The recent Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals but no clear pathway for how zero loss of important biodiversity areas and halting human-induced extinction of threatened species will be achieved. We assembled a multi-taxa tracking dataset (11 million geopositions from 15,845 tracked individuals across 121 species) to provide a global assessment of space use of highly mobile marine megafauna, showing that 63% of the area that they cover is used 80% of the time as important migratory corridors or residence areas. The GBF 30% threshold (Target 3) will be insufficient for marine megafauna’s effective conservation, leaving important areas exposed to major anthropogenic threats. Coupling area protection with mitigation strategies (e.g., fishing regulation, wildlife-traffic separation) will be essential to reach international goals and conserve biodiversity.
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Photosynthetic biofilms dominated by diatoms, cyanobacteria, and chlorophytes represent a significant fraction of biofilm biomass and contain pigments that can be detected with remote sensing. These diverse biofilm community types vary in indicator pigments and functional traits related to biogeochemical cycling and nutritional quality. We modeled and mapped spatial variation in intertidal biofilm distribution, quantity, diversity, and functional traits using multi-scale spectroscopic data collected within southern San Francisco Bay, California, USA (South SFB). We developed a new biofilm index (B-index) from 5 mm HySpex spectra to detect biofilm presence. We developed single and multiple response partial least squares regression (PLS) models of chlorophyll-a (chl-a; biomass indicator), indicator pigments: fucoxanthin and diadinoxanthin (diatoms), zeaxanthin (cyanobacteria), and chl-b (chlorophytes), and functional traits: carbohydrates, lipids, and total organic carbon from paired in situ biofilm data and field spectra. The B-index and PLS models were scaled to South SFB with a 3.7 m AVIRIS-NG hyperspectral image. The model %RMSE calculated from AVIRIS-NG test samples ranged from 12.7% for chl-a to 49% for chl-b; for six of the eight models, %RMSE was 23% or below. Mapped community types differed in mapped traits, with average lipid concentrations three times higher in areas indicated as diatoms compared to other groups. Available maps depict for the first time the spatial variation of an important shorebird food resource and inform the contribution of intertidal biofilm in carbon and nutrient cycling.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JG008520","usgsCitation":"Byrd, K.B., Palacios, S., Taylor, N.C., Woo, I., Moskal, S.M., Kokaly, R.F., Hoefen, T.M., Chapman, J., and De La Cruz, S.E., 2025, Multi-scale spectroscopy to map intertidal microbial biofilm community and trait diversity: JGR Biogeosciences, v. 130, no. 6, e2024JG008520, 23 p., https://doi.org/10.1029/2024JG008520.","productDescription":"e2024JG008520, 23 p.","ipdsId":"IP-171397","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":490197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.29763416548646,\n 37.65\n ],\n [\n -122.29763416548646,\n 37.415152793804396\n ],\n [\n -121.93300968918774,\n 37.415152793804396\n ],\n [\n -121.93300968918774,\n 37.65\n ],\n [\n -122.29763416548646,\n 37.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":939328,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palacios, Sherry L.","contributorId":356406,"corporation":false,"usgs":false,"family":"Palacios","given":"Sherry L.","affiliations":[{"id":81898,"text":"CSU Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":939329,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Nicole Chin 0000-0002-8094-2246","orcid":"https://orcid.org/0000-0002-8094-2246","contributorId":302295,"corporation":false,"usgs":true,"family":"Taylor","given":"Nicole","email":"","middleInitial":"Chin","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":939330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":939331,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moskal, Stacy M. 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thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":939334,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chapman, John","contributorId":352622,"corporation":false,"usgs":false,"family":"Chapman","given":"John","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":939335,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La 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respond to management actions is critical for making effective management decisions. This study provides important information regarding population demographics for a nonnative, hatchery-implemented population of White Sturgeon Acipenser transmontanus. We investigated the population dynamics of White Sturgeon in the upper Snake River, Idaho, and developed an age-structured population model to evaluate potential stocking and harvest scenarios (e.g., length limits and annual quotas).
White Sturgeon were sampled from June to October 2022 and from June to August 2023 using angling (i.e., rod and reel) and setlines from a 260-km-long section of the Snake River. Capture histories from 261 known-age White Sturgeon informed age and growth analysis and an evaluation of movement trends. A closed-population capture–recapture model and an estimate of setline-specific catchability were used to estimate the total abundance of White Sturgeon in the upper Snake River. Apparent survival for the population was estimated using a Cormack–Jolly–Seber model. Finally, a population model was parameterized using information on the population dynamics of White Sturgeon in the upper Snake River. The model was used to estimate the effects of varying stocking rates and harvest scenarios (i.e., harvest slot of 76–122 cm fork length [FL] and annual quotas of 0–25 White Sturgeon harvested) on the population.
In total, 340 individual White Sturgeon were captured throughout the study area, with 181 recapture events. Individuals varied in FL from 54 to 205 cm, and the mean relative weight for captures was 105.2 (SD = 14.4), suggesting relatively high body condition. Age varied from 2 to 25 years, and White Sturgeon moved an average of 8.1 km (SD = 23.5) downstream from stocking locations. Estimated abundance of White Sturgeon in the tailwaters of American Falls Dam was 428 fish (95% CI = 403–463). That abundance estimate was used to inform a total abundance estimate of 887 White Sturgeon (95% CI = 835–960) in the study area. Apparent annual survival was 0.79 (95% CI = 0.64–0.89). A stocking rate of 285 age-2 White Sturgeon/year was necessary to maintain current abundance. For every five fish harvested (harvest slot = 76–122 cm FL) per year, estimated abundance decreased by about 2.2% over 20 years.
Our research identified fast growth of White Sturgeon relative to other populations and relatively high mortality for a White Sturgeon population without exploitation. Also, like other studies evaluating harvest, a population model was used to illustrate the effect of varying rate functions on a fishery. The age-structured population model suggested that a harvest fishery is possible while still meeting management goals for the upper Snake River White Sturgeon fishery.
Changes in vegetation cover are occurring across sub-Saharan Africa and can have substantial effects on ecological communities, but limited data make understanding status and trends difficult for many taxa. We surveyed birds for several decades across Uganda using point counts. Using time-to-detection analysis in a trait-informed Bayesian multi-species occupancy framework, we model bird species richness as a function of year and local tree cover across 28 sites. We test for trends in richness and occupancy, and for the relationship between these and local and landscape-scale tree cover. Species richness increased at 75% of sites through the study period, and generalist bird species were most likely to be increasing in occupancy. Forest specialist bird species, and to a lesser extent generalists, responded positively to tree cover. Woody cover is changing across Uganda, with declines most pronounced in areas with the highest tree cover. This is likely to be causing declines in forest specialist species while favouring generalists. When tree cover decline is caused by conversion to croplands, rather than transitions to grasslands, grassland specialists are unlikely to benefit. Effects of climate and land use change and population pressure are likely to continue to alter woody plant cover and thus affect East African bird communities.
","language":"English","publisher":"Wiley","doi":"10.1111/aje.70058","usgsCitation":"Burner, R.C., Adams, E.M., Pomeroy, D., Tushabe, H., Kibuule, M., Rostad, L., Venter, Z., and Sheil, D., 2025, Trends in richness and occupancy of Ugandan birds and relation to local tree cover: African Journal of Ecology, v. 63, no. 4, e70058, 21 p., https://doi.org/10.1111/aje.70058.","productDescription":"e70058, 21 p.","ipdsId":"IP-175842","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":490660,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/aje.70058","text":"Publisher Index Page"},{"id":490399,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1SPBYBF","text":"USGS data release","linkHelpText":"Uganda bird trends"},{"id":490194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Uganda","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[31.86617,-1.02736],[30.76986,-1.01455],[30.4191,-1.13466],[29.82152,-1.44332],[29.57947,-1.34131],[29.58784,-0.58741],[29.8195,-0.2053],[29.87578,0.59738],[30.08615,1.06231],[30.46851,1.58381],[30.85267,1.8494],[31.17415,2.20447],[30.77332,2.33989],[30.83385,3.50917],[31.24556,3.7819],[31.88145,3.55827],[32.68642,3.79232],[33.39,3.79],[34.005,4.24988],[34.47913,3.5556],[34.59607,3.05374],[35.03599,1.90584],[34.6721,1.17694],[34.18,0.515],[33.89357,0.10981],[33.90371,-0.95],[31.86617,-1.02736]]]},\"properties\":{\"name\":\"Uganda\"}}]}","volume":"63","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-06-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Burner, Ryan C. 0000-0002-7314-9506","orcid":"https://orcid.org/0000-0002-7314-9506","contributorId":304152,"corporation":false,"usgs":true,"family":"Burner","given":"Ryan","email":"","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":939320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Evan M.","contributorId":139994,"corporation":false,"usgs":false,"family":"Adams","given":"Evan","email":"","middleInitial":"M.","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":939321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pomeroy, Derek","contributorId":356400,"corporation":false,"usgs":false,"family":"Pomeroy","given":"Derek","affiliations":[{"id":84992,"text":"Department of Environment Management, Makerere University, Kampala, Uganda","active":true,"usgs":false}],"preferred":false,"id":939322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tushabe, Herbert","contributorId":356401,"corporation":false,"usgs":false,"family":"Tushabe","given":"Herbert","affiliations":[{"id":84992,"text":"Department of Environment Management, Makerere University, Kampala, Uganda","active":true,"usgs":false}],"preferred":false,"id":939323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kibuule, Micheal","contributorId":356402,"corporation":false,"usgs":false,"family":"Kibuule","given":"Micheal","affiliations":[{"id":84995,"text":"NatureUganda","active":true,"usgs":false}],"preferred":false,"id":939324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rostad, Lars Jørgen","contributorId":356403,"corporation":false,"usgs":false,"family":"Rostad","given":"Lars Jørgen","affiliations":[{"id":84996,"text":"Norconsult AS","active":true,"usgs":false}],"preferred":false,"id":939325,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Venter, Zander S.","contributorId":356404,"corporation":false,"usgs":false,"family":"Venter","given":"Zander S.","affiliations":[{"id":84997,"text":"wegian Institute for Nature Research - NINA","active":true,"usgs":false}],"preferred":false,"id":939326,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sheil, Douglas","contributorId":356405,"corporation":false,"usgs":false,"family":"Sheil","given":"Douglas","affiliations":[{"id":84998,"text":"Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, Netherlands","active":true,"usgs":false}],"preferred":false,"id":939327,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70267972,"text":"70267972 - 2025 - Fomites could determine severity of SARS-CoV-2 outbreaks in low-density white-tailed deer (Odocoileus virginianus) populations","interactions":[],"lastModifiedDate":"2025-07-10T14:52:39.543071","indexId":"70267972","displayToPublicDate":"2025-06-04T09:28:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Fomites could determine severity of SARS-CoV-2 outbreaks in low-density white-tailed deer (Odocoileus virginianus) populations","docAbstract":"The establishment of a reservoir species for zoonotic diseases is concerning for both animal and human health. Severe acute respiratory syndrome coronavirus (SARS-CoV)-2, the coronavirus responsible for the COVID-19 pandemic, has been detected in white-tailed deer (Odocoileus virginianus) in the United States. Since its initial detection, various studies have documented circulation and evolution of SARS-CoV-2 in deer, with human cases suspected of spill-back from infectious deer. A priority for mitigating SARS-CoV-2 outbreaks in deer populations is determining the contribution of direct (via aerosols and physical contact) and indirect (via contaminated objects and media) transmission pathways. We expanded existing epidemiological models founded on direct transmission pathways to include three indirect transmission pathways of infection for simulated deer populations, including contaminated water, food waste, and feed piles. Despite lower infection probabilities and transmission hazards (measured by force-of-infection (FOI)) posed solely by these indirect pathways compared to direct transmission pathways, the addition of indirect transmission pathways increased FOI, which had ramifications for the severity of SARS-CoV-2 outbreaks in simulated deer populations, particularly in populations with low degrees of spread between deer (measured by basic reproductive number; R0). We used contact rate models to estimate SARS-CoV-2 spread across deer range in the United States and identified widespread potential for indirect transmission to increase the severity of outbreaks in low-density deer populations. These results indicate that indirect transmission pathways need to be considered in the management of white-tailed deer as a reservoir species for SARS-CoV-2.
","language":"English","publisher":"Wiley","doi":"10.1155/tbed/1352911","usgsCitation":"Rosenblatt, E., Cook, J.D., DiRenzo, G.V., Campbell Grant, E.H., Runge, M.C., and Mosher, B., 2025, Fomites could determine severity of SARS-CoV-2 outbreaks in low-density white-tailed deer (Odocoileus virginianus) populations: Transboundary and Emerging Diseases, v. 2025, 352911, 13 p., https://doi.org/10.1155/tbed/1352911.","productDescription":"352911, 13 p.","ipdsId":"IP-166277","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":490309,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490627,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/tbed/1352911","text":"Publisher Index Page"},{"id":491310,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P19KKRVV","text":"USGS data release","linkHelpText":"Code for Fomites could determine severity of SARS-CoV-2 outbreaks in low-density white-tailed deer Odocoileus virginianus populations"}],"volume":"2025","noUsgsAuthors":false,"publicationDate":"2025-06-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenblatt, Elias","contributorId":276324,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Elias","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":939832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, Jonathan D. 0000-0001-7000-8727","orcid":"https://orcid.org/0000-0001-7000-8727","contributorId":291411,"corporation":false,"usgs":true,"family":"Cook","given":"Jonathan","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":939833,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":939834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":939835,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":939836,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mosher, Brittany 0000-0002-8458-9056","orcid":"https://orcid.org/0000-0002-8458-9056","contributorId":216035,"corporation":false,"usgs":true,"family":"Mosher","given":"Brittany","email":"","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":939837,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268931,"text":"70268931 - 2025 - Doe diligence: A regional analysis of antlerless deer harvest regulations in the Midwestern United States of America.","interactions":[],"lastModifiedDate":"2025-07-11T15:43:07.67574","indexId":"70268931","displayToPublicDate":"2025-06-04T08:34:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Doe diligence: A regional analysis of antlerless deer harvest regulations in the Midwestern United States of America.","docAbstract":"Wildlife management in the United States of America (US) is primarily delegated to the individual states wherein state wildlife agencies manage wildlife populations to achieve multiple and sometimes conflicting objectives. White-tailed deer (Odocoileus virginianus) are an important species in the Midwestern US whose populations are primarily managed through recreational hunting. Managers aim to adjust populations by altering the harvest of antlerless (usually female) animals by changing the number of harvest permits available, hunting season lengths, or applying incentive programs like earn-a-buck, where a hunter must harvest an antlerless deer before they may harvest an antlered deer. We estimated the effect on antlerless deer harvest from changes in these regulations and changes in the number of licensed hunters across eight states in the Midwest. We used a Bayesian hierarchical model to estimate individual state and regional (i.e., across all states) effects. We found that increasing antlerless harvest permits increased antlerless harvest; however, this effect plateaued as the number of available permits increased. Providing unlimited harvest permits increased harvest, but the same increases were achieved by minimally increasing the number of limited harvest permits. Increasing the length of hunting season had a generally positive effect on antlerless harvest but the effect was non-linear and state dependent. The earn-a-buck incentive program resulted in the largest estimated increase in harvest. Finally, the number of licensed deer hunters in a state had a strong positive effect on the number of antlerless deer harvested. Our findings show that commonly applied changes in harvest regulations have a weak effect on the number of antlerless deer harvested, highlighting the challenges facing deer managers in the Midwestern US.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0324708","usgsCitation":"Draper, J., Brandell, E., Isabelle, J., Jacques, C., McCoy, C., Michel, E., Storm, D., Ott-Conn, C., Wojcik, B., Turner, W.C., and Walsh, D.P., 2025, Doe diligence: A regional analysis of antlerless deer harvest regulations in the Midwestern United States of America.: PLoS ONE, v. 20, no. 6, e0324708, 16 p., https://doi.org/10.1371/journal.pone.0324708.","productDescription":"e0324708, 16 p.","ipdsId":"IP-174116","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":492480,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0324708","text":"Publisher Index Page"},{"id":492150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, 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A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to create a continuous series of monthly Dynamic Surface Water Extent (DSWEmod) images across the Central Valley from January 2003 to January 2023. We used the timeseries to assess the climatic driving forces of winter (Oct–April) surface water variability at sub-basin and pixel scales. At the sub-basin scale, we evaluated the influences of winter precipitation, occurrence of atmospheric rivers, and antecedent soil moisture on monthly surface water extents and found that the greatest correspondence occurs in mid-winter (Dec–Feb); in contrast, non-precipitation drivers such as water management play a stronger role in autumn and spring. The pixel-level analysis identified the probabilities of precipitation-driven surface water occurrences in the Sacramento basin are highest along rivers, conveyance channels, and floodways, with higher probabilities under wetter antecedent soil moisture conditions. Precipitation-driven surface water occurrences are also common in leveed areas and outside flood boundaries designated by state and federal agencies where exposure of structures to inundation was larger in terms of their value. Finally, areas with more frequent precipitation-driven flooding have poor recharge potential but are commonly within 5 km of areas classified as having good potential. This study demonstrates a novel approach for exploring the utility of MODIS for understanding surface water dynamics in mid-winter, a period characterized by peak precipitation, flood risk, and surface water extent. This information can provide valuable insights for (1) assessing flood risks for infrastructure and populations, (2) identifying areas most suited to strategic water management investments to increase recharge, and (3) analyzing precipitation thresholds that trigger flooding to allow proactive water management strategies to minimize damage and maximize recharge.
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datasets","interactions":[],"lastModifiedDate":"2025-06-23T15:25:59.175515","indexId":"70267839","displayToPublicDate":"2025-06-02T09:11:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets","docAbstract":"Landsat Analysis Ready Data (ARD)-based time-series present challenges in monitoring surface urban heat islands (SUHI) due to rapid changes in land surface temperature (LST) compared to cloud-free satellite observations. This research investigates the use of a spatiotemporal gap-filling model as a feasible and cost-effective solution to produce Landsat time-series LST products with both high spatial resolution and temporal frequency. The study identified and filled Landsat ARD thermal times-series data gaps due to missing data, cloud and shadow effects, and data quality. The accuracy of Landsat gap-filled products was assessed using randomly selected clear observations of Landsat and uncertainty products from the gap-filling model and was evaluated using various existing temperature datasets, including climate data from NOAA Global Historical Climate Network station observations, Daily Surface Weather and Climatological Summaries (DAYMET), and LST including MODIS, VIIRS and ECOSTRESS. The result suggests that the gap-filled Landsat LST has significant correlations with existing datasets including field observation and remote sensing data derived from other sensors that have similar monthly and seasonal variation patterns. The uncertainty maps show spatial distributions of uncertainty for gap-filled pixels that have high or low uncertainties. The Landsat gap-filled time-series datasets can be used to measure annual, seasonal, or even monthly landscape thermal conditions, which are useful for SUHI and relevant research, and to perform multi-decade time-series LST change analysis under climate change conditions.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2025.2505254","usgsCitation":"Shi, H., and Xian, G.Z., 2025, Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets: International Journal of Remote Sensing, v. 46, no. 12, p. 4559-4582, https://doi.org/10.1080/01431161.2025.2505254.","productDescription":"24 p.","startPage":"4559","endPage":"4582","ipdsId":"IP-160289","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489562,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":939092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":939093,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70267826,"text":"70267826 - 2025 - Municipal and industrial wastewater treatment plant effluent contributions to per- and polyfluoroalkyl substances in the Potomac River: A basin-scale measuring and modeling approach","interactions":[],"lastModifiedDate":"2025-06-23T15:24:34.127021","indexId":"70267826","displayToPublicDate":"2025-06-02T09:01:03","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Municipal and industrial wastewater treatment plant effluent contributions to per- and polyfluoroalkyl substances in the Potomac River: A basin-scale measuring and modeling approach","docAbstract":"Managing per- and polyfluoroalkyl substances (PFAS) in water resources requires a basin-scale approach. Predicted environmental concentrations (PEC) and stream-vulnerability scores for PFAS were determined for the Potomac River watershed in the eastern United States. Approximately 15% of stream reaches contained municipal and/or industrial wastewater treatment plant (WWTP) discharges that are presumptive PFAS sources, comprising from <1 to >90% of streamflow. Mean annual PEC, based on the summed concentrations of eight PFAS detected in WWTP effluents (ΣPFASPEC), for all stream reaches in the watershed was 3.8 ng L–1, and stream reaches impacted by WWTP had perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS) PEC of 0.39 and 0.14 ng L–1. For locations where measured-environmental concentrations (MEC) were determined, municipal and industrial WWTP contributed 7.8% (0 to 65%) of the total annual streamflow and MEC were greater than PEC in 99% of the samples, indicating additional potential PFAS sources. The mean ΣPFASPEC was 9.1 ng L–1 compared to a mean sum of PFAS MEC of 34 ng L–1. Under mean-August low-flow, 17% and 9.4% of the water-supply intakes had maximum PFOA and PFOS PEC exceeding drinking water maximum contaminant levels.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.4c12167","usgsCitation":"Barber, L., Miller, S.A., Blaney, L., Bradley, P., Faunce, K.E., Fleck, J., Frick, M., He, K., Hollins, R., Lewellyn, C., Majcher, E.H., McAdoo, M.A., and Smalling, K., 2025, Municipal and industrial wastewater treatment plant effluent contributions to per- and polyfluoroalkyl substances in the Potomac River: A basin-scale measuring and modeling approach: Environmental Science and Technology, v. 59, no. 23, p. 11720-11734, https://doi.org/10.1021/acs.est.4c12167.","productDescription":"15 p.","startPage":"11720","endPage":"11734","ipdsId":"IP-159910","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":491461,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1021/acs.est.4c12167.","text":"External Repository"},{"id":489484,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia, West Virginia","otherGeospatial":"Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.39010453967498,\n 38.752691597185276\n ],\n [\n -77.41287188588434,\n 38.37965397161355\n ],\n [\n -76.30362109061913,\n 37.744281422513666\n ],\n [\n -76.21247602177843,\n 37.93634553642505\n ],\n [\n -76.74054284099122,\n 38.365258541059845\n ],\n [\n -77.04066573194639,\n 38.78820834695361\n ],\n [\n -77.39010453967498,\n 38.752691597185276\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"23","noUsgsAuthors":false,"publicationDate":"2025-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":939036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Samuel Adam 0000-0003-4225-1601","orcid":"https://orcid.org/0000-0003-4225-1601","contributorId":333495,"corporation":false,"usgs":true,"family":"Miller","given":"Samuel","email":"","middleInitial":"Adam","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blaney, Lee","contributorId":303379,"corporation":false,"usgs":false,"family":"Blaney","given":"Lee","email":"","affiliations":[{"id":38069,"text":"University of Maryland, Baltimore County","active":true,"usgs":false}],"preferred":false,"id":939038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Faunce, Kaycee E. 0000-0002-9178-0692","orcid":"https://orcid.org/0000-0002-9178-0692","contributorId":224488,"corporation":false,"usgs":true,"family":"Faunce","given":"Kaycee","email":"","middleInitial":"E.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fleck, Jacob 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":168694,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frick, Malinda 0009-0002-7252-6151","orcid":"https://orcid.org/0009-0002-7252-6151","contributorId":356288,"corporation":false,"usgs":false,"family":"Frick","given":"Malinda","affiliations":[{"id":84946,"text":"MITRE Corporation","active":true,"usgs":false}],"preferred":false,"id":939042,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"He, Ke","contributorId":356296,"corporation":false,"usgs":false,"family":"He","given":"Ke","affiliations":[{"id":15309,"text":"University of Maryland Baltimore County","active":true,"usgs":false}],"preferred":false,"id":939079,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hollins, Ryan D. 0009-0005-8414-4160","orcid":"https://orcid.org/0009-0005-8414-4160","contributorId":356289,"corporation":false,"usgs":false,"family":"Hollins","given":"Ryan D.","affiliations":[{"id":84946,"text":"MITRE Corporation","active":true,"usgs":false}],"preferred":false,"id":939044,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lewellyn, Conor J. 0000-0001-5635-8683","orcid":"https://orcid.org/0000-0001-5635-8683","contributorId":356290,"corporation":false,"usgs":false,"family":"Lewellyn","given":"Conor J.","affiliations":[{"id":84946,"text":"MITRE Corporation","active":true,"usgs":false}],"preferred":false,"id":939045,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Majcher, Emily H. 0000-0001-7144-6809","orcid":"https://orcid.org/0000-0001-7144-6809","contributorId":203335,"corporation":false,"usgs":true,"family":"Majcher","given":"Emily","middleInitial":"H.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939046,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"McAdoo, Mitchell A. 0000-0002-3895-0816 mmcadoo@usgs.gov","orcid":"https://orcid.org/0000-0002-3895-0816","contributorId":200287,"corporation":false,"usgs":true,"family":"McAdoo","given":"Mitchell","email":"mmcadoo@usgs.gov","middleInitial":"A.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":939047,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939048,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70267807,"text":"70267807 - 2025 - Relations of groundwater quality to long-term surface disposal of produced water near the Midway-Sunset and Buena Vista Oil Fields, California, USA","interactions":[],"lastModifiedDate":"2025-06-03T15:27:01.898775","indexId":"70267807","displayToPublicDate":"2025-06-02T08:22:02","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":"Relations of groundwater quality to long-term surface disposal of produced water near the Midway-Sunset and Buena Vista Oil Fields, California, USA","docAbstract":"Contamination of groundwater by oil-field fluids in proximity to oil and gas development has been an issue of concern to water users and regulators given long histories of development and legacy disposal practices. A robust set of geochemical tracers including petroleum hydrocarbon compounds, thermogenic gases, inorganic ion concentrations, stable isotopes, radioactive isotopes, and noble gases were used to assess if oil-field fluids mixed with groundwater near the Midway-Sunset and Buena Vista Oil Fields in California, USA. Results show evidence of mixing of oil-field fluids with groundwater within the study area from either anthropogenic or natural processes. Produced water plumes associated with modern surface disposal facilities, used since the late 1950s, extend up to 1.5 km and currently remain within the boundaries of the oil fields. Plumes associated with earlier routing of produced water down natural drainages and in large retention structures (Midway Basin and Sunset Basin) near the Buena Vista Lake Bed are present in groundwater east of the oil fields. Based on geochemical tracer evidence, aerial imagery, and aerial electromagnetic surveys, these legacy plumes reach the western portion of the Central Valley aquifer system, an important groundwater resource for agricultural and domestic supply. The legacy plume associated with Sunset Basin may further be detected downgradient in deeper groundwater beneath the southern extent of the Buena Vista Lake Bed based on the presence of thermogenic gases and petroleum hydrocarbon compounds.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179637","usgsCitation":"Gannon, R., Landon, M.K., Kulongoski, J.T., Stephens, M.J., Ball, L.B., Warden, J.G., Davis, T., Gillespie, J.M., and Cozzarelli, I.M., 2025, Relations of groundwater quality to long-term surface disposal of produced water near the Midway-Sunset and Buena Vista Oil Fields, California, USA: Science of the Total Environment, v. 987, 179637, 21 p., https://doi.org/10.1016/j.scitotenv.2025.179637.","productDescription":"179637, 21 p.","ipdsId":"IP-141782","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":490170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.179637","text":"Publisher Index Page"},{"id":489465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Midway-Sunset and Buena Vista Oil Fields","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.92890453836446,\n 35.40767723666424\n ],\n [\n -119.92890453836446,\n 34.8418504471874\n ],\n [\n -119.067419253165,\n 34.8418504471874\n ],\n [\n -119.067419253165,\n 35.40767723666424\n ],\n [\n -119.92890453836446,\n 35.40767723666424\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"987","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gannon, Riley 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kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938978,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stephens, Michael J. 0000-0001-8995-9928","orcid":"https://orcid.org/0000-0001-8995-9928","contributorId":205895,"corporation":false,"usgs":true,"family":"Stephens","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938979,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ball, Lyndsay B. 0000-0002-6356-4693 lbball@usgs.gov","orcid":"https://orcid.org/0000-0002-6356-4693","contributorId":1138,"corporation":false,"usgs":true,"family":"Ball","given":"Lyndsay","email":"lbball@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":938980,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warden, John G. 0000-0003-1384-458X","orcid":"https://orcid.org/0000-0003-1384-458X","contributorId":215846,"corporation":false,"usgs":true,"family":"Warden","given":"John","email":"","middleInitial":"G.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938981,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davis, Tracy 0000-0003-0253-6661 tadavis@usgs.gov","orcid":"https://orcid.org/0000-0003-0253-6661","contributorId":176921,"corporation":false,"usgs":true,"family":"Davis","given":"Tracy","email":"tadavis@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938982,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gillespie, Janice M. 0000-0003-1667-3472","orcid":"https://orcid.org/0000-0003-1667-3472","contributorId":219675,"corporation":false,"usgs":true,"family":"Gillespie","given":"Janice","email":"","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938983,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 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Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Recent large-scale prescribed fire treatments reduced Carr Fire severity at Whiskeytown National Recreation Area","docAbstract":"Background
Severe fire weather is becoming more common throughout the western United States. Changing conditions demand a better understanding of how prescribed fire treatments perform under extreme burning conditions, including the interactive influence of the age of treatments, vegetation, and fire weather. The Carr Fire of July 2018 burned nearly the entire land area of Whiskeytown National Recreation Area (NRA) under extreme fuel moisture and temperature conditions. Prior to the Carr Fire and since 1997, staff at Whiskeytown NRA treated 23% of the 15,756-ha NRA using large-scale prescribed fire (underburn) treatments ranging in size from 40 to 400 hectares.
Methods
We used simultaneous autoregressive (SAR) models to describe the effects of landscape-scale fuel treatments on wildfire severity under extreme burning conditions and across diverse biophysical settings at Whiskeytown NRA. Because vegetation type and structure are known drivers of fire severity in diverse ecosystems such as at Whiskeytown NRA, we also considered three different sources of vegetation structure data, including a 2006 physiognomic-floristic classification, a 2011 lidar-based forest structure classification, and a 2016 Landfire map of existing vegetation physiognomy-subclass.
Results
The greatest effect on 2018 Carr Fire severity was time since treatment of underburn treatments, but treatment effectiveness on fire severity dissipated rapidly—showing notable effectiveness within 5 years of underburning but virtually no effectiveness beyond 10 years post-treatment. Additional factors related to severity included vegetation structure type, topographic position index, aspect, slope, temperature, and wind gust speed. Model variance explained and model parameters, including the effect of underburn treatments, were similar regardless of the source of vegetation structure data.
Conclusions
Our results show that large-scale underburning treatments can reduce wildfire severity even under extreme fire weather conditions but suggest that frequent maintenance intervals are required to maintain treatment effectiveness ahead of severe wildfire events.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-025-00377-0","usgsCitation":"Beckman, J.J., van Mantgem, P.J., Wright, M., and Engber, E., 2025, Recent large-scale prescribed fire treatments reduced Carr Fire severity at Whiskeytown National Recreation Area: Fire Ecology, v. 21, 35, 20 p., https://doi.org/10.1186/s42408-025-00377-0.","productDescription":"35, 20 p.","ipdsId":"IP-165481","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498683,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-025-00377-0","text":"Publisher Index Page"},{"id":498548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Whiskeytown National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.77239214268279,\n 40.681212821117555\n ],\n [\n -122.77239214268279,\n 40.474214754578185\n ],\n [\n -122.47725029049735,\n 40.474214754578185\n ],\n [\n -122.47725029049735,\n 40.681212821117555\n ],\n [\n -122.77239214268279,\n 40.681212821117555\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2025-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Beckman, Jill J.","contributorId":364982,"corporation":false,"usgs":false,"family":"Beckman","given":"Jill","middleInitial":"J.","affiliations":[{"id":87020,"text":"Northern Arizona University; Former USGS","active":true,"usgs":false}],"preferred":false,"id":953552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422 pvanmantgem@usgs.gov","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":222994,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip","email":"pvanmantgem@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Micah C. 0000-0002-5324-1110","orcid":"https://orcid.org/0000-0002-5324-1110","contributorId":229071,"corporation":false,"usgs":true,"family":"Wright","given":"Micah","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engber, Eamon","contributorId":202777,"corporation":false,"usgs":false,"family":"Engber","given":"Eamon","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":953555,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268065,"text":"70268065 - 2025 - U.S. national park units as breeding bird habitat: A comparison of species prevalence and land cover across the midwestern and central United States","interactions":[],"lastModifiedDate":"2025-06-12T14:00:50.756657","indexId":"70268065","displayToPublicDate":"2025-06-01T08:47:05","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18517,"text":"Science Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SR—2025/317","title":"U.S. national park units as breeding bird habitat: A comparison of species prevalence and land cover across the midwestern and central United States","docAbstract":"The value of national parks as bird habitat depends not only on local conditions within the parks, but also on the landscape habitat matrices in which they are located. However, the influences of local and landscape habitat matrices on birds vary by species and have not been quantified. Similarly, the trends of land cover types through time have not been systematically quantified for Midwest Region national parks and the landscapes around them, despite evidence of ongoing habitat loss exacerbated by climate change and human population growth. Managers and policy makers can use this information to understand and sustain the contribution of parks to our Nation’s avifauna.
We developed models using North American Breeding Bird Survey (BBS) data collected on routes from across the central United States. The models were used to predict occupancy of bird species of concern in 32 national park units across nine Bird Conservation Regions in the Midwest based on land cover in and around those parks. We then compared these predictions with data collected through National Park Service (NPS) bird surveys at each park to determine if bird species of concern were more or less prevalent than expected.
In each park, the mean difference between observed species detections and mean predicted detections indicates that most species are less frequently detected in the parks than predicted. However, when the range of uncertainty of predictions is considered, only 21% of park-bird combinations showed strong evidence (95%) of differing from expectation. Of these, species were less common than expected in the park in all but two cases.
These results indicate that some bird species of concern occupy sites in Midwest Region national park units at a rate roughly comparable to sites with similar land cover in the Bird Conservation Region (BCR) in which they occur. However, for one in five species-park combinations, parks appear to be less occupied than comparable sites elsewhere.
Annual point counts are commonly used to monitor birds to track population densities across space and time. Palila (Loxioides bailleui) are surveyed annually in the first quarter, but we recently instituted quarterly sampling that offers a unique opportunity to improve estimator precision. We conducted point-transect distance sampling point counts during the first quarter of 2020 through 2024, and the second through fourth quarters in 2022 and 2023, and the second quarter in 2024. The reduced sampling intensity during the quarterly counts, however, requires model-based methods to estimate abundance to the entire sampling frame. We modeled spatial and temporal correlation using a soap film smoother within a generalized additive modeling framework, a density surface model, fitted to palila counts each quarter for the five-year timeseries to track changes in population abundances. Our results indicate that palila maintained a high-density hotspot throughout the five-year timeseries; however, the extent of the hotspot declined substantially over the timeseries while densities within the hotspot declined from about 3 birds/ha in 2020 to about 1 bird/ha in 2024, which resulted in a 66% decline in palila abundances over 5 years. Density surface model estimates give on average a confidence interval width that was 74.7% shorter than the associated distance sampling confidence interval widths. Our results indicate that palila may benefit most if management actions were applied within the remaining hotspot. Additionally, this temporally fine-grained sampling provides information on seasonal movement patterns and resource tracking, and population response to management and conservation actions. Our spatially explicit, model-based approach is applicable to a wide range of monitoring programs, particularly those with inconsistent, opportunistic spatial coverage.
","language":"English","publisher":"frontiers","doi":"10.3389/fcosc.2025.1564661","usgsCitation":"Camp, R.J., Asing, C.K., Hunt, N., Wang, A., Farmer, C., Neitmann, L., and Banko, P.C., 2025, Fine-grained temporal population monitoring of a declining, critically endangered Hawaiian honeycreeper: Frontiers in Conservation Science, v. 6, 1564661, 10 p., https://doi.org/10.3389/fcosc.2025.1564661.","productDescription":"1564661, 10 p.","ipdsId":"IP-177644","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":492087,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2025.1564661","text":"Publisher Index Page"},{"id":491904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Kea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.61817507930547,\n 19.93574865703343\n ],\n [\n -155.61817507930547,\n 19.691994873461468\n ],\n [\n -155.38803917004466,\n 19.691994873461468\n ],\n [\n -155.38803917004466,\n 19.93574865703343\n ],\n [\n -155.61817507930547,\n 19.93574865703343\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2025-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":942452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Asing, Chauncey K.","contributorId":272645,"corporation":false,"usgs":false,"family":"Asing","given":"Chauncey","email":"","middleInitial":"K.","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":942453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Noah J. 0009-0008-9859-7007","orcid":"https://orcid.org/0009-0008-9859-7007","contributorId":357746,"corporation":false,"usgs":false,"family":"Hunt","given":"Noah J.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":942454,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Alexander","contributorId":344103,"corporation":false,"usgs":false,"family":"Wang","given":"Alexander","email":"","affiliations":[{"id":56397,"text":"State of Hawai‘i, Division of Forestry and Wildlife","active":true,"usgs":false}],"preferred":false,"id":942455,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Farmer, Chris","contributorId":150179,"corporation":false,"usgs":false,"family":"Farmer","given":"Chris","affiliations":[{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false}],"preferred":false,"id":942456,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neitmann, Lindsey","contributorId":357751,"corporation":false,"usgs":false,"family":"Neitmann","given":"Lindsey","affiliations":[{"id":56397,"text":"State of Hawai‘i, Division of Forestry and Wildlife","active":true,"usgs":false}],"preferred":false,"id":942457,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":942458,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270043,"text":"70270043 - 2025 - Resolution sensitivities for subgrid modeling of coastal flooding","interactions":[],"lastModifiedDate":"2025-08-08T19:06:19.920299","indexId":"70270043","displayToPublicDate":"2025-05-31T08:08:53","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Resolution sensitivities for subgrid modeling of coastal flooding","docAbstract":"Flooding due to storm surge can propagate through coastal regions to threaten the built and natural environments. This propagation is controlled by geographic features of varying scales, from the largest oceans to the smallest marsh channels and sandy dunes. Numerical models to predict coastal flooding have been improved via the use of subgrid corrections, which use information about the smallest-scale flow controls to provide corrections to coarser scale grids. Although previous studies have demonstrated the benefits of subgrid models, especially how coarser models can be more efficient without a trade-off in accuracy, this study systematically investigates subgrid corrections in storm surge models across large domains. Here, we apply the widely used ADVanced CIRCulation (ADCIRC) storm surge model with revised subgrid corrections to develop guidance for resolution of coastal regions. Recent hurricanes in the South Atlantic Bight are simulated with five models, each with varying resolution of coastal islands, estuaries, rivers, and floodplains. Model performance is quantified via comparisons with observed data and high-resolution simulations. Clear degradation is observed in the subgrid model performance as minimum mesh resolution becomes coarser than the width of channels conveying flow or the barrier islands blocking flow. Therefore, subgrid model mesh resolution should account for spatial scales of local flow pathways and barrier islands to maintain proper model mass and momentum transfer. However, with subgrid modeling this can be done at much coarser (and thus computationally faster) resolutions than with conventional models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2025.104787","usgsCitation":"Woodruff, J., Dietrich, J., Wirasaet, D., Kennedy, A., Bolster, D., and Luettich, R., 2025, Resolution sensitivities for subgrid modeling of coastal flooding: Coastal Engineering, v. 201, 104787, 21 p., https://doi.org/10.1016/j.coastaleng.2025.104787.","productDescription":"104787, 21 p.","ipdsId":"IP-169265","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":493892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"south Atlantic coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.4525010504607,\n 35.026033061132466\n ],\n [\n -79.24219819510189,\n 34.328207448127046\n ],\n [\n -82.26867209188597,\n 30.740037872429845\n ],\n [\n -81.1870113426256,\n 25.032751881316358\n ],\n [\n -80.0037059437347,\n 25.16924717636583\n ],\n [\n -79.99792361076327,\n 27.312432791511757\n ],\n [\n -81.20057957391982,\n 30.892489907643174\n ],\n [\n -76.4525010504607,\n 35.026033061132466\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"201","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodruff, Johnathan Lucas 0000-0003-2806-8421","orcid":"https://orcid.org/0000-0003-2806-8421","contributorId":356688,"corporation":false,"usgs":true,"family":"Woodruff","given":"Johnathan Lucas","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dietrich, Joel C. 0000-0001-5294-2874","orcid":"https://orcid.org/0000-0001-5294-2874","contributorId":352432,"corporation":false,"usgs":false,"family":"Dietrich","given":"Joel C.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":945223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wirasaet, Damrongsak","contributorId":359382,"corporation":false,"usgs":false,"family":"Wirasaet","given":"Damrongsak","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":945224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Andrew B.","contributorId":359383,"corporation":false,"usgs":false,"family":"Kennedy","given":"Andrew B.","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":945225,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bolster, Diogo","contributorId":266171,"corporation":false,"usgs":false,"family":"Bolster","given":"Diogo","email":"","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":945226,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luettich, Richard A.","contributorId":359386,"corporation":false,"usgs":false,"family":"Luettich","given":"Richard A.","affiliations":[{"id":55603,"text":"University of North Carolina Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":945227,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267803,"text":"70267803 - 2025 - Integrated distribution modeling resolves asynchrony between bat population impacts and occupancy trends through latent abundance","interactions":[],"lastModifiedDate":"2025-06-02T14:57:26.126575","indexId":"70267803","displayToPublicDate":"2025-05-30T09:51:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5729,"text":"Communications Biology","active":true,"publicationSubtype":{"id":10}},"title":"Integrated distribution modeling resolves asynchrony between bat population impacts and occupancy trends through latent abundance","docAbstract":"Monitoring populations is challenging for cryptic species with seasonal life cycles, where data from multiple field techniques are commonly collected and analyzed as multiple lines of evidence. Data integration can provide comprehensive inferences while improving accuracy, precision, and scope but faces challenges in modeling misaligned resolutions and observational uncertainties. We developed a multi-scale, integrated species distribution model (MS-iSDM) for North American bats to combine data across monitoring types and seasons using joint likelihood methods, observational models with false-negatives and false-positives, and seasonal migratory connectivity. We applied this model to 11 years of data for an imperiled bat species (tricolored bat, Perimyotis subflavus). Relative abundance and occupancy were linked with multi-scale predictors, revealing clear patterns of population declines, but with important differences in spatial trends (abundance: corresponded with white-nose syndrome impacts, occupancy: at the range periphery) and overall severity (abundance: -74.8%, 95% CRI: -79.7% to -69.3%; occupancy: -35.5%, 95% CRI: -41.1% to -30.2%). The asynchrony between occupancy trends and population impacts was explained as an emergent pattern of spatiotemporal variation in abundance in the integrated distribution model. Compared to multiple lines of evidence, the integrated model provided consensus-estimates, increased precision and spatiotemporal scope, and strengthened evidence of population declines.
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The prosperity of our Nation depends on generating a resilient supply of domestic critical minerals; mine waste may be an untapped source of these commodities. Mine waste from centuries of legacy mining persist on the landscape and may contain critical minerals and other valuable commodities previously deemed uneconomic to recover. At modern mines, the financial viability of recovering byproduct critical minerals, which are not the primary target, may be marginal and can ultimately destine them to mine waste. Further, mine waste can be a liability for the mining company or, at legacy mines, the taxpayer because of its effect on the landscape. The U.S. Geological Survey (USGS) has several initiatives to evaluate critical mineral resources in various types of waste. This factsheet highlights studies of mine waste carried out by USGS scientists at the Geology, Energy & Minerals Science Center in collaboration with other science centers funded through the USGS Mineral Resources Program. Recovery of critical minerals from mine waste can aid in remediation efforts and increase domestic supply of vital mineral resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253026","usgsCitation":"Piatak, N., White, S.J., Hayes, S., and Seal, R.R., II, 2025, Critical minerals in mine waste: U.S. Geological Survey Fact Sheet 2025–3026, 2 p., https://doi.org/10.3133/fs20253026.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-177321","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":485789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3026/coverthb2.jpg"},{"id":485793,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3026/images/"},{"id":485790,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3026/fs20253026.pdf","text":"Report","size":"3.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3026 PDF"},{"id":485791,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253026/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3026 HTML"},{"id":485792,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3026/fs20253026.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2025-3026 XML"},{"id":499024,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118591.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The glacial deposits underlying southeastern Michigan, northeastern Indiana, and northwestern Ohio are a substantial source of water to communities, agriculture, and industry in the region. Previous efforts to characterize aquifer materials in the area cited a need for additional information about the underlying hydrogeologic characteristics and related groundwater availability as well as improved mapping of the extent and properties of the glacial deposits.
Recent U.S. Geological Survey multi-State compilations of water-well drilling records have greatly increased access to high-resolution geologic data, particularly in glacial depositional environments. This study by the U.S. Geological Survey, in cooperation with the Ohio Environmental Protection Agency, uses processed data from the State-managed collections of well records to characterize the glacial deposits in the study area using two methods. The first method creates two-dimensional maps of basic hydrogeologic information commonly required for assessments of groundwater availability, including (1) total thickness of glacial deposits, (2) total thickness of coarse-grained deposits, (3) specific-capacity-based transmissivity and hydraulic conductivity, and (4) texture-based estimated equivalent horizontal and vertical hydraulic conductivity and transmissivity. The second method builds a hydrogeologic framework of the complex glacial aquifer through construction of a volumetric geologic model by using three-dimensional kriging.
Results of the volumetric model indicate that aquifer materials are primarily concentrated in the western parts of the study area near the Indiana-Ohio border. Coarse-grained sediments are also present as surficial deposits in the north of the study area where intermixing glacial advances created complex distributions of unconsolidated deposits. Two-dimensional maps of hydrogeologic properties support the volumetric model, showing thicknesses of coarse-grained deposits that reach up to 250 feet in the western sections of the study area and progressively thin to near absence in the east. Visualization of the aquifer materials with a volumetric model generally shows a highly discontinuous distribution of coarse- and fine-grained materials, with no clearly defined boundaries to delineate the extent of the aquifer. Comparisons of cross sections derived from the volumetric model with existing published maps support previous near-surface hydrogeologic interpretations while filling gaps where data are sparse, particularly in deeper parts of the aquifer. Both the two-dimensional maps and the volumetric model provide data that can directly inform assessments of groundwater availability, in addition to having future applications to studies of groundwater flow and transport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255008","collaboration":"Prepared in cooperation with the Ohio Environmental Protection Agency","usgsCitation":"Riddle, A.D., Arihood, L.D., Naylor, S., and Lampe, D.C., 2025, Hydrogeologic mapping and three-dimensional geologic modeling of glacial deposits in a multicounty area of southeastern Michigan, northeastern Indiana, and northwestern Ohio: U.S. Geological Survey Scientific Investigations Report 2025–5008, 47 p., https://doi.org/10.3133/sir20255008.","productDescription":"Report: viii, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146138","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":486276,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5008/images/"},{"id":486275,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5008/sir20255008.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5008 XML"},{"id":486274,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5008 HTML"},{"id":486273,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5008/sir20255008.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5008 PDF"},{"id":486272,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5008/coverthb.jpg"},{"id":499040,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118589.htm","linkFileType":{"id":5,"text":"html"}},{"id":486277,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13BO3GJ","text":"USGS data release","linkHelpText":"Hydrogeologic framework of the glacial deposits in a multicounty area of southeastern Michigan, northeastern Indiana, and northwestern Ohio"}],"country":"United States","state":"Indiana, Michigan, Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.45494559947987,\n 42.39763689085959\n ],\n [\n -85.45494559947987,\n 40.651853498464675\n ],\n [\n -83.24314510969089,\n 40.651853498464675\n ],\n [\n -83.24314510969089,\n 42.39763689085959\n ],\n [\n -85.45494559947987,\n 42.39763689085959\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
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Cosmogenic 26Al/10Be isochron burial dating is a powerful tool for dating sediment burial over the past several million years. By measuring in-situ 26Al and 10Be in a suite of samples from the same depth in a buried deposit, it is possible to quantify the inventory of cosmogenic nuclides produced after burial, date the burial of shallow sediments, identify sediment reworking, and calculate paleo-erosion rates. While this approach has been used to date materials around the world for over a decade, few published codes exist for performing 26Al/10Be isochron calculations. The isochron calculation options that are available typically rely on numerous files and libraries, rendering modification and troubleshooting difficult. Moreover, the widespread use of proprietary programming languages – and their associated addon packages – can place an additional financial burden on an already costly endeavor.
Pysochron (https://code.usgs.gov/recon/pysochron) provides a solution to these issues. In its base form, it exists as a single script that can be easily modified, upgraded, and shared. Because it was developed in an open-source environment, all required computational packages are available free of charge. A user-friendly interface allows rapid modification of calculation parameters, and an automated commentary on isochron results provides insights and recommendations. Pysochron has been validated with 40 published cosmogenic 26Al/10Be burial isochrons around the world, with burial ages ranging from ∼5 Ma to ∼180 ka. As such, it is a promising option for members of the cosmogenic nuclide community seeking a straightforward, cost-effective, and flexible solution to isochron burial dating challenges.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quageo.2025.101675","usgsCitation":"Odom, W.E., 2025, Pysochron: A Python-based solution for calculating cosmogenic 26Al/10Be isochron burial ages: Quaternary Geochronology, v. 89, 101675, 13 p., https://doi.org/10.1016/j.quageo.2025.101675.","productDescription":"101675, 13 p.","ipdsId":"IP-169287","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":490662,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quageo.2025.101675","text":"Publisher Index Page"},{"id":490400,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13PCYZA","text":"USGS data release","linkHelpText":"Pysochron"},{"id":490201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, William Elijah 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","email":"","middleInitial":"Elijah","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":939256,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70269899,"text":"70269899 - 2025 - Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model","interactions":[],"lastModifiedDate":"2025-08-06T15:12:41.202838","indexId":"70269899","displayToPublicDate":"2025-05-29T08:03:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22158,"text":"Journal of Hydrology, Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model","docAbstract":"Managing water resources in semiarid watersheds is challenging due to limited supply and uncertain future climate conditions. This paper examines the impact of future climate changes on an urban watershed in southern California using an integrated hydrologic model. GSFLOW modeling software is used to simulate the nonlinear relationships between climate trends and precipitation partitioning into ET, runoff, and subsurface storage. Four global circulation models (GCMs), each with two greenhouse-gas scenarios, RCP45 and RCP85 are used to project future climate conditions. GCMs include the CanESM2, CNRM-CM5, HadGEM2-ES, and MIROC5 models. The model's simulated hydrologic conditions are compared with historical data to assess changes in water budgets and groundwater supply. Results indicate decreased groundwater storage in most scenarios due to increased natural evapotranspiration, vegetation consumptive use, and streamflow out of the watershed. Only scenarios with substantially increased future precipitation show increased groundwater storage. The study also highlights increased future aridity despite the rise in precipitation and large precipitation events forecast by GCMs, which increase the risk of urban floods and decrease stream leakage and water available to vegetation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2025.102461","usgsCitation":"Ryter, D.W., Alzraiee, A.H., and Niswonger, R., 2025, Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model: Journal of Hydrology, Regional Studies, v. 60, 102461, 16 p., https://doi.org/10.1016/j.ejrh.2025.102461.","productDescription":"102461, 16 p.","ipdsId":"IP-153865","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":493789,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2025.102461","text":"Publisher Index Page"},{"id":493644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yucaipa Valley watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.09271702186436,\n 34.0874203948469\n ],\n [\n -117.09271702186436,\n 33.9725594754417\n ],\n [\n -116.9002906646693,\n 33.9725594754417\n ],\n [\n -116.9002906646693,\n 34.0874203948469\n ],\n [\n -117.09271702186436,\n 34.0874203948469\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ryter, Derek W. 0000-0002-2488-626X dryter@usgs.gov","orcid":"https://orcid.org/0000-0002-2488-626X","contributorId":3395,"corporation":false,"usgs":true,"family":"Ryter","given":"Derek","email":"dryter@usgs.gov","middleInitial":"W.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. rniswon@usgs.gov","contributorId":146547,"corporation":false,"usgs":false,"family":"Niswonger","given":"Richard G.","email":"rniswon@usgs.gov","affiliations":[],"preferred":false,"id":944911,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271939,"text":"70271939 - 2025 - Carbonatite-hosted residual REE deposits","interactions":[],"lastModifiedDate":"2025-09-25T13:56:19.739833","indexId":"70271939","displayToPublicDate":"2025-05-28T08:52:38","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Carbonatite-hosted residual REE deposits","docAbstract":"Rare earth elements (REEs) occur in magmatic rocks but are especially enriched in carbonatite and alkaline silicates. If these rocks are chemically weathered, then the REEs may become further enriched within the regolith developed from these rocks. Primary magmatic REE minerals, as well as the various carbonate minerals and apatite, provide the REEs which, under pervasive chemical weathering, are incorporated within low-temperature REE minerals forming within the regolith. Many of these minerals, as well as their textures, are characteristic of this mode of formation. Lateritic conditions of weathering are instrumental in producing a thick, weathered, or regolith, profile, and the roles of sulfide oxidation, fluctuating groundwater tables, and downward mass wasting due to carbonate dissolution are identified as the most important controls on REE enrichment in the regolith.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geology, geochemistry and formation of supergene mineral deposits in deeply weathered terrain","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-031-75733-4_7","usgsCitation":"Siegfried, P.R., Wall, F., and Verplanck, P., 2025, Carbonatite-hosted residual REE deposits, chap. of Geology, geochemistry and formation of supergene mineral deposits in deeply weathered terrain, p. 179-206, https://doi.org/10.1007/978-3-031-75733-4_7.","productDescription":"18 p.","startPage":"179","endPage":"206","ipdsId":"IP-139371","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":502409,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":496076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-05-28","publicationStatus":"PW","contributors":{"editors":[{"text":"Bowell, Robert J.","contributorId":150175,"corporation":false,"usgs":false,"family":"Bowell","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":17927,"text":"SRK Consulting Ltd.","active":true,"usgs":false}],"preferred":false,"id":949475,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Butt, Charles R.M.","contributorId":361797,"corporation":false,"usgs":false,"family":"Butt","given":"Charles","middleInitial":"R.M.","affiliations":[],"preferred":false,"id":949476,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Siegfried, Peter R 0000-0001-8254-9889","orcid":"https://orcid.org/0000-0001-8254-9889","contributorId":361785,"corporation":false,"usgs":false,"family":"Siegfried","given":"Peter","middleInitial":"R","affiliations":[{"id":86350,"text":"Camborne School of Mines","active":true,"usgs":false}],"preferred":false,"id":949449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wall, Frances 0000-0002-5393-4400","orcid":"https://orcid.org/0000-0002-5393-4400","contributorId":361786,"corporation":false,"usgs":false,"family":"Wall","given":"Frances","affiliations":[{"id":86350,"text":"Camborne School of Mines","active":true,"usgs":false}],"preferred":false,"id":949450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Verplanck, Philip L. 0000-0002-3653-6419","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":212813,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","middleInitial":"L.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":949451,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267399,"text":"fs20253030 - 2025 - The 3D Elevation Program—Supporting Connecticut's economy","interactions":[],"lastModifiedDate":"2025-05-28T13:41:26.483587","indexId":"fs20253030","displayToPublicDate":"2025-05-27T12:20:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3030","displayTitle":"The 3D Elevation Program—Supporting Connecticut’s Economy","title":"The 3D Elevation Program—Supporting Connecticut's economy","docAbstract":"Connecticut has a diverse, largely forested landscape characterized by hills and low mountains in the Western Upland, hills in the Eastern Upland, ridges and broad valleys in the Central Lowland, and many beaches and harbors along the coast of Long Island Sound. Connecticut is manufacturing and service focused, ranking almost highest among the 50 States in the United States in personal income per capita. Due to Connecticut’s dense population, many people, especially the approximately 60 percent living near the coast, may be affected by climate-driven disasters. High-quality elevation data can inform the activities of many nongovernmental organizations and municipal and academic entities statewide, resulting in substantial economic impact. Government at the State and local levels relies on these data to support regulatory permitting, resource and infrastructure management, and various engineering and planning-level analyses. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Connecticut. The status of available and in-progress 3DEP baseline lidar data in Connecticut is shown. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $4.40 million in new benefits annually to the State. The top 10 Connecticut business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Wildlife research and management can be disruptive to wildlife. By advancing our understanding of the impacts of these activities, we can reduce adverse effects, improve decision-making, and enhance the outcomes of research and management. During 2017–2019, we observed the responses of denning female American black bears (Ursus americanus) to 3 types of routine research and management activities in Florida, USA: (1) a low-level, nonintrusive human approach near the natal den (n H 44); (2) a high-level, intrusive human approach involving cub handling (n H 42); and (3) a prescribed burn within 1 km of the den during the denning season (n H 11). We measured responses (flight distance, time away, and postdisturbance denning behavior) using Global Positioning System collars programmed to record a location every 2 hours. We observed minimal response from bears to low-level human disturbances. In contrast, all bears fled after high-level human disturbances, with responses ranging from staying nearby throughout the disturbance and quickly returning to cubs, to fleeing several kilometers and abandoning cubs. On average, bears fled approximately 380 m from the den and returned to their cubs 7 hours postdisturbance. After returning, most bears relocated their cubs to a new den site, on average 125 m away. Responses to prescribed fire ranged from no measurable response and no den relocation to den site abandonment with cub mortality. Through generalized linear modeling, we found that adult female time away was positively associated with cub age. We found that annual cub survival was negatively associated with fire exposure in the den and with continued denning at a disturbed den site following high-level disturbance. In areas where bears are easily displaced from dens, these results provide insights that may improve bear research and habitat management decisions.
","language":"English","publisher":"International Association for Bear Research and Management","doi":"10.2192/ursus-d-24-00011r1","usgsCitation":"Doran-Myers, D., Gregory, K., McGowan, C.P., Hull, V., and Scheick, B.K., 2025, Denning black bear response to anthropogenic disturbance and implications for cub survival in Florida: Ursus, v. 2525, no. 36e7, p. 1-20, https://doi.org/10.2192/ursus-d-24-00011r1.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-162854","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2192/ursus-d-24-00011r1","text":"Publisher Index Page"},{"id":494389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.70700771970081,\n 30.533149376429677\n ],\n [\n -85.70700771970081,\n 29.526013452470593\n ],\n [\n -84.16956484525814,\n 29.526013452470593\n ],\n [\n -84.16956484525814,\n 30.533149376429677\n ],\n [\n -85.70700771970081,\n 30.533149376429677\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2525","issue":"36e7","noUsgsAuthors":false,"publicationDate":"2025-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Doran-Myers, Darcy","contributorId":359973,"corporation":false,"usgs":false,"family":"Doran-Myers","given":"Darcy","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":946611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gregory, Kaili","contributorId":359974,"corporation":false,"usgs":false,"family":"Gregory","given":"Kaili","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":946612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":10145,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":946613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hull, Vanessa","contributorId":340791,"corporation":false,"usgs":false,"family":"Hull","given":"Vanessa","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":946614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scheick, Brian K.","contributorId":359976,"corporation":false,"usgs":false,"family":"Scheick","given":"Brian","middleInitial":"K.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":946615,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268074,"text":"70268074 - 2025 - Combining acoustic telemetry and side-scan sonar to estimate abundance of endangered shortnose sturgeon in the Hudson River, New York","interactions":[],"lastModifiedDate":"2025-07-10T14:53:41.778146","indexId":"70268074","displayToPublicDate":"2025-05-26T09:53:02","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":"Combining acoustic telemetry and side-scan sonar to estimate abundance of endangered shortnose sturgeon in the Hudson River, New York","docAbstract":"For endangered shortnose sturgeon (Acipenser brevirostrum), the ability to estimate and monitor population size is critical for tracking species’ recovery. Yet, contemporary abundance estimates have not been completed for many shortnose sturgeon populations, largely owing to the difficulty in using traditional abundance estimators for sturgeons. Here, we estimate the adult shortnose sturgeon population size of the Hudson River, NY by integrating data from two largely passive sampling methods – acoustic telemetry and side-scan sonar – into a Bayesian hierarchical model of abundance. We estimated the adult abundance to be 69,798 individuals (95% CI = 9,207-185,666), making the Hudson River the largest extant shortnose sturgeon population. Despite this, the population remains vulnerable to localized disturbances, as over 40% of the population congregated in a small overwintering habitat that coincides with an area of high anthropogenic activity. Accordingly, recurrent demographic surveys may be beneficial for gaining insight into the relative effects of anthropogenic and naturally stochastic processes shaping shortnose sturgeon demography. Our modeling framework provides a relatively low-cost alternative for future demographic monitoring of species of conservation concern.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0395","usgsCitation":"Higgs, A., White, S.L., Madsen, J., Kazyak, D.C., Fox, D., Pendleton, R., Bonemery, A., Smolinski, T., Simmonds, A., and Sullivan, P., 2025, Combining acoustic telemetry and side-scan sonar to estimate abundance of endangered shortnose sturgeon in the Hudson River, New York: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-12, https://doi.org/10.1139/cjfas-2024-0395.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-173509","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":493294,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2024-0395","text":"Publisher Index 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Wildlife","active":true,"usgs":false}],"preferred":false,"id":940123,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":940124,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, Dewayne","contributorId":340954,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","affiliations":[{"id":37219,"text":"Delaware State University","active":true,"usgs":false}],"preferred":false,"id":940125,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pendleton, Richard","contributorId":348720,"corporation":false,"usgs":false,"family":"Pendleton","given":"Richard","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":940126,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bonemery, Adam","contributorId":351166,"corporation":false,"usgs":false,"family":"Bonemery","given":"Adam","affiliations":[{"id":83929,"text":"Cornell University/NY Dept. of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":940127,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smolinski, Tomasz","contributorId":356810,"corporation":false,"usgs":false,"family":"Smolinski","given":"Tomasz","affiliations":[{"id":85239,"text":"Delware State University","active":true,"usgs":false}],"preferred":false,"id":940128,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Simmonds, Amanda","contributorId":351168,"corporation":false,"usgs":false,"family":"Simmonds","given":"Amanda","affiliations":[{"id":83929,"text":"Cornell University/NY Dept. of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":940129,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sullivan, Patrick","contributorId":348055,"corporation":false,"usgs":false,"family":"Sullivan","given":"Patrick","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":940130,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70268934,"text":"70268934 - 2025 - Factors associated with survival, recovery, and movements in the western Gulf Coast population of mottled ducks","interactions":[],"lastModifiedDate":"2025-07-11T15:01:30.51809","indexId":"70268934","displayToPublicDate":"2025-05-26T07:55:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Factors associated with survival, recovery, and movements in the western Gulf Coast population of mottled ducks","docAbstract":"The mottled duck (Anas fulvigula) is nonmigratory and a priority species for regional conservation and management because of its limited range and declining population trajectory in the western Gulf Coast (WGC) of Louisiana and Texas, USA. We developed multistate dead-recovery models for banding and recovery data (1997–2020) to evaluate potential drivers of survival, recovery, and post-summer movements for the WGC population of mottled ducks in Louisiana and Texas. Annual survival was most strongly associated with sex and year, with females having lower survival ( ± = 0.544 ± 0.114) than males (0.619 ± 0.062). Of the 32 environmental covariates tested, fall precipitation was the factor most strongly associated with survival. Conditional recovery probability (i.e., given mortality, the probability a bird had been shot by a hunter, retrieved, and had their band number reported) varied by sex, age, geographic state, and year, with juvenile males generally having highest conditional recovery (0.303 ± 0.072), followed by juvenile females (0.201 ± 0.100), adult males (0.156 ± 0.038), and adult females (0.095 ± 0.057). Estimates of harvest probabilities followed similar patterns as conditional recovery. Models containing effects of harvest regulations on conditional recovery were not competitive compared to models with general year effects; however, post hoc analyses suggested conditional recovery and harvest probabilities for adult and juvenile females decreased with the daily bag limit reduction in Louisiana and, for juvenile females, implementation of the 5-day closure regulation in Texas. Post-summer movement was substantially higher in the direction of Texas to Louisiana, decreased with distance to the Louisiana–Texas border, was higher for males than females, and varied with winter precipitation. These results contribute to a better understanding of the factors influencing demographic performance, harvest, and movement between states with differing harvest regulations and environmental pressures, which is important for mottled duck conservation planning. Wildlife managers can consider expanding banding effort throughout the full range of the WGC population and collecting and reporting live-recapture data to allow for stronger population-level inferences and increased power to detect differences in important demographic parameters at more refined spatial scales.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70038","usgsCitation":"Malachowski, C., Kendall, W.L., Collins, D., Kraai, K.J., Olszak, J., and Reynolds, L., 2025, Factors associated with survival, recovery, and movements in the western Gulf Coast population of mottled ducks: Journal of Wildlife Management, v. 89, no. 6, e70038, 33 p., https://doi.org/10.1002/jwmg.70038.","productDescription":"e70038, 33 p.","ipdsId":"IP-168786","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":492473,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70038","text":"Publisher Index Page"},{"id":492131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.98534654094911,\n 34.74317254350352\n ],\n [\n -99.98534654094911,\n 27.62034513580788\n ],\n [\n -89.48377067387685,\n 27.62034513580788\n ],\n [\n -89.48377067387685,\n 34.74317254350352\n ],\n [\n -99.98534654094911,\n 34.74317254350352\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Malachowski, Christopher P.","contributorId":357821,"corporation":false,"usgs":false,"family":"Malachowski","given":"Christopher P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":942661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":942662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Daniel P.","contributorId":356157,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":942663,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraai, Kevin J.","contributorId":346855,"corporation":false,"usgs":false,"family":"Kraai","given":"Kevin","email":"","middleInitial":"J.","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":942664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olszak, Jason","contributorId":357822,"corporation":false,"usgs":false,"family":"Olszak","given":"Jason","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":942665,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reynolds, Larry","contributorId":357824,"corporation":false,"usgs":false,"family":"Reynolds","given":"Larry","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":942666,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}