{"pageNumber":"102","pageRowStart":"2525","pageSize":"25","recordCount":40783,"records":[{"id":70250186,"text":"70250186 - 2023 - Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","interactions":[],"lastModifiedDate":"2023-11-28T12:52:16.525451","indexId":"70250186","displayToPublicDate":"2023-11-23T06:48:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","docAbstract":"<div class=\"JournalAbstract\"><p>Sediment connectivity is a conceptualization for the transfer and storage of sediment among different geomorphic compartments across upland landscapes and channel networks. Sediment connectivity and dysconnectivity are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review current sediment connectivity and modeling approaches evaluating and quantifying water and sediment transfer in catchment systems. Many studies highlight the interaction between sediment and water in defining landscape connectivity, but many efforts to quantify and/or simulate sediment connectivity rely on the topographic/structural controls on sediment erosion and delivery. More recent modeling efforts integrate functional and structural connectivity to capture hydrologic properties influencing sediment delivery. Though the recent modeling development is encouraging, a comprehensive sediment connectivity framework, which integrates geomorphic and hydrologic processes across spatiotemporal scales, has not yet been accomplished. Such an effort requires understanding the hydrologic and geomorphic processes that control sediment source, storage, and transport at different spatiotemporal scales and across various geophysical conditions. We propose a path for developing this new understanding through an integrated hydrologic and sediment connectivity conceptual model that broadly categorizes dominant processes and patterns relevant to understanding sediment flux dynamics. The conceptual model describes hydrologic–sediment connectivity regimes through spatial-temporal feedback between hydrologic processes and geomorphic drivers. We propose that in combining hydrologic and sediment connectivity into a single conceptual model, patterns emerge such that catchments will exist in a single characteristic behavior at a particular instance, which would shift with space and time, and with landscape disturbances. Using the conceptual model as a “thinking” tool, we extract case studies from a multidisciplinary literature review—from hydrology, geomorphology, biogeochemistry, and watershed modeling to remote-sensing technology—that correspond to each of the dominant hydrologic–sediment connectivity regimes. Sediment and water interactions in real-world examples through various observational and modeling techniques illustrate the advancements in the spatial and temporal scales of landscape connectivity observations and simulations. The conceptual model and case studies provide a foundation for advancing the understanding and predictive capability of watershed sediment processes at multiple spatiotemporal scales. Plain language summary: Soil erosion and movement across the landscape are closely linked to rain events and flow pathways. Landscape connectivity is a way to consider how soil erosion from different parts of the landscape is connected to the streams. We explore where soil erosion occurs and how eroded soil moves across the landscape through the interaction with rainfall and drainage. The comprehensive understanding of sediment connectivity and its dependence on rainfall characteristics and watershed hydrology may help to inform the effective distribution of conservation funds and management actions to address water pollution from excess sediment.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/frwa.2023.1241622","usgsCitation":"Cho, J., Karwan, D., Skalak, K., Pizzuto, J., and Huffman, M., 2023, Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time: Frontiers in Water, v. 5, 1241622, 24 p., https://doi.org/10.3389/frwa.2023.1241622.","productDescription":"1241622, 24 p.","ipdsId":"IP-153828","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":441564,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2023.1241622","text":"Publisher Index Page"},{"id":423007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2023-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Cho, Jong 0000-0001-5514-6056","orcid":"https://orcid.org/0000-0001-5514-6056","contributorId":291384,"corporation":false,"usgs":true,"family":"Cho","given":"Jong","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":888728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karwan, Diana","contributorId":331761,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":888729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":888730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pizzuto, James","contributorId":331762,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":888731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huffman, Max","contributorId":331763,"corporation":false,"usgs":false,"family":"Huffman","given":"Max","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":888732,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70250853,"text":"70250853 - 2023 - Conserved grasslands support similar pollinator diversity as pollinator-specific practice regardless of proximal cropland and pesticide exposure","interactions":[],"lastModifiedDate":"2024-01-10T15:20:56.832051","indexId":"70250853","displayToPublicDate":"2023-11-22T09:12:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3908,"text":"Royal Society Open Science","active":true,"publicationSubtype":{"id":10}},"title":"Conserved grasslands support similar pollinator diversity as pollinator-specific practice regardless of proximal cropland and pesticide exposure","docAbstract":"<p><span>Pollinator diversity and abundance are declining globally. Cropland agriculture and the corresponding use of agricultural pesticides may contribute to these declines, while increased pollinator habitat (flowering plants) can help mitigate them. Here we tested whether the relative effect of wildflower plantings on pollinator diversity and counts were modified by proportion of nearby agricultural land cover and pesticide exposure in 24 conserved grasslands in Iowa, USA. Compared with general grassland conservation practices, wildflower plantings led to only a 5% increase in pollinator diversity and no change in counts regardless of the proportion of cropland agriculture within a 1 km radius. Pollinator diversity increased earlier in the growing season and with per cent flower cover. Unexpectedly, neither insecticide nor total pesticide concentrations on above-ground passive samplers were related to pollinator diversity. However, pollinator community composition was most strongly related to date of sampling, total pesticide concentration, and forb or flower cover. Our results indicate very little difference in pollinator diversity between grassland conservation practices with and without wildflower plantings. Given the relatively high economic costs of wildflower plantings, our research provides initial evidence that investment in general grassland conservation may efficiently conserve pollinator diversity in temperate regions of intensive cropland agriculture.</span></p>","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rsos.231093","usgsCitation":"Kraus, J.M., Smalling, K., Vandever, M.W., Givens, C.E., Smith, C., Kolpin, D., and Hladik, M.L., 2023, Conserved grasslands support similar pollinator diversity as pollinator-specific practice regardless of proximal cropland and pesticide exposure: Royal Society Open Science, v. 10, no. 11, https://doi.org/10.1098/rsos.231093.","productDescription":"231093, 12 p.","startPage":"231093","ipdsId":"IP-157224","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":441567,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.231093","text":"Publisher Index Page"},{"id":435116,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q0NAF8","text":"USGS data release","linkHelpText":"Plant and insect pollinator diversity data from Conservation Reserve Program fields across an agricultural gradient in eastern Iowa"},{"id":424274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -94.06613018879129,\n              43.47459165083325\n            ],\n            [\n              -94.06613018879129,\n              40.39777384484779\n            ],\n            [\n              -90.07425648496523,\n              40.39777384484779\n            ],\n            [\n              -90.07425648496523,\n              43.47459165083325\n            ],\n            [\n              -94.06613018879129,\n              43.47459165083325\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"10","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandever, Mark W. 0000-0003-0247-2629 vandeverm@usgs.gov","orcid":"https://orcid.org/0000-0003-0247-2629","contributorId":197674,"corporation":false,"usgs":true,"family":"Vandever","given":"Mark","email":"vandeverm@usgs.gov","middleInitial":"W.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Givens, Carrie E. 0000-0003-2543-9610","orcid":"https://orcid.org/0000-0003-2543-9610","contributorId":247691,"corporation":false,"usgs":true,"family":"Givens","given":"Carrie","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Cassandra 0000-0003-1088-1772 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-1088-1772","contributorId":193491,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891798,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":204154,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":891799,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221087,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891800,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250629,"text":"70250629 - 2023 - Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir","interactions":[],"lastModifiedDate":"2023-12-21T13:08:53.986552","indexId":"70250629","displayToPublicDate":"2023-11-22T07:06:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir","docAbstract":"<div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies spillover to humans and livestock. We inferred the spatial distribution of vampire bat roosts while accounting for observation effort and environmental effects by fitting a log Gaussian Cox process model to the locations of 563 roosts in three regions of Peru. Our model explained 45% of the variance in the observed roost distribution and identified environmental drivers of roost establishment. When correcting for uneven observation effort, our model estimated a total of 2340 roosts, indicating that undetected roosts (76%) exceed known roosts (24%) by threefold. Predicted hotspots of undetected roosts in rabies-free areas revealed high-risk areas for future viral incursions. Using the predicted roost distribution to inform a spatial model of rabies spillover to livestock identified areas with disproportionate underreporting and indicated a higher rabies burden than previously recognized. We provide a transferrable approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis.</p></div></div>","language":"English","publisher":"The Royal Society","doi":"10.1098/rspb.2023.1739","usgsCitation":"Ribeiro, R., Matthiopoulos, J., Lindgre, F., Tello, C., Zariquiey, C.M., Valderrama, W., Rocke, T.E., and Streicker, D.G., 2023, Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir: Proceedings of the Royal Society B: Biological Sciences, v. 209, no. 2011, 20231739, 11 p., https://doi.org/10.1098/rspb.2023.1739.","productDescription":"20231739, 11 p.","ipdsId":"IP-152468","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":441568,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2023.1739","text":"Publisher Index Page"},{"id":423836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.35165910540015,\n              -11.766899411877915\n            ],\n            [\n              -76.35165910540015,\n              -16.706271629111924\n            ],\n            [\n              -70.68271379289993,\n              -16.706271629111924\n            ],\n            [\n              -70.68271379289993,\n              -11.766899411877915\n            ],\n            [\n              -76.35165910540015,\n              -11.766899411877915\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"209","issue":"2011","noUsgsAuthors":false,"publicationDate":"2023-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Ribeiro, Rita","contributorId":332603,"corporation":false,"usgs":false,"family":"Ribeiro","given":"Rita","email":"","affiliations":[{"id":79508,"text":"School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK","active":true,"usgs":false}],"preferred":false,"id":890628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matthiopoulos, Jason","contributorId":194337,"corporation":false,"usgs":false,"family":"Matthiopoulos","given":"Jason","email":"","affiliations":[],"preferred":false,"id":890629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindgre, Finn","contributorId":332604,"corporation":false,"usgs":false,"family":"Lindgre","given":"Finn","email":"","affiliations":[{"id":79509,"text":"School of Mathematics, University of Edinburgh, Edinburgh, UK","active":true,"usgs":false}],"preferred":false,"id":890630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tello, Carlos","contributorId":244267,"corporation":false,"usgs":false,"family":"Tello","given":"Carlos","email":"","affiliations":[{"id":48877,"text":"ILLARIY, Asociaci´on para el Desarrollo y Conservaci´on de los Recursos Naturales Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":890631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zariquiey, Carlos M.","contributorId":332605,"corporation":false,"usgs":false,"family":"Zariquiey","given":"Carlos","email":"","middleInitial":"M.","affiliations":[{"id":79511,"text":"ILLARIY (Asociación para el Desarrollo y Conservación de los Recursos Naturales), Lima, Perú","active":true,"usgs":false}],"preferred":false,"id":890632,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Valderrama, William","contributorId":244269,"corporation":false,"usgs":false,"family":"Valderrama","given":"William","email":"","affiliations":[{"id":48878,"text":"eILLARIY, Asociaci´on para el Desarrollo y Conservaci´on de los Recursos Naturales Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":890633,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":890634,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Streicker, Daniel G. 0000-0001-7475-2705","orcid":"https://orcid.org/0000-0001-7475-2705","contributorId":152378,"corporation":false,"usgs":false,"family":"Streicker","given":"Daniel","email":"","middleInitial":"G.","affiliations":[{"id":12473,"text":"University of Glasgow","active":true,"usgs":false}],"preferred":false,"id":890635,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70251598,"text":"70251598 - 2023 - Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform","interactions":[],"lastModifiedDate":"2024-02-20T12:13:11.257977","indexId":"70251598","displayToPublicDate":"2023-11-22T06:10:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Studies involving acoustic telemetry typically use stationary acoustic receivers arranged in an array or grid. Unmanned surface vehicle (USV)-based mobile receivers offer advantages over the latter approach: the USV can be programmed to autonomously carry a receiver to and from target locations, more readily adapting to a survey’s spatial scope and scale. This work examines the acoustic detection performance of a low-cost USV developed as a flexible sensing platform. The USV was fitted with an acoustic receiver and operated over multiple waypoints set at increasing distances from the transmitter in two modes: drifting and station-keeping. While drifting, the USV was allowed to drift from the waypoint; while station-keeping, the USV used its thruster to hold position. Detection performance of the USV was similar to that of stationary receivers while drifting, but significantly worse while station-keeping. Noise from the USV thruster was hypothesized as a potential cause of poor detection performance during station-keeping. Detection performance varied with the depth of the tethered receiver such that detection range was greater during the deepest (4.6&nbsp;m) trials than during shallower (1.1 and 2.9&nbsp;m) trials. These results provide insight and guidance on how a USV can be best used for acoustic telemetry, namely, navigating to a planned waypoint, drifting and lowering the receiver to a desired depth for listening, and then navigating to the next waypoint.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1186/s40317-023-00350-1","usgsCitation":"Gaskell, E.M., Funnell, T.R., Holbrook, C., Hondorp, D.W., and Tan, X., 2023, Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform: Animal Biotelemetry, v. 11, 41, 13 p., https://doi.org/10.1186/s40317-023-00350-1.","productDescription":"41, 13 p.","ipdsId":"IP-155456","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":441574,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-023-00350-1","text":"Publisher Index Page"},{"id":425784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Hammond Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -84.18848857240516,\n              45.624082075270366\n            ],\n            [\n              -84.18848857240516,\n              45.56498039215245\n            ],\n            [\n              -84.12224845490009,\n              45.56498039215245\n            ],\n            [\n              -84.12224845490009,\n              45.624082075270366\n            ],\n            [\n              -84.18848857240516,\n              45.624082075270366\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Gaskell, Eric M.","contributorId":334194,"corporation":false,"usgs":false,"family":"Gaskell","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":894996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funnell, Tyler Reid 0000-0002-9074-3531","orcid":"https://orcid.org/0000-0002-9074-3531","contributorId":334195,"corporation":false,"usgs":true,"family":"Funnell","given":"Tyler","email":"","middleInitial":"Reid","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":895000,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70250365,"text":"70250365 - 2023 - Stress gradients structure spatial variability in coastal tidal marsh plant composition and diversity in a major Pacific coast estuary","interactions":[],"lastModifiedDate":"2023-12-05T12:53:18.910274","indexId":"70250365","displayToPublicDate":"2023-11-21T06:47:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Stress gradients structure spatial variability in coastal tidal marsh plant composition and diversity in a major Pacific coast estuary","docAbstract":"<div class=\"JournalAbstract\"><p>Understanding the drivers of variability in plant diversity from local to landscape spatial scales is a challenge in ecological systems. Environmental gradients exist at several spatial scales and can be nested hierarchically, influencing patterns of plant diversity in complex ways. As plant community dynamics influence ecosystem function, understanding the drivers of plant community variability across space is paramount for predicting potential shifts in ecosystem function from global change. Determining the scales at which stress gradients influence vegetation composition is crucial to inform management and restoration of tidal marshes for specific functions. Here, we analyzed vegetation community composition in 51 tidal marshes from the San Francisco Bay Estuary, California, USA. We used model-based compositional analysis and rank abundance curves to quantify environmental (elevation/tidal frame position, distance to channel, and channel salinity) and species trait (species form, wetland indicator status, and native status) influences on plant community variability at the marsh site and estuary scales. While environmental impacts on plant diversity varied by species and their relationships to each other, overall impacts increased in strength from marsh to estuary scales. Relative species abundance was important in structuring these tidal marsh communities even with the limited species pools dominated by a few species. Rank abundance curves revealed different community structures by region with higher species evenness at plots higher in the tidal frame and adjacent to freshwater channels. By identifying interactions (species–species, species–environment, and environment–trait) at multiple scales (local, landscape), we begin to understand how variability measurements could be interpreted for conservation and land management decisions.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2023.1215964","usgsCitation":"Rankin, L.L., Jones, S., Janousek, C.N., Buffington, K., Takekawa, J., and Thorne, K., 2023, Stress gradients structure spatial variability in coastal tidal marsh plant composition and diversity in a major Pacific coast estuary: Frontiers in Ecology and Evolution, v. 11, 1215964, 16 p., https://doi.org/10.3389/fevo.2023.1215964.","productDescription":"1215964, 16 p.","ipdsId":"IP-156855","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441577,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1215964","text":"Publisher Index Page"},{"id":435118,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F802H","text":"USGS data release","linkHelpText":"Marsh Vegetation Surveys Across the San Francisco Bay Estuary, 2008-2018"},{"id":423234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.94698987792064,\n              38.31950691888645\n            ],\n            [\n              -122.94698987792064,\n              37.20784358966503\n            ],\n            [\n              -121.36495862792067,\n              37.20784358966503\n            ],\n            [\n              -121.36495862792067,\n              38.31950691888645\n            ],\n            [\n              -122.94698987792064,\n              38.31950691888645\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rankin, Lyndsay L. 0000-0003-4968-1946","orcid":"https://orcid.org/0000-0003-4968-1946","contributorId":332147,"corporation":false,"usgs":true,"family":"Rankin","given":"Lyndsay","email":"","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":889564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Scott F. 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":204137,"corporation":false,"usgs":false,"family":"Jones","given":"Scott F.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":889565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":889566,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":889567,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Takekawa, John 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203688,"corporation":false,"usgs":false,"family":"Takekawa","given":"John","affiliations":[{"id":36688,"text":"Suisun Resource Conservation District","active":true,"usgs":false}],"preferred":false,"id":889568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":889569,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70250205,"text":"70250205 - 2023 - Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty","interactions":[],"lastModifiedDate":"2023-11-28T13:21:31.650315","indexId":"70250205","displayToPublicDate":"2023-11-20T07:08:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5213,"text":"Epidemics","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41467-023-42680-x","usgsCitation":"Howerton, E., Contamin, L., Mullany, L.C., Qin, M., Reich, N.G., Bents, S., Borchering, R.K., Jung, S., Loo, S.L., Smith, C.P., Levander, J., Kerr, J., Espino, J., van Panhuis, W., Hochheiser, H., Galanti, M., Yamana, T.K., Pei, S., Shaman, J.L., Rainwater-Lovett, K., Kinsey, M., Tallaksen, K., Wilson, S., Shin, L., Lemaitre, J.C., Kaminsky, J., Dent Hulse, J., Lee, E.C., McKee, C., Hill, A., Karlen, D., Chinazzi, M., Davis, J.T., Mu, K., Xiong, X., Pastore Piontti, A., Vespignani, A., Rosenstrom, E.T., Ivy, J.S., Mayorga, M.E., Swann, J.L., Espana, G., Cavany, S., Moore, S., Perkins, A., Hladish, T.J., Pillai, A.N., Ben Toh, K., Longini, I., Chen, S., Paul, R., Janies, D., Thill, J., Bouchnita, A., Bi, K., Lachmann, M., Fox, S., Ancel Meyers, L., Srivastava, A., Porebski, P., Venkatramanan, S., Adiga, A., Lewis, B., Klahn, B., Outten, J., Hurt, B., Chen, J., Mortveit, H., Wilson, A., Marathe, M., Hoops, S., Bhattacharya, P., Machi, D., Gunnels, B.L., Healy, J.M., Slayton, R.B., Johansson, M.A., Biggerstaff, M., Truelove, S., Runge, M.C., Shea, K., Viboud, C., and Lessler, J., 2023, Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty: Epidemics, v. 14, 7260, 15 p., https://doi.org/10.1038/s41467-023-42680-x.","productDescription":"7260, 15 p.","ipdsId":"IP-154417","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":441580,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-023-42680-x","text":"Publisher Index 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,{"id":70251057,"text":"70251057 - 2023 - Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system","interactions":[],"lastModifiedDate":"2024-01-19T13:26:30.651329","indexId":"70251057","displayToPublicDate":"2023-11-19T07:24:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system","docAbstract":"<p>In coastal aquifers, dynamic stresses such as climate forcings, groundwater withdrawals, and ocean tidal fluctuations cause nonlinear responses to groundwater levels. Such responses to the stresses impact groundwater resources and related flooding and infrastructure risks at multiple scales. We used time-series models such as transfer-function models and wavelet analysis to quantify the relative contribution of these stresses to groundwater-level fluctuation in wells from the unconfined and confined aquifers in an Atlantic coastal aquifer. Climate forcings, such as precipitation and temperature, explained most of the groundwater-level variation for wells in the unconfined aquifer, whereas groundwater withdrawals were the dominant driver of groundwater levels for wells in the confined aquifer. The impact of groundwater withdrawals also was detected in several wells in the unconfined aquifer. Although the influence of ocean tides on groundwater levels commonly is observed in coastal aquifers, we found that daily groundwater withdrawals can obscure the semi-diurnal coherence signal of the two series. The magnitude of groundwater-level fluctuation that could be explained solely by tides was minor compared to that explained by climate or withdrawal stresses. Transfer-function modeling showed seasonal withdrawals from wells in confined aquifers had a significant, yet heterogeneous influence on groundwater levels in coastal aquifers, which highlights climate and withdrawals as key compounding stresses in coastal hydrology. This study demonstrates the value of time-series approaches to advance characterization of groundwater systems in areas with limited hydrogeologic parameter information.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2023.130426","usgsCitation":"Yang, G., and McCoy, K., 2023, Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system: Journal of Hydrology, v. 627, no. Part B, 130426, 12 p., https://doi.org/10.1016/j.jhydrol.2023.130426.","productDescription":"130426, 12 p.","ipdsId":"IP-150305","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":441582,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2023.130426","text":"Publisher Index Page"},{"id":424621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","city":"Virginia Beach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.26319664375336,\n              37.04695559476376\n            ],\n            [\n              -76.26319664375336,\n              36.59981472352801\n            ],\n            [\n              -75.83944187250752,\n              36.59981472352801\n            ],\n            [\n              -75.83944187250752,\n              37.04695559476376\n            ],\n            [\n              -76.26319664375336,\n              37.04695559476376\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"627","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yang, Guoxiang 0000-0001-5587-3683","orcid":"https://orcid.org/0000-0001-5587-3683","contributorId":267279,"corporation":false,"usgs":false,"family":"Yang","given":"Guoxiang","affiliations":[{"id":55459,"text":"NSA Contractor to USGS VA and WV WSC","active":true,"usgs":false}],"preferred":false,"id":892914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCoy, Kurt J. 0000-0002-9756-8238","orcid":"https://orcid.org/0000-0002-9756-8238","contributorId":216196,"corporation":false,"usgs":true,"family":"McCoy","given":"Kurt J.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892915,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70241016,"text":"ofr20231015 - 2023 - Evaluating management alternatives for Wyoming elk feedgrounds in consideration of chronic wasting disease","interactions":[],"lastModifiedDate":"2026-02-11T20:44:17.631411","indexId":"ofr20231015","displayToPublicDate":"2023-11-17T17:35:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1015","displayTitle":"Evaluating Management Alternatives for Wyoming Elk Feedgrounds in Consideration of Chronic Wasting Disease","title":"Evaluating management alternatives for Wyoming elk feedgrounds in consideration of chronic wasting disease","docAbstract":"<h1>Executive Summary</h1><p>The authors used decision and modeling analyses to evaluate management alternatives for a decision on whether to permit <i>Cervus canadensis</i> (elk) feeding on two sites on Bridger-Teton National Forest, Dell Creek and Forest Park. Supplemental feeding of elk could increase the transmission of chronic wasting disease (CWD) locally and disease spread regionally, potentially impacting elk populations over time with wider implications for <i>Odocoileus hemionus</i> (mule deer) and <i>Odocoileus virginianus</i> (white-tailed deer) populations and hunting, tourism, and regional revenue. Supplemental feeding is thought to improve overwinter elk survival and reduce the commingling of elk with cattle during months when brucellosis transmission risk is highest. We worked with the U.S. Department of Agriculture Forest Service to identify their fundamental objectives and associated performance metrics related to this feedground decision. We then developed disease and habitat selection models to quantify the effect of four management alternatives on select performance metrics. The four alternatives were to continue to permit feeding, phaseout permits to feed in three years, permit feeding on an emergency basis, or stop permitting feeding. In this report, we present methods and summarized results on disease and habitat selection models and summaries of other performance metrics analyzed by BIO-WEST, Inc. and Cirrus Ecological Solutions as part of an Environmental Impact Statement.</p><p>Data from Wyoming Game and Fish Department (WGFD) supported the assumption that supplemental elk feeding allows for larger elk populations in a region. We documented that herd units (HU) without feedgrounds had 23 percent lower densities of elk per area of winter range when compared against HUs with feedgrounds, after accounting for differences in sightability of elk during counts on and off feedgrounds. Thus, throughout our analyses, we assumed feedground closures would reduce elk carrying capacity resulting in an average decline of previously fed elk population segments by 23 percent (5th and 95th percentiles = [11 percent, 35 percent]) by year 20. Most of that decline occurred within the first few years after a feedground ceases to operate. We used a panel of CWD experts to help estimate CWD trans-mission in fed and unfed elk population segments. In aggregate, the expert panel estimated that median values of direct and indirect transmission of CWD are expected to be 1.9 and 4 times higher, respectively, in fed elk populations compared to unfed elk. We used these disease transmission estimates in combination with local elk demographic rates and carrying capacity estimates to project disease and population dynamics.</p><p>In year 20, we predicted CWD prevalence would increase to 42 percent (5th and 95th percentiles = [29 percent, 55 percent]), and 13 percent (5th and 95th percentiles = [4 percent, 26 percent]) on average for fed and unfed elk population segments, respectively, given a starting prevalence of 1.6 percent. The prevalence estimates for the unfed elk population segments are in the range of previous observations of CWD in elk in the western United States. The average CWD prevalence from 2016 to 2018 in the unfed elk population of Wind Cave National Park in South Dakota was 18 percent overall but up to 30 percent in some regions (Sargeant and others, 2021). Meanwhile, CWD prevalence in the Iron Mountain and Laramie Peak elk herds in Wyoming from 2016 to 2018 was 14 percent and 7 percent, respectively, despite being present since at least 2002 (Wyoming Game and Fish Department, 2020b).</p><p>From 2016 to 2020, elk that were fed at Dell Creek and Forest Park constituted on average 12–20 percent of the total elk on their respective HUs. As a result, the differences between management alternatives are modest when considering the closure of only one feedground on a HU. The no feeding alternative for Forest Park resulted in a CWD prevalence of 17 percent (SD = 7 percent) in the Afton HU compared to 20 percent (SD = 7 percent) with continued feeding by year 20. In the Upper Green River HU, no feeding on Dell Creek resulted in a CWD prevalence of 27 percent (SD = 6 percent) compared to 30 percent (SD = 5 percent) with continued feeding. In terms of disease-associated mortality, we predicted the closure of Forest Park and Dell Creek feedgrounds would reduce the total number of CWD mortalities by 9 percent in the Upper Green River HU and 26 percent in the Afton HU during the 20-year timespan.</p><p>Our spatial analyses predicted that management alternative effects vary by HU as a function of private property and other wildlife winter ranges proximity relative to feedground location. The predicted number of elk abortions on private land, as a proxy for brucellosis risk to cattle, may increase by 8–21 percent in the absence of feeding at Dell Creek and Forest Park.</p><p>Eight feedgrounds are located on Bridger-Teton National Forest, all of which have permits that have expired or will expire prior to 2028. In addition, WGFD could change their management of feedgrounds given new information; therefore, we also assessed the cumulative effects of continued feeding, phaseout, and no feeding management alternatives across five HUs south of Jackson, Wyoming (Afton HU, Fall Creek HU, Piney HU, Pinedale HU, and Upper Green River HU). These five HUs ranged from about 41 to 85 percent of the elk herd using feedgrounds, which corresponded to a CWD prevalence at year 20 of 23–34 percent if all feedgrounds in those five HUs remained open relative to 12 to 14 percent if all feedgrounds were closed. We predicted feedground closures may result in immediate reductions in population size relative to alternatives that continue feeding (for example, continued feeding and emergency feeding alternatives); however, over longer periods of time, CWD-associated mortality leads to larger population reductions. The no feeding alternative resulted in higher elk population sizes compared to the continued feeding alternative after about 10 years of implementation. Delayed action under a phaseout alternative resulted in increasing the CWD prevalence to 20 percent relative to 12 to 14 percent, on average, without feeding on HUs with a large population of fed elk such as the Upper Green River HU.</p><p>Summarizing our cumulative results across all five of the analyzed HUs, we predicted continued feeding will lead to fewer elk by year 20 (mean = 8,300, standard deviation [SD] = 740) compared to no feeding at U.S. Department of Agri-culture Forest Service sites (10,700, SD = 890). The closure of all feedgrounds was projected to result in the largest elk populations at year 20 (12,500, SD = 980). No feeding at all sites also resulted in the largest cumulative harvest of 57,700 (SD = 2,600) compared to 51,100 (SD = 3,800) for continued feeding at all current feedground sites on the five HUs. Continued feeding also resulted in the lowest brucellosis costs to producers ($194,600, SD = $11,500) compared to no feeding on all feedgrounds ($243,000, SD = $13,700). Assuming moderate reductions in hunter interest because of increasing CWD prevalence in elk, we predicted that no feeding resulted in regional revenues generated by hunting activities of $190 million (SD = $10 million) compared to $173 million (SD = $10 million) for continued feeding over the 20-year timeframe.</p><p>Recent CWD detections in mule deer and elk in Grand Teton National Park has elevated the importance of the cur-rent decision on whether, and how, to permit elk feeding on Dell Creek and Forest Park and the management of the other feedgrounds. Aggressive male harvest has slowed, but not stopped, the increasing prevalence of CWD in mule deer (Conner and others, 2021). It is unclear whether harvest management can be an effective tool to slow the spread of CWD in elk. There are also no effective treatments or vaccines for CWD, and it is unlikely that any will be developed that can be easily deployed in the near future. Thus, reducing artificial aggregations is one of the few management approaches suggested by the Western Association of Fish and Wildlife Agencies (Almberg and others, 2017).</p><p>Future surveillance and monitoring can be designed to resolve uncertainties that can improve future decision-making. If feedgrounds close, research could quantify elk population reductions in the absence of feeding, the redistribution of fed elk to other places, or the consequences of elk movement on private property. If feedgrounds remain open, research could assess how rapidly CWD spreads in artificial aggregations of elk; however, surveillance programs would need to be designed with sufficient power to detect initial changes of CWD prevalence. Delaying action on feedground management was projected to be costly. Results of the phaseout alternative relative to the no feeding alternative suggested a 3-year delay was enough for substantial long-term changes in CWD prevalence. The long-term persistence of infectious CWD prions in the environment suggests that feedground management decisions may have long-lasting consequences.</p><p>Our results indicated tradeoffs in the ability of a management agency to achieve all their objectives, and all management alternatives resulted in significant reductions in elk population size. This report contains the foundational elements for formal decision analysis methods, which can be implemented to help decision makers transparently evaluate the consequences of decision alternatives and identify the set of actions that best achieve agency and stakeholder priorities.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231015","collaboration":"Prepared in cooperation with U.S. Department of Agriculture, National Park Service, U.S. Fish and Wildlife Service, and Wyoming Game and Fish Department","usgsCitation":"Cook, J.D., Cross, P.C., Tomaszewski, E.M., Cole, E.K., Campbell Grant, E.H., Wilder, J.M., and Runge, M.C., 2023, Evaluating management alternatives for Wyoming Elk feedgrounds in consideration of chronic wasting disease (ver. 2.0, November 2023): U.S. Geological Survey Open-File Report 2023–1015, 50 p., https://doi.org/10.3133/ofr20231015.","productDescription":"Report: ix, 50 p.; Software Release","onlineOnly":"Y","ipdsId":"IP-145385","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":499766,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114473.htm","linkFileType":{"id":5,"text":"html"}},{"id":422707,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1015/versionHist.txt","size":"4.0kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2023-1015 history file"},{"id":422706,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1015/ofr20231015.pdf","text":"Report","size":"7.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1015"},{"id":419233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1015/coverthb2.jpg"},{"id":422704,"rank":2,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9R7XWO1","text":"USGS software release—","linkHelpText":"Simulating chronic wasting disease on Wyoming elk feedgrounds (version 2.0)."}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.03672229293583,\n              43.73180346838649\n            ],\n            [\n              -111.03672229293583,\n              42.40523773968059\n            ],\n            [\n              -109.27478197144448,\n              42.40523773968059\n            ],\n            [\n              -109.27478197144448,\n              43.73180346838649\n            ],\n            [\n              -111.03672229293583,\n              43.73180346838649\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","edition":"Version 1.0: March 2023: Version 2.0: November 2023","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/norock/\" data-mce-href=\"https://www.usgs.gov/centers/norock/\">Northern Rocky Mountain Science Center</a><br>U.S. Geological Survey<br>2327 University Way, Suite 2 <br>Bozeman, MT 59715</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Executive Summary</li><li>Introduction</li><li>Decision Framing</li><li>Chronic Wasting Disease, Population Size, and Harvest Projections</li><li>Spatio-Temporal Analysis of Elk Distributions</li><li>Consequences</li><li>Conclusions and Future Directions</li><li>References Cited</li><li>Appendix 1. Additional Chronic Wasting Disease Analysis Details</li></ul>","publishedDate":"2023-03-09","revisedDate":"2023-11-17","noUsgsAuthors":false,"publicationDate":"2023-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Jonathan D. 0000-0001-7000-8727","orcid":"https://orcid.org/0000-0001-7000-8727","contributorId":291411,"corporation":false,"usgs":true,"family":"Cook","given":"Jonathan","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":865728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":865729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tomaszewski, Emily M. 0000-0002-3766-8990","orcid":"https://orcid.org/0000-0002-3766-8990","contributorId":302889,"corporation":false,"usgs":true,"family":"Tomaszewski","given":"Emily","email":"","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":865730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Eric K.","contributorId":302890,"corporation":false,"usgs":false,"family":"Cole","given":"Eric K.","affiliations":[{"id":65572,"text":"U.S. Fish and Wildlife Service, National Elk Refuge","active":true,"usgs":false}],"preferred":false,"id":865731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilder, James M.","contributorId":302891,"corporation":false,"usgs":false,"family":"Wilder","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":865733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865734,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250099,"text":"sir20235066 - 2023 - Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013","interactions":[],"lastModifiedDate":"2026-03-09T16:53:50.063749","indexId":"sir20235066","displayToPublicDate":"2023-11-17T13:55:00","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":"2023-5066","displayTitle":"Updates to the Regional Groundwater-Flow Model of the New Jersey Coastal Plain, 1980–2013","title":"Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013","docAbstract":"<p>A 21-layer three-dimensional transient groundwater-flow model of the New Jersey Coastal Plain was developed and calibrated by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection to simulate groundwater-flow conditions during 1980–2013, incorporating average annual groundwater withdrawals and average annual groundwater recharge. This model is the third version of the New Jersey Coastal Plain regional groundwater-flow model that was initially developed as part of the USGS Regional Aquifer System Analysis (RASA) program. The model simulates groundwater flow in 11 aquifers and 10 intervening confining units of the New Jersey Coastal Plain to provide a regional overview of groundwater conditions. Averaged groundwater withdrawal data for 1980 to 2013 were used in the model. The 11 aquifers in New Jersey are, from shallowest to deepest, the Holly Beach water-bearing zone and the confined Cohansey aquifer in Cape May County; the Rio Grande water-bearing zone; the Atlantic City 800-foot sand; the Piney Point, Vincentown, and Wenonah-Mount Laurel aquifers; the Englishtown aquifer system; and the upper, middle, and lower aquifers of the Potomac-Raritan-Magothy (PRM) aquifer system.</p><p>The model was developed with the MODFLOW–2005 numerical code and the UCODE parameter estimation technique and calibrated using water-level and base-flow observations. A total of 3,453 water-level observations from 392 wells in New Jersey and 48 wells in Delaware from 1983 to 2013 were used in model calibration, which includes historical water-level trends for 29 wells in New Jersey during 1980–2013 presented in time-series hydrographs. In addition, derived observations also were included by calculating the vertical gradient at 33 pairs of nested observation wells in New Jersey, for a total of 210 observations. Changes in water levels over time were calculated for 134 wells in New Jersey and four wells in Delaware where water levels had varied substantially (approximately 10 ft) over the 30-year span of synoptic water-level measurements, for a total of 767 observations. A total of 1,485 base-flow observations in 47 surface-water basins in New Jersey from 1980 to 2013 were used in model calibration.</p><p>Updates to the groundwater-flow model include the conversion to a fully three-dimensional model from the previous quasi-three-dimensional model. The new model will allow for potential future uses such as particle tracking or simulation of variable-density groundwater flow that could not be accomplished with earlier versions of the model. Spatially and temporally variable recharge estimated by using a soil-water balance model resulted in a spatially and temporally finer discretization. The Rio Grande water-bearing zone was added to the model as an aquifer layer to refine estimates of simulated flow in Atlantic and Cape May Counties, New Jersey. Hydrogeologic parameters were updated to include the confining units in New Jersey and corresponding hydrogeologic units in Delaware and eastern Maryland.</p><p>The simulated water levels for the New Jersey Coastal Plain aquifers were compared to water-level measurements made during 1980–2013. The average residual for 4,243 water-level observations for New Jersey (simulated water levels minus measured water levels) is 1.5 feet. The simulated water-level contours for the confined aquifers for 2013 were compared to potentiometric surfaces produced from water levels measured during 2013. Simulated water levels generally matched the 2013 potentiometric surfaces of the confined aquifers in the areas of large withdrawals. Hydrographs of wells in the confined Coastal Plain aquifers of New Jersey show that simulated water levels generally match the magnitude and seasonal variation of the observed water levels. Hydrographs of base flow for the 47 streamgaging stations in New Jersey indicate that most of the simulated and estimated data match reasonably well.</p><p>Groundwater withdrawals are an important resource for water supply, agricultural, industrial, and commercial needs in the New Jersey Coastal Plain. Groundwater withdrawals from the New Jersey Coastal Plain aquifers have resulted in persistent, regionally extensive cones of depression in the Englishtown aquifer system and Wenonah-Mount Laurel aquifer in Ocean and Monmouth Counties; Wenonah-Mount Laurel and upper, middle, and lower PRM aquifers in Camden County; and Atlantic City 800-foot sand in Atlantic County. Because hydrologic stresses and water-management needs change with time, periodic updates to the groundwater-flow model are required to provide current information about hydrologic conditions in the New Jersey Coastal Plain and to maintain its usefulness as a tool to manage water resources and develop water-resource strategies. The current updates will support the continued application of this model as a tool for evaluating the regional effects of changes in groundwater withdrawals and of current and potential future water-management strategies on groundwater levels in the New Jersey Coastal Plain.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235066","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Gordon, A.D., and Carleton, G.B., 2023, Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013: U.S. Geological Survey Scientific Investigations Report 2023–5066, 116 p., https://doi.org/10.3133/sir20235066","productDescription":"Report: xii, 116 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127396","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500947,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115639.htm","linkFileType":{"id":5,"text":"html"}},{"id":422695,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5066/images/"},{"id":422693,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235066/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5066"},{"id":422696,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W6RXFC","text":"USGS data release","linkHelpText":"MODFLOW-2005 model used to simulate the regional groundwater flow system in the updated New Jersey Coastal Plain model, 1980-2013"},{"id":422694,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.XML"},{"id":422692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5066"},{"id":422691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5066/coverthb.jpg"}],"country":"United States","otherGeospatial":"New Jersey Coastal Plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -74.49018324613056,\n              41.03712838002892\n            ],\n            [\n              -75.25922621488034,\n              41.417217443631785\n            ],\n            [\n              -77.41254652738019,\n              39.17183412365296\n            ],\n            [\n              -75.22626723050551,\n              37.8132834585617\n            ],\n            [\n              -72.98505629300531,\n              40.4043207917766\n            ],\n            [\n              -74.49018324613056,\n              41.03712838002892\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/new-jersey-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/new-jersey-water-science-center\">New Jersey Water Science Center</a><br>3450 Princeton Pike, Suite 110<br>Lawrenceville, New Jersey 08648</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"https://pubs.er.usgs.gov/contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Simulation of Groundwater Flow</li><li>Summary</li><li>References Cited</li><li>Appendix 1: Soil-Water Balance Methodology</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2023-11-17","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Gordon, Alison D. 0000-0002-9502-8633","orcid":"https://orcid.org/0000-0002-9502-8633","contributorId":221457,"corporation":false,"usgs":true,"family":"Gordon","given":"Alison","email":"","middleInitial":"D.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":888330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carleton, Glen B. 0000-0002-7666-4407","orcid":"https://orcid.org/0000-0002-7666-4407","contributorId":306147,"corporation":false,"usgs":false,"family":"Carleton","given":"Glen","email":"","middleInitial":"B.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":888331,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70250011,"text":"fs20233042 - 2023 - The 3D Elevation Program—Supporting Missouri’s economy","interactions":[],"lastModifiedDate":"2024-01-25T17:25:00.353292","indexId":"fs20233042","displayToPublicDate":"2023-11-17T13:50:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3042","displayTitle":"The 3D Elevation Program—Supporting Missouri’s Economy","title":"The 3D Elevation Program—Supporting Missouri’s economy","docAbstract":"<h1>Introduction</h1><p>Because of its geography, Missouri is frequently subject to natural disasters. Ice storms, severe thunderstorms, tornadoes, and flooding are all common occurrences. Since 1990, Missouri has received 40 Federal major disaster declarations. Floods and droughts severely affect the State’s agriculture, which is a leading industry. Another potential major hazard is the New Madrid seismic zone (NMSZ), located in southeastern Missouri. Because Missouri is a major producer of lead, manufacturing and mining are very important to the State’s economy, as are restoring and reclaiming lands damaged by historical mining activities. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233042","usgsCitation":"Nail, D.S., 2023, The 3D Elevation Program—Supporting Missouri’s economy: U.S. Geological Survey Fact Sheet 2023–3042, 2 p., https://doi.org/10.3133/fs20233042","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127176","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":422536,"rank":5,"type":{"id":31,"text":"Publication 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 \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/national-geospatial-program\" data-mce-href=\"https://www.usgs.gov/programs/national-geospatial-program\">National Geospatial Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive, Mail Stop 511<br>Reston, VA 20192</p><p>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Status of 3DEP in Missouri</li><li>Agriculture and Precision Farming</li><li>Flood Risk Management</li><li>Geologic Resource Assessment and Hazard Mitigation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-11-17","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Nail, David 0000-0003-0793-2305 dnail@usgs.gov","orcid":"https://orcid.org/0000-0003-0793-2305","contributorId":331534,"corporation":false,"usgs":true,"family":"Nail","given":"David","email":"dnail@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":887991,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70250061,"text":"sir20235099 - 2023 - Machine-learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, south-central United States, with evaluation of regional geophysical aerial electromagnetic data as explanatory variables","interactions":[],"lastModifiedDate":"2026-03-13T15:15:40.637546","indexId":"sir20235099","displayToPublicDate":"2023-11-17T09:01:14","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":"2023-5099","displayTitle":"Machine-Learning Predictions of Groundwater Specific Conductance in the Mississippi Alluvial Plain, South-Central United States, With Evaluation of Regional Geophysical Aerial Electromagnetic Data as Explanatory Variables","title":"Machine-learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, south-central United States, with evaluation of regional geophysical aerial electromagnetic data as explanatory variables","docAbstract":"<p>The Mississippi Alluvial Plain, located in the south-central United States, is undergoing long-term groundwater-level declines within the surficial Mississippi River Valley alluvial aquifer (hereinafter referred to as “alluvial aquifer”), which has raised concerns about future groundwater availability. In some parts of the alluvial aquifer, groundwater availability for common uses such as irrigation, public supply, and domestic use is limited by quality (for example, high salinity) rather than quantity of water stored in the aquifer. The Mississippi Alluvial Plain region has an abundance of water-quality measurements in the alluvial aquifer and deeper aquifers; however, large areas lack direct measurements of salinity to evaluate regional groundwater availability. Statistical models can interpolate between wells to fill in spatial data gaps. In 2021, the U.S. Geological Survey trained two boosted regression tree (BRT) machine-learning models on specific conductance data available between 1942 and 2020 to predict spatially continuous surfaces of groundwater salinity at multiple depths for the alluvial aquifer and deeper aquifers. Well construction information, water levels, and surficial variables such as geomorphology and soils were included as explanatory variables in this baseline model. Additionally, subsurface electrical resistivity data from the first aquifer-wide aerial electromagnetic (AEM) survey for the region were incorporated to create a geophysical model. This work expands on prior BRT salinity predictions of the alluvial aquifer and extends predictions south to the Gulf of Mexico, where groundwater salinity is high. AEM survey data were not available for the southern extent of the alluvial aquifer at the time of modeling. A BRT model was trained without (baseline) and with (geophysical) AEM variables to test the ability of the models to predict salinity where explanatory data are missing and response data are sparse. Additionally, model sensitivity to AEM survey data was evaluated to better understand how AEM variables influence specific conductance predictions. Model performance was improved with the addition of geophysical data, which added three-dimensional information, thereby improving salinity predictions at depth. Groundwater specific conductance predictions can help inform other geophysical investigations in the southern extent of the study area, where high groundwater specific conductance can obfuscate changes in aquifer sediment resistivity and could limit groundwater resources for agricultural, public supply, and domestic uses.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235099","issn":"2328-0328","programNote":"Water Availability and Use Science Program","usgsCitation":"Killian, C.D., and Knierim, K.J., 2023, Machine-learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, south-central United States, with evaluation of regional geophysical aerial electromagnetic data as explanatory variables: U.S. Geological Survey Scientific Investigations Report 2023–5099, 36 p., 1 pl., https://doi.org/10.3133/sir20235099.","productDescription":"Report: viii, 36 p., 1 Plate: 33.04 × 37.14 inches; Dataset; Data Release","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-117784","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":501148,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115638.htm","linkFileType":{"id":5,"text":"html"}},{"id":423108,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235099/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5099 HTML"},{"id":422628,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2023/5099/sir20235099_plate01.pdf","text":"Plate 1","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5099 Plate 1","linkHelpText":"—Raster Predictions of Specific Conductance at Groundwater Wells by Depth in the Mississippi Alluvial Plain Region"},{"id":422626,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WSE8JS","text":"USGS Data Release","linkHelpText":"Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain"},{"id":422623,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5099/Images"},{"id":422622,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5099/sir20235099.pdf","size":"31.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5099"},{"id":422621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5099/coverthb.jpg"},{"id":422624,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5099/sir20235099.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5099 XML"},{"id":422627,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS Dataset","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -92.8114869520446,\n              37.89139322749202\n            ],\n            [\n              -92.8114869520446,\n              28.689695810736353\n            ],\n            [\n              -87.62594007704502,\n              28.689695810736353\n            ],\n            [\n              -87.62594007704502,\n              37.89139322749202\n            ],\n            [\n              -92.8114869520446,\n              37.89139322749202\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/lmg-water/\" href=\"https://www.usgs.gov/centers/lmg-water/\">Lower Mississippi-Gulf Water Science Center</a> <br>U.S. Geological Survey&nbsp;<br><span class=\"HQEo7\" role=\"link\" data-markjs=\"true\" data-mce-tabindex=\"0\">640 Grassmere Park, suite 100 <br>Nashville, TN 37211</span>&nbsp;</p><p><a data-mce-href=\"../\" href=\"../\"><span class=\"ContentPasted3\">Contact Pubs Warehouse</span></a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2023-11-17","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Killian, Courtney D. 0000-0002-2137-2722","orcid":"https://orcid.org/0000-0002-2137-2722","contributorId":213990,"corporation":false,"usgs":true,"family":"Killian","given":"Courtney","email":"","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":888171,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":888172,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70250515,"text":"70250515 - 2023 - Less is more: Less herbicide does more when biological control is present in Pontederia crassipes","interactions":[],"lastModifiedDate":"2023-12-14T12:43:11.880092","indexId":"70250515","displayToPublicDate":"2023-11-17T06:42:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Less is more: Less herbicide does more when biological control is present in Pontederia crassipes","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><p id=\"spara012\">An experiment along with simulation modeling was applied to study the combinations of herbicide treatment and biological control that best limit invasive water hyacinth (<i>Pontederia crassipes</i>, formerly<span>&nbsp;</span><i>Eichhornia crassipes</i>) in freshwater aquatic systems. The experiment consisted of 14 different treatments of<span>&nbsp;</span><i>P. crassipes</i><span>&nbsp;</span>in 1.67&nbsp;m<sup>2</sup><span>&nbsp;</span>outdoor tank mesocosms. Seven treatments were with and seven were without insect biological control agents,<span>&nbsp;</span><i>Neochetina eichhorniae</i>. In both of the sets of seven treatments, there was one no-herbicide treatment, a one-time full-strength herbicide treatment with 40&nbsp;%, 80&nbsp;% and 100&nbsp;% coverage of the<span>&nbsp;</span><i>P. crassipes</i>, and a one-time half-strength herbicide treatment with 40&nbsp;%, 80&nbsp;%, and 100&nbsp;% surface area coverage. An overarching hypothesis was that leaving part of a tank unsprayed, providing habitat for the maintenance of biological control agents, would optimize control. Data from the experiment, measured on five days over the 167-day period, were used to calibrate a difference equation model of<span>&nbsp;</span><i>P. crassipes</i><span>&nbsp;</span>with and without the biological control agent. The model was then used to project longer term dynamics of the system. The model predicted that an initial one-time herbicide treatment, combined with application of the biocontrol agent at 80&nbsp;% areal coverage, could maintain<span>&nbsp;</span><i>P. crassipes</i><span>&nbsp;</span>at levels lower than the carrying capacity of the plant's biomass over the long term, though not enough that<span>&nbsp;</span><i>N. eichhorniae</i><span>&nbsp;</span>would be considered, by itself, a highly effective control. However, the results suggest that a combination of biocontrol with 80&nbsp;% spraying coverage every 600 days or so would be an effective integrated biocontrol strategy for maintaining decreased<span>&nbsp;</span><i>P. crassipes</i><span>&nbsp;</span>biomass at low levels over the long term.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2023.110566","usgsCitation":"Xu, L., Goode, A.B., Tipping, P.W., Smith, M.C., Gettys, L., Knowles, B.K., Pokorny, E., Salinas, L., and DeAngelis, D., 2023, Less is more: Less herbicide does more when biological control is present in Pontederia crassipes: Ecological Modelling, v. 487, 110566, 11 p., https://doi.org/10.1016/j.ecolmodel.2023.110566.","productDescription":"110566, 11 p.","ipdsId":"IP-149426","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2023.110566","text":"Publisher Index Page"},{"id":423572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"487","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Linhao","contributorId":221358,"corporation":false,"usgs":false,"family":"Xu","given":"Linhao","email":"","affiliations":[{"id":40353,"text":"Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key","active":true,"usgs":false}],"preferred":false,"id":890219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goode, Ashley B.C.","contributorId":332463,"corporation":false,"usgs":false,"family":"Goode","given":"Ashley","middleInitial":"B.C.","affiliations":[{"id":33268,"text":"USDA-ARS Aquatic Weed Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":890220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tipping, Philip W.","contributorId":332464,"corporation":false,"usgs":false,"family":"Tipping","given":"Philip","email":"","middleInitial":"W.","affiliations":[{"id":33268,"text":"USDA-ARS Aquatic Weed Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":890221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Melissa C.","contributorId":221360,"corporation":false,"usgs":false,"family":"Smith","given":"Melissa","email":"","middleInitial":"C.","affiliations":[{"id":40354,"text":"USDA-ARS Invasive Plant Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":890222,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gettys, Lyn A.","contributorId":332465,"corporation":false,"usgs":false,"family":"Gettys","given":"Lyn A.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":890223,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knowles, Brittany K.","contributorId":332466,"corporation":false,"usgs":false,"family":"Knowles","given":"Brittany","email":"","middleInitial":"K.","affiliations":[{"id":33268,"text":"USDA-ARS Aquatic Weed Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":890224,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pokorny, Eileen","contributorId":332467,"corporation":false,"usgs":false,"family":"Pokorny","given":"Eileen","email":"","affiliations":[{"id":33268,"text":"USDA-ARS Aquatic Weed Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":890225,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Salinas, Luz","contributorId":332468,"corporation":false,"usgs":false,"family":"Salinas","given":"Luz","email":"","affiliations":[{"id":33268,"text":"USDA-ARS Aquatic Weed Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":890226,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":890227,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70250086,"text":"ofr20211030P - 2023 - System characterization report on the Pléiades Neo Imager","interactions":[{"subject":{"id":70250086,"text":"ofr20211030P - 2023 - System characterization report on the Pléiades Neo Imager","indexId":"ofr20211030P","publicationYear":"2023","noYear":false,"chapter":"P","displayTitle":"System Characterization Report on the Pléiades Neo Imager","title":"System characterization report on the Pléiades Neo Imager"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2024-06-17T19:42:45.516982","indexId":"ofr20211030P","displayToPublicDate":"2023-11-16T15:55:10","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"P","displayTitle":"System Characterization Report on the Pléiades Neo Imager","title":"System characterization report on the Pléiades Neo Imager","docAbstract":"<h1>Executive Summary</h1><p>This report addresses system characterization of the Pléiades Neo satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.</p><p>Pléiades Neo is a constellation of four identical very-high-resolution optical satellites operated by Airbus Defence and Space. The first two satellites, Pléiades Neo-3 and -4, were launched in April and August 2021, respectively. The next two satellites, launched in December 2022, did not reach orbit because of Vega-C launch vehicle failure. Pléiades Neo provides several technical improvements to previous Pléiades-HR satellites, including the addition of coastal aerosol (deep blue) and red edge spectral bands, with improved ground sample distance and swath. The Pléiades Neo satellites were designed and built by Airbus Defence and Space with the high-resolution, multispectral imager for Earth imaging and use the S950 optical satellite bus. The high-resolution sensor on Pléiades Neo collects Earth data in the visible and near-infrared region with six bands and a panchromatic band. The satellites can operate off nadir to achieve a revisit of less than 1 day. More information on Pléiades Neo satellites and sensors is available in the “Land Remote Sensing Satellites Online Compendium” (<a data-mce-href=\"https://calval.cr.usgs.gov/apps/compendium\" href=\"https://calval.cr.usgs.gov/apps/compendium\">https://calval.cr.usgs.gov/apps/compendium#</a>) and from the manufacturer (<a data-mce-href=\"https://www.intelligence-airbusds.com/imagery/constellation/pleiades-Neo/\" href=\"https://www.intelligence-airbusds.com/imagery/constellation/pleiades-Neo/\">https://www.intelligence-airbusds.com/imagery/constellation/pleiades-Neo/</a>).</p><p>The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Pléiades Neo has an interior geometric performance in the range of 0.01 meter (m; 0.008 pixel) to −0.017 m (−0.014 pixel) in band-to-band registration; an exterior geometric performance in the range of −7.015 m (−0.702 pixel) to 3.846 m (0.385 pixel) offset in comparison to Sentinel-2 using ground control points of 2.2 to 7.2 m (95-percent circular error); a radiometric performance in the range of −0.070 (minimum) to −0.053 (maximum) in offset and 1.107 (minimum) to 1.202 (maximum) in slope; and a spatial performance in the range of 1.002 to 1.226 pixels at full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.22 to 0.34 (bands 2–7).</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System Characterization of Earth Observation Sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030P","usgsCitation":"Cantrell, S.J., Sampath, A., Vrabel, J.C., Bresnahan, P., Anderson, C., Kim, M., and Park, S., 2023, System characterization report on the Pléiades Neo Imager (ver. 1.1, April 2024), chap. P <em>of</em> Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 52 p., https://doi.org/10.3133/ofr20211030P.","productDescription":"Report: vi, 52 p.; Version History","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-154436","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":422656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/p/coverthb2.jpg"},{"id":422657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/p/ofr20211030p.pdf","text":"Report","size":"21.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–P"},{"id":422658,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/p/ofr20211030p.XML"},{"id":428107,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/p/versionHist.txt","text":"Version History","size":"1.99 kB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: November 16, 2023; Version 1.1: April 29, 2024","contact":"<p>Director,&nbsp;<a href=\"https://www.usgs.gov/centers/eros\" data-mce-href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</li><li>System Description</li><li>Procedures</li><li>Measurements</li><li>Analysis</li><li>Summary and Conclusions</li><li>Selected References</li><li>Appendix 1. Explanation of Ground Control Points Method and Metadata</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-11-16","revisedDate":"2024-04-29","noUsgsAuthors":false,"publicationDate":"2023-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Cantrell, Simon J. 0000-0001-6909-1973","orcid":"https://orcid.org/0000-0001-6909-1973","contributorId":259304,"corporation":false,"usgs":false,"family":"Cantrell","given":"Simon J.","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":888269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sampath, Aparajithan 0000-0002-6922-4913","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":222486,"corporation":false,"usgs":false,"family":"Sampath","given":"Aparajithan","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":888270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vrabel, James C. 0000-0002-0120-4721","orcid":"https://orcid.org/0000-0002-0120-4721","contributorId":264751,"corporation":false,"usgs":false,"family":"Vrabel","given":"James C.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":888271,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bresnahan, Paul 0000-0002-3491-0956","orcid":"https://orcid.org/0000-0002-3491-0956","contributorId":306120,"corporation":false,"usgs":false,"family":"Bresnahan","given":"Paul","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":888272,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":888275,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kim, Minsu 0000-0003-4472-0926","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":297371,"corporation":false,"usgs":false,"family":"Kim","given":"Minsu","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":888273,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Seonkyung 0000-0003-3203-1998 seonkyungpark@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":222488,"corporation":false,"usgs":false,"family":"Park","given":"Seonkyung","email":"seonkyungpark@contractor.usgs.gov","affiliations":[{"id":40547,"text":"United Support Services, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":888274,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250271,"text":"70250271 - 2023 - Investigating permafrost carbon dynamics in Alaska with artificial intelligence","interactions":[],"lastModifiedDate":"2023-11-30T13:12:50.071693","indexId":"70250271","displayToPublicDate":"2023-11-16T07:09:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Investigating permafrost carbon dynamics in Alaska with artificial intelligence","docAbstract":"<div class=\"article-text wd-jnl-art-abstract cf\"><p>Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates<span>&nbsp;</span><i>in-situ</i><span>&nbsp;</span>measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO<sub>2</sub>), and methane flux (CH<sub>4</sub>)—with high precision and minimal loss (i.e. ALT<sup>RMSE</sup>: 1.327 cm [1969–2022]; CO<sub>2</sub><sup>RMSE</sup>: 0.697<span>&nbsp;</span><i>µ</i>molCO<sub>2</sub>m<sup>−2</sup>s<sup>−1</sup><span>&nbsp;</span>[2003–2021]; CH<sub>4</sub><sup>RMSE</sup>: 0.715 nmolCH<sub>4</sub>m<sup>−2</sup>s<sup>−1</sup><span>&nbsp;</span>[2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.</p></div>","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ad0607","usgsCitation":"Gay, B., Pastick, N., Zufle, A., Armstrong, A., Miner, K., and Qu, J., 2023, Investigating permafrost carbon dynamics in Alaska with artificial intelligence: Environmental Research Letters, v. 18, no. 12, 125001, 20 p., https://doi.org/10.1088/1748-9326/ad0607.","productDescription":"125001, 20 p.","ipdsId":"IP-158731","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":441585,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad0607","text":"Publisher Index Page"},{"id":423088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -157.71401031875757,\n              58.963334167122895\n            ],\n            [\n              -152.79213531875774,\n              62.03364889814105\n            ],\n            [\n              -147.07924469375772,\n              63.08656488912206\n            ],\n            [\n              -142.24526031875774,\n              62.44303153277835\n            ],\n            [\n              -141.01479156875783,\n              62.198069009088584\n            ],\n            [\n              -141.01479156875783,\n              66.69800816270453\n            ],\n            [\n              -141.10268219375766,\n              70.02941490604613\n            ],\n            [\n              -149.89174469375754,\n              70.79541190510929\n            ],\n            [\n              -156.39565094375757,\n              71.42141172305344\n            ],\n            [\n              -162.54799469375754,\n              70.7375061891758\n            ],\n            [\n              -165.53627594375757,\n              69.32744461858817\n            ],\n            [\n              -167.11830719375752,\n              68.01724610188819\n            ],\n            [\n              -167.38197906875752,\n              64.78513626012426\n            ],\n            [\n              -166.94252594375763,\n              61.11327463769916\n            ],\n            [\n              -166.67885406875763,\n              59.45820927210812\n            ],\n            [\n              -164.04213531875743,\n              58.64466553350218\n            ],\n            [\n              -159.73547175467303,\n              57.905238894122505\n            ],\n            [\n              -157.53820612967297,\n              57.85851039771879\n            ],\n            [\n              -157.71401031875757,\n              58.963334167122895\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Gay, Bradley 0000-0003-2617-2559","orcid":"https://orcid.org/0000-0003-2617-2559","contributorId":332010,"corporation":false,"usgs":false,"family":"Gay","given":"Bradley","email":"","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":889234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pastick, Neal 0000-0002-4321-6739","orcid":"https://orcid.org/0000-0002-4321-6739","contributorId":222683,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":889235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zufle, Andreas 0000-0001-7001-4123","orcid":"https://orcid.org/0000-0001-7001-4123","contributorId":332011,"corporation":false,"usgs":false,"family":"Zufle","given":"Andreas","email":"","affiliations":[{"id":40432,"text":"Emory University","active":true,"usgs":false}],"preferred":false,"id":889236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Armstrong, Amanda 0000-0002-9123-8924","orcid":"https://orcid.org/0000-0002-9123-8924","contributorId":332012,"corporation":false,"usgs":false,"family":"Armstrong","given":"Amanda","email":"","affiliations":[{"id":40052,"text":"NASA Goddard","active":true,"usgs":false}],"preferred":false,"id":889237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miner, Kimberly 0000-0002-1006-1283","orcid":"https://orcid.org/0000-0002-1006-1283","contributorId":329027,"corporation":false,"usgs":false,"family":"Miner","given":"Kimberly","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":889238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Qu, J.J.","contributorId":182468,"corporation":false,"usgs":false,"family":"Qu","given":"J.J.","email":"","affiliations":[],"preferred":false,"id":889239,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70250062,"text":"70250062 - 2023 - Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools","interactions":[],"lastModifiedDate":"2023-11-16T12:36:06.571747","indexId":"70250062","displayToPublicDate":"2023-11-16T06:32:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2023.1260581","usgsCitation":"White, J., Fienen, M., Moore, C.R., and Guthke, A., 2023, Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools: Frontiers in Earth Science, v. 11, 1260581, 3 p., https://doi.org/10.3389/feart.2023.1260581.","productDescription":"1260581, 3 p.","ipdsId":"IP-155062","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":441587,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2023.1260581","text":"Publisher Index Page"},{"id":422650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy","contributorId":260166,"corporation":false,"usgs":false,"family":"White","given":"Jeremy","affiliations":[{"id":52529,"text":"Interra","active":true,"usgs":false}],"preferred":false,"id":888173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":888174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Catherine R.","contributorId":251908,"corporation":false,"usgs":false,"family":"Moore","given":"Catherine","email":"","middleInitial":"R.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":888175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guthke, Anneli","contributorId":331600,"corporation":false,"usgs":false,"family":"Guthke","given":"Anneli","email":"","affiliations":[{"id":79251,"text":"Stuttgart Center for Simulation Science, Cluster of Excellence","active":true,"usgs":false}],"preferred":false,"id":888176,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70262408,"text":"70262408 - 2023 - Marshbird response to herbicide control of cattail in northwestern Minnesota","interactions":[],"lastModifiedDate":"2025-01-21T15:55:36.155755","indexId":"70262408","displayToPublicDate":"2023-11-15T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16872,"text":"The Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Marshbird response to herbicide control of cattail in northwestern Minnesota","docAbstract":"<p><span>Wetlands provide essential habitat for a wide variety of wildlife species. In the once wetland-rich Prairie Pothole Region and adjacent areas of central North America, many wetlands have been converted to agricultural production. Many remaining wetlands experience ecological change via the invasion and spread of non-native plant species, such as non-native narrowleaf (</span><i>Typha angustifolia</i><span>) and hybrid cattail (</span><i>Typha</i><span>&nbsp;x&nbsp;</span><i>glauca</i><span>), which spread aggressively and displace native vegetation, especially in large, impounded wetlands. Management of wetlands in these landscapes often includes broad-scale herbicide application intended to break up mats of cattail and restore areas to more wildlife-friendly conditions. Although restoration of wildlife habitat is a common goal of such management, marshbird response to invasive cattail control is poorly understood. To evaluate the effects of cattail management on wetland wildlife, we conducted standardized call-broadcast surveys for 5 species of marshbirds at 9 study sites that included survey locations associated with areas treated with herbicide and paired areas not treated with herbicide in wetland impoundments in northwestern Minnesota, USA, using a before-after, control-impact study design. We surveyed American bitterns (</span><i>Botaurus lentiginosus</i><span>), least bitterns (</span><i>Ixobrychus exilis</i><span>), pied-billed grebes (</span><i>Podilymbus podiceps</i><span>), soras (</span><i>Porzana carolina</i><span>), and Virginia rails (</span><i>Rallus limicola</i><span>) during the breeding season prior to herbicide application (late summer and early autumn of 2015) and during the 3 breeding seasons after herbicide application (2016–2018). We modeled species counts using a generalized linear mixed model with year-by-treatment interactions as fixed effects and site as a random effect. Before herbicide application, expected mean counts did not differ between treatment and control survey locations. Three years post-treatment, we detected significant increases in expected mean counts at treatment compared to control survey locations for soras (</span><i>t</i><sub>193</sub><span> = −3.373,&nbsp;</span><i>P</i><span> = 0.020) and Virginia rails (</span><i>t</i><sub>193</sub><span> = −3.167,&nbsp;</span><i>P</i><span> = 0.037), and point estimates for all species except least bittern were higher at treatment survey locations. Overall, our results suggest that these marshbird species responded positively to herbicide control of invasive cattail and that breeding marshbirds in these and similar wetland systems may experience positive population response over a period of at least 3 years following treatment.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22484","usgsCitation":"Hill, N., Johnson, D., Cooper, T., Archer, A., and Andersen, D.E., 2023, Marshbird response to herbicide control of cattail in northwestern Minnesota: The Journal of Wildlife Management, v. 87, no. 8, e22484, 14 p., https://doi.org/10.1002/jwmg.22484.","productDescription":"e22484, 14 p.","ipdsId":"IP-131062","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22484","text":"Publisher Index Page"},{"id":480825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Prairie Pothole Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -97.2253684104991,\n              49.07382900749482\n            ],\n            [\n              -97.2253684104991,\n              47.098948093042196\n            ],\n            [\n              -95.09634908157517,\n              47.098948093042196\n            ],\n            [\n              -95.09634908157517,\n              49.07382900749482\n            ],\n            [\n              -97.2253684104991,\n              49.07382900749482\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"87","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Hill, Nina M.","contributorId":349191,"corporation":false,"usgs":false,"family":"Hill","given":"Nina M.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Douglas H. 0000-0002-7778-6641","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":220516,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":924133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, Thomas R.","contributorId":349193,"corporation":false,"usgs":false,"family":"Cooper","given":"Thomas R.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Archer, Althea A.","contributorId":349197,"corporation":false,"usgs":false,"family":"Archer","given":"Althea A.","affiliations":[{"id":83459,"text":"St. Cloud University","active":true,"usgs":false}],"preferred":false,"id":924135,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924136,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70250954,"text":"70250954 - 2023 - A global ecological signal of extinction risk in marine ray-finned fishes (class Actinopterygii)","interactions":[],"lastModifiedDate":"2024-01-13T14:54:49.450263","indexId":"70250954","displayToPublicDate":"2023-11-14T08:52:35","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17122,"text":"Cambridge Prisms: Extinction","active":true,"publicationSubtype":{"id":10}},"title":"A global ecological signal of extinction risk in marine ray-finned fishes (class Actinopterygii)","docAbstract":"<div class=\"abstract-content\"><div class=\"abstract\" data-abstract-type=\"normal\"><p>Many marine fish species are experiencing population declines, but their extinction risk profiles are largely understudied in comparison to their terrestrial vertebrate counterparts. Selective extinction of marine fish species may result in rapid alteration of the structure and function of ocean ecosystems. In this study, we compiled an ecological trait dataset for 8,185 species of marine ray-finned fishes (class Actinopterygii) from FishBase and used phylogenetic generalized linear models to examine which ecological traits are associated with increased extinction risk, based on the International Union for the Conservation of Nature Red List. We also assessed which threat types may be driving these species toward greater extinction risk and whether threatened species face a greater average number of threat types than non-threatened species. We found that larger body size and/or fishes with life histories involving movement between marine, brackish, and freshwater environments are associated with elevated extinction risk. Commercial harvesting threatens the greatest number of species, followed by pollution, development, and then climate change. We also found that threatened species, on average, face a significantly greater number of threat types than non-threatened species. These results can be used by resource managers to help address the heightened extinction risk patterns we found.</p></div></div>","language":"English","publisher":"Cambridge University Press","doi":"10.1017/ext.2023.23.pr1","usgsCitation":"Bak, T.M., Camp, R.J., Heim, N.A., McCauley, D., Payne, J.L., and Knope, M.L., 2023, A global ecological signal of extinction risk in marine ray-finned fishes (class Actinopterygii): Cambridge Prisms: Extinction, v. 1, e25, 12 p., https://doi.org/10.1017/ext.2023.23.pr1.","productDescription":"e25, 12 p.","ipdsId":"IP-145291","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":441599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/ext.2023.23.pr1","text":"Publisher Index Page"},{"id":424416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bak, Trevor M.","contributorId":317824,"corporation":false,"usgs":false,"family":"Bak","given":"Trevor","email":"","middleInitial":"M.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":892400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":892401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heim, Noel A. 0000-0002-4528-345X","orcid":"https://orcid.org/0000-0002-4528-345X","contributorId":333307,"corporation":false,"usgs":false,"family":"Heim","given":"Noel","email":"","middleInitial":"A.","affiliations":[{"id":79842,"text":"Department of Earth & Ocean Sciences, Tufts University","active":true,"usgs":false}],"preferred":false,"id":892402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCauley, Douglas J.","contributorId":287056,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas J.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":892403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Payne, Jonathan L. 0000-0002-9601-3310","orcid":"https://orcid.org/0000-0002-9601-3310","contributorId":333308,"corporation":false,"usgs":false,"family":"Payne","given":"Jonathan","email":"","middleInitial":"L.","affiliations":[{"id":64472,"text":"Department of Geological Sciences, Stanford University","active":true,"usgs":false}],"preferred":false,"id":892404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knope, Matthew L 0000-0002-1372-6308","orcid":"https://orcid.org/0000-0002-1372-6308","contributorId":333309,"corporation":false,"usgs":false,"family":"Knope","given":"Matthew","email":"","middleInitial":"L","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":892405,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70250252,"text":"70250252 - 2023 - Assessing the ecological risk of heavy metal sediment contamination from Port Everglades Florida USA","interactions":[],"lastModifiedDate":"2023-11-30T13:03:24.940594","indexId":"70250252","displayToPublicDate":"2023-11-14T06:57:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the ecological risk of heavy metal sediment contamination from Port Everglades Florida USA","docAbstract":"<div class=\"abstract\"><p>Port sediments are often contaminated with metals and organic compounds from anthropogenic sources. Remobilization of sediment during a planned expansion of Port Everglades near Fort Lauderdale, Florida (USA) has the potential to harm adjacent benthic communities, including coral reefs. Twelve sediment cores were collected from four Port Everglades sites and a control site; surface sediment was collected at two nearby coral reef sites. Sediment cores, sampled every 5 cm, were analyzed for 14 heavy metals using inductively coupled plasma-mass spectrometry. Results for all three locations yielded concentration ranges (µg/g): As (0.607–223), Cd (n/d–0.916), Cr (0.155–56.8), Co (0.0238–7.40), Cu (0.004–215), Pb (0.0169–73.8), Mn (1.61–204), Hg (n/d–0.736), Mn (1.61–204), Ni (0.232–29.3), Se (n/d–4.79), Sn (n/d–140), V (0.160–176), and Zn (0.112–603), where n/d = non-detected. The geo-accumulation index shows moderate-to-strong contamination of As and Mo in port sediments, and potential ecological risk indicates moderate-to-significantly high overall metal contamination. All four port sites have sediment core subsamples with As concentrations above both threshold effect level (TEL, 7.24 µg/g) and probable effect level (PEL, 41.6 µg/g), while Mo geometric mean concentrations exceed the background continental crust level (1.5 µg/g) threshold. Control site sediments exceed TEL for As, while the reef sites has low to no overall heavy metal contamination. Results of this study indicate there is a moderate to high overall ecological risk from remobilized sediment due to metal contamination. Due to an imminent dredging at Port Everglades, this could have the potential to harm the threatened adjacent coral communities and surrounding protected habitats.</p></div>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.16152","usgsCitation":"Giarikos, D.G., White, L., Daniels, A., Santos, R.G., Baldauf, P.E., and Hirons, A.C., 2023, Assessing the ecological risk of heavy metal sediment contamination from Port Everglades Florida USA: PeerJ, v. 11, 35 p., https://doi.org/10.7717/peerj.16152.","productDescription":"35 p.","ipdsId":"IP-157548","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":441601,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.16152","text":"Publisher Index Page"},{"id":423086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Port Everglades","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -80.15552129212654,\n              26.104742380968872\n            ],\n            [\n              -80.15552129212654,\n              25.999255489563083\n            ],\n            [\n              -80.08685639930893,\n              25.999255489563083\n            ],\n            [\n              -80.08685639930893,\n              26.104742380968872\n            ],\n            [\n              -80.15552129212654,\n              26.104742380968872\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Giarikos, Dimitrios G.","contributorId":331918,"corporation":false,"usgs":false,"family":"Giarikos","given":"Dimitrios","email":"","middleInitial":"G.","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":889105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Laura","contributorId":331919,"corporation":false,"usgs":false,"family":"White","given":"Laura","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":889106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daniels, Andre 0000-0003-4172-2344","orcid":"https://orcid.org/0000-0003-4172-2344","contributorId":204035,"corporation":false,"usgs":true,"family":"Daniels","given":"Andre","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":889107,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Santos, Radleigh G.","contributorId":331920,"corporation":false,"usgs":false,"family":"Santos","given":"Radleigh","email":"","middleInitial":"G.","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":889108,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baldauf, Paul E.","contributorId":331923,"corporation":false,"usgs":false,"family":"Baldauf","given":"Paul","email":"","middleInitial":"E.","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":889109,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hirons, Amy C.","contributorId":331925,"corporation":false,"usgs":false,"family":"Hirons","given":"Amy","email":"","middleInitial":"C.","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":889110,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70250074,"text":"70250074 - 2023 - Living on the edge: Predicting songbird response to management and environmental changes across an ecotone","interactions":[],"lastModifiedDate":"2023-11-16T12:47:34.018042","indexId":"70250074","displayToPublicDate":"2023-11-14T06:42:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Living on the edge: Predicting songbird response to management and environmental changes across an ecotone","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Effective wildlife management requires robust information regarding population status, habitat requirements, and likely responses to changing resource conditions. Single-species management may inadequately conserve communities and result in undesired effects to non-target species. Thus, management can benefit from understanding habitat relationships for multiple species. Pinyon pine and juniper (<i>Pinus</i><span>&nbsp;</span>spp. and<span>&nbsp;</span><i>Juniperus</i><span>&nbsp;</span>spp.) are expanding into sagebrush-dominated (<i>Artemisia</i><span>&nbsp;</span>spp.) ecosystems within North America and mechanical removal of these trees is frequently conducted to restore sagebrush ecosystems and recover Greater Sage-grouse (<i>Centrocercus urophasianus</i>). However, pinyon-juniper removal effects on non-target species are poorly understood, and changing pinyon-juniper woodland dynamics, climate, and anthropogenic development may obscure conservation priorities. To better predict responses to changing resource conditions, evaluate non-target effects of pinyon-juniper removal, prioritize species for conservation, and inform species recovery within pinyon-juniper and sagebrush ecosystems, we modeled population trends and density-habitat relationships for four sagebrush-associated, four pinyon-juniper-associated, and three generalist songbird species with respect to these ecosystems. We fit hierarchical population models to point count data collected throughout the western United States from 2008 to 2020. We found regional population changes for 10 of 11 species investigated; 6 of which increased in the highest elevation region of our study. Our models indicate pinyon-juniper removal will benefit Brewer's Sparrow (<i>Spizella breweri</i>), Green-tailed Towhee (<i>Pipilo chlorurus</i>), and Sage Thrasher (<i>Oreoscoptes montanus</i>) densities. Conversely, we predict largest negative effects of pinyon-juniper removal for species occupying early successional pinyon-juniper woodlands: Bewick's Wren (<i>Thryomanes bewickii</i>), Black-throated Gray Warblers (<i>Setophaga nigrescens</i>), Gray Flycatcher (<i>Empidonax wrightii</i>), and Juniper Titmouse (<i>Baeolophus ridgwayi</i>). Our results highlight the importance of considering effects to non-target species before implementing large-scale habitat manipulations. Our modeling framework can help prioritize species and regions for conservation action, infer effects of management interventions and a changing environment on wildlife, and help land managers balance habitat requirements across ecosystems.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10648","usgsCitation":"Van Lanen, N.J., Monroe, A., and Aldridge, C.L., 2023, Living on the edge: Predicting songbird response to management and environmental changes across an ecotone: Ecology and Evolution, v. 13, no. 11, e10648, 46 p., https://doi.org/10.1002/ece3.10648.","productDescription":"e10648, 46 p.","ipdsId":"IP-147310","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":441604,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10648","text":"Publisher Index Page"},{"id":435123,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KFCBLH","text":"USGS data release","linkHelpText":"Data and analytical code assessing eleven songbird species' responses to environmental change during summertime (2008 - 2020) in the InterMountain West, USA"},{"id":422653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -119.44912777762832,\n              49.03692810275197\n            ],\n            [\n              -119.44912777762832,\n              35.60255309689437\n            ],\n            [\n              -101.60733090262836,\n              35.60255309689437\n            ],\n            [\n              -101.60733090262836,\n              49.03692810275197\n            ],\n            [\n              -119.44912777762832,\n              49.03692810275197\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"13","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":888226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":888227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":888228,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70249976,"text":"sir20235088 - 2023 - Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation","interactions":[],"lastModifiedDate":"2023-12-14T20:54:33.518942","indexId":"sir20235088","displayToPublicDate":"2023-11-13T11:15:00","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":"2023-5088","displayTitle":"Developing Fluvial Fish Species Distribution Models Across the Conterminous United States—A Scientific Framework to Support Management and Conservation","title":"Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation","docAbstract":"<p>This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the freshwater portions of the ranges for species represented 28 families. <i>Cyprinidae</i> was the family with the most species (87 of 271) modeled for this study, followed by <i>Percidae</i> (34) and <i>Ictaluridae</i> (17). Model predictive performance was evaluated using four metrics: area under the receiver operating characteristic curve, sensitivity, specificity, and True Skill Statistic, which are all from tenfold cross-validation results. The relative importance of the predictor variables in the boosted regression tree models was calculated and ranked for each species. The three strongest natural predictors of fish distributions were network catchment area, the mean annual air temperature of the local catchment, and the maximum elevation of the local catchment, while the three strongest anthropogenic predictors were downstream main stem dam density, distance to downstream main stem dam, and the percentage of pasture/hay land use area within network catchment boundaries. Study results showed 61 fish species were sensitive to climate variables, and 40 fish species were sensitive to anthropogenic stressors. The models developed in this study can be used to derive critical information regarding habitat protection priorities, anthropogenic threats, and potential effects of climate change on habitat suitability, aiding in efforts to conserve fluvial fishes now and into the future.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235088","collaboration":"Prepared in cooperation with Department of Fisheries and Wildlife, Michigan State University","programNote":"Science Analytics and Synthesis Program","usgsCitation":"Yu, H., Cooper, A.R., Ross, J., McKerrow, A., Wieferich, D.J., and Infante, D.M., 2023, Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation: U.S. Geological Survey Scientific Investigations Report 2023–5088, 41 p., https://doi.org/10.3133/sir20235088.","productDescription":"Report: vii, 41 p.; Data 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               28.97\n              ],\n              [\n                -103.94,\n                29.27\n              ],\n              [\n                -104.45697,\n                29.57196\n              ],\n              [\n                -104.70575,\n                30.12173\n              ],\n              [\n                -105.03737,\n                30.64402\n              ],\n              [\n                -105.63159,\n                31.08383\n              ],\n              [\n                -106.1429,\n                31.39995\n              ],\n              [\n                -106.50759,\n                31.75452\n              ],\n              [\n                -108.24,\n                31.75485\n              ],\n              [\n                -108.24194,\n                31.34222\n              ],\n              [\n                -109.035,\n                31.34194\n              ],\n              [\n                -111.02361,\n                31.33472\n              ],\n              [\n                -113.30498,\n                32.03914\n              ],\n              [\n                -114.815,\n                32.52528\n              ],\n              [\n                -114.72139,\n                32.72083\n              ],\n              [\n                -115.99135,\n                32.61239\n              ],\n              [\n                -117.12776,\n                32.53534\n              ],\n              [\n                -117.29594,\n                33.04622\n              ],\n              [\n                -117.944,\n                33.62124\n              ],\n              [\n                -118.4106,\n                33.74091\n              ],\n              [\n                -118.51989,\n                34.02778\n              ],\n              [\n                -119.081,\n                34.078\n              ],\n              [\n                -119.43884,\n                34.34848\n              ],\n              [\n                -120.36778,\n                34.44711\n              ],\n              [\n                -120.62286,\n                34.60855\n              ],\n              [\n                -120.74433,\n                35.15686\n              ],\n              [\n                -121.71457,\n                36.16153\n              ],\n              [\n                -122.54747,\n                37.55176\n              ],\n              [\n                -122.51201,\n                37.78339\n              ],\n              [\n                -122.95319,\n                38.11371\n              ],\n              [\n                -123.7272,\n                38.95166\n              ],\n              [\n                -123.86517,\n                39.76699\n              ],\n              [\n                -124.39807,\n                40.3132\n              ],\n              [\n                -124.17886,\n                41.14202\n              ],\n              [\n                -124.2137,\n                41.99964\n              ],\n              [\n                -124.53284,\n                42.76599\n              ],\n              [\n                -124.14214,\n                43.70838\n              ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/science-analytics-and-synthesis-sas/\" data-mce-href=\"https://www.usgs.gov/programs/science-analytics-and-synthesis-sas/\">Science Analytics and Synthesis Program</a><br>U.S. Geological Survey<br>P.O. Box 25046, Mail Stop 302<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Materials and Methods</li><li>Results</li><li>Discussion</li><li>Summary</li><li>Data Access</li><li>References Cited</li><li>Appendix 1. Fluvial Fish for Which Insufficient Occurrence Data Were Available to Support Species Distribution Modeling</li></ul>","publishedDate":"2023-11-13","noUsgsAuthors":false,"publicationDate":"2023-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Yu, Hao 0000-0003-0775-9346","orcid":"https://orcid.org/0000-0003-0775-9346","contributorId":331500,"corporation":false,"usgs":false,"family":"Yu","given":"Hao","email":"","affiliations":[{"id":79221,"text":"Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":887882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cooper, Arthur R. 0000-0002-0557-8560","orcid":"https://orcid.org/0000-0002-0557-8560","contributorId":220307,"corporation":false,"usgs":false,"family":"Cooper","given":"Arthur","email":"","middleInitial":"R.","affiliations":[{"id":7266,"text":"Michigan State University, Department of Fisheries and Wildlife","active":true,"usgs":false}],"preferred":false,"id":887883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Jared 0000-0002-0582-3589","orcid":"https://orcid.org/0000-0002-0582-3589","contributorId":289993,"corporation":false,"usgs":false,"family":"Ross","given":"Jared","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":887884,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":887885,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wieferich, Daniel J. 0000-0003-1554-7992 dwieferich@usgs.gov","orcid":"https://orcid.org/0000-0003-1554-7992","contributorId":176205,"corporation":false,"usgs":true,"family":"Wieferich","given":"Daniel","email":"dwieferich@usgs.gov","middleInitial":"J.","affiliations":[{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true},{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":887886,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Infante, Dana M. 0000-0003-1385-1587","orcid":"https://orcid.org/0000-0003-1385-1587","contributorId":150821,"corporation":false,"usgs":false,"family":"Infante","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":18112,"text":"Dept. of Fisheries and Wildlife,","active":true,"usgs":false}],"preferred":false,"id":887887,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70252450,"text":"70252450 - 2023 - Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley","interactions":[],"lastModifiedDate":"2024-03-25T14:33:08.103636","indexId":"70252450","displayToPublicDate":"2023-11-13T09:21:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3504,"text":"Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley","docAbstract":"<p><span>Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/su152215887","usgsCitation":"Mekonen, S.S., Boyce, S.E., Mohammed, A.K., Flint, L.E., Flint, A., and Disse, M., 2023, Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley: Sustainability, v. 15, no. 22, 15887, 25 p., https://doi.org/10.3390/su152215887.","productDescription":"15887, 25 p.","ipdsId":"IP-146940","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":441606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su152215887","text":"Publisher Index Page"},{"id":426968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia","otherGeospatial":"Kobo Valley, Riya Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              39.36,\n              12.88\n            ],\n            [\n              39.36,\n              11.92\n            ],\n            [\n              39.84,\n              11.92\n            ],\n            [\n              39.84,\n              12.88\n            ],\n            [\n              39.36,\n              12.88\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"22","noUsgsAuthors":false,"publicationDate":"2023-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mekonen, Sisay Simachew","contributorId":333048,"corporation":false,"usgs":false,"family":"Mekonen","given":"Sisay","email":"","middleInitial":"Simachew","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mohammed, Abdella K.","contributorId":333049,"corporation":false,"usgs":false,"family":"Mohammed","given":"Abdella","email":"","middleInitial":"K.","affiliations":[{"id":79718,"text":"Hydraulic and Water Resources Engineering, Arba Minch University","active":true,"usgs":false}],"preferred":false,"id":897194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":306090,"corporation":false,"usgs":false,"family":"Flint","given":"Lorraine","email":"","middleInitial":"E.","affiliations":[{"id":66369,"text":"Earth Knowledge, Inc.","active":true,"usgs":false}],"preferred":false,"id":897195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L 0000-0002-5118-751X","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":239656,"corporation":false,"usgs":false,"family":"Flint","given":"Alan L","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":897196,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Disse, Markus","contributorId":333050,"corporation":false,"usgs":false,"family":"Disse","given":"Markus","email":"","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897197,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70250652,"text":"70250652 - 2023 - Time-dependent weakening of granite at hydrothermal conditions","interactions":[],"lastModifiedDate":"2023-12-22T12:50:40.17831","indexId":"70250652","displayToPublicDate":"2023-11-13T06:43:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Time-dependent weakening of granite at hydrothermal conditions","docAbstract":"<div class=\"article-section__content en main\"><p>The evolution of a fault's frictional strength during the interseismic period is a critical component of the earthquake cycle, yet there have been relatively few studies that examine the time-dependent evolution of strength at conditions representative of seismogenic depths. Using a simulated fault in Westerly granite, we examined how frictional strength evolves under hydrothermal conditions up to 250°C during slide-hold-slide experiments. At temperatures ≤100°C, frictional strength generally increases with hold duration but, at 200 and 250°C, an initial increase in strength transitions to rapid time-dependent weakening for holds longer than 14&nbsp;hr. Forward modeling of long hold periods at 250°C using the rate and state friction constitutive equations requires a second, strongly negative, state variable with a long evolution distance. This implies that significant hydrothermal alteration is occurring at 250°C, consistent with microstructural observations of dissolution and secondary mineral precipitation.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GL105517","usgsCitation":"Jeppson, T.N., Lockner, D., Beeler, N.M., and Moore, D.E., 2023, Time-dependent weakening of granite at hydrothermal conditions: Geophysical Research Letters, v. 50, no. 21, e2023GL105517, 9 p., https://doi.org/10.1029/2023GL105517.","productDescription":"e2023GL105517, 9 p.","ipdsId":"IP-154283","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":441612,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gl105517","text":"Publisher Index Page"},{"id":423858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"21","noUsgsAuthors":false,"publicationDate":"2023-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jeppson, Tamara Nicole 0000-0001-5526-5530","orcid":"https://orcid.org/0000-0001-5526-5530","contributorId":248768,"corporation":false,"usgs":true,"family":"Jeppson","given":"Tamara","email":"","middleInitial":"Nicole","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":890895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lockner, David A. 0000-0001-8630-6833","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":257574,"corporation":false,"usgs":true,"family":"Lockner","given":"David A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":890896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":890897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Diane E. 0000-0002-8641-1075 dmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-8641-1075","contributorId":2704,"corporation":false,"usgs":true,"family":"Moore","given":"Diane","email":"dmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":890898,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70260882,"text":"70260882 - 2023 - VogCast: A framework for modeling volcanic air pollution and its application to the 2022 eruption of Mauna Loa Volcano, Hawai'i","interactions":[],"lastModifiedDate":"2024-11-13T16:00:47.152627","indexId":"70260882","displayToPublicDate":"2023-11-10T09:52:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8111,"text":"Journal of Geophysical Research Atmospheres","active":true,"publicationSubtype":{"id":10}},"title":"VogCast: A framework for modeling volcanic air pollution and its application to the 2022 eruption of Mauna Loa Volcano, Hawai'i","docAbstract":"<p><span>Volcanic activity and the associated gas emissions into the atmosphere often result in adverse air quality conditions and present a hazard to human health and the environment. Building on a decade-long effort to provide operational surface sulfur dioxide and sulfate aerosol forecasts for the State of Hawai'i, we present an air quality modeling framework called VogCast. VogCast is designed to simplify ensemble air quality prediction on a regional scale by linking together multiple state-of-the-art models of meteorology, emissions, and dispersion. The framework is open-source and introduces a new dynamic plume-rise algorithm for distributing pollutants vertically. Using radar and satellite data, we demonstrate that VogCast reasonably captured the mean injection height, the location, and the general envelope of the vog plume during Mauna Loa's 2022 eruption. The results suggest that during the 12-day eruption period model performance varied between days with trade and non-trade wind conditions. Our findings also highlight the importance of sulfur dioxide emission rate and vent parameter inputs for improving forecast accuracy. The broad goal of this work is to better our understanding of vog dispersion and improve air quality prediction for impacted communities.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JD039281","usgsCitation":"Moisseeva, N., Businger, S., and Elias, T., 2023, VogCast: A framework for modeling volcanic air pollution and its application to the 2022 eruption of Mauna Loa Volcano, Hawai'i: Journal of Geophysical Research Atmospheres, v. 128, no. 22, e2023JD039281, 14 p., https://doi.org/10.1029/2023JD039281.","productDescription":"e2023JD039281, 14 p.","ipdsId":"IP-153377","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jd039281","text":"Publisher Index Page"},{"id":463904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.7156069329796,\n              19.56532392435213\n            ],\n            [\n              -155.7156069329796,\n              19.345990597059426\n            ],\n            [\n              -155.48166856061957,\n              19.345990597059426\n            ],\n            [\n              -155.48166856061957,\n              19.56532392435213\n            ],\n            [\n              -155.7156069329796,\n              19.56532392435213\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"128","issue":"22","noUsgsAuthors":false,"publicationDate":"2023-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moisseeva, Nadya 0000-0001-7317-1597","orcid":"https://orcid.org/0000-0001-7317-1597","contributorId":335180,"corporation":false,"usgs":false,"family":"Moisseeva","given":"Nadya","email":"","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":918411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Businger, Steven","contributorId":345757,"corporation":false,"usgs":false,"family":"Businger","given":"Steven","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":918412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elias, Tamar 0000-0002-9592-4518 telias@usgs.gov","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":3916,"corporation":false,"usgs":true,"family":"Elias","given":"Tamar","email":"telias@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918413,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70251509,"text":"70251509 - 2023 - Georeferencing of terrestrial radar images in geomonitoring using kernel correlation","interactions":[],"lastModifiedDate":"2024-02-14T13:07:12.536551","indexId":"70251509","displayToPublicDate":"2023-11-10T07:05:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Georeferencing of terrestrial radar images in geomonitoring using kernel correlation","docAbstract":"<p>Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument’s pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over<span>&nbsp;</span><span class=\"NLM_disp-formula inline-formula rs_preserve\"><img src=\"https://:0/\" alt=\"\" data-formula-source=\"{&quot;type&quot;:&quot;mathjax&quot;}\" data-mce-src=\"https://pubs.usgs.gov:0/\"></span></p>","language":"English","publisher":"Taylor and Francis","doi":"10.1080/01431161.2023.2274321","usgsCitation":"Schmid, L., Medic, T., Collins, B.D., Meier, L., and Wieser, A., 2023, Georeferencing of terrestrial radar images in geomonitoring using kernel correlation: International Journal of Remote Sensing, v. 44, no. 21, p. 6736-6761, https://doi.org/10.1080/01431161.2023.2274321.","productDescription":"26 p.","startPage":"6736","endPage":"6761","ipdsId":"IP-149698","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":441622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2023.2274321","text":"Publisher Index Page"},{"id":425649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"21","noUsgsAuthors":false,"publicationDate":"2023-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmid, Lorenz","contributorId":334121,"corporation":false,"usgs":false,"family":"Schmid","given":"Lorenz","email":"","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":894763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Medic, Tomislav","contributorId":334122,"corporation":false,"usgs":false,"family":"Medic","given":"Tomislav","email":"","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":894764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":894765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meier, Lorenz","contributorId":334126,"corporation":false,"usgs":false,"family":"Meier","given":"Lorenz","email":"","affiliations":[{"id":80063,"text":"Geopraevent AG","active":true,"usgs":false}],"preferred":false,"id":894766,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wieser, Andreas","contributorId":334128,"corporation":false,"usgs":false,"family":"Wieser","given":"Andreas","email":"","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":894767,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70248794,"text":"70248794 - 2023 - Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses","interactions":[],"lastModifiedDate":"2023-11-30T15:55:40.634572","indexId":"70248794","displayToPublicDate":"2023-11-09T09:47:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH<sub>4</sub> sites using wavelet analyses","title":"Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses","docAbstract":"<p><span>Process-based land surface models are important tools for estimating global wetland methane (CH</span><sub>4</sub><span>) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH</span><sub>4</sub><span>&nbsp;fluxes (FCH</span><sub>4</sub><span>) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (&lt;15&nbsp;days); (b) most of the models can capture the CH</span><sub>4</sub><span>&nbsp;variability at monthly and seasonal time scales (&gt;32&nbsp;days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales &lt;5&nbsp;days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH</span><sub>4</sub><span>&nbsp;production). Our evaluation suggests the need to accurately replicate FCH</span><sub>4</sub><span>&nbsp;variability, especially at short time scales, in future wetland CH</span><sub>4</sub><span>&nbsp;model developments.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG007259","usgsCitation":"Zhang, Z., Bansal, S., Chang, K., Fluet-Chouinard, E., Delwiche, K.B., Goeckede, M., Gustafson, A., Knox, S., Leppanen, A., Liu, L., Liu, J., Malhotra, A., Markkanen, T., McNicol, G., Melton, J.R., Miller, P.A., Peng, C., Raivonen, M., Riley, W., Sonnentag, O., Aalto, T., Vargas, R., Zhang, W., Zhu, Q., Zhu, Q., Zhuang, Q., Windham-Myers, L., Jackson, R.B., and Poulter, B., 2023, Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses: JGR Biogeosciences, v. 128, no. 11, e2022JG007259, 21 p., https://doi.org/10.1029/2022JG007259.","productDescription":"e2022JG007259, 21 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