This white paper presents a series of perspectives on current and future phytobiome management, discussed at the Wild and Tamed Phytobiomes Symposium in University Park, PA, USA, in June 2018. To enhance plant productivity and health, and to translate lab- and greenhouse-based phytobiome research to field applications, the academic community and end-users need to address a variety of scientific, practical, and social challenges. Prior discussion of phytobiomes has focused heavily on plant-associated bacterial and fungal assemblages, but the phytobiomes concept covers all factors that influence plant function. Here we discuss various management considerations, including abiotic conditions (e.g. soil, nutrient applications), microorganisms (e.g. bacterial and fungal assemblages, bacterial and fungal inoculants, viruses), macroorganisms (e.g. arthropods, plant genetics), and societal factors (e.g. communication approaches, technology diffusion). An important near-term goal for this field should be to estimate the potential relative contribution of different components of the phytobiome to plant health, as well as the potential and risk of modifying each in the near-future.
","language":"English","publisher":"The American Phytopathological Society","doi":"10.1094/PBIOMES-01-19-0006-W","usgsCitation":"Bell, T.H., Hockett, K.L., Alcala-Briseno, R.I., Barbercheck, M., Beattie, G.A., Bruns, M.A., Carlson, J.E., Chung, T., Collins, A., Emmett, B., Esker, P., Garrett, K., Glenna, L., Gugino, B.K., Jimenez-Gasco, M.D., Kinkel, L., Kovac, J., Kowalski, K., Kuldau, G., Leveau, J.H., Michalska-Smith, M.J., Myrick, J., Peter, K., Salazar, M.F., Shade, A., Stopnisek, N., Tan, X., Welty, A.T., Wickings, K., and Yergeau, E., 2019, Manipulating wild and tamed phytobiomes: Challenges and opportunities: Phytobiomes Journal, v. 3, no. 1, p. 3-21, https://doi.org/10.1094/PBIOMES-01-19-0006-W.","productDescription":"19 p.","startPage":"3","endPage":"21","ipdsId":"IP-104782","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Measurement of Pb isotopic compositions in detrital K-feldspar is among the under-utilized approaches available to ascertain sedimentary source regions. While it has been long recognized that common Pb isotope compositions recorded by K-feldspar vary widely and reflect the crustal provinces from which the host basement rocks crystallized, use of the approach has suffered due to a lack of appropriate statistical models and ground truth compositional data from source regions. In this paper, we: (1) present high-throughput LA-ICPMS analysis protocols needed to generate statistically meaningful detrital K-feldspar Pb isotope data sets; (2) develop an interpretative approach based upon 208Pb/206Pb vs. 207Pb/206Pb that incorporate information from the U- and Th-decay systems into one two-dimensional plot that is amenable to analysis using two-dimensional Kolmogorov–Smirnoff statistical tests; (3) generate new Pb isotopic data from basement rocks from southwestern North America to improve knowledge of the Pb isotopic properties of potential source regions; and (4) generate new Pb isotopic data from Lower Eocene to Lower Miocene sedimentary rocks to evaluate changes in drainage patterns that occurred in response to deformation that affected the southern California margin. Through this case study, we demonstrate how our new analytical and interpretative methods could be profitably applied to future geochemical and provenance studies and tectonically driven re-organization of drainage patterns.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2018.11.040","usgsCitation":"Shulaker, D.Z., Grove, M., Hourigan, J.K., Van Buer, N., Sharman, G.R., Howard, K.A., Miller, J., and Barth, A.P., 2019, Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith: Earth and Planetary Science Letters, v. 508, p. 4-17, https://doi.org/10.1016/j.epsl.2018.11.040.","productDescription":"14 p.","startPage":"4","endPage":"17","ipdsId":"IP-103612","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"508","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shulaker, Danielle Ziva","contributorId":214181,"corporation":false,"usgs":false,"family":"Shulaker","given":"Danielle","email":"","middleInitial":"Ziva","affiliations":[{"id":38987,"text":"Stanford U.","active":true,"usgs":false}],"preferred":false,"id":759295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grove, Marty","contributorId":211570,"corporation":false,"usgs":false,"family":"Grove","given":"Marty","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":759296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hourigan, Jeremy K.","contributorId":99023,"corporation":false,"usgs":true,"family":"Hourigan","given":"Jeremy","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":759297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Buer, Nicholas","contributorId":214183,"corporation":false,"usgs":false,"family":"Van Buer","given":"Nicholas","email":"","affiliations":[{"id":38988,"text":"Cal State Poly Pomona","active":true,"usgs":false}],"preferred":false,"id":759298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":759299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howard, Keith A. 0000-0002-6462-2947 khoward@usgs.gov","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":3439,"corporation":false,"usgs":true,"family":"Howard","given":"Keith","email":"khoward@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":759294,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Jonathan","contributorId":214184,"corporation":false,"usgs":false,"family":"Miller","given":"Jonathan","affiliations":[{"id":38989,"text":"San Jose State U.","active":true,"usgs":false}],"preferred":false,"id":759300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barth, Andrew P.","contributorId":214136,"corporation":false,"usgs":false,"family":"Barth","given":"Andrew","email":"","middleInitial":"P.","affiliations":[{"id":38983,"text":"Indiana University - Purdue University","active":true,"usgs":false}],"preferred":false,"id":759301,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202630,"text":"70202630 - 2019 - Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","interactions":[],"lastModifiedDate":"2019-06-18T10:54:15","indexId":"70202630","displayToPublicDate":"2019-03-14T16:27:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","docAbstract":"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 the Greater Yellowstone Ecosystem, 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. We identified how spillover risk changes through space and time by developing resource selection functions using telemetry data from 223 female elk to predict the relative probability of female elk occurrence daily during the transmission risk period. We combined these spatiotemporal predictions with elk seroprevalence, demography, and transmission timing data to identify when and where abortions (the primary transmission route of brucellosis) were most likely to occur. Additionally, we integrated our predictions of transmission risk with spatiotemporal data on areas of potential livestock use to estimate the daily risk to livestock. We predicted that approximately half of the transmission risk occurred on areas where livestock may be present (i.e., private property or grazing allotments). Of the transmission risk that occurred in livestock areas, 98% of it was on private ranchlands as opposed to state or federal grazing allotments. Disease prevalence, transmission timing, host abundance, and host distribution were all important factors in determining the potential for spillover risk. 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":"Wiley","doi":"10.1002/jwmg.21645","usgsCitation":"Rayl, N., Proffitt, K., Almberg, E.S., Jones, J.D., Merkle, J., Gude, J., and Cross, P.C., 2019, Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover: Journal of Wildlife Management, v. 83, no. 4, p. 817-829, https://doi.org/10.1002/jwmg.21645.","productDescription":"13 p.","startPage":"817","endPage":"829","ipdsId":"IP-100336","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467814,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21645","text":"Publisher Index Page"},{"id":362077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","volume":"83","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rayl, Nathaniel D.","contributorId":199082,"corporation":false,"usgs":false,"family":"Rayl","given":"Nathaniel D.","affiliations":[],"preferred":false,"id":759303,"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":759305,"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":759304,"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":759308,"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":759306,"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":759307,"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":759302,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202298,"text":"sir20195003 - 2019 - Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","interactions":[],"lastModifiedDate":"2019-03-15T16:14:59","indexId":"sir20195003","displayToPublicDate":"2019-03-14T16:15:18","publicationYear":"2019","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":"2019-5003","displayTitle":"Climate, Streamflow, and Lake-Level Trends in the Great Lakes Basin of the United States and Canada, Water Years 1960–2015","title":"Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","docAbstract":"Water levels in the Great Lakes fluctuate substantially because of complex interactions among inputs (precipitation and streamflow), outputs (evaporation and outflow), and other factors. This report by the U.S. Geological Survey in cooperation with the Great Lakes Restoration Initiative was completed to describe trends in climate, streamflow, lake levels, and major water-budget components within the Great Lakes Basin for water years (WYs) 1960–2015 (study period). Resulting trends are applicable only to the study period and should not be considered indicative of longer-term trends.
Analyses of climate trends used monthly data from the Parameter-elevation Regressions on Independent Slopes Model, which are available only for the United States. Trend tests were completed for annual and seasonal time series of monthly means for total precipitation, daily minimum air temperature (Tmin), and daily maximum air temperature (Tmax). Statistical significance for all time-trend tests (climate, streamflow, and lake levels) was determined using the Mann‑Kendall test for probability values less than or equal to 0.10. Trend analyses were completed without adjustments for serial correlation; however, a modified Mann-Kendall test was subsequently used to examine potential effects of short-term persistence in time-series data. Effects of short-term persistence were considered inconsequential for climate data and minor for streamflow data; however, the presence of short-term persistence in water-budget components had more substantial effects on trend analyses.
Spatial distributions of trends in climatic data for WYs 1960–2015 for the U.S. part of the Great Lakes Basin (land only) indicate (1) generally ubiquitous upward trends in Tmin and (2) a sharp transition from neutral or downward trends in precipitation northwest of Lake Michigan to generally upward trends east of Lake Michigan. Trends in Tmax were not statistically significant. Analyses of annual climatic data aggregated for the U.S. land part of the Great Lakes Basin indicated statistically significant upward trends for precipitation and Tmin, and similar statistically significant trends existed for all the individual lake subbasins except Lake Superior.
Of 103 U.S. Geological Survey streamgages analyzed for streamflow trends, 71 had significant annual trends (54 upward and 17 downward). Downward trends in annual streamflow are concentrated northwest of Lake Michigan (16 streamgages), and upward trends are concentrated east of Lake Michigan (53 streamgages). Of the 71 streamgages with significant annual trends, 70 had at least one season with a significant trend that matched the annual trend direction.
Of 35 Environment and Climate Change Canada streamgages analyzed, 22 had significant upward trends in annual streamflow, and all but 1 of these 22 had at least one season with a significant upward trend. None of the Environment and Climate Change Canada streamgages had significant downward annual trends, and only one had a significant downward seasonal trend.
Trends in lake levels and several major water-budget components affecting lake levels were analyzed for the study period. Significant downward trends in lake level and outflow for Lake Superior are driven primarily by low lake levels and outflows during WYs 1998–2014. A significant downward trend in runoff from the contributing drainage area also is indicated, which is consistent with numerous streamgages northwest of Lake Michigan with significant downward trends in annual streamflow. A significant upward trend in annual overlake evaporation also is indicated, which is consistent with the spatially distributed upward trends in annual Tmin.
The sum of overlake precipitation and runoff from the contributing drainage area for each of the Great Lakes, less overlake evaporation, composes a variable called net basin supply (NBS). A significant downward trend in NBS is indicated for Lake Superior, which is consistent with significant trends for individual components of runoff (downward) and evaporation (upward) that contributed to a significant downward trend for lake outflow. Statistically significant upward trends in NBS for Lake Saint Clair and Lake Ontario offset the downward trend for Lake Superior and combine with nonsignificant upward trends in NBS for Lakes Michigan and Huron and Lake Erie to produce a neutral trend in NBS for the basin.
A predictable pattern in monthly mean lake levels is noted for Lake Superior, with the minimum for each year usually during or near March and the maximum commonly during or near September or October. When an October lake level is in a period of substantial decline, potential for an ensuing short-term period of below-mean lake levels is enhanced. Downstream from Lake Superior, monthly lake levels have sawtooth patterns that somewhat resemble those for Lake Superior but with decreased predictability in timing.
Similar to Lake Superior, Lakes Michigan and Huron, Lake Saint Clair, and Lake Erie all have a prolonged period of low lake levels around WYs 1998–2014; however, a significant downward trend is indicated only for Lakes Michigan and Huron. All these lakes also have a period of low lake levels before about WY 1968, when minimum lake levels were lower than during WYs 1998–2014. The significant downward trend of outflow from Lake Superior is carried downstream into Lakes Michigan and Huron; however, trends in outflow from the next three lakes downstream (Lakes Saint Clair, Erie, and Ontario) are offset by increased precipitation and runoff and are not significant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195003","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Norton, P.A., Driscoll, D.G., and Carter, J.M., 2019, Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015: Scientific Investigations Report 2019–5003, 47 p., https://doi.org/10.3133/sir20195003.","productDescription":"Report: vi, 47 p.; Appendix Figures; Appendix Tables: 5","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-089551","costCenters":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":362031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5003/coverthb.jpg"},{"id":362032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003.pdf","text":"Report","size":"22.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362033,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.1_to_1.103.pdf","text":"Appendix figures 1.1–1.103","size":"940 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362034,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.104_to_1.138.pdf","text":"Appendix figures 1.104–1.138","size":"333 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362035,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_tables_1.1_to_1.5.xlsx","text":"Appendix tables 1.1–1.5","size":"132 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5003"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.4716796875,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Measurements of the stable isotope ratios of nitrogen (15N/14N) and oxygen (18O/16O) in nitrate (NO3–) enable identification of sources, dispersal, and fate of natural and contaminant NO3– in aquatic environments. The 18O/16O of NO3– produced by nitrification is often assumed to reflect the proportional contribution of oxygen atom sources, water, and molecular oxygen, in a 2:1 ratio. Culture and seawater incubations, however, indicate oxygen isotopic equilibration between nitrite (NO2–) and water, and kinetic isotope effects for oxygen atom incorporation, which modulate the NO3– 18O/16O produced during nitrification. To investigate the influence of kinetic and equilibrium effects on the isotopic composition of NO3– produced from the nitrification of ammonia (NH3), we incubated streamwater supplemented with ammonium (NH4+) and increments of 18O-enriched water. Resulting NO3– 18O/16O ratios showed (1) a disproportionate sensitivity to the 18O/16O ratio of water, mediated by isotopic equilibration between water and NO2–, as well as (2) kinetic isotope discrimination during O atom incorporation from molecular oxygen and water. Empirically, the NO3– 18O/16O ratios thus produced fortuitously converge near the 18O/16O ratio of water. More elevated NO3– 18O/16O values commonly reported in soils and oxic groundwater may thus derive from processes additional to nitrification, including NO3– reduction.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b03386","usgsCitation":"Boshers, D.S., Granger, J., Tobias, C.R., Bohlke, J., and Smith, R.L., 2019, Constraining the oxygen isotopic composition of nitrate produced by nitrification: Environmental Science & Technology, v. 53, no. 3, p. 1206-1216, https://doi.org/10.1021/acs.est.8b03386.","productDescription":"11 p.","startPage":"1206","endPage":"1216","ipdsId":"IP-099166","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":362074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Boshers, Danielle S.","contributorId":214193,"corporation":false,"usgs":false,"family":"Boshers","given":"Danielle","email":"","middleInitial":"S.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":759321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Granger, Julie","contributorId":214194,"corporation":false,"usgs":false,"family":"Granger","given":"Julie","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":759322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tobias, Craig R.","contributorId":191283,"corporation":false,"usgs":false,"family":"Tobias","given":"Craig","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":759323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","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":759320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Richard L. 0000-0002-3829-0125 rlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3829-0125","contributorId":1592,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":759324,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202372,"text":"ds1106 - 2019 - Groundwater and surface-water data collection for Mason County, western Washington, 2016–18","interactions":[],"lastModifiedDate":"2019-03-15T13:42:19","indexId":"ds1106","displayToPublicDate":"2019-03-14T10:55:37","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1106","displayTitle":"Groundwater and Surface-Water Data Collection for Mason County, Western Washington, 2016–18","title":"Groundwater and surface-water data collection for Mason County, western Washington, 2016–18","docAbstract":"Groundwater levels and surface water flow measurements were collected from August 2016 to September 2018 to provide the Mason Conservation District and other stakeholders with basic knowledge of existing water resources in Mason County, Washington. Additionally, the data were collected with the intent of contributing to informed decision making about groundwater use, management, and conservation throughout the county and for future inclusion in a groundwater model. Data were collected and compiled for 130 sites—110 wells and 20 miscellaneous surface-water discharge sites. In the spring of 2016, field reconnaissance was conducted to locate suitable locations for baseflow discharge measurements to be used for estimating groundwater contribution to surface flow. In the summer of 2016, a field inventory of wells was conducted to acquire locational data and to assess the suitability of the wells for inclusion in a monthly groundwater-level monitoring network. Groundwater levels were measured bimonthly in the 64 wells over 2 years. Streamflow measurements were conducted two times each summer during two summers for each of the 20 surface water sites.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1106","collaboration":"Prepared in cooperation with the Mason Conservation District","usgsCitation":"Tecca, A.E., and Frans, L.M., 2019, Groundwater and surface-water data collection for Mason County, western Washington, 2016–18: U.S. Geological Survey Data Series 1106, 26 p., https://doi.org/10.3133/ds1106.","productDescription":"v, 26 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-102744","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":362071,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1106/coverthb.jpg"},{"id":362072,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1106/ds1106.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1106"}],"country":"United States","state":"Washington","county":"Mason County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.51379394531249,\n 46.97556750833867\n ],\n [\n -122.61016845703124,\n 46.97556750833867\n ],\n [\n -122.61016845703124,\n 47.66261271615866\n ],\n [\n -123.51379394531249,\n 47.66261271615866\n ],\n [\n -123.51379394531249,\n 46.97556750833867\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Climate change can have strong effects on aquatic ecosystems, including disrupting nutrient cycling and mediating processes that affect primary production. Past studies have been conducted mostly on individual or small groups of ecosystems, making it challenging to predict how future climate change will affect water quality at broad scales. We used a subcontinental-scale database to address three objectives: (1) identify which climate metrics best predict lake water quality, (2) examine whether climate influences different nutrient and productivity measures similarly, and (3) quantify the potential effects of a changing climate on lakes. We used climate data to predict lake water quality in ~11,000 north temperate lakes across 17 U.S. states. We developed a novel machine learning method that jointly models different measures of water quality using 48 climate metrics and accounts for properties inherent in macroscale data (e.g., spatial autocorrelation). Our results suggest that climate metrics related to winter precipitation and summer temperature were strong predictors of lake nutrients and productivity. However, we found variation in the magnitude and direction of the relationship between climate and water quality. We predict that a likely future climate change scenario of warmer summer temperatures will lead to increased nutrient concentrations and algal biomass across lakes (median ~3%–9% increase), whereas increased winter precipitation will have highly variable effects. Our results emphasize the importance of heterogeneity in the response of individual ecosystems to climate and are a caution to extrapolating relationships across space.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023088","usgsCitation":"Collins, S.M., Yuan, S., Tan, P.N., Oliver, S.K., Lapierre, J.F., Cheruvelil, K., Fergus, C., Skaff, N.K., Stachelek, J., Wagner, T., and Soranno, P., 2019, Winter precipitation and summer temperature predict lake water quality at macroscales: Water Resources Research, v. 55, no. 4, p. 2708-2721, https://doi.org/10.1029/2018WR023088.","productDescription":"14 p.","startPage":"2708","endPage":"2721","ipdsId":"IP-095436","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, S. M.","contributorId":273184,"corporation":false,"usgs":false,"family":"Collins","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yuan, S.","contributorId":273185,"corporation":false,"usgs":false,"family":"Yuan","given":"S.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tan, P. N.","contributorId":273186,"corporation":false,"usgs":false,"family":"Tan","given":"P.","email":"","middleInitial":"N.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, S. K.","contributorId":273187,"corporation":false,"usgs":false,"family":"Oliver","given":"S.","email":"","middleInitial":"K.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832673,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lapierre, J. F.","contributorId":273188,"corporation":false,"usgs":false,"family":"Lapierre","given":"J.","email":"","middleInitial":"F.","affiliations":[{"id":41192,"text":"Université de Montreal","active":true,"usgs":false}],"preferred":false,"id":832674,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheruvelil, K. S.","contributorId":273189,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"K. S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832675,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fergus, C. 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K.","contributorId":273191,"corporation":false,"usgs":false,"family":"Skaff","given":"N.","email":"","middleInitial":"K.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832677,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stachelek, J.","contributorId":273193,"corporation":false,"usgs":false,"family":"Stachelek","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832678,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832679,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Soranno, P. A.","contributorId":273195,"corporation":false,"usgs":false,"family":"Soranno","given":"P. A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832680,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70202411,"text":"sir20195009 - 2019 - Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","interactions":[],"lastModifiedDate":"2019-03-13T16:10:00","indexId":"sir20195009","displayToPublicDate":"2019-03-13T09:00:00","publicationYear":"2019","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":"2019-5009","displayTitle":"Flood-Inundation Maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","title":"Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","docAbstract":"Digital flood-inundation maps for a 16.4-mile reach of the Yellow River in Gwinnett County, Georgia, from 0.5 mile upstream from River Drive to Centerville Highway (Georgia State Route 124) were developed to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at two U.S. Geological Survey (USGS) streamgages in the mapped area. The maps for the 9.0-mile reach from 0.5 mile upstream from River Drive to Stone Mountain Highway (U.S. Route 78) are referenced to the streamgage Yellow River near Snellville, Ga. (station 02206500), and the maps for the 7.4-mile reach from Stone Mountain Highway to Centerville Highway are referenced to the streamgage Yellow River at Ga. 124, near Lithonia, Ga. (02207120). Real-time stage information from these streamgages can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgages Yellow River near Snellville, Ga. (02206500), and Yellow River at Ga. 124, near Lithonia, Ga. (02207120), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC–RAS) software for the Yellow River and was used to compute flood profiles for a 16.4-mile reach of the Yellow River. The hydraulic model was then used to simulate 16 water-surface profiles at 1.0-foot (ft) intervals at the Yellow River near Snellville streamgage and 17 water-surface profiles at 1.0-ft intervals at the Yellow River near Lithonia streamgage. At the Yellow River near Snellville streamgage, the profiles ranged from a stage of 18.0 ft, which is 819.1 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 33.0 ft, which is 834.1 ft above NAVD 88. At the Yellow River near Lithonia streamgage, the profiles ranged from the National Weather Service action stage of 13.0 ft, which is 732.5 ft above NAVD 88, to a stage of 29.0 ft, which is 748.5 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging (lidar) data having a 5.0-ft horizontal resolution—to delineate the area flooded at each 1.0-ft interval of stream stage for both streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195009","collaboration":"Prepared in cooperation with Gwinnett County, Georgia","usgsCitation":"Musser, J.W., 2019, Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia: U.S. Geological Survey Scientific Investigations Report 2019–5009, 15 p., https://doi.org/10.3133/sir20195009.","productDescription":"Report: vi, 15 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-100804","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":361975,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKB3H2","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation and flood depth for the Yellow River in Gwinnett County, Georgia based on water-surface elevation at the U.S. Geological Survey streamgages Yellow River, near Snellville, Georgia (02206500) and Yellow River at Ga. 124, near Lithonia, Georgia (02207120)"},{"id":361973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5009/coverthb.jpg"},{"id":361974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5009/sir20195009.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5009"}],"country":"United States","state":"Georgia","county":"Gwinnett County","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1667,\n 33.75\n ],\n [\n -84,\n 33.75\n ],\n [\n -84,\n 33.9167\n ],\n [\n -84.1667,\n 33.9167\n ],\n [\n -84.1667,\n 33.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Glacial−interglacial variations in CO2 and methane in polar ice cores have been attributed, in part, to changes in global wetland extent, but the wetland distribution before the Last Glacial Maximum (LGM, 21 ka to 18 ka) remains virtually unknown. We present a study of global peatland extent and carbon (C) stocks through the last glacial cycle (130 ka to present) using a newly compiled database of 1,063 detailed stratigraphic records of peat deposits buried by mineral sediments, as well as a global peatland model. Quantitative agreement between modeling and observations shows extensive peat accumulation before the LGM in northern latitudes (>40°N), particularly during warmer periods including the last interglacial (130 ka to 116 ka, MIS 5e) and the interstadial (57 ka to 29 ka, MIS 3). During cooling periods of glacial advance and permafrost formation, the burial of northern peatlands by glaciers and mineral sediments decreased active peatland extent, thickness, and modeled C stocks by 70 to 90% from warmer times. Tropical peatland extent and C stocks show little temporal variation throughout the study period. While the increased burial of northern peats was correlated with cooling periods, the burial of tropical peat was predominately driven by changes in sea level and regional hydrology. Peat burial by mineral sediments represents a mechanism for long-term terrestrial C storage in the Earth system. These results show that northern peatlands accumulate significant C stocks during warmer times, indicating their potential for C sequestration during the warming Anthropocene.
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In Osage County—a seismically quiet part of the state—continuous measurements of fluid pressure reveal that pressure in the reservoir is increasing by at least 5 kPa annually and sometimes at a much higher rate. Tidal analysis reveals that fluid level changes lead the local strain tides, with no apparent influence from transient permeability changes; this indicates a response that is inconsistent with flow in a radially extensive, confined reservoir. We investigate whether this is due to vertical flow to the water table, vertical flow within the Arbuckle, or local distortions from fractures. While none of these alternative models can fully explain both the observed tidal phases and amplitude ratios, the observed response to teleseismic waves supports a mechanism related to leakage rather than fracture effects. At this location fluid influx associated with wastewater disposal is offset by migration into surrounding layers, which include the Precambrian basement below. Thus, our findings suggest the need to monitor for changes in the induced seismicity hazard, while pore pressures increase in a leaky disposal reservoir.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB017327","usgsCitation":"Barbour, A.J., Xu, L., Roeloffs, E., and Rubinstein, J., 2019, Leakage and increasing fluid pressure detected in Oklahoma's wastewater disposal reservoir: Journal of Geophysical Research, no. 124, p. 2896-2919, https://doi.org/10.1029/2019JB017327.","productDescription":"24 p.","startPage":"2896","endPage":"2919","ipdsId":"IP-093460","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":363494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","county":"Osage 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Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Barbour, Andrew J. 0000-0002-6890-2452","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":215339,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Lian","contributorId":210946,"corporation":false,"usgs":false,"family":"Xu","given":"Lian","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":762076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roeloffs, Evelyn 0000-0002-4761-0469","orcid":"https://orcid.org/0000-0002-4761-0469","contributorId":215340,"corporation":false,"usgs":true,"family":"Roeloffs","given":"Evelyn","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":215341,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":762078,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202202,"text":"sir20195004 - 2019 - Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-10-23T09:27:27","indexId":"sir20195004","displayToPublicDate":"2019-03-11T13:38:08","publicationYear":"2019","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":"2019-5004","displayTitle":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018","title":"Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","docAbstract":"Libraries of digital flood-inundation maps that spanned a combined 37.2-mile reach of the Meramec River that extended upstream from Eureka, Missouri, to downstream near the confluence of the Meramec and Mississippi Rivers were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, and the cities of Pacific, Eureka, Wildwood, and Arnold. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages for the Meramec River near Eureka, Mo. (USGS station 07019000), the Meramec River at Valley Park, Mo. (USGS station 07019130), the Meramec River at Fenton, Mo. (USGS station 07019210), and the Meramec River at Arnold, Mo. (USGS station 07019300). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites erkm7, vllm7, fnnm7, and arnm7, respectively).
Flood profiles were computed for the stream reach by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute water-surface profiles: 1 set for the Meramec River near Eureka, Mo., streamgage (USGS station 07019000); 1 set for the Meramec River at Valley Park, Mo., streamgage (USGS station 07019130); 7 sets for the Meramec River at Fenton, Mo., streamgage (USGS station 07019210) for a range of Mississippi River conditions; and 8 sets for the Meramec River at Arnold, Mo., streamgage (USGS station 07019300) for a range of Mississippi River conditions. The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000). The simulated water-surface profiles then were combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each simulated 1-ft stage increment. Previously published flood-inundation maps were updated and incorporated in the flood map libraries for USGS stations 07019130 and 07019210 to complete the map sets corresponding to eight Mississippi River conditions.
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195004","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, the city of Pacific, the city of Eureka, the city of Wildwood, and the city of Arnold","usgsCitation":"Dietsch, B.J., and Strauch, K.R., 2019, Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5004, 12 p., https://doi.org/10.3133/sir20195004.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-096951","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5004/sir20195004.pdf","text":"Report","size":"1.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5004"},{"id":361925,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B0XLJL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018"},{"id":361923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5004/coverthb.jpg"}],"country":"United States","state":"Missouri","city":"Arnold, Eureka","otherGeospatial":"Meramec River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.66398620605469,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.38795699631396\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Water levels in lakes and wetlands in the central North Dakota Missouri Coteau region that were either dry or only sporadically held water since before the 1930s have been rising since the early 1990s in response to an extended wet period. The lakes have remained full since the mid-1990s, which has provided benefits to migratory waterfowl, fisheries, and wildlife. A small shift in climate conditions, either to drier or wetter conditions, can have a large effect on the lake levels of these water bodies. The North Dakota Game and Fish Department identified five lakes as candidates for sustaining long-term fisheries. The lakes are in Kidder, Stutsman, and Logan Counties, and some lakes might receive inflow from mostly freshwater aquifers, such as the Central Dakota and Streeter aquifers, and were mostly dry during the early 1990s. After about 1995, the lakes had filled up and were deep enough to sustain populations of game fish such as walleye, perch, and northern pike. Before investing in development of permanent fisheries and associated infrastructure, such as campgrounds and boat ramps, fisheries biologists needed to know if the lake levels are likely to remain high in coming decades.
The U.S. Geological Survey, in cooperation with the North Dakota Game and Fish Department, developed a water-balance model to determine the effects of precipitation, evapotranspiration, and groundwater interaction on lake volumes. The model was developed using climate input data and lake volumes for the calibration period 1992 through 2016, during which historical lake volumes could be estimated using land surface elevation data and Landsat images. Long-term (1940–2018) climate input data were used with the water-balance model to reconstruct historical lake volumes prior to the calibration period, and block-bootstrapping was used to simulate potential future climate input data and lake volumes for 2017 through 2067. The simulated future lake volumes were used to estimate the likelihood of annual lake volumes remaining consistent, increasing, or decreasing through the year 2067.
Of the five lakes, Sibley Lake was the most likely to sustain a long-term fishery for a period longer than 50 years. The simulated lake volumes for Alkaline Lake, Big Mallard Marsh, and Remmick Lake indicated the lakes have a 50-percent chance to fall below 75 percent of their 2016 volume by about 2030, 2067, and 2025, respectively. Simulation results for Marvin Miller Lake were substantially different compared to the other four lakes and indicated the lake has a 50-percent chance to fall below 75 percent of its 2016 volume prior to 2025.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195007","collaboration":"Prepared in cooperation with the North Dakota Game and Fish Department","usgsCitation":"Lundgren, R.F., York, B.C., Stroh, N.A., and Vecchia, A.V., 2019, Water-balance modeling of selected lakes for evaluating viability as long-term fisheries in Kidder, Logan, and Stutsman Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5007, 22 p., https://doi.org/10.3133/sir20195007.","productDescription":"Report: v, 22 p.; Downloads","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102504","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361913,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5007/coverthb.jpg"},{"id":361914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5007/sir20195007.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5007"},{"id":361915,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2019/5007/downloads/","text":"Water-Balance Model R code scripts","linkFileType":{"id":6,"text":"zip"},"description":"Water-Balance Model R code scripts"}],"country":"United States","state":"North Dakota","county":"Kidder County, Logan County, Stutsman County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1667,\n 46.5\n ],\n [\n -99.1667,\n 46.5\n ],\n [\n -99.1667,\n 47.5\n ],\n [\n -100.1667,\n 47.5\n ],\n [\n -100.1667,\n 46.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Context
Movement of prey on hydrologically pulsed, spatially heterogeneous wetlands can result in transient, high prey concentrations, when changes in landscape features such as connectivity between flooded areas alternately facilitate and impede prey movement. Predators track and exploit these concentrations, depleting them as they arise.
Objectives
We sought to describe how prey pulses of fish rapidly form and persist on wetland landscapes, while enduring constant consumption by wading birds, without being fully depleted. Specifically, we questioned how is the predator–prey relationship mediated by interactions between animal movement and dynamic landscape connectivity?
Methods
Two models were developed of the predator–prey-landscape system with qualitatively different representations of space, to identify and quantify prey pulsing dynamics that were robust across modeled assumptions. The first included a homogeneous landscape described by simple geometry, and implicit fish movement as wetland volume contracts. The second modeled transverse movement across a heterogeneous landscape, with isolated drying patches.
Results
Both models produced rapid fish prey concentrations as the wetland dried to shallow water depths. These conditions are critical for making prey available to wading birds. Fish were also rapidly depleted by birds, representing daily caloric intake supporting birds. Model 1 provided average estimates across the modeled domain. Model 2 mapped locations of emerging prey hotspots on the landscape through time.
Conclusions
Our models tracked predator, prey, and landscape dynamics in parallel, inducing systems dynamics from empirical observations. Explicit inclusion of dynamic wetland hydrologic connectivity, a key landscape mechanism, allowed for a comprehensive picture of links between landscape dynamics and the adapted predator–prey system.
We apply the Community Ice Sheet Model (CISM2) to determine the extent to which the Last Glacial Maximum (LGM) temperature and precipitation climatologies from the Paleoclimate Modelling Intercomparison Project 3 (PMIP3) simulations support the large North American ice sheets that were prescribed as a boundary condition. We force CISM2 with eight PMIP3 general circulation models (GCMs), and an additional model, GENMOM. Seven GCMs simulate LGM climatologies that support positive surface mass balances of the Laurentide and Cordilleran ice sheets (LIS, CIS) consistent with where ice was prescribed in the GCMs. Two GCMs simulate July temperatures that are too warm to support the ice sheets. Four of the nine CISM2 simulations support ice sheets in Beringia, in absence of prescribed ice in the driving GCMs and in disagreement with geologic evidence that indicates the area remained ice-free during the LGM. We test the sensitivity of our results to a range of snow and ice positive degree-day factors, daily, monthly, and climatological temperature and precipitation inputs, and we evaluate the role of albedo and snow in the simulations. Areas with perennial snow in the GCM simulations correspond well to the presence of ice in the CISM2 simulation. GCMs with unrealistically low surface albedos over the LIS yield simulations that fail to simulate realistic ice sheets.
","language":"English","publisher":"Springer","doi":"10.1007/s00382-019-04663-x","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, Applying the Community Ice Sheet Model to evaluate PMIP3 LGM climatologies over the North American ice sheets: Climate Dynamics, v. 53, p. 2807-2824, https://doi.org/10.1007/s00382-019-04663-x.","productDescription":"18 p.","startPage":"2807","endPage":"2824","ipdsId":"IP-088834","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":366848,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","noUsgsAuthors":false,"publicationDate":"2019-03-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":769152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":769153,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202540,"text":"70202540 - 2019 - Flooding regimes increase avian predation on wildlife prey in tidal marsh ecosystems","interactions":[],"lastModifiedDate":"2019-03-11T13:00:05","indexId":"70202540","displayToPublicDate":"2019-03-08T10:14:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Flooding regimes increase avian predation on wildlife prey in tidal marsh ecosystems","docAbstract":"Within isolated and fragmented populations, species interactions such as predation can cause shifts in community structure and demographics in tidal marsh ecosystems. It is critical to incorporate species interactions into our understanding when evaluating the effects of sea‐level rise and storm surges on tidal marshes. In this study, we hypothesize that avian predators will increase their presence and hunting activities during high tides when increased inundation makes their prey more vulnerable. We present evidence that there is a relationship between tidal inundation depth and time of day on the presence, abundance, and behavior of avian predators. We introduce predation pressure as a combined probability of predator presence related to water level. Focal surveys were conducted at four tidal marshes in the San Francisco Bay, California where tidal inundation patterns were monitored across 6 months of the winter. Sixteen avian predator species were observed. During high tide at Tolay Slough marsh, ardeids had a 29‐fold increase in capture attempts and 4 times greater apparent success rate compared with low tide. Significantly fewer raptors and ardeids were found on low tides than on high tides across all sites. There were more raptors in December and January and more ardeids in January than in other months. Ardeids were more prevalent in the morning, while raptors did not exhibit a significant response to time of day. Modeling results showed that raptors had a unimodal response to water level with a peak at 0.5 m over the marsh platform, while ardeids had an increasing response with water level. We found that predation pressure is related to flooding of the marsh surface, and short‐term increases in sea levels from high astronomical tides, sea‐level rise, and storm surges increase vulnerability of tidal marsh wildlife.
In 2016, digital flood-inundation maps along the shoreline of Lake Champlain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York were created by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission (IJC). This report discusses the creation of updated static digital flood-inundation mapping, in 2018, to include the entire shoreline of Lake Champlain in the United States. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water-surface elevations (stages) at the USGS lake gages on Lake Champlain.
As a result of the record setting floods of May 2011 in Lake Champlain and the Richelieu River, the U.S. and Canadian governments requested that the IJC issue a reference for a study to identify how flood forecasting, preparedness, and mitigation could be improved in the Lake Champlain–Richelieu River Basin. The IJC submitted the Lake Champlain–Richelieu River Plan of Study to the governments of Canada and the United States in 2013. The flood-inundation maps in this study are one aspect of the task work outlined in the IJC 2013 Plan of Study.
Wind and seiche effects (standing oscillating wave with a long wavelength) that can influence flooding along the Lake Champlain shoreline were not represented. The flood-inundation maps reflect 11 stages for Lake Champlain that are static for the entire area of the lake. Near-real-time stages at the USGS gages on Lake Champlain may be obtained from the USGS National Water Information System website at http://waterdata.usgs.gov/ (https://doi.org/10.5066/F7P55KJN) or from the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/.
Updated static flood-inundation boundary extents were created for Lake Champlain in Franklin, Chittenden, Addison, Rutland, and Grand Isle Counties in Vermont and Clinton, Essex, and Washington Counties in New York by using recently acquired (2009, 2012, 2014, and 2015) light detection and ranging (lidar) data. The corresponding flood-inundation maps may be referenced to any of the four active USGS lake gages on Lake Champlain. Of these four active lake gages, USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y.; USGS lake gage 04294500, Lake Champlain at Burlington, Vt.; USGS lake gage 04279085 Lake Champlain north of Whitehall, N.Y.; and USGS lake gage 04294413, Lake Champlain at Port Henry, N.Y., only the Richelieu River (Lake Champlain) at Rouses Point, N.Y., gage also serves as a National Weather Service prediction location. Lake Champlain static flood-inundation map boundary extents corresponding to the May 2011 peak flood stage (103.20 feet [ft], National Geodetic Vertical Datum of 1929 [NGVD 29], as recorded at the USGS Rouses Point lake gage, were compared to the flood-inundation area extents determined from satellite imagery for the May 2011 flood (which incorporated documented high-water marks from the flood of May 2011) and were found to be in good agreement. The May 2011 flood is the highest recorded lake water level (stage) at the Rouses Point, N.Y., and Burlington, Vt., lake gages. Flood stages greater than 101.5 ft (NGVD 29) exceed the “major flood stage” as defined by the National Weather Service for USGS lake gage 04295000.
Updated digital elevation models (DEMs) were created from the recent lidar data for Lake Champlain in Vermont and New York. These DEMs were used in determining the flood-inundation boundary and associated depth grids for 11 flood stages at 0.5-ft or 1-ft intervals from 100.0 to 106.0 ft (NGVD 29) as referenced to the USGS lake gages. In addition, the May 2011 flood-inundation area for elevation 103.20 ft (NGVD 29) (102.77 ft, North American Vertical Datum of 1988) was determined from these updated DEMs.
The availability of these maps, along with online information regarding current stages at the USGS lake gages and forecasted high-flow stages from the National Weather Service at USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185169","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Flynn, R.H., and Hayes, L., 2019, Flood-inundation maps for Lake Champlain in Vermont and New York: U.S. Geological Survey Scientific Investigations Report 2018–5169, 14 p., https://doi.org/10.3133/sir20185169. [Supersedes USGS Scientific Investigations Report 2016–5060.]","productDescription":"Report: v, 14 p.; Application Site; Data release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101452","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437545,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S","text":"USGS data release","linkHelpText":"Flood-Inundation Shapefiles and Grids for Lake Champlain in Vermont and New York"},{"id":361774,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html ","linkHelpText":"- Flood Inundation Mapper"},{"id":361771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5169/coverthb.jpg"},{"id":361772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5169/sir20185169.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5169"},{"id":361773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S ","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation shapefiles and grids for Lake Champlain in Vermont and New York"}],"country":"United States","state":"New York, Vermont","county":"Addison, Chittenden, Clinton, Franklin, Grand Isle ","otherGeospatial":"Lake Champlain","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.7081,43.5785 ], [ -73.7081,45.0891 ], [ -72.8948,45.0891 ], [ -72.8948,43.5785 ], [ -73.7081,43.5785 ] ] ] } } ] }","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The Florida Coastal Mapping Program is a nascent but highly relevant program that has the potential to greatly enhance the “Blue Economy” of Florida by coordinating and facilitating sea-floor mapping efforts and aligning partner and stakeholder activities for increased efficiency and cost reduction. Sustained acquisition of modern coastal mapping information for Florida may improve management of resources and reduce costs by eliminating redundancy. Economic growth could be aided by improved data to support emerging sectors such as aquaculture and renewable energy.
The present focus of the Florida Coastal Mapping Program is on modern, high-resolution bathymetric and coastal topobathymetric data, which can be immediately used to update navigational charts and identify navigation hazards, provide fundamental baseline data for scientific research, and provide information for use by emergency managers and responders. Derivative products include identifying sand resources for beach nourishment, creating vastly improved models for coastal erosion and flooding, identifying coastal springs, and creating benthic habitat maps. The uses and applications of the data generated could grow over time. The process of creating a steering committee and technical team, conducting an inventory and gaps analysis, soliciting feedback from the stakeholder and partner communities, and developing a prioritization process has provided a framework on which a successful program can develop a sustainable funding strategy that may be an investment the citizens of Florida could benefit from for decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191017","collaboration":"Prepared in cooperation with the Florida Institute of Oceanography, Florida Fish and Wildlife Research Institute, and Florida Department of Environmental Protection","usgsCitation":"Hapke, C.J., Kramer, P.A., Fetherston-Resch, E.H., Baumstark, R.D., Druyor, R., Fredericks, X., and Fitos, E., 2019, Florida Coastal Mapping Program—Overview and 2018 workshop report: U.S. Geological Survey Open-File Report 2019–1017, 19 p., https://doi.org/10.3133/ofr20191017.","productDescription":"vii, 19 p.","numberOfPages":"28","ipdsId":"IP-099357","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":361829,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1017/ofr20191017.pdf","text":"Report","size":"5.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1017"},{"id":361828,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1017/coverthb.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 -88.00,\n 24.5\n ],\n [\n -80,\n 24.5\n ],\n [\n -80,\n 30.75\n ],\n [\n -88.00,\n 30.75\n ],\n [\n -88.00,\n 24.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 Fourth Street South
St. Petersburg, FL 33701
We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass − percent cover distribution through space and time, as part of dynamic systems rather than static systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2018.09.004","usgsCitation":"Boyte, S.P., Wylie, B.K., and Major, D.J., 2019, Validating a time series of annual grass percent cover in the sagebrush ecosystem: Rangeland Ecology and Management, v. 72, no. 2, p. 347-359, https://doi.org/10.1016/j.rama.2018.09.004.","productDescription":"13 p.","startPage":"347","endPage":"359","ipdsId":"IP-101002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467831,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2018.09.004","text":"Publisher Index Page"},{"id":437546,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71J98QK","text":"USGS data release","linkHelpText":"A Time Series of Herbaceous Annual Cover in the Sagebrush Ecosystem"},{"id":361853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","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":758988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","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":758989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Major, Donald J.","contributorId":83405,"corporation":false,"usgs":false,"family":"Major","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":758990,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202528,"text":"70202528 - 2019 - Evidence for plunging river plume deposits in the Pahrump Hills member of the Murray formation, Gale crater, Mars","interactions":[],"lastModifiedDate":"2019-07-23T12:29:25","indexId":"70202528","displayToPublicDate":"2019-03-07T11:15:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3369,"text":"Sedimentology","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for plunging river plume deposits in the Pahrump Hills member of the Murray formation, Gale crater, Mars","docAbstract":"Recent robotic missions to Mars have offered new insights into the extent, diversity and habitability of the Martian sedimentary rock record. Since the Curiosity rover landed in Gale crater in August 2012, the Mars Science Laboratory Science Team has explored the origins and habitability of ancient fluvial, deltaic, lacustrine and aeolian deposits preserved within the crater. This study describes the sedimentology of a ca 13 m thick succession named the Pahrump Hills member of the Murray formation, the first thick fine‐grained deposit discovered in situ on Mars. This work evaluates the depositional processes responsible for its formation and reconstructs its palaeoenvironmental setting. The Pahrump Hills succession can be sub‐divided into four distinct sedimentary facies: (i) thinly laminated mudstone; (ii) low‐angle cross‐stratified mudstone; (iii) cross‐stratified sandstone; and (iv) thickly laminated mudstone–sandstone. The very fine grain size of the mudstone facies and abundant millimetre‐scale and sub‐millimetre‐scale laminations exhibiting quasi‐uniform thickness throughout the Pahrump Hills succession are most consistent with lacustrine deposition. Low‐angle geometric discordances in the mudstone facies are interpreted as ‘scour and drape’ structures and suggest the action of currents, such as those associated with hyperpycnal river‐generated plumes plunging into a lake. Observation of an overall upward coarsening in grain size and thickening of laminae throughout the Pahrump Hills succession is consistent with deposition from basinward progradation of a fluvial‐deltaic system derived from the northern crater rim into the Gale crater lake. Palaeohydraulic modelling constrains the salinity of the ancient lake in Gale crater: assuming river sediment concentrations typical of floods on Earth, plunging river plumes and sedimentary structures like those observed at Pahrump Hills would have required lake densities near freshwater to form. The depositional model for the Pahrump Hills member presented here implies the presence of an ancient sustained, habitable freshwater lake in Gale crater for at least ca 103 to 107 Earth years.
","language":"English","publisher":"Wiley","doi":"10.1111/sed.12558","usgsCitation":"Stack, K.M., Grotzinger, J.P., Lamb, M.P., Gupta, S., Rubin, D.M., Kah, L.C., Edgar, L.A., Fey, D.M., Hurowitz, J.A., McBride, M.J., Rivera-Hernandez, F., Sumner, D.Y., Van Beek, J.K., Williams, R.M., and Yingst, R.A., 2019, Evidence for plunging river plume deposits in the Pahrump Hills member of the Murray formation, Gale crater, Mars: Sedimentology, v. 66, no. 5, p. 1768-1801, https://doi.org/10.1111/sed.12558.","productDescription":"34 p.","startPage":"1768","endPage":"1801","ipdsId":"IP-101054","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":467833,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/sed.12558","text":"Publisher Index Page"},{"id":361830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"66","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Stack, Kathryn M. 0000-0003-3444-6695","orcid":"https://orcid.org/0000-0003-3444-6695","contributorId":146791,"corporation":false,"usgs":false,"family":"Stack","given":"Kathryn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":758957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grotzinger, John P.","contributorId":181502,"corporation":false,"usgs":false,"family":"Grotzinger","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":758958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamb, Michael P.","contributorId":214027,"corporation":false,"usgs":false,"family":"Lamb","given":"Michael","email":"","middleInitial":"P.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":758959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gupta, Sanjeev","contributorId":172302,"corporation":false,"usgs":false,"family":"Gupta","given":"Sanjeev","email":"","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":758960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubin, David M.","contributorId":206587,"corporation":false,"usgs":false,"family":"Rubin","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":758961,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kah, Linda C.","contributorId":181497,"corporation":false,"usgs":false,"family":"Kah","given":"Linda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":758962,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":758956,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fey, Deirdra M.","contributorId":214028,"corporation":false,"usgs":false,"family":"Fey","given":"Deirdra","email":"","middleInitial":"M.","affiliations":[{"id":36716,"text":"Malin Space Science Systems","active":true,"usgs":false}],"preferred":false,"id":758963,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hurowitz, Joel A.","contributorId":200390,"corporation":false,"usgs":false,"family":"Hurowitz","given":"Joel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":758964,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McBride, Marie J.","contributorId":167693,"corporation":false,"usgs":false,"family":"McBride","given":"Marie","email":"","middleInitial":"J.","affiliations":[{"id":24734,"text":"Malin Space Science Systems, San Diego","active":true,"usgs":false}],"preferred":false,"id":758965,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rivera-Hernandez, Frances","contributorId":203793,"corporation":false,"usgs":false,"family":"Rivera-Hernandez","given":"Frances","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":758966,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sumner, Dawn Y.","contributorId":200403,"corporation":false,"usgs":false,"family":"Sumner","given":"Dawn","email":"","middleInitial":"Y.","affiliations":[],"preferred":false,"id":758967,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Van Beek, Jason K.","contributorId":200399,"corporation":false,"usgs":false,"family":"Van Beek","given":"Jason","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":758968,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Williams, Rebecca M. E.","contributorId":214029,"corporation":false,"usgs":false,"family":"Williams","given":"Rebecca","email":"","middleInitial":"M. E.","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":758969,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Yingst, R. Aileen","contributorId":214030,"corporation":false,"usgs":false,"family":"Yingst","given":"R.","email":"","middleInitial":"Aileen","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":758970,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70202489,"text":"70202489 - 2019 - Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature, and treatment rules","interactions":[],"lastModifiedDate":"2019-06-18T10:39:36","indexId":"70202489","displayToPublicDate":"2019-03-06T11:26:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"title":"Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature, and treatment rules","docAbstract":"Atlantic salmon farming is one of the largest aquaculture sectors in the world. A major impact on farm economics, fish welfare, and potentially nearby wild salmonid populations, is the sea louse ectoparasite Lepeophtheirus salmonis. Sea louse infestations are most often controlled through application of chemicals, but in most farming regions sea lice have evolved resistance to the small set of available chemicals. Therefore, alternative treatment methodologies are becoming more widely used. One increasingly common alternative treatment involves the co-culture of farmed salmon with cleaner fish, which prey on sea lice. However, despite their wide use, little is understood about the situations in which cleaner fish are most effective. For example, previous work suggests that a low parasite density results in sea lice finding it difficult to acquire mates, reducing fecundity and population growth. Other work suggests that environmental conditions such as temperature and external sea louse pressure have substantial impact on this mate limitation threshold and may even remove the effect entirely. We use an Agent-Based Model (ABM) to simulate cleaner fish on a salmon farm to explore interactions between sea louse mating behaviour, cleaner fish feeding rate, temperature, and external sea lice pressure. We found that sea louse mating has a substantial effect on sea louse infestations under a variety of environmental conditions. Our results suggest that cleaner fish can control sea louse infestations most effectively by maintaining the population below critical density thresholds.
","language":"English","publisher":"Inter-Research","doi":"10.3354/dao03329","usgsCitation":"McEwan, G.F., Groner, M.L., Cohen, A.A., Imsland, A.K., and Revie, C.W., 2019, Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature, and treatment rules: Diseases of Aquatic Organisms, v. 133, no. 1, p. 69-82, https://doi.org/10.3354/dao03329.","productDescription":"14 p.","startPage":"69","endPage":"82","ipdsId":"IP-099338","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":467835,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://strathprints.strath.ac.uk/68921/1/McEwan_etal_DoAO_2019_Modelling_sea_lice_control.pdf","text":"External Repository"},{"id":361798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"133","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McEwan, Gregor F.","contributorId":213961,"corporation":false,"usgs":false,"family":"McEwan","given":"Gregor","email":"","middleInitial":"F.","affiliations":[{"id":38940,"text":"Department of Health Management, University of Prince Edward Island, Charlottetown, PE, Canada, C1A 4P3","active":true,"usgs":false}],"preferred":false,"id":758813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Groner, Maya L. 0000-0002-3381-6415","orcid":"https://orcid.org/0000-0002-3381-6415","contributorId":213541,"corporation":false,"usgs":true,"family":"Groner","given":"Maya","email":"","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":758814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cohen, Allegra A. B.","contributorId":213963,"corporation":false,"usgs":false,"family":"Cohen","given":"Allegra","email":"","middleInitial":"A. B.","affiliations":[{"id":38941,"text":"Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA 32611-0570","active":true,"usgs":false}],"preferred":false,"id":758815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Imsland, Albert K. D.","contributorId":213964,"corporation":false,"usgs":false,"family":"Imsland","given":"Albert","email":"","middleInitial":"K. D.","affiliations":[{"id":38942,"text":"Akvaplan-niva Iceland Office, Akralind 4, 201 Kópavogur, Iceland","active":true,"usgs":false}],"preferred":false,"id":758816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Revie, Crawford W.","contributorId":213965,"corporation":false,"usgs":false,"family":"Revie","given":"Crawford","email":"","middleInitial":"W.","affiliations":[{"id":38940,"text":"Department of Health Management, University of Prince Edward Island, Charlottetown, PE, Canada, C1A 4P3","active":true,"usgs":false}],"preferred":false,"id":758817,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}