{"pageNumber":"112","pageRowStart":"2775","pageSize":"25","recordCount":41032,"records":[{"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":"

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.

","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":"

Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey 
640 Grassmere Park, suite 100
Nashville, TN 37211 

Contact Pubs Warehouse

","tableOfContents":"","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":"

An experiment along with simulation modeling was applied to study the combinations of herbicide treatment and biological control that best limit invasive water hyacinth (Pontederia crassipes, formerly Eichhornia crassipes) in freshwater aquatic systems. The experiment consisted of 14 different treatments of P. crassipes in 1.67 m2 outdoor tank mesocosms. Seven treatments were with and seven were without insect biological control agents, Neochetina eichhorniae. In both of the sets of seven treatments, there was one no-herbicide treatment, a one-time full-strength herbicide treatment with 40 %, 80 % and 100 % coverage of the P. crassipes, and a one-time half-strength herbicide treatment with 40 %, 80 %, and 100 % 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 P. crassipes 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 % areal coverage, could maintain P. crassipes at levels lower than the carrying capacity of the plant's biomass over the long term, though not enough that N. eichhorniae would be considered, by itself, a highly effective control. However, the results suggest that a combination of biocontrol with 80 % spraying coverage every 600 days or so would be an effective integrated biocontrol strategy for maintaining decreased P. crassipes biomass at low levels over the long term.

","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":"

Executive Summary

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.

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” (https://calval.cr.usgs.gov/apps/compendium#) and from the manufacturer (https://www.intelligence-airbusds.com/imagery/constellation/pleiades-Neo/).

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).

","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 of 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":"

Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

Contact Pubs Warehouse

","tableOfContents":"","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":"

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 in-situ 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 (CO2), and methane flux (CH4)—with high precision and minimal loss (i.e. ALTRMSE: 1.327 cm [1969–2022]; CO2RMSE: 0.697 µmolCO2m−2s−1 [2003–2021]; CH4RMSE: 0.715 nmolCH4m−2s−1 [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.

","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":"

No abstract available.

","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":"

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 (Typha angustifolia) and hybrid cattail (Typha x glauca), 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 (Botaurus lentiginosus), least bitterns (Ixobrychus exilis), pied-billed grebes (Podilymbus podiceps), soras (Porzana carolina), and Virginia rails (Rallus limicola) 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 (t193 = −3.373, P = 0.020) and Virginia rails (t193 = −3.167, P = 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.

","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":"

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.

","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":"

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.

","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":"

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 (Pinus spp. and Juniperus spp.) are expanding into sagebrush-dominated (Artemisia spp.) ecosystems within North America and mechanical removal of these trees is frequently conducted to restore sagebrush ecosystems and recover Greater Sage-grouse (Centrocercus urophasianus). 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 (Spizella breweri), Green-tailed Towhee (Pipilo chlorurus), and Sage Thrasher (Oreoscoptes montanus) densities. Conversely, we predict largest negative effects of pinyon-juniper removal for species occupying early successional pinyon-juniper woodlands: Bewick's Wren (Thryomanes bewickii), Black-throated Gray Warblers (Setophaga nigrescens), Gray Flycatcher (Empidonax wrightii), and Juniper Titmouse (Baeolophus ridgwayi). 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.

","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":"

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. Cyprinidae was the family with the most species (87 of 271) modeled for this study, followed by Percidae (34) and Ictaluridae (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.

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\"name\": \"United States\"\n }\n }\n ]\n}","contact":"

Director, Science Analytics and Synthesis Program
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225

","tableOfContents":"","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":"

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.

","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 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","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":"

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 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.

","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":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","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":"

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.

","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":"

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 \"\"

","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-CH4 sites using wavelet analyses","title":"Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses","docAbstract":"

Process-based land surface models are important tools for estimating global wetland methane (CH4) 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 CH4 fluxes (FCH4) 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 (<15 days); (b) most of the models can capture the CH4 variability at monthly and seasonal time scales (>32 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 <5 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 CH4 production). Our evaluation suggests the need to accurately replicate FCH4 variability, especially at short time scales, in future wetland CH4 model developments.

","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 p.","ipdsId":"IP-154074","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":441629,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Coral reefs are iconic ecosystems that support diverse, productive communities in both shallow and deep waters. However, our incomplete knowledge of cold-water coral (CWC) niche space limits our understanding of their distribution and precludes a complete accounting of the ecosystem services they provide. Here, we present the results of recent surveys of the CWC mound province on the Blake Plateau off the U.S. east coast, an area of intense human activity including fisheries and naval operations, and potentially energy and mineral extraction. At one site, CWC mounds are arranged in lines that total over 150 km in length, making this one of the largest reef complexes discovered in the deep ocean. This site experiences rapid and extreme shifts in temperature between 4.3 and 10.7 °C, and currents approaching 1 m s−1. Carbon is transported to depth by mesopelagic micronekton and nutrient cycling on the reef results in some of the highest nitrate concentrations recorded in the region. Predictive models reveal expanded areas of highly suitable habitat that currently remain unexplored. Multidisciplinary exploration of this new site has expanded understanding of the cold-water coral niche, improved our accounting of the ecosystem services of the reef habitat, and emphasizes the importance of properly managing these systems.

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Debris flows regularly traverse bedrock channels that dissect steep landscapes, but our understanding of bedrock erosion by debris flows and their impact on steepland morphology is still rudimentary. Quantitative models of steep bedrock channel networks are based on geomorphic transport laws designed to represent erosion by water-dominated flows. To quantify the impact of debris flow erosion on steep channel network form, it is first necessary to develop methods to estimate spatial variations in bulk debris flow properties (e.g., flow depth, velocity) throughout the channel network that can be integrated into landscape evolution models. Here, we propose and evaluate two methods to estimate spatial variations in bulk debris flow properties along the length of a channel profile. We incorporate both methods into a model designed to simulate the evolution of longitudinal channel profiles that evolve in response to debris flow and fluvial processes. To explore this model framework, we propose a general family of debris flow erosion laws where erosion rate is a function of debris flow depth and channel slope. Model results indicate that erosion by debris flows can explain the occurrence of a scaling break in the slope–area curve at low-drainage areas and that upper-network channel morphology may be useful for inferring catchment-averaged erosion rates in quasi-steady landscapes. Validating specific forms of a debris flow incision law, however, would require more detailed model–data comparisons in specific landscapes where input parameters and channel morphometry can be better constrained. Results improve our ability to interpret topographic signals within steep channel networks and identify observational targets critical for constraining a debris flow incision law.


","language":"English","publisher":"European Geoscience Union","doi":"10.5194/esurf-11-1117-2023","usgsCitation":"McGuire, L.A., McCoy, S., Marc, O., Struble, W., and Barnhart, K.R., 2023, Steady-state forms of channel profiles shaped by debris flow and fluvial processes: Earth Surface Dynamics, v. 11, no. 6, p. 1117-1143, https://doi.org/10.5194/esurf-11-1117-2023.","productDescription":"27 p.","startPage":"1117","endPage":"1143","ipdsId":"IP-140745","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":441646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esurf-11-1117-2023","text":"Publisher Index Page"},{"id":422513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":887888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCoy, Scott W.","contributorId":267182,"corporation":false,"usgs":false,"family":"McCoy","given":"Scott W.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":887889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marc, Odin","contributorId":198732,"corporation":false,"usgs":false,"family":"Marc","given":"Odin","email":"","affiliations":[],"preferred":false,"id":887890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Struble, William 0000-0002-8163-5088","orcid":"https://orcid.org/0000-0002-8163-5088","contributorId":241913,"corporation":false,"usgs":false,"family":"Struble","given":"William","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":887891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":887892,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250069,"text":"70250069 - 2023 - Expanding our view of the cold-water coral niche and accounting of the ecosystem services of the reef habitat","interactions":[],"lastModifiedDate":"2023-11-16T13:04:49.894681","indexId":"70250069","displayToPublicDate":"2023-11-09T06:57:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Expanding our view of the cold-water coral niche and accounting of the ecosystem services of the reef habitat","docAbstract":"

Coral reefs are iconic ecosystems that support diverse, productive communities in both shallow and deep waters. However, our incomplete knowledge of cold-water coral (CWC) niche space limits our understanding of their distribution and precludes a complete accounting of the ecosystem services they provide. Here, we present the results of recent surveys of the CWC mound province on the Blake Plateau off the U.S. east coast, an area of intense human activity including fisheries and naval operations, and potentially energy and mineral extraction. At one site, CWC mounds are arranged in lines that total over 150 km in length, making this one of the largest reef complexes discovered in the deep ocean. This site experiences rapid and extreme shifts in temperature between 4.3 and 10.7 °C, and currents approaching 1 m s−1. Carbon is transported to depth by mesopelagic micronekton and nutrient cycling on the reef results in some of the highest nitrate concentrations recorded in the region. Predictive models reveal expanded areas of highly suitable habitat that currently remain unexplored. Multidisciplinary exploration of this new site has expanded understanding of the cold-water coral niche, improved our accounting of the ecosystem services of the reef habitat, and emphasizes the importance of properly managing these systems.

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Eutrophication is one of the largest threats to aquatic ecosystems and chlorophyll a measurements are relevant indicators of trophic state and algal abundance. Many studies have modeled chlorophyll a in rivers but model development and testing has largely occurred at individual sites which hampers creating generalized models capable of making broad-scale predictions. To address this gap, we compiled a large data set of chlorophyll a concentrations matched to other water quality, meteorological, and reach characteristic data for a diverse set of 82 streams and rivers across the United States. We used this data set and extreme gradient boosting, a tree-based machine learning algorithm, to predict daily chlorophyll a concentrations. Furthermore, we tested several practical considerations of broad-scale models, such as making predictions at sites not included in model training or the utility of in situ water quality data versus universally available remotely estimated model inputs. Predictions were very strongly correlated to observations when compared against a randomly withheld subset of days; however, the model had lower accuracy when applied to completely novel sites withheld from model training. Turbidity and total nitrogen were the two most important variables for predicting chlorophyll a. Although in situ variables improved modeled estimates and were identified as more important during model interpretation, using only remote inputs still resulted in highly correlated predictions with small bias. Testing a model across many sites allowed for identification of common variables relevant to chlorophyll a and highlighted several challenges for applying data-driven models to new sites or at larger spatial scales.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR034215","usgsCitation":"Savoy, P., and Harvey, J., 2023, Predicting daily river chlorophyll concentrations at a continental scale: Water Resources Research, v. 59, no. 11, e2022WR034215, 16 p., https://doi.org/10.1029/2022WR034215.","productDescription":"e2022WR034215, 16 p.","ipdsId":"IP-154516","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":441652,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr034215","text":"Publisher Index Page"},{"id":422613,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Savoy, Philip 0000-0002-6075-837X","orcid":"https://orcid.org/0000-0002-6075-837X","contributorId":300288,"corporation":false,"usgs":true,"family":"Savoy","given":"Philip","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":888117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":888118,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250116,"text":"70250116 - 2023 - Snag dynamics and surface fuel loads in the Sierra Nevada: Predicting the impact of the 2012–2016 drought","interactions":[],"lastModifiedDate":"2023-11-21T12:38:22.847886","indexId":"70250116","displayToPublicDate":"2023-11-09T06:36:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Snag dynamics and surface fuel loads in the Sierra Nevada: Predicting the impact of the 2012–2016 drought","docAbstract":"Forest die-backs linked to extreme droughts are expected to increase as the climate dries and warms. An example is the 2012-2016 hotter drought in California that induced widespread tree mortality in the Sierra Nevada, California. The sudden increase in snags (i.e., standing dead trees) raised immediate concerns about their impact on wildfire hazard and longer-term questions about their impact on ecosystem structure and function. We quantified the likely progression of snag fall and fuel succession following the recent extensive mortality event in the southern Sierra Nevada mixed conifer forest. Our results used data from a long-term demography study to project trends in surface fuel loads at three study sites in Yosemite and Sequoia Kings Canyon National Parks. In the short term (2017-2021), fine woody debris and litter + duff significantly increased across all three sites (>145% and >55%, respectively); coarse woody debris increased significantly at one site (48.6%); and total fuel loads increased significantly at two of the three sites (38% and 69%). Snag longevity increased with size, with the relationship varying by species. Yellow pine was a notable outlier: size played a small role in influencing its fall rates. Overall, species-specific snag fall rates in the southern Sierra Nevada were 20% to 40% slower than previously reported. By 2040, projected median cumulative inputs of biomass from future snag fall range from 49.4 Mg ha-1 to 136.1 Mg ha-1across our three sites, which exceeds the amounts currently present (47.17-89.97 Mg ha-1) and is well above estimates of historical coarse woody debris amounts in the Sierra Nevada (17.7 Mg ha -1). These results provide a robust empirical basis to refine the snag fall algorithm in vegetation simulation models. Options to manage the impact of extreme number of snags and their large surface combustible biomass include salvage operations and prescribed burning, with both methods having operational, financial, and legal limitations that need to be considered.","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2023.121521","usgsCitation":"Northrop, H., Axelson, J.N., Das, A., Stephenson, N.L., Vilanova, E., Stephens, S.L., and Battles, J.J., 2023, Snag dynamics and surface fuel loads in the Sierra Nevada: Predicting the impact of the 2012–2016 drought: Forest Ecology and Management, v. 551, 121521, 11 p., https://doi.org/10.1016/j.foreco.2023.121521.","productDescription":"121521, 11 p.","ipdsId":"IP-158944","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441654,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2023.121521","text":"Publisher Index Page"},{"id":435128,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P938EGYD","text":"USGS data release","linkHelpText":"Snag Fall Data from Long Term Forest Dynamics Plots in the Sierra Nevada of California through 2021"},{"id":422777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"551","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Northrop, Hudson","contributorId":331674,"corporation":false,"usgs":false,"family":"Northrop","given":"Hudson","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":888423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Axelson, Jodi N.","contributorId":331675,"corporation":false,"usgs":false,"family":"Axelson","given":"Jodi","email":"","middleInitial":"N.","affiliations":[{"id":51972,"text":"British Columbia Ministry of Forests","active":true,"usgs":false}],"preferred":false,"id":888424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":888425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":888426,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vilanova, Emilio","contributorId":331676,"corporation":false,"usgs":false,"family":"Vilanova","given":"Emilio","email":"","affiliations":[{"id":79262,"text":"Wildlife Conservation Society, New York","active":true,"usgs":false}],"preferred":false,"id":888427,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephens, Scott L.","contributorId":46022,"corporation":false,"usgs":false,"family":"Stephens","given":"Scott","email":"","middleInitial":"L.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":888428,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Battles, John J.","contributorId":102006,"corporation":false,"usgs":false,"family":"Battles","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":888429,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250986,"text":"70250986 - 2023 - Marginal value analysis reveals shifting importance of migration habitat for waterfowl under a changing climate","interactions":[],"lastModifiedDate":"2024-01-18T11:47:53.770787","indexId":"70250986","displayToPublicDate":"2023-11-09T05:46:03","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":"Marginal value analysis reveals shifting importance of migration habitat for waterfowl under a changing climate","docAbstract":"

Migratory waterfowl are an important resource for consumptive and non-consumptive users alike and provide tremendous economic value in North America. These birds rely on a complex matrix of public and private land for forage and roosting during migration and wintering periods, and substantial conservation effort focuses on increasing the amount and quality of target habitat. Yet, the value of habitat is a function not only of a site's resources but also of its geographic position and weather. To quantify this value, we used a continental-scale energetics-based model of daily dabbling duck movement to assess the marginal value of lands across the contiguous United States during the non-breeding period (September to May). We examined effects of eliminating each habitat node (32 × 32 km) in both a particularly cold and a particularly warm winter, asking which nodes had the largest effect on survival. The marginal value of habitat nodes for migrating dabbling ducks was a function of forage and roosting habitat but, more importantly, of geography (especially latitude and region). Irrespective of weather, nodes in the Southeast, central East Coast, and California made the largest positive contributions to survival. Conversely, nodes in the Midwest, Northeast, Florida, and the Pacific Northwest had consistent negative effects. Effects (positive and negative) of more northerly nodes occurred in late fall or early spring when climate was often severe and was most variable. Importance and effects of many nodes varied considerably between a cold and a warm winter. Much of the Midwest and central Great Plains benefited duck survival in a warm winter, and projected future warming may improve the value of lands in these regions, including many National Wildlife Refuges, for migrating dabbling ducks. Our results highlight the geographic variability in habitat value, as well as shifts that may occur in these values due to climate change.

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0000-0003-0568-6702","orcid":"https://orcid.org/0000-0003-0568-6702","contributorId":333396,"corporation":false,"usgs":true,"family":"Golas","given":"Benjamin","email":"","middleInitial":"Donald","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":892665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aagaard, Kevin J.","contributorId":302397,"corporation":false,"usgs":false,"family":"Aagaard","given":"Kevin","email":"","middleInitial":"J.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":892666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lonsdorf, Eric V.","contributorId":149495,"corporation":false,"usgs":false,"family":"Lonsdorf","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":17752,"text":"Chicago Botanic 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studies","interactions":[],"lastModifiedDate":"2024-01-12T18:31:49.724753","indexId":"tm5D5","displayToPublicDate":"2023-11-08T12:29:39","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-D5","displayTitle":"Creating Oriented and Precisely Sectioned Mineral Mounts for In Situ Chemical Analyses—An Example Using Olivine for Diffusion Chronometry Studies","title":"Creating oriented and precisely sectioned mineral mounts for in situ chemical analyses—An example using olivine for diffusion chronometry studies","docAbstract":"

Diffusion chronometry is now a widely applied methodology for determining the rates and timescales of geologic processes from the chemical zoning observed in minerals. Despite the popularity of the method, several challenges still remain during its application, including: (1) the random sectioning of minerals either in thin sections or grain mounts in which both off-center and oblique sections contribute substantial uncertainty to modeled timescales and (2) diffusion anisotropy needs to be accounted for in models, which generally requires determining the principal crystallographic axes of the mineral using electron backscatter diffraction, a technique that is both challenging and limiting because few scanning electron microscopes have an electron backscatter detector. This guide developed by the U.S. Geological Survey focuses on a step-by-step methodology for mounting individually oriented minerals that are sectioned through their cores prior to polishing for analytical work. Using this technique, one can significantly reduce the uncertainties associated with off-center sections and minimize or completely remove the need for determining crystallographic orientation via electron backscatter diffraction analyses. This report is presented as a guide for using the technique on olivine crystals but can be applied to any minerals that can be extracted for analysis. Two variations of the methodology are included here: (1) The individual crystal method that entails mounting individually sectioned single crystals and crystal groups and (2) the whole mount method in which multiple single crystals or crystal clusters are mounted and sectioned at the same time.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5D5","programNote":"Volcano Hazards Program","usgsCitation":"Lynn, K.J., and DeSmither, L.G., 2023, Creating oriented and precisely sectioned mineral mounts for in situ chemical analyses—An example using olivine for diffusion chronometry studies: U.S. Geological Survey Techniques and Methods, book 5, chap. D5, 36 p., https://doi.org/10.3133/tm5D5.","productDescription":"ix, 36 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-142373","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":422457,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/d5/covrthb.jpg"},{"id":422458,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/d5/tm5d5.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":422459,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/05/d5/tm5d5.xml"},{"id":422460,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/05/d5/images"},{"id":422461,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm5D5/full"}],"contact":"

Director, 
Volcano Science Center
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA 38683

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-11-08","noUsgsAuthors":false,"publicationDate":"2023-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":887825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Desmither, Liliana G. 0000-0002-2422-3490","orcid":"https://orcid.org/0000-0002-2422-3490","contributorId":215610,"corporation":false,"usgs":true,"family":"Desmither","given":"Liliana","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":887826,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256441,"text":"70256441 - 2023 - Effects of landcover on mesocarnivore density and detection rate along an urban to rural gradient","interactions":[],"lastModifiedDate":"2024-08-02T15:31:51.494526","indexId":"70256441","displayToPublicDate":"2023-11-08T10:28:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Effects of landcover on mesocarnivore density and detection rate along an urban to rural gradient","docAbstract":"

Human development has major implications for wildlife populations. Urban-exploiter species can benefit from human subsidized resources, whereas urban-avoider species can vanish from wildlife communities in highly developed areas. Therefore, understanding how the density of different species varies in response to landcover changes associated with human development can provide important insight into how wildlife communities are likely to change and provide a starting point for predicting the consequences of those changes. Here, we estimated the population density of five common mesocarnivore species (coyote (Canis latrans), bobcat (Lynx rufus), red fox (Vulpes vulpes), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana)) at 12 study sites along an urban to rural gradient in the greater Fayetteville Area, Northwest Arkansas, USA between November 2021, and March 2022. At each study site, we applied the Random Encounter Model (REM) to data from camera traps to calculate the density of five focal species. Coyote density ranged from 0.5 to 0.93 individuals/km2. Raccoon density ranged from 0.19 to 20.25 individuals/km2. Bobcat density ranged from 0 to 1.06 individuals/km2. Opossum density ranged from 0 to 3.43 individuals/km2. Red fox density ranged from 0 to 0.10 individuals/km2. Coyote and raccoon density showed a positive relationship with anthropogenic noise. Opossum density increased with HUD. Red Fox and bobcat density showed a negative relationship with forest area and a positive relationship with distance to water respectively, however confidence intervals for both species overlapped zero. The density estimates we report based on camera trap data of unmarked animals were consistent with reports from the literature for these same species derived from traditional methods, providing additional support to the REM as a viable, non-invasive method to calculate density of unmarked species. Our second analysis consisted of taking camera level density estimates and treating them as detection rates corrected for camera viewshed and animal movement. Coyote and raccoon detection rate showed a positive relationship with anthropogenic noise. Red Fox detection rate was positively related to developed open space, and negatively related to distance to water. Similarly to red fox, opossums detection rate was higher in areas with more developed open space. We found no evidence that bobcat density or detection rate varied with any of the landcover or anthropogenic variables we measured.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2023.e02716","usgsCitation":"McTigue, L., and DeGregorio, B.A., 2023, Effects of landcover on mesocarnivore density and detection rate along an urban to rural gradient: Global Ecology and Conservation, v. 48, e02716, 14 p., https://doi.org/10.1016/j.gecco.2023.e02716.","productDescription":"e02716, 14 p.","ipdsId":"IP-149643","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":441657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2023.e02716","text":"Publisher Index Page"},{"id":432148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","city":"Fayetteville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.703264661285,\n 36.581012567484365\n ],\n [\n -94.703264661285,\n 35.69179592709919\n ],\n [\n -93.38479477781208,\n 35.69179592709919\n ],\n [\n -93.38479477781208,\n 36.581012567484365\n ],\n [\n -94.703264661285,\n 36.581012567484365\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McTigue, Leah","contributorId":310420,"corporation":false,"usgs":false,"family":"McTigue","given":"Leah","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907389,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250113,"text":"70250113 - 2023 - Shifted sediment-transport regimes by climate change and amplified hydrological variability in cryosphere-fed rivers","interactions":[],"lastModifiedDate":"2023-11-20T15:15:03.789762","indexId":"70250113","displayToPublicDate":"2023-11-08T09:10:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Shifted sediment-transport regimes by climate change and amplified hydrological variability in cryosphere-fed rivers","docAbstract":"

Climate change affects cryosphere-fed rivers and alters seasonal sediment dynamics, affecting cyclical fluvial material supply and year-round water-food-energy provisions to downstream communities. Here, we demonstrate seasonal sediment-transport regime shifts from the 1960s to 2000s in four cryosphere-fed rivers characterized by glacial, nival, pluvial, and mixed regimes, respectively. Spring sees a shift toward pluvial-dominated sediment transport due to less snowmelt and more erosive rainfall. Summer is characterized by intensified glacier meltwater pulses and pluvial events that exceptionally increase sediment fluxes. Our study highlights that the increases in hydroclimatic extremes and cryosphere degradation lead to amplified variability in fluvial fluxes and higher summer sediment peaks, which can threaten downstream river infrastructure safety and ecosystems and worsen glacial/pluvial floods. We further offer a monthly-scale sediment-availability-transport model that can reproduce such regime shifts and thus help facilitate sustainable reservoir operation and river management in wider cryospheric regions under future climate and hydrological change.

","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.adi5019","usgsCitation":"Zhang, T., Li, D., East, A.E., Kettner, A.J., Best, J., Ni, J., and Lu, X., 2023, Shifted sediment-transport regimes by climate change and amplified hydrological variability in cryosphere-fed rivers: Science Advances, v. 9, no. 45, eadi5019, 12 p., https://doi.org/10.1126/sciadv.adi5019.","productDescription":"eadi5019, 12 p.","ipdsId":"IP-147639","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":441660,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adi5019","text":"Publisher Index Page"},{"id":422725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Tinghu","contributorId":210005,"corporation":false,"usgs":false,"family":"Zhang","given":"Tinghu","email":"","affiliations":[],"preferred":false,"id":888409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Dongfeng","contributorId":297068,"corporation":false,"usgs":false,"family":"Li","given":"Dongfeng","email":"","affiliations":[{"id":64287,"text":"National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":888410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kettner, Albert J.","contributorId":331669,"corporation":false,"usgs":false,"family":"Kettner","given":"Albert","email":"","middleInitial":"J.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":888412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Best, James L.","contributorId":331670,"corporation":false,"usgs":false,"family":"Best","given":"James L.","affiliations":[{"id":35161,"text":"University of Illinois, Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":888413,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ni, Jinren","contributorId":331671,"corporation":false,"usgs":false,"family":"Ni","given":"Jinren","email":"","affiliations":[{"id":79261,"text":"Peking University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":888414,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lu, Xixi","contributorId":298889,"corporation":false,"usgs":false,"family":"Lu","given":"Xixi","email":"","affiliations":[{"id":64287,"text":"National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":888415,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250689,"text":"70250689 - 2023 - Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere","interactions":[],"lastModifiedDate":"2023-12-27T12:49:16.223157","indexId":"70250689","displayToPublicDate":"2023-11-08T06:46:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7120,"text":"Limnology & Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere","docAbstract":"

In lakes, the rates of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) are often controlled by resource availability. Herein, we explore how catchment vs. within lake predictors of metabolism compare using data from 16 lakes spanning 39°N to 64°N, a range of inflowing streams, and trophic status. For each lake, we combined stream loads of dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) with lake DOC, TN, and TP concentrations and high frequency in situ monitoring of dissolved oxygen. We found that stream load stoichiometry indicated lake stoichiometry for C : N and C : P (r2 = 0.74 and r2 = 0.84, respectively), but not for N : P (r2 = 0.04). As we found a strong positive correlation between TN and TP, we only used TP in our statistical models. For the catchment model, GPP and R were best predicted by DOC load, TP load, and load N : P (R2 = 0.85 and R2 = 0.82, respectively). For the lake model, GPP and R were best predicted by TP concentrations (R2 = 0.86 and R2 = 0.67, respectively). The inclusion of N : P in the catchment model, but not the lake model, suggests that both N and P regulate metabolism and that organisms may be responding more strongly to catchment inputs than lake resources. Our models predicted NEP poorly, though it is unclear why. Overall, our work stresses the importance of characterizing lake catchment loads to predict metabolic rates, a result that may be particularly important in catchments experiencing changing hydrologic regimes related to global environmental change.

","language":"English","publisher":"Wiley","doi":"10.1002/lno.12449","usgsCitation":"Corman, J.R., Zwart, J.A., Klug, J., Bruesewitz, D.A., de Eyto, E., Klaus, M., Knoll, L.B., Rusak, J.A., Vanni, M.J., Alfonso, M.B., Fernandez, R.L., Yao, H., Austnes, K., Couture, R., de Wit, H.A., Karlsson, J., and Laas, A., 2023, Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere: Limnology & Oceanography, v. 68, no. 12, p. 2617-2631, https://doi.org/10.1002/lno.12449.","productDescription":"15 p.","startPage":"2617","endPage":"2631","ipdsId":"IP-148966","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":441665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.12449","text":"Publisher Index 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