Differentiation in physiological activity is a critical component of resource partitioning in resource-limited environments. For example, it is crucial to understand how plant physiological performance varies through time for different functional groups to forecast how terrestrial ecosystems will respond to change. Here, we tracked the seasonal progress of 13 plant species representing C3 shrub, perennial C3 and C4 grass, and annual forb functional groups of the Colorado Plateau, USA. We tested for differences in carbon assimilation strategies and how photosynthetic rates related to recent, seasonal, and annual precipitation and temperature variables. Despite seasonal shifts in species presence and activity, we found small differences in seasonally weighted annual photosynthetic rates among groups. However, differences in the timing of maximum assimilation (Anet) were strongly functional group-dependent. C3 shrubs employed a relatively consistent, low carbon capture strategy and maintained activity year-round but switched to a rapid growth strategy in response to recent climate conditions. In contrast, grasses maintained higher carbon capture during spring months when all perennials had maximum photosynthetic rates, but grasses were dormant during months when shrubs remained active. Perennial grass Anet rates were explained in part by precipitation accumulated during the preceding year and average maximum temperatures during the past 48 h, a result opposite to shrubs. These results lend insight into diverse physiological strategies and their connections to climate, and also point to the potential for shrubs to increase in abundance in response to increased climatic variability in drylands, given shrubs’ ability to respond rapidly to changing conditions.
The
We examine three continuously recording data sets related to the aurora: all‐sky camera images, three‐component magnetometer data, and vertical‐component, broadband seismic data as part of the EarthScope project (2014 to present). Across Alaska there are six all‐sky cameras, 13 magnetometers, and
No abstract available.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020EO147385","usgsCitation":"Bender, A., Lease, R.O., Jones, J.V., and Kreiner, D.C., 2020, Ancient rivers and critical minerals in eastern Alaska: Eos, American Geophysical Union, HTML Document, https://doi.org/10.1029/2020EO147385.","productDescription":"HTML Document","ipdsId":"IP-120232","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":455843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020eo147385","text":"Publisher Index Page"},{"id":408082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.390625,\n 62.226996036319726\n ],\n [\n -141.240234375,\n 62.226996036319726\n ],\n [\n -141.240234375,\n 68.64055504059381\n ],\n [\n -155.390625,\n 68.64055504059381\n ],\n [\n -155.390625,\n 62.226996036319726\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bender, Adrian 0000-0001-7469-1957","orcid":"https://orcid.org/0000-0001-7469-1957","contributorId":219952,"corporation":false,"usgs":true,"family":"Bender","given":"Adrian","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":854113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lease, Richard O. 0000-0003-2582-8966 rlease@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-8966","contributorId":5098,"corporation":false,"usgs":true,"family":"Lease","given":"Richard","email":"rlease@usgs.gov","middleInitial":"O.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":854114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":854116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kreiner, Douglas C. 0000-0002-4405-1403","orcid":"https://orcid.org/0000-0002-4405-1403","contributorId":220474,"corporation":false,"usgs":true,"family":"Kreiner","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":854115,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211634,"text":"70211634 - 2020 - Legacy effects of hydrologic alteration in playa wetland responses to droughts","interactions":[],"lastModifiedDate":"2020-12-29T21:21:31.795804","indexId":"70211634","displayToPublicDate":"2020-07-28T15:46:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Legacy effects of hydrologic alteration in playa wetland responses to droughts","docAbstract":"Wetland conservation increasingly must account for climate change and legacies of previous land-use practices. Playa wetlands provide critical wildlife habitat, but may be impacted by intensifying droughts and previous hydrologic modifications. To inform playa restoration planning, we asked: (1) what are the trends in playa inundation? (2) what are the factors influencing inundation? (3) how is playa inundation affected by increasingly severe drought? (4) do certain playas provide hydrologic refugia during droughts, and (5) if so, how are refugia patterns related to historical modifications? Using remotely sensed surface-water data, we evaluated a 30-year time series (1985–2015) of inundation for 153 playas of the Great Basin, USA. Inundation likelihood and duration increased with wetter weather conditions and were greater in modified playas. Inundation probability was projected to decrease from 22% under average conditions to 11% under extreme drought, with respective annual inundation decreasing from 1.7 to 0.9 months. Only 4% of playas were inundated for at least 2 months in each of the 5 driest years, suggesting their potential as drought refugia. Refugial playas were larger and more likely to have been modified, possibly because previous land managers selected refugial playas for modification. These inundation patterns can inform efforts to restore wetland functions and to conserve playa habitats as climate conditions change.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01334-0","usgsCitation":"Russell, M.T., Cartwright, J.M., Collins, G.H., Long, R.A., and Eitel, J., 2020, Legacy effects of hydrologic alteration in playa wetland responses to droughts: Wetlands, v. 40, p. 2011-2024, https://doi.org/10.1007/s13157-020-01334-0.","productDescription":"14 p.","startPage":"2011","endPage":"2024","ipdsId":"IP-111867","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":455846,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-020-01334-0","text":"Publisher Index Page"},{"id":377103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Oregon","otherGeospatial":"Sheldon-Hart Mountain National Wildlife Refuge Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.96246337890624,\n 41.52091689636249\n ],\n [\n -118.69354248046875,\n 41.52091689636249\n ],\n [\n -118.69354248046875,\n 42.84777884235988\n ],\n [\n -119.96246337890624,\n 42.84777884235988\n ],\n [\n -119.96246337890624,\n 41.52091689636249\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Russell, Micah T.","contributorId":236988,"corporation":false,"usgs":false,"family":"Russell","given":"Micah","email":"","middleInitial":"T.","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":794877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Gail H.","contributorId":59170,"corporation":false,"usgs":false,"family":"Collins","given":"Gail","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":794879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, Ryan A.","contributorId":236989,"corporation":false,"usgs":false,"family":"Long","given":"Ryan","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":794880,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eitel, Jan H.","contributorId":236991,"corporation":false,"usgs":false,"family":"Eitel","given":"Jan H.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":794881,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211358,"text":"70211358 - 2020 - Quantifying development to inform management of Mojave and Sonoran desert tortoise habitat in the American southwest","interactions":[],"lastModifiedDate":"2020-08-27T14:47:07.029758","indexId":"70211358","displayToPublicDate":"2020-07-28T13:40:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying development to inform management of Mojave and Sonoran desert tortoise habitat in the American southwest","docAbstract":"Two tortoise species native to the American southwest have experienced significant habitat loss from development and are vulnerable to ongoing threats associated with continued development. Mojave desert tortoises Gopherus agassizii are listed as threatened under the US Endangered Species Act, and Sonoran desert tortoises G. morafkai are protected in Arizona (USA) and Mexico. Substantial habitat for both species occurs on multiple-use public lands, where development associated with traditional and renewable energy production, recreation, and other activities is likely to continue. Our goal was to quantify development to inform and evaluate actions implemented to protect and manage desert tortoise habitat. We quantified a landscape-level index of development across the Mojave and Sonoran desert tortoise ranges using models of potential habitat for each species (152485 total observations). We used 13 years of Mojave desert tortoise monitoring data (4732 observations) to inform the levels and spatial scales at which tortoises may be affected by development. Most (66–70%) desert tortoise habitat has some development within 1 km. Development levels on desert tortoise habitat are lower inside versus outside areas protected by actions at national, state, and local levels, suggesting that protection efforts may be having the desired effects and providing a needed baseline for future effectiveness evaluations. Of the relatively undeveloped desert tortoise habitat, 43% (74030 km2) occurs outside of existing protections. These lands are managed by multiple federal, state, and local entities and private landowners, and may provide opportunities for future land acquisition or protection, including as mitigation for energy development on public lands.","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01045","usgsCitation":"Carter, S.K., Nussear, K., Esque, T., Leinwand, I.I., Masters, E.H., Inman, R.D., Carr, N.B., and Allison, L.J., 2020, Quantifying development to inform management of Mojave and Sonoran desert tortoise habitat in the American southwest: Endangered Species Research, v. 42, p. 167-184, https://doi.org/10.3354/esr01045.","productDescription":"18 p.","startPage":"167","endPage":"184","ipdsId":"IP-088248","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":455849,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01045","text":"Publisher Index Page"},{"id":377921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada, Utah","otherGeospatial":"Mojave Desert, Sonoran Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.137451171875,\n 31.868227816180674\n ],\n [\n -110.863037109375,\n 34.134541681937364\n ],\n [\n -112.34619140625,\n 35.69299463209881\n ],\n [\n -113.785400390625,\n 36.85325222344018\n ],\n [\n -113.48876953125,\n 37.69251435532741\n ],\n [\n -115.68603515624999,\n 38.40194908237822\n ],\n [\n -119.10278320312499,\n 38.35027253825765\n ],\n [\n -119.871826171875,\n 38.20365531807149\n ],\n [\n -118.30078125,\n 35.639441068973944\n ],\n [\n -115.29052734375,\n 32.667124733120325\n ],\n [\n -112.181396484375,\n 31.737511125687828\n ],\n [\n -112.137451171875,\n 31.868227816180674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":794011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nussear, Kenneth","contributorId":194538,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":794012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":794013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leinwand, Ian IF","contributorId":229704,"corporation":false,"usgs":false,"family":"Leinwand","given":"Ian","email":"","middleInitial":"IF","affiliations":[{"id":41706,"text":"Cherokee Services Group Inc.","active":true,"usgs":false}],"preferred":false,"id":794014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Masters, Elroy H.","contributorId":229705,"corporation":false,"usgs":false,"family":"Masters","given":"Elroy","email":"","middleInitial":"H.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":794015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Inman, Richard D. 0000-0002-1982-7791 rdinman@usgs.gov","orcid":"https://orcid.org/0000-0002-1982-7791","contributorId":187754,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":794016,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carr, Natasha B. 0000-0002-4842-0632 carrn@usgs.gov","orcid":"https://orcid.org/0000-0002-4842-0632","contributorId":1918,"corporation":false,"usgs":true,"family":"Carr","given":"Natasha","email":"carrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":794017,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Allison, Linda J. 0000-0003-1983-901X","orcid":"https://orcid.org/0000-0003-1983-901X","contributorId":229706,"corporation":false,"usgs":false,"family":"Allison","given":"Linda","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":794018,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227774,"text":"70227774 - 2020 - Vegetation sampling and management","interactions":[],"lastModifiedDate":"2022-01-31T17:47:50.412067","indexId":"70227774","displayToPublicDate":"2020-07-28T11:39:15","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"19","title":"Vegetation sampling and management","docAbstract":"What is the utility of vegetation measurements for wildlife managers? In the prairie, savanna, tundra, forest, steppe, and wetland regions of the world, mixtures of plant species provide wildlife with food, cover and, in some circumstances, water; the 3 essential habitat elements necessary to sustain viable wildlife populations. We define habitat in reference to use of a vegetation type by an animal (e.g., deer habitat) and vegetation type when referring to differences in vegetation stands (e.g., marsh vegetation type versus tall grass prairie vegetation type; Hall et al. 1997). In strict definition, the variety of wildlife using plants ranges from snails and voles (Microtus spp.) to bison (Bison bison) and elephants (Loxodonta spp.) in uplands and from mosquitoes and ducks to muskrats (Ondatra zibethicus) and manatees (Trichechus manatus) in wetlands. Through evolutionary processes, some wildlife species are totally dependent on vegetation for all annual life requirements, whereas other species use vegetation only for cover or food. Regardless of the role of vegetation in the sustenance of wildlife, any management or research project that requires evaluation of wildlife and vegetation type relationships on a unit of land will necessitate some form of vegetation measurement.\nThe term vegetation can refer to a single plant or species on a specific site or a community in the landscape. Vegetation may occur naturally or be introduced, and may be live or dead. Uses of vegetation measurements are many: (1) evaluation of vegetation response to management practices, (2) estimation of carrying capacity and/or forage production, (3) characterization of cover and habitat components for an endangered species, or (4) long-term monitoring of the general trend of plant vigor or vegetation type condition.\nSurveying and measuring quantity and quality of vegetation within habitats are basic to wildlife research and management. Grassland, shrubland, and woodland vegetation types are comprised of populations in which individual plants are usually too numerous to inventory completely. Consequently, wildlife biologists usually use sampling techniques to make inferences about the total plant population within a given vegetation type.\nVegetation sampling methodologies have evolved within several ecological disciplines (e.g., plant ecology, forestry, rangeland science) and for a variety of management or research objectives (e.g., estimating forage for ungulates, describing habitat use by passerine birds). Description of every method that has been used to sample vegetation is beyond the scope of this chapter. We describe how to measure vegetation structure, which Dansereau (1957) defined as the spatial organization (distribution) of individuals that form a stand. We have organized this chapter into a description of basic methods of vegetation sampling with examples of how those methods have been applied or modified in wildlife research and management. We assume the investigator/reader has adequate knowledge of the concepts of wildlife ecology, primary habitat requirements of wildlife species under study, and ability to systematically identify the species of wildlife and vascular plants within the geographical area of investigation.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The wildlife techniques manual. Volume 1: Research. Volume 2: Management.","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","collaboration":"The Wildlife Society","usgsCitation":"Higgins, K., Jenkins, K., Uresk, D.W., Perkins, L.B., Jensen, K., Norland, J., Klaver, R.W., and Naugle, D.E., 2020, Vegetation sampling and management, chap. 19 of The wildlife techniques manual. Volume 1: Research. Volume 2: Management., p. 409-438.","productDescription":"30 p.","startPage":"409","endPage":"438","ipdsId":"IP-092410","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":395165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Higgins, Kenneth F.","contributorId":272584,"corporation":false,"usgs":false,"family":"Higgins","given":"Kenneth F.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Kurt 0000-0003-1415-6607","orcid":"https://orcid.org/0000-0003-1415-6607","contributorId":221472,"corporation":false,"usgs":true,"family":"Jenkins","given":"Kurt","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":832183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uresk, Daniel W.","contributorId":272585,"corporation":false,"usgs":false,"family":"Uresk","given":"Daniel","email":"","middleInitial":"W.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":832181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Lora B.","contributorId":272586,"corporation":false,"usgs":false,"family":"Perkins","given":"Lora","email":"","middleInitial":"B.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jensen, Kent C.","contributorId":272587,"corporation":false,"usgs":false,"family":"Jensen","given":"Kent C.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Norland, Jack E.","contributorId":272588,"corporation":false,"usgs":false,"family":"Norland","given":"Jack E.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":832185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":832180,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Naugle, David E.","contributorId":272589,"corporation":false,"usgs":false,"family":"Naugle","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":832186,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227723,"text":"70227723 - 2020 - Research and experimental design","interactions":[],"lastModifiedDate":"2022-03-07T17:07:00.35531","indexId":"70227723","displayToPublicDate":"2020-07-28T11:03:30","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Research and experimental design","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The wildlife techniques manual, volume 1:Research","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Garton, E., Aycrigg, J.L., Conway, C.J., and Horne, J., 2020, Research and experimental design, chap. of The wildlife techniques manual, volume 1:Research, p. 1-39.","productDescription":"39 p.","startPage":"1","endPage":"39","ipdsId":"IP-087905","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"8th Edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Garton, Edward O.","contributorId":272292,"corporation":false,"usgs":false,"family":"Garton","given":"Edward O.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":831921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aycrigg, Jocelyn L.","contributorId":272293,"corporation":false,"usgs":false,"family":"Aycrigg","given":"Jocelyn","email":"","middleInitial":"L.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":831922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horne, Jon S.","contributorId":272294,"corporation":false,"usgs":false,"family":"Horne","given":"Jon S.","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":831923,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228377,"text":"70228377 - 2020 - Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands","interactions":[],"lastModifiedDate":"2022-02-09T16:41:59.820113","indexId":"70228377","displayToPublicDate":"2020-07-28T10:31:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands","title":"Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands","docAbstract":"Due to geologic processes and recent anthropogenic introductions, patterns of genetic and morphological diversity within the Smallmouth Bass (Micropterus dolomieu), which are endemic to the central and eastern United States (USA), are poorly understood. We assessed genetic and morphological differentiation between the widespread Northern Smallmouth Bass (M. d. dolomieu) and the more restricted Neosho Smallmouth Bass (M. d. velox) where their ranges meet in the Central Interior Highlands ecoregion (CIH). Data from 14 microsatellite loci were used to conduct STRUCTURE and principal components analyses to evaluate diversity across populations and screen for hybridization with sympatric Spotted Bass (M. punctulatus). We also tested for morphological differences using five morphometric traits and one meristic trait. We found support for three genetic clusters corresponding to previously described taxonomic variation; five clusters largely corresponding to river systems; and nine clusters representing hierarchical population structure within both ranges. We found evidence of a unique genetic cluster in tributaries of the White River within the Northern Smallmouth Bass range and admixture between the subspecies throughout the Neosho range. We also found evidence of morphological differentiation between subspecies; Neosho Smallmouth Bass exhibited larger head length than Northern Smallmouth Bass relative to total length, and there was a significant interaction of subspecies and orbital length, possibly indicating differential growth patterns between subspecies. Our results reveal multiple levels of divergence, suggesting the CIH harbors greater and more complex Smallmouth Bass diversity than previously thought.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-020-01295-1","usgsCitation":"Gunn, J.C., Berkman, L.K., Koppelman, J.K., Taylor, A.T., Brewer, S.K., Long, J.M., and Eggert, L.S., 2020, Complex patterns of genetic and morphological differentiation in the Smallmouth Bass subspecies (Micropterus dolomieu dolomieu and M. d. velox) of the Central Interior Highlands: Conservation Genetics, v. 21, p. 891-904, https://doi.org/10.1007/s10592-020-01295-1.","productDescription":"14 p.","startPage":"891","endPage":"904","ipdsId":"IP-111223","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Kansas, Mississippi, Missouri, Oklahoma, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.7890625,\n 33.99802726234877\n ],\n [\n -88.41796875,\n 33.99802726234877\n ],\n [\n -88.41796875,\n 39.80853604144591\n ],\n [\n -98.7890625,\n 39.80853604144591\n ],\n [\n -98.7890625,\n 33.99802726234877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2020-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gunn, Joe C.","contributorId":275348,"corporation":false,"usgs":false,"family":"Gunn","given":"Joe","email":"","middleInitial":"C.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":834024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkman, Leah K.","contributorId":275349,"corporation":false,"usgs":false,"family":"Berkman","given":"Leah","email":"","middleInitial":"K.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":834025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koppelman, Jeff K.","contributorId":275350,"corporation":false,"usgs":false,"family":"Koppelman","given":"Jeff","email":"","middleInitial":"K.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":834026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, A. T.","contributorId":275351,"corporation":false,"usgs":false,"family":"Taylor","given":"A.","email":"","middleInitial":"T.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":834027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834029,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eggert, Lori S.","contributorId":106325,"corporation":false,"usgs":false,"family":"Eggert","given":"Lori","email":"","middleInitial":"S.","affiliations":[{"id":13259,"text":"USDA Forest Service Northern Research Station","active":true,"usgs":false}],"preferred":false,"id":834030,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211356,"text":"sir20205075 - 2020 - Estimates of groundwater discharge by evapotranspiration, Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18","interactions":[],"lastModifiedDate":"2025-05-14T18:35:24.827452","indexId":"sir20205075","displayToPublicDate":"2020-07-28T09:24:17","publicationYear":"2020","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":"2020-5075","displayTitle":"Estimates of Groundwater Discharge by Evapotranspiration, Stump Spring and Hiko Springs, Clark County, Southern Nevada, 2016–18","title":"Estimates of groundwater discharge by evapotranspiration, Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18","docAbstract":"This report documents methodology and results of a study that estimated groundwater discharge by evapotranspiration (GWET) from phreatophytic vegetation in two desert riparian areas with ephemeral spring discharge in Clark County, southern Nevada. The phreatophytes consisted primarily of western honey mesquite [Prosopis glandulosa var. torreyana (L.D. Benson) M.C. Johnst.] at Stump Spring and mixed shrubs at Hiko Springs. An eddy-covariance station and precipitation gage were established to concurrently measure actual evapotranspiration (AET) and precipitation. Site-scale GWET rates—computed by subtracting measured precipitation from AET—were 239 ±45 millimeters per year (mm/yr) based on measurements over one growing season at Stump Spring and 109 ±27 mm/yr averaged over two growing seasons at Hiko Springs.
The volume of GWET for each groundwater discharge area (GDA) was estimated by developing relations between site-scale computed GWET rates and phreatophytic vegetation represented by a Normalized Difference Vegetation Index (NDVI). A GDA was delineated for the natural drainage in each area by mapping the extent of phreatophytes using high-resolution imagery. A second GDA was delineated at Stump Spring by mapping the extent of phreatophytes in the Area of Critical Environmental Concern (ACEC). Site-scale GWET rates were scaled up by applying the site-based GWET -NDVI relations to NDVI distributions in each GDA. The areas of phreatophytic vegetation within each GDA, area-weighted mean GWET rates, and GWET volumes were as follows: (1) Stump Spring—59 hectares (ha), 126 mm/yr, 7.4 ± 1.4 ×104 cubic meters per year (m3/yr) (60 ± 11 acre-feet/yr); Stump Spring ACEC—49 ha, 98 mm/yr, 4.9 ± 0.9 × 104 m3/yr (39 ± 7 acre-feet/yr); and (2) Hiko Springs—7.2 ha, 112 mm/yr, 0.8 ±0.2 × 104 m3/yr (6.6 ±1.6 acre-feet/yr). The GWET rate computed at Stump Spring compared favorably with published GWET rates for mesquite.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205075","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Moreo, M.T., Buto, S.G., Smith, D.W., and Nelson, N.C., 2020, Estimates of groundwater discharge by evapotranspiration, Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5075, 39 p., https://doi.org/ 10.3133/ sir20205075.","productDescription":"Report: vii, 39 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-098487","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":376763,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5075/sir20205075.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5075"},{"id":376762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5075/coverthb.jpg"},{"id":376765,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KD11GX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental evapotranspiration gap-filled datasets from Stump Spring and Hiko Springs, Clark County, southern Nevada, 2016–18"},{"id":376764,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BG3VKP","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial data to support estimates of annual groundwater discharge by evapotranspiration from areas of spring-fed riparian vegetation, Stump Spring and Hiko Springs, Clark County, southern Nevada"}],"country":"United States","state":"Nevada","county":"Clark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-115.894,36.8425],[-115.737,36.843],[-115.737,36.8539],[-115.303,36.854],[-115.299,36.8539],[-115.283,36.8541],[-114.979,36.8536],[-114.976,36.8535],[-114.954,36.8535],[-114.943,36.8533],[-114.925,36.8533],[-114.889,36.853],[-114.871,36.8531],[-114.867,36.853],[-114.853,36.8531],[-114.784,36.853],[-114.757,36.8533],[-114.757,36.8437],[-114.665,36.843],[-114.649,36.8435],[-114.561,36.8432],[-114.541,36.8432],[-114.495,36.8429],[-114.429,36.8426],[-114.218,36.843],[-114.111,36.8427],[-114.109,36.8427],[-114.05,36.8428],[-114.049,36.8242],[-114.049,36.8092],[-114.049,36.8061],[-114.049,36.7121],[-114.048,36.5807],[-114.048,36.5136],[-114.048,36.4888],[-114.048,36.231],[-114.048,36.2047],[-114.049,36.1951],[-114.055,36.194],[-114.062,36.1893],[-114.063,36.1887],[-114.072,36.1777],[-114.073,36.1765],[-114.073,36.1754],[-114.078,36.1651],[-114.084,36.1554],[-114.088,36.1481],[-114.095,36.1416],[-114.098,36.1353],[-114.1,36.1277],[-114.103,36.1241],[-114.109,36.1214],[-114.116,36.1195],[-114.12,36.1172],[-114.123,36.113],[-114.123,36.1084],[-114.12,36.1054],[-114.117,36.1023],[-114.115,36.1],[-114.116,36.0964],[-114.118,36.0933],[-114.123,36.0889],[-114.128,36.0794],[-114.134,36.0678],[-114.136,36.0597],[-114.137,36.0525],[-114.137,36.0502],[-114.137,36.0457],[-114.14,36.0416],[-114.143,36.0381],[-114.145,36.0364],[-114.146,36.0346],[-114.147,36.0314],[-114.147,36.0305],[-114.151,36.0288],[-114.154,36.0285],[-114.161,36.0291],[-114.165,36.0288],[-114.171,36.0296],[-114.176,36.0292],[-114.178,36.029],[-114.184,36.0266],[-114.188,36.0239],[-114.193,36.0227],[-114.202,36.02],[-114.213,36.0166],[-114.224,36.0145],[-114.234,36.0139],[-114.241,36.0151],[-114.246,36.0178],[-114.258,36.0232],[-114.267,36.0297],[-114.269,36.0359],[-114.269,36.0423],[-114.272,36.0467],[-114.276,36.048],[-114.283,36.0492],[-114.287,36.051],[-114.29,36.0544],[-114.295,36.0568],[-114.299,36.0585],[-114.305,36.058],[-114.314,36.0596],[-114.316,36.0628],[-114.316,36.0668],[-114.312,36.07],[-114.311,36.0705],[-114.307,36.0735],[-114.305,36.078],[-114.306,36.0826],[-114.309,36.0867],[-114.312,36.09],[-114.316,36.0945],[-114.319,36.0988],[-114.321,36.102],[-114.324,36.1044],[-114.328,36.1057],[-114.329,36.106],[-114.335,36.1082],[-114.339,36.1106],[-114.341,36.1143],[-114.346,36.1211],[-114.351,36.1241],[-114.355,36.1268],[-114.36,36.1316],[-114.362,36.1362],[-114.368,36.1414],[-114.374,36.1438],[-114.382,36.144],[-114.389,36.1457],[-114.397,36.1473],[-114.404,36.1467],[-114.411,36.1461],[-114.418,36.1419],[-114.424,36.1377],[-114.424,36.1373],[-114.433,36.132],[-114.44,36.1273],[-114.445,36.1297],[-114.45,36.1317],[-114.454,36.1371],[-114.458,36.141],[-114.462,36.1424],[-114.466,36.1427],[-114.471,36.1404],[-114.48,36.1356],[-114.491,36.1305],[-114.498,36.1288],[-114.504,36.1283],[-114.508,36.1309],[-114.51,36.1341],[-114.51,36.1373],[-114.508,36.14],[-114.504,36.1422],[-114.502,36.1444],[-114.501,36.1462],[-114.504,36.1501],[-114.507,36.1518],[-114.511,36.1528],[-114.515,36.1524],[-114.521,36.1516],[-114.524,36.1495],[-114.527,36.1482],[-114.531,36.1483],[-114.535,36.1502],[-114.539,36.153],[-114.546,36.1547],[-114.551,36.1536],[-114.559,36.1526],[-114.565,36.1515],[-114.573,36.1509],[-114.583,36.1486],[-114.592,36.1439],[-114.597,36.1409],[-114.598,36.1403],[-114.603,36.1367],[-114.608,36.1338],[-114.612,36.1328],[-114.616,36.1331],[-114.62,36.1341],[-114.621,36.1369],[-114.623,36.1394],[-114.626,36.142],[-114.63,36.1421],[-114.632,36.1408],[-114.636,36.1391],[-114.64,36.1363],[-114.646,36.1347],[-114.649,36.1329],[-114.653,36.1299],[-114.656,36.1268],[-114.658,36.125],[-114.661,36.1224],[-114.662,36.1217],[-114.664,36.1197],[-114.669,36.1174],[-114.67,36.1171],[-114.675,36.1149],[-114.675,36.1147],[-114.68,36.1129],[-114.682,36.1127],[-114.687,36.1132],[-114.691,36.114],[-114.695,36.1123],[-114.698,36.111],[-114.701,36.1101],[-114.706,36.1094],[-114.711,36.1084],[-114.722,36.1079],[-114.73,36.1061],[-114.737,36.1042],[-114.743,36.1007],[-114.749,36.0952],[-114.753,36.0919],[-114.755,36.0851],[-114.754,36.0811],[-114.75,36.0771],[-114.745,36.0736],[-114.742,36.07],[-114.74,36.0657],[-114.738,36.061],[-114.736,36.0579],[-114.735,36.0542],[-114.738,36.0515],[-114.74,36.0498],[-114.74,36.0471],[-114.74,36.0428],[-114.738,36.0407],[-114.734,36.0393],[-114.731,36.038],[-114.728,36.0349],[-114.724,36.0326],[-114.724,36.03],[-114.726,36.0264],[-114.729,36.0231],[-114.734,36.0188],[-114.739,36.0156],[-114.741,36.0135],[-114.742,36.0119],[-114.742,36.0081],[-114.741,36.0028],[-114.741,35.9992],[-114.741,35.9951],[-114.743,35.9921],[-114.745,35.9891],[-114.745,35.9869],[-114.745,35.9846],[-114.743,35.9798],[-114.741,35.9743],[-114.736,35.9671],[-114.732,35.964],[-114.731,35.9591],[-114.731,35.9547],[-114.731,35.9482],[-114.732,35.9462],[-114.732,35.9445],[-114.729,35.9414],[-114.724,35.9393],[-114.72,35.9357],[-114.712,35.9312],[-114.709,35.9255],[-114.708,35.922],[-114.709,35.917],[-114.709,35.912],[-114.707,35.9086],[-114.705,35.9047],[-114.702,35.9024],[-114.7,35.9003],[-114.698,35.8983],[-114.684,35.8874],[-114.679,35.8826],[-114.678,35.8818],[-114.676,35.8795],[-114.671,35.8757],[-114.667,35.8747],[-114.664,35.8733],[-114.663,35.8719],[-114.663,35.8698],[-114.666,35.8688],[-114.668,35.8682],[-114.68,35.8641],[-114.685,35.8624],[-114.689,35.8611],[-114.69,35.8602],[-114.691,35.8596],[-114.696,35.855],[-114.699,35.8546],[-114.7,35.8545],[-114.706,35.853],[-114.707,35.8511],[-114.707,35.8489],[-114.706,35.8458],[-114.703,35.842],[-114.702,35.8399],[-114.699,35.8364],[-114.697,35.8341],[-114.697,35.8314],[-114.697,35.8305],[-114.701,35.8261],[-114.703,35.8243],[-114.703,35.8217],[-114.704,35.8194],[-114.707,35.8145],[-114.708,35.8137],[-114.709,35.8129],[-114.711,35.8108],[-114.713,35.8086],[-114.713,35.8064],[-114.711,35.8027],[-114.709,35.8008],[-114.707,35.7994],[-114.705,35.7966],[-114.702,35.7907],[-114.702,35.7854],[-114.702,35.7834],[-114.701,35.777],[-114.701,35.7718],[-114.701,35.7679],[-114.699,35.7647],[-114.696,35.7597],[-114.696,35.7583],[-114.697,35.7551],[-114.699,35.7525],[-114.699,35.7493],[-114.698,35.7474],[-114.696,35.7402],[-114.697,35.7374],[-114.697,35.7347],[-114.699,35.7279],[-114.702,35.7225],[-114.703,35.7212],[-114.705,35.7178],[-114.706,35.7136],[-114.705,35.7091],[-114.705,35.705],[-114.702,35.7006],[-114.698,35.6966],[-114.697,35.6958],[-114.693,35.693],[-114.688,35.69],[-114.685,35.6881],[-114.683,35.6856],[-114.682,35.6831],[-114.682,35.6804],[-114.683,35.679],[-114.684,35.6754],[-114.685,35.6727],[-114.686,35.6704],[-114.687,35.6668],[-114.688,35.6636],[-114.689,35.6628],[-114.69,35.6573],[-114.689,35.6491],[-114.687,35.646],[-114.681,35.6402],[-114.677,35.6367],[-114.674,35.6335],[-114.672,35.6302],[-114.672,35.6279],[-114.668,35.6219],[-114.664,35.62],[-114.661,35.6184],[-114.659,35.6162],[-114.657,35.6117],[-114.656,35.6102],[-114.656,35.6022],[-114.657,35.5971],[-114.658,35.5935],[-114.66,35.5912],[-114.662,35.5898],[-114.663,35.5889],[-114.668,35.5862],[-114.676,35.5827],[-114.676,35.5804],[-114.677,35.5772],[-114.674,35.5752],[-114.673,35.5746],[-114.668,35.5697],[-114.666,35.5643],[-114.665,35.561],[-114.665,35.5583],[-114.666,35.5561],[-114.665,35.5506],[-114.665,35.5493],[-114.664,35.5446],[-114.662,35.5399],[-114.66,35.5373],[-114.659,35.5354],[-114.66,35.5332],[-114.662,35.5301],[-114.664,35.5279],[-114.666,35.5268],[-114.667,35.5258],[-114.67,35.5234],[-114.672,35.5208],[-114.675,35.5184],[-114.676,35.5177],[-114.678,35.515],[-114.679,35.5114],[-114.68,35.5104],[-114.68,35.5005],[-114.68,35.4986],[-114.678,35.4942],[-114.677,35.4904],[-114.677,35.4896],[-114.675,35.4841],[-114.673,35.4788],[-114.67,35.4697],[-114.667,35.4639],[-114.665,35.4606],[-114.657,35.4489],[-114.657,35.4482],[-114.649,35.4362],[-114.622,35.3906],[-114.621,35.3886],[-114.615,35.372],[-114.614,35.3699],[-114.61,35.3599],[-114.605,35.3516],[-114.604,35.3504],[-114.602,35.3438],[-114.601,35.3429],[-114.6,35.3365],[-114.599,35.3337],[-114.596,35.3246],[-114.597,35.3188],[-114.598,35.3137],[-114.599,35.3065],[-114.599,35.3052],[-114.599,35.3033],[-114.599,35.2993],[-114.597,35.2925],[-114.597,35.2905],[-114.596,35.2866],[-114.592,35.2794],[-114.589,35.2708],[-114.589,35.2699],[-114.588,35.2622],[-114.587,35.2599],[-114.587,35.2555],[-114.587,35.2508],[-114.586,35.246],[-114.586,35.2426],[-114.586,35.2387],[-114.584,35.2339],[-114.583,35.2297],[-114.583,35.2257],[-114.582,35.2242],[-114.582,35.2202],[-114.582,35.2174],[-114.583,35.2116],[-114.58,35.2075],[-114.577,35.2048],[-114.576,35.2028],[-114.573,35.1969],[-114.574,35.1922],[-114.572,35.1859],[-114.571,35.18],[-114.571,35.1772],[-114.571,35.1759],[-114.572,35.1737],[-114.572,35.1704],[-114.572,35.1695],[-114.572,35.165],[-114.572,35.1623],[-114.575,35.1551],[-114.576,35.151],[-114.576,35.1494],[-114.576,35.1463],[-114.575,35.1447],[-114.575,35.1404],[-114.576,35.1374],[-114.579,35.1318],[-114.584,35.1272],[-114.59,35.1255],[-114.598,35.1244],[-114.608,35.1238],[-114.617,35.1236],[-114.623,35.1226],[-114.635,35.1189],[-114.641,35.1163],[-114.647,35.1105],[-114.649,35.107],[-114.649,35.1038],[-114.646,35.0992],[-114.644,35.0979],[-114.64,35.0958],[-114.634,35.0933],[-114.628,35.0912],[-114.627,35.0907],[-114.625,35.09],[-114.623,35.0892],[-114.619,35.0866],[-114.615,35.0836],[-114.611,35.0806],[-114.607,35.0769],[-114.605,35.073],[-114.604,35.0677],[-114.606,35.0614],[-114.607,35.0598],[-114.61,35.0565],[-114.614,35.0545],[-114.618,35.0526],[-114.619,35.052],[-114.625,35.0485],[-114.63,35.0443],[-114.632,35.0416],[-114.635,35.0353],[-114.636,35.0291],[-114.636,35.0237],[-114.635,35.0205],[-114.636,35.0176],[-114.636,35.0153],[-114.636,35.0132],[-114.635,35.0094],[-114.635,35.0076],[-114.633,35.0021],[-114.659,35.0234],[-114.675,35.0363],[-114.677,35.0378],[-114.695,35.0527],[-114.712,35.0668],[-114.722,35.0742],[-114.743,35.0905],[-114.753,35.0997],[-114.774,35.1165],[-114.828,35.1609],[-114.935,35.2469],[-114.95,35.2584],[-114.982,35.2837],[-115.027,35.3204],[-115.044,35.3338],[-115.073,35.3567],[-115.091,35.371],[-115.109,35.3858],[-115.156,35.4234],[-115.207,35.463],[-115.21,35.4658],[-115.221,35.4744],[-115.415,35.6279],[-115.427,35.6375],[-115.522,35.7115],[-115.536,35.7229],[-115.591,35.7661],[-115.628,35.7954],[-115.648,35.8111],[-115.651,35.8111],[-115.688,35.8402],[-115.895,36.0015],[-115.895,36.0927],[-115.895,36.1163],[-115.894,36.1608],[-115.894,36.1957],[-115.894,36.2923],[-115.895,36.3087],[-115.894,36.457],[-115.895,36.462],[-115.894,36.4806],[-115.895,36.5005],[-115.895,36.5858],[-115.895,36.5962],[-115.895,36.598],[-115.894,36.6702],[-115.894,36.686],[-115.894,36.8425]]]},\"properties\":{\"name\":\"Clark\",\"state\":\"NV\"}}]}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The wildlife techniques manual","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Organ, J.F., Decker, D.J., Riley, S.J., Mcdonald, J.E., and Mahoney, S.P., 2020, Adaptive management in wildlife conservation, chap. 31 of The wildlife techniques manual.","ipdsId":"IP-099032","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":382594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":798066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Decker, Daniel J.","contributorId":114044,"corporation":false,"usgs":true,"family":"Decker","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":809134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riley, Shawn J.","contributorId":202177,"corporation":false,"usgs":false,"family":"Riley","given":"Shawn","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":809135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mcdonald, John E. Jr.","contributorId":171604,"corporation":false,"usgs":false,"family":"Mcdonald","given":"John","suffix":"Jr.","email":"","middleInitial":"E.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":809136,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahoney, Shane P.","contributorId":199084,"corporation":false,"usgs":false,"family":"Mahoney","given":"Shane","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":809137,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227091,"text":"70227091 - 2020 - Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia","interactions":[],"lastModifiedDate":"2021-12-29T15:11:29.100926","indexId":"70227091","displayToPublicDate":"2020-07-28T08:54:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia","title":"Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia","docAbstract":"Little is known about hypogeous, sequestrate (i.e., truffles) fungi in the eastern United States. Since the fruiting bodies of these fungi are part of the diet of multiple rodent species, filling data gaps is important to understanding more about truffle species distribution and habitat associations. During a microhabitat study on radio-collared Virginia Northern Flying Squirrels (Glaucomys sabrinus fuscus Miller) in 2013, we opportunistically sampled truffles at small mammal digs and scratches within our microhabitat plots. All sampling was conducted within known squirrel foraging home ranges. We found three Elaphomyces species: Elaphomyces macrosporus Castellano and Elliott, E. verruculosus Castellano, and E. americanum Castellano. Our observations of E. macroporus are the first from West Virginia. Herein, we describe the microhabitat associations for each fungal species. We suggest using small mammal digs and scratches as potential indicators to opportunistically gather more information on truffle species in coniferous forests of the eastern United States.","language":"English","publisher":"Humboldt Field Research Institute","doi":"10.1656/045.027.0305","usgsCitation":"Diggins, C., Castellano, M., and Ford, W., 2020, Hypogeous, sequestrate fungi (genus Elaphomyces) found at small-mammal foraging sites in high-elevation conifer forests of West Virginia: Northeastern Naturalist, v. 27, no. 3, p. N40-N47, https://doi.org/10.1656/045.027.0305.","productDescription":"8 p.","startPage":"N40","endPage":"N47","ipdsId":"IP-117955","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455853,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/102440","text":"External Repository"},{"id":393584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","county":"Pocahontas County, Randolph County, Webster County","otherGeospatial":"Kumbrabow State Forest, Monogahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.54901123046875,\n 38.16209595668554\n ],\n [\n -79.92828369140625,\n 38.16209595668554\n ],\n [\n -79.92828369140625,\n 38.52668162061619\n ],\n [\n -80.54901123046875,\n 38.52668162061619\n ],\n [\n -80.54901123046875,\n 38.16209595668554\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Richardson, David ","contributorId":223903,"corporation":false,"usgs":false,"family":"Richardson","given":"David ","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":829651,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Diggins, Corinne A.","contributorId":270604,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":829609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castellano, Michael A.","contributorId":270606,"corporation":false,"usgs":false,"family":"Castellano","given":"Michael A.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":829610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":829608,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213045,"text":"70213045 - 2020 - Managing state lands for wildlife","interactions":[],"lastModifiedDate":"2021-01-26T14:37:21.462527","indexId":"70213045","displayToPublicDate":"2020-07-28T08:36:41","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"40","title":"Managing state lands for wildlife","docAbstract":"State-owned lands are a vital component of state fish and wildlife management programs because they contain valuable habitats for a diversity of wild species and often provide important public access. The Association of Fish and Wildlife Agencies (AFWA 2017) reported state agencies manage or administer approximately 188 million hectares of land, including 10 million hectares under fee title ownership with the remainder of those lands leased or licensed in conservation agreements, grazing allotments, or rights-of-ways.\nState agencies commonly strive to enhance wildlife habitats within their purview, but have also improved areas not directly owned by them. In total, an estimated 22,953,364 additional hectares have been improved by state fish and wildlife agencies through private landowner agreements (AFWA 2017). States also own 192,000 individual water rights and have developed at least 53,000 formal partnership agreements to conserve wildlife.\nToday, there are a plethora of state land categories including wildlife refuges, wildlife management areas, state forests, parks and trails, state trust lands, research and natural areas, and public fishing areas. Acquisition and/or leasing of properties to provide access for fishing and hunting are often vitally important in meeting local constituents’ desires for consumptive uses of their natural resources. However, state agencies also provide areas for forest conservation, boating, camping, hiking, rock hounding, skiing, wildlife viewing, and other nonconsumptive recreational pursuits. Lastly, states often acquire properties primarily for wildlife habitat management/conservation or to preserve areas of cultural and historic value to their citizens.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The wildlife techniques manual.","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Ryder, T., and Organ, J.F., 2020, Managing state lands for wildlife, chap. 40 of The wildlife techniques manual., p. 264-272.","productDescription":"9 p.","startPage":"264","endPage":"272","ipdsId":"IP-103548","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":382591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"8th","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ryder, Thomas","contributorId":239899,"corporation":false,"usgs":false,"family":"Ryder","given":"Thomas","email":"","affiliations":[{"id":48037,"text":"Wyoming Game and Fish Department (Retired)","active":true,"usgs":false}],"preferred":false,"id":798064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":798065,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223184,"text":"70223184 - 2020 - Cryptic evolved melts beneath monotonous basaltic shield volcanoes in the Galápagos Archipelago","interactions":[],"lastModifiedDate":"2021-08-17T13:35:37.373175","indexId":"70223184","displayToPublicDate":"2020-07-28T07:49:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Cryptic evolved melts beneath monotonous basaltic shield volcanoes in the Galápagos Archipelago","docAbstract":"Many volcanoes erupt compositionally homogeneous magmas over timescales ranging from decades to millennia. This monotonous activity is thought to reflect a high degree of chemical homogeneity in their magmatic systems, leading to predictable eruptive behaviour. We combine petrological analyses of erupted crystals with new thermodynamic models to characterise the diversity of melts in magmatic systems beneath monotonous shield volcanoes in the Galápagos Archipelago (Wolf and Fernandina). In contrast with the uniform basaltic magmas erupted at the surface over long timescales, we find that the sub-volcanic systems contain extreme heterogeneity, with melts extending to rhyolitic compositions. Evolved melts are in low abundance and large volumes of basalt flushing through the crust from depth overprint their chemical signatures. This process will only maintain monotonous activity while the volume of melt entering the crust is high, raising the possibility of transitions to more silicic activity given a decrease in the crustal melt flux.
Because of a lack of clarity about the status of golden eagles (Aquila chrysaetos) in coastal southern California, the U.S. Geological Survey, in collaboration with U.S. Fish and Wildlife Service, California Department of Fish and Wildlife, Bureau of Land Management, and San Diego Management and Monitoring Program, began a multi-year survey and tracking program of golden eagles to address questions regarding habitat use, movement behavior, nest occupancy, genetic population structure, and human impacts on eagles. Golden eagle trapping and tracking efforts began in September 2014. During trapping efforts from September 29, 2014, to February 23, 2017, 37 golden eagles were captured. During trapping efforts from February 24, 2017, to December 2, 2019, an additional 7 golden eagles (4 females and 3 males) were captured, and one previously captured female was recaptured in San Diego County. Biotelemetry data for 27 of the 44 golden eagles that were transmitting data from February 24, 2017, to December 2, 2019, are presented. These eagles ranged as far north as British Columbia, Canada, and as far south as Ciudad Insurgentes, Baja California, Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1128","collaboration":"Prepared in cooperation with the San Diego Association of Governments (SANDAG), U.S. Fish and Wildlife Service (USFWS), California Department of Fish and Wildlife (CDFW), Bureau of Land Management (BLM), and San Diego Management and Monitoring Program (SDMMP)","usgsCitation":"Tracey, J.A., Madden, M.C., Molden, J.C., Sebes, J.B., Bloom, P.H., and Fisher, R.N., 2020, Biotelemetry data for Golden Eagles (Aquila chrysaetos) captured in coastal southern California, February 2017–December 2019: U.S. Geological Survey Data Series 1128, 34 p., https://doi.org/ 10.3133/ ds1128.","productDescription":"vii, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-117961","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":376715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1128/covrthb.jpg"},{"id":376716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1128/ds1128.pdf","text":"Report","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0810546875,\n 32.62087018318113\n ],\n [\n -115.81787109375,\n 32.62087018318113\n ],\n [\n -115.81787109375,\n 33.925129700072\n ],\n [\n -118.0810546875,\n 33.925129700072\n ],\n [\n -118.0810546875,\n 32.62087018318113\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Understanding groundwater quality in transboundary aquifers like the Hueco Bolson is important for the 2.7 million people along the United States and Mexico border living in and near the combined metropolitan areas of Ciudad Juárez, Mexico, and El Paso, Texas, who rely on groundwater for water supply. To better understand water-quality conditions in the Mexico–New Mexico–Texas transboundary area, 23 water-supply wells were sampled in the Hueco Bolson within the United States near El Paso, Tex., during August–September 2016 and May–June 2017. Groundwater samples were analyzed for physical properties, major ions, dissolved solids, nutrients, trace elements, organic compounds, and selected isotopes such as strontium, hydrogen, oxygen, tritium, and carbon-14.
Most of the water samples from the Hueco Bolson water-supply wells were classified as a sodium-chloride type water. Only four wells sampled in the study area had dissolved-solids concentrations greater than 1,000 milligrams per liter (mg/L), with three of those wells closest to the Rio Grande/Río Bravo del Norte (hereinafter referred to as the Rio Grande).
Nitrate concentrations in the groundwater samples collected in the study area ranged from below the long-term method detection level of 0.04 to 6.2 mg/L. Arsenic was the only trace element detected in the wells sampled that had concentrations exceeding the designated drinking-water standard of 10 micrograms per liter (μg/L). Four of the 23 wells had arsenic concentrations greater than 10 μg/L, and these wells were all located near the Rio Grande. Three of the wells with the highest uranium concentrations (greater than 10 μg/L) were also located near the Rio Grande, and two of those wells were the same wells that had arsenic concentrations greater than 10 μg/L. Groundwater samples were analyzed for 83 organic compounds, but only 6 were detected—simazine, prometryn, prometon, atrazine, deethylatrazine, and dichloroaniline. All concentrations for the organic compounds detected were less than 0.03 μg/L, and the detections were only in five groundwater wells, three of which were located near the Rio Grande.
Strontium, hydrogen, and oxygen isotopic values indicate that recharge water to the central and northern sections of the study area originates from near the Franklin Mountains, whereas groundwater in the southern section of the study area is likely from the Rio Grande valley. Tritium and carbon-14 values indicate that most of the wells that were sampled contained water that is considered premodern, which means that it is more than several hundred years old. Three wells with modern groundwater (approximately less than 70 years old) are located near the Rio Grande and are the same wells that had elevated arsenic or uranium concentrations and organic compound detections. Most of the results of the geochemical analyses indicate that groundwater near the Rio Grande has higher dissolved-solids concentrations, higher concentrations of several trace elements, and slightly more organic compound detections than the groundwater farther away from the Rio Grande; therefore, the groundwater may be affected by the Rio Grande and surrounding land-use activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205056","usgsCitation":"Ging, P.B., Humberson, D.G., and Ikard, S.J., 2020, Geochemical assessment of the Hueco Bolson, New Mexico and Texas, 2016–17: U.S. Geological Survey Scientific Investigations Report 2020–5056, 30 p., https://doi.org/10.3133/sir20205056.","productDescription":"Report: viii, 30 p.; Data Release","numberOfPages":"42","onlineOnly":"N","ipdsId":"IP-112001","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":376690,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5056/sir20205056.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 22020–5056"},{"id":376689,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5056/coverthb.jpg"},{"id":376691,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73T9GHR","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Transboundary Aquifer Assessment Program—Water-quality data from groundwater wells in the Hueco Bolson near El Paso, Texas, August 2016–June 2017"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Hueco Bolson","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.4462890625,\n 31.12819929911196\n ],\n [\n -105.53466796874999,\n 31.12819929911196\n ],\n [\n -105.53466796874999,\n 32.491230287947594\n ],\n [\n -107.4462890625,\n 32.491230287947594\n ],\n [\n -107.4462890625,\n 31.12819929911196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Conceptual models of magma storage and transport under calderas favor a connected system of sills and dikes. These features are individually below the resolution of standard seismic tomography, but radial seismic anisotropy can reveal where they exist in aggregate. We model radial anisotropy at Okmok caldera, Alaska, to demonstrate the presence of a caldera-centered stacked sill complex and surrounding dike system. We show that ascending magma, inferred from seismicity, either intersects the sill complex, resulting in a larger volume eruption of evolved magma, or bypasses the overlying sill complex via dikes, resulting in a low-volume mafic eruption. Our results exemplify how the locations of magma storage and paths of transport impact eruption size and composition. As this type of crustal storage is likely common to many calderas, this analysis offers a potential new framework for volcano observatories to forecast the size of impending eruptions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088122","usgsCitation":"Miller, D., Bennington, N., Haney, M.M., Bedrosian, P.A., Key, K., Thurber, C., Hart, L., and Ohlendorf, S., 2020, Linking magma storage and ascent to eruption volume and composition at an arc caldera: Geophysical Research Letters, v. 47, no. 14, e2020GL088122, 9 p., https://doi.org/10.1029/2020GL088122.","productDescription":"e2020GL088122, 9 p.","ipdsId":"IP-109819","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":501607,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/c8e18fc1eae64dc78c6b03f108287254","text":"External Repository"},{"id":501580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian Islands, Okmok caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -167.12198767983367,\n 55.318525399141095\n ],\n [\n -167.12198767983367,\n 53.798526218879715\n ],\n [\n -162.2019428127277,\n 53.798526218879715\n ],\n [\n -162.2019428127277,\n 55.318525399141095\n ],\n [\n -167.12198767983367,\n 55.318525399141095\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, David","contributorId":367856,"corporation":false,"usgs":false,"family":"Miller","given":"David","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":957822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bennington, Ninfa","contributorId":367857,"corporation":false,"usgs":false,"family":"Bennington","given":"Ninfa","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":957823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":957824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":957825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Key, Kerry","contributorId":367858,"corporation":false,"usgs":false,"family":"Key","given":"Kerry","affiliations":[{"id":28041,"text":"Lamont-Doherty Earth Observatory, Columbia University","active":true,"usgs":false}],"preferred":false,"id":957826,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thurber, Cliff","contributorId":367859,"corporation":false,"usgs":false,"family":"Thurber","given":"Cliff","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":957827,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Laney","contributorId":367860,"corporation":false,"usgs":false,"family":"Hart","given":"Laney","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":957828,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ohlendorf, Summer","contributorId":367861,"corporation":false,"usgs":false,"family":"Ohlendorf","given":"Summer","affiliations":[{"id":87631,"text":"National Tsunami Warning Center","active":true,"usgs":false}],"preferred":false,"id":957829,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211345,"text":"sir20205072 - 2020 - Quality of pesticide data for groundwater analyzed for the National Water-Quality Assessment Project, 2013–18","interactions":[],"lastModifiedDate":"2021-05-27T13:25:01.758364","indexId":"sir20205072","displayToPublicDate":"2020-07-27T15:40:18","publicationYear":"2020","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":"2020-5072","displayTitle":"Quality of Pesticide Data for Groundwater Analyzed for the National Water-Quality Assessment Project, 2013–18","title":"Quality of pesticide data for groundwater analyzed for the National Water-Quality Assessment Project, 2013–18","docAbstract":"The National Water-Quality Assessment (NAWQA) Project of the U.S. Geological Survey (USGS) submitted nearly 1,900 samples collected from groundwater sites across the United States in 2013–18 for analysis of 225 pesticide compounds (pesticides and pesticide degradates, hereafter referred to as “pesticides”) by USGS National Water Quality Laboratory schedule 2437 (S2437). For the associated NAWQA study of pesticide occurrence and concentration in groundwater, and for other studies using pesticide results determined by S2437, it is necessary to assess the ability of reported results to meet data-quality requirements that will allow study objectives to be achieved. This assessment of the quality of S2437 results reported in 2013–18 examined data from field and laboratory quality-control samples, along with third-party performance assessment samples, to estimate bias and variability and to identify their potential sources, with an emphasis on implications for the interpretation of pesticide data for groundwater. Results indicate that measurements produced by the S2437 method for most pesticides have bias and variability that would be considered acceptable for many interpretative studies, which could therefore use the results without qualification or censoring. However, the reported data for a subset of pesticides have the potential for unacceptable contamination bias, high or low recovery bias, or high variability as a consequence of method performance and (or) nonlaboratory factors that could preclude their use for certain common objectives or could necessitate adjustment or qualification to meet those objectives.
Based on data for laboratory blanks, censoring of some detections for a subset of pesticides reported by the laboratory in environmental samples might be necessary or desirable to avoid an unacceptably high likelihood of a false-positive result caused by laboratory contamination. The 90-percent upper confidence limit for the 95th percentile of laboratory blank concentration equals or exceeds the minimum reported groundwater concentration in at least 1 water year for 28 pesticides. During at least 1 water year, this upper confidence limit exceeds the maximum laboratory detection limit for 17 pesticides and exceeds the maximum laboratory reporting limit for 3 pesticides (ametryn, atrazine, and diazinon). The level of contamination indicated by this upper confidence limit should not substantially affect the suitability of reported environmental concentrations for any compound for comparison with corresponding human-health benchmarks.
Despite being subjected to the same laboratory processes as laboratory blanks, field blanks indicated little evidence of contamination bias. This observation could largely be the consequence of data-reporting practices, which utilize detections in laboratory blanks to censor results in associated field samples (including blanks and environmental samples) when relative concentrations indicate that a result could have a substantial contribution from laboratory contamination. Laboratory censoring appears likely to reduce the risk of false-positive results in environmental samples below the level that laboratory blank results alone would imply.
Whereas data available for third-party blind blank samples analyzed in 2018 indicate that only propoxur had any false-positive results, data for pesticides that were not spiked into blind spike samples analyzed in 2013–18 indicate that the false-positive rates for 31 pesticides exceeded 1 percent when considering only detections reported at concentrations greater than the maximum detection limit. Although about half of these pesticides lack substantial supporting evidence of contamination bias based on laboratory blank or field blank detections, indicating that spiking issues or degradation of parent compounds within the spiked samples might be a contributing factor to some false-positive results, these results indicate the need to closely examine detections reported for some pesticides in environmental samples analyzed during a similar period for possible contributions from contamination bias. Data for blind spike samples that were spiked at concentrations above the maximum reporting limit indicate that false-negative rates for eight pesticides exceed 10 percent; substantial low bias could affect results reported for these pesticides in environmental samples analyzed during a similar period.
Data for laboratory reagent spikes, which measure recovery of pesticides in blank water, show little evidence for unacceptable recovery bias for S2437 pesticides. However, field matrix spikes, which measure recovery of pesticides in environmental matrices, indicate that degradation and (or) matrix effects could result in moderate to substantial low bias for groundwater results for several pesticides. Low bias could cause some reported concentrations to be categorized as being below a benchmark when the actual concentration in groundwater is greater than the benchmark. Occurrence and concentrations in groundwater could be substantially underrepresented for six pesticides with benchmarks (1H-1,2,4-triazole, asulam, bifenthrin, cis-permethrin, fenbutatin oxide, and naled) that have median recoveries between zero and 50 percent in field matrix spikes. Two compounds (didealkylatrazine and 2-hydroxy-6-ethylamino-4-amino-s-triazine) have median recoveries near or greater than 150 percent in field matrix spikes, indicating a substantial high bias. Plots of data for all spike types show clear changes in the typical recovery with time for some pesticides, which would require further examination for evaluation of temporal trends in environmental concentrations.
Data for laboratory reagent spikes indicate that nearly all S2437 pesticides have acceptable variability resulting from random measurement error. Only two compounds (fenbutatin oxide and naled) have F-pseudosigma values greater than 30 percent for recovery, which implies the potential for relatively high variability in reported concentrations and could affect comparison of concentrations to benchmarks and determination of whether concentrations for samples collected at separate locations or times are truly different with a specified level of confidence. Data for third-party blind spike samples show relatively high variability for a greater number of pesticides, although these results likely reflect the influence of degradation and (or) differences in the magnitude and variability of concentrations used for blind spikes relative to laboratory reagent spikes. Detailed analysis of variability using field replicate data is possible for only 12 pesticides on S2437; low variability in analyte detection and concentration is indicated for most of these pesticides in groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205072","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Bexfield, L.M., Belitz, K., Sandstrom, M.W., Beaty, D., Medalie, L., Lindsey, B.D., and Nowell, L.H., 2020, Quality of pesticide data for groundwater analyzed for the National Water-Quality Assessment Project, 2013–18: U.S. Geological Survey Scientific Investigations Report 2020–5072, 35 p., https://doi.org/10.3133/sir20205072.","productDescription":"Report: vi, 35 p.; 11 Tables; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111029","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":376742,"rank":17,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table11.xlsx","text":"Table 11","size":"34.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 11","linkHelpText":"— Summary of results of data-quality assessment for schedule 2437 pesticide compounds based on all quality-control sample types, May 2013 through September 2018"},{"id":376741,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table08.csv","text":"Table 8","size":"22.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 8","linkHelpText":"— Summary statistics for the recovery of schedule 2437 pesticide compounds in field matrix spikes, May 2013 through September 2018"},{"id":376740,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table08.xlsx","text":"Table 8","size":"64.7 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 8","linkHelpText":"— Summary statistics for the recovery of schedule 2437 pesticide compounds in field matrix spikes, May 2013 through September 2018"},{"id":376739,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table07.csv","text":"Table 7","size":"22.4 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 7","linkHelpText":"— Summary statistics for the recovery of schedule 2437 pesticide compounds in third-party blind spike samples, May 2013 through September 2018"},{"id":376738,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table07.xlsx","text":"Table 7","size":"55.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 7","linkHelpText":"— Summary statistics for the recovery of schedule 2437 pesticide compounds in third-party blind spike samples, May 2013 through September 2018"},{"id":376737,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table06.csv","text":"Table 6","size":"23.4 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 6","linkHelpText":"— Summary statistics for the recovery of schedule 2437 pesticide compounds in laboratory reagent spikes, May 2013 through September 2018"},{"id":376736,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table06.xlsx","text":"Table 6","size":"58.7 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 6","linkHelpText":"— Summary statistics for the recovery of schedule 2437 pesticide compounds in laboratory reagent spikes, May 2013 through September 2018"},{"id":376735,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table05.csv","text":"Table 5","size":"19.4 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 5","linkHelpText":"— Summary of false-positive and false-negative results for schedule 2437 pesticide compounds based on third-party blind spike samples, May 2013 through September 2018"},{"id":376734,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table05.xlsx","text":"Table 5","size":"47.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 5","linkHelpText":"— Summary of false-positive and false-negative results for schedule 2437 pesticide compounds based on third-party blind spike samples, May 2013 through September 2018"},{"id":376733,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table04.csv","text":"Table 4","size":"54.7 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 4","linkHelpText":"— Comparison of the 90-percent upper confidence limit for the 90th, 95th, and 99th percentiles of schedule 2437 pesticide compounds in laboratory blanks by water year, with minimum groundwater concentrations, maximum laboratory limits, and human-health benchmarks"},{"id":376732,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table04.xlsx","text":"Table 4","size":"91.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 4","linkHelpText":"— Comparison of the 90-percent upper confidence limit for the 90th, 95th, and 99th percentiles of schedule 2437 pesticide compounds in laboratory blanks by water year, with minimum groundwater concentrations, maximum laboratory limits, and human-health benchmarks"},{"id":376731,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table03.csv","text":"Table 3","size":"23.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 3","linkHelpText":"— Summary of detections of schedule 2437 pesticide compounds in groundwater, field blanks, and laboratory blanks, May 2013 through September 2018"},{"id":376730,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table03.xlsx","text":"Table 3","size":"53.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 3","linkHelpText":"— Summary of detections of schedule 2437 pesticide compounds in groundwater, field blanks, and laboratory blanks, May 2013 through September 2018"},{"id":376729,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table01.csv","text":"Table 1","size":"39.1 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 1","linkHelpText":"— Information about pesticide compounds included on the National Water Quality Laboratory schedule 2437, with human-health benchmarks and ranges of detection and laboratory reporting limits"},{"id":376728,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table01.xlsx","text":"Table 1","size":"55.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5072 Table 1","linkHelpText":"— Information about pesticide compounds included on the National Water Quality Laboratory schedule 2437, with human-health benchmarks and ranges of detection and laboratory reporting limits"},{"id":376727,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072.pdf","text":"Report","size":"3.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5072"},{"id":376744,"rank":19,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BFKA4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Field, laboratory, and third-party data for assessment of the quality of pesticide results reported by the National Water Quality Laboratory for groundwater samples collected by the National Water-Quality Assessment Project, 2013–18"},{"id":376743,"rank":18,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5072/sir20205072_table11.csv","text":"Table 11","size":"18.6 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5072 Table 11","linkHelpText":"— Summary of results of data-quality assessment for schedule 2437 pesticide compounds based on all quality-control sample types, May 2013 through September 2018"},{"id":376726,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5072/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The U.S. Geological Survey, in cooperation with the University of Minnesota-Duluth Natural Resources Research Institute, completed an assessment of regional water quality in areas of potential base-metal mining in Minnesota. Bedrock, soil, streambed sediment, and surface-water samples were collected in three watersheds that cross the basal part of the Duluth Complex with different mineral-deposit settings: (1) copper-nickel-platinum group element mineralization (Filson Creek), (2) iron-titanium-oxide mineralization (headwaters of the St. Louis River), and (3) no identified mineralization (Keeley Creek). At least 10 bedrock, 30 soil (2 each from 15 sites), and as many as 13 streambed sediment samples were collected in each watershed and analyzed for 44 major and trace elements, total and inorganic carbon, and 10 loosely bound metals (when possible). Surface-water samples were collected at four to nine locations in each watershed three to four times per year for 2 years (total of 141 environmental samples). Surface-water samples were analyzed for 10 trace metals (total and dissolved concentrations), 8 trace elements, 8 major ions (dissolved concentrations), alkalinity, and total and dissolved organic carbon.
Metal and element concentrations in solid media varied by watershed, representing local geology. Copper-nickel sulfide mineralization in the Filson Creek watershed was evidenced in bedrock, soil, and streambed sediments. In the Keeley Creek watershed, silicate mineralogy of underlying bedrock contributed metals to streambed sediments. Thick glacial cover masked potential bedrock contributions to solid media in the St. Louis River watershed. Water-quality data indicate that waters in all three watersheds are dilute. Water quality is more similar between the Filson and Keeley Creek watersheds, compared to the St. Louis River watershed, because of the difference in glacial cover. Metal concentrations (copper and nickel, in particular) in surface-water samples follow similar patterns of concentrations in solid media, indicating the influence of bedrock on water quality in Filson and Keeley Creeks. Data from this study provide a baseline of metal concentrations and general water quality within an area of active mineral exploration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205039","collaboration":"Prepared in cooperation with the University of Minnesota-Duluth Natural Resources Research Institute","usgsCitation":"Elliott, S.M., Jones, P.M., Woodruff, L.G., Jennings, C.E., Krall, A.L., and Morel, D.L., 2020, Assessing the influence of natural copper-nickel-bearing bedrocks of the Duluth Complex on water quality in Minnesota, 2013–15: U.S. Geological Survey Scientific Investigations Report 2020–5039, 51 p., https://doi.org/10.3133/sir20205039.","productDescription":"Report: x, 51 p.; 1 Table; 2 Appendices; Data Release","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-110010","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376695,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039_appendix_tables.xlsx","text":"Appendix Tables 1.1 and 1.2","size":"207 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5039 Appendix Tables 1.1–1.2"},{"id":376694,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039_tables5to7.xlsx","text":"Tables 5 to 7","size":"42.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5039 Tables 5–7"},{"id":376696,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VO251H","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geochemical characterization of solid media from three watersheds that transect the basal contact of the Duluth Complex, northeastern Minnesota"},{"id":376692,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5039/coverthb.jpg"},{"id":376693,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5039/sir20205039.pdf","text":"Report","size":"28.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5039"}],"country":"United States","state":"Minnesota","otherGeospatial":"Duluth Complex, Filson Creek, Keeley Creek, headwaters of the St. Louis River watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.6806640625,\n 47.1075227853425\n ],\n [\n -92.076416015625,\n 47.12995075666307\n ],\n [\n -91.38427734374999,\n 47.1075227853425\n ],\n [\n -91.373291015625,\n 47.98256841921405\n ],\n [\n -92.691650390625,\n 47.98256841921405\n ],\n [\n -92.6806640625,\n 47.1075227853425\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Citizen science is science undertaken by the public, usually in collaboration with professional scientific institutions. It encourages citizens to tackle real-world scientific problems and augments traditional science by expanding the coverage of data collection and by reducing costs of fieldwork in remote locations. Information collected by volunteers enables us all to gain a deeper understanding of how the natural world works and how it is changing. The U.S. Geological Survey is a leading Federal agency in fostering citizen-science collaborations, and the examples in this publication show the value of those collaborations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203026","usgsCitation":"Powers, E.M., and Williams, D.M., 2020, Citizen science collaboration with the U.S. Geological Survey in Alaska: U.S. Geological Survey Fact Sheet 2020-3026, 2 p., https://doi.org/10.3133/fs20203026.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-114073","costCenters":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"links":[{"id":376751,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3026/fs20203026.pdf","text":"Report","size":"428 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3026"},{"id":376750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3026/coverthb.jpg"}],"country":"United States","state":"Alaska","county":"Anchorage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.314453125,\n 59.0405546167585\n ],\n [\n -147.12890625,\n 59.0405546167585\n ],\n [\n -147.12890625,\n 62.14497603754045\n ],\n [\n -152.314453125,\n 62.14497603754045\n ],\n [\n -152.314453125,\n 59.0405546167585\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Science Coordinator, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska, 99508
The arrival and establishment of nonnative insects in North America is increasingly problematic. International trade has created opportunities to move wood products and nursery stock worldwide, which has increased the risk of insect introduction to regions or countries where they are not native. One group of researchers, the High-impact Insect Invasions Working Group (HIIWG), has developed a predictive model that can be used to estimate the likelihood that a newly arriving nonnative insect may significantly impact North American conifers. The HIIWG examined several traits and factors associated with nonnative insects feeding on conifers (a conifer specialist) already established in North America. Using these data, the HIIWG identified which combination of factors best predicted the risk that a conifer specialist would have a high impact. The researchers then developed a statistical model to predict the probability that a conifer specialist yet to arrive in North America would cause significant damage to conifers if the insect became established. Using three factors, the model calculates the odds of any particular conifer specialist having a high impact on a North American conifer in a range between 1 in 6.5 to 1 in 2,858. This model is a valuable tool to help identify invading insects with the potential to be the most damaging if the insect becomes established in North America. In addition, application of tools like this model can increase positive environmental outcomes for land managers by focusing their efforts on conifer populations that are deemed most vulnerable to extensive mortality
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203039","usgsCitation":"Durden, L.A., Schulz, A.N., Mech, A., and Thomas, K.A., 2020, A not so sudden impact—Historical relations between conifers and insects can help predict damage by nonnative insects: U.S. Geological Survey Fact Sheet 2020-3039, 4 p., https://doi.org/10.3133/fs20203039.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-117878","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":376753,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3039/fs20203039.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376752,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3039/covrthb.jpg"}],"contact":"A multiscale magnetic survey of the northern basin of Yellowstone Lake was undertaken in 2016 as part of the Hydrothermal Dynamics of Yellowstone Lake Project (HD‐YLAKE)—a broad research effort to characterize the cause‐and‐effect relationships between geologic and environmental processes and hydrothermal activity on the lake floor. The magnetic survey includes lake surface, regional aeromagnetic, and near‐bottom autonomous underwater vehicle (AUV) data. The study reveals a strong contrast between the northeastern lake basin, characterized by a regional magnetic low punctuated by stronger local magnetic lows, many of which host hydrothermal vent activity, and the northwestern lake basin with higher‐amplitude magnetic anomalies and no obvious hydrothermal activity or punctuated magnetic lows. The boundary between these two regions is marked by a steep gradient in heat flow and magnetic values, likely reflecting a significant structure within the currently active ~20‐km‐long Eagle Bay‐Lake Hotel fault zone that may be related to the ~2.08‐Ma Huckleberry Ridge caldera rim. Modeling suggests that the broad northeastern magnetic low reflects both a shallower Curie isotherm and widespread hydrothermal activity that has demagnetized the rock. Along the western lake shoreline are sinuous‐shaped, high‐amplitude magnetic anomaly highs, interpreted as lava flow fronts of upper units of the West Thumb rhyolite. The AUV magnetic survey shows decreased magnetization at the periphery of the active Deep Hole hydrothermal vent. We postulate that lower magnetization in the outer zone results from enhanced hydrothermal alteration of rhyolite by hydrothermal condensates while the vapor‐dominated center of the vent is less altered.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB019743","usgsCitation":"Bouligand, C., Tivey, M.A., Finn, C., Morgan, L.A., Shanks, W.C., and Sohn, R., 2020, Geological and thermal control of the hydrothermal system in northern Yellowstone Lake: Inferences from high resolution magnetic surveys: Journal of Geophysical Research Solid Earth, v. 125, no. 9, e2020JB019743, 20 p., https://doi.org/10.1029/2020JB019743.","productDescription":"e2020JB019743, 20 p.","ipdsId":"IP-117428","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":455857,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020jb019743","text":"External Repository"},{"id":378514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.55816650390625,\n 44.44162421758805\n ],\n [\n -110.21759033203125,\n 44.44162421758805\n ],\n [\n -110.21759033203125,\n 44.574817404670306\n ],\n [\n -110.55816650390625,\n 44.574817404670306\n ],\n [\n -110.55816650390625,\n 44.44162421758805\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Bouligand, Claire","contributorId":240831,"corporation":false,"usgs":false,"family":"Bouligand","given":"Claire","affiliations":[{"id":34188,"text":"University of Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":798998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tivey, Maurice A.","contributorId":240832,"corporation":false,"usgs":false,"family":"Tivey","given":"Maurice","email":"","middleInitial":"A.","affiliations":[{"id":13294,"text":"Woods Hole Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":798999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":205010,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":799000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morgan, Lisa A","contributorId":240835,"corporation":false,"usgs":false,"family":"Morgan","given":"Lisa","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":799001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shanks, W. C. Pat III 0000-0001-5336-3954","orcid":"https://orcid.org/0000-0001-5336-3954","contributorId":240915,"corporation":false,"usgs":true,"family":"Shanks","given":"W.","suffix":"III","email":"","middleInitial":"C. Pat","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":799002,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sohn, Robert A.","contributorId":240840,"corporation":false,"usgs":false,"family":"Sohn","given":"Robert A.","affiliations":[{"id":13294,"text":"Woods Hole Oceanographic Institute","active":true,"usgs":false}],"preferred":false,"id":799003,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211317,"text":"ofr20201059 - 2020 - Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019","interactions":[],"lastModifiedDate":"2020-07-28T14:34:33.855203","indexId":"ofr20201059","displayToPublicDate":"2020-07-27T11:05:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1059","displayTitle":"Chemical Constituent Concentrations in Stream Water, Streambed Sediment, and Soils of Fort Belvoir, Virginia— A Characterization of Ambient Conditions in 2019","title":"Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019","docAbstract":"The U.S. Army Fort Belvoir (FTBL) installation is on the banks of the Potomac River in Fairfax County, northeastern Virginia. The installation was founded by the U.S. Army during World War I. It has been home to a variety of military organizations over the course of its more than 100-year history and currently houses more than 145 mission partners. The installation consists of two noncontiguous units, the Main Post, and a smaller area to the northwest, Fort Belvoir North Area (FTNA). FTBL encompasses 8.91 square miles.
There is concern that activities on FTBL, including a long history of training, operations, and maintenance, may have resulted in contamination of stream water, streambed sediment, and (or) soils. Of particular concern is the U.S. Environmental Protection Agency (EPA) Target Analyte List (TAL). TAL refers to “the list of inorganic compounds/elements designated for analysis as contained in the version of the EPA Contract Laboratory Program Statement of Work for Inorganics Analysis, Multi-Media, Multi-Concentration in effect as of the date on which the laboratory is performing the analysis” (https://www.nj.gov/dep/srp/guidance/tcl_tal/). Because of the potential for TAL contamination at FTBL, the U.S. Geological Survey (USGS), in cooperation with U.S. Army Fort Belvoir, conducted a survey of FTBL’s stream water, streambed sediment, and soils during calendar year 2019.
The terminology “ambient concentrations” is used in this report to represent the concentrations of the TAL and other constituents at the time of sampling. This is in contrast to “background concentrations,” a term that “refers to areas in which the concentrations of chemicals have not been elevated by site activities”. Although some of the samples collected for this project may represent “background concentrations,” there is no assurance that they do, so all data collected are described as having “ambient concentrations.”
The purpose of the study was to obtain environmental data to characterize ambient concentrations of EPA TAL constituents in stream water, streambed sediment, and soils in FTBL, Virginia. This report describes methods and results of sampling stream water, streambed sediment, and soils during 2019. The purpose of this report is four-fold: (1) to describe the field sampling methods used to collect stream water, streambed sediment, and soils; (2) to describe the laboratory methods used to analyze the samples; (3) to report summaries of the field and laboratory results; and (4) to report the quality assurance and quality control results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201059","collaboration":"Prepared in cooperation with the U.S. Army, Fort Belvoir","usgsCitation":"Rice, K.C., and Chambers, D.B., 2020, Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019: U.S. Geological Survey Open-File Report 2020–1059, 20 p., https://doi.org/10.3133/ofr20201059.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116519","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":376670,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91P7OZJ","text":"USGS data release","linkHelpText":"Fort Belvoir, Virginia, stream-water, streambed-sediment, and soil data collected in 2019"},{"id":376668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1059/coverthb.jpg"},{"id":376669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1059/ofr20201059.pdf","text":"Report","size":"6.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1059"}],"country":"United States","state":"Virginia","city":"Fort Belvoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.19388961791992,\n 38.71096464102174\n ],\n [\n -77.20212936401367,\n 38.69033348573663\n ],\n [\n -77.12900161743163,\n 38.67117065551123\n ],\n [\n -77.11492538452148,\n 38.695290864945804\n ],\n [\n -77.12608337402344,\n 38.741766321754575\n ],\n [\n -77.14050292968749,\n 38.753012320665185\n ],\n [\n -77.16659545898438,\n 38.74390855335671\n ],\n [\n -77.18238830566406,\n 38.73051855149164\n ],\n [\n -77.19388961791992,\n 38.71096464102174\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.2184371948242,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.73935623438332\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Despite the reliance on groundwater by approximately 2.4 million rural Pennsylvania residents, publicly available data to characterize the quality of private well water are limited. As part of a regional effort to characterize groundwater in rural areas of Pennsylvania, samples from 54 domestic wells in Clinton County were collected and analyzed in 2017. The samples were evaluated for a wide range of constituents and compared to drinking-water health standards and geochemical characteristics. The sampled wells were completed to depths ranging from 46 to 500 feet in bedrock that was of predominantly sandstone, shale, or carbonate lithology. Results of this study show that the sampled groundwater quality in Clinton County generally met most drinking-water standards that apply to public water supplies. However, a percentage of samples exceeded drinking-water maximum contaminant levels (MCLs) for total coliform bacteria (57.4 percent), Escherichia coli (E. coli) (25.9 percent), nitrate (1.9 percent), and arsenic (1.9 percent); and secondary maximum contaminant levels (SMCLs) for pH (31.5 percent), manganese (29.6 percent), iron (13 percent), total dissolved solids (7.4 percent), aluminum (1.9 percent), and chloride (1.9 percent). Sodium concentrations exceeded the U.S. Environmental Protection Agency drinking-water advisory recommendation in 16.7 percent of the samples. Radon-222 activities exceeded the proposed drinking-water standard of 300 picocuries per liter (pCi/L) in 59.3 percent of the samples. The only volatile organic compounds (VOCs) detected were acetone and methyl ethyl ketone in two separate samples; neither constituent exceeded drinking-water standards.
Higher median nitrate concentrations were found in the carbonate (3.26 milligrams per liter [mg/L]) versus shale (less than 0.04 mg/L) and sandstone (0.27 mg/L) aquifer subsets. Most of the elevated nitrate concentrations were associated with E. coli detections in the carbonate aquifers, where transmissive bedrock can facilitate groundwater contamination by human activities at the land surface.
The median pH of groundwater from the sandstone aquifers (6.53) was less than those for the shale aquifers (7.31) and carbonate aquifers (7.43). Generally, the lower pH samples had greater potential for elevated concentrations of dissolved metals, including beryllium, copper, lead, nickel, and zinc, whereas the higher pH samples had greater potential for elevated concentrations of total dissolved solids, sodium, fluoride, boron, and uranium. Near-neutral samples (pH 6.5 to 7.5) had greater hardness and alkalinity concentrations than other samples with pH outside this range. Many samples from the shale or sandstone aquifers, particularly those with pH less than 6.5, were identified as having serious potential corrosivity based on the combination of the calcite saturation index and the chloride to sulfate mass ratio; however, none of the samples from the carbonate aquifers was identified as seriously corrosive.
Groundwater from 3.7 percent of the wells had concentrations of methane greater than the Pennsylvania action level of 7 mg/L, and 48 of the 54 wells (88.9 percent) had detectable concentrations of methane greater than the 0.0002 mg/L detection limit. Greater methane concentrations were found more frequently in groundwater sampled from the shale aquifers than the carbonate or sandstone aquifers in the study area. Most of the samples containing elevated methane (greater than 0.2 mg/L) were located outside the area of the Appalachian Plateaus. The elevated concentrations of methane generally were associated with suboxic groundwater (dissolved oxygen less than 0.5 mg/L) that had near-neutral to alkaline pH and were correlated with concentrations of iron, manganese, ammonia, sodium, lithium, barium, fluoride, and boron. The stable carbon and hydrogen isotopic compositions of methane in two of four samples analyzed for isotopes were consistent with compositions reported for mud-gas logging samples from gas-bearing geologic units (thermogenic gas) in the Appalachian Plateaus region, whereas two others were consistent with methane of microbial origin or a mixture of microbial and thermogenic gas.
Forty-two percent of samples had chloride concentrations greater than 20 mg/L with variable bromide concentrations. Corresponding chloride/bromide ratios are consistent with low-bromide sources such as road-deicing salt and septic effluent or animal waste, or, in a few cases, high-bromide brine. Brines characterized by relatively high bromide are naturally present in deeper parts of the regional groundwater system and, in some cases, may be mobilized by gas drilling. The chloride, bromide, and other constituents in road-deicing salt or brine solutions tend to be diluted by mixing with fresh groundwater in shallow aquifers used for water supply. One of the four groundwater samples with methane concentrations greater than 4 mg/L had chloride and bromide concentrations and a chloride/bromide ratio that indicates mixing with a salinity source such as road-deicing salt, whereas the chloride and bromide concentrations and ratios for the other three high-methane samples indicate mixing with a small amount of brine (0.03 percent or less). In two other eastern Pennsylvania county studies where gas drilling is absent, groundwater with comparable chloride/bromide ratios, bromide, and chloride concentrations plus other element associations have been reported. Additional sampling and analysis, such as isotopic analysis of the dissolved gas, fracture analysis, and more detailed evaluation of surrounding land uses, may be warranted to better understand the origin of the methane and brine constituents in groundwater at specific locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205022","collaboration":"Prepared in cooperation with the Clinton County Commissioners","usgsCitation":"Clune, J.W., and Cravotta, C.A., III, 2020, Groundwater quality in relation to drinking water health standards and geochemical characteristics for 54 domestic wells in Clinton County, Pennsylvania, 2017 (ver 1.1, July 2020): U.S. Geological Survey Scientific Investigations Report 2020–5022, 72 p., https://doi.org/10.3133/sir20205022.","productDescription":"Report: vii, 72 p.; Data Release; Appendix","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109062","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":376698,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5022/sir20205022_appendix3.pdf","text":"Appendix 3","size":"130 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Spearman rank correlation coefficient matrix for groundwater chemical data"},{"id":376697,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5022/versionHist.txt","text":"Version History","size":"677 B","linkFileType":{"id":2,"text":"txt"}},{"id":374843,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q86Z7D","text":"USGS data release","linkHelpText":"Compilation of data not available in the National Water Information System for domestic wells sampled by the U.S. Geological Survey in Clinton County, Pennsylvania, May–September 2017"},{"id":374847,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5022/coverthb2.jpg"},{"id":374846,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5022/sir20205022.pdf","text":"Report","size":"7.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5022"}],"country":"United States","state":"Pennsylvania","county":"Clinton County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-77.5978,41.4784],[-77.5977,41.453],[-77.5971,41.4412],[-77.5971,41.4398],[-77.5818,41.4394],[-77.5812,41.4371],[-77.5806,41.4353],[-77.5787,41.4308],[-77.5757,41.4222],[-77.575,41.4208],[-77.572,41.4167],[-77.5658,41.4131],[-77.564,41.4122],[-77.5634,41.4117],[-77.5628,41.4113],[-77.5597,41.4067],[-77.5585,41.4054],[-77.5554,41.4017],[-77.5499,41.3954],[-77.5468,41.3899],[-77.5432,41.3836],[-77.5425,41.3827],[-77.5419,41.3818],[-77.5407,41.3704],[-77.5395,41.3673],[-77.5382,41.3659],[-77.537,41.3645],[-77.5358,41.3636],[-77.534,41.3623],[-77.5285,41.3596],[-77.5266,41.3586],[-77.5242,41.3577],[-77.5217,41.3573],[-77.5162,41.3559],[-77.5089,41.355],[-77.5028,41.3546],[-77.5009,41.3541],[-77.4997,41.3536],[-77.493,41.35],[-77.4881,41.3473],[-77.4856,41.3455],[-77.4838,41.3441],[-77.4789,41.3405],[-77.4771,41.3391],[-77.4765,41.3387],[-77.4759,41.3382],[-77.4752,41.3373],[-77.4746,41.3359],[-77.4746,41.3332],[-77.4752,41.3296],[-77.4752,41.326],[-77.474,41.3219],[-77.4734,41.3192],[-77.471,41.3133],[-77.4642,41.3083],[-77.4594,41.306],[-77.4563,41.3046],[-77.4557,41.3042],[-77.4538,41.3024],[-77.4526,41.3005],[-77.452,41.2987],[-77.4514,41.296],[-77.4502,41.2933],[-77.4471,41.2896],[-77.4435,41.2869],[-77.4343,41.2846],[-77.43,41.2828],[-77.4294,41.2824],[-77.4282,41.2801],[-77.427,41.2783],[-77.4251,41.2729],[-77.4221,41.2715],[-77.4166,41.2701],[-77.4154,41.2697],[-77.4117,41.2674],[-77.4111,41.2669],[-77.4105,41.2665],[-77.4062,41.2624],[-77.4032,41.2588],[-77.3946,41.2497],[-77.3763,41.2379],[-77.3672,41.2324],[-77.3586,41.2274],[-77.3513,41.222],[-77.3495,41.2206],[-77.3464,41.2211],[-77.3397,41.2211],[-77.3354,41.2206],[-77.3202,41.2192],[-77.3135,41.2169],[-77.3049,41.2106],[-77.2903,41.1965],[-77.2903,41.1919],[-77.2928,41.1888],[-77.2897,41.1847],[-77.2849,41.1838],[-77.2775,41.1815],[-77.2727,41.1774],[-77.2684,41.171],[-77.2666,41.1692],[-77.2265,41.1306],[-77.2186,41.1224],[-77.2064,41.1092],[-77.1888,41.0914],[-77.1657,41.0692],[-77.1536,41.0687],[-77.1402,41.0695],[-77.1427,41.0478],[-77.1427,41.0441],[-77.1915,41.0302],[-77.1988,41.028],[-77.2464,41.0145],[-77.2494,41.0135],[-77.2854,41.0041],[-77.2964,41.0009],[-77.3006,40.9996],[-77.3067,40.9978],[-77.311,40.9964],[-77.3116,40.9959],[-77.3207,40.9928],[-77.3317,40.9887],[-77.3445,40.9874],[-77.3859,40.9797],[-77.3956,40.9779],[-77.4035,40.977],[-77.4102,40.977],[-77.4358,40.9752],[-77.5082,40.963],[-77.5405,41.0002],[-77.5557,41.0183],[-77.5588,41.0219],[-77.5801,41.0496],[-77.5631,41.0591],[-77.5698,41.0668],[-77.5777,41.0691],[-77.5814,41.0691],[-77.5869,41.0718],[-77.5911,41.0727],[-77.5948,41.0741],[-77.596,41.0764],[-77.5978,41.0777],[-77.6015,41.0795],[-77.607,41.0804],[-77.61,41.0818],[-77.6113,41.0836],[-77.6167,41.0868],[-77.6241,41.0899],[-77.6277,41.0909],[-77.6302,41.0936],[-77.6326,41.0954],[-77.6387,41.0972],[-77.6576,41.1022],[-77.6594,41.1049],[-77.6613,41.1081],[-77.6631,41.1094],[-77.6643,41.1094],[-77.6674,41.109],[-77.6704,41.1076],[-77.6729,41.1071],[-77.6753,41.1076],[-77.6777,41.1085],[-77.6802,41.1098],[-77.6826,41.1107],[-77.6851,41.1107],[-77.6881,41.1098],[-77.6906,41.1094],[-77.6948,41.1098],[-77.7052,41.1148],[-77.7083,41.1143],[-77.7107,41.1125],[-77.7125,41.1102],[-77.7143,41.1089],[-77.7168,41.108],[-77.7216,41.1084],[-77.7241,41.1098],[-77.7265,41.112],[-77.729,41.1129],[-77.7339,41.112],[-77.7387,41.1111],[-77.7454,41.1093],[-77.7663,41.1347],[-77.7767,41.1473],[-77.7816,41.1532],[-77.7847,41.1568],[-77.7988,41.1731],[-77.8055,41.1804],[-77.8403,41.1798],[-77.8934,41.177],[-77.8947,41.2001],[-77.8956,41.2496],[-77.8962,41.2514],[-77.8968,41.2537],[-77.9023,41.2495],[-77.9072,41.2454],[-77.9108,41.2441],[-77.9127,41.2418],[-77.9163,41.235],[-77.9205,41.2291],[-77.9242,41.2272],[-77.9278,41.2268],[-77.9333,41.2267],[-77.9364,41.2258],[-77.9376,41.224],[-77.9382,41.2213],[-77.9351,41.2168],[-77.9345,41.2159],[-77.9351,41.2127],[-77.9369,41.2104],[-77.9412,41.2104],[-77.9485,41.2131],[-77.9559,41.2181],[-77.9595,41.2176],[-77.962,41.2149],[-77.9638,41.2126],[-77.9656,41.2094],[-77.9686,41.2076],[-77.9723,41.2053],[-77.9729,41.2035],[-77.9729,41.1998],[-77.9728,41.1953],[-77.9734,41.1889],[-77.9721,41.1831],[-77.9715,41.1772],[-77.9715,41.1744],[-77.9739,41.1731],[-77.98,41.1717],[-77.9824,41.1712],[-77.9843,41.1703],[-77.9848,41.1667],[-77.986,41.1644],[-77.9885,41.163],[-77.9928,41.1639],[-77.9964,41.1648],[-77.9983,41.1634],[-77.9995,41.1612],[-78.0006,41.1535],[-78.0006,41.1489],[-78.0024,41.1462],[-78.0061,41.1448],[-78.0103,41.1448],[-78.0128,41.147],[-78.014,41.1502],[-78.0153,41.1534],[-78.0183,41.1543],[-78.0195,41.1538],[-78.0232,41.1529],[-78.0262,41.1524],[-78.0305,41.1533],[-78.0354,41.1542],[-78.0372,41.1533],[-78.039,41.1515],[-78.052,41.1655],[-78.0594,41.175],[-78.0661,41.1831],[-78.0791,41.2008],[-78.0865,41.2098],[-78.0921,41.2184],[-78.0516,41.2776],[-78.0165,41.3286],[-77.9887,41.37],[-77.9887,41.3781],[-77.9887,41.3795],[-77.9888,41.3809],[-77.9888,41.3886],[-77.9889,41.4013],[-77.9884,41.4212],[-77.9885,41.4312],[-77.9886,41.443],[-77.9875,41.4639],[-77.9875,41.4675],[-77.9876,41.4757],[-77.9796,41.4757],[-77.9514,41.4754],[-77.922,41.4755],[-77.903,41.476],[-77.8742,41.4761],[-77.8454,41.4766],[-77.8282,41.4767],[-77.8123,41.4772],[-77.8006,41.4772],[-77.7774,41.4772],[-77.7645,41.4777],[-77.7498,41.4778],[-77.7093,41.4778],[-77.6989,41.4779],[-77.6977,41.4779],[-77.664,41.4784],[-77.6155,41.4784],[-77.5978,41.4784]]]},\"properties\":{\"name\":\"Clinton\",\"state\":\"PA\"}}]}","edition":"Version 1.1: July 2020; Version 1.0: May 2020","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
This document describes a standard operating procedure (SOP) for the preparation of purposely killed juvenile salmon, implanted with telemetry transmitters, to be released into rivers, lakes, or streams to test one of the survival model assumptions. Procedures for releases of purposely killed fish (hereinafter dead fish releases) were developed by staff from the U.S. Geological Survey’s Columbia River Research Laboratory, on the basis of laboratory experiments and practical experience with telemetry studies in the Columbia River Basin. Initially, we used extended exposure to high dose anesthetic baths to euthanize fish for dead fish releases. This approach was selected on the basis of euthanization procedures described in the literature for studies that required an effective and rapid procedure, such as stress physiology assessments. Ultimately, this technique was deemed insufficient because detection records suggested that some fish seemed to revive and continue their migration with limited effect. That is, the detection histories of dead fish were very similar to those of live fish. To overcome this challenge, we adapted our procedures to require a combination of euthanization procedures on individual fish to ensure that there was no opportunity for revival. A combination of euthanization procedures for dead fish releases was used in one study in Germany. This SOP has been used by the U.S. Geological Survey to test survival model assumptions in several field studies and has consistently performed well. In addition, limited laboratory tests were completed to ensure that no live juvenile salmon were found in holding tanks for 24 hours following the procedures described in this SOP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201083","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Tomka, R.G., Liedtke, T.L., Frost, C., and Smith, C.D., 2020, A standard operating procedure for the preparation of purposely killed juvenile salmon used to test survival model assumptions: U.S. Geological Survey Open-File Report 2020–1083, 11 p., https://doi.org/10.3133/ofr20201083.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-116988","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":376754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1083/coverthb.jpg"},{"id":376755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1083/ofr20201083.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1083"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016