Geostatistics is the most commonly used probabilistic approach for modeling earth systems, including quality parameters of various geoenergy resources. In geostatistics, estimates, either on a point or block support, are generated as a spatially-weighted average of surrounding samples. The optimal weights are determined through the stationary variogram model which accounts for the spatial structure of the samples. Recently, efficient modeling workflows using various machine learning algorithms (MLAs) have been expanded to the spatial context for modeling geological heterogeneity. The flexible use of MLAs as a spatial estimation tool stems mainly from the fact that unlike kriging, they do not require any variogram, nor do they depend strongly on a prior stationarity assumption (i.e., second order stationarity). This study evaluates the performance of two MLAs (ensemble super learner and elliptical radial basis neural network), ordinary kriging, and hybrid spatial modeling approaches using ordinary intrinsic collocated cokriging. The aforementioned modeling techniques are compared for estimating resources for four coal variables (wash yield, ash yield, calorific value and thickness) as an example. The results suggest that MLAs, when implemented alone, do not outperform ordinary kriging, but the estimation accuracy of the final model, measured by the root mean squared error tends to subtly improve (
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2023.104328","usgsCitation":"Erdogan Erten, G., Erten, O., Karacan, C.O., Boisvert, J., and Deutsch, C.V., 2023, Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources: International Journal of Coal Geology, v. 276, 104328, 16 p., https://doi.org/10.1016/j.coal.2023.104328.","productDescription":"104328, 16 p.","ipdsId":"IP-149602","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":419585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.33693408888138,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.563642070051216\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erdogan Erten, Gamze","contributorId":317909,"corporation":false,"usgs":false,"family":"Erdogan Erten","given":"Gamze","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erten, Oktay","contributorId":300145,"corporation":false,"usgs":false,"family":"Erten","given":"Oktay","email":"","affiliations":[],"preferred":false,"id":879701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":879702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boisvert, Jeff","contributorId":317910,"corporation":false,"usgs":false,"family":"Boisvert","given":"Jeff","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deutsch, Clayton V.","contributorId":317911,"corporation":false,"usgs":false,"family":"Deutsch","given":"Clayton","email":"","middleInitial":"V.","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879704,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256432,"text":"70256432 - 2023 - Effects of sucker gigging on fish populations in Oklahoma scenic rivers","interactions":[],"lastModifiedDate":"2024-09-09T15:31:56.213633","indexId":"70256432","displayToPublicDate":"2023-08-04T10:26:42","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-151-2023","title":"Effects of sucker gigging on fish populations in Oklahoma scenic rivers","docAbstract":"Suckers (Catostomidae) are ecologically important, and some support popular fisheries, despite not being considered ‘sport fish’ in most states. Gigging suckers is a popular and culturally significant pastime in the Ozark Highlands, but little is known about the effect of gigging harvest on population dynamics of suckers. Therefore, research is needed to determine safe levels of sucker harvest that ensure sustainability of sucker gigging and protect overall ecosystem function. The objectives of this study were to: 1) determine the spatial distribution of common sucker species during spawning season (when sucker gigging is most effective), 2) determine the population size, age structure , and total mortality rate for common sucker species, and 3) model the effects of different harvest rates on sucker populations to determine the harvest rate at which growth overfishing and recruitment overfishing begin. Suckers were sampled using electrofishing, modified fyke netting, gillnetting, hoop netting, and seining and marked with passive integrated transponder (PIT) tags to provide information about population size, demographics, and coarse-scale movement patterns. A subset of fish sampled using the above gears and additional fish collected during gigging tournaments in 2017-2019 and 2021-2022 (no tournament was held in 2020) were used for age analyses. Tournament data collected prior to the initiation of this project were obtained from the state agency. Data from gigging tournaments indicated Golden Redhorse Moxostoma erythrurum, Black Redhorse M. duquesnei, White Sucker Catostomus commersonii, and Spotted Sucker Minytrema melanops were vulnerable to gigging harvest. Selection by giggers for larger individuals was apparent for all species except Golden Redhorse in 2019. Spotted Suckers constituted most fish harvested, but the proportion of each species harvested still varied among years. A total of 943 fish were aged from samples obtained from 2017 to 2022 and results from subsequent analyses indicated a high degree of variation in growth rates within and among species. Over 4,700 suckers were tagged with PIT tags and over 400 recaptures of these tagged fish were made since autumn 2018. Preliminary analyses indicate survival was consistent across samples and species, and detection rates varied by sampling event (3-month periods). Our most likely top multistrata model suggested that a large portion of fish within the upper Spavinaw, lower Spavinaw, and reservoir sections remain in these locations year-round (means: 0.46 – 0.67). Despite this, transition probabilities are still high for movement from upper Spavinaw to lower Spavinaw (mean: 0.32) and from lower Spavinaw to upper Spavinaw (mean: 0.38). Likewise, transition probabilities were high for movement from lower Spavinaw to the reservoir (mean: 0.15) and from the reservoir to lower Spavinaw (mean: 0.32). Transition probabilities between upper Spavinaw and the reservoir were low in both directions (means < 0.01). Population sizes, growth trajectories and length-weight relationships varied among species. Preliminary harvest models suggest species-specific regulation may be scientifically appropriate; however, it may be difficult for giggers to identify species while gigging. Based on our model results, there appears to be little risk of recruitment or growth overfishing for any species at current exploitation levels.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Zetner, D., Shoup, D., and Brewer, S., 2023, Effects of sucker gigging on fish populations in Oklahoma scenic rivers: Cooperator Science Series FWS/CSS-151-2023, ii, 60 p.","productDescription":"ii, 60 p.","ipdsId":"IP-153396","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431783,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/effects-sucker-gigging-fish-populations-oklahoma-scenic-rivers"},{"id":433623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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E.","contributorId":242905,"corporation":false,"usgs":false,"family":"Shoup","given":"D. E.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907354,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907355,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247436,"text":"70247436 - 2023 - DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards","interactions":[],"lastModifiedDate":"2023-08-08T13:37:18.248038","indexId":"70247436","displayToPublicDate":"2023-08-04T08:30:10","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards","docAbstract":"Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"KDD '23: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"ACM SIGKDD Conference on Knowledge Discovery & Data Mining","conferenceDate":"August 6-10, 2023","conferenceLocation":"Long Beach, CA","language":"English","publisher":"Association for Computing Machinery","doi":"10.1145/3580305.3599807","usgsCitation":"Li, X., Burgi, P.M., Ma, W., Noh, H., Wald, D.J., and Xu, S., 2023, DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards, in KDD '23: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, v. 29, Long Beach, CA, August 6-10, 2023, p. 4391-4403, https://doi.org/10.1145/3580305.3599807.","productDescription":"13 p.","startPage":"4391","endPage":"4403","ipdsId":"IP-149697","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":419595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","noUsgsAuthors":false,"publicationDate":"2023-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Xuechun","contributorId":317874,"corporation":false,"usgs":false,"family":"Li","given":"Xuechun","email":"","affiliations":[{"id":69176,"text":"Stonybrook University","active":true,"usgs":false}],"preferred":false,"id":879620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgi, Paula Madeline 0000-0003-3001-5759","orcid":"https://orcid.org/0000-0003-3001-5759","contributorId":317875,"corporation":false,"usgs":true,"family":"Burgi","given":"Paula","email":"","middleInitial":"Madeline","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":879621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ma, Wei","contributorId":317876,"corporation":false,"usgs":false,"family":"Ma","given":"Wei","email":"","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":879622,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, Haeyoung","contributorId":317877,"corporation":false,"usgs":false,"family":"Noh","given":"Haeyoung","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":879623,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":879624,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":879625,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247383,"text":"sir20235073 - 2023 - Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin","interactions":[],"lastModifiedDate":"2026-03-12T20:43:24.534885","indexId":"sir20235073","displayToPublicDate":"2023-08-03T14:21:59","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5073","displayTitle":"Response in the Water Quality of Delavan Lake, Wisconsin, to Changes in Phosphorus Loading—Setting New Goals for Loading from its Drainage Basin","title":"Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin","docAbstract":"During 1989–92, an extensive rehabilitation project was completed in and around Delavan Lake, Wisconsin, to improve the lake’s water quality. However, in 2016, the lake was listed by the Wisconsin Department of Natural Resources as impaired for excessive algal growth (high chlorophyll a concentrations), and high phosphorus input was listed as its likely cause. In addition, the recent (2017–21) mean summer water clarity (as measured with a Secchi disk) was shallower than the goal set by the community (3.0 meters). Based primarily on flow and water-quality data collected in Jackson Creek, which is the main tributary of the lake, the mean annual phosphorus loading to the lake during water years (WYs) 2017–21 was 6,570 kilograms per year (kg/yr), and 306 kg/yr came from uncontrollable sources (atmospheric deposition and groundwater). Phosphorus loading during these years was about 48 percent higher than the long-term mean loading from WY 1984 to WY 2021. Based on results from Canfield-Bachmann phosphorus models, Carlson trophic state index relations, and the Jones and Bachmann chlorophyll a relation, external phosphorus loading would need to be decreased from 6,570 to 5,270 kg/yr (a 21-percent reduction in the potentially controllable external phosphorus load from the base period of WYs 2017–21) for chlorophyll a concentrations greater than 20 micrograms per liter to be detected no more than 5.0 percent of the time (the Wisconsin Department of Natural Resources criterion for chlorophyll a impairment for the lake). Based on Carlson trophic state index relations, external loading would need to be decreased from 6,570 to 4,380 kg/yr (a 35-percent reduction in the potentially controllable external phosphorus load) for summer mean Secchi depths to increase to 3.0 meters. Therefore, for Delavan Lake to reach the water-quality criteria for impairment and the goals for all three water-quality constituents, a 35-percent reduction in the potentially controllable phosphorus load is needed, which equates to a reduction in total phosphorus loading from 6,570 to 4,380 kg/yr. A 35-percent reduction in phosphorus loading to improve the water quality of Delavan Lake is less than the 49-percent reduction in phosphorus loading required for the area near Delavan Lake to improve the water quality of the Rock River and its tributaries indicated in the Rock River total maximum daily load.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235073","collaboration":"Prepared in cooperation with the Town of Delavan and the Delavan Lake Sanitary District","usgsCitation":"Robertson, D.M., Siebers, B.J., and Fredrick, R.A., 2023, Response in the water quality of Delavan Lake, Wisconsin, to changes in phosphorus loading—Setting new goals for loading from its drainage basin: U.S. Geological Survey Scientific Investigations Report 2023–5073, 28 p., https://doi.org/10.3133/sir20235073.","productDescription":"Report: viii, 28 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-148703","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501038,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115123.htm","linkFileType":{"id":5,"text":"html"}},{"id":419534,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235073/full","text":"Report"},{"id":419467,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":419466,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H85BK0","text":"USGS data release","linkHelpText":"Eutrophication models to simulate changes in the water quality of Green Lake, Wisconsin in response to changes in phosphorus loading, with supporting water-quality data for the lake, its tributaries, and atmospheric deposition"},{"id":419465,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5073/images/"},{"id":419464,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5073/sir20235073.XML","linkFileType":{"id":8,"text":"xml"}},{"id":419463,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5073/sir20235073.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5073"},{"id":419462,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5073/coverthb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Delavan Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.69602097806326,\n 42.574403384923556\n ],\n [\n -88.52306051926304,\n 42.574403384923556\n ],\n [\n -88.52306051926304,\n 42.66531414833537\n ],\n [\n -88.69602097806326,\n 42.66531414833537\n ],\n [\n -88.69602097806326,\n 42.574403384923556\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The Mississippi Alluvial Plain is one of the most important agricultural regions in the United States, and crop productivity relies on groundwater irrigation from an aquifer system whose full capacity is unknown. Groundwater withdrawals from the Mississippi River Valley alluvial aquifer have resulted in substantial groundwater-level declines and reductions in base flow in streams within the Mississippi Alluvial Plain. These effects are limiting well production and threatening future water availability in the region.
A comprehensive assessment of water availability in the Mississippi Alluvial Plain is critically important for making well-informed management decisions about sustainability, establishing best practices for water use, and predicting changes to water levels in the Mississippi Alluvial Plain over the next 50–100 years. The first step in the new regional modeling effort was to run the existing Mississippi Embayment Regional Aquifer Study (MERAS) model and perform data-worth and uncertainty analyses to prioritize data collection efforts to improve model forecasts. Parameter estimation indicated that streambed conductance was one of the variables that the model was most sensitive to, but little data were available to constrain those general estimates.
From this characterization of the existing data, a map of the streams that the MERAS model was most sensitive to was created by the U.S. Geological Survey to guide the collection of 862 kilometers of waterborne resistivity surveys within the Delta region of Mississippi to characterize streambed lithology. This technique characterizes the streambed itself and the 15–30 meters below the streambed that control the exchange of water between the stream and the alluvial aquifer. These data can be used to map changes in the lithology of the streambed and identify areas of potential groundwater/surface-water exchange. Additionally, electrical and nuclear well logs from the study area were compared to facilitate the development of a petrophysical relation between the waterborne resistivity data and hydraulic conductivity. Resistivity values may then be used as a cost-effective way to approximate aquifer hydraulic conductivity distributions for use in regional groundwater models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3500","issn":"2329-132X","collaboration":"Prepared in cooperation with the Arkansas Department of Health, Arkansas Game and Fish Commission, Delta Council, Delta FARM, Delta Sustainable Water Resources Task Force, Delta Wildlife HydroGeophysics Group, Aarhus University, Mississippi Department of Environmental Quality, Mississippi State University, Missouri Department of Natural Resources, The Nature Conservancy, U.S. Army Corps of Engineers, U.S. Department of Agriculture-Agricultural Research Service, University of Arkansas, University of Mississippi, Yazoo Mississippi Delta Joint Water Management District","usgsCitation":"Adams, R.F., Miller, B.V., Kress, W.H., Minsley, B.J., and Rigby, J.R., 2023, Estimating streambed hydraulic conductivity for selected streams in the Mississippi Alluvial Plain using continuous resistivity profiling methods—Delta region: U.S. Geological Survey Scientific Investigations Map 3500, 2 sheets, https://doi.org/10.3133/sim3500.","productDescription":"2 Sheets: 45.00 x 34.25 inches and 45.00 x 34.55 inches","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-115128","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":419523,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WQPRFB","text":"USGS—Waterborne resistivity inverted models, Mississippi Alluvial Plain, 2016–2018"},{"id":419522,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3500/sim3500_sheet2.pdf","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3500 sheet 2"},{"id":419521,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3500/sim3500_sheet1.pdf","size":"15.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3500 sheet 1"},{"id":419520,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3500/coverthb.jpg"},{"id":500206,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115125.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arkansas, Louisiana, Mississippi","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90,\n 35\n ],\n [\n -91.25,\n 35\n ],\n [\n -91.25,\n 31\n ],\n [\n -90,\n 31\n ],\n [\n -90,\n 35\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"For more information about this publication, contact
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
For additional information, visit
https://www.usgs.gov/centers/lmg-water/
Ophidiomycosis (snake fungal disease) is an infectious disease caused by the fungus Ophidiomyces ophidiicola to which all snake species appear to be susceptible. Significant variation has been observed in clinical presentation, progression of disease, and response to treatment, which may be due to genetic variation in the causative agent. Recent phylogenetic analysis based on whole-genome sequencing identified that O. ophidiicola strains from the United States formed a clade distinct from European strains, and that multiple clonal lineages of the clade are present in the United States. The purpose of this study was to design a qPCR-based genotyping assay for O. ophidiicola, then apply that assay to swab-extracted DNA samples to investigate whether the multiple O. ophidiicola clades and clonal lineages in the United States have specific geographic, taxonomic, or temporal predilections. To this end, six full genome sequences of O. ophidiicola representing different clades and clonal lineages were aligned to identify genomic areas shared between subsets of the isolates. Eleven hydrolysis-based Taqman primer-probe sets were designed to amplify selected gene segments and produce unique amplification patterns for each isolate, each with a limit of detection of 10 or fewer copies of the target sequence and an amplification efficiency of 90–110%. The qPCR-based approach was validated using samples from strains known to belong to specific clades and applied to swab-extracted O. ophidiicola DNA samples from multiple snake species, states, and years. When compared to full-genome sequencing, the qPCR-based genotyping assay assigned 75% of samples to the same major clade (Cohen’s kappa = 0.360, 95% Confidence Interval = 0.154–0.567) with 67–77% sensitivity and 88–100% specificity, depending on clade/clonal lineage. Swab-extracted O. ophidiicola DNA samples from across the United States were assigned to six different clonal lineages, including four of the six established lineages and two newly defined groups, which likely represent recombinant strains of O. ophidiicola. Using multinomial logistic regression modeling to predict clade based on snake taxonomic group, state of origin, and year of collection, state was the most significant predictor of clonal lineage. Furthermore, clonal lineage was not associated with disease severity in the most intensely sampled species, the Lake Erie watersnake (Nerodia sipedon insularum). Overall, this assay represents a rapid, cost-effective genotyping method for O. ophidiicola that can be used to better understand the epidemiology of ophidiomycosis.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0289159","usgsCitation":"Haynes, E., Lorch, J., and Allender, M.C., 2023, Development and application of a qPCR-based genotyping assay for Ophidiomyces ophidiicola to investigate the epidemiology of ophidiomycosis: PLoS ONE, v. 18, no. 8, e0289159, 24 p., https://doi.org/10.1371/journal.pone.0289159.","productDescription":"e0289159, 24 p.","ipdsId":"IP-153457","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":442520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0289159","text":"Publisher Index Page"},{"id":419734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Haynes, Ellen","contributorId":302417,"corporation":false,"usgs":false,"family":"Haynes","given":"Ellen","email":"","affiliations":[{"id":65476,"text":"Southeastern Cooperative Wildlife Disease Study, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":880033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":260164,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":880034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allender, Matthew C.","contributorId":192522,"corporation":false,"usgs":false,"family":"Allender","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":880035,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247412,"text":"70247412 - 2023 - Tracking carbon from subduction to outgassing along the Aleutian-Alaska Volcanic Arc","interactions":[],"lastModifiedDate":"2023-08-03T12:48:44.073761","indexId":"70247412","displayToPublicDate":"2023-08-03T07:37:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Tracking carbon from subduction to outgassing along the Aleutian-Alaska Volcanic Arc","docAbstract":"Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.
Invasive annual grasses can promote ecosystem state changes and habitat loss in the American Southwest. Non-native annual grasses such as Bromus spp. and Schismus spp. have invaded the Mojave Desert and degraded habitat through increased fire occurrence, severity, and shifting plant community composition. Thus, it is important to identify and characterize the areas where persistent invasion has occurred, identifying where subsequent habitat degradation has increased. Previous plot and landscape-scale analyses have revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these studies have been limited in spatial and temporal scales. Here we use Landsat imagery validated using an extensive network of plot data to map persistent and productive populations of invasive annual grass, called hot spots, across the entire Mojave Desert ecoregion over 12 years (2009–2020). We also identify important variables for predicting hot spot distribution using the Random Forest algorithm and identifying the most invaded subregions. We identified hot spots in over 5% of the Mojave Desert mostly on the western and eastern edges of the ecoregion, and invasive grasses were detected in over 90% of the Mojave Desert at least once in that time. Across the entire Mojave Desert, our results indicate that soil texture, aspect, winter precipitation, and elevation are the highest-ranking predictive variables of invasive grass hot spots, while anthropogenic variables contributed the least to the accuracy of the predictive model. The total area covered by hot spots varied significantly among subregions of the Mojave Desert. We found that anthropogenic variables became more important in explaining invasive annual establishment and persistence as spatial scale was reduced to the subregional level. Our findings have important implications for informing where land management actions can prioritize reducing invasive annual persistence and promoting restoration efforts.
Various interference reactions producing unwanted Ar isotopes from K, Ca, Cl and Ar require correction to satisfy the 40Ar/39Ar age equation. Using GEANT4, we design and build a model Cadmium Lined In Core Irradiation Tube (CLICIT) irradiation facility, as used in the Oregon State TRIGA Reactor (OSTR). We illustrate the complexity of the irradiation of geologic samples within this framework and determine an overlooked production channel of 36Ar. The production of 36Ar is fed from the 39K(n,α)36Cl nuclear channel, 36Cl subsequently decays to 36Ar (39K(n,α,β) 36Ar). Simulations in this work using a 235U fission neutron energy spectrum and modelled CLICIT facility, determine a production ratio for this reaction (36Cl/39Ar)K = 0.40 ± 0.01 (1σ); greater than an order of magnitude larger than any other K interference. The magnitude of the resulting age bias for an unknown sample will be a function of the integrated neutron flux, the length of irradiation (fluence), the time elapsed since irradiation, and the age relationship between the unknown and neutron fluence monitor. We show using the raw data of (Niespolo et al., 2017) that the age of Alder Creek sanidine can be modified to be ca. 0.1% older (1σ), at the 2σ level of current analytical precision for the Alder Creek age for this study. The 39K(n,α,β)36Ar inference should be incorporated into routine data analysis and may be especially important in the intercalibration of the 40Ar/39Ar system with other chronometers (e.g., 206Pb/238U).
The extent and seasonality of Arctic sea ice during the Last Interglacial (129,000 to 115,000 years before present) is poorly known. Sediment-based reconstructions have suggested extensive ice cover in summer, while climate model outputs indicate year-round conditions in the Arctic Ocean ranging from ice free to fully ice covered. Here we use microfossil records from across the central Arctic Ocean to show that sea-ice extent was substantially reduced and summers were probably ice free. The evidence comes from high abundances of the subpolar planktic foraminifera Turborotalita quinqueloba in five newly analysed cores. The northern occurrence of this species is incompatible with perennial sea ice, which would be associated with a thick, low-salinity surface water. Instead, T. quinqueloba’s ecological preference implies largely ice-free surface waters with seasonally elevated levels of primary productivity. In the modern ocean, this species thrives in the Fram Strait–Barents Sea ‘Arctic–Atlantic gateway’ region, implying that the necessary Atlantic Ocean-sourced water masses shoaled towards the surface during the Last Interglacial. This process reflects the ongoing Atlantification of the Arctic Ocean, currently restricted to the Eurasian Basin. Our results establish the Last Interglacial as a prime analogue for studying a seasonally ice-free Arctic Ocean, expected to occur this century.
The goal of this paper was to review the evidence of population-level impacts of the Deepwater Horizon Oil Spill (DWH) on Gulf of Mexico (GOM) continental shelf taxa, as well as evidence of resiliency following the DWH. There is considerable environmental and biological evidence that GOM shelf taxa were exposed to and suffered direct and indirect impacts of the DWH. Numerous assessments, from mesocosm studies to analysis of biopsied tissue or tissue samples from necropsied animals, revealed a constellation of physiological effects related to DWH impacts on GOM biota, some of which clearly or likely resulted in mortality. While the estimated concentrations of hydrocarbons in shelf waters and sediments were orders of magnitude lower than measured in inshore or deep GOM environments, the level of mortality observed or predicted was substantial for many shelf taxa. In some cases, such as for zooplankton, community shifts following the spill were ephemeral, likely reflecting high rates of population turnover and productivity. In other taxa, such as GOM reef fishes, impacts of the spill are confounded with other stressors, such as fishing mortality or the appearance and rapid population growth of invasive lionfish (Pterois spp.). In yet others, such as cetaceans, modeling efforts to predict population-level effects of the DWH made conservative assumptions given the species’ protected status, which post-DWH population assessments either failed to detect or population increases were estimated. A persistent theme that emerged was the lack of precise population-level data or assessments prior to the DWH for many taxa, but even when data or assessments did exist, examining evidence of population resiliency was confounded by other stressors impacting GOM biota. Unless efforts are made to increase the resolution of the data or precision of population assessments, difficulties will likely remain in estimating the scale of population-level effects or resiliency in the case of future large-scale environmental catastrophes.
In this article, we consider modeling arbitrarily censored survival data with spatio-temporal covariates. We demonstrate that under the piecewise constant hazard function, the likelihood for uncensored or right-censored subjects is proportional to the likelihood of multiple conditionally independent Poisson random variables. To address left- or interval-censored subjects, we propose to impute the exact event times and convert them into uncensored subjects, enabling the application of the integrated nested Laplace approximation to update model parameters using the imputed data. We introduce an iterative algorithm that alternates between imputing event times for left- and interval-censored subjects and re-estimating model parameters. The proposed method is assessed through a simulation study and applied to analyze a spatio-temporal survival dataset in a wildlife disease study investigating bovine tuberculosis in white-tailed deer in Michigan.
","language":"English","publisher":"Wiley","doi":"10.1002/env.2823","usgsCitation":"Yao, K., Zhu, J., O'Brien, D., and Walsh, D.P., 2023, Bayesian spatio-temporal survival analysis for all types of censoring with application to a wildlife disease study: Environmetrics, v. 34, no. 8, e2823, 13 p., https://doi.org/10.1002/env.2823.","productDescription":"e2823, 13 p.","ipdsId":"IP-146224","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":442561,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1002/env.2823","text":"Publisher Index Page"},{"id":430134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yao, Kehui","contributorId":339161,"corporation":false,"usgs":false,"family":"Yao","given":"Kehui","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":903822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Jun","contributorId":73485,"corporation":false,"usgs":true,"family":"Zhu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":903823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Brien, Daniel J.","contributorId":339164,"corporation":false,"usgs":false,"family":"O'Brien","given":"Daniel J.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903825,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257347,"text":"70257347 - 2023 - Understanding drivers of mercury in lake trout (Salvelinus namaycush), a top-predator fish in southwest Alaska's parklands","interactions":[],"lastModifiedDate":"2024-08-28T16:23:55.197568","indexId":"70257347","displayToPublicDate":"2023-08-01T09:11:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Understanding drivers of mercury in lake trout (Salvelinus namaycush), a top-predator fish in southwest Alaska's parklands","docAbstract":"Mercury (Hg) is a widespread element and persistent pollutant, harmful to fish, wildlife, and humans in its organic, methylated form. The risk of Hg contamination is driven by factors that regulate Hg loading, methylation, bioaccumulation, and biomagnification. In remote locations, with infrequent access and limited data, understanding the relative importance of these factors can pose a challenge. Here, we assessed Hg concentrations in an apex predator fish species, lake trout (Salvelinus namaycush), collected from 14 lakes spanning two National Parks in southwest Alaska, U.S.A. We then examined factors associated with the variation in fish Hg concentrations using a Bayesian hierarchical model. We found that total Hg concentrations in water were consistently low among lakes (0.11–0.50 ng L− 1). Conversely, total Hg concentrations in lake trout spanned a thirty-fold range (101–3046 ng g− 1 dry weight), with median values at 7 lakes exceeding Alaska’s human consumption threshold. Model results showed that fish age and, to a lesser extent, body condition best explained variation in Hg concentration among fish within a lake, with Hg elevated in older, thinner lake trout. Other factors, including plankton methyl Hg content, fish species richness, volcano proximity, and glacier loss, best explained variation in lake trout Hg concentration among lakes. Collectively, these results provide evidence that multiple, hierarchically nested factors control fish Hg levels in these lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2023.121678","usgsCitation":"Bartz, K.K., Hannam, M.P., Wilson, T.L., Lepak, R., Ogorek, J.M., Young, D.B., Eagles-Smith, C., and Krabbenhoft, D.P., 2023, Understanding drivers of mercury in lake trout (Salvelinus namaycush), a top-predator fish in southwest Alaska's parklands: Environmental Pollution, v. 330, 121678, 11 p., https://doi.org/10.1016/j.envpol.2023.121678.","productDescription":"121678, 11 p.","ipdsId":"IP-149237","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":442564,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2023.121678","text":"Publisher Index Page"},{"id":433253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and Preserve, Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.59006149445355,\n 60.857310763857726\n ],\n [\n -155.59006149445355,\n 58.42599213711503\n ],\n [\n -152.59863331288238,\n 58.42599213711503\n ],\n [\n -152.59863331288238,\n 60.857310763857726\n ],\n [\n -155.59006149445355,\n 60.857310763857726\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"330","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":910037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hannam, Michael P.","contributorId":199775,"corporation":false,"usgs":false,"family":"Hannam","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":910038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Tammy L. 0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lepak, Ryan F. 0000-0003-2806-1895","orcid":"https://orcid.org/0000-0003-2806-1895","contributorId":210990,"corporation":false,"usgs":false,"family":"Lepak","given":"Ryan F.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Young, Daniel","contributorId":58468,"corporation":false,"usgs":false,"family":"Young","given":"Daniel","affiliations":[{"id":35763,"text":"National Park Service, Lake Clark National Park and Preserve, Port Alsworth, AK","active":true,"usgs":false}],"preferred":false,"id":910042,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":910043,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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The experiments were conducted on the North Carolina coast by a multidisciplinary group of over 30 research scientists from 18 academic and federal institutions supporting over 30 graduate students and deploying over 300 instruments from 2019 to 2021. The overarching goal of DUNEX was to gather information collaboratively to improve understanding of the interactions of coastal water levels, waves, currents, beach and dune evolution, soil behavior, vegetation, and groundwater during major coastal storms that affect infrastructure, habitats, and communities. In the short term, these high-quality field measurements will lead to better understanding of during-storm processes and impacts and will enhance U.S. academic coastal research programs by providing opportunities for students to learn about field data collection and to potentially analyze data as part of their studies. Longer-term, DUNEX data and outcomes will improve the ability to predict extreme event physical processes and impacts, validate coastal processes numerical models, and improve coastal resilience strategies and communication methods for coastal communities impacted by storms. The purpose of this paper is to describe the motivation for and science goals of the experiment, how stakeholder needs led to these goals, collaborations amongst researchers, and the knowledge gained that will lead to tools to improve coastal resilience. Herein, we first describe how researchers worked with stakeholders to structure their community-driven needs into science-based requirements. Next, we summarize how federal, academic, and stakeholder researchers worked together to design and execute a multi-organizational experiment aligned with those requirements. Finally, we articulate early findings and lessons learned from the experiment. This paper does not summarize all the research findings from DUNEX, as analyses are still ongoing. An American Geophysical Union (AGU) Special Collection on Coastal Storm Research will be published in 2025 including outcomes from DUNEX research.
","language":"English","publisher":"American Shore & Beach Preservation Association (ASBPA)","doi":"10.34237/1009133","usgsCitation":"Straub, J.A., Cialone, M.A., Raubenheimer, B., Brown, J., Elko, N., and Brodie, K., 2023, The During Nearshore Event Experiment (DUNEX): A collaborative coastal community experiment to address coastal resilience: Shore & Beach, v. 91, no. 3, p. 23-29, https://doi.org/10.34237/1009133.","productDescription":"7 p.","startPage":"23","endPage":"29","ipdsId":"IP-154913","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":420560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.96092024961042,\n 36.52798524878611\n ],\n [\n -75.70287550901757,\n 35.7225387686793\n ],\n [\n -75.58916087756994,\n 35.32743140299219\n ],\n [\n -76.31955870263806,\n 34.95189496181462\n ],\n [\n -76.57760344323091,\n 34.71855336952575\n ],\n [\n -77.17241843849592,\n 34.682596002484786\n ],\n [\n -77.95092502360447,\n 34.188407272265394\n ],\n [\n -78.06901329472343,\n 33.956547416969514\n ],\n [\n -78.55448729821158,\n 33.89485306093033\n ],\n [\n -78.46264086511943,\n 33.75678659018945\n ],\n [\n -77.87219950952516,\n 33.78223663539556\n ],\n [\n -77.48294557880051,\n 34.383544699373275\n ],\n [\n -76.50325029248162,\n 34.54581268018919\n ],\n [\n -75.39672570300596,\n 35.17026943934029\n ],\n [\n -75.42296896493126,\n 35.83252729550989\n ],\n [\n -75.74661830059003,\n 36.5525741300887\n ],\n [\n -75.96092024961042,\n 36.52798524878611\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"91","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Straub, Jessamin A. 0000-0001-5630-5741","orcid":"https://orcid.org/0000-0001-5630-5741","contributorId":267195,"corporation":false,"usgs":false,"family":"Straub","given":"Jessamin","email":"","middleInitial":"A.","affiliations":[{"id":55437,"text":"U.S. Army Engineer Research and Development Center, Field Research Facility, Duck, NC, USA","active":true,"usgs":false}],"preferred":false,"id":882088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cialone, Mary A.","contributorId":329373,"corporation":false,"usgs":false,"family":"Cialone","given":"Mary","email":"","middleInitial":"A.","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":882089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Raubenheimer, Britt","contributorId":194340,"corporation":false,"usgs":false,"family":"Raubenheimer","given":"Britt","email":"","affiliations":[],"preferred":false,"id":882090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Jenna A. 0000-0003-3137-7073","orcid":"https://orcid.org/0000-0003-3137-7073","contributorId":208564,"corporation":false,"usgs":true,"family":"Brown","given":"Jenna A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882091,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elko, Nicole","contributorId":287920,"corporation":false,"usgs":false,"family":"Elko","given":"Nicole","affiliations":[{"id":61663,"text":"American Shore and Beach Preservation Association","active":true,"usgs":false}],"preferred":false,"id":882092,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brodie, Katherine L.","contributorId":271224,"corporation":false,"usgs":false,"family":"Brodie","given":"Katherine L.","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":882093,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247909,"text":"70247909 - 2023 - Towards improved code-based performance objectives for liquefaction hazard analysis","interactions":[],"lastModifiedDate":"2023-08-24T13:31:28.043712","indexId":"70247909","displayToPublicDate":"2023-08-01T08:27:02","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Towards improved code-based performance objectives for liquefaction hazard analysis","docAbstract":"Ground failure due to liquefaction in loose sand deposits poses substantial risks to the built environment, and has caused significant damage in past earthquakes to a wide range of infrastructure. Advances in liquefaction hazard analysis in practice have largely stagnated in recent years; the state of practice remains rooted in simplified procedures that ignore considerable uncertainties in liquefaction phenomena, and are largely conditional on single-return period ground motions. As a result, they lack any sort of liquefaction-specific design criteria or performance objective. Presented herein is a roadmap for using probabilistic liquefaction hazard analysis (PLHA) to address many of these limitations and improve liquefaction design guidelines. PLHA incorporates hazard contributions from the full ground motion hazard space in conjunction with probabilistic liquefaction models, to produce hazard curves for various types of liquefaction-related demands. In this study, PLHA is utilized to assess the current, implied liquefaction design levels at 76 study sites throughout the U.S. using ASCE 7 guidelines, by computing effective return periods of liquefaction factor of safety FSL, and liquefaction potential index LPI. The results indicate broad inconsistencies in these design levels across different parts of the U.S, with return periods varying from about 300 years in deterministically-capped parts of California, to nearly 3,000 years on the Pacific Northwest coast and in the Charleston Fault zone region. These results are also used to inform potential strategies for establishing consistent, liquefaction-specific design objectives in the future, based on return period averaging methods that weight the importance of a study site according to both the population and relative liquefaction hazard level.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 4th international conference on applications of statistics and probability in civil engineering (ICASP14)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"4th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14)","conferenceDate":"Dublin, Ireland","conferenceLocation":"July 9-13, 2023","language":"English","usgsCitation":"Makdisi, A.J., and Kramer, S.L., 2023, Towards improved code-based performance objectives for liquefaction hazard analysis, in Proceedings, 4th international conference on applications of statistics and probability in civil engineering (ICASP14), July 9-13, 2023, Dublin, Ireland, 8 p.","productDescription":"8 p.","ipdsId":"IP-150700","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":420116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":420115,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/2262/103375","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":880974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Steven L.","contributorId":328669,"corporation":false,"usgs":false,"family":"Kramer","given":"Steven","email":"","middleInitial":"L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":880975,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248782,"text":"70248782 - 2023 - CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanalysis over the CONUS","interactions":[],"lastModifiedDate":"2023-12-11T16:43:40.955033","indexId":"70248782","displayToPublicDate":"2023-08-01T08:24:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanalysis over the CONUS","docAbstract":"A unique, high-resolution, hydroclimate reanalysis, 40-plus-year (October 1979–September 2021), 4 km (named as CONUS404), has been created using the Weather Research and Forecasting Model by dynamically downscaling of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous United States. The paper describes the approach for generating the dataset, provides an initial evaluation, including biases, and indicates how interested users can access the data. The motivation for creating this National Center for Atmospheric Research (NCAR)–U.S. Geological Survey (USGS) collaborative dataset is to provide research and end-user communities with a high-resolution, self-consistent, long-term, continental-scale hydroclimate dataset appropriate for forcing hydrological models and conducting hydroclimate scientific analyses over the conterminous United States. The data are archived and accessible on the USGS Black Pearl tape system and on the NCAR supercomputer Campaign storage system.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-21-0326.1","usgsCitation":"Rasmussen, R.M., Chen, F., Liu, C.H., Ikeda, K., Prein, A., Kim, J., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Xue, L., Chen, S., Newman, A., Dougherty, E., Abolafia-Rozenzweig, R., Lybarger, N., Viger, R.J., Lesmes, D.P., Skalak, K., Brakebill, J., Cline, D.W., Dunne, K.A., Rasmussen, K., and Miguez-Macho, G., 2023, CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanalysis over the CONUS: Bulletin of the American Meteorological Society, v. 104, no. 8, p. 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The pond is hydraulically connected to the surrounding groundwater-flow system, and water levels in the pond fluctuate in response to recharge to the aquifer from precipitation and wastewater return flows through septic systems, to withdrawals from the aquifer at nearby wells, and to precipitation directly on the pond surface. Concerns about the effects of withdrawals on water levels in the pond prompted an investigation to better understand the hydrology of Kingsbury Pond and its response to groundwater withdrawals and to determine if withdrawals from wells in Franklin, Mass., can be modified to simultaneously reduce the effect on water levels in the pond and yet meet the water-supply demands of the Town of Franklin.
An existing, transient groundwater-flow model of the Upper Charles River Basin was modified for this study in the area near Kingsbury Pond to improve representation of the hydrologic system near the pond. The mean annual water-level altitude simulated for the pond for nonpumping conditions using the modified model is 136 feet (ft), which falls within the range of likely annual pond-altitude fluctuations of 135 to 140 ft estimated for average hydrologic conditions before the beginning of withdrawals at two nearby wells operated by the Town of Franklin (wells FR–04 and FR–05). The mean annual water-level altitude at the pond decreased by 3.8 ft to 132.2 ft for simulated mean monthly withdrawal rates at all wells within the Upper Charles River Basin from 2010 to 2019 (referred to as the baseline withdrawal condition).
A groundwater management model that links the groundwater-flow model with a mathematical optimization method was developed to evaluate the effects of three alternative groundwater-withdrawal scenarios for the Franklin public-water system on water levels in Kingsbury Pond. In the first scenario, monthly withdrawal rates at wells FR–04 and FR–05 were increased from the baseline withdrawal rates to their maximum authorized rates for all months of the year; all other Franklin wells were specified at their baseline withdrawal rates. This scenario resulted in a mean annual water-level altitude at the pond of 129.3 ft, or a mean annual decline of 6.7 ft compared with nonpumping conditions and a decline of 2.9 ft compared with baseline conditions.
The results of the second scenario showed that water levels in the pond can be increased relative to 2010–19 conditions while meeting Franklin’s 2010–19 monthly water-supply demands if withdrawals at wells FR–04 and FR–05 were shifted to other Franklin wells. In this scenario, monthly withdrawal rates at wells FR–04 and FR–05 were decreased from their baseline rates to one-third their maximum practical rates for all months of the year; increased withdrawal rates at other Franklin wells were determined by the management model. The decrease in withdrawal rates at wells FR–04 and FR–05 resulted in a mean water-level altitude at the pond of 134.1 ft, which was equivalent to a 51 percent increase (improvement) in the mean annual water level of the pond relative to the baseline condition.
A third scenario was done to determine if Franklin’s existing water-supply system has the capacity to meet the mean annual maximum permitted withdrawal rate of the system of 3.45 million gallons per day while maintaining monthly withdrawal rates at wells FR–04 and FR–05 at their 2010–19 rates and water levels in Kingsbury Pond at baseline conditions. The analysis indicated that the capacity of the system cannot meet the increased demand during some months of the year with withdrawal rates at the two wells fixed at their monthly 2010–19 rates; however, the existing system is capable of meeting about 90 percent of the maximum permitted rate (3.10 million gallons per day) by increasing withdrawal rates at other Franklin wells above their 2010–19 rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235083","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Barlow, P.M., Friesz, P.J., and Barbaro, J.R., 2023, Evaluation of alternative groundwater-withdrawal scenarios on water levels in Kingsbury Pond, upper Charles River Basin, eastern Massachusetts: U.S. Geological Survey Scientific Investigations Report 2023–5083, 36 p., https://doi.org/10.3133/sir20235083.","productDescription":"Report: viii, 36 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-141684","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501046,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115124.htm","linkFileType":{"id":5,"text":"html"}},{"id":419446,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V49N3R","text":"USGS data release","linkHelpText":"MODFLOW–2000 and management-optimization models used to evaluate alternative groundwater-withdrawal scenarios on water levels in Kingsbury Pond, upper Charles River Basin, eastern Massachusetts"},{"id":419445,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5083/images/"},{"id":419444,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5083/sir20235083.XML"},{"id":419442,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5083/sir20235083.pdf","text":"Report","size":"3.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5083"},{"id":419441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5083/coverthb.jpg"},{"id":419443,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235083/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5083"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Upper Charles River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.61642753245717,\n 42.29469041974528\n ],\n [\n -71.61642753245717,\n 41.90357856449015\n ],\n [\n -71.16069045053953,\n 41.90357856449015\n ],\n [\n -71.16069045053953,\n 42.29469041974528\n ],\n [\n -71.61642753245717,\n 42.29469041974528\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Influenza A viruses in wild birds pose threats to the poultry industry, wild birds, and human health under certain conditions. Of particular importance are wild waterfowl, which are the primary reservoir of low pathogenicity influenza viruses that ultimately cause high pathogenicity outbreaks in poultry farms. Despite much work on the drivers of influenza A virus prevalence, the underlying viral subtype dynamics are still mostly unexplored. Nevertheless, understanding these dynamics, particularly for the agriculturally significant H5 and H7 subtypes, is important for mitigating the risk of outbreaks in domestic poultry farms. Here, using an expansive surveillance database, we take a large-scale look at the spatial, temporal, and taxonomic drivers in the prevalence of these two subtypes among influenza A positive wild waterfowl. We document spatiotemporal trends that are consistent with past work, particularly an uptick in H5 viruses in late autumn and H7 viruses in spring. Interestingly, despite large species differences in temporal trends in overall influenza A virus prevalence, we document only modest differences in the relative abundance of these two subtypes and little, if any, temporal differences among species. As such, it appears that differences in species' phenology, physiology, and behaviors that influence overall susceptibility to influenza A viruses play a much lesser role in relative susceptibility to different subtypes. Instead, species likely freely pass viruses among each other regardless of subtype. Importantly, despite the similarities among species documented here, individual species still may play important roles in moving viruses across large geographic areas or sustaining local outbreaks through their different migratory behaviors.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2906","usgsCitation":"Kent, C.M., Bevins, S.N., Mullinax, J.M., Sullivan, J.D., and Prosser, D., 2023, Waterfowl show spatiotemporal trends in influenza A H5 and H7 infections but limited taxonomic variation: Ecological Applications, v. 33, no. 7, e2906, 11 p., https://doi.org/10.1002/eap.2906.","productDescription":"e2906, 11 p.","ipdsId":"IP-147544","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442588,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2906","text":"Publisher Index Page"},{"id":435237,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K4ARTI","text":"USGS data release","linkHelpText":"Predicted H5 and H7 subtype Avian Influenza Prevalence for Wild Waterfowl Species Across the Continental United States"},{"id":419539,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kent, Cody M.","contributorId":265823,"corporation":false,"usgs":false,"family":"Kent","given":"Cody","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":879543,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bevins, Sarah N.","contributorId":212845,"corporation":false,"usgs":false,"family":"Bevins","given":"Sarah","email":"","middleInitial":"N.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":879544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":879545,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":879546,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":879547,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247471,"text":"70247471 - 2023 - First observation of the ground-state electron-capture of 40K","interactions":[],"lastModifiedDate":"2023-08-09T11:36:54.603942","indexId":"70247471","displayToPublicDate":"2023-07-31T06:35:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16675,"text":"Physical Review C","active":true,"publicationSubtype":{"id":10}},"title":"First observation of the ground-state electron-capture of 40K","docAbstract":"Potassium-40 is a widespread, naturally occurring isotope whose radioactivity impacts estimated geological ages spanning billions of years, nuclear structure theory, and subatomic rare-event searches—including those for dark matter and neutrinoless double-beta decay. The decays of this long-lived isotope must be precisely known for its use as a geochronometer, and to account for its presence in low-background experiments. There are several known decay modes for potassium-40, but a predicted electron-capture decay directly to the ground state of argon-40 has never been observed. The existence of this decay mode impacts several fields, while theoretical predictions span an order of magnitude. Here we report on the first, successful observation of this rare decay mode, obtained by the KDK (potassium decay) Collaboration using a novel combination of a low-threshold x-ray detector surrounded by a tonne-scale, high-efficiency γ-ray tagger at Oak Ridge National Laboratory. A blinded analysis reveals a distinctly nonzero ratio of intensities of ground-state electron-captures (IEC0) over excited-state ones (IEC∗) of IEC0/IEC∗=0.0095stat±0.0022sys±0.0010 (68% CL), with the null hypothesis rejected at 4σ [Stukel et al., Phys. Rev. Lett. 131, 052503 (2023)]. In terms of branching ratio, this unambiguous signal yields IEC0=0.098%stat±0.023%sys±0.010%, roughly half of the commonly used prediction. This first observation of a third-forbidden unique electron capture improves our understanding of low-energy backgrounds in dark-matter searches and has implications for nuclear-structure calculations. For example, a shell-model based theoretical estimate for the neutrinoless double-beta decay half-life of calcium-48 is increased by a factor of 7+3−2. Our nonzero measurement shifts geochronological ages by up to a percent; implications are illustrated for Earth and solar system chronologies.
","language":"English","publisher":"American Physical Society","doi":"10.1103/PhysRevC.108.014327","usgsCitation":"Hariasz, L., Stukel, M., Di Stefano, P., Rasco, B., Rykaczewski, K., Brewer, N., Stracener, D., Liu, Y., Gai, Z., Rouleau, C., Carter, J.B., Kostensalo, J., Suhonen, J., Davis, H., Lukosi, E., Goetz, K., Grzywacz, R., Mancuso, M., Petricca, F., Fijalkowska, A., Wolinska-Cichocka, M., Ninkovic, J., Lechner, P., Ickert, R., Morgan, L.E., Renne, P., and Yavin, I., 2023, First observation of the ground-state electron-capture of 40K: Physical Review C, v. 108, 014327, https://doi.org/10.1103/PhysRevC.108.014327.","productDescription":"014327","ipdsId":"IP-147097","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":442593,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://urn.fi/URN:NBN:fi:jyu-202308304844","text":"Publisher Index Page"},{"id":419654,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"108","noUsgsAuthors":false,"publicationDate":"2023-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Hariasz, L.","contributorId":317927,"corporation":false,"usgs":false,"family":"Hariasz","given":"L.","email":"","affiliations":[{"id":40753,"text":"Queen's University","active":true,"usgs":false}],"preferred":false,"id":879791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stukel, M.","contributorId":317926,"corporation":false,"usgs":false,"family":"Stukel","given":"M.","email":"","affiliations":[{"id":40753,"text":"Queen's University","active":true,"usgs":false}],"preferred":false,"id":879792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Di Stefano, P.C.F.","contributorId":317928,"corporation":false,"usgs":false,"family":"Di Stefano","given":"P.C.F.","email":"","affiliations":[{"id":40753,"text":"Queen's University","active":true,"usgs":false}],"preferred":false,"id":879793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rasco, B.C.","contributorId":317929,"corporation":false,"usgs":false,"family":"Rasco","given":"B.C.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rykaczewski, K.P.","contributorId":317930,"corporation":false,"usgs":false,"family":"Rykaczewski","given":"K.P.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brewer, N.T.","contributorId":317931,"corporation":false,"usgs":false,"family":"Brewer","given":"N.T.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879796,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stracener, D.W.","contributorId":317932,"corporation":false,"usgs":false,"family":"Stracener","given":"D.W.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879797,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Liu, Y.","contributorId":127400,"corporation":false,"usgs":false,"family":"Liu","given":"Y.","email":"","affiliations":[{"id":6940,"text":"State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":879798,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gai, Z.","contributorId":317933,"corporation":false,"usgs":false,"family":"Gai","given":"Z.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879799,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rouleau, C.","contributorId":317935,"corporation":false,"usgs":false,"family":"Rouleau","given":"C.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879800,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carter, J. 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Tennessee","active":true,"usgs":false}],"preferred":false,"id":879804,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lukosi, E.D.","contributorId":317943,"corporation":false,"usgs":false,"family":"Lukosi","given":"E.D.","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":879805,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Goetz, K.C.","contributorId":317944,"corporation":false,"usgs":false,"family":"Goetz","given":"K.C.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879806,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Grzywacz, R.K.","contributorId":317946,"corporation":false,"usgs":false,"family":"Grzywacz","given":"R.K.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National 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Letters","active":true,"publicationSubtype":{"id":10}},"title":"Rare 40K decay with implications for fundamental physics and geochronology","docAbstract":"Potassium-40 is a widespread, naturally occurring isotope whose radioactivity impacts subatomic rare-event searches, nuclear structure theory, and estimated geological ages. A predicted electron-capture decay directly to the ground state of argon-40 has never been observed. The KDK (potassium decay) collaboration reports strong evidence of this rare decay mode. A blinded analysis reveals a nonzero ratio of intensities of ground-state electron-captures (IEC0) over excited-state ones (IEC∗) of IEC0/IEC∗=0.0095stat±0.0022sys±0.0010 (68% C.L.), with the null hypothesis rejected at 4σ. In terms of branching ratio, this signal yields IEC0=0.098%stat±0.023%sys±0.010%, roughly half of the commonly used prediction, with consequences for various fields [L. Hariasz et al., companion paper, Phys. Rev. C 108, 014327 (2023)].
","language":"English","publisher":"American Physical Society","doi":"10.1103/PhysRevLett.131.052503","usgsCitation":"Stukel, M., Hariasz, L., Di Stefano, P., Rasco, B., Rykaczewski, K., Brewer, N., Stracener, D., Liu, Y., Gai, Z., Rouleau, C., Carter, J.B., Kostensalo, J., Suhonen, J., Davis, H., Lukosi, E., Goetz, K., Grzywacz, R., Mancuso, M., Petricca, F., Fijalkowska, A., Wolinska-Cichocka, M., Ninkovic, J., Lechner, P., Ickert, R., Morgan, L.E., Renne, P., and Yavin, I., 2023, Rare 40K decay with implications for fundamental physics and geochronology: Physical Review Letters, v. 131, 052503, https://doi.org/10.1103/PhysRevLett.131.052503.","productDescription":"052503","ipdsId":"IP-146164","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":442596,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://urn.fi/URN:NBN:fi:jyu-202308244749","text":"Publisher Index Page"},{"id":419653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"131","noUsgsAuthors":false,"publicationDate":"2023-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Stukel, M.","contributorId":317926,"corporation":false,"usgs":false,"family":"Stukel","given":"M.","email":"","affiliations":[{"id":40753,"text":"Queen's University","active":true,"usgs":false}],"preferred":false,"id":879762,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hariasz, L.","contributorId":317927,"corporation":false,"usgs":false,"family":"Hariasz","given":"L.","email":"","affiliations":[{"id":40753,"text":"Queen's University","active":true,"usgs":false}],"preferred":false,"id":879763,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Di Stefano, P.C.F.","contributorId":317928,"corporation":false,"usgs":false,"family":"Di Stefano","given":"P.C.F.","email":"","affiliations":[{"id":40753,"text":"Queen's University","active":true,"usgs":false}],"preferred":false,"id":879764,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rasco, B.C.","contributorId":317929,"corporation":false,"usgs":false,"family":"Rasco","given":"B.C.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879765,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rykaczewski, K.P.","contributorId":317930,"corporation":false,"usgs":false,"family":"Rykaczewski","given":"K.P.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879766,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brewer, N.T.","contributorId":317931,"corporation":false,"usgs":false,"family":"Brewer","given":"N.T.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879767,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stracener, D.W.","contributorId":317932,"corporation":false,"usgs":false,"family":"Stracener","given":"D.W.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879768,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Liu, Y.","contributorId":127400,"corporation":false,"usgs":false,"family":"Liu","given":"Y.","email":"","affiliations":[{"id":6940,"text":"State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":879769,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gai, Z.","contributorId":317933,"corporation":false,"usgs":false,"family":"Gai","given":"Z.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879770,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rouleau, C.","contributorId":317935,"corporation":false,"usgs":false,"family":"Rouleau","given":"C.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879771,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carter, J. B.","contributorId":205584,"corporation":false,"usgs":false,"family":"Carter","given":"J.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":879772,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kostensalo, J.","contributorId":317937,"corporation":false,"usgs":false,"family":"Kostensalo","given":"J.","email":"","affiliations":[{"id":40380,"text":"Natural Resources Institute Finland","active":true,"usgs":false}],"preferred":false,"id":879773,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Suhonen, J.","contributorId":317939,"corporation":false,"usgs":false,"family":"Suhonen","given":"J.","email":"","affiliations":[{"id":25498,"text":"University of Jyvaskyla","active":true,"usgs":false}],"preferred":false,"id":879774,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Davis, H.","contributorId":317941,"corporation":false,"usgs":false,"family":"Davis","given":"H.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":879775,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lukosi, E.D.","contributorId":317943,"corporation":false,"usgs":false,"family":"Lukosi","given":"E.D.","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":879776,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Goetz, K.C.","contributorId":317944,"corporation":false,"usgs":false,"family":"Goetz","given":"K.C.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879777,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Grzywacz, R.K.","contributorId":317946,"corporation":false,"usgs":false,"family":"Grzywacz","given":"R.K.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National 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Warsaw","active":true,"usgs":false}],"preferred":false,"id":879781,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Wolinska-Cichocka, M.","contributorId":317953,"corporation":false,"usgs":false,"family":"Wolinska-Cichocka","given":"M.","email":"","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":879782,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Ninkovic, J.","contributorId":317955,"corporation":false,"usgs":false,"family":"Ninkovic","given":"J.","email":"","affiliations":[{"id":69198,"text":"MPG Semiconductor Laboratory","active":true,"usgs":false}],"preferred":false,"id":879783,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Lechner, P.","contributorId":317957,"corporation":false,"usgs":false,"family":"Lechner","given":"P.","email":"","affiliations":[{"id":69198,"text":"MPG Semiconductor Laboratory","active":true,"usgs":false}],"preferred":false,"id":879784,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Ickert, R.B.","contributorId":317960,"corporation":false,"usgs":false,"family":"Ickert","given":"R.B.","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":879785,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":879786,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Renne, P.R.","contributorId":317964,"corporation":false,"usgs":false,"family":"Renne","given":"P.R.","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":879787,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Yavin, I.","contributorId":317967,"corporation":false,"usgs":false,"family":"Yavin","given":"I.","email":"","affiliations":[{"id":18047,"text":"n/a","active":true,"usgs":false}],"preferred":false,"id":879788,"contributorType":{"id":1,"text":"Authors"},"rank":27}]}} ,{"id":70247915,"text":"70247915 - 2023 - Satellite tracking reveals use of Biscayne National Park by sea turtles tagged in multiple locations","interactions":[],"lastModifiedDate":"2023-08-29T15:21:22.255682","indexId":"70247915","displayToPublicDate":"2023-07-29T07:23:48","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5094,"text":"Regional Studies in Marine Science","onlineIssn":"2352-4855","active":true,"publicationSubtype":{"id":10}},"title":"Satellite tracking reveals use of Biscayne National Park by sea turtles tagged in multiple locations","docAbstract":"Although historical observations date back to the 1800’s, there is little information on sea turtle occupancy within Biscayne National Park (BNP). The park is located along the Florida reef tract and is dominated by the Gulfstream, which acts as a corridor for many marine animals. Here we used satellite telemetry to determine areas of use in BNP for two species of imperiled sea turtles, loggerhead (Caretta caretta) and green (Chelonia mydas) turtles. We included data for turtles tagged between 2009–2021 at sites both within park waters and in five locations outside the park boundary; individuals were captured both in the water and on land. We tagged 60 individuals (female, n
In the Mojave Desert, timing and amounts of precipitation profoundly affect availability of water and annual plant foods necessary for the threatened Agassiz’s desert tortoise (Gopherus agassizii) to survive, especially during prolonged droughts. As part of recovery actions to increase declining populations, we translocated 83 juvenile and young desert tortoises raised in head-start pens for 4–10 years to a new location 15 km away during fall of 2013 and 2014. We tracked them for 9 years during a megadrought, during multiple years of low rainfall and a few years when precipitation neared or exceeded long-term norms. We evaluated behaviors and how precipitation and forage availability affected survival. At the end of the study, 21.6% of tortoises were alive and 6 had grown to adulthood. Annual models of survival indicated that tortoise size was the driving variable in most years, followed by number of repeatedly used burrows during periods of temperature extremes. Other variables affecting survival in ≥1 year were vegetation, movements during the first 2 years post-translocation, and condition index, a measure of health. Tortoises moved more, expanded home ranges, and grew rapidly in years when winter rainfall approached or exceeded long-term norms and annual plants were available to eat. During dry years, movements and growth were limited. Exceptions to this pattern occurred in the last year of study, a dry year: tortoises grew, moved more, and home ranges increased. The increase in size and approaching adulthood may have stimulated greater travelling. Some left the study area, indicating a need for large release areas. We may have aided survival by offering water twice yearly when handling, because some tortoises drank and increased in mass up to 40%. Prolonged droughts and hotter temperatures can limit recovery of populations, reduce survival of young tortoises, and increase the time to maturity.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2023.1164050","usgsCitation":"Berry, K.H., Mack, J., and Anderson, K.M., 2023, Variations in climate drive behavior and survival of small desert tortoises: Frontiers in Ecology and Evolution, v. 11, 1164050, 19 p., https://doi.org/10.3389/fevo.2023.1164050.","productDescription":"1164050, 19 p.","ipdsId":"IP-155005","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":442605,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1164050","text":"Publisher Index Page"},{"id":419448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern","otherGeospatial":"Edwards Air Force Base, Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.8488372578013,\n 34.86175283945724\n ],\n [\n -117.8488372578013,\n 34.82274737275513\n ],\n [\n -117.8249371796763,\n 34.82274737275513\n ],\n [\n -117.8249371796763,\n 34.86175283945724\n ],\n [\n -117.8488372578013,\n 34.86175283945724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":879370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mack, Jeremy S 0000-0002-3394-8493","orcid":"https://orcid.org/0000-0002-3394-8493","contributorId":206166,"corporation":false,"usgs":false,"family":"Mack","given":"Jeremy S","affiliations":[{"id":37269,"text":"Crater Lake National Park (formerly USGS - WERC)","active":true,"usgs":false}],"preferred":false,"id":879371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kemp M.","contributorId":139382,"corporation":false,"usgs":false,"family":"Anderson","given":"Kemp","email":"","middleInitial":"M.","affiliations":[{"id":12757,"text":"Seal Beach, California 90740","active":true,"usgs":false}],"preferred":false,"id":879372,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269298,"text":"70269298 - 2023 - Vertebrate population changes induced by hunting in Amazonian sustainable-use protected areas","interactions":[],"lastModifiedDate":"2025-07-18T15:09:41.242703","indexId":"70269298","displayToPublicDate":"2023-07-28T09:56:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Vertebrate population changes induced by hunting in Amazonian sustainable-use protected areas","docAbstract":"The purported sustainability of sustainable-use reserves (SURs) has been questioned in recent decades due to anthropogenic disturbance, including widespread game hunting. A fuller understanding of the drivers of harvest-induced game population changes in SURs is needed to inform this debate. We deployed 720 camera traps around 100 local communities both inside and outside nine SURs in central-western Brazilian Amazonia to generate detection records of 29 mammal and bird species. We used Royle-Nichols multi-species occupancy models to evaluate if (i) distance to and size of local communities, (ii) local human population density, (iii) distance to and size of urban areas, (iv) local level of protection, and (v) alternative availability of aquatic protein affected the (a) species richness, (b) aggregated abundance and (c) biomass, (d) mean reproductive rate of species, and (e) mean abundance of functional groups and (f) individual species. Community distance was the main determinant of wildlife declines, impacting species up to 5-km from communities, but three game species exhibited higher abundances within this distance. Other drivers, such as community size and urban neighbourhood, also contributed to species declines. Availability of alternative aquatic protein buffered declines of only two species and local protection increased species richness and aggregate abundance. These findings can help inform evidence-based conservation strategies in tropical SURs. Our results suggest that preventing habitat loss beyond 5-km radius from communities can promote a healthy source-sink dynamic for populations of game species. Furthermore, game management measures could encourage targeting harvest-tolerant species and the protection of all game species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.110206","usgsCitation":"Sampaio, R., Morato, R., Royle, A., Abrahams, M., Peres, C., and Chiarello, A., 2023, Vertebrate population changes induced by hunting in Amazonian sustainable-use protected areas: Biological Conservation, v. 284, 110206, 12 p., https://doi.org/10.1016/j.biocon.2023.110206.","productDescription":"110206, 12 p.","ipdsId":"IP-149705","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":492540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bolivia, Brazil, Colombia, Ecuador, French Guiana, Peru, Venezuela","otherGeospatial":"Amazonia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -43.637408253195986,\n -2.3060326068290067\n ],\n [\n -48.14555339905098,\n -0.1940974895162526\n ],\n [\n -49.637615237798286,\n 0.7143229361070951\n ],\n [\n -51.33131765072363,\n 4.944450698236253\n ],\n [\n -53.7256643067451,\n 2.2972655401073467\n ],\n [\n -55.96904383423603,\n 2.423987519980571\n ],\n [\n -58.70893349914384,\n 1.5174393194595694\n ],\n [\n -59.96691587416443,\n 2.2658727368582845\n ],\n [\n -59.589929970999336,\n 4.051694701023223\n ],\n [\n -60.96781422230684,\n 5.659735807716402\n ],\n [\n -61.231933841432635,\n 6.674442717678929\n ],\n [\n -65.03176714852142,\n 5.729607550626682\n ],\n [\n -64.85261613935633,\n 4.423513387321805\n ],\n [\n -66.09631744138863,\n 4.6175141482436\n ],\n [\n -67.5836202891718,\n 4.278648073117438\n ],\n [\n -75.30203138662513,\n 3.296749983278147\n ],\n [\n -77.99136888731351,\n -1.9187154982584076\n ],\n [\n -78.57612587231063,\n -4.632831136658638\n ],\n [\n -78.42265374822121,\n -6.080577280462858\n ],\n [\n -77.26543480240852,\n -8.668445675562559\n ],\n [\n -74.62185995137612,\n -12.27373941680095\n ],\n [\n -71.37336343298678,\n -14.580335381469894\n ],\n [\n -70.36900675103054,\n -13.888418641293228\n ],\n [\n -66.30868649128317,\n -17.935168274255616\n ],\n [\n -62.90282496866929,\n -18.617013572434885\n ],\n [\n -59.840583612385714,\n -15.896682735242365\n ],\n [\n -58.53060682293865,\n -13.70495505291602\n ],\n [\n -56.20785533225906,\n -14.814552312120384\n ],\n [\n -48.958427214778396,\n -12.661802525180462\n ],\n [\n -43.637408253195986,\n -2.3060326068290067\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"284","noUsgsAuthors":false,"publicationDate":"2023-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Sampaio, Ricardo","contributorId":358252,"corporation":false,"usgs":false,"family":"Sampaio","given":"Ricardo","affiliations":[{"id":85590,"text":"Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros (CENAP)","active":true,"usgs":false}],"preferred":false,"id":943385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morato, Ronaldo G.","contributorId":358253,"corporation":false,"usgs":false,"family":"Morato","given":"Ronaldo G.","affiliations":[{"id":85590,"text":"Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros (CENAP)","active":true,"usgs":false}],"preferred":false,"id":943386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":943387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abrahams, Mark I.","contributorId":358254,"corporation":false,"usgs":false,"family":"Abrahams","given":"Mark I.","affiliations":[{"id":85591,"text":"Bristol Zoological Society, Field Conservation and Science Department, Bristol, UK","active":true,"usgs":false}],"preferred":false,"id":943388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peres, Carlos A.","contributorId":358255,"corporation":false,"usgs":false,"family":"Peres","given":"Carlos A.","affiliations":[{"id":85593,"text":"School Environmental Sciences, University of East Anglia, Norwich, UK","active":true,"usgs":false}],"preferred":false,"id":943389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chiarello, Adriano G.","contributorId":358256,"corporation":false,"usgs":false,"family":"Chiarello","given":"Adriano G.","affiliations":[{"id":85594,"text":"Departamento de Biologia, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo","active":true,"usgs":false}],"preferred":false,"id":943390,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247386,"text":"70247386 - 2023 - Incorporating metapopulation dynamics to inform invasive species management: Evaluating bighead and silver carp control strategies in the Illinois River","interactions":[],"lastModifiedDate":"2023-09-22T16:30:13.063527","indexId":"70247386","displayToPublicDate":"2023-07-28T08:55:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating metapopulation dynamics to inform invasive species management: Evaluating bighead and silver carp control strategies in the Illinois River","docAbstract":"1. Invasive species management can benefit from predictive models that incorporate spatially explicit demographics and dispersal to guide resource allocation decisions.
2. We used invasive bigheaded carps (Hypophthalmichthys spp.) in the Illinois River, USA as a case study to create a spatially explicit model to evaluate the allocation of future management efforts. Specifically, we compared additional harvest (e.g. near the invasion front vs. source populations) and enhanced movement deterrents to meet the management goal of reducing abundance at the invasion front.
3. We found additional harvest in lower river pools (i.e. targeting source populations) more effectively limited population sizes upriver at the invasion front compared to allocating the same harvest levels near the invasion front. Likewise, decreasing passage (i.e. lock and dam structures) at the farthest, feasible downriver location limited invasion front population size more than placing movement deterrents farther upriver.
4. Synthesis and applications. Our work highlights the benefits of adopting a multipronged approach for invasive species management, combining suppression of source populations with disrupting movement between source and sink populations thereby producing compounding benefits for control. Our results also demonstrate the importance of considering metapopulation dynamics for invasive species control programs when achieving long-term management goals.
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