Avian species endemic to desert grasslands of North America contend with significant ecological challenges, including monsoonal rains, droughts, and variable temperatures. These birds have evolved physiological and behavioral means of coping with such extremes, but ongoing changes to temperature and precipitation patterns are affecting their breeding phenology, reproductive success, and population growth rates. We examined how seasonal and daily weather conditions and habitat structure were associated with the nest survival of Arizona Grasshopper Sparrows (Ammodramus savannarum ammolegus) in the semidesert and plains grasslands of southeastern Arizona, USA. The mean ± SE daily survival rate (DSR) of nests was 0.960 ± 0.006, corresponding to overall nest success of 46%. The previous season's precipitation, large rain events, and nest concealment were the most important factors explaining DSR. Grasshopper Sparrow nest survival decreased with a wetter previous growing season and with large rain events on previous days. Nests that were more concealed had lower survival rates. There was some evidence that nest survival was lower later in the nesting season. In addition, when nest concealment was included in models, there were positive but weak associations between other vegetation variables and DSR—nests with higher visual obstruction at the nest and nest plot scales, and nests that were farther from shrubs >2 m tall, showed higher survival rates. Predation was the major cause of nest failure, suggesting complex interactions among predation, precipitation, and nest concealment. Further, our findings suggest tradeoffs in the potential effects of future climate change on A. s. ammolegus. The increased frequency of extreme storm events predicted for the region may result in reduced nest survival of A. s. ammolegus, but, conversely, lower seasonal precipitation prior to nesting may positively influence nest survival.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-17-128.1","usgsCitation":"Ruth, J.M., and Skagen, S., 2018, Reproductive response of Arizona Grasshopper Sparrows to weather patterns and habitat structure: The Condor, v. 120, no. 3, p. 596-616, https://doi.org/10.1650/CONDOR-17-128.1.","productDescription":"21 p.","startPage":"596","endPage":"616","ipdsId":"IP-073346","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468540,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.1650/CONDOR-17-128.1","text":"External Repository"},{"id":437804,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9USE8CH","text":"USGS data release","linkHelpText":"Arizona Grasshopper Sparrow nest monitoring data 2011 to 2013"},{"id":356782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","volume":"120","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a295e4b0702d0e842f6d","contributors":{"authors":[{"text":"Ruth, Janet M. 0000-0003-1576-5957 janet_ruth@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-5957","contributorId":1408,"corporation":false,"usgs":true,"family":"Ruth","given":"Janet","email":"janet_ruth@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":743279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skagen, Susan K. 0000-0002-6744-1244 skagens@usgs.gov","orcid":"https://orcid.org/0000-0002-6744-1244","contributorId":167829,"corporation":false,"usgs":true,"family":"Skagen","given":"Susan K.","email":"skagens@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":743280,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198357,"text":"70198357 - 2018 - Broad‐scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage‐grouse and other sensitive prey","interactions":[],"lastModifiedDate":"2018-10-23T16:59:03","indexId":"70198357","displayToPublicDate":"2018-08-01T11:14:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Broad‐scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage‐grouse and other sensitive prey","docAbstract":"Expanding human enterprise across remote environments impacts numerous wildlife species. Anthropogenic resources provide subsidies for generalist predators that can lead to cascading effects on prey species at lower trophic levels. A fundamental challenge for applied ecologists is to disentangle natural and anthropogenic influences on species occurrence, and subsequently develop spatially explicit models to help inform management and conservation decisions.
Using Bayesian hierarchical occupancy models, we mapped the broad‐scale occurrence of common ravens Corvus corax as a function of natural and anthropogenic landscape covariates using >15,000 point count surveys performed during 2007–2016 within the Great Basin region, USA. Raven abundance and distribution is substantially increasing across the American west due to unintended anthropogenic resource subsidies. Importantly, ravens prey on eggs and chicks of numerous species including greater sage‐grouse Centrocercus urophasianus, an indicator species whose decline is at the centre of national conservation strategies and land use policies.Anthropogenic factors that contributed to greater raven occurrence were: increased road density, presence of transmission lines, agricultural activity, and presence of roadside rest areas. Natural landscape characteristics included lower elevations with greener vegetation (NDVI), greater stream and habitat edge densities, and lower percentages of big sagebrush A. tridentata spp.
Interactions between anthropogenic sources of nesting substrate and food subsidies suggested that raven occurrence increased multiplicatively when these resource subsidies co‐occurred. Overall, the average probability of raven occurrence estimated within sagebrush ecosystems of the study area was ~0.83.
Synthesis and applications. We demonstrate how anthropogenic factors can be disentangled from natural effects when making spatially‐explicit predictions of subsidized predators occurring across expansive landscapes. This approach can guide management decisions where subsidized predators overlap sensitive prey habitats. For example, we identify areas where elevated raven occurrence coincides with breeding sage‐grouse concentration areas and appears to be largely driven by anthropogenic factors. Management applications could focus on reducing raven access to anthropogenic subsidies in these areas, while prioritizing habitat improvements for sage‐grouse elsewhere. Our approach is applicable to other species where widespread survey data are available.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13249","usgsCitation":"O’Neil, S.T., Coates, P.S., Brussee, B.E., Jackson, P.J., Howe, K., Moser, A.M., Foster, L.J., and Delehanty, D.J., 2018, Broad‐scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage‐grouse and other sensitive prey: Journal of Applied Ecology, v. 55, no. 6, p. 2641-2652, https://doi.org/10.1111/1365-2664.13249.","productDescription":"12 p.","startPage":"2641","endPage":"2652","ipdsId":"IP-097721","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468542,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13249","text":"Publisher Index Page"},{"id":437805,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93ONIQT","text":"USGS data release","linkHelpText":"Data from: Broad-scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage-grouse and other sensitive prey"},{"id":356082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-08","publicationStatus":"PW","scienceBaseUri":"5b6fc3ece4b0f5d57878e93b","contributors":{"authors":[{"text":"O’Neil, Shawn T.","contributorId":62533,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":741233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":741235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howe, Kristy B.","contributorId":192078,"corporation":false,"usgs":false,"family":"Howe","given":"Kristy B.","affiliations":[],"preferred":false,"id":741236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moser, Ann M.","contributorId":206592,"corporation":false,"usgs":false,"family":"Moser","given":"Ann","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":741237,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Foster, Lee J.","contributorId":201654,"corporation":false,"usgs":false,"family":"Foster","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":741238,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":741239,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70200933,"text":"70200933 - 2018 - Seasonal streamflow extremes are key drivers of Brook Trout young‐of‐the‐year abundance","interactions":[],"lastModifiedDate":"2018-11-16T11:12:47","indexId":"70200933","displayToPublicDate":"2018-08-01T11:12:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal streamflow extremes are key drivers of Brook Trout young‐of‐the‐year abundance","docAbstract":"To manage ecosystems in the context of climate change, we need to understand the relationship between extreme events and population dynamics. Floods and droughts are projected to occur more frequently, but how aquatic species will respond to these extreme events remains uncertain. Based on counts of Brook Trout (Salvelinus fontinalis) collected over 28 yr at 115 sites in Shenandoah National Park, we developed mixed‐effects models to (1) assess how well extreme streamflow, as compared to mean flows and total precipitation, can explain young‐of‐the‐year (YOY) abundance, (2) identify potential nonlinear relationships between seasonal environmental covariates and abundance using nonlinear generalized additive mixed models, and (3) explore likely impacts of expected future weather and streamflow conditions. We found that (1) using covariates of streamflow extremes improved prediction of YOY abundance compared to use of mean seasonal flow values or precipitation as a proxy, (2) warmer maximum daily spring temperatures were associated with increased YOY abundance up to about 1.5 standard deviations, above which abundance declined, and (3) a strong negative effect of extreme winter streamflow, unlikely to be offset by possibly positive effects from other seasons, is expected to have a detrimental impact on Brook Trout populations given predicted increases in winter precipitation. Because YOY abundance is a strong determinant of population dynamics for these short‐lived species, extreme events will have the potential to exert a strong influence on population persistence of Brook Trout in a changing climate. Management actions that maximize resiliency of populations in response to extreme events, such as restoration of habitat connectivity, should be prioritized to buffer negative impacts.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2356","usgsCitation":"Blum, A., Kanno, Y., and Letcher, B., 2018, Seasonal streamflow extremes are key drivers of Brook Trout young‐of‐the‐year abundance: Ecosphere, v. 9, no. 8, p. 1-16, https://doi.org/10.1002/ecs2.2356.","productDescription":"e02356; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-097891","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":468543,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2356","text":"Publisher Index Page"},{"id":359511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.9202880859375,\n 38.03078569382294\n ],\n [\n -78.06060791015624,\n 38.03078569382294\n ],\n [\n -78.06060791015624,\n 38.92095542046727\n ],\n [\n -78.9202880859375,\n 38.92095542046727\n ],\n [\n -78.9202880859375,\n 38.03078569382294\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-20","publicationStatus":"PW","scienceBaseUri":"5befe5bce4b045bfcadf7f40","contributors":{"authors":[{"text":"Blum, Annalise G.","contributorId":193846,"corporation":false,"usgs":false,"family":"Blum","given":"Annalise G.","affiliations":[],"preferred":false,"id":751378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":751379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":751377,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211859,"text":"70211859 - 2018 - Quartz solubility in the H2O-NaCl system: A framework for understanding vein formation in porphyry copper deposits","interactions":[],"lastModifiedDate":"2020-08-10T16:10:51.88747","indexId":"70211859","displayToPublicDate":"2018-08-01T11:04:26","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Quartz solubility in the H2O-NaCl system: A framework for understanding vein formation in porphyry copper deposits","title":"Quartz solubility in the H2O-NaCl system: A framework for understanding vein formation in porphyry copper deposits","docAbstract":"Porphyry copper deposits consist of low-grade stockwork and disseminated sulfide zones that contain characteristic vein generations formed during the evolution of the magmatic-hydrothermal systems. The present contribution proposes an interpretive framework for the formation of porphyry veins that is based on quartz solubility calculations in the H2O-NaCl system at temperatures of 100° to 1,000°C and pressures of 1 to 2,000 bar. The model predicts that high-temperature (≳500°C) quartz in A veins of deep (≳4 km) porphyry deposits forms as a result of the cooling of ascending intermediate-density fluids at lithostatic conditions. In deposits of intermediate depths (~1.5–4 km), A vein quartz is mostly formed through cooling of ascending hydrothermal fluids under closed-system conditions or quasi-isobaric cooling under open-system conditions within the two-phase field of the H2O-NaCl system. In shallow (≲1.5 km) porphyry deposits, rapid decompression can also result in quartz precipitation, forming so-called banded veins. The high-temperature quartz in A veins is associated with potassic alteration. During continued cooling of the magmatic-hydrothermal system, quartz is formed at intermediate temperatures (≳375°–500°C). This quartz overprints earlier A veins and forms B veins. The fluid inclusion inventory of this quartz generation suggests formation at fluctuating pressure conditions, marking the lithostatic to hydrostatic transition, and the change of wall-rock behavior from ductile to brittle conditions. The quartz is precipitated because of cooling and decompression of the magmatic-hydrothermal fluids under K-feldspar-stable conditions. Textural evidence from many porphyry veins suggests that hypogene sulfide minerals present in A and B veins postdate the quartz, as contacts between quartz and sulfide minerals commonly show dissolution textures. Hypogene sulfide minerals in C veins form at conditions of retrograde quartz solubility, explaining why these veins contain little to no quartz. The quartz solubility calculations suggest that C vein formation occurs at temperatures of ~375° to 450°C from low-salinity, single-phase fluids escaping from the lithostatic to the hydrostatic environment. At the upper end of this temperature range, C veins are biotite stable. However, these veins are associated with chlorite, chlorite-K-feldspar, or chlorite-sericite alteration in most deposits. Late quartz is formed during continued cooling of the hydrothermal fluids at ≲375°C within the single-phase field of the H2O-NaCl system as quartz solubility under these conditions decreases with temperature. This process is responsible for the formation of quartz in D veins and later base metal-bearing E veins, which are associated with phyllic, advanced argillic, or argillic alteration.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.2018.4580","usgsCitation":"Monecke, T., Monecke, J., Reynolds, T., Tsuruoka, S., Bennett, M.M., Skewes, W.B., and Palin, R.M., 2018, Quartz solubility in the H2O-NaCl system: A framework for understanding vein formation in porphyry copper deposits: Economic Geology, v. 113, no. 5, p. 1007-1046, https://doi.org/10.5382/econgeo.2018.4580.","productDescription":"40 p.","startPage":"1007","endPage":"1046","ipdsId":"IP-086994","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":377280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Monecke, Thomas","contributorId":210730,"corporation":false,"usgs":false,"family":"Monecke","given":"Thomas","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":795433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monecke, Jochen","contributorId":237834,"corporation":false,"usgs":false,"family":"Monecke","given":"Jochen","email":"","affiliations":[{"id":47621,"text":"Institute of Theoretical Physics, TU Bergakademie Freiberg, Leipziger Strae 23, 09596 Freiberg, Germany","active":true,"usgs":false}],"preferred":false,"id":795434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reynolds, T James","contributorId":237835,"corporation":false,"usgs":false,"family":"Reynolds","given":"T James","affiliations":[{"id":47622,"text":"FLUID INC., 1401 Wewatta St. #PH3, Denver, Colorado 80202","active":true,"usgs":false}],"preferred":false,"id":795435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tsuruoka, Subaru","contributorId":237836,"corporation":false,"usgs":false,"family":"Tsuruoka","given":"Subaru","email":"","affiliations":[{"id":47623,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, 1516 Illinois Street, Golden, Colorado 80401","active":true,"usgs":false}],"preferred":false,"id":795440,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennett, Mitchell M. 0000-0001-9533-9557 mbennett@usgs.gov","orcid":"https://orcid.org/0000-0001-9533-9557","contributorId":199379,"corporation":false,"usgs":true,"family":"Bennett","given":"Mitchell","email":"mbennett@usgs.gov","middleInitial":"M.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":795441,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Skewes, Wiley B","contributorId":237837,"corporation":false,"usgs":false,"family":"Skewes","given":"Wiley","email":"","middleInitial":"B","affiliations":[{"id":47623,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, 1516 Illinois Street, Golden, Colorado 80401","active":true,"usgs":false}],"preferred":false,"id":795442,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Palin, Richard M.","contributorId":237838,"corporation":false,"usgs":false,"family":"Palin","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":795443,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227936,"text":"70227936 - 2018 - The perpetual state of emergency that sacrifices protected areas in a changing climate","interactions":[],"lastModifiedDate":"2022-02-02T17:50:13.341661","indexId":"70227936","displayToPublicDate":"2018-08-01T10:59:35","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"The perpetual state of emergency that sacrifices protected areas in a changing climate","docAbstract":"A modern challenge for conservation biology is to assess the consequences of policies that adhere to assumptions of stationarity (e.g. historic norms) in an era of global environmental change. Such policies may result in unexpected and surprising levels of mitigation given future climate change trajectories, especially as agriculture looks to protected areas to buffer against production losses during periods of environmental extremes. Here, we conduct a scenario impact assessment to determine the potential impact of climate change scenarios on the rates at which lands enrolled in the Conservation Reserve Program (CRP) lands are authorized for emergency biomass removal. Grassland biomass on CRP lands is authorized for ‘emergency’ harvesting for agricultural use when precipitation for the last four months falls below 40 percent of the normal, or ‘historical’ mean, precipitation for that four-month period. We develop and analyze scenarios under the condition that policy will continue to operate under assumptions of stationarity, thereby authorizing emergency biomass harvesting solely as a function of precipitation departure from historic norms. Model projections show the historical likelihood of authorizing emergency biomass harvesting in any given year in the northern Great Plains was 33.28 ± 0.96%, according to long-term weather records. Emergency biomass harvesting became the norm (>50% of years) in the scenario reflecting continued increases in emissions and a decrease in growing season precipitation, and areas in the Great Plains with higher historical mean annual rainfall were disproportionately affected and experienced a greater increase in emergency biomass removal. Emergency biomass harvesting decreased only in the scenario reflecting rapid reductions in emissions. Our scenario impact analysis indicates that biomass from lands enrolled in the CRP will be used primarily as a buffer for agriculture in an era of climatic change, unless policy guidelines are adapted or climate change projections significantly depart from the current consensus.","language":"English","publisher":"Wiley","doi":"10.1111/cobi.13099","usgsCitation":"Twidwell, D., Wonkka, C.L., Bielski, C.H., Allen, C.R., Angeler, D., Drozda, J., Garmestani, A.S., Johnson, J., Powell, L., and Roberts, C.P., 2018, The perpetual state of emergency that sacrifices protected areas in a changing climate: Conservation Biology, v. 32, no. 4, p. 905-915, https://doi.org/10.1111/cobi.13099.","productDescription":"11 p.","startPage":"905","endPage":"915","ipdsId":"IP-094023","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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L.","contributorId":197668,"corporation":false,"usgs":false,"family":"Wonkka","given":"Carissa","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":832617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bielski, Christine H.","contributorId":197669,"corporation":false,"usgs":false,"family":"Bielski","given":"Christine","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":832618,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832619,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Angeler, David G.","contributorId":25027,"corporation":false,"usgs":true,"family":"Angeler","given":"David G.","affiliations":[],"preferred":false,"id":832620,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drozda, Jacob","contributorId":273637,"corporation":false,"usgs":false,"family":"Drozda","given":"Jacob","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":832747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garmestani, Ahjond S.","contributorId":77285,"corporation":false,"usgs":true,"family":"Garmestani","given":"Ahjond","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":832621,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Julia","contributorId":273638,"corporation":false,"usgs":false,"family":"Johnson","given":"Julia","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":832748,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Powell, Larkin A.","contributorId":15100,"corporation":false,"usgs":true,"family":"Powell","given":"Larkin A.","affiliations":[],"preferred":false,"id":832623,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Roberts, Caleb P. 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":197604,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","middleInitial":"P.","affiliations":[],"preferred":false,"id":832624,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70198353,"text":"70198353 - 2018 - Complex bedding geometry in the upper portion of Aeolis Mons, Gale crater, Mars","interactions":[],"lastModifiedDate":"2018-08-01T10:56:17","indexId":"70198353","displayToPublicDate":"2018-08-01T10:56:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Complex bedding geometry in the upper portion of Aeolis Mons, Gale crater, Mars","docAbstract":"The Upper formation of Aeolis Mons in Gale crater exhibits curvilinear bedding patterns on the surfaces of several erosional benches that have been interpreted as cross-bedding. We use High Resolution Imaging Science Experiment (HiRISE) stereo topography to test this hypothesis by measuring the bedding geometry within these benches. The bedding geometry is consistent with aeolian cross-beds: measured dips rarely exceed the angle of repose, and the distribution of dip azimuths is non-random, allowing dune morphology and paleo-transport directions to be inferred using computer models of bedforms. The inferred dune type and transport direction vary between the benches of the Upper formation, indicating that the benches are separated by sufficient time for the wind regime to change. The paleo-wind directions derived from bedding geometry measurements differ from modern wind modeling results, suggesting that the conditions during deposition of the Upper formation were unlike modern conditions. The concentric bedding patterns in some locations indicate that the rate of deposition approached the rate of bedform migration. The evidence for lithified hundred-meter-scale dunes in the Upper formation of Aeolis Mons indicates that the area was a sediment sink at the time of formation, and any hypothesis for the formation of Aeolis Mons must be compatible with these results. We present one possible sequence of events for the formation of Aeolis Mons.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2018.06.009","usgsCitation":"Anderson, R.B., Edgar, L.A., Rubin, D.M., Lewis, K.W., and Newman, C., 2018, Complex bedding geometry in the upper portion of Aeolis Mons, Gale crater, Mars: Icarus, v. 314, p. 246-264, https://doi.org/10.1016/j.icarus.2018.06.009.","productDescription":"19 p.","startPage":"246","endPage":"264","ipdsId":"IP-096721","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":356079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"314","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc3ede4b0f5d57878e93d","contributors":{"authors":[{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":741197,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":741198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":741199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewis, Kevin W.","contributorId":203787,"corporation":false,"usgs":false,"family":"Lewis","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":741200,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newman, Claire","contributorId":206588,"corporation":false,"usgs":false,"family":"Newman","given":"Claire","affiliations":[{"id":37347,"text":"Aeolis Research","active":true,"usgs":false}],"preferred":false,"id":741201,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228742,"text":"70228742 - 2018 - Paleoclimate Records: Providing context and understanding of current Arctic change","interactions":[],"lastModifiedDate":"2022-02-18T17:15:28.160551","indexId":"70228742","displayToPublicDate":"2018-08-01T10:40:53","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10118,"text":"Bulletin American Meteorological Society","active":true,"publicationSubtype":{"id":10}},"title":"Paleoclimate Records: Providing context and understanding of current Arctic change","docAbstract":"At present, the Arctic Ocean is experiencing changes in ocean surface temperature and sea ice extent that are unprecedented in the era of satellite observations, which extend from the 1980s to the present (see sections 5c,d). To provide context for current changes, scientists turn to paleoclimate records to document and study anthropogenic influence and natural decadal and multidecadal climate variability in the Arctic system. Paleoceanographic records extend limited Arctic instrumental measurements back in time and are central to improving our understanding of climate dynamics and the predictive capability of climate models. By comparing paleoceanographic records with modern observations, scientists can place the rates and magnitudes of modern Arctic change in the context of those inferred from the geological record. \n\nOver geological time, paleoceanographic reconstructions using, for instance, marine sediment cores indicate that the Arctic has experienced huge sea ice fluctuations. These fluctuations range from nearly completely ice-free to totally ice-covered conditions. The appearance of ice-rafted debris and sea ice-dependent diatoms in Arctic marine sediments indicate that the first Arctic sea ice formed approxi-mately 47 million years ago (St. John 2008; Stickley et al. 2009; Fig. SB5.1), coincident with an interval of declining atmospheric carbon dioxide (CO2) concentration, global climate cooling, and expansion of Earths cryosphere during the middle Eocene. The development of year-round (i.e., perennial) sea ice in the central Arctic Ocean, similar to conditions that exist today, is evident in sediment records as early as 1418 million years ago (Darby 2008). These records suggest that transitions in sea ice cover occur over many millennia and often vary in concert with the waxing and waning of circum-Arctic land ice sheets, ice shelves, and long-term fluctuations in ocean and atmospheric temperature and atmospheric CO2 concentrations (Stein et al. 2012; Jakobsson et al. 2014). Over shorter time scales, shallow sediment records from Arctic Ocean continental shelves allow more detailed, higher-resolution (hundreds of years resolution) reconstructions of sea ice history extending through the Holocene (11 700 years ago to present), the most recent interglacial period.\nA notable feature of these records is an early Holocene sea ice minimum, corresponding to a thermal maximum (warm) period from 11 000 to 5000 years ago, when the Arctic may have been warmer and had less summertime sea ice than today (Kaufman et al. 2004). However, it is not clear that the Arctic was ice-free at any point during the Holocene (Polyak et al. 2010). High-resolution paleosea ice records from the western Arctic in the Chukchi and East Siberian Seas indicate that sea ice concentrations increased through the Holocene in concert with decreasing summer solar insolation (sunlight). Sea ice extent in this region also varied in response to the volume of Pacific water delivered via the Bering Strait into the Arctic Basin (Stein et al. 2017; Polyak et al. 2016). Records from the Fram Strait (Mller et al. 2012), Laptev Sea (Hrner et al. 2016), and Canadian Arctic Archipelago (Vare et al. 2009) also indicate a similar long-term expansion of sea ice and suggest sea ice extent in these regions is modulated by the varying influx of warm Atlantic water into the Arctic Basin (e.g., Werner et al. 2013). Taken together, available records support a circum-Arctic sea ice expansion during the late Holocene. \n\nA notably high-resolution summer sea ice history (<5-year resolution) has been established for the last 1450 years using a network of terrestrial records (tree ring , lake sediment, and ice core records) located around the margins of the Arctic Ocean (Kinnard et al. 2011). Results summarized in Fig. SB5.2 indicate a pronounced decline in summer sea ice extent beginning in the 20th century, with exceptionally low ice extent recorded since the mid-1990s, consistent with the satellite record (see section 5d). While several episodes of reduced and expanded sea ice extent occur in association with climate anomalies such as the Medieval Climate Warm Period (AD 8001300) and the Little Ice Age (AD 14501850), the magnitude and pace of the modern decline in sea ice is outside of the range of natural variability and unprecedented in the 1450-year reconstruction (Kinnard et al. 2011). A radiocarbon-dated driftwood record of the Ellesmere ice shelf in the Canadian High Arctic, the oldest landfast ice in the Northern Hemisphere, also demonstrates a substantial reduction in ice extents over the 20th century (England et al. 2017). A supporting sediment record indicates that inflowing Atlantic water in Fram Strait has warmed by 2C since 1900, driving break up and melt of sea ice (Spielhagen et al. 2011). Complementary mooring and satellite observations show the Atlantification of the eastern Arctic due to enhanced inflow of warm saline water through Fram Strait (Nilsen et al. 2016) and nutrient-rich Pacific water via the Bering has increased by more than 50% (Woodgate et al. 2012), further driving sea ice melt and warming seas. Similar high-resolution proxy records from Arctic regions also indicate that the modern rate of increasing annual surface air temperatures has not been observed over at least the last 2000 years (McKay and Kaufman 2014). Scientists conclude that broad-scale sea ice variations recorded in the paleo record were dominantly driven by changes in basin-scale changes in atmospheric circulation patterns, fluctuations in air temperature, strength of incoming solar radiation, and changes in the inflow of warm water via Pacific and Atlantic inflows (Polyak et al. 2010). \n\nThere is general consensus that ice-free Arctic summers are likely before the end of the 21st century (e.g., Stroeve et al. 2007; Massonnet et al. 2012), while some climate model projections suggest ice-free Arctic summers as early as 2030 (Wang and Overland 2009). Paleoclimate studies and observational time series attribute the decline in sea ice extent and thickness over the last decade to both enhanced greenhouse warming and natural climate variability. While understanding the interplay of these factors is critical for future projections of Arctic sea ice and ecosystems, most observational time series records cover only a few decades. This highlights the need for additional paleoceanographic reconstructions across multiple spatial and temporal domains to better understand the drivers and implications of present and future Arctic Ocean change.","language":"English","doi":"10.1175/2018BAMSStateoftheClimate.1","usgsCitation":"Osborne, E., Cronin, T.M., and Farmer, J., 2018, Paleoclimate Records: Providing context and understanding of current Arctic change: Bulletin American Meteorological Society, v. 99, no. 8, p. s150-s152, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.","productDescription":"3 p.","startPage":"s150","endPage":"s152","ipdsId":"IP-098816","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":468548,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1175/2018bamsstateoftheclimate.1","text":"External Repository"},{"id":396176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osborne, Emily","contributorId":279642,"corporation":false,"usgs":false,"family":"Osborne","given":"Emily","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":835253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":835254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farmer, Jesse","contributorId":279643,"corporation":false,"usgs":false,"family":"Farmer","given":"Jesse","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":835255,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199099,"text":"70199099 - 2018 - Dynamic modeling of barrier island response to hurricane storm surge under future sea level rise","interactions":[],"lastModifiedDate":"2018-09-04T10:31:27","indexId":"70199099","displayToPublicDate":"2018-08-01T10:31:13","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic modeling of barrier island response to hurricane storm surge under future sea level rise","docAbstract":"Sea level rise (SLR) has the potential to exacerbate the impacts of extreme storm events on the coastal landscape. This study examines the coupled interactions of SLR on storm-driven hydrodynamics and barrier island morphology. A numerical model is used to simulate the hydrodynamic and morphodynamic impacts of two Gulf of Mexico hurricanes under present-day and future sea levels. SLR increased surge heights and caused overwash to occur at more locations and for longer durations. During surge recession, water level gradients resulted in seaward sediment transport. The duration of the seaward-directed water level gradients was altered under SLR; longer durations caused more seaward-directed cross-barrier transport and a larger net loss in the subaerial island volume due to increased sand deposition in the nearshore. Determining how SLR and the method of SLR implementation (static or dynamic) modulate storm-driven morphologic change is important for understanding and managing longer-term coastal evolution.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-018-2245-8","usgsCitation":"Passeri, D., Bilskie, M.V., Plant, N.G., Long, J., and Hagen, S.C., 2018, Dynamic modeling of barrier island response to hurricane storm surge under future sea level rise: Climatic Change, v. 149, no. 3-4, p. 413-425, https://doi.org/10.1007/s10584-018-2245-8.","productDescription":"13 p.","startPage":"413","endPage":"425","ipdsId":"IP-095725","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468549,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://link.springer.com/10.1007/s10584-018-2245-8","text":"External Repository"},{"id":357043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35685729980469,\n 30.203300547277813\n ],\n [\n -88.05816650390625,\n 30.203300547277813\n ],\n [\n -88.05816650390625,\n 30.287531589298727\n ],\n [\n -88.35685729980469,\n 30.287531589298727\n ],\n [\n -88.35685729980469,\n 30.203300547277813\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"3-4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-12","publicationStatus":"PW","scienceBaseUri":"5b98a296e4b0702d0e842f75","contributors":{"authors":[{"text":"Passeri, Davina L. 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":744074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bilskie, Matthew V.","contributorId":166891,"corporation":false,"usgs":false,"family":"Bilskie","given":"Matthew","email":"","middleInitial":"V.","affiliations":[{"id":16154,"text":"LSU","active":true,"usgs":false}],"preferred":false,"id":744075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":744076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":202183,"corporation":false,"usgs":true,"family":"Long","given":"Joseph W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":744077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hagen, Scott C.","contributorId":166890,"corporation":false,"usgs":false,"family":"Hagen","given":"Scott","email":"","middleInitial":"C.","affiliations":[{"id":16154,"text":"LSU","active":true,"usgs":false}],"preferred":false,"id":744078,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196978,"text":"70196978 - 2018 - A case study and a meta-analysis of seasonal variation in fish mercury concentrations","interactions":[],"lastModifiedDate":"2021-02-04T15:41:43.602161","indexId":"70196978","displayToPublicDate":"2018-08-01T09:31:32","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"A case study and a meta-analysis of seasonal variation in fish mercury concentrations","docAbstract":"Mercury contamination in aquatic ecosystems is a concern due to health risks of consuming fish. Fish mercury concentrations are highly variable and influenced by a range of environmental factors. However, seasonal variation in mercury levels are typically overlooked when monitoring fish mercury concentrations, establishing consumption advisories, or creating accumulation models. Temporal variation in sampling could bias mercury concentration estimates of accumulation potential. Thus, the objectives of this study were to first evaluate seasonal variation of largemouth bass (Micropterus salmoides) axial muscle mercury concentration from two Iowa, USA impoundments. Second, we conducted a meta-analysis to evaluate if seasonal variation in mercury concentration is dependent upon mean mercury concentration, waterbody type, or fish trophic level or mean length. Largemouth bass were collected four times between May and October (24–36 fish per month) from Twelve Mile (2013) and Red Haw (2014) lakes. Largemouth bass axial muscle mercury concentrations were variable within and between lakes, ranging from undetectable ( < 0.05 mg/kg) to 0.54 mg/kg. Largemouth bass mercury concentrations were similar across months in Twelve Mile but varied temporally in Red Haw and were highest in July, intermediate in May and September, and lowest during October. Results of the meta-analysis suggest that seasonal variation in mercury concentrations is more likely to occur as mean mercury concentration of the population increases but is unrelated to waterbody type, trophic status, and fish size. Understanding seasonal variation in fish mercury concentrations will aid in the development of standardized sampling programs for long-term monitoring programs and fish consumption advisories.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10646-018-1942-4","usgsCitation":"Mills, N., Cashatt, D., Weber, M., and Pierce, C., 2018, A case study and a meta-analysis of seasonal variation in fish mercury concentrations: Ecotoxicology, v. 27, p. 641-649, https://doi.org/10.1007/s10646-018-1942-4.","productDescription":"9 p.","startPage":"641","endPage":"649","ipdsId":"IP-088547","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":487219,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/277","text":"External Repository"},{"id":382950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","otherGeospatial":"Red Haw Lake, Twelve Mile Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.85784530639647,\n 43.28295400639641\n ],\n [\n -94.844970703125,\n 43.28295400639641\n ],\n [\n -94.844970703125,\n 43.29329402211397\n ],\n [\n -94.85784530639647,\n 43.29329402211397\n ],\n [\n -94.85784530639647,\n 43.28295400639641\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.2830238342285,\n 40.98969788697535\n ],\n [\n -93.26444149017334,\n 40.98969788697535\n ],\n [\n -93.26444149017334,\n 41.00244356919737\n ],\n [\n -93.2830238342285,\n 41.00244356919737\n ],\n [\n -93.2830238342285,\n 40.98969788697535\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Mills, Nathan","contributorId":248785,"corporation":false,"usgs":false,"family":"Mills","given":"Nathan","email":"","affiliations":[],"preferred":false,"id":809831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cashatt, Darcy","contributorId":248786,"corporation":false,"usgs":false,"family":"Cashatt","given":"Darcy","email":"","affiliations":[],"preferred":false,"id":809832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weber, Michael","contributorId":213318,"corporation":false,"usgs":false,"family":"Weber","given":"Michael","affiliations":[],"preferred":false,"id":809833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pierce, Clay 0000-0001-5088-5431 cpierce@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":150492,"corporation":false,"usgs":true,"family":"Pierce","given":"Clay","email":"cpierce@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":735166,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203197,"text":"70203197 - 2018 - Evaluating and managing environmental water regimes in a water-scarce and uncertain future","interactions":[],"lastModifiedDate":"2019-04-29T09:29:43","indexId":"70203197","displayToPublicDate":"2018-08-01T09:29:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating and managing environmental water regimes in a water-scarce and uncertain future","docAbstract":"A steady-state three-dimensional groundwater-flow model that simulates present conditions was coupled with the particle-tracking program MODPATH to delineate zones of contribution to wells pumping from the Magothy aquifer near a chlorinated volatile organic compound (VOC) plume. This modeling was part of a study by the U.S. Geological Survey in cooperation with the Naval Facilities Engineering Command to delineate groundwater near the Naval Weapons Industrial Reserve Plant in Bethpage, New York. Because rates of advection within the coarse-grained sediments typically exceed 0.1 foot per day, transport by dispersion and (or) diffusion was assumed to be negligible. Resulting zones of contribution are complex shapes, influenced by hydrogeologic features including confining beds and a basal gravel zone, and the interplay of nearby hydrologic stresses. The use of two particle tracking techniques identified zones of contribution to wells. Particles are backtracked from pumping well screens, and particles are forward tracked from the location of a VOC plume, as defined by surfaces of equal total VOC concentration. During any period of 5 years or less, about 1 to 3 percent of particles backtracked from pumping wells within a focus area intersected the 5-part per billion (ppb) VOC plume shell, indicating that the vast majority of particles were not sourced from the plume. During 5 years or less, none of the particles backtracked from pumping wells intersected the 50-ppb VOC plume shell. Forward-tracking techniques identified the fate of water within the VOC plume after 5 years as it moves toward ultimate well capture and (or) discharge to model constant head and drain boundaries. Out of 4,813 forward tracked particles started within the 50-ppb VOC plume shell, 1 forward-tracked particle was captured by well ANY8480. Out of 22,958 forward tracked particles started within the 5-ppb VOC plume shell, 100 were captured by production wells (less than 1 percent). The subset of forward pathlines that represent well plume capture are similar in number and shape to those of backtracked pathlines.
Model simulations were conducted to assess uncertainties and improve understanding of how variability in hydraulic properties, pumpage rates, and maximum particle traveltime affect delineation of zones of contribution. By use of driller’s’ logs, a transitional probability approach generated nine alternative realizations of heterogeneity within the Magothy aquifer to assess uncertainty in model representation. Fine-grained sediments with low hydraulic conductivity were realized as laterally discontinuous, thickening towards the south, and comprising about 27 percent of the total aquifer volume within the transitional probability subgrid. Model simulations with alternative pumpage rates, porosity terms, and alternative maximum particle traveltime were also used to demonstrate how the size and shape of zones of contribution may vary.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175161","collaboration":"Prepared in cooperation with the Naval Facilities Engineering Command","usgsCitation":"Misut, P.E., 2018, Simulation of zones of groundwater contribution to wells south of the Naval Weapons Industrial Reserve Plant in Bethpage, New York: U.S. Geological Survey Scientific Investigations Report 2017–5161, 45 p., https://doi.org/10.3133/sir20175161.","productDescription":"Report: vii, 45 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087126","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":355559,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F770809V","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 model archive for simulation of zones of groundwater contribution to wells south of the Naval Weapons Industrial Reserve Plant in Bethpage, New York"},{"id":355557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5161/coverthb.jpg"},{"id":355558,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5161/sir20175161.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5161"}],"country":"United States","state":"New York","city":"Bethpage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.5167,\n 40.7167\n ],\n [\n -73.45,\n 40.7167\n ],\n [\n -73.45,\n 40.7667\n ],\n [\n -73.5167,\n 40.7667\n ],\n [\n -73.5167,\n 40.7167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
Coram, NY 11727
The U.S. Geological Survey’s Soil-Water-Balance (SWB) code was developed as a tool to estimate distribution and timing of net infiltration out of the root zone by means of an approach that uses readily available data and minimizes user effort required to begin a SWB application. SWB calculates other components of the water balance, including soil moisture, reference and actual evapotranspiration, snowfall, snowmelt, canopy interception, and crop-water demand. SWB is based on a modified Thornthwaite-Mather soil-water-balance approach, with components of the soil-water balance calculated at a daily time step. Net-infiltration calculations are computed by means of a rectangular grid of computational elements, which allows the calculated infiltration rates to be imported into grid-based regional groundwater-flow models. SWB makes use of gridded datasets, including datasets describing hydrologic soil groups, moisture-retaining capacity, flow direction, and land use. Climate data may be supplied in gridded or tabular form. The SWB 2.0 code described in this report extends capabilities of the original SWB version 1.0 model by adding new options for representing physical processes and additional data input and output capabilities. New methods included in SWB 2.0 allow for direct gridded input of externally calculated water-budget components (fog, septic, and storm-sewer leakage), simulation of canopy interception by several alternative processes, and a crop-water demand method for estimating irrigation amounts. New input and output capabilities allow for grids with differing spatial extents and projections to be combined without requiring the user to resample and resize the grids before use.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A59","collaboration":"Water Availability and Use Science ProgramDirector, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Sandbars along the Colorado River are used as campsites by river runners and hikers and are an important recreational resource within Grand Canyon National Park, Arizona. Regulation of the flow of river water through Glen Canyon Dam has reduced the amount of sediment available to be deposited as sandbars, has reduced the magnitude and frequency of flooding events, and has increased the magnitude of baseflows. This has caused widespread erosion of sandbars and has allowed native and non-native vegetation to expand on open sand. Previous studies show an overall decline in campsite area despite the use of controlled floods to rebuild sandbars. Monitoring of campsites since 1998 has shown changes in campsite area, but the factors that cause gains and losses in campsite area have not been quantified. These factors include, among others, changes in sandbar volume and slope under different dam flow regimes that include controlled floods, gullying caused by monsoonal rains, vegetation expansion, and reworking of sediment by aeolian processes.
Using 4-band aerial imagery and digital elevation models (DEMs) derived from total-station survey data, we analyzed topographic and vegetation change at 35 of 37 long-term monitoring sites (2 sites were excluded because topographic measurements do not overlap with measurements of campsite area) using data collected between 2002 and 2009 to quantify the factors affecting the size of campsite area. Over the course of the study period, there was a net loss in campsite area of 2,431 square meters (m2). We find that (1) 53 percent of the net loss was caused by topographic change associated with controlled floods and erosion of those flood deposits, (2) 47 percent of the net loss was caused by increases in vegetation cover, the majority of which occurred in high-elevation campsite area, and (3) gullying was significant at certain sites but overall was a minor factor.
Sites in critical reaches—sections of river where campsites are infrequent or where there is high demand by river runners—were subjected to more erosion and changes in sandbar slope than sites in noncritical reaches, suggesting that campsite area is less stable in those reaches. There was also a greater increase in vegetation cover at sites in noncritical reaches than at sites in critical reaches. Our results show a continuation of sandbar erosion and vegetation encroachment that has been occurring at campsites since construction of the dam.
A new campsite survey methodology using a tablet-based geographic information system (GIS) approach was also developed in an effort to map campsite area on digital orthophotographs. Using a series of repeat measurements, we evaluated the inherent uncertainty in mapping campsite area, the accuracy of the new tablet-based method, and if there is any bias between the tablet method and the total-station method that is currently used. We find that uncertainty associated with surveyor judgment while using the total-station method is about 15 percent, which is higher than a previously reported uncertainty of 10 percent. Use of the tablet method adds additional uncertainty; however, the benefits of being able to quantify factors that lead to campsite-area change in the field may outweigh the additional error. Future campsite monitoring may need to consist of a combination of total-station and orthophotograph techniques.
SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Vegetation response to wildfire has been studied extensively in upland ecosystems, but fire effects on temperate wetlands are less understood. We evaluated vegetation response to extensive wildfire in wetlands of Okefenokee National Wildlife Refuge (ONWR), USA, with a spatially explicit Bayesian belief network model informed with data recorded during 1990–2012. We assessed model accuracy and effects of fire frequency on vegetation composition with predictive scenarios of fire absence or a fire return interval (FRI) every 5 or 10 years during 2012–2032. In fire absence, shrubs increased 100%, primarily in the northern half of the Refuge, while the herbaceous class that was widespread in 2012 was eliminated. Areas dominated by forest during the past ~65 years were maintained with the 5- and 10-year FRI. Herbaceous-dominated areas maintained with the 5-year FRI decreased (90%) with the 10-year FRI. Shrub coverage increased with fire (17%, 5-year FRI; 20%, 10-year FRI), while scrub/shrub decreased (12%; 5-year FRI) or increased (6%; 10-year FRI). A 5-year FRI during conditions promoting severe fire may maintain the distribution of herbaceous and forested areas that followed an extensive drought and fires in 2011, and may limit scrub/shrub expansion that previously occurred with longer FRIs in the ONWR.
","language":"English","publisher":"Springer Link","doi":"10.1007/s13157-018-1033-6","usgsCitation":"Loftin, C., Guyette, M., and Wetzel, P., 2018, Evaluation of vegetation-fire dynamics in the Okefenokee National Wildlife Refuge, Georgia, USA, with a Bayesian belief network: Wetlands, v. 38, p. 819-834, https://doi.org/10.1007/s13157-018-1033-6.","productDescription":"16 p.","startPage":"819","endPage":"834","ipdsId":"IP-059938","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":394873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Okefenokee National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.5567626953125,\n 30.552800413453546\n ],\n [\n -82.08984375,\n 30.552800413453546\n ],\n [\n -82.08984375,\n 31.07\n ],\n [\n -82.5567626953125,\n 31.07\n ],\n [\n -82.5567626953125,\n 30.552800413453546\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationDate":"2018-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guyette, Margaret Q.","contributorId":272181,"corporation":false,"usgs":false,"family":"Guyette","given":"Margaret Q.","affiliations":[{"id":56366,"text":"St. Johns River Management District, Bureau of Water Resource Information, Palatka, FL","active":true,"usgs":false}],"preferred":false,"id":831680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wetzel, Paul R.","contributorId":272182,"corporation":false,"usgs":false,"family":"Wetzel","given":"Paul R.","affiliations":[{"id":56367,"text":"Center for the Environment, Ecological Design, and Sustainability, Smith College, Northampton, MA","active":true,"usgs":false}],"preferred":false,"id":831681,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198300,"text":"70198300 - 2018 - Host feeding ecology and trophic position significantly influence isotopic discrimination between a generalist ectoparasite and its hosts: Implications for parasite-host trophic studies","interactions":[],"lastModifiedDate":"2018-07-30T09:49:12","indexId":"70198300","displayToPublicDate":"2018-07-27T20:11:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5453,"text":"Food Webs","active":true,"publicationSubtype":{"id":10}},"title":"Host feeding ecology and trophic position significantly influence isotopic discrimination between a generalist ectoparasite and its hosts: Implications for parasite-host trophic studies","docAbstract":"Despite being one of the most prevalent forms of consumerism in ecological communities, parasitism has largely been excluded from food-web models. Stable isotope analysis of consumers and their diets has been widely used in the study of food webs for decades. However, the amount of information regarding parasite stable isotope ecology is limited, restricting the ability of ecologists to use stable isotope analysis to study parasites in food webs. This study took advantage of distinct differences in the feeding ecology and trophic position of different species of fish known to host the same common micropredatory gnathiid isopod to study the effects of host stable isotope ecology on that of the associated micropredator. Blood engorged juvenile gnathiids were in most cases indistinguishable from their hosts' blood, but significant isotope discrimination was observed for adults. Males were generally lower in δ13C and δ15N than host blood whereas host-specific isotopic discrimination for females varied among the different host species. Model predictions indicated that there is a significant effect of host blood isotope ratios on the rate of carbon and nitrogen isotopic discrimination between gnathiids and their host’s blood. As such, general differences in the feeding ecology and trophic positions of the different host species were reflected in their associated gnathiids, indicating that stable isotope analysis of gnathiids can provide significant details concerning previous hosts. The results presented herein have significant implications for how stable isotopes may be used as a tool to study the trophic dynamics and feeding ecology of gnathiids.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2018.e00092","usgsCitation":"Jenkins, W.G., Demopoulos, A.W., and Sikkel, P.C., 2018, Host feeding ecology and trophic position significantly influence isotopic discrimination between a generalist ectoparasite and its hosts: Implications for parasite-host trophic studies: Food Webs, v. 16, Article e00092, https://doi.org/10.1016/j.fooweb.2018.e00092.","productDescription":"Article e00092","ipdsId":"IP-095826","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468561,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fooweb.2018.e00092","text":"Publisher Index Page"},{"id":437818,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7416WBQ","text":"USGS data release","linkHelpText":"Host Feeding Ecology and Trophic Position Significantly influence Isotopic discrimination between a Generalist Ectoparasite and its hosts: Implications for parasite host trophic studies"},{"id":355993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc3f2e4b0f5d57878e95b","contributors":{"authors":[{"text":"Jenkins, William G. 0000-0001-5133-2628","orcid":"https://orcid.org/0000-0001-5133-2628","contributorId":200936,"corporation":false,"usgs":false,"family":"Jenkins","given":"William","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":740949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demopoulos, Amanda W.J. 0000-0003-2096-4694 ademopoulos@usgs.gov","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":196216,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","email":"ademopoulos@usgs.gov","middleInitial":"W.J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":740950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sikkel, Paul C.","contributorId":140403,"corporation":false,"usgs":false,"family":"Sikkel","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":13476,"text":"Arkansas State University, State University, AR","active":true,"usgs":false}],"preferred":false,"id":740951,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198295,"text":"70198295 - 2018 - Using reverse-time egg transport analysis for predicting Asian Carp spawning grounds in the Illinois River","interactions":[],"lastModifiedDate":"2018-07-27T11:13:22","indexId":"70198295","displayToPublicDate":"2018-07-27T11:13:19","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Using reverse-time egg transport analysis for predicting Asian Carp spawning grounds in the Illinois River","docAbstract":"Identifying spawning grounds of Asian carp is important for determining the reproductive front of invasive populations. Ichthyoplankton monitoring along the Illinois Waterway (IWW) has provided information on abundances of Asian carp eggs in the IWW's navigation pools. Post-fertilization times derived from egg development stages and water temperatures can be used to estimate spawning times of Asian carp eggs, but estimating how far these eggs have drifted requires information on river hydraulics. A Fluvial Egg Drift Simulator (FluEgg) program was designed to predict the drift of Asian carp eggs in the riverine environment with egg growth considered. This paper presents a reverse-time particle tracking (RTPT) algorithm for back-casting the spawning location of eggs from their collection site. The RTPT algorithm was implemented as a module in FluEgg. The new version of FluEgg was coupled with an unsteady hydrodynamic model of the IWW to predict the spawning locations for 530 eggs that were collected in June 2015. The results indicate that tailwater sections below the Locks and Dams (L&Ds) in each navigation pool appear to be preferred spawning locations for Silver Carp. From the data analyzed, the most upstream spawning location for the June 2015 spawning period was in the upper Marseilles navigation pool, downstream of the Dresden Island L&D. The RTPT algorithm can efficiently estimate spawning locations for multiple egg samples.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.06.003","usgsCitation":"Zhu, Z., Soong, D., Garcia, T., Behrouz, M.S., Butler, S.E., Murphy, E.A., Diana, M.J., Duncker, J.J., and Wahl, D.H., 2018, Using reverse-time egg transport analysis for predicting Asian Carp spawning grounds in the Illinois River: Ecological Modelling, v. 384, p. 53-62, https://doi.org/10.1016/j.ecolmodel.2018.06.003.","productDescription":"10 p.","startPage":"53","endPage":"62","ipdsId":"IP-093404","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":355987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.7415771484375,\n 38.839707613545144\n ],\n [\n -87.506103515625,\n 38.839707613545144\n ],\n [\n -87.506103515625,\n 41.97174336327968\n ],\n [\n -90.7415771484375,\n 41.97174336327968\n ],\n [\n -90.7415771484375,\n 38.839707613545144\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"384","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc3f3e4b0f5d57878e95f","contributors":{"authors":[{"text":"Zhu, Zhenduo","contributorId":206524,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","email":"","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":740921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Tatiana","contributorId":206525,"corporation":false,"usgs":false,"family":"Garcia","given":"Tatiana","affiliations":[{"id":37335,"text":"Optimatics","active":true,"usgs":false}],"preferred":false,"id":740922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Behrouz, Mina Shahed","contributorId":206526,"corporation":false,"usgs":false,"family":"Behrouz","given":"Mina","email":"","middleInitial":"Shahed","affiliations":[],"preferred":false,"id":740923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Steven E.","contributorId":206527,"corporation":false,"usgs":false,"family":"Butler","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":740924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Elizabeth A. 0000-0002-8939-7678 emurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-8939-7678","contributorId":196368,"corporation":false,"usgs":true,"family":"Murphy","given":"Elizabeth","email":"emurphy@usgs.gov","middleInitial":"A.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740925,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Diana, Matthew J.","contributorId":206528,"corporation":false,"usgs":false,"family":"Diana","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":740927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740926,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wahl, David H.","contributorId":206529,"corporation":false,"usgs":false,"family":"Wahl","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":740928,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70194500,"text":"sir20175137 - 2018 - Rating stability, and frequency and magnitude of shifts, for streamgages in Virginia through water year 2013","interactions":[],"lastModifiedDate":"2018-07-27T10:16:52","indexId":"sir20175137","displayToPublicDate":"2018-07-26T16:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5137","title":"Rating stability, and frequency and magnitude of shifts, for streamgages in Virginia through water year 2013","docAbstract":"The U.S. Geological Survey, in cooperation with the Virginia Department of Environmental Quality, has quantified several measures of rating stability and the frequency and magnitude of changes to ratings through time for 174 real-time continuous streamgages active in Virginia as of September 30, 2013. Generalized additive models (GAMs) were fitted through all available flow measurements for all the streamgages in Virginia’s real-time network as of September 30, 2013, with at least 20 flow measurements with positive flow values. For each measurement with a positive flow value, residuals from the GAM curve were calculated. Time series of these residuals were used to identify major changes to the control (the stream feature or features which control the relation between stage and flow); the measurements in the periods of equilibrium between major changes were assigned to rating families. Of the 127 rating families that were identified as being distinct at sites, documented explanations were found for 67 of them. The most common reasons for the control to change enough to warrant a new rating family are moving the streamgage (28 times), floods (26 times), and construction activities (13 times). Provisional flow data from any streamgage that has recently experienced a major flood, regardless of historical stability, are more uncertain than usual until post-flood evidence emerges that the rating is stable, or if the rating has changed, until it is known to be well defined.
A direct comparison between provisional flow data (those data originally displayed on the web in near-real time) and flow data approved for publication following subsequent flow measurements and review could not be made because provisional flow data have not been archived. As a substitute, alternative flow (AltFlow) tables were constructed for periods with complete records of shifts and ratings. Alternate flows consist of $Q$same, the flow value from the shifted rating table used to compute the daily flow value at the time of the most recent flow measurement that corresponds to the gage height of each day’s daily flow value, and $Q$prev, the flow value from the shifted rating table in effect at the time of the previous flow measurement that corresponds to the gage height of each day’s daily flow value.
Several metrics that summarize AltFlow tables were computed and evaluated; particular importance was given to how well the metrics agreed with the descriptive stability class developed from interviews with hydrographers. Of these stability metrics, at least four were determined to be meaningful and to represent different aspects of control stability that might be relevant to water managers: total root mean square error between log-transformed $Q$prev and $Q$same, percentage of days when the difference between $Q$prev and $Q$same is greater than (>) $Q$same, the sum of absolute AltFlow error divided by total flow, and percentage of days with zero difference between $Q$prev and $Q$same.
Three other meaningful metrics of control stability or provisional flow-data quality were computed: R2 (coefficient of determination) of GAMs from the flow measurements, percentage of total estimated days, and percentage of estimated days in the winter. Correlations among metrics varied, indicating they responded to different aspects of control stability. Relations among the various stability metrics and quantitative basin and site characteristics were weak. Although quantitative relations between stability metrics and basin and site characteristics were all weak, some common patterns still emerged. Controls and ratings on large streams [>500-square mile (mi2) drainage area] and at high elevations (>1,000 feet) were more likely to be stable than controls and ratings on small streams (less than (<) 100-mi2 drainage area) and at low elevations (<500 feet). There were exceptions to both generalizations, and streamgages that were intermediate in both characteristics varied widely in stability.
Typical timing of record computation changed during water years 1991–2013. From 1991 through 2001, the median number of days between the start date of the shift and the date it was created fluctuated between about 240 and about 300 days and decreased by about 4 months from 2001 to 2002. Only in 2012 and 2013 did one-half of the shifts have a delay of about 60 days between start date and final modification.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175137","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"Messinger, T., and Burgholzer, R.W., 2018, Rating stability, and frequency and magnitude of shifts, for streamgages in Virginia through water year 2013: U.S. Geological Survey Scientific Investigations Report 2017–5137, 91 p., https://doi.org/10.3133/sir20175137. ","productDescription":"Report: viii, 91 p.; 5 Plates: 24.0 x 30.0 inches; 5 Tables; Appendixes; Data Release; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084661","costCenters":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":437819,"rank":19,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72R3PXB","text":"USGS data release","linkHelpText":"Ratings and estimated provisional streamflow for streamgages in Virginia, water years 1991 through 2013"},{"id":355791,"rank":11,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate05.pdf","text":"Plate 5","size":"25 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Scatterplot matrixes of stage-flow relation stability metrics in relation to selected basin characteristics for A, 64 plots and, B, 88 plots, for streamgages in Virginia, water years 1991–2013"},{"id":355792,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table08.xlsx","text":"Table 8","size":"57.6 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Hydrographer descriptions of controls for streamgages in Virginia at which real-time flow data were collected as of September 30, 2013, and descriptions of associated features of interest "},{"id":355783,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix01a.pdf","text":"Appendix 1a","size":"3.60 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Plots for real-time streamgages in Virginia (All available measurements)"},{"id":355788,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate02.pdf","text":"Plate 2","size":"212 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Patterns of variation in, A, long-term stage-flow relations at selected streamgages in Virginia, B, residual of flow and date with a LOESS smoother, and C, residual of flow and Julian date with a LOESS smoother"},{"id":355787,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate01.pdf","text":"Plate 1","size":"230 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- A, Long-term stage-flow relations at selected streamgages in Virginia with a generalized additive model smoother, B, seasonal variation of relations between residual of flow and date with a LOESS smoother, and C, seasonal variation of relations between residual of flow and Julian date with a LOESS smoother"},{"id":355789,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate03.pdf","text":"Plate 3","size":"360 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- AltFlow hydrographs for four selected streamgages in Virginia, water years 1991–2013"},{"id":355786,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix04.pdf","text":"Appendix 4","size":"24.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Plots showing daily time series of $Q$same and $Q$prev"},{"id":355790,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_plate04.pdf","text":"Plate 4","size":"20.6 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Matrix of proposed stability metrics developed using daily flow, shifted ratings, and flow measurements for streamgages in Virginia, water years 1991–2013"},{"id":355793,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table20.xlsx","text":"Table 20","size":"50.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary of selected rating stability metrics for streamgages in Virginia, water years 1991–2013"},{"id":355794,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table24.xlsx","text":"Table 24","size":"482 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Average of daily percent errors for flow in Virginia streams, by month and percentile range"},{"id":355796,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table26.xlsx","text":"Table 26","size":"228 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Proportion of days when the difference between Qprev and Qsame was zero at streamgages in Virginia, by month, 1990–2013"},{"id":355797,"rank":17,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F72R3PXB","text":"USGS data release","description":"USGS data release","linkHelpText":"Ratings and estimated provisional streamflow for streamgages in Virginia, water years 1991 through 2013"},{"id":355841,"rank":18,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_readme.txt","size":"8 MB","linkFileType":{"id":2,"text":"txt"}},{"id":355784,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix01b.pdf","text":"Appendix 1b","size":"5.11 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Plots for real-time streamgages in Virginia (Measurements grouped by rating families )"},{"id":355782,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137.pdf","text":"Report","size":"3.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5137"},{"id":355795,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_table25.xlsx","text":"Table 25","size":"373 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Root-mean-square error of AltFlows and number of days of record for streamgages on Virginia streams by month and percentile of monthly flow, water years 1991–2013 "},{"id":355781,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5137/coverthb.jpg"},{"id":355785,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5137/sir20175137_appendix03.xlsx","text":"Appendix 3","size":"640 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- UNIX shell scripts used to develop AltFlow record"}],"country":"United 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\"}}]}","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
A recent study posits that much of the 240-m-deep inner gorge of Grand Canyon was carved between 500 and 400 ka via passage of a migrating knickzone with incision rates of ~1600 m/Ma during that time period; this was based on dating of a ca. 500 ka travertine deposit perched on the rim of the inner gorge, near Hermit Rapid, and a ca. 400 ka travertine drape that extends to within 60 m of river level nearby. However, a new U/Th age of 517 ± 13 ka on the same travertine drape challenges this model of a migrating knickzone and punctuated incision. The presence of ca. 500 ka travertine just 95 m above river level requires that most of the inner gorge was carved before that time. The resulting maximum bedrock incision rate of 230 m/Ma is consistent with independent results from sites up and downstream and with models for semi-steady Quaternary bedrock incision and dispels problems with the transient incision model. Downstream from the Hermit Rapid area, dikes present on both sides of the canyon have been used to support the migrating knickzone model. We report a new 40Ar/39Ar age of 517 ± 16 ka on one of these dikes, but argue that they don’t necessarily gauge incision.
Field observations suggest that the discontinuous travertine deposits, near Hermit Rapid, were deposited by springs that emanated from the Redwall-Muav aquifer, mantled the Tonto Platform, and locally built downwards into the inner gorge and tributary canyons. The range of U/Th ages from ca. 10–600 ka suggests these were long-lived spring systems. The travertine cements predominantly angular to subrounded locally derived clasts consistent with deposition on hillslopes and by tributaries. Well-rounded gravels are exceedingly rare but have been used to suggest that the Colorado River was at the rim of the inner gorge at ca. 500 ka. No exotic Colorado River clasts, derived from the area outside of Grand Canyon, were observed by us. In-place gravel from the main stem or tributaries (e.g., from paleo–Hermit Creek) within the travertine deposits can be reconciled with existing data, if: (1) travertine was deposited at ca. 2 Ma, which is approximately when the steady incision model suggests the inner gorge began to incise; (2) a 500 ka lava dam in the Lava Falls Rapid area, 140 km downstream, backed water and sediment up to the rim of the inner gorge in the Hermit area; or (3) regional climate-driven aggradation took place at 500 ka.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01562.1","usgsCitation":"Crow, R.S., Karlstrom, K.E., Crossey, L.J., Polyak, V., Asmerom, Y., and McIntosh, W.C., 2018, Carving Grand Canyon’s inner gorge: A test of steady incision versus rapid knickzone migration: Geosphere, v. 14, no. 5, p. 1-17, https://doi.org/10.1130/GES01562.1.","productDescription":"17 p.","startPage":"1","endPage":"17","ipdsId":"IP-084123","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":468562,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01562.1","text":"Publisher Index Page"},{"id":356636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Grand Canyon ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.14794921875,\n 35.572448615622804\n ],\n [\n -111.67053222656249,\n 35.572448615622804\n ],\n [\n -111.67053222656249,\n 37.21283151445594\n ],\n [\n -114.14794921875,\n 37.21283151445594\n ],\n [\n -114.14794921875,\n 35.572448615622804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-26","publicationStatus":"PW","scienceBaseUri":"5b98a297e4b0702d0e842f85","contributors":{"authors":[{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":742965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlstrom, Karl E.","contributorId":75597,"corporation":false,"usgs":true,"family":"Karlstrom","given":"Karl","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":742966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crossey, Laura J.","contributorId":56265,"corporation":false,"usgs":true,"family":"Crossey","given":"Laura","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":742967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Polyak, Victor","contributorId":207162,"corporation":false,"usgs":false,"family":"Polyak","given":"Victor","email":"","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":742968,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asmerom, Yemane","contributorId":146278,"corporation":false,"usgs":false,"family":"Asmerom","given":"Yemane","email":"","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":742969,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McIntosh, William C.","contributorId":191163,"corporation":false,"usgs":false,"family":"McIntosh","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":742970,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70198055,"text":"ds1093 - 2018 - Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2017 breeding season","interactions":[],"lastModifiedDate":"2018-07-27T10:27:10","indexId":"ds1093","displayToPublicDate":"2018-07-26T12:39:42","publicationYear":"2018","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":"1093","displayTitle":"Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2017 breeding season","title":"Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2017 breeding season","docAbstract":"Marbled murrelets (Brachyramphus marmoratus) have been listed as “Endangered” by the State of California and “Threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management and evaluation of conservation efficacy, and ultimately to meet Federal- and State-mandated recovery efforts for this species. During June–August 2017, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1999–2016), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (central California—San Francisco Bay to Monterey Bay). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 189 detections of 321 individuals observed on nine unique surveys. The abundance estimated for the entire study area using all surveys in 2017 was 530 birds (95-percent confidence interval, 384–732 birds). Estimated abundance from 2017 is comparable to most prior years of study, except for 2008 and 2015, which had anomalously low abundances. We estimated productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) in 2017 using three detections of three individuals observed in six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.022 ± 0.014 standard error. We created a synthesized database of all marbled murrelet survey data from 1999 to 2017 to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates. Future modifications of survey design could help reduce variance in abundance estimation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1093","usgsCitation":"Felis, J.J., Adams, J., and Kelsey, E.C.., 2018, Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2017 breeding season: U.S. Geological Survey Data Series 1093, 12 p., https://doi.org/10.3133/ds1093.","productDescription":"Report: iv, 12 p.; Data release","onlineOnly":"Y","ipdsId":"IP-098753","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":355984,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01RW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Annual marbled murrelet abundance and productivity surveys off central California (Zone 6), 1999-2017"},{"id":355983,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1093/ds1093.pdf","text":"Report","size":"749 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1093"},{"id":355982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1093/coverthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.53189086914061,\n 36.92464553397128\n ],\n [\n -121.93450927734375,\n 36.92464553397128\n ],\n [\n -121.93450927734375,\n 37.528242717975054\n ],\n [\n -122.53189086914061,\n 37.528242717975054\n ],\n [\n -122.53189086914061,\n 36.92464553397128\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive
Modoc Hall, Room 4004
Sacramento, California 95819
The Community for Data Integration (CDI) is a group that helps members grow their expertise on all aspects of working with scientific data. The CDI’s activities advance data and information integration capabilities in the U.S. Geological Survey and in the wider Earth and biological sciences. This annual report describes the presentations, activities, collaboration areas, workshop, and other CDI-sponsored events in fiscal year 2017. The report also describes the objectives of the 11 CDI-funded projects in fiscal year 2017. The report shows how the CDI activities fulfill the strategic objective of the U.S. Geological Survey’s Core Science Systems Mission Area to develop a workplace model for interdisciplinary science.
Core Science Analytics, Synthesis, and Library
U.S. Geological Survey
108 National Center
12201 Sunrise Valley Drive,
Reston, VA 20192
The UFINCH (Unit Flows In Networks of Channels) computer application can be used to simulate daily and unit flows in networks of streams based on geospatial data in the National Hydrography Dataset NHDPlus (with value added attributes), and U.S. Geoogical Survey daily streamflow data from a downstream (or base) streamgage. Among streamflow augmentation methods, UFINCH has the unique capability to estimate time series of flows from a single base (downstream) streamgage to many upstream reaches, while conserving flows within the basin. UFINCH also provides a simple statistical model to adjust simulated flows to better match continuous flows from data at an upstream streamgage. Parameters of the statistical model are estimated using overlapping periods of record at the two streamgages, but the adjustment can be applied to all years of record available at the base streamgage. This report describes the main features of UFINCH and presents results from a sample application. Interactive graphical user interfaces and automated geographical information processing facilitate flow-data retrievals provide an intuitive environment for efficient and effective generation of flow information in a network. UFINCH is coded in the Matlab programming language and can be run in the Matlab programming environment, with supporting statistical, optimization, and mapping toolboxes, or from compiled code on a Microsoft Windows computer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165074","collaboration":"National Water Quality Program","usgsCitation":"Holtschlag, D.J., 2018, UFINCH—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages: U.S. Geological Survey Scientific Investigations Report 2016-5074, 17 p., https://doi.org/10.3133/sir20165074.","productDescription":"Report: iv, 17 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073436","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":355884,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5074/sir20165074.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5074"},{"id":355883,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5074/coverthb.jpg"},{"id":355885,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319TC5","text":"USGS data release","description":"USGS data release","linkHelpText":"Code, data, executables, and other information used to run Unit Flows in Networks of Channels (UFINCH)—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages"}],"contact":"Upper Midwest Water Science Center
U.S. Geological Survey
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