mangroves have gradually expanded as progressively more healthy forests, while human-disturbed mangroves exhibited three patterns: (1) continuous degradation, (2) flourishing-degradation, and (3) degradation-regeneration. Anthropogenic activities, such as seawall construction, aquaculture activity, agricultural expansion, logging, and urbanization have significantly reversed the natural health of mangroves such that any conservation and restoration strategy used for mangroves globally could inherently consider anthropogenic factors along with natural environmental change for better outcomes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2025.104950","usgsCitation":"Zhang, Y., Zhao, G., Krauss, K., Pan, L., Xu, Y., and Meng, X., 2025, Anthropogenic activities have greatly altered mangroves over the last hundred years: Global and Planetary Change, v. 253, 104950, 17 p., https://doi.org/10.1016/j.gloplacha.2025.104950.","productDescription":"104950, 17 p.","ipdsId":"IP-172933","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":492905,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"253","noUsgsAuthors":false,"publicationDate":"2025-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Yao","contributorId":358653,"corporation":false,"usgs":false,"family":"Zhang","given":"Yao","affiliations":[{"id":78354,"text":"China Geological Survey","active":true,"usgs":false}],"preferred":false,"id":944017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Guangming","contributorId":173535,"corporation":false,"usgs":false,"family":"Zhao","given":"Guangming","email":"","affiliations":[{"id":27244,"text":"Qingdao Institute of Marine Geology, China","active":true,"usgs":false}],"preferred":false,"id":944018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":223022,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":944019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pan, Lianghao","contributorId":358641,"corporation":false,"usgs":false,"family":"Pan","given":"Lianghao","affiliations":[{"id":85663,"text":"Guangxi Key Lab of Mangrove Conservation and Utilization, Guangxi Mangrove Research Center, Guangxi Academy of Marine Sciences, Beihai, China","active":true,"usgs":false}],"preferred":false,"id":944020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Yuanqin","contributorId":358642,"corporation":false,"usgs":false,"family":"Xu","given":"Yuanqin","affiliations":[{"id":85666,"text":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China","active":true,"usgs":false}],"preferred":false,"id":944021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meng, Xianwei","contributorId":358643,"corporation":false,"usgs":false,"family":"Meng","given":"Xianwei","affiliations":[{"id":85666,"text":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China","active":true,"usgs":false}],"preferred":false,"id":944022,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268395,"text":"sir20255040 - 2025 - Aquifer storage change and storage properties, Rio Rancho, New Mexico, 2019–23","interactions":[],"lastModifiedDate":"2025-06-25T13:56:15.826186","indexId":"sir20255040","displayToPublicDate":"2025-06-25T08:36:27","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5040","displayTitle":"Aquifer Storage Change and Storage Properties, Rio Rancho, New Mexico, 2019–23","title":"Aquifer storage change and storage properties, Rio Rancho, New Mexico, 2019–23","docAbstract":"To better understand changes in groundwater storage and groundwater elevations, the U.S. Geological Survey, in cooperation with the City of Rio Rancho, New Mexico, carried out a multiyear groundwater monitoring project. Groundwater-level data were collected at 27 locations, including sites having multiple wells screened at different depths and those having long-term records. A repeat microgravity network of 20 stations was established, and surveys were carried out three times per year. The microgravity method provides a direct, quantitative measurement of mass change caused by aquifer filling or draining. Data collected during the 2019–23 study period indicate generally stable groundwater conditions, with small fluctuations in groundwater levels (increasing at some wells, declining at others), and small declines in groundwater storage over the period of record at most gravity locations (average = −0.33 foot of water per year). The discrepancy between the water-level and microgravity data may have been caused by a loss of soil moisture in the unsaturated zone, which is as much as 1,000 feet thick in some areas. At the Rio Rancho Advanced Water Treatment Facility, where the city recharges water through direct injection, there may be seasonal correlations in storage related to injection but no longer-term accumulation of recharged water in the immediate vicinity of the facility, indicating water is moving efficiently into the aquifer.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Contact Us- USGS Publications Warehouse
","tableOfContents":"The report focuses on the screening of previously published concentration data associated with 12 elements of concern (aluminum, arsenic, cadmium, chromium, copper, iron, mercury, manganese, lead, selenium, uranium, and zinc) measured in stream surface waters of three hydrologic basins (Delaware River Basin, Illinois River Basin, and the Upper Colorado River Basin). The purpose of this analysis is to determine what subsets of the original dataset (containing more than 1,500,000 observations) may be most suitable for each of two types of modeling efforts. The first type of modeling envisions a machine learning approach to determine which geospatial attributes are most significant in describing the spatial distribution of elemental concentrations within a basin. The second type of modeling envisions a stepwise regression approach to develop multivariable models that can be used to determine high resolution time-series estimates of elemental concentrations or loads at discrete U.S. Geological Survey real-time stream surface water sites. These site-specific temporal models are based on continuous measurements of available discharge and (or) in situ sensor data (temperature, pH, turbidity, dissolved oxygen, specific conductance, and (or) fluorescent dissolved organic matter) as the explanatory variables. The data screening for both model types considered historical trends in analytical methods and detection quantitation limits, the extent of censored data, data density, and environmental relevance with respect to three U.S. Environmental Protection Agency water quality thresholds (drinking water guidelines, human health criteria, and aquatic life criteria). The result of this analysis was the production of a final list of potential models deemed suitable for further development based upon the data exclusion (or inclusion) scheme developed herein for each model type. In both cases, the final models included mostly the three crustal elements (iron, manganese, and aluminum) that are found at comparatively high concentrations in surface water, whereas most of the more pernicious elements were excluded from the final model lists owing to various data limitations. The one exception to this was arsenic, for which the existing data were sufficient at three U.S. Geological Survey real-time sites for potential further development of time-series models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251033","programNote":"Water Quality Program","usgsCitation":"Marvin-DiPasquale, M.C., McCleskey, R.B., Sullivan, S.L., Root, J.C., Seawolf, S.M., Ransom, K.M., Wherry, S.A., Kakouros, E., and Baesman, S., 2025, Select elements of concern in surface water of three hydrologic basins (Delaware River, Illinois River, and Upper Colorado River)—Data screening for the development of spatial and temporal models: U.S. Geological Survey Open-File Report 2025–1033, 25 p., https://doi.org/10.3133/ofr20251033.","productDescription":"Report: v, 25 p.; 2 Data Releases","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151463","costCenters":[{"id":37277,"text":"WMA - Earth System Processes 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Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Ave, Suite 1950
Portland, OR 97204
Thiamine (vitamin B1) deficiency in marine systems is a globally significant threat to marine life. In 2020, newly hatched Chinook salmon (Oncorhynchus tshawytscha) fry in California’s Central Valley (CCV) hatcheries swam in corkscrew patterns and died at unusually high rates due to a lack of this essential vitamin. We subsequently investigated the impacts and causes of thiamine deficiency in California’s anadromous salmonids. Our laboratory studies defined the relationship between thiamine concentrations in Chinook salmon eggs and early life-stage survival in offspring; we used these data to develop a model that estimated 26 to 48% thiamine-dependent fry mortality across consecutive years (2020–2021) for winter-run Chinook salmon. We established an egg surveillance effort that found widespread thiamine deficiency in CCV Chinook salmon in 2020 and 2021, and emerging thiamine deficiency in Klamath River and Trinity River coho salmon (Oncorhynchus kisutch) in 2021. We determined that thiamine injections into adults raised egg thiamine concentrations above levels found to impact early life-stage survival and swimming behavior. Ocean surveys, prey nutrition, salmon gut contents, and stable isotope data link thiamine deficiency to an ocean diet dominated by a booming population of northern anchovy (Engraulis mordax). This forage fish had low thiamine, high lipid, and high thiaminase activity levels consistent with both a thiaminase and oxidative stress hypothesis for causing thiamine deficiency in California salmon. Our research suggests California’s already stressed anadromous salmonids will continue to be impacted by thiamine deficiency as long as their ocean forage base and diet are dominated by northern anchovy.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2426011122","usgsCitation":"Mantua, N., Bell, H.M., Todgham, A.E., Daniels, M.E., Rinchard, J., Ludwig, J.R., Field, J., Lindley, S., Rowland, F.E., Richter, C.A., Walters, D., Finney, B., Haskell, A., Tillitt, D., Honeyfield, D.C., Lipscomb, T.N., Kwak, K., Kindopp, J., Cocherell, D.E., Ward, A., Williams, T.H., Harding, J., Fangue, N., Jeffres, C., Ruiz-Cooley, R., Litvin, S., Foott, S., Adkison, M., Kormos, B., Harte, P., Colwell, F.S., Suffridge, C., Shannon, K., Cranford, A., Ambrose, C., Reed, A.N., and Johnson, R.C., 2025, Widespread thiamine deficiency in California salmon linked to an anchovy-dominated marine prey base: Proceedings of the National Academy of Sciences, v. 122, no. 26, e2426011122, 12 p., https://doi.org/10.1073/pnas.2426011122.","productDescription":"e2426011122, 12 p.","ipdsId":"IP-168561","costCenters":[{"id":192,"text":"Columbia Environmental Research 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2025 - Automated methods for processing camera trap video data for distance sampling","interactions":[],"lastModifiedDate":"2025-11-13T16:59:28.553199","indexId":"70272025","displayToPublicDate":"2025-06-23T09:52:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2984,"text":"Pacific Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Automated methods for processing camera trap video data for distance sampling","docAbstract":"Context
Population monitoring is an essential need for tracking biodiversity and judging efficacy of conservation management actions, both globally and in the Pacific. However, population monitoring efforts are often temporally inconsistent and limited to small scales. Motion-activated cameras (‘camera traps’) offer a way to cost-effectively monitor populations, but they also generate large amounts of data that are time intensive to process.
Aims
To develop an automated pipeline for processing videos of ungulates (Philippine deer, Rusa marianna; and pigs, Sus scrofa) on Andersen Air Force Base in Guam.
Methods
We processed camera videos with a machine learning model for object detection and classification. To estimate density using distance sampling methods, we used a separate machine learning model to estimate the distance of target animals from the camera. We compared density estimates generated using manual versus automated methods and assessed accuracy and processing time saved.
Key results
The object detection and classification model achieved an overall accuracy >80% and F1 score ≥0.9 and saved 36.9 h of processing time. The automated distance estimation was fairly accurate, with a 1.1 m (±1.4 m) difference from manual distance estimates, and saved 16.8 h of processing time. Density estimates did not differ substantially between manual and automated distance estimation.
Conclusions
Machine learning models accurately processed camera videos, allowing efficient estimates of density from camera data.
Implications
Further adoption of motion-activated cameras coupled with automated processing could lead to continuous, large-scale monitoring of populations, helping to understand and address changes in biodiversity.
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Here we examine the presence, location and style of pocket beach rotation along the 1700 km coast of California using a comprehensive 22-year satellite-derived shoreline dataset. These analyses identify 23 pocket beaches that exhibit annual cycles of rotation, and these beaches have two general types. In southern California, pocket beaches rotate clockwise, or towards the south, in the winter season (‘winter southward’ transport of sand). These beaches have symmetric rotation patterns and strong seasonality in wave direction (winter west swell and summer south swell), which is indicative of rotation from seasonal oscillations in longshore sediment transport. In northern California, pocket beaches rotate counterclockwise, or towards the north, in the winter (‘winter northward’ transport of sand), and they are characterized by strong asymmetry (winter beach is overall narrower than the summer beach) and strong seasonality in wave power. Rotation of these northern California beaches is related to both cross-shore and longshore sediment transport, caused by large west-to-northwest swell of the winter and smaller northwest wind waves of the summer. We acknowledge that many more rotating pocket beaches likely exist in California owing to the undersampling of the smallest beaches in the source data. In the end, we conclude that seasonally rotating pocket beaches are a fundamental coastal landform type of the California coast, owing to its wave seasonality and rocky and cliff-backed morphology.","language":"English","publisher":"British Society for Geomorphology","doi":"10.1002/esp.70115","usgsCitation":"Warrick, J.A., Buscombe, D.D., Vos, K., Ritchie, A., and Battalio, B., 2025, Seasonal rotation of California pocket beaches: Earth Surface Processes and Landforms, v. 50, no. 8, e70115, 21 p., https://doi.org/10.1002/esp.70115.","productDescription":"e70115, 21 p.","ipdsId":"IP-174144","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491464,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.70115","text":"Publisher Index Page"},{"id":491194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.53754214833282,\n 42.01939137685051\n ],\n [\n -124.58232174888042,\n 40.669027141445056\n ],\n [\n -123.78043869005253,\n 38.57555641017706\n ],\n [\n -122.29825799326892,\n 36.337324446738684\n ],\n [\n -117.97924443937916,\n 32.361668474994005\n ],\n [\n -116.72630257932812,\n 32.61972232338513\n ],\n [\n -121.30393894039285,\n 36.51381193501618\n ],\n [\n -123.36405835149245,\n 39.878218221096176\n ],\n [\n -123.38617364905127,\n 41.9363016901882\n ],\n [\n -124.53754214833282,\n 42.01939137685051\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":941189,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vos, Kilian 0000-0002-9518-1582","orcid":"https://orcid.org/0000-0002-9518-1582","contributorId":229435,"corporation":false,"usgs":false,"family":"Vos","given":"Kilian","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":941190,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ritchie, Andrew C. 0000-0001-5826-9983","orcid":"https://orcid.org/0000-0001-5826-9983","contributorId":333630,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941191,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Battalio, Bob","contributorId":357321,"corporation":false,"usgs":false,"family":"Battalio","given":"Bob","affiliations":[{"id":85409,"text":"Consulting Coastal Engineer, Pacifica, California, USA","active":true,"usgs":false}],"preferred":false,"id":941192,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268393,"text":"70268393 - 2025 - A method to obtain remotely sensed grain size distributions from nonplanar granular deposits","interactions":[],"lastModifiedDate":"2025-06-24T14:50:40.182916","indexId":"70268393","displayToPublicDate":"2025-06-21T09:42:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"A method to obtain remotely sensed grain size distributions from nonplanar granular deposits","docAbstract":"Constraining the grain size distribution of granular deposits with complex surfaces is difficult with existing approaches. Field and laboratory techniques are time consuming and limited by the maximum grain size that laboratories can accommodate. In this study, we present a new method to identify the coarse fraction of the grain size distribution at a debris-flow fan deposit surveyed with terrestrial laser scanning (TLS) in Glenwood Canyon, Colorado, USA. This method is a novel grain segmentation algorithm developed for application to point cloud data of deposits with complex surfaces and angular grains ranging in size from centimeters to a meter. This approach combines an existing random forest machine learning method with a novel iterative clustering algorithm. We compared the grain size distribution from our algorithm with a Wolman pebble count conducted in the field, and found a root mean squared error of less than 2 cm from the 5th to 95th percentile of the grain size distribution of grains ranging from cobble to boulder sized (6.3–78 cm in our application). Finally, we compared our new algorithm with an existing open-source grain segregation algorithm, and our method outperformed the selected alternative when applied to the debris-flow deposit point cloud.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025EA004376","usgsCitation":"Jacobson, H., Walton, G., Barnhart, K.R., and Rengers, F.K., 2025, A method to obtain remotely sensed grain size distributions from nonplanar granular deposits: Earth and Space Science, v. 12, e2025EA004376, 18 p., https://doi.org/10.1029/2025EA004376.","productDescription":"e2025EA004376, 18 p.","ipdsId":"IP-159354","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":491499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025ea004376","text":"Publisher Index Page"},{"id":491193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado River, Grizzly Creek Fire area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.03314321384126,\n 39.68363920671021\n ],\n [\n -107.34507070999592,\n 39.68363920671021\n ],\n [\n -107.34507070999592,\n 39.525758441449966\n ],\n [\n -107.03314321384126,\n 39.525758441449966\n ],\n [\n -107.03314321384126,\n 39.68363920671021\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Jacobson, Hayden L. 0000-0003-4777-6626","orcid":"https://orcid.org/0000-0003-4777-6626","contributorId":357323,"corporation":false,"usgs":false,"family":"Jacobson","given":"Hayden L.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":941193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton, Gabriel 0000-0002-9214-0021","orcid":"https://orcid.org/0000-0002-9214-0021","contributorId":357324,"corporation":false,"usgs":false,"family":"Walton","given":"Gabriel","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":941194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":941195,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":941196,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273158,"text":"70273158 - 2025 - Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration","interactions":[],"lastModifiedDate":"2025-12-17T16:03:03.075808","indexId":"70273158","displayToPublicDate":"2025-06-20T09:54:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration","docAbstract":"Often found along the estuarine-marine interface, barrier islands and mainland coastal zones are shaped by tides, currents, extreme storms, and relative sea-level rise. These systems provide ecosystem services such as storm surge and wave attenuation, erosion protection to inland areas, habitat for fish and wildlife, recreation, and tourism. Given the importance of these ecosystems coupled with their dynamic nature, information on how these coastal systems are changing can help to inform natural resource management. Remote sensing advancements have led to an abundance of data for monitoring change in coastal settings. This study developed a multiscale framework that can provide trajectory information from screening-level analyses by using existing or custom moderate spatial resolution land cover maps. Using the north-central Gulf Coast as a case study, the trajectory of land cover area for barrier islands and mainland coastal zones was assessed using several geospatial data sets, including: (1) long-term moderate-resolution remote sensing products with an annual (or more frequent) temporal frequency; (2) a restoration database (e.g., beach/dune restoration, sediment placement, and dune enhancement); and (3) a tropical storm database. Due to the coarser spatial resolution of data sets used for screening-level analyses, detailed or application-specific analyses are often needed to reduce uncertainty in smaller changes that may not be captured. These may include land cover change analyses (i.e. this study), periodic land cover maps with higher spatial resolution and more detailed land cover classes, or elevation-related analyses (e.g., dune change or inundation change). Using this framework, abrupt changes in land cover on Dauphin Island, Alabama, resulting from extreme storms were detected using moderate spatial resolution screening-level data, while restoration impact analyses may require higher resolution data. Further, land cover change analyses that incorporate change allocation provide robust information for understanding land cover change in dynamic coastal settings.
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While geophysical methods have a long history in resource exploration and studies of Earth’s interior, the subdiscipline of “near-surface geophysics” has evolved in recent decades for examination of the shallow, near-surface environment for a range of purposes ranging from archaeological or forensic investigations to assessment of geologic, hydrologic, biologic, and geochemical properties and processes. “Environmental geophysics” are near-surface geophysical studies and methods that focus on understanding natural systems (e.g., watershed hydrology, groundwater–surface water connections, biophysical processes) as well as research pertaining to anthropogenic impacts and land management, (e.g., contamination and remediation, saltwater intrusion, agricultural practices). This field can be further subdivided into subdisciplines focused on specific topics and applications, such as water resources and hydrology (hydrogeophysics) or biologic and microbial processes (biogeophysics). Studies in environmental geophysics span a range of scales, from pore-scale laboratory tests to watershed-scale or regional field experiments. Methods vary by the nature of physics employed, the specific measurement acquired, and how that data is ultimately processed and analyzed to produce interpretable results. There exists further diversity in the acquisition logistics, geometry, and timing of data collection. Geophysical data can be collected in boreholes (one-dimensional, 1-D, vertical profiles), along survey lines (two-dimensional, 2-D, cross-sections), or in dense sensor arrays or gridded profiles (three-dimensional, 3-D, models). Regarding the temporal aspect, studies can conduct one-time geophysical surveys to obtain detailed imaging of subsurface structure or use timelapse and continuous monitoring to investigate variations in subsurface properties over time. The cumulation of all possible permutations of these factors (method, acquisition geometry, survey design, and target application) results in an immense diversity among environmental geophysical studies. Nevertheless, this field remains unified in the pursuit of understanding natural and human-impacted near-surface environments through geophysical investigations. Here we highlight some key references within environmental geophysics. Resources on geophysical theory, acquisition logistics, processing and inversion workflows, and example case studies are categorized into the most common geophysical classes within Geophysical Methods. Lastly, example references for the dominant types of applications in environmental geophysical studies are catalogued in Environmental Applications.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oxford Bibliographies","largerWorkSubtype":{"id":11,"text":"Bibliography"},"language":"English","publisher":"Oxford University Press","doi":"10.1093/obo/9780199363445-0146","usgsCitation":"James, S.R., Glaser, D.R., and Garcia, A., 2025, Near-surface geophysics: Environmental applications, chap. of Oxford Bibliographies, HTML Document, https://doi.org/10.1093/obo/9780199363445-0146.","productDescription":"HTML Document","ipdsId":"IP-172909","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"James, Stephanie R. 0000-0001-5715-253X","orcid":"https://orcid.org/0000-0001-5715-253X","contributorId":260620,"corporation":false,"usgs":true,"family":"James","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":958466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glaser, Dan R.","contributorId":292710,"corporation":false,"usgs":false,"family":"Glaser","given":"Dan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":958467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Alejandro","contributorId":369112,"corporation":false,"usgs":false,"family":"Garcia","given":"Alejandro","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":958468,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273971,"text":"70273971 - 2025 - Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales","interactions":[],"lastModifiedDate":"2026-02-20T17:06:56.875893","indexId":"70273971","displayToPublicDate":"2025-06-19T09:57:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales","docAbstract":"The boreal ecosystem has experienced significant changes over recent decades as wildfires become more frequent, intense, and severe. As streams are highly prevalent and ecologically relevant, understanding interactions among wildfire and hydrologic patterns is important for effective aquatic ecosystem management. This study used a Bayesian mixture model to classify streamflow regimes from modeled streamflow data for 32,730 stream reaches (totaling 295,880 km) across the Yukon and Kuskokwim basins and the Northwestern Boreal Ecosystem in Alaska, USA, and Yukon Territory, Canada. We assessed time since burn and calculated the total length of stream (km) within burn perimeters for each streamflow class from 1985 to 2015. Additionally, we used field observations (2018–2022) to compare streamflow regimes in four burned and four unburned headwater streams (drainage basins ≤150 km2) in interior Alaska. Modeled stream reaches were grouped into twenty-two classes and reduced to eleven metaclasses based on similarities in streamflow statistics. These metaclasses formed two broad groups: 1) large rivers with lower variability and strong seasonal signals, and 2) mid- to small-sized tributaries with high variability, frequent high flow events, and weaker seasonal signals. The stream length burned analysis indicated an average increase of 47 km per year with first- and second-order streams experiencing more frequent fire. Empirical streamflow metrics from headwater stream gages revealed additional differences in streamflow patterns between burned and unburned streams. This streamflow classification establishes a baseline for understanding boreal stream responses to wildfire, detecting climate-induced regime shifts, and facilitating management and conservation of important boreal aquatic species.
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Estimates of daily domestic and CII per capita deliveries were generated from these models for approximately 18,800 water service areas, covering most public water systems in the CONUS. Domestic delivery was found to be lowest in the midwestern region and higher in the southern and southwest regions of the United States. Results indicate that climate and land use can be associated with regional differences in DPC delivery. Population metrics and land use were identified as significant contributors to CII delivery estimates.
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domartin@usgs.gov","orcid":"https://orcid.org/0000-0001-5913-2372","contributorId":4450,"corporation":false,"usgs":true,"family":"Martin","given":"Donald","email":"domartin@usgs.gov","affiliations":[],"preferred":true,"id":941110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buchwald, Cheryl A. 0000-0001-8968-5023 cabuchwa@usgs.gov","orcid":"https://orcid.org/0000-0001-8968-5023","contributorId":1943,"corporation":false,"usgs":true,"family":"Buchwald","given":"Cheryl","email":"cabuchwa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dieter, Cheryl A. 0000-0002-5786-4091","orcid":"https://orcid.org/0000-0002-5786-4091","contributorId":220502,"corporation":false,"usgs":true,"family":"Dieter","given":"Cheryl A.","affiliations":[],"preferred":true,"id":941112,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luukkonen, Carol L. 0000-0001-7056-8599","orcid":"https://orcid.org/0000-0001-7056-8599","contributorId":208181,"corporation":false,"usgs":true,"family":"Luukkonen","given":"Carol","email":"","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941113,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stewart, Jana S. 0000-0002-8121-1373","orcid":"https://orcid.org/0000-0002-8121-1373","contributorId":211037,"corporation":false,"usgs":true,"family":"Stewart","given":"Jana","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941114,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Paulinski, Scott 0000-0001-6548-8164","orcid":"https://orcid.org/0000-0001-6548-8164","contributorId":357291,"corporation":false,"usgs":false,"family":"Paulinski","given":"Scott","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":941115,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, Lisa D. 0000-0002-3523-0768 ldmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-3523-0768","contributorId":1125,"corporation":false,"usgs":true,"family":"Miller","given":"Lisa","email":"ldmiller@usgs.gov","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941116,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941117,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70270396,"text":"70270396 - 2025 - Biological implications for contaminants of emerging concern in the Great Lakes–Upper St Lawrence River drainage: An effect-based ecological hazard assessment in fish","interactions":[],"lastModifiedDate":"2025-11-20T16:51:50.588392","indexId":"70270396","displayToPublicDate":"2025-06-18T08:43:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Biological implications for contaminants of emerging concern in the Great Lakes–Upper St Lawrence River drainage: An effect-based ecological hazard assessment in fish","docAbstract":"Contaminants of emerging concern (CECs) are released widely and continuously into the Great Lakes Basin–Upper St Lawrence River study area, with many detected in surface water at concentrations known to adversely affect fish. We applied a recent ecological hazard assessment methodology to identify the biological significance of a database of 21,441 surface water CEC concentrations compiled from 7,162 surface water samples collected at 1,021 sampling sites in 387 individual waterbodies throughout the Great Lakes Basin. We assessed hazard to fish in 12 effect categories (e.g., mortality, developmental, reproductive) from aqueous exposure to 16 emerging contaminants. Our hazard assessment used pairs of screening values to generate contaminant- and effect-specific ordinal hazard scores. Using this novel methodology, we generated a database of 93,864 hazard scores. We found the highest level of hazard to fish, indicating probable adverse impacts, was broadly distributed and often associated with municipalities. Mortality, reproductive, and developmental effect categories combined accounted for 17.5% of high hazard observations. Low hazard, indicating possible adverse effects, was prevalent for numerous effect categories and occurred throughout the period 1991–2021. For mortality, reproductive, and developmental effect categories, the incidence of elevated hazard (low or high hazard) among assessed water samples was 20.4%, 39.5%, and 20.3%, respectively. On a local scale, effect-based assessment is an efficient and conceptually simple tool for natural resource managers to obtain effect- and site-specific hazard information concerning CEC effects in fish that can be used in project planning and results interpretation for natural resource monitoring, restoration, and protection.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/etojnl/vgaf147","usgsCitation":"Gefell, D.J., Bellamy, A.R., Kiesling, R.L., Elliott, S.M., and Hummel, S.L., 2025, Biological implications for contaminants of emerging concern in the Great Lakes–Upper St Lawrence River drainage: An effect-based ecological hazard assessment in fish: Environmental Toxicology and Chemistry, v. 44, no. 10, p. 3004-3023, https://doi.org/10.1093/etojnl/vgaf147.","productDescription":"20 p.","startPage":"3004","endPage":"3023","ipdsId":"IP-147082","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":494969,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93MA2EU","text":"USGS data release","linkHelpText":"Compilation of contaminant of emerging concern concentrations (1991 - 2021) and associated hazard scores for assessment of potential hazard to fish in the Great Lakes Basin"},{"id":494295,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes–Upper St Lawrence River study area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.12093747053447,\n 50.24282738097176\n ],\n [\n -93.18555897072864,\n 46.549737302479485\n ],\n [\n -89.35016735282879,\n 41.120841480697436\n ],\n [\n -81.2766505886645,\n 40.818536101438454\n ],\n [\n -73.52805108109916,\n 43.298089640713215\n ],\n [\n -68.77067561630659,\n 48.516842742090205\n ],\n [\n -77.03461005100947,\n 47.16888316144038\n ],\n [\n -86.53622259614193,\n 49.82835744099062\n ],\n [\n -90.12093747053447,\n 50.24282738097176\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Gefell, Daniel J.","contributorId":138671,"corporation":false,"usgs":false,"family":"Gefell","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":946302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellamy, Amber R","contributorId":265773,"corporation":false,"usgs":false,"family":"Bellamy","given":"Amber","email":"","middleInitial":"R","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":946304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":946303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":946305,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hummel, Stephanie L.","contributorId":359795,"corporation":false,"usgs":false,"family":"Hummel","given":"Stephanie","middleInitial":"L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":946306,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268250,"text":"70268250 - 2025 - Mixed natal origins present management challenges for a non-native fish established throughout a modified river network","interactions":[],"lastModifiedDate":"2025-07-10T14:56:29.491021","indexId":"70268250","displayToPublicDate":"2025-06-18T08:25:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Mixed natal origins present management challenges for a non-native fish established throughout a modified river network","docAbstract":"Expansion of non-native brown trout (Salmo trutta) in the Colorado River below Glen Canyon Dam motivated reevaluation of suppression strategies to minimize potential impacts to native fishes in the Grand Canyon, Arizona, USA. Brown trout are one of several non-native fish species of management concern in this river reach, and understanding their natal sources and movement patterns may assist managers in planning suppression strategies. We identified trace elements in brown trout otoliths, which, when coupled with location-specific water chemistry data, identified brown trout natal origins over 19 years. Strontium and manganese concentrations revealed distinct emigration patterns from natal tributary streams and the mainstem Colorado River over two periods. Adult brown trout collected from throughout our study area showed mixed tributary and mainstem natal origins, which persisted during suppression efforts in a known spawning tributary. Unexpectedly, we found evidence of brown trout reproduction in the Colorado River for at least a decade before documentation through field monitoring. Our findings may inform but complicate the development of management strategies for system-wide brown trout suppression.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0267","usgsCitation":"Akland, M., Limburg, K., Healy, B.D., and Pine, W.E., 2025, Mixed natal origins present management challenges for a non-native fish established throughout a modified river network: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-13, https://doi.org/10.1139/cjfas-2024-0267.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-168922","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":490920,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.63077077053144,\n 36.80786020404963\n ],\n [\n -111.65750425155792,\n 36.81141589124036\n ],\n [\n -111.76698028633918,\n 36.68345426369245\n ],\n [\n -111.88230163058687,\n 36.503023995019646\n ],\n [\n -111.84019781092644,\n 36.499299737249814\n ],\n [\n -111.68048884893217,\n 36.68934775789845\n ],\n [\n -111.63077077053144,\n 36.80786020404963\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Akland, Michael K.","contributorId":357023,"corporation":false,"usgs":false,"family":"Akland","given":"Michael K.","affiliations":[{"id":85311,"text":"Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, New York, USA","active":true,"usgs":false}],"preferred":false,"id":940600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Limburg, Karin E.","contributorId":356369,"corporation":false,"usgs":false,"family":"Limburg","given":"Karin E.","affiliations":[{"id":33387,"text":"SUNY-ESF","active":true,"usgs":false}],"preferred":false,"id":940601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Healy, Brian D. 0000-0002-4402-638X","orcid":"https://orcid.org/0000-0002-4402-638X","contributorId":304257,"corporation":false,"usgs":true,"family":"Healy","given":"Brian","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":940602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pine, William E. III","contributorId":139959,"corporation":false,"usgs":false,"family":"Pine","given":"William","suffix":"III","email":"","middleInitial":"E.","affiliations":[{"id":13332,"text":"Uni. of Florida Department of Wildlife Ecology and Conservation","active":true,"usgs":false}],"preferred":false,"id":940603,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269667,"text":"70269667 - 2025 - Fine-scale spatial risk models to predict avian collisions with power lines","interactions":[],"lastModifiedDate":"2025-08-19T15:31:40.484785","indexId":"70269667","displayToPublicDate":"2025-06-18T08:12:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fine-scale spatial risk models to predict avian collisions with power lines","docAbstract":"1. Avian fatalities caused by collisions with overhead power lines are an important conservation issue worldwide. Although mitigation strategies can help reduce mortalities, given their considerable cost and the vast scale of power line infrastructure, cost-effective action requires that these efforts be prioritised to areas with the highest potential risk to birds. To date, this risk assessment has usually been guided by potentially biased information on the location of recorded fatalities.
2. Here we use five years of GPS tracking data from endangered Tasmanian wedge-tailed eagles to develop an alternative approach to risk assessment: fine-scale spatial risk models based on behavioural analyses. We built and cross-validated a model that generates spatially explicit predictions of the probability that eagles would cross power lines at hazardous altitudes throughout the entire Tasmanian electricity distribution network.
3. In our model, probability of power line crossings was most strongly associated with the proportion of forest edges, wet forest, open habitat, freshwater sources, and rural residential developments in the area surrounding the power lines. Cross-validation indicated that the model effectively predicted where Tasmanian wedge-tailed eagles cross power lines at low altitude.
4. Model validation suggested our approach was a powerful predictor of the locations of power line collisions involving eagles. The locations of almost all (94%) confirmed eagle fatalities were in the half of the total Tasmanian power line area assigned the higher risk by the model, and 50% of incidents occurred in the 30% of the power line area estimated to be highest risk.
5. Synthesis and applications. Our study illustrates a framework for using bird movement data to provide insights into avian behaviour and the risk they encounter around power line infrastructure. Electricity delivery industries can use these models to identify the electrical infrastructure that poses the highest risk to avian survival and prioritise mitigation efforts, thereby optimizing the benefit of investments to reduce detrimental effects on biodiversity. Our model can direct pre-emptive mitigation across Tasmania’s 20,310 km of distribution infrastructure to meet management targets aiming to reduce the negative effects of power lines on the Tasmanian wedge-tailed eagle.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.70076","usgsCitation":"Pay, J.M., Cameron, E.Z., Hawkins, C.E., Johnson, C., Koch, A.J., Wiersma, J., and Katzner, T., 2025, Fine-scale spatial risk models to predict avian collisions with power lines: Journal of Applied Ecology, v. 62, no. 8, p. 1820-1830, https://doi.org/10.1111/1365-2664.70076.","productDescription":"11 p.","startPage":"1820","endPage":"1830","ipdsId":"IP-163294","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":493113,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":493326,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.70076","text":"Publisher Index Page"}],"country":"Australia","otherGeospatial":"Tasmania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 143.86088831282336,\n -40.405798345451885\n ],\n [\n 143.86088831282336,\n -44.063653865697944\n ],\n [\n 149.28385412411444,\n -44.063653865697944\n ],\n [\n 149.28385412411444,\n -40.405798345451885\n ],\n [\n 143.86088831282336,\n -40.405798345451885\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Pay, James M.","contributorId":245078,"corporation":false,"usgs":false,"family":"Pay","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":944335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cameron, Elissa Z.","contributorId":245084,"corporation":false,"usgs":false,"family":"Cameron","given":"Elissa","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":944336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawkins, Clare E.","contributorId":245079,"corporation":false,"usgs":false,"family":"Hawkins","given":"Clare","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":944337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Christopher","contributorId":334072,"corporation":false,"usgs":false,"family":"Johnson","given":"Christopher","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":944338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koch, Amelia J.","contributorId":245080,"corporation":false,"usgs":false,"family":"Koch","given":"Amelia","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":944339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wiersma, Jason M.","contributorId":358878,"corporation":false,"usgs":false,"family":"Wiersma","given":"Jason M.","affiliations":[{"id":85698,"text":"Forest Practices Authority, 30 Patrick St, Hobart, TAS, AustraliaForest Practices Authority, 30 Patrick St, Hobart, TAS, Australia","active":true,"usgs":false}],"preferred":false,"id":944340,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":944341,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268377,"text":"70268377 - 2025 - Borehole geophysical time-series logging to monitor passive ISCO treatment of residual chlorinated-ethenes in a confining bed, NAS Pensacola, Florida","interactions":[],"lastModifiedDate":"2025-06-24T14:49:19.213209","indexId":"70268377","displayToPublicDate":"2025-06-18T07:43:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21827,"text":"Hydrology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Borehole geophysical time-series logging to monitor passive ISCO treatment of residual chlorinated-ethenes in a confining bed, NAS Pensacola, Florida","docAbstract":"In-situ chemical oxidation (ISCO) is a common method to remediate chlorinated ethene contaminants in groundwater. Monitoring the effectiveness of ISCO can be hindered because of insufficient observations to assess oxidant delivery. Advantageously, potassium permanganate, one type of oxidant, provides the opportunity to use its strong electrical signal as a surrogate to track oxidant delivery using time-series borehole geophysical methods, like electromagnetic (EM) induction logging. Here we report a passive ISCO (P-ISCO) experiment, using potassium permanganate cylinders emplaced in boreholes, at a chlorinated ethene contamination site, Naval Air Station Pensacola, Florida. The contaminants are found primarily at the base of a shallow sandy aquifer in contact with an underlying silty-clay confining bed. We used results of the time-series borehole logging collected between 2017 and 2022 in 4 monitoring wells to track oxidant delivery. The EM-induction logs from the monitoring wells showed an increase in EM response primarily along the contact, likely from pooling of the oxidant, during P-ISCO treatment in 2021. Interestingly, concurrent natural gamma-ray (NGR) logging showed a decrease in NGR response at 3 of the 4 wells possibly from the formation of manganese precipitates coating sediments. The coupling of time-series logging and well-chemistry data allowed for an improved assessment of passive ISCO treatment effectiveness.
","language":"English","publisher":"MDPI","doi":"10.3390/hydrology12060155","usgsCitation":"Harte, P., Singletary, M., and Landmeyer, J., 2025, Borehole geophysical time-series logging to monitor passive ISCO treatment of residual chlorinated-ethenes in a confining bed, NAS Pensacola, Florida: Hydrology Journal, v. 12, no. 6, 155, 21 p., https://doi.org/10.3390/hydrology12060155.","productDescription":"155, 21 p.","ipdsId":"IP-172305","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":491498,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrology12060155","text":"Publisher Index Page"},{"id":491184,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","city":"Pensacola","otherGeospatial":"NAS Pensacola","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.35238712915951,\n 30.378828812173666\n ],\n [\n -87.35238712915951,\n 30.32983549008638\n ],\n [\n -87.23991634365869,\n 30.32983549008638\n ],\n [\n -87.23991634365869,\n 30.378828812173666\n ],\n [\n -87.35238712915951,\n 30.378828812173666\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Harte, Philip 0000-0002-7718-1204","orcid":"https://orcid.org/0000-0002-7718-1204","contributorId":217273,"corporation":false,"usgs":true,"family":"Harte","given":"Philip","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Singletary, Michael A.","contributorId":357307,"corporation":false,"usgs":false,"family":"Singletary","given":"Michael A.","affiliations":[{"id":85401,"text":"U.S. Navy Facilities Command, Southeast","active":true,"usgs":false}],"preferred":false,"id":941146,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landmeyer, James E. 0000-0002-5640-3816","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":346430,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James E.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941147,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274278,"text":"70274278 - 2025 - Considerations for using tag-returns to monitor targeted removal of invasive fishes","interactions":[],"lastModifiedDate":"2026-03-24T16:46:08.980961","indexId":"70274278","displayToPublicDate":"2025-06-18T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Considerations for using tag-returns to monitor targeted removal of invasive fishes","docAbstract":"Objective
Targeted removals are used for management of some invasive fish populations. Tag–return studies are one approach that can be used to assess the efficacy of targeted removals. However, there are many decisions to make when designing a tag–return study. We used simulation modeling to outline general guidelines for consideration when designing efficient tag–return studies to measure annual removal rates of invasive fish, particularly invasive carps.
Methods
We simulated data sets using scenarios with varying numbers of fish tagged per year, removal rates, tag reporting rates, tag retention rates, and study durations. We generated the data sets under a set of “known” parameters with added stochasticity; we then fitted the simulated data sets to a Bayesian tag–return model and measured the precision and accuracy of the model-estimated removal rates.
Results
We found that the model was able to predict removal rates without bias for most of the scenarios. However, we did find patterns in the precision of the predictions that could help to inform tag–return studies. When the proportion of the population removed through harvest was constant, the proportion of the population removed per year and the probability that harvested tags were reported had the largest effect on precision. The number of tags released per year and the study duration also had moderate effects. For scenarios testing the ability of the model to predict removal rates in stochastic populations, the precision of the model was primarily influenced by the number of fish tagged, the underlying nature of the stochasticity, and whether fish were tagged during the year of the prediction.
Conclusions
Based on our simulations, we outline how study objectives, the underlying population variability, and the tolerance range for error can guide decisions regarding the number of fish to tag, how to monitor tag return rates, and how long to conduct a study.
","language":"English","publisher":"American Fisheries Society","doi":"10.1093/najfmt/vqaf049","usgsCitation":"Stanton, J.C., Marcek, B.J., and Brey, M.K., 2025, Considerations for using tag-returns to monitor targeted removal of invasive fishes: North American Journal of Fisheries Management, v. 45, no. 4, p. 669-683, https://doi.org/10.1093/najfmt/vqaf049.","productDescription":"15 p.","startPage":"669","endPage":"683","ipdsId":"IP-164064","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":501964,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13DJCBK","text":"USGS data release","linkHelpText":"Code release for simulated tag-return study for monitoring invasive fish removals"},{"id":501681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/najfmt/vqaf049","text":"Publisher Index Page"},{"id":501474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":957555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marcek, Benjamin J.","contributorId":367732,"corporation":false,"usgs":false,"family":"Marcek","given":"Benjamin","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":957556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":957557,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268060,"text":"sir20255051 - 2025 - Estimated hydrogeologic, spatial, and temporal distribution of self-supplied domestic groundwater withdrawals for aquifers of the Virginia Coastal Plain","interactions":[],"lastModifiedDate":"2025-08-14T19:36:34.867035","indexId":"sir20255051","displayToPublicDate":"2025-06-17T10:40:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5051","displayTitle":"Estimated Hydrogeologic, Spatial, and Temporal Distribution of Self-Supplied Domestic Groundwater Withdrawals for Aquifers of the Virginia Coastal Plain","title":"Estimated hydrogeologic, spatial, and temporal distribution of self-supplied domestic groundwater withdrawals for aquifers of the Virginia Coastal Plain","docAbstract":"Water use from private-domestic wells accounts for nearly 40 percent of total groundwater withdrawals in the Virginia Coastal Plain Physiographic Province (henceforth called the Virginia Coastal Plain). However, because self-supplied domestic water use generally falls below the Virginia Department of Environmental Quality (VDEQ) reporting and management threshold of 300,000 gallons per month, quantifying these withdrawals is challenging. This report builds upon the foundation of previous U.S. Geological Survey investigations by providing revised techniques to improve estimates of the aquifer source, spatial distribution, and monthly magnitude of these groundwater withdrawals.
The aquifer sources of private-domestic wells in the Virginia Coastal Plain were estimated by cross-referencing 8,264 well records from the VDEQ and the Virginia Department of Health to a digital model of the Virginia Coastal Plain hydrogeologic framework. This analysis highlights the regional importance of the Yorktown-Eastover, Potomac, and surficial aquifers. Collectively, these three aquifers account for 80 percent of self-supplied domestic groundwater withdrawals.
The population using self-supplied domestic water was estimated using census blocks, well-use ratios, building footprints, and land-use and land-cover data to produce a high-resolution, disaggregated, raster-based dataset. This approach improves upon previous models at the census-block or road-network scale by reducing the low-density spread of the self-supplied domestic population across undeveloped areas and concentrating the population and its corresponding water use in the areas where it is most likely to occur. Results show that an estimated 475,332 people comprise the 2020 self-supplied domestic population of the Virginia Coastal Plain, an increase of 5.7 percent since 2010, and the greatest concentrations of self-supplied domestic population surround large cities. Estimates could be further refined with the addition of current and complete spatial data on public water-system service areas.
The quantity of water used by the self-supplied domestic population was estimated by modifying published state per-capita water-use coefficients with the corresponding monthly variability assessed from Virginia Coastal Plain public water-system withdrawal data. This analysis estimates an average increase of 12 percent from June through August and an average decrease of 8 percent from December through March from the baseline annual average of 80 gallons per day per capita, which generally matches similar studies in the eastern United States.
The application of these revised methodologies for the estimation of private-domestic wells and the self-supplied domestic population improves understanding of domestic groundwater use in the Virginia Coastal Plain across hydrogeologic, spatial, and temporal scales. These revisions help better inform water-resource managers and decision makers and support higher resolution groundwater modeling. Furthermore, these methods are transferrable to other areas where self-supplied domestic water withdrawals are important to the overall water budget.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255051","isbn":"978-1-4113-4609-3","collaboration":"Prepared in cooperation with Virginia Department of Environmental Quality","usgsCitation":"Kearns, M.R., and Pope, J.P., 2025, Estimated hydrogeologic, spatial, and temporal distribution of self-supplied domestic groundwater withdrawals for aquifers of the Virginia Coastal Plain: U.S. Geological Survey Scientific Investigations Report 2025–5051, 45 p., https://doi.org/10.3133/sir20255051.","productDescription":"Report: vii, 45 p.; Data release","numberOfPages":"45","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-168850","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":494146,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118651.htm","linkFileType":{"id":5,"text":"html"}},{"id":490433,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QJQ2CB","text":"USGS data release","linkHelpText":"Estimated aquifer distribution for private domestic wells; estimated spatial distribution of the self-supplied domestic population for 2020 and 2010; and estimated monthly domestic self-supplied withdrawals of groundwater for the Virginia Coastal Plain"},{"id":490432,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5051/images/"},{"id":490431,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5051/sir20255051.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5051 XML"},{"id":490428,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5051/coverthb.jpg"},{"id":490429,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5051/sir20255051.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5051 PDF"},{"id":490430,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255051/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5051 HTML"}],"country":"United States","state":"Virginia","otherGeospatial":"Virginia Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.32710341724686,\n 36.556784944765795\n ],\n [\n -75.86947557927473,\n 36.55265452745853\n ],\n [\n -75.8900431225546,\n 37.161549677245205\n ],\n [\n -75.32957756818404,\n 38.02121666772172\n ],\n [\n -76.21912381502888,\n 37.907709637048185\n ],\n [\n -76.67160976718064,\n 38.146680392024706\n ],\n [\n -77.04182554621384,\n 38.31631983320398\n ],\n [\n -77.04182554621384,\n 38.40904986490523\n ],\n [\n -77.23721720737078,\n 38.34052172806261\n ],\n [\n -77.30406172302983,\n 38.39293143681613\n ],\n [\n -77.18579834917193,\n 38.64236292442064\n ],\n [\n -77.00583234547452,\n 38.722640223692224\n ],\n [\n -77.03717799709146,\n 38.83873960192227\n ],\n [\n -77.1282913174048,\n 38.95928536652022\n ],\n [\n -77.23580503537455,\n 38.99328476178536\n ],\n [\n -77.29958450463103,\n 39.054158449342225\n ],\n [\n -77.6385260561967,\n 39.00744529354114\n ],\n [\n -77.6421696520891,\n 38.07245238381515\n ],\n [\n -77.5875012530419,\n 36.77147021603372\n ],\n [\n -77.58567879299983,\n 36.54488432804379\n ],\n [\n -76.32710341724686,\n 36.556784944765795\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
Yes. Equilibrium shoreline models, which simulate wave-driven cross-shore erosion and accretion, are mathematically equivalent to a discrete convolution (i.e., a weighted, moving average) of a time series of wave-forcing conditions with a parameterized memory-decay kernel function. The direct equivalence between equilibrium shoreline models and convolutions reveals key theoretical aspects of equilibrium behavior. Convolutions (representing quasi-low-pass filter operations) provide an intuitive theoretical description of shoreline erosion and accretion behavior in response to waves: that is, shoreline position often mirrors the weighted moving average of wave time series. Model-convolution equivalence also provides a conceptual basis to interpret, evaluate, and construct data-driven Machine-Learning/Deep-Learning (ML/DL) models that use convolutions to extract features from data and then apply them for prediction (e.g., Convolutional Neural Networks (CNNs)). Finally, our findings provide a methodological pathway (based on Fourier transforms) for future understanding of wave-driven shoreline change, which can be used to interpret the coherence between the frequency spectrum of the processes of waves and shoreline change and construct more computationally efficient and effective shoreline-modeling approaches.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JF008452","usgsCitation":"Vitousek, S., Buscombe, D.D., Gomez-de la Peña, E., Calcraft, K., Lundine, M.A., Splinter, K., Giovanni Coco, and Barnard, P.L., 2025, Are equilibrium shoreline models just convolutions?: JGR Earth Surface, v. 130, no. 6, e2025JF008452, 30 p., https://doi.org/10.1029/2025JF008452.","productDescription":"e2025JF008452, 30 p.","ipdsId":"IP-172699","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jf008452","text":"Publisher Index Page"},{"id":491025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"130","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":940731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gomez-de la Peña, Eduardo","contributorId":357091,"corporation":false,"usgs":false,"family":"Gomez-de la Peña","given":"Eduardo","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":940732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calcraft, Kit","contributorId":357094,"corporation":false,"usgs":false,"family":"Calcraft","given":"Kit","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":940733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lundine, Mark A. 0000-0002-2878-1713","orcid":"https://orcid.org/0000-0002-2878-1713","contributorId":339934,"corporation":false,"usgs":true,"family":"Lundine","given":"Mark","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Splinter, Kristen D.","contributorId":357097,"corporation":false,"usgs":false,"family":"Splinter","given":"Kristen D.","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":940735,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Giovanni Coco","contributorId":357100,"corporation":false,"usgs":false,"family":"Giovanni Coco","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":940736,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940737,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268936,"text":"70268936 - 2025 - Evaluating mark–resight survey design performance using simulation: A case study of endangered Steller sea lions","interactions":[],"lastModifiedDate":"2025-07-11T14:54:44.035754","indexId":"70268936","displayToPublicDate":"2025-06-17T09:36:31","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":"Evaluating mark–resight survey design performance using simulation: A case study of endangered Steller sea lions","docAbstract":"Effective monitoring is fundamental to estimating wildlife population parameters with a level of accuracy and precision that is adequate to inform management decisions. However, managers must balance trade-offs between the costs of monitoring and the resulting data quality to identify cost-effective monitoring survey designs. As such, evaluating the expected performance of monitoring surveys relative to monitoring objectives prior to survey implementation is critical. In this study, we present a simulation framework for examining the accuracy and precision of age-specific survival estimates and the probability of detecting a change in survival within the context of mark–resight monitoring programs. We consider 90 survey designs that vary across marked cohort size, marking frequency, study duration, and resight probability (i.e., detection of marked individuals). We apply this approach to the design of a monitoring program for Steller sea lions (Eumetopias jubatus), which is complicated by heterogeneity in rookery accessibility, population sizes, and abundance trends across the species' range. To identify cost-effective survey designs in the absence of actual survey costs, we evaluated performance with respect to a relative-costs schema. Our results highlight survey designs that reliably meet pre-defined precision targets, with precision and accuracy strongly affected by marked cohort size, marking frequency, and study duration. We found that historical mark–resight survey effort for Steller sea lions has been sufficient to reliably achieve precision targets for younger age class survival probabilities only for rookeries where abundance has been stable or increasing. In contrast, the probability of achieving survival estimates with target levels of precision at rookeries where abundance has been declining is low (<25%) due to smaller marked cohort sizes, less frequent marking at remote sites, and fewer years of available data. Our results indicate that the precision of survival estimates for subpopulations of conservation concern can be improved by longer-term monitoring, although the constraints of monitoring small populations may limit the ability of biologists to detect changes in population dynamics on management-relevant time horizons. Our survey design evaluation framework can be applied in a variety of contexts to assist natural resource managers in developing cost-effective monitoring programs.
","language":"English","doi":"10.1002/ecs2.70269","usgsCitation":"Warlick, A., Fadely, B., Mahoney, P., Melin, S., Gelatt, T., Raum-Suryan, K., and Converse, S.J., 2025, Evaluating mark–resight survey design performance using simulation: A case study of endangered Steller sea lions: Ecosphere, v. 16, no. 6, e70269, 17 p., https://doi.org/10.1002/ecs2.70269.","productDescription":"e70269, 17 p.","ipdsId":"IP-168290","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":492472,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70269","text":"Publisher Index Page"},{"id":492130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n 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The ability to accurately estimate daily public supply water use could help water supply planners in New Jersey better understand and manage the state’s limited water resources and balance the competing needs for freshwater resources. Data sources for this work include daily public supply water-use data from 2016 through 2020 acquired from New Jersey American Water for 15 drinking water service areas and monthly data exported from the New Jersey Department of Environmental Protection’s online water transfer data model database (known as NJWaTr). The two datasets were compared by aggregating the daily data to a monthly timescale. Statistical regression analysis was applied to the daily data, along with climate data, to evaluate what factors are influential in estimating daily fluctuations and trends in daily public supply water use. Fifteen regression equations were developed, one for each of the 15 drinking water service area systems for which daily data were acquired. Regression equations for systems that had seasonal patterns performed better than equations for non-seasonal systems. For the test year (2020), the average adjusted coefficient of determination for the linear regression with autoregressive errors model among systems with seasonality was 0.78; the average adjusted coefficient of determination for the linear regression with autoregressive errors model among systems with little or no seasonality was 0.25. The effects of anomalous data in the regression analysis were examined by comparing adjusted coefficient of determination values when the atypical data points were removed versus when they were retained in the analysis. Overall, including the anomalous data did not have a large effect on the results, and thus the data were retained for this study.
In addition to developing regression equations, all 589 unique drinking water service area systems in New Jersey were characterized based on socio-economic data and monthly water-use data from NJWaTr. Systems that are located near the New Jersey coast, serve populations larger than 1,970 people, or serve areas that have median property values over $256,250 tended to demonstrate seasonal water-use behaviors. Systems that have mostly urban residential land use tended to show little to no seasonal water-use behaviors. Finally, a method was developed to disaggregate monthly data to a daily timescale and was tested against systems for which daily data were not available. Two regression equation forms were developed to be applied to systems beyond the 15 systems from which the original equations were developed; one equation was developed for use when all drinking water service area systems showed little to no seasonality, and the other equation was developed for use when systems displayed seasonal behavior.
To the extent possible, uncertainty and possible sources of error were identified and examined in relation to the regression model equations developed. Additional daily data from these 15 systems (over different years) and daily data from different systems could be used to further evaluate the results of the disaggregation through a comprehensive assessment of error. Further adjustments to the regression equations could be made, ultimately enhancing their accuracy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245061","collaboration":"Prepared in cooperation with New Jersey Department of Environmental Protection","programNote":"Water Availability and Use Science Program","usgsCitation":"Shourds, J.L., and Scott, M.H., 2025, Estimating daily public supply water use by drinking water service area in New Jersey: U.S. Geological Survey Scientific Investigations Report 2024–5061, 90 p., https://doi.org/10.3133/sir20245061.","productDescription":"Report: xi, 90 p.; Appendix","numberOfPages":"90","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-151668","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":485928,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5061/sir20245061.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5061 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike
Suite 110
Lawrenceville, NJ 08648
Anthropogenic climate change is an existential threat to our planet, impacting everything from the delicate balance of ecosystems to the availability of vital resources. Coastal regions, particularly vulnerable to the impacts of climate change due to rising sea levels and changing weather patterns, are experiencing increased erosion, flooding, and habitat loss. Understanding how coastal regions responded to past warming is crucial for developing effective adaptation and mitigation strategies. One past interval commonly used to examine and compare with climate model projections of near future conditions is the mid-Piacenzian Warm Period (MPWP) which occurred between*3.3 and 3.0 Ma. Here we review the stratigraphy of Atlantic Coastal Plain (ACP) sediments to determine the stratigraphic position of the MPWP by evaluating ages based upon existing and new planktic foraminifer occurrence data calibrated to the current geologic time scale (GTS2020). We identify geologic formations representing pre-, syn-, and post-MPWP environments. The Sunken Meadow Member of the Yorktown Formation in Virginia and North Carolina and the Wabasso beds in the subsurface of Georgia and Florida both fall within Planktic Foraminiferal Zone PL1 and represent pre-MPWP Pliocene deposits. Parts of the Yorktown Formation in southeastern Virginia and northern North Carolina, the Duplin Formation in North Carolina and South Carolina, and the Raysor Formation in South Carolina and Georgia, fall within Planktic Foraminiferal Zone PL3 and were deposited following a major regression associated with a global drop in sea level during Marine Isotope Stage (MIS) M2 and represent syn-MPWP deposits. Representing the immediately post-MPWP climate conditions (Planktic Foraminiferal Zone PL5) are the Chowan River, Bear Bluff, and Cypresshead Formations. This work provides a record of the MPWP from Georgia to Virginia and provides a stratigraphic framework within which the impacts of a profound global warming on the east coast of the United States can be assessed.
","language":"English","publisher":"Micropaleontological Press","doi":"10.47894/stra.22.2.00","usgsCitation":"Dowsett, H., and Spivey, W., 2025, The stratigraphic record of the mid-Piacenzian warm period on the Atlantic Coastal Plain: Stratigraphy, v. 22, no. 2, p. 81-97, https://doi.org/10.47894/stra.22.2.00.","productDescription":"17 p.","startPage":"81","endPage":"97","ipdsId":"IP-167251","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":490297,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.micropress.org/microaccess/stratigraphy/issue-412/article-2424","linkFileType":{"id":5,"text":"html"}},{"id":495251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina, South Carolina, Virginia","otherGeospatial":"Atlantic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.2540064137628,\n 37.9661310516864\n ],\n [\n -77.45393664738447,\n 37.57788480662157\n ],\n [\n -82.81553889394615,\n 31.49173122752032\n ],\n [\n -82.4849451212944,\n 30.69984073209814\n ],\n [\n -81.58807068387608,\n 30.841401404258605\n ],\n [\n -78.7009680731669,\n 33.43137207763918\n ],\n [\n -75.88995103042635,\n 34.91166522689612\n ],\n [\n -75.2540064137628,\n 37.9661310516864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":316789,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":939852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spivey, Whittney 0000-0003-1111-3361 wspivey@usgs.gov","orcid":"https://orcid.org/0000-0003-1111-3361","contributorId":214849,"corporation":false,"usgs":true,"family":"Spivey","given":"Whittney","email":"wspivey@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":939853,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70268280,"text":"70268280 - 2025 - Scoping decision-maker needs and science availability to support regional natural capital accounting in the U.S. Colorado River Basin","interactions":[],"lastModifiedDate":"2025-06-20T14:25:55.873314","indexId":"70268280","displayToPublicDate":"2025-06-16T09:18:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"Scoping decision-maker needs and science availability to support regional natural capital accounting in the U.S. Colorado River Basin","docAbstract":"Natural capital accounting has the potential to yield important policy insights at multiple scales, but there remains a disconnect between regional-scale natural capital accounts and their use for informing policy. In this paper, we propose a roadmap that could lead to the creation of policy-relevant regional accounts, with steps split across an initial scoping phase and a subsequent development phase. We demonstrate the scoping steps in action with an application to the Colorado River Basin (“Basin”), a large watershed in the southwestern United States (U.S.) that has faced aridification and substantial high-profile tradeoffs around the use of its water and other natural resources. Drawing on prior U.S. Geological Survey science co-production efforts, we conducted a series of eight discussion sessions with 41 scientists and science representatives whose work is relevant to Basin water, riparian and riverine ecosystems, upland ecosystems and energy and minerals. We summarise participants' thoughts on key topics and economic linkages, their insights and questions of interest and their recommendations on existing scientific data sources and gaps. We evaluate the suitability of the available data for construction of System of Environmental-Economic Accounting (SEEA) Central Framework and SEEA Ecosystem Accounting accounts, including those for land, water, forests, energy and minerals and ecosystems (covering extent, condition and ecosystem services). We present a series of lessons learned during the scoping phase, as well as lessons that could be relevant for future practitioners engaging in the development phase. The information can help guide the development of timely and relevant regional-scale environmental-economic accounts in the U.S. and beyond.
","language":"English","publisher":"Pensoft","doi":"10.3897/oneeco.10.e147848","usgsCitation":"Enriquez, A.J., Bagstad, K.J., Dahm, K., Torregrosa, A.A., and Schuster, R., 2025, Scoping decision-maker needs and science availability to support regional natural capital accounting in the U.S. Colorado River Basin: One Ecosystem, v. 10, e147848, 52 p., https://doi.org/10.3897/oneeco.10.e147848.","productDescription":"e147848, 52 p.","ipdsId":"IP-174023","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":491491,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.10.e147848","text":"Publisher Index Page"},{"id":491021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"U.S. Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.17764090281825,\n 35.12554291280361\n ],\n [\n -115.22388348576852,\n 32.6714960288822\n ],\n [\n -114.64523348546022,\n 32.6959720389348\n ],\n [\n -114.5582296837352,\n 32.46385000374262\n ],\n [\n -110.89393838404563,\n 31.28075591535938\n ],\n [\n -108.2173723487556,\n 31.3251446765358\n ],\n [\n -108.06400182207712,\n 32.5291268823845\n ],\n [\n -107.30272225391106,\n 34.32342052943805\n ],\n [\n -106.69789963396444,\n 36.602443067829526\n ],\n [\n -106.09388936806188,\n 39.1986266984284\n ],\n [\n -106.25789166159434,\n 41.01272550000448\n ],\n [\n -107.60805391608476,\n 43.093170001656574\n ],\n [\n -109.76114321368557,\n 43.59470695967647\n ],\n [\n -110.44713900844467,\n 40.552785978119346\n ],\n [\n -111.67118335490603,\n 38.90891919021175\n ],\n [\n -113.11999353237276,\n 37.78177191496151\n ],\n [\n -114.42766368760466,\n 37.956578120783476\n ],\n [\n -115.13223455603163,\n 39.522533859344065\n ],\n [\n -116.8897305478193,\n 39.50565015228386\n ],\n [\n -116.08173233860414,\n 36.32307104415649\n ],\n [\n -115.17764090281825,\n 35.12554291280361\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2025-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Enriquez, Aaron Joey 0000-0002-0305-4333","orcid":"https://orcid.org/0000-0002-0305-4333","contributorId":346485,"corporation":false,"usgs":true,"family":"Enriquez","given":"Aaron","email":"","middleInitial":"Joey","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":940694,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":940695,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahm, Katharine G.","contributorId":357078,"corporation":false,"usgs":false,"family":"Dahm","given":"Katharine G.","affiliations":[{"id":85322,"text":"Office of Natural Resources Revenue","active":true,"usgs":false}],"preferred":false,"id":940696,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":940697,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schuster, Rudy 0000-0003-2353-8500 schusterr@usgs.gov","orcid":"https://orcid.org/0000-0003-2353-8500","contributorId":3119,"corporation":false,"usgs":true,"family":"Schuster","given":"Rudy","email":"schusterr@usgs.gov","affiliations":[],"preferred":true,"id":940698,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268362,"text":"70268362 - 2025 - Characterizing water-quality response after the 2020 Cameron Peak Fire using a novel application of the Weighted Regressions on Time, Discharge, and Season method","interactions":[],"lastModifiedDate":"2025-06-24T14:08:12.18671","indexId":"70268362","displayToPublicDate":"2025-06-16T09:02:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing water-quality response after the 2020 Cameron Peak Fire using a novel application of the Weighted Regressions on Time, Discharge, and Season method","docAbstract":"The frequency and severity of wildfire activity in the western United States emphasises the utility of hydrologic models to predict water-quality response. This study presents a novel application of the Weighted Regressions on Time, Discharge and Season (WRTDS) method to assess potential changes in water quality in two watersheds draining the North Fork Big Thompson River and Buckhorn Creek in Larimer County, Colorado that were affected by the 2020 Cameron Peak Fire. WRTDS models were developed using 12 years of pre-fire data and used to estimate the expected constituent concentrations for each sample collected in the post-fire record. The predicted constituent concentrations modelled in this manner are representative of conditions in the absence of fire and allow pre-fire and post-fire stream chemistry to be quantitatively compared. Nitrate and total phosphorus concentrations showed the greatest differences between the observed and predicted concentrations, which were up to 153% greater than expected. We linked changes in source inputs and elevation as likely controls on the difference in magnitude and timing of response between the two watersheds. Post-fire arsenic and manganese concentrations were greater than the predicted concentrations in both watersheds, with arsenic up to 42% greater and manganese up to 85% greater than the model predictions. Post-fire calcium, magnesium, chloride and sulphate concentrations were greater than model predictions at the North Fork and less than the predictions at Buckhorn. We argue that greater burn severity at Buckhorn likely reduced soil–water infiltration and led to bypassed subsurface flow paths through a major lithologic source of these constituents. Post-fire changes in total organic carbon and dissolved iron concentrations were weakly supported by the model results, as observed concentrations were largely within the bounds of expected values calculated from the pre-fire model. The novel approach to WRTDS presented in this study could be a useful tool for water-quality assessments after land disturbances in the western United States.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70178","usgsCitation":"Ruckhaus, M.H., Clow, D.W., Hirsch, R.M., and Chapin, T.W., 2025, Characterizing water-quality response after the 2020 Cameron Peak Fire using a novel application of the Weighted Regressions on Time, Discharge, and Season method: Hydrological Processes, v. 39, no. 6, e70178, 21 p., https://doi.org/10.1002/hyp.70178.","productDescription":"e70178, 21 p.","ipdsId":"IP-171701","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":491489,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70178","text":"Publisher Index Page"},{"id":491178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Larimer County","otherGeospatial":"Buckhorn Creek watershed, North Fork Big Thompson River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1667,\n 40.75\n ],\n [\n -106,\n 40.75\n ],\n [\n -106,\n 40.333\n ],\n [\n -105.1667,\n 40.333\n ],\n [\n -105.1667,\n 40.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruckhaus, Manya Helene 0009-0006-3111-1127","orcid":"https://orcid.org/0009-0006-3111-1127","contributorId":344234,"corporation":false,"usgs":true,"family":"Ruckhaus","given":"Manya","email":"","middleInitial":"Helene","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":941105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapin, Tanner William 0000-0003-3905-3241","orcid":"https://orcid.org/0000-0003-3905-3241","contributorId":297923,"corporation":false,"usgs":true,"family":"Chapin","given":"Tanner","email":"","middleInitial":"William","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941106,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273481,"text":"70273481 - 2025 - Evaluating slash piles as habitat for a threatened salamander","interactions":[],"lastModifiedDate":"2026-01-16T15:07:00.084464","indexId":"70273481","displayToPublicDate":"2025-06-16T09:02:27","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating slash piles as habitat for a threatened salamander","docAbstract":"Amplified wildfire activity in forests of the western United States threatens biodiversity. Fuel treatments can reduce fire severity, modify fire behavior, and restore forest structure and composition, yet impacts of some treatments, including slash piling and burning, on wildlife have received little attention. Piling of residual woody material may create habitable microenvironments for species that require cool, moist microclimates for all biological and ecological functions. One such species, the Sacramento Mountain salamander (Aneides hardii Taylor), a relictual, endemic salamander narrowly distributed in the mountains of south-central New Mexico, USA, has been found below constructed slash piles within its range, but the characteristics of occupied slash piles and the extent of their occupancy has not yet been quantified. We surveyed for Sacramento Mountain salamanders in slash piles and under logs (cover objects) adjacent to piles and within a surrounding survey plot, and related salamander occupancy to slash pile and cover object characteristics, soil moisture and temperature, and environmental setting.
We found Sacramento Mountain salamanders in 50% of surveyed slash piles. About 90% of salamanders were found in piles that contained black plastic sheeting, which held accumulations of moist leaf litter and other forest debris. We found no differences in pile characteristics, soil variables, or environmental setting between piles occupied by salamanders and piles in which no salamanders were detected. Salamander density was highest in slash piles, ~ 10% lower under cover objects in the survey area, and ~ 23% lower under cover objects adjacent to slash piles.
Slash piles serve as habitat for Sacramento Mountain salamanders. Our results suggest that within our study area or similar environments within the species range, any comparable slash pile has the potential to be occupied by salamanders. Species and habitat conservation measures indicated by this study and the timing of historical detections into mid-October include constructing smaller, pyramidal piles that minimize log-on-log or log-ground contact, avoiding the inclusion of black plastic in piles, limiting residence time of piles on the landscape, and initiating pile burning in late October or early November, when most salamanders are likely to have retreated below the ground surface.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-025-00381-4","collaboration":"University of Rhode Island","usgsCitation":"Loehman, R.A., and Karraker, N.E., 2025, Evaluating slash piles as habitat for a threatened salamander: Fire Ecology, v. 21, 36, 18 p., https://doi.org/10.1186/s42408-025-00381-4.","productDescription":"36, 18 p.","ipdsId":"IP-124527","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":498916,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-025-00381-4","text":"Publisher Index Page"},{"id":498739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Lincoln National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.4616018244409,\n 32.86352310900293\n ],\n [\n -105.4616018244409,\n 32.775897714349966\n ],\n [\n -105.30671714574738,\n 32.775897714349966\n ],\n [\n -105.30671714574738,\n 32.86352310900293\n ],\n [\n -105.4616018244409,\n 32.86352310900293\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2025-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":953895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karraker, Nancy E","contributorId":365192,"corporation":false,"usgs":false,"family":"Karraker","given":"Nancy","middleInitial":"E","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":953896,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}