“Bartram’s Bass” Micropterus sp. cf. coosae is endemic to the upper Savannah River basin of the southeastern United States and is threatened by hybridization with invasive Alabama Bass Micropterus henshalli. Bartram’s Bass have been functionally extirpated from reservoirs, and hybrid individuals have been detected in several tributaries. However, the extent of introgression in tributaries is currently unknown. Our objectives were to (1) assess the distribution of Bartram’s Bass, native Largemouth Bass M. salmoides, invasive Alabama Bass, and their hybrids in streams of the upper Savannah River basin and (2) quantify effects of abiotic variables on the distribution of each species. We sampled 154 locations in 2017 and 2018 and assigned genetic identity using hydrolysis probes and microsatellites. We used conditional inference trees to quantify variables affecting the occurrence of each species and hybrids. We observed widespread hybridization across the basin. Pure Bartram’s Bass were collected at 27% (42) of sites, among which only 12 sites contained pure Bartram’s Bass and no other congeners. Thirty sites where pure Bartram’s Bass were collected contained hybrids. In the montane Blue Ridge ecoregion, occurrence of pure Bartram’s Bass was negatively affected by low levels of local-scale developed land cover. In the lower-relief Piedmont ecoregion, pure Bartram’s Bass were positively associated with watershed-scale forest land cover and stream gradient. Distance from a reservoir was positively associated with occurrence of pure Bartram’s Bass in both ecoregions. Pure Bartram’s Bass are likely to occur with high probability in only 16% of nonimpounded stream segments; this represents a conservative estimate, and the true number is likely lower. However, future work accounting for incomplete detection of Bartram’s Bass will help to improve confidence in true extirpations. Conservation efforts may be more successful if implemented on stream segments farther from reservoirs or upstream of dispersal barriers preventing colonization of Alabama Bass.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10637","usgsCitation":"Peoples, B., Judson, E., Darden, T.L., Farrae, D.J., Kubach, K., Leitner, J., and Scott, M.C., 2021, Modeling distribution of endemic Bartram’s Bass Micropterus sp. cf. coosae: Disturbance and proximity to invasion source increase hybridization with invasive Alabama Bass: North American Journal of Fisheries Management, v. 41, no. 5, p. 1309-1321, https://doi.org/10.1002/nafm.10637.","productDescription":"13 p.","startPage":"1309","endPage":"1321","ipdsId":"IP-152697","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina, South Carolina","otherGeospatial":"Savannah River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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Kevin","contributorId":341212,"corporation":false,"usgs":false,"family":"Kubach","given":"Kevin","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907848,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leitner, Jean","contributorId":177201,"corporation":false,"usgs":false,"family":"Leitner","given":"Jean","email":"","affiliations":[],"preferred":false,"id":910836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Mark C.","contributorId":341033,"corporation":false,"usgs":false,"family":"Scott","given":"Mark","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":907849,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224620,"text":"sir20215065 - 2021 - Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013","interactions":[],"lastModifiedDate":"2021-10-01T12:09:28.489755","indexId":"sir20215065","displayToPublicDate":"2021-09-30T21:14:22","publicationYear":"2021","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":"2021-5065","displayTitle":"Conceptual and Numerical Groundwater Flow Model of the Cedar River Alluvial Aquifer System with Simulation of Drought Stress on Groundwater Availability near Cedar Rapids, Iowa, for 2011 through 2013","title":"Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013","docAbstract":"Between July 2011 and February 2013, the City of Cedar Rapids observed water level declines in their horizontal collector wells approaching 11 meters. As a result, pumping from these production wells had to be halted, and questions were raised about the reliability of the alluvial aquifer under future drought conditions. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, completed a study to better understand the effects of drought stress on the Cedar River alluvial aquifer using a numerical groundwater flow model. Previously published groundwater flow models were combined with newly collected airborne, waterborne, down-hole, and land-based geophysical survey data and provided a detailed three-dimensional lithologic model of the Cedar River alluvial aquifer and surrounding area. An improved conceptual model for the groundwater flow system and a lithologic model were used to build and inform a numerical groundwater flow model capable of simulating water levels observed in the City of Cedar Rapids horizontal collector wells during the 2012 drought. Model performance was assessed primarily on the ability of the model to simulate water table elevation at six monitoring wells. Statistical tests were used to assess the numerical model during the calibration period, and results varied from satisfactory to unsatisfactory, likely because of stage changes in the Cedar River and production well withdrawal rates near monitoring wells. Simulated water levels during the 2012 drought indicated a depression near the horizontal collector wells, although simulated elevations at these locations and at monitoring wells were generally overestimated compared to measured values. The numerical groundwater flow model was modified to account for a decrease in seepage rate caused by low flow in the Cedar River and increased production. With seepage rate modification, model results improved; the simulated water table elevations were like those observed in horizontal collector and monitoring wells. Results demonstrated the ability of the model to simulate water levels observed in the horizontal collector wells during the 2012 drought when accounting for a decrease in infiltration from the Cedar River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215065","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Haj, A.E., Ha, W.S., Gruhn, L.R., Bristow, E.L., Gahala, A.M., Valder, J.F., Johnson, C.D., White, E.A., and Sterner, S.P., 2021, Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013: U.S. Geological Survey Scientific Investigations Report 2021–5065, 59 p., https://doi.org/10.3133/sir20215065.","productDescription":"Report: ix, 59 p.; Appendix; 3 Data Releases; Dataset","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-118762","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5065/coverthb.jpg"},{"id":390067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.pdf","text":"Report","size":"8.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065"},{"id":390069,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS882S","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Airborne electromagnetic and magnetic survey data and inverted resistivity models, Cedar Rapids, Iowa, May 2017"},{"id":390070,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96CF4L5","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Cedar River alluvial aquifer near Cedar Rapids, Iowa"},{"id":390071,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXJDHX","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Geophysical data collected in the Cedar River floodplain, Cedar Rapids, Iowa, 2015–2017"},{"id":390072,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":390068,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065_appendix.pdf","text":"Poster","size":"3.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065 Appendix","linkHelpText":"— Geophysical methods used to better characterize surface water, alluvial aquifer, and bedrock aquifer interaction in the Cedar River Valley, Iowa"},{"id":390073,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.xml","size":"367 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5065 xml"},{"id":390074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5065/images"}],"country":"United States","state":"Iowa","city":"Cedar Rapids","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.76759719848633,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 41.99139471889533\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The challenges associated with field measurements of turbidity are well known and result primarily from differences in reported values that depend on instrument design and the resulting need for reporting units that are specific to those designs. A critical challenge for making comparable turbidity measurements is the selection and use of appropriate turbidity standards for sensor calibration. The accepted primary standards for turbidity measurements use formazin made from scratch; all others should relate back to readings obtained using standard formazin. However, because turbidity is a qualitative property of water, comparing standards is not as simple as it is for many chemical measurements. The U.S. Geological Survey “National Field Manual for the Collection of Water-Quality Data” currently allows for the use of two standards, formazin and polymer beads, for the calibration of field turbidimeters. Another challenge for making comparable turbidity measurements is selection of turbidity sensors. A turbidity sensor commonly used in the U.S. Geological Survey, the Yellow Springs Instruments (YSI) 6136, has been replaced by the manufacturer with the YSI EXO turbidity sensor. Both sensors operate on the same principles but have slight design differences that result in readings that are not directly comparable on a 1:1 basis.
Differences in calibration standards and sensors are a cause of concern in ongoing studies that require switching calibration standards or sensor types, and for comparisons of data collected with sensors calibrated by using different calibration standards, different sensor types, or both. The objectives of this study were to evaluate the response of two YSI turbidity sensors in both formazin-based standards (StablCal) and polymer turbidity standards (in this case YSI brand; however, other brands are available) and to compare the performance of the YSI EXO and YSI 6136 turbidity sensors under similar laboratory and environmental (field) conditions. To quantify these differences, a series of laboratory and field side-by-side comparisons were conducted. Nine field comparisons of YSI EXO and YSI 6136 sensors were performed at site locations in Kansas and Virginia. Two field comparisons of StablCal and polymer calibration standards were performed in Kansas, both using YSI EXO turbidity sensors. Five laboratory comparisons between the YSI EXO and YSI 6136 turbidity sensors were performed, and seven laboratory comparisons between StablCal and polymer turbidity standards were performed using YSI EXO turbidity sensors. The results can help the USGS and others better understand how turbidity data can differ depending on the sensors and calibration standards used.
Key findings and conclusions include the following—
Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
A U.S. Geological Survey-led assessment of data gaps and science needs across the Great Lakes ecosystem indicated the following:
• Expanded data collection or monitoring would provide basic ecosystem, social, and public health data to manage the Great Lakes system and to develop and test models and decision support tools.
• New science and advanced technologies (for example, sensors and high-performance computing capability) would improve the understanding of critical threats, such as harmful algae blooms and high-water levels.
Although there is significant scientific knowledge in specific areas or for specific topics, managers could use improved models and decision support tools, strengthened by extensive data collection and developed at multiple scales, to better inform decision making in the future. Enhanced coordination of agency efforts and associated data collection across data types (for example, prey fish populations and water levels) is needed to effectively manage the Great Lakes.
This report highlights the data gaps; benefits of better, more structured coordination; and areas of concern specifically related to data collection/measurement and science efforts. It summarizes and analyzes stakeholder feedback and information from review of scientific literature. Finally, the report outlines steps necessary to create an integrated Great Lakes science plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211096","usgsCitation":"Carl, L.M., Hortness, J.E., and Strach, R.M., 2021, U.S. Geological Survey Great Lakes Science Forum—Summary of remaining data and science needs and next steps: U.S. Geological Survey Open-File Report 2021–1096, 4 p., https://doi.org/10.3133/ofr20211096.","productDescription":"iii, 4 p.","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-133589","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":390007,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.xml","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1096 xml"},{"id":390006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.pdf","text":"Report","size":"655 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1096"},{"id":390005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1096/coverthb.jpg"}],"contact":"Director, Midwest Regional Director’s Office
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Stocking of Rainbow Trout Oncorhynchus mykiss commonly provides seasonal or mitigation fisheries; however, these fish are usually small and ecosystem effects are spatially or temporally limited. Yet agencies receive requests to stock Rainbow Trout in relatively natural settings (i.e., not tailwater or mitigation fisheries), where introductions may have greater ecosystem consequences. The size of introduced fish is an important factor in determining biotic interactions with native species; therefore, our objectives were to assess the seasonal feeding ecology and microhabitat use of large (265–530 mm TL) nonnative Emmerson strain Rainbow Trout in a relatively unaltered, groundwater-influenced, warmwater stream of the Ozark Highlands. Rainbow Trout consumed a variety of prey; however, diets differed between cool (winter and spring) and warm (summer) seasons. Cool-season Rainbow Trout exhibited a mixed feeding strategy, with individual specialization on crayfishes and fishes and generalist feeding on Ephemeroptera and Diptera, but Gastropoda were the dominant prey. Feeding strategy in the warm season switched to individual specialization on numerous prey types. Overall, larger prey resources were important components of Rainbow Trout diets. Piscivory was relatively high in both seasons, and crayfishes were one of the most important prey types across seasons. Selection of coarse substrates and deeper-water microhabitats (>0.95 m) was similar between seasons. Rainbow Trout selected the lowest-velocity microhabitats available during the warm season and moderate velocities in the cool season. Rainbow Trout were five times more likely to be associated with cover in the warm season. Due to their higher temperature tolerance, Emmerson strain Rainbow Trout may persist in Ozark Highland streams, where they disrupt local food webs and occupy habitat otherwise selected by native fish, such as Neosho Smallmouth Bass Micropterus dolomieu velox. If native species conservation is a priority for agencies, then caution regarding Rainbow Trout stockings may be warranted.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10694","usgsCitation":"Rodger, A.W., Wolf, S.L., Starks, T.A., Burroughs, J.P., and Brewer, S.K., 2021, Seasonal diet and habitat use of large, introduced Rainbow Trout in an Ozark Highland stream: North American Journal of Fisheries Management, v. 41, no. 6, p. 1764-1780, https://doi.org/10.1002/nafm.10694.","productDescription":"17 p.","startPage":"1764","endPage":"1780","ipdsId":"IP-129494","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":397173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Spavinaw Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13336181640625,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.22987352301491\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodger, A. W.","contributorId":288610,"corporation":false,"usgs":false,"family":"Rodger","given":"A.","email":"","middleInitial":"W.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolf, S. L.","contributorId":288613,"corporation":false,"usgs":false,"family":"Wolf","given":"S.","email":"","middleInitial":"L.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":838136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Starks, T. A.","contributorId":288616,"corporation":false,"usgs":false,"family":"Starks","given":"T.","email":"","middleInitial":"A.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838137,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burroughs, J. P.","contributorId":288619,"corporation":false,"usgs":false,"family":"Burroughs","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838138,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838139,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229778,"text":"70229778 - 2021 - Livestock grazing, climatic variation, and breeding phenology jointly shape disease dynamics and survival in a wild amphibian","interactions":[],"lastModifiedDate":"2022-03-17T16:05:37.252377","indexId":"70229778","displayToPublicDate":"2021-09-30T10:49:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Livestock grazing, climatic variation, and breeding phenology jointly shape disease dynamics and survival in a wild amphibian","docAbstract":"Wildlife responses to infectious disease can be influenced by environmental stressors that alter host-pathogen dynamics. We investigated how livestock grazing, climatic variation, and breeding phenology influence disease prevalence and annual survival in boreal toad (Anaxyrus boreas boreas) populations challenged with Batrachochytrium dendrobatidis (Bd), a fungal pathogen implicated in global amphibian declines. We conducted a five-year (2015–2019) capture-recapture study of boreal toads (n = 1301) inhabiting pastures grazed by cattle in western Wyoming, USA. We employed structural equation models to determine whether the effects of climatic variation on Bd prevalence were direct or mediated through effects on breeding phenology and multi-state models to explore the interplay of grazing, weather, and Bd infection on adult survival. Higher winter snowpack was linked with shorter spring breeding seasons, which were associated with lower Bd prevalence. Boreal toads infected with Bd suffered increased mortality, but only at relatively cool temperatures. Although cattle grazing created warmer microclimates, likely by reducing vegetation cover, grazing-induced habitat changes did not scale up to influence adult survival. Our results suggest that boreal toads in cooler environments face increased risk of disease-induced mortality, possibly because infected individuals are not able to elevate body temperature to reduce or clear infection. More generally, we demonstrate that host-pathogen dynamics can be shaped jointly by independent and interactive effects of livestock grazing, breeding season length, and climatic variation. Future investigations of wildlife responses to disease therefore may benefit from considering anthropogenic land use and climatic regimes, including the effect of weather on host phenology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2021.109247","usgsCitation":"Barrile, G., Walters, A.W., and Chalfoun, A.D., 2021, Livestock grazing, climatic variation, and breeding phenology jointly shape disease dynamics and survival in a wild amphibian: Biological Conservation, v. 261, 109247, 10 p., https://doi.org/10.1016/j.biocon.2021.109247.","productDescription":"109247, 10 p.","ipdsId":"IP-127277","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2021.109247","text":"Publisher Index Page"},{"id":397254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Range, Buck Creek,Chall Creek, Wind River Range, Lower Gypsum Creek , Upper Gypsum Creek;","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.050048828125,\n 42.73289174571287\n ],\n [\n -109.16015624999999,\n 42.73289174571287\n ],\n [\n -109.16015624999999,\n 43.375108633273086\n ],\n [\n -110.050048828125,\n 43.375108633273086\n ],\n [\n -110.050048828125,\n 42.73289174571287\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"261","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":288734,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":838252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838253,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229775,"text":"70229775 - 2021 - Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis)","interactions":[],"lastModifiedDate":"2024-09-16T15:52:28.120247","indexId":"70229775","displayToPublicDate":"2021-09-30T10:39:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2286,"text":"Journal of Fish Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis)","title":"Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis)","docAbstract":"Pacific salmon (Oncorhynchus spp.) rearing in lakes and reservoirs above dams have been known to become heavily infected with an ectoparasitic copepod (Salmincola californiensis). Little is known about the factors that affect the parasite infection prevalence and intensity. However, previous research suggests that the parasite may negatively affect the fitness and survival of the host fish. The effect of water temperature, confinement and the density of the free-swimming infectious stage of S. californiensis, the copepodid, on infection prevalence and intensity was evaluated by experimentally exposing juvenile Chinook Salmon (O. tshawytscha). Infection rates observed in wild populations were achieved under warm water (15–16°C) and high copepodid density (150–300/L) treatment conditions. Infection prevalence and intensity were also significantly higher in larger fish. During the infection experiment, 4.5% of infected fish died within 54 days with mortality significantly related to copepod infection intensity. The potential for autoinfection was compared to cross-infection by cohabitation of infected fish with naïve fish. Previously infected fish had significantly greater infection intensity compared with naïve fish, indicating that infected fish can be reinfected and that they may be more susceptible than naïve fish.
","language":"English","publisher":"Wiley","doi":"10.1111/jfd.13450","usgsCitation":"Neal, T., Kent, M., Sanders, J., Schreck, C., and Peterson, J., 2021, Laboratory infection rates and associated mortality of juvenile Chinook Salmon (Oncorhynchus tshawytscha) from parasitic copepod (Salmincola californiensis): Journal of Fish Diseases, v. 44, no. 9, p. 1423-1434, https://doi.org/10.1111/jfd.13450.","productDescription":"12 p.","startPage":"1423","endPage":"1434","ipdsId":"IP-127527","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":397251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Ohio","otherGeospatial":"Great Lakes region, Ottawa National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.5345458984375,\n 41.372686481864655\n ],\n [\n -82.9742431640625,\n 41.372686481864655\n ],\n [\n -82.9742431640625,\n 41.713930073371294\n ],\n [\n -83.5345458984375,\n 41.713930073371294\n ],\n [\n -83.5345458984375,\n 41.372686481864655\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.3258056640625,\n 43.09697190802465\n ],\n [\n -82.628173828125,\n 43.09697190802465\n ],\n [\n -82.628173828125,\n 43.54456658436357\n ],\n [\n -83.3258056640625,\n 43.54456658436357\n ],\n [\n -83.3258056640625,\n 43.09697190802465\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.473876953125,\n 41.934976500546604\n ],\n [\n -83.6993408203125,\n 41.934976500546604\n ],\n [\n -83.6993408203125,\n 42.48830197960227\n ],\n [\n -84.473876953125,\n 42.48830197960227\n ],\n [\n -84.473876953125,\n 41.934976500546604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Neal, Travis","contributorId":341105,"corporation":false,"usgs":false,"family":"Neal","given":"Travis","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":838241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kent, Michael L.","contributorId":288715,"corporation":false,"usgs":false,"family":"Kent","given":"Michael L.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":838242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanders, Justin","contributorId":288718,"corporation":false,"usgs":false,"family":"Sanders","given":"Justin","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":838243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schreck, Carl B.","contributorId":288720,"corporation":false,"usgs":false,"family":"Schreck","given":"Carl B.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":838244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838240,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239438,"text":"70239438 - 2021 - Intelligent monitoring system for real-time geologic storage, optimization, and reservoir management","interactions":[],"lastModifiedDate":"2024-03-28T15:39:15.900396","indexId":"70239438","displayToPublicDate":"2021-09-30T10:36:32","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":91,"text":"Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"DOE-USGS-FE0026517-1","title":"Intelligent monitoring system for real-time geologic storage, optimization, and reservoir management","docAbstract":"The objective of the subtask was to develop a near-real-time monitoring system for seismic data at the Decatur, IL, geologic carbon sequestration (GCS) site and specifically include fiber-optic cable derived distributed acoustic signal (DAS) data in the process. Owing to the large volumes of data, we opted to utilize existing deep borehole conventional seismic sensors for detection and pull DAS and shallow borehole seismic data once a detection has been made. Unfortunately, the horizontal fiber-optic cables did not yield microseismic signals for use in locating events near the GCS site. Various stacking and filtering approaches were tested without any coherent detection becoming apparent. We attribute the insensitivity to local microseismic events to a lack of coupling in downhole, vertical cable and the non-ideal alignment of the horizontal fiber-optic cable to the vertically polarized seismic energy. Despite the inability to detect and utilize the DAS data from the fiber-optic cables, we developed a general processing framework that enables easy adaptation for future deployment of fiber-optic cables with better suited alignment at Decatur or elsewhere.
","language":"English","publisher":"Department of Energy","doi":"10.2172/1834620","usgsCitation":"Kaven, J., 2021, Intelligent monitoring system for real-time geologic storage, optimization, and reservoir management: Technical Report DOE-USGS-FE0026517-1, 24 p., https://doi.org/10.2172/1834620.","productDescription":"24 p.","ipdsId":"IP-132449","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":450600,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.2172/1834620","text":"External Repository"},{"id":427218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kaven, J. Ole 0000-0003-2625-2786 okaven@usgs.gov","orcid":"https://orcid.org/0000-0003-2625-2786","contributorId":3993,"corporation":false,"usgs":true,"family":"Kaven","given":"J. Ole","email":"okaven@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":861574,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70232197,"text":"70232197 - 2021 - Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation","interactions":[],"lastModifiedDate":"2022-06-13T15:41:33.151946","indexId":"70232197","displayToPublicDate":"2021-09-30T10:32:31","publicationYear":"2021","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}},"displayTitle":"Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation","title":"Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation","docAbstract":"Forest birds, particularly those associated with late-successional forests, are of widespread conservation interest. Although birds are among the more widely studied taxa of forest wildlife, relatively few studies have examined the long-term effects of active management (i.e., intentional stand density reduction) on the forest bird assemblage. This is an important omission, as changes in stand structure and composition over the decades following harvesting may influence wildlife utilization of forest stands. We developed an observational study to evaluate forest bird response to stand structural development multiple decades following harvesting with differing levels of overstory density reduction, and in unmanaged stands. We focused, in particular, on songbirds and cavity excavating species associated with old-growth Douglas-fir forests, and the active management strategies that we examined – thinning and structural retention harvesting – were selected based on their potential to accelerate stand structural development. Our 18 mature stands (age range: 107–187 years) were located in the western hemlock zone of western Oregon, and bird surveys were conducted an average of 41 years and 22 years post-harvest in thinned and retention harvest stands, respectively. Poisson generalized linear models were formulated to evaluate the effects of management condition on forest birds. Although five species were associated with specific management conditions, relative abundance was not statistically different between unmanaged, thinned and retention harvest stands for a majority of the species in our analysis. Species richness was also relatively invariant across management conditions. Our results do suggest that retention harvesting adversely affects some songbird species associated with old-growth forests – Pacific-slope flycatcher (Empidonax difficilis) and Pacific wren (Troglodytes pacificus) for example - but our findings also indicate that retention harvesting has long-term benefits for birds associated with early-successional forests. This includes migrant species that have experienced significant population declines in Western North America over recent decades, such as Wilson’s and MacGillivray’s warblers (Cardellina pusilla and Geothlypis tolmiei, respectively). Overall, our results imply that for many species of forest birds, including those associated with old-growth forests, managers have some flexibility in overstory density management where long-term species persistence is an objective. Equally significant, our results provide strong support for the application of variable retention harvesting as a tool for the conservation of bird species associated with early-successional forests, while also reinforcing the value of mature structural legacies for bird species associated with late-successional forests. While also reinforcing the value of mature structural legacies for birds associated with late-successional forests.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119609","usgsCitation":"Williams, N., Hagar, J., and Powers, M., 2021, Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation: Forest Ecology and Management, v. 502, 119609, 15 p., https://doi.org/10.1016/j.foreco.2021.119609.","productDescription":"119609, 15 p.","ipdsId":"IP-126150","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":450602,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119609","text":"Publisher Index Page"},{"id":402089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.5849609375,\n 43.004647127794435\n ],\n [\n -121.11328124999999,\n 43.004647127794435\n ],\n [\n -121.11328124999999,\n 44.96479793033101\n ],\n [\n -124.5849609375,\n 44.96479793033101\n ],\n [\n -124.5849609375,\n 43.004647127794435\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"502","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Neil","contributorId":292425,"corporation":false,"usgs":false,"family":"Williams","given":"Neil","email":"","affiliations":[{"id":62230,"text":"Oregon State University, Corvallis","active":true,"usgs":false}],"preferred":false,"id":844544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagar, Joan 0000-0002-3044-6607 joan_hagar@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-6607","contributorId":3369,"corporation":false,"usgs":true,"family":"Hagar","given":"Joan","email":"joan_hagar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":844545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powers, Matthew","contributorId":202554,"corporation":false,"usgs":false,"family":"Powers","given":"Matthew","affiliations":[{"id":36477,"text":"Department of Forest Engineering Resources and Management, Oregon State University, Corvallis, OR 97331 USA. matthew.powers@oregonstate.edu","active":true,"usgs":false}],"preferred":false,"id":844546,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226660,"text":"70226660 - 2021 - Accuracy of flowmeters measuring horizontal flow in fractured-rock simulators","interactions":[],"lastModifiedDate":"2021-12-02T16:33:07.928752","indexId":"70226660","displayToPublicDate":"2021-09-30T10:29:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1866,"text":"Groundwater Monitoring & Remediation","active":true,"publicationSubtype":{"id":10}},"title":"Accuracy of flowmeters measuring horizontal flow in fractured-rock simulators","docAbstract":"Laboratory evaluations of flowmeter response to flow in fractured-rock simulators are needed to improve understanding of data collected in field settings. The ability of flowmeters to accurately measure the velocity and direction of water flowing between parallel plates was used as a surrogate for instrument response in fractured-rock aquifers. A colloidal borescope flowmeter and a heat-pulse flowmeter were deployed in a fractured rock simulator with 4-inch and 6-inch inner-diameter, uncased wells with 0.39- and 1.0-inch fracture apertures and groundwater velocities from 35 to 975 ft/d. The colloidal borescope measurements and applied velocities were positively correlated in all wells and apertures (the coefficient of determination [r2] = 0.61–0.89) and most accurately measured direction at higher velocities. The mean directional error in colloidal borescope measurements was less than 17° in 6-inch wells and 31° in the 4-inch wells at velocities between 92 and 958 ft/d. Heat-pulse flowmeter measurements were 0.001 to 0.004 times less than applied rates and may indicate that water was moving around rather than through the instrument's integrated packer. The mean directional error of heat-pulse flowmeter measurements were about 18 and 42° in the 0.39- and 1.0-inch fractures, respectively, for groundwater velocities within the manufacturer's suggested range of application (0.5–100 ft/d). Measurements made at vertical increments and fracture positions in the well using the colloidal borescope indicate that laminar flow occurs within the central 50% of the fracture but measurements above or below are likely affected by eddy currents.
The regeneration process is a sensitive period within life cycles of floodplain tree species and can strongly influence forest community composition. Yet, fundamental information remains limited on the relationship between regeneration processes and the flood disturbances that, together, construct floodplain forest landscapes. In a controlled greenhouse experiment we tested the effects of complete submergence on six temperate floodplain forest species to understand how flood timing and duration influence seedling survival. Groups of overcup oak (Quercus lyrata), Nuttall oak (Quercus texana), willow oak (Quercus phellos), sugarberry (Celtis laevigata), green ash (Fraxinus pennsylvanica), and American elm (Ulmus americana) seedlings were submerged for either 5, 15, 25, or 0 days (control) at the ages of 3-weeks, 6-weeks, and 9-weeks post-emergence. All species demonstrated a higher sensitivity to flooding at age 3-weeks compared to 6- and 9- weeks, indicating substantial changes in seedling resilience within the first months following emergence. Additionally, the heavier-seeded Q. lyrata, Q. texana, and Q. phellos were less or equally vulnerable to flooding compared to the lighter-seeded C. laevigata, F. pennsylvanica, and U. americana across all age groups, especially at 3-weeks post-emergence. The results of this study have implications for understanding woody species regeneration ecology and changes in floodplain forest composition, particularly in the context of hydrologic modifications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119660","usgsCitation":"Kroschel, W., and King, S.L., 2021, Floodplain forest tree seedling response to variation in flood timing and duration: Forest Ecology and Management, v. 502, 119660, 10 p., https://doi.org/10.1016/j.foreco.2021.119660.","productDescription":"119660, 10 p.","ipdsId":"IP-127728","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"502","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kroschel, W.A.","contributorId":341796,"corporation":false,"usgs":false,"family":"Kroschel","given":"W.A.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908902,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230396,"text":"70230396 - 2021 - Strategic considerations for invasive species managers in the utilization of environmental DNA (eDNA): Steps for incorporating this powerful surveillance tool","interactions":[],"lastModifiedDate":"2022-04-12T11:09:56.160523","indexId":"70230396","displayToPublicDate":"2021-09-30T10:14:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Strategic considerations for invasive species managers in the utilization of environmental DNA (eDNA): Steps for incorporating this powerful surveillance tool","docAbstract":"Invasive species surveillance programs can utilize environmental DNA sampling and analysis to provide information on the presence of invasive species. Wider utilization of eDNA techniques for invasive species surveillance may be warranted. This paper covers topics directed towards invasive species managers and eDNA practitioners working at the intersection of eDNA techniques and invasive species surveillance. It provides background information on the utility of eDNA for invasive species management and points to various examples of its use across federal and international programs. It provides information on 1) why an invasive species manager should consider using eDNA, 2) deciding if eDNA can help with the manager’s surveillance needs, 3) important components to operational implementation, and 4) a high-level overview of the technical steps necessary for eDNA analysis. The goal of this paper is to assist invasive species managers in deciding if, when, and how to use eDNA for surveillance. If eDNA use is elected, the paper provides guidance on steps to ensure a clear understanding of the strengths and limitation of the methods and how results can be best utilized in the context of invasive species surveillance.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2021.12.3.15","usgsCitation":"Morisette, J., Burgiel, S., Brantley, K., Daniel, W., Darling, J., Davis, J., Franklin, T.W., Gaddis, K., Hunter, M., Lance, R., Leskey, T., Passamaneck, Y., Piaggio, A.J., Rector, B., Sepulveda, A., Smith, M., Stepien, C.A., and Wilcox, T., 2021, Strategic considerations for invasive species managers in the utilization of environmental DNA (eDNA): Steps for incorporating this powerful surveillance tool: Management of Biological Invasions, v. 12, no. 3, p. 747-775, https://doi.org/10.3391/mbi.2021.12.3.15.","productDescription":"29 p.","startPage":"747","endPage":"775","ipdsId":"IP-130309","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2021.12.3.15","text":"Publisher Index 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The overall goal of this project was to model, map, and share information essential for the conservation of LSOG forest ecosystems in the U.S. Pacific Northwest, within a diverse co-production team of state and federal land managers. We developed statistical models of contemporary (2002-2017) fire refugia, non-stand-replacing fire (NSR), and high-severity fire based on topography, fuels, fire weather, fire behavior and climate. Independent models were built for two ecoregions (Figure 1), one encompassing the Douglas-fir/western hemlock forests of the northwestern portion of our study area and the other encompassing dry-mixed conifer forests of the eastern Cascades and Klamath-Siskiyou region. We used these models to produce probability surface maps for fire refugia, NSR, and high-severity fire under low, moderate, and extreme fire weather and fire growth scenarios. These maps and associated products provide timely information about the likely persistence, change, and loss of LSOG forests under current and future climate conditions.","language":"English","publisher":"Oregon State University","usgsCitation":"Naficy, C.E., Meigs, G., Gregory, M., Davis, R., Bell, D.M., Dugger, K., Wiens, J.D., and Krawchuk, M.A., 2021, Fire refugia in old-growth forests: Predicting habitat persistence to support land management in an era of rapid global change: Final Report, 39 p.","productDescription":"39 p.","ipdsId":"IP-135786","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489323,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://firerefugia.forestry.oregonstate.edu/findings"},{"id":489389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.03230454175225,\n 48.98129296044047\n ],\n [\n -123.23629505743158,\n 49.021189699759475\n ],\n [\n -122.36411639022937,\n 47.53290523042941\n ],\n [\n -122.735187412137,\n 48.16641453033125\n ],\n [\n -124.8905678391362,\n 48.456759846434124\n ],\n [\n -124.28837165472417,\n 44.57003722150395\n ],\n [\n -124.68283057104817,\n 42.67310902261141\n ],\n [\n -124.20999230992997,\n 41.18665658280105\n ],\n [\n -124.55296007164978,\n 40.23706044199622\n ],\n [\n -123.88224360324604,\n 39.64402305418474\n ],\n [\n -123.79771977835343,\n 38.75178009790977\n ],\n [\n -122.3664015891323,\n 37.62043867256661\n ],\n [\n -120.98881658816403,\n 38.95590221404629\n ],\n [\n -119.90347242862897,\n 40.51176766538154\n ],\n [\n -119.03230454175225,\n 48.98129296044047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Naficy, Cameron E.","contributorId":298154,"corporation":false,"usgs":false,"family":"Naficy","given":"Cameron","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meigs, Garrett W.","contributorId":356212,"corporation":false,"usgs":false,"family":"Meigs","given":"Garrett W.","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":938882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gregory, Matt J.","contributorId":356213,"corporation":false,"usgs":false,"family":"Gregory","given":"Matt J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Ray","contributorId":356214,"corporation":false,"usgs":false,"family":"Davis","given":"Ray","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":938884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bell, David M.","contributorId":34423,"corporation":false,"usgs":true,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":938885,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938886,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wiens, J. 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Leaking wastewater conveyance infrastructure commonly contaminates receiving waters. Methods to quantify such contamination can be time consuming, expensive, and often nonspecific. Human-associated bacteria are wastewater specific but require discrete sampling and laboratory analyses, introducing latency. Human sewage has fluorescence and absorbance properties different than those of natural waters. To assist real-time field sensor development, this study investigated optical properties for use as surrogates for human-associated bacteria to estimate wastewater prevalence in environmental waters. Three spatial scales were studied: Eight watershed-scale sites, five subwatershed-scale sites, and 213 storm sewers and open channels within three small watersheds (small-scale sites) were sampled (996 total samples) for optical properties, human-associated bacteria, fecal indicator bacteria, and, for selected samples, human viruses. Regression analysis indicated that bacteria concentrations could be estimated by optical properties used in existing field sensors for watershed and subwatershed scales. Human virus occurrence increased with modeled human-associated bacteria concentration, providing confidence in these regressions as surrogates for wastewater contamination. Adequate regressions were not found for small-scale sites to reliably estimate bacteria concentrations likely due to inconsistent local sanitary sewer inputs.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.1c02644","usgsCitation":"Corsi, S., DeCicco, L.A., Hansen, A., Lenaker, P.L., Bergamaschi, B.A., Pellerin, B., Dila, D., Bootsma, M., Spencer, S., Borchardt, M.A., and McLellan, S.L., 2021, Optical properties of water for prediction of wastewater contamination, human-associated bacteria, and fecal indicator bacteria in surface water at three watershed scales: Environmental Science and Technology, v. 55, no. 20, p. 13770-13782, https://doi.org/10.1021/acs.est.1c02644.","productDescription":"13 p.","startPage":"13770","endPage":"13782","ipdsId":"IP-132758","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":450607,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index 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- Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California","interactions":[],"lastModifiedDate":"2022-02-07T15:35:39.438342","indexId":"70228198","displayToPublicDate":"2021-09-30T09:30:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California","docAbstract":"Migratory waterfowl are the primary reservoir of avian influenza viruses (AIV) which can be spread to commercial poultry. Surveillance efforts that track the location and abundance of wild waterfowl and link those data to inform assessments of risk and sampling for AIV currently do not exist. To assist surveillance and minimize poultry exposure to AIV, here we explored the utility of remotely sensed MODerate Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with land-based climate measurements (e.g., temperature and precipitation) to predict waterfowl location and abundance in near real-time in the California Central Valley (CCV), where both wild waterfowl and domestic poultry are densely located. Specifically, remotely collected MODIS and climate data were integrated into a previously developed Boosted Regression Tree (BRT) model to predict and visualize waterfowl distributions across the CCV. Daily model-based predictions are publicly available during the winter as part of the dynamic California Waterfowl Tracker (CWT) web-app hosted on the University of California’s Cooperative Extension webpage. In this study, we analyzed 52 days of model predictions and produced daily spatio-temporal maps of waterfowl concentrations near the 605 commercial poultry farms in the CCV during January and February of 2019. Exposure of each poultry farm to waterfowl during each day was classified as “high”, “medium”, “low”, or “none” depending on the density of waterfowl within 4 km of a farm. Results indicated that farms were at substantially greater risk of “exposure” in January, when CCV waterfowl populations peak, than in February. For example, during January, 33% (199/605) of the farms were exposed ≥ 1 day to “high” waterfowl density versus 19% (115/605) of the farms in February. In addition to demonstrating the overall variability of waterfowl location and density, these data demonstrate how remote sensing can be used to better triage AIV surveillance and biosecurity efforts via the utilization of a functional web-app based tool. The ability to leverage remote sensing is an integral advancement toward improving AIV surveillance in waterfowl in close proximity to commercial poultry. Expansion of these types of remote sensing methods linked to a user-friendly web-tool could be further developed across the continental U.S. The BRT model incorporated into the CWT reflects a first attempt to give an accurate representation of waterfowl distribution and density relative to commercial poultry.","language":"English","publisher":"American Association of Avian Pathologists","doi":"10.1637/aviandiseases-D-20-00137","usgsCitation":"Acosta, S., Kelman, T., Feirer, S., Matchett, E., Smolinsky, J.A., Pitesky, M.E., and Buler, J.J., 2021, Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California: Avian Diseases, v. 65, no. 3, p. 483-492, https://doi.org/10.1637/aviandiseases-D-20-00137.","productDescription":"10 p.","startPage":"483","endPage":"492","ipdsId":"IP-125341","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":395530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley of California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 40.44694705960048\n ],\n [\n -122.51953124999999,\n 38.95940879245423\n ],\n [\n -121.70654296874999,\n 37.54457732085582\n ],\n [\n -120.08056640625,\n 35.92464453144099\n ],\n [\n -119.20166015625,\n 35.15584570226544\n ],\n [\n -118.43261718749999,\n 35.38904996691167\n ],\n [\n -119.0478515625,\n 36.73888412439431\n ],\n [\n -120.89355468749999,\n 38.238180119798635\n ],\n [\n -122.3876953125,\n 40.29628651711716\n ],\n [\n -122.958984375,\n 40.44694705960048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Acosta, Sarai","contributorId":274842,"corporation":false,"usgs":false,"family":"Acosta","given":"Sarai","email":"","affiliations":[{"id":56669,"text":"Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA USA","active":true,"usgs":false}],"preferred":false,"id":833381,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelman, Todd","contributorId":274843,"corporation":false,"usgs":false,"family":"Kelman","given":"Todd","email":"","affiliations":[{"id":56669,"text":"Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA USA","active":true,"usgs":false}],"preferred":false,"id":833382,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feirer, Shane","contributorId":274844,"corporation":false,"usgs":false,"family":"Feirer","given":"Shane","email":"","affiliations":[{"id":56670,"text":"Hopland Research & Extension Center, UC-Agriculture and Natural Resources. 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The southeast coast of Florida (USA) has been identified as the original introduction location, but genetic analyses including Florida lionfish have yet to investigate introduction scenarios. Here, we assessed the potential lionfish invasion pathways using 1795 sequences from previously published mitochondrial D-loop sequences (n = 1558) and new samples (n = 237) from 6 locations: The Bahamas, Florida Keys, northwest Florida, North Carolina, Panamá, and southeast Florida. None of the assessed Florida lionfish (n = 394) contained the H05-H09 D-loop haplotypes found in The Bahamas, North Carolina, and Bermuda (the Northern Region), indicating that Florida was not the source for these haplotypes. Assessing the mitochondrial population structure, the Florida east coast lionfish grouped with the Caribbean/Gulf of Mexico, as opposed to the Northern Region. To further explore connectivity and invasion pathways, 14 nuclear microsatellite loci were multiplexed on lionfish collected from 15 locations (n = 394). As found in other nuclear lionfish studies, the analyses identified a lack of population structure likely due to founding effects and/or inbreeding in aquaculture brood stocks. Together, the significant haplotype differences and H01-H04 haplotypes refute Florida as the sole source of red lionfish introduction. The results of this study support alternative invasion scenarios, in which Florida was colonized as a secondary introduction site or by individuals from the Northern Region. Understanding invasive species’ population boundaries and dispersal patterns informs local control efforts and management planning for future invasive species introductions.
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0000-0003-1443-4873","orcid":"https://orcid.org/0000-0003-1443-4873","contributorId":289826,"corporation":false,"usgs":false,"family":"Mignucci-Giannoni","given":"Antonio","email":"","middleInitial":"A.","affiliations":[{"id":62259,"text":"Centro de Conservación de Manatíes del Caribe, Universidad Interamericana de Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":839860,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Silliman, Brian R. 0000-0001-6360-650X","orcid":"https://orcid.org/0000-0001-6360-650X","contributorId":289827,"corporation":false,"usgs":false,"family":"Silliman","given":"Brian","email":"","middleInitial":"R.","affiliations":[{"id":62261,"text":"Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University","active":true,"usgs":false}],"preferred":false,"id":839861,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buddo, 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A.","contributorId":289858,"corporation":false,"usgs":false,"family":"Ramos","given":"E.","email":"","middleInitial":"A.","affiliations":[{"id":62267,"text":"Fundación Internacional para la Naturaleza y la Sustentabilidad, Mexico","active":true,"usgs":false}],"preferred":false,"id":839936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Álvarez-Alemán, Anmari","contributorId":265539,"corporation":false,"usgs":false,"family":"Álvarez-Alemán","given":"Anmari","affiliations":[{"id":54719,"text":"Clearwater Marine Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":839937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Attademo, Fernanda L. N.","contributorId":289859,"corporation":false,"usgs":false,"family":"Attademo","given":"Fernanda L. N.","affiliations":[{"id":62268,"text":"Centro Nacional de Pesquisa e Conservação de Mamíferos Aquáticos / Instituto Chico Mendes de Conservação da Biodiversidade, Brazil","active":true,"usgs":false}],"preferred":false,"id":839938,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beck, Cathy A.","contributorId":289860,"corporation":false,"usgs":false,"family":"Beck","given":"Cathy A.","affiliations":[{"id":54719,"text":"Clearwater Marine Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":839939,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bonde, Robert K.","contributorId":289663,"corporation":false,"usgs":false,"family":"Bonde","given":"Robert","email":"","middleInitial":"K.","affiliations":[{"id":54719,"text":"Clearwater Marine Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":839940,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Butler, Susan M. 0000-0003-3676-9332 sbutler@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-9332","contributorId":195796,"corporation":false,"usgs":true,"family":"Butler","given":"Susan","email":"sbutler@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839941,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cabrias-Contreras, L. J.","contributorId":289861,"corporation":false,"usgs":false,"family":"Cabrias-Contreras","given":"L.","email":"","middleInitial":"J.","affiliations":[{"id":62269,"text":"Caribbean Manatee Conservation Center, Inter American University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":839942,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Caicedo-Herrera, D.","contributorId":289862,"corporation":false,"usgs":false,"family":"Caicedo-Herrera","given":"D.","affiliations":[{"id":62270,"text":"Fundación Omacha, Colombia","active":true,"usgs":false}],"preferred":false,"id":839943,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Galves, Jamal","contributorId":289863,"corporation":false,"usgs":false,"family":"Galves","given":"Jamal","email":"","affiliations":[{"id":54719,"text":"Clearwater Marine Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":839944,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gomez-Camelo, I. V.","contributorId":289864,"corporation":false,"usgs":false,"family":"Gomez-Camelo","given":"I.","email":"","middleInitial":"V.","affiliations":[{"id":62270,"text":"Fundación Omacha, Colombia","active":true,"usgs":false}],"preferred":false,"id":839945,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gonzalez-Socoloske, D.","contributorId":289865,"corporation":false,"usgs":false,"family":"Gonzalez-Socoloske","given":"D.","email":"","affiliations":[{"id":62271,"text":"Andrews University","active":true,"usgs":false}],"preferred":false,"id":839946,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Jiménez-Domínguez, D.","contributorId":289866,"corporation":false,"usgs":false,"family":"Jiménez-Domínguez","given":"D.","affiliations":[{"id":62272,"text":"Universidad Juarez Autónoma de Tabasco, Mexico","active":true,"usgs":false}],"preferred":false,"id":839947,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Luna, Fabia O.","contributorId":256966,"corporation":false,"usgs":false,"family":"Luna","given":"Fabia","email":"","middleInitial":"O.","affiliations":[{"id":51921,"text":"Instituto Chico Mendes de Conservação da Biodiversidade/Centro Nacional de Pesquisa e Conservação de Mamíferos Aquáticos (ICMBio/CMA), Santos, São Paulo, Brazil","active":true,"usgs":false}],"preferred":false,"id":839948,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Mona-Sanabria, Y.","contributorId":289867,"corporation":false,"usgs":false,"family":"Mona-Sanabria","given":"Y.","email":"","affiliations":[{"id":62270,"text":"Fundación Omacha, Colombia","active":true,"usgs":false}],"preferred":false,"id":839949,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Morales-Vela, J. B.","contributorId":289868,"corporation":false,"usgs":false,"family":"Morales-Vela","given":"J.","email":"","middleInitial":"B.","affiliations":[{"id":62273,"text":"El Colegio de la Frontera Sur, Mexico","active":true,"usgs":false}],"preferred":false,"id":839950,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Olivera-Gomez, L. D.","contributorId":289869,"corporation":false,"usgs":false,"family":"Olivera-Gomez","given":"L. D.","affiliations":[{"id":62272,"text":"Universidad Juarez Autónoma de Tabasco, Mexico","active":true,"usgs":false}],"preferred":false,"id":839951,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Padilla-Saldivar, Janneth Adriana","contributorId":140524,"corporation":false,"usgs":false,"family":"Padilla-Saldivar","given":"Janneth","email":"","middleInitial":"Adriana","affiliations":[{"id":13524,"text":"El Colegio de la Frontera Sur, Quintana Roo, Mexico","active":true,"usgs":false}],"preferred":false,"id":839952,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Powell, James A.","contributorId":288150,"corporation":false,"usgs":false,"family":"Powell","given":"James A.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":839953,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Reid, James P. 0000-0002-8497-1132","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":206849,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839954,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Rieucau, G.","contributorId":289870,"corporation":false,"usgs":false,"family":"Rieucau","given":"G.","email":"","affiliations":[{"id":62267,"text":"Fundación Internacional para la Naturaleza y la Sustentabilidad, Mexico","active":true,"usgs":false}],"preferred":false,"id":839955,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Mignucci-Gianonni, Antonio A.","contributorId":289871,"corporation":false,"usgs":false,"family":"Mignucci-Gianonni","given":"Antonio","email":"","middleInitial":"A.","affiliations":[{"id":62269,"text":"Caribbean Manatee Conservation Center, Inter American University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":839956,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70224634,"text":"70224634 - 2021 - Machine learning can assign geologic basin to produced water samples using major ion geochemistry","interactions":[],"lastModifiedDate":"2021-11-16T15:48:00.581979","indexId":"70224634","displayToPublicDate":"2021-09-30T08:16:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning can assign geologic basin to produced water samples using major ion geochemistry","docAbstract":"Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.
","language":"English","publisher":"Springer Link","doi":"10.1007/s11053-021-09949-8","usgsCitation":"Shelton, J., Jubb, A., Saxe, S., Attanasi, E., Milkov, A., Engle, M.A., Freeman, P., Shaffer, C., and Blondes, M., 2021, Machine learning can assign geologic basin to produced water samples using major ion geochemistry: Natural Resources Research, v. 30, p. 4147-4163, https://doi.org/10.1007/s11053-021-09949-8.","productDescription":"17 p.","startPage":"4147","endPage":"4163","ipdsId":"IP-126045","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11053-021-09949-8","text":"Publisher Index Page"},{"id":390110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":824456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milkov, Alexei","contributorId":266160,"corporation":false,"usgs":false,"family":"Milkov","given":"Alexei","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":824458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engle, Mark A 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":228981,"corporation":false,"usgs":false,"family":"Engle","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":41535,"text":"The University of Texas at El Paso, Department of Geological Sciences, El Paso, TX 79968","active":true,"usgs":false}],"preferred":false,"id":824459,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824460,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaffer, Christopher","contributorId":266161,"corporation":false,"usgs":false,"family":"Shaffer","given":"Christopher","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":824461,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824462,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225551,"text":"70225551 - 2021 - Clays are not created equal: How clay mineral type affects soil parameterization","interactions":[],"lastModifiedDate":"2021-10-22T12:42:02.867477","indexId":"70225551","displayToPublicDate":"2021-09-30T07:38:20","publicationYear":"2021","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":"Clays are not created equal: How clay mineral type affects soil parameterization","docAbstract":"Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral-informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.
To determine trends in either frog distribution or abundance in the State of Louisiana, we reviewed and analyzed frog call data from the Louisiana Amphibian Monitoring Program (LAMP). The data were collected between 1997 and 2017 using North American Amphibian Monitoring Program protocols. Louisiana was divided into three survey regions for administration and analysis: the Florida Parishes, and 2 areas west of the Florida parishes called North and South. Fifty-four routes were surveyed with over 12,792 stops and 1,066 hours of observation. Observers heard 26 species of the 31 species reported to be in Louisiana. Three of the species not heard were natives with ranges that did not overlap with survey routes. The other two species were introduced species, the Rio Grande Chirping Frog (Eleutherodactylus cystignathoides) and the Cuban Treefrog (Osteopilus septentrionalis). Both seem to be limited to urban areas with little to no route coverage. The 15 most commonly occurring species were examined in detail using the percentage of stops at which they observed along a given survey and their call indices. Most species exhibited a multimodal, concave, or convex pattern of abundance over a 15-year period. Among LAMP survey regions, none of the species had synchronous population trends. Only one group of species, winter callers, regularly co-occur. Based on the species lists, the North region could be seen as a subset of the South. However, based on relative abundance, the North was more similar to Florida parishes for both the winter and summer survey runs. Our analyses demonstrate that long-term monitoring (10 years or more) may be necessary to determine population and occupancy trends, and that frog species may have different local demographic patterns across large geographic areas.
Long-term lake ice phenological records from around the Northern Hemisphere provide unique sensitive indicators of climatic variations, even prior to the existence of physical meteorological measurement stations. Here, we updated ice phenology records for 60 lakes with time-series ranging from 107–204 years to provide the first re-assessment of Northern Hemispheric ice trends since 2004 by adding 15 additional years of ice phenology records and 40 lakes to our study. We found that, on average, ice-on was 11.0 days later, ice-off was 6.8 days earlier, and ice duration was 17.0 days shorter per century over the entire record for each lake. Trends in ice-on and ice duration were six times faster in the last 25-year period (1992–2016) than previous quarter centuries. More extreme events in recent decades, including late ice-on, early ice-off, shorter periods of ice cover, or no ice cover at all, contribute to the increasing rate of lake ice loss. Reductions in greenhouse gas emissions could limit increases in air temperature and abate losses in lake ice cover that would subsequently limit ecological, cultural, and socioeconomic consequences, such as increased evaporation rates, warmer water temperatures, degraded water quality, and the formation of toxic algal blooms.