Signatures developed from metal concentrations in water and fish bony structures can be used to demonstrate migration of individual fish between connected water bodies. The U.S. Geological Survey (USGS), in cooperation with the National Park Service and the Missouri Department of Conservation, compared two protocols for collecting and analyzing water samples for concentrations of several metals commonly used to develop metal signatures. In 2015, paired seasonal water samples were collected in two study areas incorporating three National Park Service units; paired water samples were collected using USGS protocols and simpler research protocols. Metal concentrations obtained using USGS and research protocols were compared using t-tests, percent differences, and simple linear regression analyses. Graphical plots of median values and measured ranges were used to compare ratios of strontium to calcium (Sr:Ca) and barium to calcium (Ba:Ca) obtained using the different protocols among individual stations within the two study areas. For stations on the Mississippi and St. Croix Rivers, ranges in concentrations of calcium, barium, and strontium (obtained using USGS protocols) were compared between samples collected from 1995 through 2012 and samples collected in this study. Comparisons were used to evaluate the long-term stability of metal concentrations in the environment.
Collectively, results presented in this report demonstrated that research protocols provided metal concentration data that were similar to data obtained using USGS protocols for all compared metals except manganese. Holding times of 6–33 weeks prior to filtration and analyses for samples collected using research protocols may have caused greater changes in manganese concentrations compared to other metals. Strontium, barium, and calcium are the metals most commonly used in studies of fish migration, and concentrations of these metals were similar using different protocols. However, rivers within each study area were more easily distinguished from each other using metal concentration data obtained using USGS protocols compared to data obtained using research protocols. Information presented in this report can be used to develop studies that use identified metal signatures in connected water bodies and bony fish structures to demonstrate fish migration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185143","collaboration":"Prepared in cooperation with the National Park Service and the Missouri Department of Conservation","usgsCitation":"Ziegeweid, J.R., Zigler, S.J., Maki, R.P., Karns, B.N., and Love, S.A., 2018, Method comparisons for determining concentrations of metals in water samples used in studies of fish migratory histories: U.S. Geological Survey Scientific Investigations Report 2018–5143, 20 p., https://doi.org/10.3133/sir20185143.","productDescription":"Report: vii; 20 p.; Appendixes: 3","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097379","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359070,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5143/sir20185143_appendix2_table2-1.xlsx","text":"Appendix 2","size":"40.7 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2018–5143 Appendix 2"},{"id":359071,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5143/sir20185143_appendix3_table3-1.xlsx","text":"Appendix 3","size":"19.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2018–5143 Appendix 3"},{"id":359068,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5143/sir20185143.pdf","text":"Report ","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5143"},{"id":359067,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5143/coverthb.jpg"},{"id":359069,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5143/sir20185143_appendix1","text":"Appendix 1","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2018–5143 Appendix 1"}],"country":"Canada, United States","otherGeospatial":"Mississippi National River and Recreation Area, Namakan Reservoir, St. Croix National Scenic Riverway","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 N Goodwin Ave
Urbana, IL 61801
Wildlife reservoirs of infectious disease are a major source of human-wildlife conflict because of the risk of potential spillover associated with commingling of wildlife and livestock. In Montana, the presence of brucellosis (Brucella abortus) in free-ranging elk (Cervus canadensis) populations is of significant management concern because of the risk of disease transmission from elk to livestock. To help mitigate potential conflict, we identified how spillover risk changes through space and time using a combination of elk population, disease, and movement data. We developed resource selection functions using telemetry data from 223 female elk to predict the relative probability of female elk occurrence on a daily basis during the 15 February-30 June transmission risk period. We combined these spatiotemporal predictions with elk seroprevalence, demography, and abortion timing data to identify when and where abortions (the primary transmission route of brucellosis) were most likely to occur. Additionally, we integrated these predictions with spatiotemporal data on livestock distribution to estimate the daily risk of livestock encountering brucellosis-induced elk abortions. We estimated that a minimum of ~17,500 adult female elk lived within our study area, which resulted in a conservative estimate of ~525 brucellosis-induced abortions each year. We predicted that approximately half of the transmission events occurred on livestock properties and 98% of those properties were private ranchlands as opposed to state or federal grazing allotments. Our fine-resolution (250-m spatial, 1-day temporal), large-scale (17,732 km2) predictions of potential elk-to-livestock transmission risk provide wildlife and livestock managers with a useful tool to identify higher risk areas in space and time and proactively focus actions in these areas to separate elk and livestock to reduce spillover risk.
","language":"English","publisher":"Montana Fish, Wildlife and Parks","usgsCitation":"Rayl, N.D., Proffitt, K., Almberg, E.S., Jones, J.D., Merkle, J., Gude, J., and Cross, P.C., 2018, Estimating the risk of elk-to-livestock brucellosis transmission in Montana, 56 p.","productDescription":"56 p.","ipdsId":"IP-098928","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":359521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":358823,"type":{"id":15,"text":"Index Page"},"url":"https://fwp.mt.gov/fwpDoc.html?id=87528"}],"country":"United States","state":"Montana","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5befe5bbe4b045bfcadf7f38","contributors":{"authors":[{"text":"Rayl, Nathaniel D. 0000-0003-3846-2764","orcid":"https://orcid.org/0000-0003-3846-2764","contributorId":202350,"corporation":false,"usgs":true,"family":"Rayl","given":"Nathaniel","email":"","middleInitial":"D.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":749810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Proffitt, Kelly 0000-0001-5528-3309","orcid":"https://orcid.org/0000-0001-5528-3309","contributorId":210093,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","email":"","affiliations":[{"id":38065,"text":"Montana Fish, Wildlife and Parks, Bozeman, Montana","active":true,"usgs":false}],"preferred":false,"id":749811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Almberg, Emily S.","contributorId":207014,"corporation":false,"usgs":false,"family":"Almberg","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":749812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Jennifer D.","contributorId":145754,"corporation":false,"usgs":false,"family":"Jones","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[{"id":16227,"text":"Institute on Ecosystems,Montana State University MT, 59715 USA","active":true,"usgs":false}],"preferred":false,"id":749813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":749814,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gude, Justin A.","contributorId":210094,"corporation":false,"usgs":false,"family":"Gude","given":"Justin A.","affiliations":[{"id":38066,"text":"Montana Fish, Wildlife and Parks,","active":true,"usgs":false}],"preferred":false,"id":749815,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":749809,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202215,"text":"70202215 - 2018 - Effects of temperature and exposure duration on four potential rapid-response tools for zebra mussel (Dreissena polymorpha) eradication","interactions":[],"lastModifiedDate":"2019-02-14T13:21:24","indexId":"70202215","displayToPublicDate":"2018-11-01T13:21:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Effects of temperature and exposure duration on four potential rapid-response tools for zebra mussel (Dreissena polymorpha) eradication","title":"Effects of temperature and exposure duration on four potential rapid-response tools for zebra mussel (Dreissena polymorpha) eradication","docAbstract":"Zebra mussels (Dreissena polymorpha) have continued their spread within inland lakes and rivers in North America despite diligent containment and decontamination efforts by natural resource agencies and other stakeholders. Identification of newly infested waterways by early detection surveillance programs allows for rapid response zebra mussel eradication treatments in some situations. Previous eradication treatments have occurred over a broad range of water temperatures which have influenced the efficacy of molluscicides. Natural resource managers will benefit from knowledge regarding the impacts of water temperature and exposure duration on the toxicity of molluscicides to zebra mussels. In particular, temperature specific data are needed to inform the selection of an effective molluscicide and the proper dose that will induce 100% zebra mussel mortality. We evaluated the influences of temperature and exposure duration on the toxicity of two U.S. EPA-registered (EarthTec QZ and Zequanox) and two nonregistered (niclosamide and potassium chloride) molluscicides to zebra mussels at water temperatures of 7, 12, 17, and 22 °C. Our results indicate that treatment options for the eradication of zebra mussels in waters ≤ 12 °C include 336 h or longer treatments with EarthTec QZ and KCl as well as treatments with niclosamide ≥ 24 h in duration. In waters ≥ 17 °C, multiple toxicant and exposure duration combinations are potentially effective for zebra mussel eradication. On-site or in situ zebra mussel bioassays are a useful tool for the evaluation of treatment efficacy.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2018.9.4.06","usgsCitation":"Luoma, J.A., Severson, T.J., Barbour, M., and Wise, J.K., 2018, Effects of temperature and exposure duration on four potential rapid-response tools for zebra mussel (Dreissena polymorpha) eradication: Management of Biological Invasions, v. 9, no. 4, p. 425-438, https://doi.org/10.3391/mbi.2018.9.4.06.","productDescription":"14 p.","startPage":"425","endPage":"438","ipdsId":"IP-095367","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468271,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2018.9.4.06","text":"Publisher Index Page"},{"id":361263,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barbour, Matthew T. 0000-0002-0095-9188 mbarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-9188","contributorId":195580,"corporation":false,"usgs":true,"family":"Barbour","given":"Matthew","email":"mbarbour@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wise, Jeremy K. 0000-0003-0184-6959 jwise@usgs.gov","orcid":"https://orcid.org/0000-0003-0184-6959","contributorId":5009,"corporation":false,"usgs":true,"family":"Wise","given":"Jeremy","email":"jwise@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757281,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200703,"text":"70200703 - 2018 - Gravity signature of basaltic fill in Kīlauea caldera, Island of Hawai‘i","interactions":[],"lastModifiedDate":"2019-10-28T09:35:22","indexId":"70200703","displayToPublicDate":"2018-11-01T13:06:15","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"13","title":"Gravity signature of basaltic fill in Kīlauea caldera, Island of Hawai‘i","docAbstract":"Characterization of the subsurface structure of a volcanic edifice is essential to understanding volcanic behavior. One of the best-studied volcanoes is Kīlauea (Island of Hawai‘i). Geological evidence suggests that the formation of the summit caldera of Kīlauea is cyclic, with repeated collapse followed by filling with lava. The most recent collapse occurred ca. 1500 CE, producing a basin that is several hundred meters deeper than the current caldera. In this study, we used two- and three-dimensional gravity modeling of spatially dense gravity data covering the summit area to suggest that, since its formation in 1500 CE, the caldera has been progressively filled by lava flows that are slightly denser than those found in the rim and outboard of the caldera. The geometry of this fill, inferred from gravity data, enables us to reconstruct the morphology of the 1500 CE caldera before its subsequent filling. The coincidence of fumarolic zones and thermal anomalies observed at the surface with the interpreted 1500 CE caldera rim suggests that hydrothermal fluid circulation is guided by the more permeable inner faults bounding the main caldera.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Field volcanology: A tribute to the distinguished career of Don Swanson: Geological Society of America Special Paper 538","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"The Geological Society of America","doi":"10.1130/2018.2538(13)","isbn":"9780813795386","usgsCitation":"Gailler, L., and Kauahikaua, J.P., 2018, Gravity signature of basaltic fill in Kīlauea caldera, Island of Hawai‘i, chap. 13 of Field volcanology: A tribute to the distinguished career of Don Swanson: Geological Society of America Special Paper 538, v. 538, p. 297-306, https://doi.org/10.1130/2018.2538(13).","productDescription":"10 p.","startPage":"297","endPage":"306","ipdsId":"IP-080434","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":460819,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2018.2538(13)","text":"Publisher Index Page"},{"id":359516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.35354614257812,\n 19.330582575049508\n ],\n [\n -155.15853881835938,\n 19.330582575049508\n ],\n [\n -155.15853881835938,\n 19.47500813674322\n ],\n [\n -155.35354614257812,\n 19.47500813674322\n ],\n [\n -155.35354614257812,\n 19.330582575049508\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"538","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5befe5bbe4b045bfcadf7f3a","contributors":{"authors":[{"text":"Gailler, Lydie 0000-0002-8132-2428","orcid":"https://orcid.org/0000-0002-8132-2428","contributorId":192584,"corporation":false,"usgs":false,"family":"Gailler","given":"Lydie","email":"","affiliations":[],"preferred":false,"id":750172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":750171,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201612,"text":"70201612 - 2018 - The approaching obsolescence of 137Cs dating of wetland soils in North America","interactions":[],"lastModifiedDate":"2018-12-18T12:49:01","indexId":"70201612","displayToPublicDate":"2018-11-01T12:49:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The approaching obsolescence of 137Cs dating of wetland soils in North America","title":"The approaching obsolescence of 137Cs dating of wetland soils in North America","docAbstract":"The peak fallout in 1963 of the radionuclide 137Cs has been used to date lake, reservoir, continental shelf, and wetland sedimentary deposits. In wetlands such dating is used to project the ability of wetlands to keep pace with sea level rise and develop strategies for mitigating carbon pollution using biological carbon sequestration. Here we demonstrate that reliable 137Cs profiles are increasingly difficult to obtain from wetland soils. Among 58 soil cores recently collected from a range of wetland types and 137Cs fallout densities across the United States, 25% contain no identifiable 137Cs peaks. Less than 40% of 137Cs ages are consistent with 210Pb dating. We provide a new measure of 137Cs peak clarity (τ) for our core dataset by comparing the 50% interquartile range of data around the 137Cs peak for “ideal” cores profiles determined using 137Cs fallout data to that of observed core profiles. Our results show that overall τ is approximately 10 times greater for observed cores than ideal cores. The deterioration in the 137Cs peak has occurred due to radionuclide decay, 137Cs migration in situ, which is ubiquitous in this study, and 137Cs amendments from surface waters. Such deterioration likely extends to both Mexican and non-permafrost, Canadian wetlands. We recommend continued use of 137Cs only if the full bound of dating uncertainty for both 137Cs and an additional method such as 210Pb is propagated into estimates of wetland vertical accretion and carbon sequestration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2018.08.028","usgsCitation":"Drexler, J.Z., Fuller, C.C., and Archfield, S.A., 2018, The approaching obsolescence of 137Cs dating of wetland soils in North America: Quaternary Science Reviews, v. 199, p. 83-96, https://doi.org/10.1016/j.quascirev.2018.08.028.","productDescription":"14 p.","startPage":"83","endPage":"96","ipdsId":"IP-095998","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":468273,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2018.08.028","text":"Publisher Index Page"},{"id":360460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"199","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c1a1532e4b0708288c2352c","contributors":{"authors":[{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":754535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":754536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":754537,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202559,"text":"70202559 - 2018 - Overwintering behavior of juvenile sea turtles at a temperate foraging ground","interactions":[],"lastModifiedDate":"2019-03-11T12:25:27","indexId":"70202559","displayToPublicDate":"2018-11-01T12:25:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Overwintering behavior of juvenile sea turtles at a temperate foraging ground","docAbstract":"Most freshwater and terrestrial turtle species that inhabit temperate environments hibernate to survive extreme cold periods. However, for sea turtles, the question of whether these species use hibernation as an overwintering strategy has not been resolved (Ultsch 2006). Felger et al. (1976) suggested that sea turtles bury themselves in mud on the seafloor and remain dormant throughout the winter, presumably not surfacing during that time. Additional researchers have described sea turtles in temperatures <15°C as lethargic, mud‐covered and buried in bottom sediment (Carr et al. 1980, Mendonça 1983, Ogren and McVea 1995). However, more recent studies suggest that sea turtles may not be as dormant in cold temperatures as previously suggested (Hochscheid et al. 2007). Resolving this question is difficult due to the unpredictability of winter weather patterns and the cost of advanced tracking tools required to assess these fine‐scale behaviors. However, in January 2018, unusually calm and clear marine conditions coupled with exceptionally cold weather provided us the opportunity to observe and film turtle behavior at a foraging ground in the northern Gulf of Mexico. These images, combined with previously gathered data from vessel‐based surveys and water temperature loggers, have enabled us to piece together one of the most comprehensive views of sea turtle overwintering behavior to date.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2439","usgsCitation":"Lamont, M.M., Seay, D.R., and Gault, K., 2018, Overwintering behavior of juvenile sea turtles at a temperate foraging ground: Ecology, v. 99, no. 11, p. 2621-2624, https://doi.org/10.1002/ecy.2439.","productDescription":"4 p.","startPage":"2621","endPage":"2624","ipdsId":"IP-095734","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":361942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"11","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Lamont, Margaret M. 0000-0001-7520-6669 mlamont@usgs.gov","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":4525,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"mlamont@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":759111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seay, David R. 0000-0001-5473-9474","orcid":"https://orcid.org/0000-0001-5473-9474","contributorId":214086,"corporation":false,"usgs":false,"family":"Seay","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":27063,"text":"Cherokee Nations Technology","active":true,"usgs":false}],"preferred":false,"id":759112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gault, Kathleen","contributorId":214087,"corporation":false,"usgs":false,"family":"Gault","given":"Kathleen","email":"","affiliations":[{"id":38979,"text":"Eglin Air Force Base","active":true,"usgs":false}],"preferred":false,"id":759113,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201039,"text":"70201039 - 2018 - Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems","interactions":[],"lastModifiedDate":"2018-11-26T11:55:00","indexId":"70201039","displayToPublicDate":"2018-11-01T11:54:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems","docAbstract":"Earth observation data are increasingly used to provide consistent eco-physiological information over large areas through time. Production efficiency models (PEMs) estimate Gross Primary Production (GPP) as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from Earth observation. GPP can be summed over the growing season and adjusted by a crop-specific harvest index to estimate yield. Although PEMs have many advantages over other crop yield models, they are not widely used, because performance is relatively poor. Here, a new PEM is presented that addresses deficiencies for macro-scale application: Production Efficiency Model Optimized for Crops (PEMOC). It was developed by optimizing functions from the literature with GPP estimated by eddy covariance flux towers in the United States. The model was evaluated using newly developed Earth observation products and county-level yield statistics for major crops. PEMOC generally performed better at the field and county level than another commonly used PEM, the Moderate Resolution Imaging Spectroradiometer GPP (MOD17). PEMOC and MOD17 estimates of GPP had an R2 and root mean squared error (RMSE) over the growing season of 0.71–0.89 (9.87–17.47 g CO2 d−1) and 0.59–0.83 (6.86–22.20 g CO2 d−1) with flux tower GPP. PEMOC produced R2s and RMSE of 0.70 (0.52), 0.60 (0.61), and 0.62 (0.59), while MOD17 produced R2s and RMSE of 0.65 (0.57), 0.53 (0.66), and 0.65 (0.57) with corn, soybean, and winter wheat crop yield anomalies. The sample size of rice was small, so yields were compared directly. PEMOC and MOD17 produced R2s and RMSE of 0.53 (3.42 t ha−1) and 0.40 (4.89 t ha−1). The most sizeable model improvements were seen for C3 and C4 crops during emergence/senescence and peak season, respectively. These improvements were attributed to C3 and C4 partitioning, optimized temperature and moisture constraints, and an evapotranspiration-based soil moisture index.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2018.08.001","usgsCitation":"Marshall, M., Tu, K., and Brown, J.F., 2018, Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems: Remote Sensing of Environment, v. 217, p. 258-271, https://doi.org/10.1016/j.rse.2018.08.001.","productDescription":"14 p.","startPage":"258","endPage":"271","ipdsId":"IP-082657","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2018.08.001","text":"Publisher Index Page"},{"id":359657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"217","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bfd146ee4b0815414ca38f6","contributors":{"authors":[{"text":"Marshall, Michael","contributorId":65216,"corporation":false,"usgs":true,"family":"Marshall","given":"Michael","email":"","affiliations":[],"preferred":false,"id":751963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tu, Kevin","contributorId":210791,"corporation":false,"usgs":false,"family":"Tu","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":751964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":751962,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200791,"text":"70200791 - 2018 - Changes in growth of Rainbow Trout in a Catskill Mountain Reservoir following Alewife and White Perch Introductions","interactions":[],"lastModifiedDate":"2018-11-01T11:49:26","indexId":"70200791","displayToPublicDate":"2018-11-01T11:49:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Changes in growth of Rainbow Trout in a Catskill Mountain Reservoir following Alewife and White Perch Introductions","docAbstract":"Rainbow Trout Oncorhynchus mykiss were introduced to the Esopus Creek watershed in the Catskill Mountains of New York in the early 1880s. This introduction created a renowned naturalized fishery that remains important to the local economy today. The objective of this study was to determine whether the growth and condition of Rainbow Trout in the Ashokan Reservoir changed following the establishment of (1) Alewives Alosa pseudoharengus in the 1970s and (2) White Perch Morone americana in the 2000s by analyzing historical scale samples from 502 Rainbow Trout. The resulting data were used to compare length at age, von Bertalanffy growth curves, age‐specific annual growth increments, and relative weight before and after each introduction. Results indicated that growth of Rainbow Trout of ages <5 years generally increased following each introduction, while insufficient data for ages 5 and 6 made trends for older fish unclear. Rainbow Trout of ages ≤2 are believed to primarily use riverine habitats in this watershed, and therefore fish of ages >2 may best reflect reservoir growth. The mean relative weight of Rainbow Trout also increased between each period. The largest increases in both growth and condition were observed during the period after the introduction of White Perch, which was unexpected considering this species may have some diet overlap with Rainbow Trout and should be a poor forage species. Changes in watershed management and density‐dependent growth effects may explain these unexpected results. Our results, which largely suggest increased growth and condition over time, eliminate growth effects as a possible explanation for declining Rainbow Trout populations and suggest recruitment issues in the watershed require further investigation. This study contributes to our understanding of the interactions between introduced species and underscores the value of maintaining long‐term monitoring programs for assessing biological trends.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10203","usgsCitation":"George, S.D., Baldigo, B.P., Flaherty, M.J., and Randall, E.A., 2018, Changes in growth of Rainbow Trout in a Catskill Mountain Reservoir following Alewife and White Perch Introductions: North American Journal of Fisheries Management, v. 38, no. 5, p. 1027-1038, https://doi.org/10.1002/nafm.10203.","productDescription":"12 p.","startPage":"1027","endPage":"1038","ipdsId":"IP-080228","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":359066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Ashokan Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.5,\n 41.8333\n ],\n [\n -74,\n 41.8333\n ],\n [\n -74,\n 42.25\n ],\n [\n -74.5,\n 42.25\n ],\n [\n -74.5,\n 41.8333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"5","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-12","publicationStatus":"PW","scienceBaseUri":"5c10a8fee4b034bf6a7e4ed8","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flaherty, Michael J.","contributorId":210348,"corporation":false,"usgs":false,"family":"Flaherty","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":750530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Randall, Eileen A.","contributorId":210349,"corporation":false,"usgs":false,"family":"Randall","given":"Eileen","email":"","middleInitial":"A.","affiliations":[{"id":38104,"text":"EcoLogic LLC","active":true,"usgs":false}],"preferred":false,"id":750531,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201085,"text":"70201085 - 2018 - Introduction and dispersal of non-native bullseye snakehead Channa marulius (Hamilton, 1822) in the canal system of southeastern Florida, USA","interactions":[],"lastModifiedDate":"2018-11-28T11:17:51","indexId":"70201085","displayToPublicDate":"2018-11-01T11:17:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":994,"text":"BioInvasions Records","active":true,"publicationSubtype":{"id":10}},"title":"Introduction and dispersal of non-native bullseye snakehead Channa marulius (Hamilton, 1822) in the canal system of southeastern Florida, USA","docAbstract":"An established population of bullseye snakehead (Channa marulius), a large predatory fish from southeastern Asia, was identified for the first time in North America from waters in southeastern Florida, USA, in the year 2000. Since then, it has dispersed throughout the extensive canal system in the area from West Palm Beach south to Miramar. Collection data were compiled to determine the extent of the distribution. The range encompasses three separate areas totaling approximately 830 km2. Over an 18-year period, the range increased an average of approximately 46 km2 per year. There is concern that this non-native species may threaten the fauna in unique protected natural areas of southern Florida, such as Everglades National Park.
","language":"English","publisher":"REABIC","doi":"10.3391/bir.2018.7.4.17","usgsCitation":"Benson, A.J., Schofield, P.J., and Gestring, K.B., 2018, Introduction and dispersal of non-native bullseye snakehead Channa marulius (Hamilton, 1822) in the canal system of southeastern Florida, USA: BioInvasions Records, v. 7, no. 4, p. 451-457, https://doi.org/10.3391/bir.2018.7.4.17.","productDescription":"7 p.","startPage":"451","endPage":"457","ipdsId":"IP-092762","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460821,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/bir.2018.7.4.17","text":"Publisher Index Page"},{"id":437699,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H70F02","text":"USGS data release","linkHelpText":"Observations of bullseye snakehead (Channa marulius) in Florida"},{"id":359761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.53115844726562,\n 25.828324988459716\n ],\n [\n -80.013427734375,\n 25.828324988459716\n ],\n [\n -80.013427734375,\n 26.713720362159577\n ],\n [\n -80.53115844726562,\n 26.713720362159577\n ],\n [\n -80.53115844726562,\n 25.828324988459716\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"4","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bffb75ce4b0815414ca8e48","contributors":{"authors":[{"text":"Benson, Amy J. 0000-0002-4517-1466 abenson@usgs.gov","orcid":"https://orcid.org/0000-0002-4517-1466","contributorId":3836,"corporation":false,"usgs":true,"family":"Benson","given":"Amy","email":"abenson@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":752355,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schofield, Pamela J. 0000-0002-8752-2797 pschofield@usgs.gov","orcid":"https://orcid.org/0000-0002-8752-2797","contributorId":168659,"corporation":false,"usgs":true,"family":"Schofield","given":"Pamela","email":"pschofield@usgs.gov","middleInitial":"J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":752357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gestring, Kelly B.","contributorId":210849,"corporation":false,"usgs":false,"family":"Gestring","given":"Kelly","email":"","middleInitial":"B.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":752356,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227673,"text":"70227673 - 2018 - Age and growth of a native, lightly exploited population of Coregonus clupeaformis (Lake Whitefish) in a small natural lake in Maine","interactions":[],"lastModifiedDate":"2022-01-26T16:33:43.599359","indexId":"70227673","displayToPublicDate":"2018-11-01T10:22:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Age and growth of a native, lightly exploited population of Coregonus clupeaformis (Lake Whitefish) in a small natural lake in Maine","docAbstract":"We assessed annual growth of Coregonus clupeaformis (Lake Whitefish) from a natural, lightly exploited population in a small lake in northern Maine using observed and back-calculated length-at-age data. We sampled Lake Whitefish from Clear Lake, ME, with gill nets and extracted otoliths from 57 fish. We incorporated age-at-length data into a von Bertalanffy growth function, which we employed to model growth trajectories from individual fish. We used these estimates to evaluate length-at-age variability within this population. Ages for Lake Whitefish varied from 8 y to 30 y. Among all fish, we characterized incremental growth by an average-growth coefficient of K = 0.156 and an estimated L∞ of 484 mm. The oldest individuals demonstrated the slowest incremental growth (K = 0.106) when compared to younger cohorts (K = 0.218). We observed an inverse relationship between L∞ and K and the estimated age-at-capture (R2 = 0.178 and 0.723, respectively), which suggests relatively slow growth and a smaller maximum size for the longest living members of the population. Our estimated parameters serve as a reference to inform management of populations of Lake Whitefish.
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Rainfall data from station observations are inadequate over many parts of the world due to sparse or non-existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite-based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and quasi-global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10-day) and monthly time-scales. The evaluation was done by comparing the satellite products with rain-gauge data from about 1,200 stations. The CHIRP and CHIRPS products were also compared with two similar operational satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time-scales, while TAMSAT performed better at the daily time-scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.
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Tufa","contributorId":330695,"corporation":false,"usgs":false,"family":"Dinku","given":"Tufa","email":"","affiliations":[{"id":78970,"text":"IRI","active":true,"usgs":false}],"preferred":false,"id":885603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Pete","contributorId":192379,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[],"preferred":false,"id":885605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maidment, Ross","contributorId":330747,"corporation":false,"usgs":false,"family":"Maidment","given":"Ross","email":"","affiliations":[],"preferred":false,"id":885787,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tadesse, Tsegaye 0000-0002-4102-1137","orcid":"https://orcid.org/0000-0002-4102-1137","contributorId":147617,"corporation":false,"usgs":false,"family":"Tadesse","given":"Tsegaye","email":"","affiliations":[],"preferred":false,"id":885606,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ceccato, Pietro","contributorId":330696,"corporation":false,"usgs":false,"family":"Ceccato","given":"Pietro","affiliations":[{"id":78970,"text":"IRI","active":true,"usgs":false}],"preferred":false,"id":885607,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202420,"text":"70202420 - 2018 - Gaps in kelp cover may threaten the recovery of California sea otters","interactions":[],"lastModifiedDate":"2019-02-28T09:35:39","indexId":"70202420","displayToPublicDate":"2018-11-01T09:35:31","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Gaps in kelp cover may threaten the recovery of California sea otters","docAbstract":"Despite more than a century of federal protection, the California sea otter Enhydra lutris nereis remains threatened under the U.S. Endangered Species Act (ESA), and the population has not appreciably expanded its range in two decades. Here, we examine a novel dataset of 725 sea otter live strandings from 1984–2015 to gain insights into demographic and environmental factors underlying threats to sea otter recovery. Using multinomial logistic regression to evaluate spatiotemporal patterns of stranding causes, we demonstrate that increases in stranding rates, particularly outside the range center, are related to a substantial increase in shark bites. By contrast, trauma linked to human activities has declined dramatically, and now accounts for less than 5% of stranding cases. Within the range core, where the sea otter population seems regulated by prey availability, symptoms of energetic stress represent more than 63% of all strandings and are strongly associated with high sea otter density. Conversely, in range peripheries, the majority of strandings are caused by shark bite and neurological disease. Notably, these threats are virtually absent where nearshore habitat is characterized by at least 10% kelp canopy cover. Our analyses reveal that declining kelp cover may therefore constrain the population's spatial expansion and recovery in two key ways. Absence of kelp intensifies density‐independent threats in the range peripheries, and likely limits dispersal of reproductive females, which depend on kelp canopy for nursery habitat. These results highlight the significance of both top‐down and bottom‐up processes in population dynamics, and inform an ecosystem‐based approach to conservation planning.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.03561","usgsCitation":"Nicholson, T.E., Mayer, K.A., Staedler, M.M., Fujii, J.A., Murray, M.J., Johnson, A.B., Tinker, M.T., and Van Houtan, K.S., 2018, Gaps in kelp cover may threaten the recovery of California sea otters: Ecography, v. 41, no. 11, p. 1751-1762, https://doi.org/10.1111/ecog.03561.","productDescription":"12 p.","startPage":"1751","endPage":"1762","ipdsId":"IP-089177","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468278,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.03561","text":"Publisher Index Page"},{"id":361606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -122.6019287109375,\n 34.14818102254435\n ],\n [\n -119.16320800781249,\n 34.14818102254435\n ],\n [\n -119.16320800781249,\n 37.81846319511331\n ],\n [\n -122.6019287109375,\n 37.81846319511331\n ],\n [\n -122.6019287109375,\n 34.14818102254435\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"11","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Nicholson, Teri E.","contributorId":213741,"corporation":false,"usgs":false,"family":"Nicholson","given":"Teri","email":"","middleInitial":"E.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":758383,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayer, Karl A.","contributorId":203504,"corporation":false,"usgs":false,"family":"Mayer","given":"Karl","email":"","middleInitial":"A.","affiliations":[{"id":36639,"text":"University of Wisconsin Zoological Museum, 250 North Mills Street, Madison, WI 53706 (PMH) Sea Otter Research and Conservation Program, Monterey Bay Aquarium, 886 Cannery Row, Monterey, CA 93940","active":true,"usgs":false}],"preferred":false,"id":758384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":213742,"corporation":false,"usgs":false,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":758385,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fujii, Jessica A. 0000-0003-4794-479X","orcid":"https://orcid.org/0000-0003-4794-479X","contributorId":196602,"corporation":false,"usgs":false,"family":"Fujii","given":"Jessica","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":758386,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murray, Michael J.","contributorId":206852,"corporation":false,"usgs":false,"family":"Murray","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":37418,"text":"Monterey Bay Aquarium, Monterey, CA","active":true,"usgs":false}],"preferred":false,"id":758387,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Andrew B.","contributorId":127459,"corporation":false,"usgs":false,"family":"Johnson","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":758388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":758382,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Van Houtan, Kyle S.","contributorId":213743,"corporation":false,"usgs":false,"family":"Van Houtan","given":"Kyle","email":"","middleInitial":"S.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":758389,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273348,"text":"70273348 - 2018 - Magmatic origin for sediment-hosted Au deposits, Guizhou Province, China: In situ chemistry and sulfur isotope composition of pyrites, Shuiyindong and Jinfeng deposits","interactions":[],"lastModifiedDate":"2026-01-07T16:01:30.702015","indexId":"70273348","displayToPublicDate":"2018-11-01T09:04:59","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Magmatic origin for sediment-hosted Au deposits, Guizhou Province, China: In situ chemistry and sulfur isotope composition of pyrites, Shuiyindong and Jinfeng deposits","docAbstract":"The southwest Guizhou Province, China, contains numerous sediment-hosted Au deposits with Au reserves greater than 700 tonnes. To date, the source of ore fluids that formed the Guizhou sediment-hosted Au deposits is controversial, hampering the formulation of genetic models. In this study, we selected the Shuiyindong and Jinfeng Au deposits, the largest strata-bound and fault-controlled deposits in Guizhou, respectively, for detailed research on pyrite chemistry and S isotope composition using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and laser ablation-multicollector-inductively coupled plasma-mass spectrometry (LA-MC-ICP-MS), respectively.
Petrography and pyrite chemistry studies distinguished five generations of pyrite. Among these, pre-ore pyrite 2 and ore pyrite are the most abundant types in the deposits. Pre-ore pyrite 2 is anhedral to euhedral and with ~2,639 ppm As and wider ranges of Cu, Sb, and Pb (<~22–4,837 ppm, <~6 to 532 ppm, and <~4 to 1,344 ppm, respectively). Gold in pre-ore pyrite 2 is below the detection limit of LA-ICP-MS (~2 ppm). Pre-ore pyrite 2 is interpreted to have a sedimentary (syngenetic or diagenetic) origin. Ore pyrite commonly rims the four identified pre-ore pyrites or occurs as individual, anhedral to euhedral crystals. Ore pyrite is enriched in Au (~641 ppm), As (~9,147 ppm), Cu (~1,043 ppm), Sb (~188 ppm), Hg (~43 ppm), and Tl (~22 ppm) in both deposits. Ore pyrite formed mainly by sulfidation of Fe in Fe-bearing host rocks, mainly Fe dolomite, and As, Cu, Sb, Hg, and Tl, also in ore fluids, were incorporated into ore pyrite.
In situ δ34S isotope ratios for pre-ore pyrite 2 and ore pyrite were measured by LA-MC-ICP-MS. Pre-ore pyrite 2 from Shuiyindong and Jinfeng deposits resulted in δ34S values ranging from −0.8 to +3.4‰ and from 5.1 to 10.5‰, respectively. Analyses of ore pyrite from the Shuiyindong have δ34S values that vary from −3.3 to +2.5‰, with a median of 0.7‰; analyses of ore pyrite from the Jinfeng range from 8.9 to 11.2‰, with a median at 10.3‰. Available bulk and in situ δ34S data in the literature for pre-ore pyrites 1 and 2 and ore-related sulfide minerals including ore pyrite, arsenopyrite, and late ore-stage stibnite, realgar, orpiment, and cinnabar from several Guizhou sediment-hosted Au deposits were compiled for comparison. Pre-ore-stage pyrites from Guizhou sediment-hosted Au deposits have a broad range of δ34S values, from −33.8 to + 17.9‰ (including in situ and available bulk δ34S data). Ore-related sulfide minerals in all Guizhou sediment-hosted Au deposits, except Jinfeng, have very similar δ34S values, and most data plot between ~−5 and +5‰. In the Jinfeng deposit, the ore-related sulfide minerals exhibit δ34S values ranging from 1.9 to 18.1‰, with most data plotting between 6 and 12‰.
The broad range of S isotope compositions for the sedimentary pyrites (pre-ore pyrites 1 and 2) indicate that S in these pre-ore pyrites was most likely generated by bacterial reduction from marine sulfate. The narrow range of δ34S values (~−5–+5‰) for ore-related sulfide minerals in all Guizhou sediment-hosted Au deposits, excepting the Jinfeng deposit, suggests that the deposits may have formed in response to a single widespread metallogenic event. As the S isotope fractionation between hydrothermal fluids and sulfide minerals in a sulfide-dominated system is small (<2‰) at ~250°C, the initial ore fluids that formed the Guizhou sediment-hosted Au deposits would have had δ34S values similar to the ore-related sulfide minerals, between ~−5 and +5‰. At Jinfeng, initial ore fluids may have mixed with local fluids with heavier δ34S, possibly basin brine (δ34Sbasin brine >18‰), resulting in elevated δ34S values of ore-related sulfide minerals and especially late ore-stage sulfide minerals.
Although few igneous rocks are exposed in the mining area around these deposits, there is evidence of magmatic activity ~20 km away. Furthermore, gravity and magnetic geophysical investigations indicate the presence of a pluton ~5 km below the surface of the Shuiyindong district. Based on in situ S isotope results and recent data indicating proximal intrusions, we interpret a deep magmatic S source for the ore fluids that formed the Guizhou sediment-hosted Au deposits. However, as the age for Au mineralization of Guizhou sediment-hosted Au deposits is still debated, the mineralization-magma connection remains hypothetical. Identifying an ore fluid source and time frame for Guizhou Au mineralization continues to be a critically important research goal for this district.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.2018.4607","usgsCitation":"Xie, Z., Xia, Y., Cline, J., Pribil, M., Koenig, A., Tan, Q., Wei, D., Wang, Z., and Yan, J., 2018, Magmatic origin for sediment-hosted Au deposits, Guizhou Province, China: In situ chemistry and sulfur isotope composition of pyrites, Shuiyindong and Jinfeng deposits: Economic Geology, v. 7, no. 113, p. 1627-1652, https://doi.org/10.5382/econgeo.2018.4607.","productDescription":"26 p.","startPage":"1627","endPage":"1652","ipdsId":"IP-097173","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":498381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Yunnan-Guizhou-Guangxi region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 102,\n 27\n ],\n [\n 102,\n 22\n ],\n [\n 108.5,\n 22\n ],\n [\n 108.5,\n 27\n ],\n [\n 102,\n 27\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","issue":"113","noUsgsAuthors":false,"publicationDate":"2018-11-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Xie, Zhuojun","contributorId":364901,"corporation":false,"usgs":false,"family":"Xie","given":"Zhuojun","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":953401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xia, Yong","contributorId":364903,"corporation":false,"usgs":false,"family":"Xia","given":"Yong","affiliations":[{"id":87003,"text":"State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":953402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cline, Jean","contributorId":364905,"corporation":false,"usgs":false,"family":"Cline","given":"Jean","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":953403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pribil, Michael J. 0000-0003-4859-8673 mpribil@usgs.gov","orcid":"https://orcid.org/0000-0003-4859-8673","contributorId":141158,"corporation":false,"usgs":true,"family":"Pribil","given":"Michael","email":"mpribil@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":953404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koenig, Alan 0000-0002-5230-0924","orcid":"https://orcid.org/0000-0002-5230-0924","contributorId":206119,"corporation":false,"usgs":true,"family":"Koenig","given":"Alan","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":953405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tan, Qingping","contributorId":364906,"corporation":false,"usgs":false,"family":"Tan","given":"Qingping","affiliations":[],"preferred":false,"id":953406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wei, Dongtian","contributorId":364907,"corporation":false,"usgs":false,"family":"Wei","given":"Dongtian","affiliations":[],"preferred":false,"id":953407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Zepeng","contributorId":364908,"corporation":false,"usgs":false,"family":"Wang","given":"Zepeng","affiliations":[],"preferred":false,"id":953408,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yan, Jun","contributorId":364909,"corporation":false,"usgs":false,"family":"Yan","given":"Jun","affiliations":[],"preferred":false,"id":953409,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70200780,"text":"70200780 - 2018 - Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands","interactions":[],"lastModifiedDate":"2018-10-31T14:31:01","indexId":"70200780","displayToPublicDate":"2018-10-31T14:30:58","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands","docAbstract":"Restoration and rehabilitation of native vegetation in dryland ecosystems, which encompass over 40% of terrestrial ecosystems, is a common challenge that continues to grow as wildfire and biological invasions transform dryland plant communities. The difficulty in part stems from low and variable precipitation, combined with limited understanding about how weather conditions influence restoration outcomes, and increasing recognition that one‐time seeding approaches can fail if they do not occur during appropriate plant establishment conditions. The sagebrush biome, which once covered over 620,000 km2 of western North America, is a prime example of a pressing dryland restoration challenge for which restoration success has been variable. We analyzed field data on Artemisia tridentata (big sagebrush) restoration collected at 771 plots in 177 wildfire sites across its western range, and used process‐based ecohydrological modeling to identify factors leading to its establishment. Our results indicate big sagebrush occurrence is most strongly associated with relatively cool temperatures and wet soils in the first spring after seeding. In particular, the amount of winter snowpack, but not total precipitation, helped explain the availability of spring soil moisture and restoration success. We also find considerable interannual variability in the probability of sagebrush establishment. Adaptive management strategies that target seeding during cool, wet years or mitigate effects of variability through repeated seeding may improve the likelihood of successful restoration in dryland ecosystems. Given consistent projections of increasing temperatures, declining snowpack, and increasing weather variability throughout midlatitude drylands, weather‐centric adaptive management approaches to restoration will be increasingly important for dryland restoration success.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14374","usgsCitation":"Shriver, R.K., Andrews, C.M., Pilliod, D.S., Arkle, R., Welty, J.L., Germino, M., Duniway, M.C., Pyke, D.A., and Bradford, J.B., 2018, Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands: Global Change Biology, v. 24, no. 10, p. 4972-4982, https://doi.org/10.1111/gcb.14374.","productDescription":"11 p.","startPage":"4972","endPage":"4982","ipdsId":"IP-095817","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437704,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U67LQX","text":"USGS data release","linkHelpText":"Environmental conditions, covariate data used in model fitting, and long-term establishment predictions from 1979 to 2016 in the Great Basin, USA"},{"id":359048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -110,\n 37.5\n ],\n [\n -110,\n 45\n ],\n [\n -122,\n 45\n ],\n [\n -122,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-20","publicationStatus":"PW","scienceBaseUri":"5c10a900e4b034bf6a7e4ee0","contributors":{"authors":[{"text":"Shriver, Robert K. 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dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":750485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arkle, Robert 0000-0003-3021-1389 rarkle@usgs.gov","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":149893,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","email":"rarkle@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":750486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Welty, Justin L. 0000-0001-7829-7324 jwelty@usgs.gov","orcid":"https://orcid.org/0000-0001-7829-7324","contributorId":4206,"corporation":false,"usgs":true,"family":"Welty","given":"Justin","email":"jwelty@usgs.gov","middleInitial":"L.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":750487,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":750488,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":750489,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":750490,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":750491,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70200758,"text":"70200758 - 2018 - Tropical storm-induced landslide potential using combined field monitoring and numerical modeling","interactions":[],"lastModifiedDate":"2018-10-31T14:10:54","indexId":"70200758","displayToPublicDate":"2018-10-31T14:10:50","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2327,"text":"Journal of Geotechnical and Geoenvironmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Tropical storm-induced landslide potential using combined field monitoring and numerical modeling","docAbstract":"When heavy rainfall, such as that associated with tropical storms, falls on steep hillsides, shallow landslides are often one of the damaging consequences. To assess landslide potential from heavy rainfall, a strategy of combined numerical simulation and field monitoring of variably saturated hillslope conditions is developed. To test the combined method, hillslope hydrologic data from paired field monitoring sites in western North Carolina are examined. The hydrologic data collected from the field monitoring site where no shallow landslide has occurred is used to identify and calibrate the hydromechanical parameters used in a numerical ground water flow model. The identified parameters are then used to simulate landslide potential at the two hillslopes during heavy rainfall associated with hurricanes Frances and Ivan (HFI) that impacted western North Carolina in 2004. Results identify the timing of instability at the shallow landslide site and show that the stable site remains stable during rainfall associated with the HFI tropical storms. Thus, the results demonstrate the effectiveness of combined numerical modeling and field monitoring to evaluate landslide potential under variably saturated conditions.
In 1974, the U.S. Geological Survey (USGS), in cooperation with the San Bernardino Valley Municipal Water District, initiated a study to assess the regional groundwater resources in the Bunker Hill Subbasin of the Upper Santa Ana Valley Groundwater Basin in San Bernardino County, California. The study area expanded east into the Yucaipa Subbasin in 1996. This report compiles the geologic (borehole lithology and geophysical logs) and hydrologic (water-quality and water-level) data collected from 1974–2016 for 11 multiple-well monitoring sites (48 individual wells) constructed by the USGS in the Bunker Hill (7 sites) and Yucaipa (4 sites) Groundwater Subbasins.
Approximately 240 water-quality samples from the 11 sites were analyzed for constituents including major and minor ions, nutrients, selected trace elements, organic wastewater compounds (OWCs), volatile organic compounds (VOCs), pesticides and pesticide degradates, the stable isotopes of hydrogen, oxygen, and nitrogen, and the radiogenic isotopes of tritium and carbon-14. All environmental data associated with these sites are available on the project web page for the San Bernardino Optimal Basin Management study (https://ca.water.usgs.gov/sanbern/) and the Yucaipa Valley Hydrogeology study (https://ca.water.usgs.gov/yucaipa/).
Quality-assurance blank samples were processed periodically throughout the study and show that approximately 2.4 percent of the analytical results for major and minor ions, trace elements, and nutrients, and 1.5 percent of the results for VOCs fall below the acceptable study reporting limits and therefore are censored.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1096","collaboration":"Prepared in cooperation with the San Bernardino Valley Municipal Water District","usgsCitation":"Mendez, G.O., Anders, R., McPherson, K.R., and Danskin, W.R., 2018, Geologic, hydrologic, and water-quality data from multiple-well monitoring sites in the Bunker Hill and Yucaipa Groundwater Subbasins, San Bernardino County, California, 1974–2016 (ver 1.1): U.S. Geological Survey Data Series 1096, 215 p., https://doi.org/10.3133/ds1096.","productDescription":"viii, 215 p.","onlineOnly":"Y","temporalStart":"1974-01-01","temporalEnd":"2016-12-31","ipdsId":"IP-077227","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":358988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1096/coverthb.jpg"},{"id":359774,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/1096/versionHist.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"DS 1096 Version History"},{"id":358989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1096/ds1096_v1.1.pdf","text":"Report","size":"25.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1096"}],"country":"United States","state":"California","county":"San Bernardino County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.54547119140624,\n 33.863573814253485\n ],\n [\n -116.54022216796875,\n 33.863573814253485\n ],\n [\n -116.54022216796875,\n 34.34343606848294\n ],\n [\n -117.54547119140624,\n 34.34343606848294\n ],\n [\n -117.54547119140624,\n 33.863573814253485\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: November 2018; Version 1.0: October 2018","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey, in cooperation with the St. Johns River Water Management District, conducted a study to examine water fluxes in two small study areas in the Indian River Lagoon. Vertical arrays of temperature sensors were placed at multiple locations in the lagoon bed to measure temperature time series in the vertical profile. These data at one of the study areas, Eau Gallie, were used in two numerical models, 1DTempPro and VFLUX, to estimate seepage flux rates into the lagoon. 1DTempPro uses an inverse-modeling approach to calibrate groundwater flux to the measured temperature time series. VFLUX isolates the fundamental frequency signal in the temperature data and utilizes the resulting amplitude and phase differences between sensor locations to determine vertical water flux.
Field measurements were made during two time periods, March 23 to April 28, 2017, and June 1 to November 3, 2017. Simulating the first, drier period at one location with 1DTempPro helped determine reasonable seepage fluctuations and provided guidelines for choosing which temperature sensor pairs used in the VFLUX simulations would produce the best results. VFLUX simulations at eight locations indicated daily average seepage flux rates of less than 20 centimeters per day (cm/d) and substantial seepage flux out to a distance of at least 110 meters from shore. The spatial variation in average seepage flux rates within 40 meters of shore seemed large, ranging from about 3 to 20 cm/d.
In the VFLUX application using the June 1–November 3, 2017 data, the seepage flux has a higher magnitude and fluctuation than the first simulation period, making the isolation of the fundamental temperature frequency signal in the temperature data difficult. However, useful partial or full simulations were achieved at 6 of the 10 locations. The storm surge of Hurricane Irma on September 10, 2017, changed the depths of the sensors relative to the lagoon bed and disrupted the ability of VFLUX to compute seepage flux for the posthurricane period. The June 1 to November 3, 2017, computed seepage flux rates were higher than those for the March 24 to April 28, 2017, period and were sometimes as great as 40 cm/d, and more than 60 cm/d at one location. The seepage time-series data collected during Hurricane Irma indicates a downward seepage flux as a result of the storm surge, followed by upwelling from precipitation recharge inland. The average seepage flux rates are higher than those during the March–April period and are over 25 cm/d near the coast and about 20 cm/d 130 meters offshore.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181151","collaboration":"Prepared in cooperation with the St. Johns River Water Management District","usgsCitation":"Swain, E.D., and Prinos, S.T., 2018, Using heat as a tracer to determine groundwater seepage in the Indian River Lagoon, Florida, April–November, 2017: U.S. Geological Survey Open-File Report 2018–1151, 18 p., https://doi.org/10.3133/ofr20181151.","productDescription":"Report: vi, 18 p.; Data Releases","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-096716","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":358771,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q8JGAO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model data sets for 1DTempPro and VFLUX simulation experiments to determine groundwater seepage in the Indian River Lagoon, Florida"},{"id":358770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1151/ofr20181151.pdf","text":"Report","size":"7.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1151"},{"id":358769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1151/coverthb.jpg"},{"id":358772,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VM4B41","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Temperature data collected in the Indian River Lagoon to evaluate groundwater seepage, Brevard County, Florida, 2017–2018"}],"country":"United States","state":"Florida","otherGeospatial":"Indian River Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.91156005859375,\n 28.10832614221258\n ],\n [\n -80.452880859375,\n 28.10832614221258\n ],\n [\n -80.452880859375,\n 28.84707946871795\n ],\n [\n -80.91156005859375,\n 28.84707946871795\n ],\n [\n -80.91156005859375,\n 28.10832614221258\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Demonstrating disease impacts on the vital rates of free‐ranging mammalian hosts typically requires intensive, long‐term study. Evidence for chronic pathogens affecting reproduction but not survival is rare, but has the potential for wide‐ranging effects. Accurately quantifying disease‐associated reductions in fecundity is important for advancing theory, generating accurate predictive models, and achieving effective management. We investigated the impacts of brucellosis (Brucella abortus) on elk (Cervus canadensis) productivity using serological data from over 6,000 captures since 1990 in the Greater Yellowstone Ecosystem, USA. Over 1,000 of these records included known age and pregnancy status. Using Bayesian multilevel models, we estimated the age‐specific pregnancy probabilities of exposed and naïve elk. We then used repeat‐capture data to investigate the full effects of the disease on life history. Brucellosis exposure reduced pregnancy rates of elk captured in mid‐ and late‐winter. In an average year, we found 60% of exposed 2‐year‐old elk were pregnant compared to 91% of their naïve counterparts (a 31 percentage point reduction, 89% HPDI = 20%–42%), whereas exposed 3‐ to 9‐year‐olds were 7 percentage points less likely to be pregnant than naïve elk of their same age (89% HPDI = 2%–11%). We found these reduced rates of pregnancy to be independent from disease‐induced abortions, which afflict a portion of exposed elk. We estimate that the combination of reduced pregnancy by mid‐winter and the abortions following mid‐winter reduces the reproductive output of exposed female elk by 24%, which affects population dynamics to a similar extent as severe winters or droughts. Exposing hidden reproductive costs of disease is essential to avoid conflating them with the effects of climate and predation. Such reproductive costs cause complex population dynamics, and the magnitude of the effect we found should drive a strong selection gradient if there is heritable resistance.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4521","usgsCitation":"Cotterill, G., Cross, P.C., Middleton, A.D., Rogerson, J.D., Scurlock, B., and Du Toit, J.T., 2018, Hidden cost of disease in a free‐ranging ungulate: brucellosis reduces mid‐winter pregnancy in elk: Ecology and Evolution, v. 8, no. 22, p. 10733-10742, https://doi.org/10.1002/ece3.4521.","productDescription":"10 p.","startPage":"10733","endPage":"10742","ipdsId":"IP-096975","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468280,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4521","text":"Publisher Index Page"},{"id":358977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"22","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-28","publicationStatus":"PW","scienceBaseUri":"5c08f1c6e4b0815414d0bbfd","contributors":{"authors":[{"text":"Cotterill, Gavin G.","contributorId":203301,"corporation":false,"usgs":false,"family":"Cotterill","given":"Gavin G.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":750321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":750320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Middleton, Arthur D.","contributorId":210264,"corporation":false,"usgs":false,"family":"Middleton","given":"Arthur","email":"","middleInitial":"D.","affiliations":[{"id":33770,"text":"University of California at Berkeley","active":true,"usgs":false}],"preferred":false,"id":750322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogerson, Jared D.","contributorId":210265,"corporation":false,"usgs":false,"family":"Rogerson","given":"Jared","email":"","middleInitial":"D.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":750323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scurlock, Brandon","contributorId":145744,"corporation":false,"usgs":false,"family":"Scurlock","given":"Brandon","email":"","affiliations":[{"id":16219,"text":"Wyoming Game and Fish Department, PO Box 850, Pinedale, Wyoming","active":true,"usgs":false}],"preferred":false,"id":750324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Du Toit, Johan T. 0000-0003-0705-7117","orcid":"https://orcid.org/0000-0003-0705-7117","contributorId":210266,"corporation":false,"usgs":false,"family":"Du Toit","given":"Johan","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":750325,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70200679,"text":"70200679 - 2018 - Interisland genetic structure of two endangered Hawaiian waterbirds: The Hawaiian Coot and Hawaiian Gallinule","interactions":[],"lastModifiedDate":"2018-10-30T13:58:58","indexId":"70200679","displayToPublicDate":"2018-10-30T13:57:44","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Interisland genetic structure of two endangered Hawaiian waterbirds: The Hawaiian Coot and Hawaiian Gallinule","docAbstract":"Most of Hawaii's endemic avifauna are species of conservation concern. Some of Hawaii's endangered waterbirds, however, have increased in number as a result of intensive management of wetlands. To inform these conservation efforts, we examined interisland genetic structure and gene flow within 2 Hawaiian endemic waterbirds, the Hawaiian Coot (Fulica alai) and the Hawaiian subspecies of the Common Gallinule (Gallinula galeata sandvicensis), using microsatellite and mitochondrial loci. Hawaiian Coots and Hawaiian Gallinules occupy coastal wetlands and exhibit similar life history characteristics and generation times, although they may differ in dispersal propensity. Mark–resight data for Hawaiian Coot indicate interisland movements, whereas Hawaiian Gallinules are sedentary. Genetic diversity is partitioned across the landscape differently for Hawaiian Coots and Hawaiian Gallinules; patterns of variation are likely influenced by behavioral and ecological mechanisms. Hawaiian Coots exhibit low levels of structure at microsatellite loci (FST = 0.029) and high levels of gene flow among islands. Conversely, Hawaiian Gallinules are highly structured across marker types (microsatellite FST = 0.205, mtDNA control region FST = 0.370, mtDNA ND2 FST = 0.087), with restricted recent gene flow. Patterns of gene flow have changed after the population declines in the early to mid-1900s. Gene flow estimates indicate historical dispersal from Kauai to Oahu in both species, while recent estimates show individual Hawaiian Coots dispersing from Oahu and restricted gene flow between islands for the Hawaiian Gallinule. Changes in gene flow through time suggest that patterns of dispersal may be an artifact of the availability of habitat, which may be indirectly associated with the synergistic influences of population density and wetland quality. Despite recent population size increases for both species, continued threats to Hawaiian waterbirds (i.e. nonnative mammalian predators and invasive plants, avian disease, altered hydrology, and saltwater inundation of freshwater wetlands) will likely require continued active management to maintain viable populations.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-18-98.1","usgsCitation":"Sonsthagen, S.A., Wilson, R.E., and Underwood, J.G., 2018, Interisland genetic structure of two endangered Hawaiian waterbirds: The Hawaiian Coot and Hawaiian Gallinule: The Condor, v. 120, no. 4, p. 863-873, https://doi.org/10.1650/CONDOR-18-98.1.","productDescription":"11 p.","startPage":"863","endPage":"873","ipdsId":"IP-099058","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":460825,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-18-98.1","text":"Publisher Index Page"},{"id":437707,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74Q7SXC","text":"USGS data release","linkHelpText":"Hawaiian Coot (Fulica alai) and Hawaiian Gallinule (Gallinula galeata sandvicensis) Microsatellite and Mitochondrial DNA Data, 2014-2016, Oahu, Kauai, and Molokai, Hawaii"},{"id":358969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.499267578125,\n 18.760712758499565\n ],\n [\n -154.7314453125,\n 18.760712758499565\n ],\n [\n -154.7314453125,\n 22.370396344320053\n ],\n [\n -160.499267578125,\n 22.370396344320053\n ],\n [\n -160.499267578125,\n 18.760712758499565\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10a902e4b034bf6a7e4ef5","contributors":{"authors":[{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":750108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":750109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Underwood, Jared G.","contributorId":198606,"corporation":false,"usgs":false,"family":"Underwood","given":"Jared","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":750110,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202164,"text":"70202164 - 2018 - Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models","interactions":[],"lastModifiedDate":"2019-02-12T11:09:23","indexId":"70202164","displayToPublicDate":"2018-10-30T11:09:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models","docAbstract":"We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of allowable proximity and backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model (GMM) that doesn’t include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF therefore may prove to be a powerful ‘spatially aware’ alternative to other discriminative classifiers.
","language":"English","publisher":"MDPI","doi":"10.3390/geosciences8110395","usgsCitation":"Buscombe, D.D., and Grams, P.E., 2018, Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models: Geosciences, v. 8, no. 11, p. 1-21, https://doi.org/10.3390/geosciences8110395.","productDescription":"Article 395; 21 p.","startPage":"1","endPage":"21","ipdsId":"IP-095788","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468281,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geosciences8110395","text":"Publisher Index Page"},{"id":361166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":757055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":757054,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206274,"text":"70206274 - 2018 - Effects of an extreme flood event on federally endangered Diamond Darter abundances","interactions":[],"lastModifiedDate":"2019-10-29T08:13:31","indexId":"70206274","displayToPublicDate":"2018-10-29T08:12:27","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5153,"text":"The American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Effects of an extreme flood event on federally endangered Diamond Darter abundances","docAbstract":"Extreme flood events can substantially affect riverine systems, modifying instream habitat and influencing fish assemblages and densities. Rare species are especially vulnerable to these disturbance events because of their small population size and often reduced phenotypic heterogeneity. In June 2016 the lower Elk River in West Virginia experienced severe flooding, resulting in a peak discharge that exceeded the 0.005 annual exceedance probability (>200 y flood) in the main stem. We obtained pre-flood and postflood population count data and estimated abundances for one cohort of the federally endangered Diamond Darter (Crystallaria cincotta) at 15 sites. While both the total count data and total estimated abundance decreased following the flood, our analyses did not indicate the extreme flood event strongly impacted Diamond Darter abundance. This indicates individuals are able to withstand high velocities and resist displacement or mortality. In addition site-level abundances were estimated at three sentinel sites during 2015 and 2016 using a multinomial N-mixture model that accounted for variation in detectability resulting from water temperature. Mean estimated abundance varied among the three sites and between the 2 y. Our results suggest there is substantial variation in year-class strength between the two cohorts we sampled. It is suggested that survey efforts at established sentinel sites be continued on an annual basis in order to help determine factors influencing year-class strength.
","language":"English","publisher":"United States Department of Agriculture","doi":"10.1674/0003-0031-180.1.108","usgsCitation":"Welsh, S., 2018, Effects of an extreme flood event on federally endangered Diamond Darter abundances: The American Midland Naturalist, v. 180, p. 108-118, https://doi.org/10.1674/0003-0031-180.1.108.","productDescription":"11 p.","startPage":"108","endPage":"118","ipdsId":"IP-088254","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":368688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"180","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":774050,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70200657,"text":"70200657 - 2018 - Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters","interactions":[],"lastModifiedDate":"2018-12-05T14:09:21","indexId":"70200657","displayToPublicDate":"2018-10-26T16:35:43","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters","docAbstract":"In the Anthropocene, watershed chemical transport is increasingly dominated by novel combinations of elements, which are hydrologically linked together as ‘chemical cocktails.’ Chemical cocktails are novel because human activities greatly enhance elemental concentrations and their probability for biogeochemical interactions and shared transport along hydrologic flowpaths. A new chemical cocktail approach advances our ability to: trace contaminant mixtures in watersheds, develop chemical proxies with high-resolution sensor data, and manage multiple water quality problems. We explore the following questions: (1) Can we classify elemental transport in watersheds as chemical cocktails using a new approach? (2) What is the role of climate and land use in enhancing the formation and transport of chemical cocktails in watersheds? To address these questions, we first analyze trends in concentrations of carbon, nutrients, metals, and salts in fresh waters over 100 years. Next, we explore how climate and land use enhance the probability of formation of chemical cocktails of carbon, nutrients, metals, and salts. Ultimately, we classify transport of chemical cocktails based on solubility, mobility, reactivity, and dominant phases: (1) sieved chemical cocktails (e.g., particulate forms of nutrients, metals and organic matter); (2) filtered chemical cocktails (e.g., dissolved organic matter and associated metal complexes); (3) chromatographic chemical cocktails (e.g., ions eluted from soil exchange sites); and (4) reactive chemical cocktails (e.g., limiting nutrients and redox sensitive elements). Typically, contaminants are regulated and managed one element at a time, even though combinations of elements interact to influence many water quality problems such as toxicity to life, eutrophication, infrastructure corrosion, and water treatment. A chemical cocktail approach significantly expands evaluations of water quality signatures and impacts beyond single elements to mixtures. High-frequency sensor data (pH, specific conductance, turbidity, etc.) can serve as proxies for chemical cocktails and improve real-time analyses of water quality violations, identify regulatory needs, and track water quality recovery following storms and extreme climate events. Ultimately, a watershed chemical cocktail approach is necessary for effectively co-managing groups of contaminants and provides a more holistic approach for studying, monitoring, and managing water quality in the Anthropocene.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-018-0502-6","usgsCitation":"Kaushal, S., Gold, A.J., Bernal, S., Newcomer Johnson, T., Addy, K., Burgin, A., Burns, D., Coble, A.A., Hood, E.W., Lu, Y., Mayer, P., Minor, E.C., Schroth, A.W., Vidon, P., Wilson, H.F., Xenopolous, M.A., Doody, T., Galella, J.G., Goodling, P., Haviland, K., Haq, S., Wessel, B., Wood, K.L., Jaworski, N., and Belt, K., 2018, Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters: Biogeochemistry, v. 141, no. 3, p. 281-305, https://doi.org/10.1007/s10533-018-0502-6.","productDescription":"25 p.","startPage":"281","endPage":"305","ipdsId":"IP-093496","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":468284,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/407","text":"External 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Susana","contributorId":210126,"corporation":false,"usgs":false,"family":"Bernal","given":"Susana","email":"","affiliations":[{"id":38075,"text":"Center for Advanced Studies of Blanes, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":749990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newcomer Johnson, Tammy A.","contributorId":210127,"corporation":false,"usgs":false,"family":"Newcomer Johnson","given":"Tammy A.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":749991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Addy, Kelly","contributorId":210128,"corporation":false,"usgs":false,"family":"Addy","given":"Kelly","email":"","affiliations":[{"id":38076,"text":"Univ of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":749992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burgin, 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Shahan","contributorId":210138,"corporation":false,"usgs":false,"family":"Haq","given":"Shahan","email":"","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":750007,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wessel, Barret","contributorId":210139,"corporation":false,"usgs":false,"family":"Wessel","given":"Barret","email":"","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":750008,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Wood, Kelsey L.","contributorId":210140,"corporation":false,"usgs":false,"family":"Wood","given":"Kelsey","email":"","middleInitial":"L.","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":750010,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Jaworski, Norbert","contributorId":210141,"corporation":false,"usgs":false,"family":"Jaworski","given":"Norbert","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":750011,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Belt, Kenneth T.","contributorId":210142,"corporation":false,"usgs":false,"family":"Belt","given":"Kenneth T.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":750012,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70223856,"text":"70223856 - 2018 - A dirty dozen ways to die: Metrics and modifiers of mortality driven by drought and warming for a tree species","interactions":[],"lastModifiedDate":"2021-09-10T14:34:55.369322","indexId":"70223856","displayToPublicDate":"2018-10-26T09:06:02","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"A dirty dozen ways to die: Metrics and modifiers of mortality driven by drought and warming for a tree species","docAbstract":"Tree mortality events driven by drought and warmer temperature, often amplified by pests and pathogens, are emerging as one of the predominant climate change impacts on plants. Understanding and predicting widespread tree mortality events in the future is vital as they affect ecosystem goods and services provided by forests and woodlands, including carbon storage needed to help offset warming. Additionally, if extensive enough, tree die-off events can influence not only local climate but also climate and vegetation elsewhere via ecoclimate teleconnections. Consequently, recent efforts have focused on improving predictions of tree mortality. One of the most commercially important genera of trees is Pinus, and the most studied species globally for drought-induced tree mortality is piñon pine, Pinus edulis. Numerous metrics have been developed in association with predicting mortality thresholds or variations in mortality for this species. In this article, we compiled metrics associated with drought and warming related mortality that were developed for P. edulis or for which P. edulis was a key example species used in a calculation or prediction. We grouped these metrics into three categories: (i) those related to simple climate variables, (ii) those related to physiological responses, and (iii) those that require multi-step calculations or modeling using climate, ecohydrological, and/or ecophysiological data; and we identified the spatial-temporal scale of each of these metrics. We also compiled factors shown to modify rates or sensitivities of mortality. The metrics to predict mortality include empirical ones which often have implicit linkages to expected mechanisms, and more mechanistic ones related to physiological drivers. The metrics for P. edulis have similarities with those available for other species of Pinus. Expected future mortality events will provide an opportunity to observationally and experimentally test and compare these metrics related to tree mortality for P. edulis via near-term ecological forecasting. The metrics for P. edulis may also be useful as potential analogs for other genera. Improving predictions of tree mortality for this species and others will be increasingly important as an aid to move toward anticipatory management.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2018.00004","usgsCitation":"Breshears, D.D., Carroll, C.J., Redmond, M.D., Wion, A.P., Allen, C.D., Cobb, N.S., Meneses, N., Field, J.P., Wilson, L.A., Law, D., McCabe, L.M., and Newell-Bauer, O., 2018, A dirty dozen ways to die: Metrics and modifiers of mortality driven by drought and warming for a tree species: Frontiers in Forests and Global Change, v. 1, 4, 10 p., https://doi.org/10.3389/ffgc.2018.00004.","productDescription":"4, 10 p.","ipdsId":"IP-099758","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468285,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2018.00004","text":"Publisher Index Page"},{"id":389056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","noUsgsAuthors":false,"publicationDate":"2018-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Breshears, David D.","contributorId":51620,"corporation":false,"usgs":false,"family":"Breshears","given":"David","email":"","middleInitial":"D.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":822993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carroll, Charles J. W.","contributorId":187575,"corporation":false,"usgs":false,"family":"Carroll","given":"Charles","email":"","middleInitial":"J. W.","affiliations":[],"preferred":false,"id":822994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Redmond, Miranda D.","contributorId":256888,"corporation":false,"usgs":false,"family":"Redmond","given":"Miranda","email":"","middleInitial":"D.","affiliations":[{"id":51890,"text":"Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO, USA","active":true,"usgs":false}],"preferred":false,"id":822995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wion, Andreas P.","contributorId":256899,"corporation":false,"usgs":false,"family":"Wion","given":"Andreas","email":"","middleInitial":"P.","affiliations":[{"id":51890,"text":"Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO, USA","active":true,"usgs":false}],"preferred":false,"id":822996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allen, Craig D. 0000-0002-8777-5989 craig_allen@usgs.gov","orcid":"https://orcid.org/0000-0002-8777-5989","contributorId":2597,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"craig_allen@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":822997,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cobb, Neil S.","contributorId":200776,"corporation":false,"usgs":false,"family":"Cobb","given":"Neil","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":822998,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meneses, Nashelly","contributorId":265576,"corporation":false,"usgs":false,"family":"Meneses","given":"Nashelly","email":"","affiliations":[],"preferred":false,"id":822999,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Field, Jason P.","contributorId":216389,"corporation":false,"usgs":false,"family":"Field","given":"Jason","email":"","middleInitial":"P.","affiliations":[{"id":39400,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":823000,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, Luke A.","contributorId":265577,"corporation":false,"usgs":false,"family":"Wilson","given":"Luke","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":823001,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Law, Darin J.","contributorId":98627,"corporation":false,"usgs":true,"family":"Law","given":"Darin J.","affiliations":[],"preferred":false,"id":823002,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McCabe, Lindsie M.","contributorId":265578,"corporation":false,"usgs":false,"family":"McCabe","given":"Lindsie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":823003,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Newell-Bauer, Olivia","contributorId":265579,"corporation":false,"usgs":false,"family":"Newell-Bauer","given":"Olivia","email":"","affiliations":[],"preferred":false,"id":823004,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70199372,"text":"sir20185124 - 2018 - Concentrations of nutrients at the water table beneath forage fields receiving seasonal applications of manure, Whatcom County, Washington, autumn 2011–spring 2015","interactions":[],"lastModifiedDate":"2018-10-29T12:54:27","indexId":"sir20185124","displayToPublicDate":"2018-10-26T08:39:48","publicationYear":"2018","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":"2018-5124","title":"Concentrations of nutrients at the water table beneath forage fields receiving seasonal applications of manure, Whatcom County, Washington, autumn 2011–spring 2015","docAbstract":"The U.S. Geological Survey, in cooperation with the Whatcom Conservation District (WCD), collected groundwater-quality data for roughly 3 years (October 2011–May 2015) from near the water table beneath forage fields receiving regular seasonal applications of liquid dairy manure in Whatcom County, Washington. The work was done as part of an evaluation of WCD’s prototypical Application Risk Management (ARM) decision support system. The ARM system uses a combination of field-specific hydrology, stage of crop-growth, manure management practices, soil conditions, and precipitation forecast to evaluate the timing of manure application via a set of decision support tools (Manure Spreading Advisory, ARM Worksheet, manure application setback distances) in order to reduce the risk of contamination of surface water and groundwater. The ARM system’s effectiveness in reducing leaching of nitrate to groundwater was evaluated by monitoring nitrate concentrations in recently recharged groundwater beneath paired test plots receiving manure application scheduled using either conventional (CON) manure scheduling procedures, which utilize fixed start and end dates for manure application along with projected crop nutrient requirements or ARM manure scheduling procedures using an approach to manure application timing based on projected crop nutrient needs, field conditions, and weather forecast. Water-quality samples from the surface of the water table were collected synoptically from paired test plots (2–5 monitoring wells per test plot) at approximately monthly intervals at three different dairy field sites. Water-quality samples from near the water table were isolated from the underlying aquifer using a combination of an inflatable packer and a fine-grained sand pack encompassing the well-screen interval.
Concentrations of nitrate and chloride measured at the water table beneath test plots were highly variable. Concentrations of nitrate ranged from non-detectable to 116 milligrams nitrogen per liter (mg-N/L), and chloride ranged from 1.15 to 153 mg/L. In each test plot, seasonal variations were much greater than spatial variations. Differences in nitrate concentrations in groundwater between the two treatments were inconclusive. Nitrate concentrations in groundwater at paired treatment plots (Mann Whitney, p<0.05) were significantly lower beneath the ARM treatment plot at site B, yet significantly higher beneath the ARM treatment plot at site C. Nitrate concentrations in ground water varied significantly among individual wells at each site (Kruskal-Wallis, p<0.05), indicating that leaching of nitrates from soil following manure application is spatially variable at the field scale tested regardless of manure application strategy. At all three paired test plots, average concentrations of nitrate and chloride at the water table were lowest near the end of the growing season (September) and increased rapidly with the onset of autumn rains (October–December). Under both the conventional (calendar-based) and treatment (ARM-based) manure application scheduling systems, high soil nitrate concentrations in autumn were coincident with rising groundwater levels, suggesting that nitrate and chloride were flushed from soil to groundwater by recharge from the seasonal rains. Under both treatments, concentrations of nitrate in shallow (10–25 feet) groundwater beneath forage fields receiving manure applications were greater than the nitrate drinking water standard of 10 mg-N/L in approximately 85 percent of samples. Yearly mass loading of nitrogen to the groundwater system calculated from nitrate concentrations at the water table and estimates of recharge volume ranged from 86 to 196 pounds-N per acre, which was equivalent to approximately 16–37 percent of the recommended manure application rate for projected forage production yield of 7 dry tons per acre per year. Manure nitrogen applied in the autumn, when crop nutrient needs decrease due to reduced sunlight and cooler temperatures and commensurate with ongoing mineralization of soil organic-nitrogen and increased seasonal precipitation, are more likely to exceed the immediate plant nutritional requirements and hence be flushed to groundwater than manure applications occurring near the peak of the growing season.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185124","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the Whatcom Conservation District","usgsCitation":"Cox, S.E., Spanjer, A.R., Huffman, R.L., Black, R.W., Barbash, J.E., and Embertson, N.M., 2018, Concentrations of nutrients at the water table beneath forage fields receiving seasonal applications of manure, Whatcom County, Washington, autumn 2011–spring 2015: U.S. Geological Survey Scientific Investigations Report 2018-5124, 41 p.,\nhttps://doi.org/10.3133/sir20185124.","productDescription":"Report: vii, 41 p.; Data release","onlineOnly":"Y","ipdsId":"IP-092676","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":437710,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7D50K3F","text":"USGS data release","linkHelpText":"Concentration of nitrate and other water-quality constituents in groundwater from the water table beneath forage fields receiving seasonal applications of dairy manure, Whatcom County, Washington (2015)"},{"id":358358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5124/coverthb.jpg"},{"id":358359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5124/sir20185124.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5124"},{"id":358360,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7D50K3F","text":"USGS data release","description":"USGS Data Realase","linkHelpText":"Concentration of nitrate and other water-quality constituents in groundwater from the water table beneath forage fields receiving seasonal applications of dairy manure, Whatcom County, Washington (2015)"}],"country":"United States","state":"Washington","county":"Whatcom County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.48554229736328,\n 48.90286905393369\n ],\n [\n -122.21260070800781,\n 48.90286905393369\n ],\n [\n -122.21260070800781,\n 48.99711382864934\n ],\n [\n -122.48554229736328,\n 48.99711382864934\n ],\n [\n -122.48554229736328,\n 48.90286905393369\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director Washington Water Science Center
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