To determine the most relevant climatic and economic factors driving water demand for Providence, Rhode Island, and to further the understanding of human interactions with water availability, linear regression models were developed to estimate single-family and multifamily residential, commercial, and industrial water demand for the service area of Providence Water for 2014–21. Monthly water use delivery data were provided by Providence Water. An array of climatic and economic data, the drought index, and binary variables to represent seasonal water use and the onset of the coronavirus (COVID–19) were investigated as possible explanatory variables for the water demand models. The water demand model with the best fit with the least amount of error was the single-family residential water demand followed in descending order of accuracy by the commercial, multifamily residential, and industrial water demand. Seasonal variables were significant in all models, and the COVID–19 binary variable was significant in the commercial and industrial models. One or two economic variables were significant in all models and one climatic variable was significant in all models except the commercial model.
Overall residential, commercial, and industrial water demand in the Providence, Rhode Island, service area has decreased during the study period most likely because of widescale drought conditions and policies designed to improve water efficiencies. The linear regression models developed for single-family and multifamily residential, commercial, and industrial water use explained 94, 85, 91, and 77 percent, respectively, of the variability in monthly water use. Multifamily residential water demand displayed a less distinct seasonal trend than that observed for single-family residential customers, likely because multifamily homes tend to use less water outdoors. The commercial water-demand model included no climatic variables, one economic variable, the COVID–19 pandemic variable, and the high and low water use seasonal variables—the latter two variables indicating the importance of seasonal fluctuations in water use. The COVID–19 pandemic and a concomitant State executive order had the immediate effect of severely reducing commercial water use. The industrial water-demand model did not perform as well as the other models because industrial water delivery data display a greater range of values, both seasonally and for the overall study period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235057","collaboration":"Prepared in cooperation with the Rhode Island Water Resources Board","usgsCitation":"Stagnitta, T.J., and Medalie, L., 2023, Assessment of factors that influence human water demand for Providence, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2023–5057, 18 p., https://doi.org/10.3133/sir20235057.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-142026","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":419046,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5057/coverthb.jpg"},{"id":500938,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114971.htm","linkFileType":{"id":5,"text":"html"}},{"id":419051,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91H5QOY","text":"USGS data release","linkHelpText":"Data for regression models to estimate water use in Providence, Rhode Island, 2014–2021"},{"id":419050,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5057/images/"},{"id":419049,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5057/sir20235057.XML"},{"id":419048,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235057/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 20023-5057"},{"id":419047,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5057/sir20235057.pdf","text":"Report","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 20023-5057"}],"country":"United States","state":"Rhode Island","city":"Providence","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.496115199193,\n 41.87371310909353\n ],\n [\n -71.496115199193,\n 41.785379633702576\n ],\n [\n -71.37573267926174,\n 41.785379633702576\n ],\n [\n -71.37573267926174,\n 41.87371310909353\n ],\n [\n -71.496115199193,\n 41.87371310909353\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
A telemetry study was conducted during March–October 2022 to evaluate behavior and movement patterns of adult smallmouth bass (Micropterus dolomieu) in the forebay of Bonneville Dam, on the Columbia River in Washington and Oregon. This study was a follow-up to a previous study conducted at the site during August–December 2020. In 2022, a total of 41 smallmouth bass were collected, tagged, and released during March–May in three distinct areas of the dam forebay and monitored until late-October. Movement data from 39 tagged smallmouth bass were used in behavior analyses with an average detection duration (elapsed time from release to last detection) of 121.5 days. Most tagged smallmouth bass had site fidelity while present in the forebay of Bonneville Dam, primarily remaining within their zone of release, or moving into nearby adjacent zones. Although site fidelity was common during the study, we found that some tagged smallmouth bass left the forebay of Bonneville Dam and moved substantial distances upstream or downstream. Thirty-six percent of the tagged smallmouth bass were detected at least 8 kilometers upstream or downstream from the Bonneville Dam at some point during the study period (several of these fish eventually returned to the forebay), and 10 percent of the tagged fish were detected at sites located 24 river kilometers upstream or downstream from the dam. Results from this study build upon previous data collected during 2020 and provide new insights into behavior patterns of smallmouth bass collected and tagged in the Bonneville Dam forebay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231046","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Kock, T.J., and Hansen, G.S., 2023, Behavior and movement of smallmouth bass (Micropterus dolomieu) near Bonneville Dam, Columbia River, Washington and Oregon, March–October 2022: U.S. Geological Survey Open-File Report 2023–1046, 14 p., https://doi.org/10.3133/ofr20231046.","productDescription":"vii, 14 p.","onlineOnly":"Y","ipdsId":"IP-150147","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":419025,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231046/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1046"},{"id":419024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1046/ofr20231046.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1046"},{"id":419023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1046/coverthb.jpg"},{"id":419027,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1046/ofr20231046.XML"},{"id":419026,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1046/images"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Bonneville Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.98471940891719,\n 45.66526448575243\n ],\n [\n -121.98471940891719,\n 45.62085268028778\n ],\n [\n -121.90466791018856,\n 45.62085268028778\n ],\n [\n -121.90466791018856,\n 45.66526448575243\n ],\n [\n -121.98471940891719,\n 45.66526448575243\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The Poso Creek Oil Field is one of the many fields selected for regional groundwater mapping and monitoring by the California State Water Resources Control Board as part of the Oil and Gas Regional Monitoring Program (RMP; California State Water Resources Control Board, 2015, 2022b; U.S. Geological Survey, 2022a). The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations to groundwater and the geologic materials between them; (3) evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2022a). As part of this evaluation, the USGS installed a multiple-well monitoring site within the administrative boundary of the Poso Creek Oil Field about 12 miles north of Bakersfield, California (fig. 1). Data collected at the Poso Creek multiple-well monitoring site (PCCT) provide information about the geology, hydrology, geophysical properties, and water quality of the aquifer system overlying the oil-bearing zone, thus enhancing understanding of relations between adjacent groundwater and the Poso Creek Oil Field in an area where groundwater data are limited, particularly at different depths in the aquifer. This report presents construction information for the PCCT and initial geohydrologic data collected from the site. Similar sites installed on the east side of the Lost Hills Oil Field and North and South Belridge Oil Fields were described by Everett and others (2020a, b).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231047","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Everett, R.R., McMahon, P.B., Stephens, M.J., Gillespie, J.M., Shepherd, M.M., and Fenton, N.C., 2023, Multiple-well monitoring site within the Poso Creek Oil Field, Kern County, California: U.S. Geological Survey Open-File Report 2023-1047, 11 p., https://doi.org/10.3133/ofr20231047.","productDescription":"11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-143467","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499783,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114968.htm","linkFileType":{"id":5,"text":"html"}},{"id":418877,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/ofr20231047/full"},{"id":418876,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1047/images"},{"id":418875,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1047/ofr20231047.xml"},{"id":418874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1047/ofr20231047.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":418873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1047/covrthb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Poso Creek Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.02751948230053,\n 35.55715768005527\n ],\n [\n -119.02751948230053,\n 35.37156425616723\n ],\n [\n -118.8051417495576,\n 35.37156425616723\n ],\n [\n -118.8051417495576,\n 35.55715768005527\n ],\n [\n -119.02751948230053,\n 35.55715768005527\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Beginning in September 2010, the U.S. Geological Survey installed continuous water-quality monitors at several streamgages across Indiana as part of a network of supergages to meet cooperator information needs. Two types (or models) of water-quality monitors deployed at each site measured and recorded water temperature, dissolved oxygen, specific conductance, pH, and turbidity every 15 minutes during the study period. Associated discrete water samples were collected at regular intervals and analyzed for concentrations of suspended sediment and total phosphorus. Surrogate regression models were developed between the continuously measured turbidity values and turbidity values in the associated samples to compute continuous concentrations and loads of suspended sediment and total phosphorus. Starting in November 2018, the original extended deployment system monitors were replaced with the newest model of multiparameter water-quality monitors and were equipped with turbidity smart sensors because the older monitors were phased out of production. The updated monitor and smart sensor yield different but relatable turbidity values.
Turbidity data collected concurrently by the two sensors from November 2018 to December 2021 were compared and analyzed to quantify the relation between them at six supergage sites in northwestern Indiana and one site in the town of Zionsville in central Indiana. Ordinary least squares regression was used to calculate site-specific conversion factors so that turbidity data from the newer monitors can be used in published surrogate models based on the older monitor data. Regression analyses explained approximately 98 percent of the variation in turbidity readings between the two sensors. From these analyses, conversion factors were developed that may be applied to older turbidity readings to calculate near real-time concentrations of phosphorus and suspended sediment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235077","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management, Iroquois River Conservancy District, Kankakee River Basin and Yellow River Basin Development Commission, and the Town of Zionsville","usgsCitation":"Messner, M.L., Perkins, M.K., and Bunch, A.R., 2023, Comparison of turbidity sensors at U.S. Geological Survey supergages in Indiana from November 2018 to December 2021: U.S. Geological Survey Scientific Investigations Report 2023–5077, 13 p., https://doi.org/10.3133/sir20235077.","productDescription":"Report: iv, 13 p.; Dataset","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129902","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":501040,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114970.htm","linkFileType":{"id":5,"text":"html"}},{"id":419012,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","linkHelpText":"- U.S. Geological Survey water data for the nation"},{"id":419011,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5077/images/"},{"id":419010,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5077/sir20235077.XML"},{"id":419009,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5077"},{"id":419008,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5077/sir20235077.pdf","text":"Report","size":"3.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5077"},{"id":419007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5077/coverthb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Kankakee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.47825888394271,\n 41.7055943665263\n ],\n [\n -87.47825888394271,\n 40.75718260396678\n ],\n [\n -86.31031406433961,\n 40.75718260396678\n ],\n [\n -86.31031406433961,\n 41.7055943665263\n ],\n [\n -87.47825888394271,\n 41.7055943665263\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278
Ecological interactions among species often lead to parasitic lineages coevolving with host resources, which is often suggested as the primary driver of parasite diversification. Freshwater mussels are bivalves that possess a parasitic life cycle requiring larval encystment on freshwater vertebrates to complete metamorphosis. The North American freshwater mussel tribe Quadrulini has a suite of life history adaptations including highly specialized patterns of host use, infection strategies, and variable larval morphologies. However, the evolution of life histories has yet to be explored using phylogenetic comparative methods. In this study, we use a holistic approach incorporating biogeographical, ecological, molecular, and morphological datasets to reconstruct the evolution of Quadrulini. Comparative phylogenetic analyses suggested the diversification of Quadrulini has been driven, at least in part, by codiversification with their primary host fishes in Ictaluridae. Major diversification events in both ictalurids and quadrulines were estimated to have occurred in the Mississippi River basin throughout the Miocene. Life history characteristics associated with parasitism were supported to have coevolved with host repertories, supporting the hypothesis that ecological interactions with host fishes have shaped the evolution of highly specialized traits in this group. Our findings demonstrate the importance of ecological interactions with host resources in shaping the evolutionary history of freshwater mussels.
","language":"English","publisher":"Society of Systematic Biologists","doi":"10.18061/bssb.v2i1.8998","usgsCitation":"Neemuchwala, S., Johnson, N., Pfeiffer, J., Lopes-Lima, M., Gomes-dos-Santos, A., Froufe, E., Hillis, D.M., and Smith, C.H., 2023, Coevolution with host fishes shapes parasitic life histories in a group of freshwater mussels (Unionidae: Quadrulini): Bulletin of the Society of Systematic Biologists, v. 2, no. 1, 8998, 25 p.; Data Release, https://doi.org/10.18061/bssb.v2i1.8998.","productDescription":"8998, 25 p.; Data Release","ipdsId":"IP-141233","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":442731,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.18061/bssb.v2i1.8998","text":"Publisher Index Page"},{"id":419392,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419597,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OEDFC3","text":"Molecular data and results needed to better understand codiversification of freshwater mussels (Unionidae: Quadrulini) and their parasitic larval hosts"}],"volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Neemuchwala, Sakina","contributorId":317738,"corporation":false,"usgs":false,"family":"Neemuchwala","given":"Sakina","email":"","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":879207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nathan 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":216876,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":879208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pfeiffer, John M.","contributorId":202521,"corporation":false,"usgs":false,"family":"Pfeiffer","given":"John M.","affiliations":[{"id":36469,"text":"Florida Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":879209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopes-Lima, Manuel","contributorId":213286,"corporation":false,"usgs":false,"family":"Lopes-Lima","given":"Manuel","email":"","affiliations":[],"preferred":false,"id":879210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomes-dos-Santos, Andre","contributorId":317739,"corporation":false,"usgs":false,"family":"Gomes-dos-Santos","given":"Andre","email":"","affiliations":[{"id":69139,"text":"University of Porto","active":true,"usgs":false}],"preferred":false,"id":879211,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Froufe, Elsa","contributorId":213253,"corporation":false,"usgs":false,"family":"Froufe","given":"Elsa","email":"","affiliations":[],"preferred":false,"id":879212,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hillis, David M.","contributorId":317740,"corporation":false,"usgs":false,"family":"Hillis","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":879213,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Chase H. 0000-0002-1499-0311","orcid":"https://orcid.org/0000-0002-1499-0311","contributorId":225140,"corporation":false,"usgs":false,"family":"Smith","given":"Chase","email":"","middleInitial":"H.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":879214,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70249843,"text":"70249843 - 2023 - New insights into the relationship between mass eruption rate and volcanic column height based on the IVESPA dataset","interactions":[],"lastModifiedDate":"2024-09-16T22:23:08.941426","indexId":"70249843","displayToPublicDate":"2023-07-18T09:23:11","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":"New insights into the relationship between mass eruption rate and volcanic column height based on the IVESPA dataset","docAbstract":"Rapid and simple estimation of the mass eruption rate (MER) from column height is essential for real-time volcanic hazard management and reconstruction of past explosive eruptions. Using 134 eruptive events from the new Independent Volcanic Eruption Source Parameter Archive (IVESPA, v1.0), we explore empirical MER-height relationships for four measures of column height: spreading level, sulfur dioxide height, and top height from direct observations and as reconstructed from deposits. These relationships show significant differences and highlight limitations of empirical models currently used in operational and research applications. The roles of atmospheric stratification, wind, and humidity remain challenging to detect across the wide range of eruptive conditions spanned in IVESPA, ultimately resulting in empirical relationships outperforming analytical models that account for atmospheric conditions. This finding highlights challenges in constraining the MER-height relation using heterogeneous observations and empirical models, which reinforces the need for improved eruption source parameter data sets and physics-based models.
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In 2017, the latest science, data, and modeling tools were used to develop revised Watershed Implementation Plans (WIPs). In this article, we examine the vulnerability of the Chesapeake Bay watershed to the combined pressures of climate change and growth in population, agricultural intensity, and economic activity for the 60-year period 1995–2055. The results will be used to revise WIPs, as needed, to account for expected increases in loads. Assessing changes relative to 1995 for the years 2025, 2035, 2045, and 2055, mean annual precipitation increases of 3.11%, 4.21%, 5.34%, and 6.91%, respectively, air temperature increases of 1.12, 1.45, 1.84, and 2.12°C, respectively, and potential evapotranspiration increases of 3.36%, 4.43%, 5.54%, and 6.35%, respectively, are projected. Population in the watershed is expected to grow by 3.5 million between 2025 and 2055. Watershed model results show incremental increases in streamflow (2.3%–6.2%), nitrogen (2.6%–10.8%), phosphorus (4.5%–26.7%), and sediment (3.8%–18.8%) loads to the tidal Bay due to climate change. Growth in population, agricultural intensity, development, and economic activity resulted in relatively smaller increases in loads compared to climate change.
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0000-0002-2737-2379","orcid":"https://orcid.org/0000-0002-2737-2379","contributorId":306024,"corporation":false,"usgs":false,"family":"Hinson","given":"Kyle","email":"","middleInitial":"E.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":880058,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Claggett, Peter 0000-0002-5335-2857","orcid":"https://orcid.org/0000-0002-5335-2857","contributorId":238920,"corporation":false,"usgs":true,"family":"Claggett","given":"Peter","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":880059,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248826,"text":"70248826 - 2023 - Current and future sinkhole susceptibility in karst and pseudokarst areas of the conterminous United States","interactions":[],"lastModifiedDate":"2023-09-22T13:52:11.321375","indexId":"70248826","displayToPublicDate":"2023-07-18T08:49:29","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":"Current and future sinkhole susceptibility in karst and pseudokarst areas of the conterminous United States","docAbstract":"Sinkholes in karst and pseudokarst regions threaten infrastructure, property, and lives. We mapped closed depressions in karst and pseudokarst regions of the conterminous United States (U.S.) from 10-m-resolution elevation data using high-performance computing, and then created a heuristic additive model of sinkhole susceptibility that also included nationally consistent data for factors related to geology, soils, precipitation extremes, and development. Maps identify potential sinkhole hotspots based on current conditions and projections for 50 years into the future (the years 2070–2079) based on climate change and urban development scenarios. Areas characterized as having either high or very high sinkhole susceptibility contain 94%–99% of known or probable sinkhole locations from three U.S. state databases. States and counties with the highest amounts and percentages of land in zones of highest sinkhole susceptibility are identified. Projected changes in extreme precipitation and development did not substantially change current hotspots of highest sinkhole susceptibility. Results provide a uniform index of sinkhole potential that can support national planning, instead of existing assessments produced through various methods within individual states or smaller areas.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2023.1207689","usgsCitation":"Wood, N.J., Doctor, D.H., Alder, J.R., and Jones, J.M., 2023, Current and future sinkhole susceptibility in karst and pseudokarst areas of the conterminous United States: Frontiers in Earth Science, v. 11, 1207689, 15 p., https://doi.org/10.3389/feart.2023.1207689.","productDescription":"1207689, 15 p.","ipdsId":"IP-143024","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":442742,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2023.1207689","text":"Publisher Index Page"},{"id":435253,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97YWD2D","text":"USGS data release","linkHelpText":"Geospatial files and tabular exposure estimates of sinkhole susceptibility for counties in the conterminous United States for current conditions and 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The role of large datasets, secondary data, and associated analysis techniques in outdoor recreation research","interactions":[],"lastModifiedDate":"2023-12-05T11:57:12.531833","indexId":"70247702","displayToPublicDate":"2023-07-18T07:27:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5520,"text":"Journal of Outdoor Recreation and Tourism","active":true,"publicationSubtype":{"id":10}},"title":"What is “big data” and how should we use it? The role of large datasets, secondary data, and associated analysis techniques in outdoor recreation research","docAbstract":"With researchers increasingly interested in big data research, this conceptual paper describes how large datasets, secondary data, and associated analysis techniques can be used to understand outdoor recreation. Some types of large, secondary datasets that have been increasingly used in outdoor recreation research include social media, mobile device data, and trip reports or online reviews. First, we give a brief overview of big data terms and outline the steps involved in conducting big data research. In doing so, we describe data sources and analysis techniques relevant for outdoor recreation, and review how they have been applied in previous published works. We then describe opportunities, limitations, and considerations of using big data. Finally, we outline several questions researchers may consider when designing, conducting, reporting, and reviewing outdoor recreation research using big data. Overall, big data approaches can expand our understanding of outdoor recreation and, by addressing key questions, may help researchers harness the strengths of big data while ensuring quality and integrity.
Increasingly frequent megafires are dramatically altering landscapes and critical habitats around the world. Across the western United States, megafires have become an almost annual occurrence, but the implication of these fires for the conservation of native wildlife remains relatively unknown. Woodland savannas are among the world's most biodiverse ecosystems and provide important food and structural resources to a variety of wildlife, but they are threatened by megafires. Despite this, the great majority of fire impact studies have only been conducted in coniferous forests. Understanding the resistance and resilience of wildlife assemblages following these extreme perturbations can help inform future management interventions that limit biodiversity loss due to megafire. We assessed the resistance of a woodland savanna mammal community to the short-term impacts of megafire using camera trap data collected before, during, and after the fire. Specifically, we utilized a 5-year camera trap data set (2016–2020) from the Hopland Research and Extension Center to examine the impacts of the 2018 Mendocino Complex Fire, California's largest recorded wildfire at the time, on the distributions of eight observed mammal species. We used a multispecies occupancy model to quantify the effects of megafire on species' space use, to assess the impact on species size and diet groups, and to create robust estimates of fire's impacts on species diversity across space and time. Megafire had a negative effect on the detection of certain mammal species, but overall, most species showed high resistance to the disturbance and returned to detection and site use levels comparable to unburned sites by the end of the study period. Following megafire, species richness was higher in burned areas that retained higher canopy cover relative to unburned and burned sites with low canopy cover. Fire management that prevents large-scale canopy loss is critical to providing refugia for vulnerable species immediately following fire in oak woodlands, and likely other mixed-forest landscapes.
On May 25, 2014, a 54.5-million cubic meter rock avalanche in the West Salt Creek valley, Mesa County, Colorado, traveled 4.6 kilometers, leaving a deposit that covers about 2.2 square kilometers. To check the particle-size distribution of the deposit for information about the high mobility of the avalanche, we estimated boulder distribution density for the entire deposit by counting 1-meter (m) or larger diameter boulders of sedimentary rock derived from the Green River Formation that are visible in high-resolution imagery collected from the area in July 2014. Basalt boulders were excluded from the count because field observations indicated that they generally stayed intact as the avalanche moved downslope, whereas sedimentary boulders showed evidence of fragmentation during downslope movement. Variable clarity, contrast, and resolution of the imagery precluded mapping smaller boulders. Experimentation with 5-, 10-, and 20-m resolution grids indicated that a 20-m resolution grid showed the spatial pattern of boulder density across the deposit at a scale that allowed statistically meaningful variations to be determined. In addition to counting the boulders in each 20×20-m grid cell, six categories of successively increasing boulder distribution density were created to help visualize variations in the distribution of 1 meter or larger boulders across the avalanche deposit. Analysis indicates that boulder distribution density gradually decreases with increasing distance from the avalanche source.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1178","usgsCitation":"Lewis, A.C., Baum, R.L., and Coe, J.A., 2023, Distribution of large boulders on the deposit of the West Salt Creek rock avalanche, western Colorado: U.S. Geological Survey Data Report 1178, 6 p., https://doi.org/10.3133/dr1178.","productDescription":"Report: iv, 6 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-139567","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":418867,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1178/coverthb2.jpg"},{"id":418869,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MWDI9P","text":"USGS data release","linkHelpText":"Distribution of large boulders on the deposit of the West Salt Creek rock avalanche, western Colorado"},{"id":418983,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1178/dr1178.xml"},{"id":418982,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1178/images"},{"id":418868,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1178/dr1178.pdf","text":"Report","size":"33.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1178"},{"id":419151,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1178/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1178"},{"id":499553,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114967.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.55,\n 39.12\n ],\n [\n -107.55,\n 39.08\n ],\n [\n -107.50,\n 39.08\n ],\n [\n -107.50,\n 39.12\n ],\n [\n -107.55,\n 39.12\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
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Lake Sturgeon Acipenser fulvescens are listed as threatened or endangered in 15 states or provinces within their native range. Accordingly, investments in habitat and population restoration for this species have increased throughout the Great Lakes. To aide evaluation of restoration efficacy, robust population parameters are needed to inform management decisions. The St. Clair – Detroit River System (SCDRS) contains one of the largest self-sustaining Lake Sturgeon populations in the Great Lakes; however recent estimates of population abundance and growth parameters have not been assessed. Our study used baited setline and mark-recapture data collected between 2001 – 2019 to estimate whether the number of Lake Sturgeon captured varied annually and/or with water temperature and whether population abundance and population growth rate varied among three sub-populations located in the SCDRS. Trends in the number of Lake Sturgeon captured on setlines varied among sub-populations and by life stage. Annual trends in the number of Lake Sturgeon captured remained consistent over time in the upper St. Clair River, decreased for adults and increased for subadults in the lower St. Clair River, and increased in the Detroit River. With sub-population abundance of 20,184 (95% CI = 12,533 – 27,816) in the upper St. Clair River/southern Lake Huron, 6,523 (95% CI = 5,720 – 7,327) in the lower St. Clair River, and 6,416 (95% CI = 4,065 – 8,767) in the Detroit River, our study confirms that the SCDRS contains the largest Lake Sturgeon population with unimpeded access to the Great Lakes. The geometric mean population growth rate (λ) for all sub-populations indicated stable populations and ranged from 1.00 – 1.16. Our study provides an updated assessment of Lake Sturgeon population parameters that serve as a baseline to evaluate habitat restoration efforts and inform management of the SCDRS recreational Lake Sturgeon fishery.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10917","usgsCitation":"Chiotti, J., Boase, J., Briggs, A.S., Davis, C., Drouin, R., Hondorp, D.W., Mohr, L., Roseman, E., Thomas, M.V., and Wills, T.C., 2023, Lake sturgeon population trends in the St. Clair–Detroit River System, 2001–2019: North American Journal of Fisheries Management, v. 43, no. 4, p. 1066-1080, https://doi.org/10.1002/nafm.10917.","productDescription":"15 p.","startPage":"1066","endPage":"1080","ipdsId":"IP-137433","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":442766,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10917","text":"Publisher Index Page"},{"id":418800,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.37937675719795,\n 41.841019809758876\n ],\n [\n -82.98007655844503,\n 41.97565564942232\n ],\n [\n -82.98651225027805,\n 42.20799521065484\n ],\n [\n -82.38332005697309,\n 42.350470301178746\n ],\n [\n -82.40688308860425,\n 42.584079482850626\n ],\n [\n -82.3163144315291,\n 43.00425817508719\n ],\n [\n -82.6321265850878,\n 43.08619329541759\n ],\n [\n -83.04444510888004,\n 42.572640973304175\n ],\n [\n -83.32788082965169,\n 42.08612558362202\n ],\n [\n -83.37937675719795,\n 41.841019809758876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Chiotti, Justin A.","contributorId":26629,"corporation":false,"usgs":false,"family":"Chiotti","given":"Justin A.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":877240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":877241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Andrew S 0000-0002-0268-9310","orcid":"https://orcid.org/0000-0002-0268-9310","contributorId":215596,"corporation":false,"usgs":false,"family":"Briggs","given":"Andrew","email":"","middleInitial":"S","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":877242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Chris","contributorId":316266,"corporation":false,"usgs":false,"family":"Davis","given":"Chris","affiliations":[],"preferred":false,"id":877243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drouin, Richard","contributorId":70288,"corporation":false,"usgs":false,"family":"Drouin","given":"Richard","email":"","affiliations":[{"id":6780,"text":"Ontario Ministry of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":877244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":877245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mohr, Lloyd","contributorId":34001,"corporation":false,"usgs":true,"family":"Mohr","given":"Lloyd","affiliations":[],"preferred":false,"id":877246,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":877247,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thomas, Michael V.","contributorId":195401,"corporation":false,"usgs":false,"family":"Thomas","given":"Michael","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":877248,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wills, Todd C.","contributorId":195402,"corporation":false,"usgs":false,"family":"Wills","given":"Todd","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":877249,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70247818,"text":"70247818 - 2023 - River geomorphology affects biogeochemical responses to hydrologic events in a large river ecosystem","interactions":[],"lastModifiedDate":"2023-08-21T11:54:49.113964","indexId":"70247818","displayToPublicDate":"2023-07-13T06:50:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"River geomorphology affects biogeochemical responses to hydrologic events in a large river ecosystem","docAbstract":"Shifts in the frequency and intensity of high discharge events due to climate change may have important consequences for the hydrology and biogeochemistry of rivers. However, our understanding of event-scale biogeochemical dynamics in large rivers lags that of small streams. To fill this gap, we used high-frequency sensor data collected during four consecutive summers from a main channel and backwater site of the Upper Mississippi River. We identified high discharge events and calculated event concentration-discharge responses for both physical-chemical (nitrate, turbidity, and fluorescent dissolved organic matter) and biological (chlorophyll-a and cyanobacteria) constituents using metrics of hysteresis and slope. We found a range of responses across events, particularly for nitrate. Although fluorescent dissolved organic matter (FDOM) and turbidity exhibited more consistent responses across events, contrasting hysteresis metrics indicated that FDOM was flushed to the river from more distant sources than turbidity. Biological responses (chlorophyll a and cyanobacteria) differed more between sites than physical and chemical constituents. Lastly, we found that the event characteristics best explaining concentration responses differed between sites, with event magnitude more frequently related to responses in the main channel, and antecedent wetness conditions associated with response variation in the backwater. Our results indicate that event responses in large rivers are distinct across the diverse habitats and biogeochemical components of a large floodplain river, which has implications for local and downstream ecosystems as the climate shifts.
As larger tracts of land experience degradation, seed-based restoration (SBR) will be a primary tool to reestablish vegetation and ecosystem function. SBR has advanced in terms of technical and technological approaches, yet plant recruitment remains a major barrier in some systems, notably drylands. There is an unmet opportunity to test science-based approaches to seed mix design and application, based not only on diversity or local provenance, but on the unique recruitment strategies of species. We lay out a framework that uses a quantitative representation of species' recruitment niches to match them to targeted goals (e.g. drought or invasion resistance) and methods (e.g. precision tools and technologies) in SBR. We first describe how to quantify the recruitment niche with seed and seedling traits tied to observed recruitment responses to environmental factors. We then show how a quantified recruitment niche framework can serve as the foundation to address three major restoration challenges: (1) designing forward-looking seed mixes that increase resilience to future climate and disturbance, (2) accounting for natural recovery in SBR planning, and (3) applying precision seeding practices to maximize restoration success. Finally, we demonstrate these ideas with existing data and discuss key challenges to adoption in SBR practice. While the ideas in this framework are based in ecological theory, they will require substantial testing and refinement by scientists engaged in SBR efforts. If this framework is integrated into research agendas, we believe it has the potential to unify and advance diverse elements of seed-based restoration ecology and improve restoration outcomes.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13959","usgsCitation":"Larson, J.E., Agneray, A.C., Boyd, C.S., Bradford, J., Kildisheva, O.A., Suding, K.N., and Copeland, S., 2023, A recruitment niche framework for improving seed-based restoration: Restoration Ecology, v. 31, no. 7, e13959, 15 p., https://doi.org/10.1111/rec.13959.","productDescription":"e13959, 15 p.","ipdsId":"IP-150987","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":442774,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13959","text":"Publisher Index Page"},{"id":419229,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, Julie E.","contributorId":261604,"corporation":false,"usgs":false,"family":"Larson","given":"Julie","email":"","middleInitial":"E.","affiliations":[{"id":52914,"text":"Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA","active":true,"usgs":false}],"preferred":false,"id":878516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Agneray, A. C.","contributorId":316842,"corporation":false,"usgs":false,"family":"Agneray","given":"A.","email":"","middleInitial":"C.","affiliations":[{"id":68710,"text":"Bureau of Land Management, Nevada State Office, 1340 Financial Blvd, Reno, NV, US 89502","active":true,"usgs":false}],"preferred":false,"id":878517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Chad S.","contributorId":255106,"corporation":false,"usgs":false,"family":"Boyd","given":"Chad","email":"","middleInitial":"S.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":878518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":878519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kildisheva, O. A.","contributorId":316844,"corporation":false,"usgs":false,"family":"Kildisheva","given":"O.","email":"","middleInitial":"A.","affiliations":[{"id":68711,"text":"The Nature Conservancy, 999 Disk Drive, Suite 104, Bend, OR, US 97702","active":true,"usgs":false}],"preferred":false,"id":878520,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suding, Katharine N. 0000-0002-5357-0176","orcid":"https://orcid.org/0000-0002-5357-0176","contributorId":168385,"corporation":false,"usgs":false,"family":"Suding","given":"Katharine","email":"","middleInitial":"N.","affiliations":[{"id":6709,"text":"University of Colorado, Denver","active":true,"usgs":false}],"preferred":false,"id":878521,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Copeland, Stella M.","contributorId":196218,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":878522,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70246656,"text":"sir20235070 - 2023 - Spatiotemporal variations in copper, arsenic, cadmium, and zinc concentrations in surface water, fine-grained bed sediment, and aquatic macroinvertebrates in the upper Clark Fork Basin, western Montana—A 20-year synthesis, 1996–2016","interactions":[],"lastModifiedDate":"2026-03-09T17:10:18.876972","indexId":"sir20235070","displayToPublicDate":"2023-07-12T15:04:42","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":"2023-5070","displayTitle":"Spatiotemporal Variations in Copper, Arsenic, Cadmium, and Zinc Concentrations in Surface Water, Fine-Grained Bed Sediment, and Aquatic Macroinvertebrates in the Upper Clark Fork Basin, Western Montana—A 20-Year Synthesis, 1996–2016","title":"Spatiotemporal variations in copper, arsenic, cadmium, and zinc concentrations in surface water, fine-grained bed sediment, and aquatic macroinvertebrates in the upper Clark Fork Basin, western Montana—A 20-year synthesis, 1996–2016","docAbstract":"The legacy of mining-related contamination in the upper Clark Fork Basin created an extensive longitudinal gradient in metal concentrations, extending from Silver Bow Creek to Lake Pend Oreille, Idaho. Downstream metal concentrations continue to decline, but, despite such improvements, the ecological health of much of the river remains uncertain. Understanding the long-term consequences of the Clark Fork River mining legacy may be supported by environmental monitoring techniques that include a holistic assessment of biological health or response to define organism exposure to complex contaminant mixtures and the consequences of such exposures. This report presents the spatiotemporal patterns of mining-related contaminants, copper, arsenic, cadmium, and zinc, in surface water, fine-grained bed sediment, and macroinvertebrate (aquatic insect) tissue in the upper Clark Fork from near Butte to Missoula, Montana. Overall, the patterns in water column sample concentrations observed in this study were consistent with previously observed trends, but bed sediment concentrations and concentrations of copper and arsenic varied more in tissue samples among sites. Trace element concentrations, especially copper, often exceeded the chronic aquatic life criteria and consistently exceeded the sediment probable effects level PEL for copper, particularly in the upper and middle river segments. The 20 years considered here were the wettest period since remediation started, and this increase in precipitation may have affected patterns in contaminant concentrations.
Results of this study demonstrated the utility of a continued, comprehensive biomonitoring program to help guide and evaluate future environmental cleanup activities in the Clark Fork. Despite variation in defining complete restoration in these watersheds, using multiple lines of evidence in this study provided quantifiable measures of the timing and completeness of recovery relative to reference conditions. Successful recovery in the Clark Fork may benefit from an adaptive management strategy to continue collecting a comprehensive, multivariate dataset to evaluate whether established goals are being met and for subsequent adjustments and management, as needed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235070","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Caldwell Eldridge, S.L., and Hornberger, M.I., 2023, Spatiotemporal variations in copper, arsenic, cadmium, and zinc concentrations in surface water, fine-grained bed sediment, and aquatic macroinvertebrates in the upper Clark Fork Basin, western Montana—A 20-year synthesis, 1996–2016: U.S. Geological Survey Scientific Investigations Report 2023–5070, 55 p., https://doi.org/10.3133/sir20235070.","productDescription":"Report: viii, 55 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-124462","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":500950,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114966.htm","linkFileType":{"id":5,"text":"html"}},{"id":418908,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235070/full"},{"id":418905,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":418904,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5070/images/"},{"id":418903,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5070/sir20235070.XML"},{"id":418902,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5070/sir20235070.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5070"},{"id":418901,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5070/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Upper Clark Fork Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.19960466889883,\n 47.19600162794333\n ],\n [\n -114.19960466889883,\n 45.651910037647326\n ],\n [\n -110.76235872576048,\n 45.651910037647326\n ],\n [\n -110.76235872576048,\n 47.19600162794333\n ],\n [\n -114.19960466889883,\n 47.19600162794333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Differential Optical Absorption Spectroscopy (DOAS) is commonly used to measure gas emissions from volcanoes. DOAS instruments measure the absorption of solar ultraviolet (UV) radiation scattered in the atmosphere by sulfur dioxide (SO2) and other trace gases contained in volcanic plumes. The standard spectral retrieval methods assume that all measured light comes from behind the plume and has passed through the plume along a straight line. However, a fraction of the light that reaches the instrument may have been scattered beneath the plume and thus has passed around it. Since this component does not contain the absorption signatures of gases in the plume, it effectively “dilutes” the measurements and causes underestimation of the gas abundance in the plume. This dilution effect is small for clean-air conditions and short distances between instrument and plume. However, plume measurements made at long distance and/or in conditions with significant atmospheric aerosol, haze, or clouds may be severely affected. Thus, light dilution is regarded as a major error source in DOAS measurements of volcanic degassing. Several attempts have been made to model the phenomena and the physical mechanisms are today relatively well understood. However, these models require knowledge of the local atmospheric aerosol composition and distribution, parameters that are almost always unknown. Thus, a practical algorithm to quantitatively correct for the dilution effect is still lacking. Here, we propose such an algorithm focused specifically on SO2 measurements. The method relies on the fact that light absorption becomes non-linear for high SO2 loads, and that strong and weak SO2 absorption bands are unequally affected by the diluting signal. These differences can be used to identify when dilution is occurring. Moreover, if we assume that the spectral radiance of the diluting light is identical to the spectrum of light measured away from the plume, a measured clean air spectrum can be used to represent the dilution component. A correction can then be implemented by iteratively subtracting fractions of this clean air spectrum from the measured spectrum until the respective absorption signals on strong and weak SO2 absorption bands are consistent with a single overhead SO2 abundance. In this manner, we can quantify the magnitude of light dilution in each individual measurement spectrum as well as obtaining a dilution-corrected value for the SO2 column density along the line of sight of the instrument. This paper first presents the theory behind the method, then discusses validation experiments using a radiative transfer model, as well as applications to field data obtained under different measurement conditions at three different locations; Fagradalsfjall located on the Reykjanaes peninsula in south Island, Manam located off the northeast coast of mainland Papua New Guinea and Holuhraun located in the inland of north east Island.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2023.1088768","usgsCitation":"Galle, B., Arellano, S., Johansson, M., Kern, C., and Pfeffer, M., 2023, An algorithm for correction of atmospheric scattering dilution effects in volcanic gas emission measurements using skylight differential optical absorption spectroscopy: Frontiers in Earth Science, v. 11, 1088768, 14 p., https://doi.org/10.3389/feart.2023.1088768.","productDescription":"1088768, 14 p.","ipdsId":"IP-151840","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":442778,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2023.1088768","text":"Publisher Index Page"},{"id":419499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Galle, Bo","contributorId":255645,"corporation":false,"usgs":false,"family":"Galle","given":"Bo","email":"","affiliations":[{"id":51629,"text":"Chalmers University, Sweden","active":true,"usgs":false}],"preferred":false,"id":879452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arellano, Santiago","contributorId":205719,"corporation":false,"usgs":false,"family":"Arellano","given":"Santiago","affiliations":[{"id":37153,"text":"Department of Earth and Space Sciences – Chalmers University of Technology, Göteborg, Sweden","active":true,"usgs":false}],"preferred":false,"id":879453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johansson, Mattias","contributorId":255657,"corporation":false,"usgs":false,"family":"Johansson","given":"Mattias","email":"","affiliations":[{"id":51629,"text":"Chalmers University, Sweden","active":true,"usgs":false}],"preferred":false,"id":879454,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":879455,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pfeffer, Melissa","contributorId":199349,"corporation":false,"usgs":false,"family":"Pfeffer","given":"Melissa","affiliations":[],"preferred":false,"id":879456,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247285,"text":"70247285 - 2023 - Slip deficit rates on southern Cascadia faults resolved with viscoelastic earthquake cycle modeling of geodetic deformation","interactions":[],"lastModifiedDate":"2023-12-04T17:00:29.407097","indexId":"70247285","displayToPublicDate":"2023-07-12T08:49:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Slip deficit rates on southern Cascadia faults resolved with viscoelastic earthquake cycle modeling of geodetic deformation","docAbstract":"The fore‐arc of the southern Cascadia subduction zone (CSZ), north of the Mendocino triple junction (MTJ), is home to a network of Quaternary‐active crustal faults that accumulate strain due to the interaction of the North American, Juan de Fuca (Gorda), and Pacific plates. These faults, including the Little Salmon and Mad River fault (LSF and MRF) zones, are located near the most populated parts of California’s north coast and show paleoseismic evidence for three slip events of several‐meter scale in the past 1700 yr. However, the geodetic slip rates of these faults are poorly constrained. In this work, we analyze a new compilation of interseismic geodetic velocities from Global Navigation Satellite Systems, leveling, and tide gauge data near the MTJ to constrain present‐day slip deficit rates on upper‐plate faults and coupling on the megathrust. We construct Green’s functions for interseismic slip deficit for discrete faults embedded in an elastic plate overlying a viscoelastic mantle. We then use a constrained least‐squares inversion to determine best‐fitting slip rates on the major faults and investigate slip rate trade‐offs between faults. Results indicate that the LSF and MRF systems together accumulate 4–5 mm/yr of reverse‐slip deficit, although their separate slip rates cannot be determined independently. Modeling of the horizontal and vertical velocities suggests that the southernmost CSZ is coupled interseismically to deeper than 25 km depth. We also find that 6–17 mm/yr of right‐lateral slip deficit extends north of the MTJ and into the southern Cascadia fore‐arc. These results reinforce the notion that both the southernmost Cascadia megathrust and the smaller fore‐arc faults above it contribute to regional seismic hazard.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230007","usgsCitation":"Materna, K.Z., Murray, J.R., Pollitz, F., and Patton, J.R., 2023, Slip deficit rates on southern Cascadia faults resolved with viscoelastic earthquake cycle modeling of geodetic deformation: Bulletin of the Seismological Society of America, v. 113, no. 6, p. 2505-2518, https://doi.org/10.1785/0120230007.","productDescription":"14 p.","startPage":"2505","endPage":"2518","ipdsId":"IP-145972","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":419347,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascadia fault zone, Mendocino triple junction","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.02213582197189,\n 46.78612877852626\n ],\n [\n -126.15645523900434,\n 46.78612877852626\n ],\n [\n -126.15645523900434,\n 36.43464818238357\n ],\n [\n -120.02213582197189,\n 36.43464818238357\n ],\n [\n -120.02213582197189,\n 46.78612877852626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"113","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":879116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":879117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":879118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patton, Jason R.","contributorId":317714,"corporation":false,"usgs":false,"family":"Patton","given":"Jason","email":"","middleInitial":"R.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":879119,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70246685,"text":"70246685 - 2023 - Minimal shift of eastern wild turkey nesting phenology associated with projected climate change","interactions":[],"lastModifiedDate":"2023-07-26T14:50:52.169626","indexId":"70246685","displayToPublicDate":"2023-07-12T06:57:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Minimal shift of eastern wild turkey nesting phenology associated with projected climate change","docAbstract":"Climate change may induce mismatches between wildlife reproductive phenology and temporal occurrence of resources necessary for reproductive success. Verifying and elucidating the causal mechanisms behind potential mismatches requires large-scale, longer-duration data. We used eastern wild turkey (Meleagris gallopavo silvestris) nesting data collected across the southeastern U.S. over eight years to investigate potential climatic drivers of variation in nest initiation dates. We investigated climactic relationships with two datasets, one inclusive of successful and unsuccessful nests (full dataset) and another of just successful nests (successfully hatched dataset), to determine whether successfully hatched nests responded differently to weather changes than all nests did. In the full dataset, each 10 cm increase in January precipitation was associated with nesting occurring 0.46-0.66 days earlier, and each 10 cm increase in precipitation during the 30 days preceding nesting was associated with nesting occurring 0.17-0.21 days later. In the successfully hatched dataset, a 10 cm increase in March precipitation was associated with nesting occurring 0.67-0.74 days earlier, and an increase of one unit of variation in February maximum temperature was associated with nesting occurring 0.02 days later. We combined the results of these modeled relationships with multiple climate scenarios to understand potential implications of future climate change on wild turkey nesting phenology; results indicated that mean nest initiation date is projected to change by <0.1 day by 2040-2060. Wild turkey nesting phenology did not track changes in spring green-up timing, which could result in phenological mismatch between the timing of nesting and the availability of resources critical for successful reproduction.
A priority of the U.S. Geological Survey (USGS) Coastal and Marine Hazards and Resources Program focus on coastal change hazards is to provide accessible and actionable science that meets user needs. To understand these needs, 10 virtual Coastal Data Delivery Listening Sessions were completed with 5 coastal data user types that coastal change hazards data are intended to serve: resource managers, consultants, local planners, State planners, and non-USGS researchers.
During these listening sessions, participants revealed challenges to coastal data use including being overwhelmed by too many webtools, having a lack of capacity to search for and understand new information, facing difficulties finding data, and not understanding how to apply data.
The specific coastal data and information needs described by participants are also detailed in the report and describe data gaps, a need for simpler tools, data needs that differ across spatial and temporal scales, and more outreach on coastal topics and climate change. Participants also suggested leveraging data across study sites and regions to help improve capacity issues and called for more communication and collaboration among and within Federal agencies.
The synthesized information from the Coastal Data Delivery Listening Sessions provided in this report can help the USGS and those working on coastal challenges better understand barriers to coastal information use and the exact data requirements of different coastal data users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235081","programNote":"Coastal and Marine Hazards and Resources Program","usgsCitation":"Stoltz, A.D., Cravens, A.E., Lentz, E., and Himmelstoss, E., 2023, User engagement to improve coastal data access and\ndelivery: U.S. Geological Survey Scientific Investigations Report 2023–5081, 29 p., https://doi.org/10.3133/sir20235081.","productDescription":"iv, 29 p.","onlineOnly":"Y","ipdsId":"IP-146290","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":418931,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235081/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5081"},{"id":418834,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5081/sir20235081.pdf","text":"Report","size":"1.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5081"},{"id":418833,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5081/coverthb.jpg"},{"id":418929,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5081/sir20235081.xml"},{"id":418928,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5081/images"}],"contact":"
Center Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Rd.
Woods Hole, MA 02543
When responding to a wildlife disease outbreak, managers depend on consistent and clear data to make decisions. However, diagnostic methods for detecting pathogens of wildlife often lack the level of procedural and interpretational standardization that occurs in the investigation of human and domestic animal diseases. This lack of standardization can hamper diagnostic reliability in two ways. First is the inappropriate application of tests to new species or in situations that are outside of the original (in other words, validated) purpose. Second is the use of laboratory-specific modifications or analytical parameters without thorough investigation of how those changes affect result comparisons across institutions or the ability to make broader conclusions about pathogen or disease.
White-nose syndrome (WNS) is a disease caused by the fungal pathogen Pseudogymnoascus destructans (Pd), which has spread rapidly and is causing population-level declines in some species of North American bats. During the last decade, quantitative polymerase chain reaction (qPCR) has become the most common method of testing for Pd because of qPCR’s speed, accuracy, and simplicity across a wide range of invasive and noninvasive sample types. Its widespread use by many State, Federal, Provincial, and academic institutions has inevitably led to variations in methodology and interpretation among laboratories. The progressive geographic spread of fungus and disease has also led to sampling contexts and strategies that differ from those for which the qPCR assay was originally developed and validated. These factors have resulted in inconsistencies among results tested in different laboratories and, subsequently, confusion for managers and decision makers.
To address these challenges, the WNS National Response Team Diagnostic Working Group launched a project congruent with increased calls for the harmonization of wildlife disease diagnostic results, and reporting standards across disparate methodologies and laboratories. Beginning in 2019, interlaboratory testing was done to better understand how variations to Pd qPCR methodology affect diagnostic consistency and to reassess the assay’s fit for purpose in new testing contexts. This information led to expanded conversations within the Diagnostic Working Group related to best practices in Pd qPCR diagnostic testing, the development of common interpretation language for classifying test results, and the incorporation of that language into an updated WNS case definition. This handbook is the resulting product and is intended to help further harmonize Pd qPCR diagnostic testing by establishing recommendations related to voluntary participation in a WNS Diagnostic Laboratory Network, documenting the currently (2022) practiced Pd qPCR methodologies, discussing general best practices for molecular diagnostics and laboratory networks, and elaborating on the epidemiologic and diagnostic basis of the agreed-upon classification language for Pd qPCR results. Through this voluntary, consensus-based approach to diagnostic harmonization, this work aims to improve the confidence of management agencies in reported Pd qPCR results and can serve as an example of national diagnostic coordination for other unregulated wildlife diseases.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm15E1","usgsCitation":"Alger, K., and White Nose Syndrome National Response Team Diagnostic Working Group, 2023, White-Nose Syndrome Diagnostic Laboratory Network handbook: U.S. Geological Survey Techniques and Methods, book 15, chap. E1, 50 p., https://doi.org/10.3133/tm15E1.","productDescription":"Report: x, 50 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-137564","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":418802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/15/e01/coverthb.jpg"},{"id":418803,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/15/e01/tm15e1.pdf","text":"Report","size":"4.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 15–E1"},{"id":418805,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/15/e01/tm15e1.XML"},{"id":418806,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/15/e01/images/"},{"id":418808,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93SXYL0","text":"USGS Data Release","linkHelpText":"Pd qPCR interlaboratory testing results"}],"contact":"Director, National Wildlife Health Center
U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711
Fluvial fishes are threatened globally from intensive human landscape stressors degrading aquatic ecosystems. However, impacts vary regionally, as stressors and natural environmental factors differ between ecoregions and continents. To date, a comparison of fish responses to landscape stressors over continents is lacking, limiting understanding of consistency of impacts and hampering efficiencies in conserving fishes over large regions. This study addresses these shortcomings through a novel, integrative assessment of fluvial fishes throughout Europe and the conterminous United States. Using large-scale datasets, including information on fish assemblages from more than 30,000 locations on both continents, we identified threshold responses of fishes summarized by functional traits to landscape stressors including agriculture, pasture, urban area, road crossings, and human population density. After summarizing stressors by catchment unit (local and network) and constraining analyses by stream size (creeks vs. rivers), we analyzed stressor frequency (number of significant thresholds) and stressor severity (value of identified thresholds) within ecoregions across Europe and the United States. We document hundreds of responses of fish metrics to multi-scale stressors in ecoregions across two continents, providing rich findings to aid in understanding and comparing threats to fishes across the study regions. Collectively, we found that lithophilic species and, as expected, intolerant species are most sensitive to stressors in both continents, while migratory and rheophilic species are similarly strongly affected in the United States. Also, urban land use and human population density were most frequently associated with declines in fish assemblages, underscoring the pervasiveness of these stressors in both continents. This study offers an unprecedented comparison of landscape stressor effects on fluvial fishes in a consistent and comparable manner, supporting conservation of freshwater habitats in both continents and worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.165101","usgsCitation":"Ublacker, M.M., Infante, D.M., Cooper, A.R., Daniel, W., Schmutz, S., and Schinegger, R., 2023, Cross-continental evaluation of landscape-scale drivers and their impacts to fluvial fishes: Understanding frequency and severity to improve fish conservation in Europe and the United States: Science of the Total Environment, v. 897, 165101, 14 p., https://doi.org/10.1016/j.scitotenv.2023.165101.","productDescription":"165101, 14 p.","ipdsId":"IP-154519","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":442798,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2023.165101","text":"Publisher Index Page"},{"id":419565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Arthur R.","contributorId":187646,"corporation":false,"usgs":false,"family":"Cooper","given":"Arthur","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":879589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219320,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":879590,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmutz, Stefan","contributorId":317869,"corporation":false,"usgs":false,"family":"Schmutz","given":"Stefan","email":"","affiliations":[{"id":69172,"text":"University of Natural Resources and Life, Sciences, Vienna, Austria","active":true,"usgs":false}],"preferred":false,"id":879591,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schinegger, Rafaela","contributorId":305614,"corporation":false,"usgs":false,"family":"Schinegger","given":"Rafaela","email":"","affiliations":[{"id":34867,"text":"University of Natural Resources and Life Sciences","active":true,"usgs":false}],"preferred":false,"id":879592,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70245537,"text":"sir20235071 - 2023 - Assessment of salinity retention or mobilization by sediment-retention ponds near Delta, Colorado, 2019","interactions":[],"lastModifiedDate":"2026-03-09T17:12:27.818572","indexId":"sir20235071","displayToPublicDate":"2023-07-10T17:45:00","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":"2023-5071","displayTitle":"Assessment of Salinity Retention or Mobilization by Sediment-Retention Ponds near Delta, Colorado, 2019","title":"Assessment of salinity retention or mobilization by sediment-retention ponds near Delta, Colorado, 2019","docAbstract":"Salinity control efforts in the Colorado River Basin have focused on mobilization of salts from irrigated land, but nonirrigated rangelands are also a source of salinity. In particular, lands where soils have formed from the Late Cretaceous Mancos Shale under arid and semiarid climates contain considerable quantities of salt, mainly in the subsurface. Hundreds of thousands of contour furrows and check dams (gully plugs) were constructed by the Bureau of Land Management (BLM) and Bureau of Reclamation in the late 1950s and 1960s to reduce runoff, sedimentation, and salt mobilization from ephemeral stream channels on rangelands. Sediment-retention ponds associated with check dams are dry most of the year, except immediately following substantial rain events. Generally, no maintenance has been performed on these structures, some have degraded over time, and their current and past influence on salinity is poorly understood. To assess the influence of check dams and their associated ponds on salt retention and mobilization, the U.S. Geological Survey, in cooperation with the BLM, conducted a study of such ponds within the Gunnison Gorge National Conservation Area (GGNCA) near Delta, Colorado.
This report includes conceptual models of how sediment-retention ponds function relative to salinity, and a collection of environmental data to evaluate the conceptual models. An inventory of 69 ponds indicated that 38 percent no longer had water holding capacity, and another 20 percent could hold 1 foot or less of water. Check-dam degradation was the main cause, but sediment infill of ponds contributed as well. Water content of soil profiles collected beneath ponds and immediately downstream from check dams indicated little penetration of water below 60 centimeters for most ponds and little evidence for lateral movement of water beneath check dams. Patterns of salt content in the soil profiles indicated no accumulation of salts at the pond surface from evaporating waters and little evidence for salt redistribution in the form of salt bulges or salt depletion curves at intermediate depths. Based on the conceptual models presented and interpretations of data collected by this study, it appears that the sediment-retention ponds in the GGNCA have neither mobilized nor retained substantial quantities of salt during their lifetimes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235071","collaboration":"Prepared in cooperation with Bureau of Land Management","programNote":"Water Availability and Use Science Program","usgsCitation":"Richards, R.J., Bern, C.R., and Moreno, V., 2023, Assessment of salinity retention or mobilization by sediment-retention ponds near Delta, Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2023–5071, 21 p., https://doi.org/10.3133/sir20235071.","productDescription":"Report: v, 21 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-134766","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":418430,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WZNJL6","text":"USGS data release","linkHelpText":"Data from the assessment of sediment-retention ponds near Delta, Colorado, 2019"},{"id":418428,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5071/coverthb.jpg"},{"id":418429,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5071/sir20235071.pdf","text":"Report","size":"4.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5071"},{"id":500951,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114965.htm","linkFileType":{"id":5,"text":"html"}},{"id":418866,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235071/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5071"},{"id":418837,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5071/sir20235071.xml"},{"id":418836,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5071/images"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.55,\n 38.4\n ],\n [\n -107.55,\n 38.36\n ],\n [\n -107.53,\n 38.36\n ],\n [\n -107.53,\n 38.4\n ],\n [\n -107.55,\n 38.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25048, Mail Stop 415
Denver, Colorado 80225
Remote cameras have become a widespread data-collection tool for terrestrial mammals, but classifying images can be labor intensive and limit the usefulness of cameras for broad-scale population monitoring. Machine learning algorithms for automated image classification can expedite data processing, but image misclassifications may influence inferences. Here, we used camera data for three sympatric species with disparate body sizes and life histories – black-tailed jackrabbits (Lepus californicus), kit foxes (Vulpes macrotis), and pronghorns (Antilocapra americana) – as a model system to evaluate the influence of competing image classification approaches on estimates of occupancy and inferences about space use. We classified images with: (i) single review (manual), (ii) double review (manual by two observers), (iii) an automated-manual review (machine learning to cull empty images and single review of remaining images), (iv) a pretrained machine-learning algorithm that classifies images to species (base model), (v) the base model accepting only classifications with ≥95% confidence, (vi) the base model trained with regional images (trained model), and (vii) the trained model accepting only classifications with ≥95% confidence. We compared species-specific results from alternative approaches to results from double review, which reduces the potential for misclassifications and was assumed to be the best approximation of truth. Despite high classification success, species-level misclassification rates for the base and trained models were sufficiently high to produce erroneous occupancy estimates and inferences related to space use across species. Increasing the confidence thresholds for image classification to 95% did not consistently improve performance. Classifying images as empty (or not) offered a reasonable approach to reduce effort (by 97.7%) and facilitated a semi-automated workflow that produced reliable estimates and inferences. Thus, camera-based monitoring combined with machine learning algorithms for image classification could facilitate monitoring with limited manual image classification.
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Email: nfm@usgs.gov
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