Introduction Although the Petersburg Granite had long been in practical use as a building stone since the 1830s (Watson, 1906; 1907; 1910; Darton, 1911; Steidtmann, 1945), it was first formally defined as a geologic unit by Anna Jonas on the 1928 geologic map of Virginia. Anna Jonas defined this unit as a Precambrian coarse-grained porphyritic biotite granite that was intruded by finer grained granite and cut by pegmatite (Nelson, 1928). This belt of mostly granitic rocks extends from near Ashland, Virginia north of Richmond, to near Stony Creek, south of Petersburg, Virginia (e.g., Virginia Division of Mineral Resources, 1993) and is bounded by the Hylas fault zone to the northwest, the Mesozoic Richmond basin to the west, and the newly recognized Nottoway River fault zone to the southwest (e.g., Carter and others, 2020; 2021). The eastern boundary of this belt is covered by Coastal Plain sediments, but geophysical and deep borehole data suggest an orogen-scale suture separates it from the Neoproterozoic Chesapeake block to the east (Figure 1; Carter and others, 2021).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the Eastern Piedmont to the Coastal Plain: a cross section through the Richmond Area Fall Zone: Guidebook for 2021 Virginia Geologic Field Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"William and Mary","usgsCitation":"Carter, M.W., McAleer, R.J., Occhi, M., Holm-Denoma, C., Vazquez, J.A., and Owens, B.E., 2021, Stop 3 – The Petersburg “Granite” redefined: Recognition and implications of Silurian to Devonian rocks in central-eastern Virginia, in From the Eastern Piedmont to the Coastal Plain: a cross section through the Richmond Area Fall Zone: Guidebook for 2021 Virginia Geologic Field Conference, p. 18-25.","productDescription":"8 p.","startPage":"18","endPage":"25","ipdsId":"IP-137667","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":400054,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399998,"type":{"id":15,"text":"Index Page"},"url":"https://vgfc.blogs.wm.edu/past-conferences/"}],"country":"United States","state":"Virginia","otherGeospatial":"Petersburg granite","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78,\n 36.75\n ],\n [\n -77.25,\n 36.75\n ],\n [\n -77.25,\n 38\n ],\n [\n -78,\n 38\n ],\n [\n -78,\n 36.75\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":841878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":841879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Occhi, Marcie","contributorId":191116,"corporation":false,"usgs":false,"family":"Occhi","given":"Marcie","affiliations":[],"preferred":false,"id":841880,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":841881,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":841882,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Owens, Brent E.","contributorId":178190,"corporation":false,"usgs":false,"family":"Owens","given":"Brent","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":841883,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229239,"text":"70229239 - 2021 - Numerical modelling of mine pollution to inform remediation decision-making in watersheds","interactions":[],"lastModifiedDate":"2022-03-03T15:20:56.513912","indexId":"70229239","displayToPublicDate":"2021-12-31T09:09:08","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Numerical modelling of mine pollution to inform remediation decision-making in watersheds","docAbstract":"Prioritisation of mine pollution sources for remediation is a key challenge facing environmental managers. This paper presents a numerical modelling methodology to evaluate potential improvements in stream water quality from remediation of important mine pollution sources. High spatial resolution synoptic sampling data from a Welsh watershed were used to calibrate the OTIS solute transport model. Simulation of mine pollution remediation scenarios using OTIS revealed decreases in stream Zn concentrations between 9% and 62% under mean streamflow conditions. Remediation scenarios under low streamflow conditions were less effective (<1% to 17% decrease in Zn concentrations), due to diffuse and metal-rich groundwater inflows.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of international mine water association 2021","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Mine Water Management for Future Generations","conferenceLocation":"Cardiff, Wales","language":"English","publisher":"ISI Thomson","usgsCitation":"Byrne, P., Onnis, P., Runkel, R.L., Frau, I., Lynch, S.F., Brown, A.M., Robertson, I., and Edwards, P., 2021, Numerical modelling of mine pollution to inform remediation decision-making in watersheds, in Proceedings of international mine water association 2021, Cardiff, Wales, p. 66-71.","productDescription":"6 p.","startPage":"66","endPage":"71","ipdsId":"IP-129772","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":396699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396694,"type":{"id":15,"text":"Index Page"},"url":"https://www.imwa.info/imwaconferencesandcongresses/proceedings/325-proceedings-2021.html"}],"country":"Wales","otherGeospatial":"Nant Cwmnewyddion watershed","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrne, Patrick","contributorId":192845,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","affiliations":[],"preferred":false,"id":837016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Onnis, Patrizia","contributorId":209909,"corporation":false,"usgs":false,"family":"Onnis","given":"Patrizia","email":"","affiliations":[{"id":16820,"text":"University of Cagliari","active":true,"usgs":false}],"preferred":false,"id":837017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frau, Ilaria","contributorId":247580,"corporation":false,"usgs":false,"family":"Frau","given":"Ilaria","email":"","affiliations":[{"id":49583,"text":"Liverpool John Moores University","active":true,"usgs":false}],"preferred":false,"id":837019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lynch, Sarah F. L.","contributorId":247581,"corporation":false,"usgs":false,"family":"Lynch","given":"Sarah","email":"","middleInitial":"F. L.","affiliations":[{"id":13386,"text":"AECOM","active":true,"usgs":false}],"preferred":false,"id":837020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Aaron M. L.","contributorId":287684,"corporation":false,"usgs":false,"family":"Brown","given":"Aaron","email":"","middleInitial":"M. L.","affiliations":[{"id":16759,"text":"Swansea University","active":true,"usgs":false}],"preferred":false,"id":837021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Robertson, Iain","contributorId":257646,"corporation":false,"usgs":false,"family":"Robertson","given":"Iain","email":"","affiliations":[],"preferred":false,"id":837022,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Edwards, Paul","contributorId":247582,"corporation":false,"usgs":false,"family":"Edwards","given":"Paul","email":"","affiliations":[{"id":16759,"text":"Swansea University","active":true,"usgs":false}],"preferred":false,"id":837023,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219213,"text":"70219213 - 2021 - Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 2: Probabilistic numerical simulation of bank erosion","interactions":[],"lastModifiedDate":"2024-02-21T15:47:15.093087","indexId":"70219213","displayToPublicDate":"2021-12-31T08:40:40","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 2: Probabilistic numerical simulation of bank erosion","docAbstract":"USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper, the second of two parts, addresses processing the collected data to inform inputs for probabilistic bank erosion estimates in BSTEM. Measuring the intrinsic soil properties for BSTEM is discussed in part one. Soil critical shear stress and soil erodibility coefficients were calibrated by Unified Soil Classification soil type to observed erosion on the American River. Adjustments were made in the probability density functions for these parameters to reflect field-measured variability and carry forward the reduction in error achieved during calibration. The resulting calibrated values were tested at additional sites, validating the resulting critical shear stress and soil erodibility coefficient values and probability density functions for more robust probabilistic bank erosion estimates using BSTEM.","conferenceTitle":"10th International Conference on Scour and Erosion (ICSE-10)","conferenceDate":"October 18-20, 2021","language":"English","publisher":"ASCE","usgsCitation":"Rivas, T.M., AuBuchon, J., Shidlovskaya, A., Langendoen, E., Work, P.A., Livsey, D.N., Timchenko, A., Jemes, K., and Briaud, J., 2021, Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 2: Probabilistic numerical simulation of bank erosion, 10th International Conference on Scour and Erosion (ICSE-10), October 18-20, 2021, 10 p.","productDescription":"10 p.","ipdsId":"IP-116981","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425819,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/publications/publication/risk-informed-levee-erosion-countermeasure-site-selection-and-design-in-the-sacramento-area-part-2-probabilistic-numerical-simulation-of-bank-erosion","linkFileType":{"id":5,"text":"html"}},{"id":425814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Sacramento","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62841736139654,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.61545545028389\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivas, Todd M.","contributorId":256771,"corporation":false,"usgs":false,"family":"Rivas","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":51857,"text":"Sacramento District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"AuBuchon, Jonathan","contributorId":256772,"corporation":false,"usgs":false,"family":"AuBuchon","given":"Jonathan","email":"","affiliations":[{"id":51859,"text":"Albuquerque District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shidlovskaya, Anna","contributorId":256773,"corporation":false,"usgs":false,"family":"Shidlovskaya","given":"Anna","email":"","affiliations":[{"id":51860,"text":"Department of Civil Engineering, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":813242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Timchenko, Anna","contributorId":334257,"corporation":false,"usgs":false,"family":"Timchenko","given":"Anna","email":"","affiliations":[],"preferred":false,"id":895117,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jemes, Kellie","contributorId":334259,"corporation":false,"usgs":false,"family":"Jemes","given":"Kellie","email":"","affiliations":[],"preferred":false,"id":895118,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Briaud, Jean-Louis","contributorId":334258,"corporation":false,"usgs":false,"family":"Briaud","given":"Jean-Louis","email":"","affiliations":[],"preferred":false,"id":895119,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219211,"text":"70219211 - 2021 - Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing","interactions":[],"lastModifiedDate":"2024-02-21T14:39:05.70841","indexId":"70219211","displayToPublicDate":"2021-12-31T08:31:46","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing","docAbstract":"USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper discusses the measurement of the intrinsic erosion and geotechnical properties of the soil for improved BSTEM results and is the first of two parts. Sampling and testing were conducted at select sites to obtain the soil stratigraphy and collect samples for estimation of engineering properties. Soil erodibility was measured using the Erosion Function Apparatus test, the Borehole Erosion Test, the Pocket Erodometer Test, and the mini-Jet Erosion Test. Critical evaluation of previously existing datasets and the collection of these new datasets helped to provide better definition of the range in erosion parameters for improved probabilistic erosion estimates using BSTEM for risk-informed levee erosion countermeasure site selection and design.","conferenceTitle":"10th International Conference on Scour and Erosion (ICSE-10)","conferenceDate":"October 18-20, 2021","language":"English","publisher":"ASCE","collaboration":"US Army Corps of Engineers","usgsCitation":"Rivas, T.M., AuBuchon, J., Shidlovskaya, A., Langendoen, E., Work, P.A., Livsey, D.N., Timchenko, A., and Briaud, J., 2021, Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing, 10th International Conference on Scour and Erosion (ICSE-10), October 18-20, 2021, 11 p.","productDescription":"11 p.","ipdsId":"IP-117504","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425812,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/publications/publication/risk-informed-levee-erosion-countermeasure-site-selection-and-design-in-the-sacramento-area-part-2-probabilistic-numerical-simulation-of-bank-erosion","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","city":"Sacramento","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62841736139654,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.61545545028389\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivas, Todd M.","contributorId":256771,"corporation":false,"usgs":false,"family":"Rivas","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":51857,"text":"Sacramento District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"AuBuchon, Jonathan","contributorId":256772,"corporation":false,"usgs":false,"family":"AuBuchon","given":"Jonathan","email":"","affiliations":[{"id":51859,"text":"Albuquerque District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shidlovskaya, Anna","contributorId":256773,"corporation":false,"usgs":false,"family":"Shidlovskaya","given":"Anna","email":"","affiliations":[{"id":51860,"text":"Department of Civil Engineering, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":813236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Timchenko, Anna","contributorId":334257,"corporation":false,"usgs":false,"family":"Timchenko","given":"Anna","email":"","affiliations":[],"preferred":false,"id":895115,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Briaud, Jean-Louis","contributorId":334258,"corporation":false,"usgs":false,"family":"Briaud","given":"Jean-Louis","email":"","affiliations":[],"preferred":false,"id":895116,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70241610,"text":"70241610 - 2021 - Estimating trends of common raven populations in North America, 1966—2018","interactions":[],"lastModifiedDate":"2024-09-11T16:27:03.40755","indexId":"70241610","displayToPublicDate":"2021-12-31T08:28:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Estimating trends of common raven populations in North America, 1966—2018","docAbstract":"Over the last half century, common raven (Corvus corax; raven) populations have increased in abundance across much of North America. Ravens are generalist predators known to depredate the eggs and young of several sensitive species. Quantifying raven population increases at multiple spatial scales across North America will help wildlife resource managers identify areas where population increases present the greatest risk to species conservation. We used a hierarchical Bayesian modeling approach to analyze trends of standardized raven counts from 1966 to 2018 using Breeding Bird Survey data within each Level I and II ecoregion of the United States and Canada. We also compared raven abundance within and outside the distributions of 9 sensitive or endangered species. Although we found substantial evidence that raven populations have increased across North America, populations varied in growth rates and relative abundances among regions. We found 73% of Level I (11/15) and II (25/34) ecoregions demonstrated positive annual population growth rates ranging from 0.2–9.4%. We found higher raven abundance inside versus outside the distributions of 7 of the 9 sensitive species included in our analysis. Gunnison sage-grouse (Centrocercus minimus) had the highest discrepancy, with 293% more ravens within compared to outside of their range, followed by greater sandhill crane (Antigone canadensis tabida; 280%), and greater sage-grouse (C. urophasianus; 204%). Only 2 species, least tern (Sternula antillarum) and piping plover (Charadrius melodus), indicated lower raven abundance within relative to outside their distributions. Our findings will help wildlife resource managers identify regional trends in abundance of ravens and anticipate which sensitive species are at greatest risk from elevated raven populations. Future research directed at identifying the underlying regional drivers of these trends could help elucidate the most appropriate and responsive management actions and, thereby, guide the development of raven population management plans to mitigate impacts to sensitive species.
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Seth M. 0000-0003-0444-7881","orcid":"https://orcid.org/0000-0003-0444-7881","contributorId":238889,"corporation":false,"usgs":false,"family":"Harju","given":"Seth","email":"","middleInitial":"M.","affiliations":[{"id":47817,"text":"Heron Ecological","active":true,"usgs":false}],"preferred":false,"id":867486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dettenmaier, Seth J. 0000-0001-6325-8808","orcid":"https://orcid.org/0000-0001-6325-8808","contributorId":302087,"corporation":false,"usgs":true,"family":"Dettenmaier","given":"Seth","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dinkins, Jonathan B.","contributorId":177565,"corporation":false,"usgs":false,"family":"Dinkins","given":"Jonathan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":867489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":867490,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chenaille, Michael P. 0000-0003-3387-7899 mchenaille@usgs.gov","orcid":"https://orcid.org/0000-0003-3387-7899","contributorId":194661,"corporation":false,"usgs":true,"family":"Chenaille","given":"Michael","email":"mchenaille@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867491,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259394,"text":"70259394 - 2021 - Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019","interactions":[],"lastModifiedDate":"2024-10-07T14:00:24.63834","indexId":"70259394","displayToPublicDate":"2021-12-31T08:26:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18741,"text":"Incasciences, Revista del Instituto Geológico, Minero y Metalúrgico","active":true,"publicationSubtype":{"id":10}},"title":"Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019","docAbstract":"Using a volcanic monitoring data set from Ubinas volcano, we applied a process-based model of pre-eruptive seismic patterns to the 2019 eruptive episode with the goal of identifying possible seismic precursors in order to help forecast future eruptions. This conceptual model, based on geologic processes, is divided into four seismicity stages: Stage 1. Characterized by the occurrence of deep seismicity associated with deep intrusion(s); Stage 2. Occurrence of distal volcano – tectonic seismicity in response to magma intrusion(s) into the upper crustal reservoir; Stage 3. Dominated by seismicity associated with vent – clearing; and Stage 4. Corresponding to the occurrence of repetitive seismicity related with final magma ascent. \nIn the 2019 eruptive episode, we identified the last three stages: seismicity associated with the intrusion of new magma (Stage 2), seismicity associated with an opened and vent – clearing inside of the volcanic system (Stage 3) and repetitive seismicity that suggested the magma ascent towards shallower depths (Stage 4), however, no surficial lava was observed. Because Ubinas is an active system with frequent eruptions, Stage 2 was very brief; however, we were still able to identify the transition from phreatomagmatic to magmatic activity.\nThe model allows us to provide a process-based interpretation to the volcanic monitoring observations from Ubinas volcano. Additionally, this model will aid in future assessment of unrest and contribute to eruption forecasting.","language":"English","publisher":"Instituto Geológico, Minero y Metalúrgico","usgsCitation":"Ortega, M.A., McCausland, W., White, R., Anccasi, R.M., and Ccallata, B., 2021, Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019: Incasciences, Revista del Instituto Geológico, Minero y Metalúrgico, v. 1, no. 1, p. 53-61.","productDescription":"9 p.","startPage":"53","endPage":"61","ipdsId":"IP-122050","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","otherGeospatial":"Ubinas Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.92669701072916,\n -16.328296011041587\n ],\n [\n -70.92669701072916,\n -16.37059339221304\n ],\n [\n -70.87807952769546,\n -16.37059339221304\n ],\n [\n -70.87807952769546,\n -16.328296011041587\n ],\n [\n -70.92669701072916,\n -16.328296011041587\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ortega, Mayra A.","contributorId":344962,"corporation":false,"usgs":false,"family":"Ortega","given":"Mayra","email":"","middleInitial":"A.","affiliations":[{"id":82443,"text":"INGEMMET (Peru)","active":true,"usgs":false}],"preferred":false,"id":915137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":344963,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Randall A. 0000-0003-4074-8577","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":344964,"corporation":false,"usgs":false,"family":"White","given":"Randall A.","affiliations":[{"id":82444,"text":"none, retired USGS","active":true,"usgs":false}],"preferred":false,"id":915139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anccasi, Rosa M.","contributorId":344965,"corporation":false,"usgs":false,"family":"Anccasi","given":"Rosa","email":"","middleInitial":"M.","affiliations":[{"id":82443,"text":"INGEMMET (Peru)","active":true,"usgs":false}],"preferred":false,"id":915140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ccallata, Beto","contributorId":190928,"corporation":false,"usgs":false,"family":"Ccallata","given":"Beto","email":"","affiliations":[],"preferred":false,"id":915141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241053,"text":"70241053 - 2021 - Common ravens disrupt greater sage-grouse lekking behavior in the Great Basin, USA","interactions":[],"lastModifiedDate":"2023-03-08T13:24:14.029751","indexId":"70241053","displayToPublicDate":"2021-12-31T07:20:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Common ravens disrupt greater sage-grouse lekking behavior in the Great Basin, USA","docAbstract":"Expansion of human enterprise has contributed to increased abundance and distribution of common ravens (Corvus corax; ravens) across sagebrush (Artemisia spp.) ecosystems within western North America. Ravens are highly effective nest predators of greater sage-grouse (Centrocercus urophasianus; sage-grouse), a species of high conservation concern. Sage-grouse population trends are estimated using count survey data of males attending traditional breeding grounds, known as leks. We sought to investigate associations of ravens to sage-grouse lek sites and document interactions between the sage-grouse and ravens as well as those between sage-grouse and other animals observed around leks. First, we used extensive raven point counts and sage-grouse lek observation data collected across Nevada and California, USA, from 2009–2019 to evaluate spatial associations between sage-grouse and ravens while accounting for other environmental covariates. We found that ravens were more likely to be observed closer to lek sites, especially as leks increased in size. Second, we used a subset of the lek dataset from 2006–2019 to describe behavioral changes of male sage-grouse in the presence of ravens and other predators. Our analysis indicated that ravens are attracted to lek sites and were associated with disrupting lekking sage-grouse by causing flushes or ceasing displaying behaviors. These results suggest that adult and yearling sage-grouse perceive ravens as a reason to alter breeding activity, and ravens may adversely influence their reproduction during the lekking stage. Additionally, standardized techniques to count sage-grouse on leks for population trend analyses could be biased low if raven presence during surveys is not accounted for.
The black-capped petrel Pterodroma hasitata is an Endangered seabird endemic to the western North Atlantic. Although estimated at ~1000 breeding pairs, only ~100 nests have been located at 2 sites in Haiti and 3 sites in the Dominican Republic. At sea, the species primarily occupies waters of the western Gulf Stream in the Atlantic and the Caribbean Sea. Due to limited data, there is currently no consensus on the geographic marine range of the species although no current proposed ranges include the Gulf of Mexico. Here, we report on observations of black-capped petrels during 2 vessel-based survey efforts throughout the northern Gulf of Mexico from 2010-2011 and 2017-2019. During 558 d and ~54700 km of surveys, we tallied 40 black-capped petrels. Most observations occurred in the eastern Gulf, although birds were observed over much of the east-west and north-south footprint of the survey area. Predictive models indicated that habitat suitability for black-capped petrels was highest in areas associated with dynamic waters of the Loop Current. We used the extent of occurrence and area of occupancy concepts to delimit the geographic range of the species within the northern Gulf. We suggest that the marine range for black-capped petrels be modified to include the northern Gulf of Mexico, recognizing that distribution may be more clumped in the eastern Gulf and that occurrence in the southern Gulf remains unknown due to a lack of surveys there. To date, however, it remains unclear which nesting areas are linked to the Gulf of Mexico.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.1101/2021.01.19.427288","usgsCitation":"Jodice, P.G., Michael, P., Gleason, J., Haney, J., and Satge, Y., 2021, Revising the marine range of the endangered black-capped petrel Pterodroma hasitata: occurrence in the northern Gulf of Mexico and exposure to conservation threats: Endangered Species Research, v. 46, p. 49-65, https://doi.org/10.1101/2021.01.19.427288.","productDescription":"17 p.","startPage":"49","endPage":"65","ipdsId":"IP-124873","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1101/2021.01.19.427288","text":"Publisher Index Page"},{"id":396779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.96484375,\n 25.799891182088334\n ],\n [\n -80.771484375,\n 25.799891182088334\n ],\n [\n -80.771484375,\n 31.42866311735861\n ],\n [\n -98.96484375,\n 31.42866311735861\n ],\n [\n -98.96484375,\n 25.799891182088334\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michael, P.E.","contributorId":288015,"corporation":false,"usgs":false,"family":"Michael","given":"P.E.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":837281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gleason, J.S.","contributorId":288017,"corporation":false,"usgs":false,"family":"Gleason","given":"J.S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":837282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haney, J.C.","contributorId":288019,"corporation":false,"usgs":false,"family":"Haney","given":"J.C.","email":"","affiliations":[{"id":61685,"text":"Terra Mar Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":837283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Satge, Y.G.","contributorId":279816,"corporation":false,"usgs":false,"family":"Satge","given":"Y.G.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":837284,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239027,"text":"70239027 - 2021 - Monitoring multi-decadal variations of urban heat island intensity","interactions":[],"lastModifiedDate":"2022-12-21T12:57:11.647921","indexId":"70239027","displayToPublicDate":"2021-12-31T06:55:07","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Monitoring multi-decadal variations of urban heat island intensity","docAbstract":"This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 2 (April–June), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211105","usgsCitation":"Micijevic, E., Rengarajan, R., Haque, M.O., Lubke, M., Tuli, F.T., Shaw, J.L., Hasan, N., Denevan, A., Franks, S., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2021, ECCOE Landsat quarterly Calibration and Validation report — Quarter 2, 2021: U.S. Geological Survey Open-File Report 2021–1105, 40 p., https://doi.org/10.3133/ofr20211105.","productDescription":"vii, 40 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-130990","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":393445,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1105/images"},{"id":393443,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1105/ofr20211105.pdf","text":"Report","size":"4.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1105"},{"id":393442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1105/coverthb.jpg"},{"id":393444,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1105/ofr20211105.XML","size":"118 kB","description":"OFR 2021–1105 xml"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Eastern Shore of Virginia and Fisherman Island National Wildlife Refuges in Virginia. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of six marsh management units within the refuges, totaling about 575 hectares, and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $143,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than $143,000 included digging runnels by hand to improve drainage from the marsh surface, breaching a road to restore natural hydrology, trapping predators to enhance nest success of tidal marsh birds, and reducing the abundance of Odocoileus virginianus (white-tailed deer) to minimize their effects on marsh vegetation. The potential management benefits were derived from expected increases in number of tidal marsh obligate breeding birds, species richness of nekton, and density of spiders (as an indicator of trophic health); and an expected decrease in duration of surface flooding. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Eastern Shore of Virginia and Fisherman Island National Wildlife Refuges that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211117","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Denmon, P., and Leffel, R., 2021, Optimization of salt marsh management at the Eastern Shore of Virginia and Fisherman Island National Wildlife Refuges, Virginia, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1117, 32 p., https://doi.org/10.3133/ofr20211117.","productDescription":"Report: vi, 32 p.; Database","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131973","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":393431,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":393427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1117/coverthb.jpg"},{"id":393428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1117/ofr20211117.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1117"},{"id":393429,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1117/images/"},{"id":393430,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1117/ofr20211117.XML"}],"country":"United States","state":"Virginia","otherGeospatial":"Fisherman Island 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 -75.99586486816406,\n 37.072162624715375\n ],\n [\n -75.92857360839844,\n 37.072162624715375\n ],\n [\n -75.92857360839844,\n 37.14061402065652\n ],\n [\n -75.99586486816406,\n 37.14061402065652\n ],\n [\n -75.99586486816406,\n 37.072162624715375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Moosehorn National Wildlife Refuge in Maine. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of four marsh management units within the refuge, totaling about 13 hectares, and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to $1,000, and may continue to increase at a lower rate with further expenditures. Potential management actions in optimal portfolios at total costs less than or equal to $1,000 included improving nesting habitat for Ammodramus nelsoni (Nelson’s sparrow) or restoring hydrologic connections to the upper marsh in one marsh management unit (Hobart Stream West). The potential management benefits were derived from expected increases in the density of nekton and of spiders (as an indicator of trophic health). The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Moosehorn National Wildlife Refuge that can be updated for implementation as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211115","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Mills, M., Brown, R.E., and Ramos, K., 2021, Optimization of salt marsh management at the Moosehorn National Wildlife Refuge, Maine, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1115, 28 p., https://doi.org/10.3133/ofr20211115.","productDescription":"Report: vi, 28 p.; Database","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131976","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":393375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1115/coverthb.jpg"},{"id":393376,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1115/ofr20211115.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":393377,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1115/ofr20211115.xml"},{"id":393379,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":393378,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1115/images"}],"country":"United States","state":"Maine","otherGeospatial":"Moosehorn 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 -67.27890014648438,\n 44.80132682904856\n ],\n [\n -67.15,\n 44.80132682904856\n ],\n [\n -67.15,\n 44.918625522424925\n ],\n [\n -67.27890014648438,\n 44.918625522424925\n ],\n [\n -67.27890014648438,\n 44.80132682904856\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
A reliable chlorophyll–nutrient relationship (CNR) is essential for lake eutrophication management. Although the spatial variability of CNRs has been extensively explored, temporal variations of CNRs at the individual lake scale has rarely been discussed. The paucity of information about temporal dependence in CNRs may in part be due to the lack of a suitable statistical framework that helps guide such investigations. In order to reveal temporal dependence of CNR, this study develop a novel statistical framework. In the framework, we employ quantile regression to generate overall (the entire dataset), annual (subsets for each year), and accumulative (subsets collected before a certain year) CNRs. We aim to 1) show biases of annual relationships by comparing the overall and annual relationships and 2) determine whether or not data accumulation is enough to develop a reliable CNR. We use Lake Champlain and Lake Kasumigaura as case studies to illustrate the necessary steps needed to utilize this novel framework. Results show that large interannual variations exist for CNRs. Accumulative relationships tend to converge to the overall relationship, indicating that overall relationships are reliable for informing lake-specific eutrophication management in the two case study lakes. The novel statistical framework that we propose for a procedure to estimate reliable CNRs is important for informing lake-specific eutrophication control decision-making processes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.125883","usgsCitation":"Qiu, Q., Liang, Z., Xu, Y., Matsuzaki, S.S., Komatsu, K., and Wagner, T., 2021, A statistical framework to track temporal dependence of chlorophyll–nutrient relationships with implications for lake eutrophication management: Journal of Hydrology, v. 603, no. Part D, 127134, 10 p., https://doi.org/10.1016/j.jhydrol.2020.125883.","productDescription":"127134, 10 p.","ipdsId":"IP-119115","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":449983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2020.125883","text":"Publisher Index Page"},{"id":396460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan, United States","otherGeospatial":"Ibaraki Prefecture, Lake Champlain, Lake Kasumigaura","volume":"603","issue":"Part D","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Qiu, Qianlinglin","contributorId":280020,"corporation":false,"usgs":false,"family":"Qiu","given":"Qianlinglin","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":835891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liang, Zhongyao","contributorId":280018,"corporation":false,"usgs":false,"family":"Liang","given":"Zhongyao","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":835889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xu, Yaoyang","contributorId":280019,"corporation":false,"usgs":false,"family":"Xu","given":"Yaoyang","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":835890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matsuzaki, Shin-Ichiro S.","contributorId":203197,"corporation":false,"usgs":false,"family":"Matsuzaki","given":"Shin-Ichiro","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":836003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Komatsu, Kazuhiro","contributorId":280073,"corporation":false,"usgs":false,"family":"Komatsu","given":"Kazuhiro","email":"","affiliations":[],"preferred":false,"id":836005,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":835888,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226934,"text":"sir20215131 - 2021 - Completion summary for boreholes USGS 148, 148A, and 149 at the Materials and Fuels Complex, Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2021-12-27T13:33:25.050244","indexId":"sir20215131","displayToPublicDate":"2021-12-23T07:24:54","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":"2021-5131","displayTitle":"Completion Summary for Boreholes USGS 148, 148A, and 149 at the Materials and Fuels Complex, Idaho National Laboratory, Idaho","title":"Completion summary for boreholes USGS 148, 148A, and 149 at the Materials and Fuels Complex, Idaho National Laboratory, Idaho","docAbstract":"In 2019, the U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy, drilled and constructed boreholes USGS 148A and USGS 149 for stratigraphic framework analyses and long-term groundwater monitoring of the eastern Snake River Plain aquifer at the Idaho National Laboratory (INL) in southeastern Idaho. Initially, boreholes USGS 148A and USGS 149 were continuously cored to allow the USGS and INL subcontractor to collect select geophysical and seismic data and evaluate properties of recovered core material. The USGS geophysical data and descriptions of core material are described in this report; however, data collected by the INL contractor, including seismic data, are not included as part of the report.
The unsaturated zone at both borehole locations is relatively thick, depth to water was measured at approximately 663.6 feet (ft) below land surface (BLS) in USGS 148A, and at approximately 654.1 ft BLS at USGS 149. On completion of coring and data collection, both boreholes (USGS 148A and USGS 149) were repurposed as monitoring wells. Well USGS 148A was constructed to a depth of 759 ft BLS and instrumented with a dedicated submersible pump and measurement line; well USGS 149 was constructed to a depth of 974 ft BLS and instrumented with a multilevel monitoring system (WestbayTM).
Geophysical data, collected by the USGS, were used to characterize the subsurface geology and aquifer conditions. Natural gamma log measurements were used to assess sediment-layer thickness and location. Neutron and gamma-gamma source logs were used to confirm fractured and vesicular basalt identified for aquifer testing and multilevel monitoring well zone testing. Acoustic televiewer logs, collected for well USGS 149, were used to identify fractures and assess groundwater movement when compared with neutron measurements. Furthermore, gyroscopic deviation measurements were used to measure horizontal and vertical displacement for the constructed boreholes USGS 148A and USGS 149.
A single-well aquifer test was done in well USGS 148A during November 6–7, 2019, to provide estimates of transmissivity and hydraulic conductivity. Estimates for transmissivity and hydraulic conductivity were 6.34×103 feet squared per day and 3.17 feet per day, respectively. The aquifer test was run overnight (21.3 hours) and measured drawdown was relatively small (0.09 ft) at sustained pumping rates ranging from 15.7 to 16.1 gallons per minute. The transmissivity estimates for well USGS 148A were slightly lower than those determined from previous aquifer tests for wells near the Materials and Fuels Complex, but well within range of other aquifer tests done at the INL.
Water-quality samples, collected from well USGS 148A and from four zones in well USGS 149, were analyzed for cations, anions, metals, nutrients, volatile organic compounds, stable isotopes, and radionuclides. Water samples for most of the inorganic constituents showed similar chemistry in USGS 148A and all four zones in USGS 149. Water samples for stable isotopes of oxygen and hydrogen indicated some possible influence of irrigation on the water quality. Nitrate plus nitrite concentrations indicated influence from anthropogenic sources. The volatile organic compound and radiochemical data indicated that wastewater disposal practices at the Materials and Fuels Complex or from drilling had no detectable influence on these wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215131","collaboration":"DOE/ID-22255Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Least Terns (Sternula antillarum) are known to forage away from nesting colonies, yet little information is available about movement rates and distances. We used VHF transmitters and a network of datalogging receivers to monitor movements of 23 Least Terns on the central Platte River, Nebraska, USA. We typically detected incubating and brood-rearing birds within 8 km of colonies during daylight hours, and up to 17.5 km away at night. Movement distances were even longer during post-fledging (up to 20 km) and nonbreeding (up to 31 km) periods. Colony attendance differed notably by reproductive stage, being highest for incubating and lowest for post-fledging birds. Birds were most frequently detected on the study area during brood-rearing and nonbreeding periods, and most likely to go undetected during incubation and after fledging a brood. Frequency and success of foraging behaviors were lowest on sandpit sites, intermediate on riverine sites, and highest at the Kearney Diversion Dam on the Platte River, where flow patterns likely enhanced forage fish availability. Foraging movements of Least Terns were temporally and spatially variable, with time of day, reproductive stage, and availability of prey hotspots appearing to be key factors. Management of habitat complexes for breeding Least Terns may benefit from emphasizing the availability of profitable foraging habitat within 8 km of nesting areas, and considering foraging habitat within 8 - 30 km as available.
The rapid technological development of small Unmanned Aircraft Systems (sUAS) has led to an increase in capabilities of aerial image collection and analysis for monitoring a variety of wildlife species including waterfowl. Biologists mainly rely on conducting ocular surveys from fixed-wing aircraft or helicopters to estimate waterfowl abundance. sUAS provide an alternative that is safer, less expensive, and more flexible. Researchers have attempted to estimate waterfowl abundance from aerial imagery, but this method has proven to be too time consuming. Machine learning provides the opportunity to more efficiently estimate waterfowl abundance from aerial imagery. In this paper, we present a new integrated system of sUAS and machine learning for waterfowl population surveys. This system provides a user-friendly process for sUAS survey design, deployment, and data post-processing using deep learning methods to automatically detect and count waterfowl. To develop this system, we conducted many sUAS flights to capture a diversity of imagery and assembled six datasets of imagery taken from both fix-winged aircraft and sUAS flights. We used these datasets to develop and evaluate state-of-the-art deep learning models for waterfowl detection. Our system of using a combination of sUAS and machine learning has proved to be an efficient and accurate approach for collecting, analyzing, and estimating waterfowl abundance.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)","conferenceDate":"November 1-3, 2021","language":"English","doi":"10.1109/ICTAI52525.2021.00084","usgsCitation":"Tang, Z., Zhang, Y., Wang, Y.Q., Shang, Y., Viegut, R., Webb, E.B., Raedeke, A., and Sartwell, J., 2021, SUAS and machine learning integration in waterfowl population surveys, in 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), November 1-3, 2021, p. 517-521, https://doi.org/10.1109/ICTAI52525.2021.00084.","productDescription":"6 p.","startPage":"517","endPage":"521","ipdsId":"IP-131231","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tang, Z.","contributorId":341913,"corporation":false,"usgs":false,"family":"Tang","given":"Z.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":909151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Y.","contributorId":274978,"corporation":false,"usgs":false,"family":"Zhang","given":"Y.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":909152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Y. Q.","contributorId":221210,"corporation":false,"usgs":false,"family":"Wang","given":"Y.","email":"","middleInitial":"Q.","affiliations":[],"preferred":false,"id":909153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shang, Y.","contributorId":341914,"corporation":false,"usgs":false,"family":"Shang","given":"Y.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":909154,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Viegut, R.","contributorId":341915,"corporation":false,"usgs":false,"family":"Viegut","given":"R.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":909155,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":909156,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raedeke, Andy","contributorId":341916,"corporation":false,"usgs":false,"family":"Raedeke","given":"Andy","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":909157,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sartwell, J.","contributorId":341917,"corporation":false,"usgs":false,"family":"Sartwell","given":"J.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":909158,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70226913,"text":"70226913 - 2021 - Invasive sea lamprey detection and characterization using interdigitated electrode (IDE) contact sensor","interactions":[],"lastModifiedDate":"2021-12-21T15:30:33.915647","indexId":"70226913","displayToPublicDate":"2021-12-21T09:22:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9956,"text":"IEEE Sensors Journal","active":true,"publicationSubtype":{"id":10}},"title":"Invasive sea lamprey detection and characterization using interdigitated electrode (IDE) contact sensor","docAbstract":"The ability to monitor invasive sea lamprey (Petromyzon marinus) populations in the Laurentian Great Lakes is critical to protecting the region’s $ 7 billion USD fishing industry and preserving its biodiversity. Monitoring these invaders requires considerable fieldwork and human power, making remote lamprey detection systems attractive for their continuous monitoring capabilities and potential for workload reduction. However, a lack of available methods for detecting sea lamprey hampers development of such systems. Here we present a sensor composed of two exposed planar interdigitated electrodes (IDE) along with a DC measurement system for the detection of lamprey attachment underwater. Measuring voltage instead of impedance, reduces cost and signal processing complexity, making the device more attractive for field deployment. The system is calibrated to a baseline output voltage and deviations from this baseline occur when objects touch the IDE. Validation was done through testing on live adult sea lampreys using video recordings to correlate lamprey attachments to the sensor response. Three response types were identified corresponding to different attachments: sustained, short and sliding-sustained. Sensor response to sustained and sliding-sustained attachments showed a characteristic exponential decay whereas the response due to short attachments was indistinguishable from measurement noise. Lamprey size was found to have a weak linear correlation with both response parameters, positive for the voltage drop and negative for the time constant of voltage drop. A representative circuit for the lamprey-sensor interaction is proposed and simulated using element values calculated from the response parameters. The response of the model shows agreement with experimental data.","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/JSEN.2021.3122884","usgsCitation":"Gonzalez-Afanador, I., Shi, H., Holbrook, C., Tan, X., and Sepulveda, N., 2021, Invasive sea lamprey detection and characterization using interdigitated electrode (IDE) contact sensor: IEEE Sensors Journal, v. 21, no. 24, p. 27947-27956, https://doi.org/10.1109/JSEN.2021.3122884.","productDescription":"10 p.","startPage":"27947","endPage":"27956","ipdsId":"IP-132564","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450001,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jsen.2021.3122884","text":"Publisher Index Page"},{"id":393194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great 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Center","active":true,"usgs":true}],"preferred":true,"id":828765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":828766,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sepulveda, Nelson","contributorId":264255,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":828767,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226907,"text":"sir20215132 - 2021 - Kootenai River white sturgeon (Acipenser transmontanus) fine-scale habitat selection and preference, Kootenai River near Bonners Ferry, Idaho, 2017","interactions":[],"lastModifiedDate":"2023-05-31T11:20:34.120912","indexId":"sir20215132","displayToPublicDate":"2021-12-20T12:52:33","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":"2021-5132","displayTitle":"Kootenai River White Sturgeon (Acipenser transmontanus) Fine-Scale Habitat Selection and Preference, Kootenai River near Bonners Ferry, Idaho, 2017","title":"Kootenai River white sturgeon (Acipenser transmontanus) fine-scale habitat selection and preference, Kootenai River near Bonners Ferry, Idaho, 2017","docAbstract":"To quantify fine-scale Kootenai River white sturgeon (Acipenser transmontanus) staging and spawning habitat selection and preference within a recently restored reach of the Kootenai River, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, integrated acoustic telemetry data with two-dimensional hydraulic model simulations within a 1.5-kilometer reach of the Kootenai River near Bonners Ferry, northern Idaho. Twenty-seven individual Kootenai River white sturgeon were detected in the study reach during May 6–June 30, 2017. The largest concentration of fish positions occurred near the edge of the gravel bar adjacent to the right bank pool-forming structure and additional concentrations of fish positions occurred near two recently constructed rock substrate clusters. The difference in preferred and available depth distributions quantifies that Kootenai River white sturgeon generally preferred depths of 7–11.5 meters, deeper than the most frequently available depths. About 71 percent of the detections occurred within the lower one-third of the water column, placing Kootenai River white sturgeon at or near the channel bed. The difference in available and preferred water velocities indicated that Kootenai River white sturgeon generally preferred a wide range of velocities from 0.0 to 1.0 meters per second, and generally preferred velocities that were less than the most frequently occurring available velocities. Kootenai River white sturgeon generally preferred the downstream part of the study area where water velocities were less than those in the upstream part. This study concludes that Kootenai River white sturgeon generally avoided shallow areas with increased velocities and generally favored deep areas with lower velocities near recently constructed restoration structures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215132","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Fosness, R.L., Dudunake, T.J., McDonald, R.R., Hardy, R.S., Young, S., Ireland, S., and Hoffman, G.C., 2021, Kootenai River white sturgeon (Acipenser transmontanus) fine-scale habitat selection and preference, Kootenai River near Bonners Ferry, Idaho, 2017: U.S. Geological Survey Scientific Investigations Report 2021–5132, 21 p., https://doi.org/10.3133/sir20215132.","productDescription":"Report: vii, 21 p; Data Release","onlineOnly":"Y","ipdsId":"IP-105165","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":396731,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5132/sir20215132.XML","description":"SIR 2021-5132"},{"id":393132,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97TMY3D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"White sturgeon fine-scale habitat model archive, Kootenai River near Bonners Ferry, Idaho, 2017"},{"id":393131,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5132/sir20215132.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5132"},{"id":393130,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5132/coverthb.jpg"},{"id":396944,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5132/images"},{"id":402990,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215132/full","description":"SIR 2021-5132"}],"country":"United States","state":"Idaho","city":"Bonners Ferry","otherGeospatial":"Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.6802978515625,\n 48.58205840283824\n ],\n [\n -116.05957031249999,\n 48.58205840283824\n ],\n [\n -116.05957031249999,\n 48.99824008113872\n ],\n [\n -116.6802978515625,\n 48.99824008113872\n ],\n [\n -116.6802978515625,\n 48.58205840283824\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
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Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter, Clear Lake), California, are experiencing long-term decreases in abundance. Upper Klamath Lake populations are decreasing not only due to adult mortality, which is relatively low, but also because they are not being balanced by recruitment of young adult suckers into known adult spawning aggregations.
Long-term monitoring of juvenile sucker populations is conducted to (1) determine if there are annual and species-specific differences in production, survival, and growth, (2) better understand when juvenile sucker mortality is greatest, and (3) help identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake. The U.S. Geological Survey monitoring program, that began in 2015, tracks cohorts through summer months and among years in Upper Klamath and Clear Lakes. Data on juvenile suckers captured in trap nets are used to provide information on annual variability in age-0 sucker apparent production, juvenile sucker apparent survival, apparent growth, species composition, and health.
Upper Klamath Lake indices of year-class strength indicated that the 2019 year-class was the strongest in the past 5 years of monitoring. Low detections of age-1 and older suckers indicate that the 2018 cohort experienced poor survival within the first year of life. Shortnose suckers constituted the smallest proportion and suckers with uncertain species identification constituted the largest proportion of the 2019 year-class. Small numbers of Lost River sucker were captured consistently throughout the sampling season.
The relative abundance of age-0 suckers is not a good indicator of year-class strength in Clear Lake. There were no age-0 suckers captured in Clear Lake during the 2015 and 2019 sampling seasons. Most suckers captured were age-1 Klamath largescale/shortnose suckers, which indicated a relatively strong 2018 cohort. Four-year old juveniles from the 2015 cohort were present in 2019 in Clear Lake. Cohorts that do not recruit to our sampling gear until a year or more of age seem to indicate that (1) a stream resident life history is contributing to the lake population and (2) juvenile suckers occupy the Willow Creek drainage for a full year or more. Although these suckers could be either the non-endangered Klamath largescale or the endangered shortnose suckers, a stream resident life history is consistent with these fish being Klamath largescale suckers. Survival of all distinguishable taxa of juvenile suckers is much higher in Clear Lake than in Upper Klamath Lake, with non-trivial numbers of suckers surviving to join spawning aggregations in most years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211119","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Bart, R.J., Kelsey, C.M., Burdick, S.M., Hoy, M.S., and Ostberg, C.O., 2021, Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2019 Monitoring Report: U.S. Geological Survey Open-File Report 2021–1119, 26 p., https://doi.org/10.3133/ofr20211119.","productDescription":"vi, 26 p.","onlineOnly":"Y","ipdsId":"IP-125594","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":393120,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1119/ofr20211119.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1119"},{"id":393119,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1119/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16384887695312,\n 42.21733067375916\n ],\n [\n -121.75048828124999,\n 42.21733067375916\n ],\n [\n -121.75048828124999,\n 42.595554553719204\n ],\n [\n -122.16384887695312,\n 42.595554553719204\n ],\n [\n -122.16384887695312,\n 42.21733067375916\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.25473022460936,\n 41.78052894057897\n ],\n [\n -121.04324340820312,\n 41.78052894057897\n ],\n [\n -121.04324340820312,\n 41.94621306986162\n ],\n [\n -121.25473022460936,\n 41.94621306986162\n ],\n [\n -121.25473022460936,\n 41.78052894057897\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
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6505 NE 65th Street
Seattle, Washington 98115-5016
Flow management is complex in the Willamette River Basin where the U.S. Army Corps of Engineers owns and operates a system of 13 dams and reservoirs (hereinafter Willamette Project), which are spread throughout three large tributaries including the Middle Fork Willamette, McKenzie, and Santiam Rivers. The primary purpose of the Willamette Project is flood-risk management, which provides critical protection to the Willamette Valley, but flow managers must also consider factors such as power generation, water-quality improvement, irrigation, recreation, and protection for aquatic species such as U.S. Endangered Species Act-listed Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss). Flow-management decision-making in the basin can benefit from models that allow for flow-scenario comparisons and a wide range of modeling methods are available. For this study, we examined existing datasets and modeling efforts in the basin and provided an overview of available options. Most previous studies used Physical Habitat Simulation System, habitat data were collected from a series of transects within modeled reaches, and habitat suitability indices were obtained from the literature, or using expert opinion. These studies provide information for specific reaches of the Willamette River Basin, which limits their ability to provide broad-scale predictive capability. Recent efforts to develop a two-dimensional hydraulic model in the mainstem Willamette River, and in specific reaches of primary tributaries downstream from Project dams, have bolstered modeling capabilities in the basin. This work has developed spatially continuous water depth and velocity data in more than 250 kilometers (km) of river downstream from Project dams and has predictive capability throughout the year at flows up to normal peak levels. Additionally, other methods are described for estimating habitat availability, which include habitat suitability criteria, logistic regression, occupancy and abundance modeling, and energetic based approaches. There are strengths and weaknesses to each approach and selection of the preferred approach in the Willamette River Basin will depend on the desired metrics of interest and the risk tolerance of managers and stakeholders in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211114","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Kock, T.J., Perry, R.W., Hansen, G.S., White, J., Stratton Garvin, L., and Wallick, J.R., 2021, Synthesis of habitat availability and carrying capacity research to support water management decisions and enhance conditions for Pacific salmon in the Willamette River, Oregon: U.S. Geological Survey Open-File Report 2021–1114, 24 p., https://doi.org/10.3133/ofr20211114.","productDescription":"vii, 24 p.","onlineOnly":"Y","ipdsId":"IP-127909","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":393073,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1114/ofr20211114.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1114"},{"id":393072,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1114/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.33251953125,\n 43.41302868475145\n ],\n [\n -121.59667968749999,\n 43.41302868475145\n ],\n [\n -121.59667968749999,\n 45.79050946752472\n ],\n [\n -123.33251953125,\n 45.79050946752472\n ],\n [\n -123.33251953125,\n 43.41302868475145\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Sockeye salmon (Oncorhynchus nerka) spawn at beaches along Lake Ozette’s shoreline and within its tributary streams including Umbrella Creek and Big River in western Washington. The tributary-spawning aggregate of the Lake Ozette sockeye salmon population has been increasing from very low abundance through hatchery supplementation, but the beach-spawning aggregate has decreased from the early 20th century resulting in an Endangered Species Act listing of the Lake Ozette sockeye salmon population as “Threatened” in 1999. Among several factors inhibiting the recovery of beach spawning sockeye salmon, the quality of spawning habitat in beaches and low dissolved-oxygen concentrations in beach gravels during incubation were identified as important limitations on the recovery of this population. Proliferation of primarily native near-shore vegetation during the 20th century, as a result of alterations in the lake hydroperiod, accompanied by fine-grained sediment deposition was hypothesized as a potential cause of low rates of water circulation and dissolved-oxygen concentrations in beach spawning gravels. The potential for shoreline vegetation removal to restore spawning habitat function, including dissolved-oxygen concentrations, was evaluated in this report by measuring continuous dissolved-oxygen concentrations with data-logging dissolved-oxygen sensors, by measuring particle-size distribution of beach sediment, and by estimating groundwater/surface-water exchange using vertical sediment temperature profiles at three shoreline sampling areas. These sampling areas included an area of current spawning devoid of shoreline vegetation, an adjacent vegetated area, and an adjacent treatment area where a 3-meter-wide swath of existing above-ground vegetation was removed in 2018 prior to the study. Substrate particle-size distributions, dissolved-oxygen concentrations, sediment temperatures, and groundwater/surface-water exchange were compared among the three shoreline sampling areas. Median grain size (D50) of sediment varied at sampling stations from medium sand fine to coarse gravel. The coarsest sediment generally occurred in the current spawning area that was devoid of vegetation; whereas the vegetated shoreline and the shoreline where above-ground vegetation was removed were characterized by finer sediment. Removal of above-ground vegetation resulted in increased D50 at the most shoreward station at the treatment sampling area from 8.2 millimeters in 2018 to 21.6 millimeters in 2019 but other changes in substrate particle-size distribution in the treatment area were negligible. Increased grain size from 2018 to 2019 at this site suggests that while higher wave energy was allowed to mobilize sediment in the backshore area of the treatment area and winnow fine sediment during the winter, residual root structure in the treatment area may have limited the ability of wave energy to mobilize sediment after removal. During the November 2018 to March 2019 incubation period for sockeye salmon, dissolved-oxygen concentrations at the depth of sockeye salmon egg pockets (15–25 centimeters) within all three shoreline sampling areas were less than 1 milligram per liter throughout the deployment time (October 2018—May 2019) and below the threshold to sustain sockeye salmon embryo development (3 milligrams per liter). In addition, the similarity of dissolved-oxygen concentrations among all three shoreline sampling areas indicates that above-ground vegetation removal did not increase subsurface dissolved-oxygen concentrations. Groundwater/surface-water exchange measured from sediment temperature profiles were variable both within and across shoreline sampling areas. At the most lakeward stations, groundwater discharge to the lake ranged from 0.25 to 0.007 meter per day and was highest at the control station and lowest at the vegetated station. However, in general, the differences in groundwater/surface-water exchange across the three shoreline sampling areas were negligible. Collectively, these results suggest the process of removing above-ground vegetation had little effect on subsurface dissolved-oxygen concentrations and groundwater/surface-water exchange during the study period, but limitations of the study design, including retention of below-surface root cohesion after above-ground vegetation removal, too narrow of a band of vegetation removal, and a limited duration of the monitoring period, may have prevented wave energy from winnowing fine-grained sediment along the shoreline and altering subsurface dissolved-oxygen concentrations during the study period. Response of the substrate particle-size distribution, groundwater/surface-water exchange, and subsurface dissolved-oxygen concentrations to shoreline vegetation removal that includes root-zone removal over a larger extent and longer periods than the 7-month study period from October 2018 to May 2019, however, remain unknown and warrant further investigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215134","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Gendaszek, A.S., and Sheibley, R.W., 2021, Substrate particle-size distribution, dissolved-oxygen concentrations, sediment temperatures, and groundwater/surface-water exchange in shoreline spawning habitat of sockeye salmon (Oncorhynchus nerka) of Lake Ozette, Western Washington: U.S. Geological Survey Scientific Investigations Report 2021–5134, 21 p., https://doi.org/10.3133/sir20215134.","productDescription":"Report: v, 21 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-126474","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":402991,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215134/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5134"},{"id":396954,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5134/sir20215134.XML"},{"id":396953,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5134/images"},{"id":393022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XC9XPR","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Dissolved Oxygen, temperature, particle-size distribution, and groundwater flux in the nearshore of Lake Ozette, WA, October 2018 to May 2019"},{"id":393021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5134/sir20215134.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5134"},{"id":393020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5134/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Ozette","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.69001770019531,\n 48.02988662072008\n ],\n [\n -124.58015441894531,\n 48.02988662072008\n ],\n [\n -124.58015441894531,\n 48.1642534885474\n ],\n [\n -124.69001770019531,\n 48.1642534885474\n ],\n [\n -124.69001770019531,\n 48.02988662072008\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Migrating animals may benefit from social or experiential learning, yet whether and how these learning processes interact or change over time to produce observed migration patterns remains unexplored. Using 16 years of satellite-tracking data from 105 reintroduced whooping cranes, we reveal an interplay between social and experiential learning in migration timing. Both processes dramatically improved individuals’ abilities to dynamically adjust their timing to track environmental conditions along the migration path. However, results revealed an ontogenetic shift in the dominant learning process, whereby subadult birds relied on social information, while mature birds primarily relied on experiential information. These results indicate that the adjustment of migration phenology in response to the environment is a learned skill that depends on both social context and individual age. Assessing how animals successfully learn to time migrations as environmental conditions change is critical for understanding intraspecific differences in migration patterns and for anticipating responses to global change.
","language":"English","publisher":"Nature Publishing","doi":"10.1038/s41467-021-27626-5","usgsCitation":"Abrahms, B., Teitelbaum, C., Mueller, T., and Converse, S.J., 2021, Ontogenetic shifts from social to experiential learning drive avian migration timing: Nature Communications, v. 12, 7326, 8 p., https://doi.org/10.1038/s41467-021-27626-5.","productDescription":"7326, 8 p.","ipdsId":"IP-130971","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450013,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-021-27626-5","text":"Publisher Index Page"},{"id":429751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2021-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Abrahms, Briana","contributorId":337674,"corporation":false,"usgs":false,"family":"Abrahms","given":"Briana","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":902611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teitelbaum, Claire S.","contributorId":337675,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":902612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mueller, Thomas","contributorId":337677,"corporation":false,"usgs":false,"family":"Mueller","given":"Thomas","affiliations":[{"id":81034,"text":"Goethe-University Frankfurt and Senckenberg Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":902613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":902610,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}