Many species are distributed as metapopulations in dynamic landscapes, where habitats change through space and time. Individuals locate habitat through dispersal, and the relationship between a species and landscape characteristics can have profound effects on population persistence. Despite the importance of connectivity in dynamic environments, few empirical studies have examined temporal variability in dispersal or its effect on metapopulation dynamics. In response to this knowledge gap, we studied the dispersal, demography, and viability of a metapopulation of an endangered, disturbance-dependent shorebird. We examined three subpopulations of piping plovers (Charadrius melodus) on the lower Platte and Missouri rivers from 2008–2013. High flow events from an upstream dam on the Missouri River in 2010 and 2011 allowed us to assess the effect of total habitat loss and the subsequent creation of new habitat associated with a large disturbance at one ‘natural’ study location. The other two sites within the metapopulation, which were maintained by anthropogenic activities (e.g., mining, development, habitat restoration), were largely unaffected by this disturbance, resulting in a controlled natural experiment.
\nHigh flow events were associated with increased emigration, decreased immigration, and decreased survival in the subpopulation that experienced high flows. Following the high flow event, immigration into that subpopulation increased. Dispersal rates among subpopulations were negatively correlated with distance. The metapopulation had a low probability of extinction over 100 years (0%) under the current disturbance interval and associated dispersal and survival rates. However, persistence depended on relatively stable, human-created habitats, not the dynamic, natural habitat (47.7 % extinction probability for this subpopulation).
\nWe found that functional connectivity, as measured by the rate of dispersal among subpopulations, increased as a result of the high flow event in our study metapopulation. Plovers also increased reproductive output following this event. Although the study metapopulation had a low overall probability of extinction, metapopulation persistence depended on anthropogenically created habitats that provided a small but stable source of nesting habitat and dispersers through time. However, all subpopulations remained small, even if persistent, making them individually vulnerable to extinction through stochastic events. Given the highly dynamic nature of habitat availability in this system, maintaining several subpopulations within the metapopulation and stable sources of habitat will be critical, and this species will likely remain conservation-reliant.
\nExfoliation of rock deteriorates cliffs through the formation and subsequent opening of fractures, which in turn can lead to potentially hazardous rockfalls. Although a number of mechanisms are known to trigger rockfalls, many rockfalls occur during periods when likely triggers such as precipitation, seismic activity and freezing conditions are absent. It has been suggested that these enigmatic rockfalls may occur due to solar heating of rock surfaces, which can cause outward expansion. Here we use data from 3.5 years of field monitoring of an exfoliating granite cliff in Yosemite National Park in California, USA, to assess the magnitude and temporal pattern of thermally induced rock deformation. From a thermodynamic analysis, we find that daily, seasonal and annual temperature variations are sufficient to drive cyclic and cumulative opening of fractures. Application of fracture theory suggests that these changes can lead to further fracture propagation and the consequent detachment of rock. Our data indicate that the warmest times of the day and year are particularly conducive to triggering rockfalls, and that cyclic thermal forcing may enhance the efficacy of other, more typical rockfall triggers.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/ngeo2686","collaboration":"National Park Service","usgsCitation":"Collins, B.D., and Stock, G.M., 2016, Rockfall triggering by cyclic thermal stressing of exfoliation fractures: Nature Geoscience, v. 9, p. 395-400, https://doi.org/10.1038/ngeo2686.","productDescription":"7 p.","startPage":"395","endPage":"400","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064900","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":323258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.66308593749999,\n 37.496652341233364\n ],\n [\n -119.57244873046874,\n 37.49556277942662\n ],\n [\n -119.57382202148439,\n 37.53368798315969\n ],\n [\n -119.43923950195312,\n 37.536954951447285\n ],\n [\n -119.388427734375,\n 37.554376365024865\n ],\n [\n -119.36920166015624,\n 37.63054716639914\n ],\n [\n -119.32388305664064,\n 37.63380988687157\n ],\n [\n -119.25659179687499,\n 37.72836644908416\n ],\n [\n -119.26895141601562,\n 37.74248523826606\n ],\n [\n -119.20166015625,\n 37.80001858607365\n ],\n [\n -119.20028686523438,\n 37.89002800137122\n ],\n [\n -119.23599243164062,\n 37.91061711049342\n ],\n [\n -119.31289672851562,\n 37.9593578107923\n ],\n [\n -119.32113647460936,\n 37.97451499202459\n ],\n [\n -119.31015014648438,\n 38.04484662140698\n ],\n [\n -119.34722900390625,\n 38.08593231319764\n ],\n [\n -119.42825317382811,\n 38.11943249695316\n ],\n [\n -119.44198608398438,\n 38.098901948321256\n ],\n [\n -119.46533203125,\n 38.098901948321256\n ],\n [\n -119.47219848632812,\n 38.13023573104302\n ],\n [\n -119.50241088867188,\n 38.158316657442\n ],\n [\n -119.50790405273436,\n 38.135636748588574\n ],\n [\n -119.57382202148439,\n 38.161556068786886\n ],\n [\n -119.58755493164061,\n 38.187466178077905\n ],\n [\n -119.62326049804688,\n 38.1680344597114\n ],\n [\n -119.62600708007812,\n 38.15291731872143\n ],\n [\n -119.7015380859375,\n 38.13239618602294\n ],\n [\n -119.73724365234375,\n 38.09998264736481\n ],\n [\n -119.81414794921875,\n 38.09674050228651\n ],\n [\n -119.83886718750001,\n 38.089174937729794\n ],\n [\n -119.87457275390625,\n 38.05566088242076\n ],\n [\n -119.88693237304688,\n 37.98642201062662\n ],\n [\n -119.88555908203126,\n 37.89327929625019\n ],\n [\n -119.82650756835938,\n 37.89219554724437\n ],\n [\n -119.827880859375,\n 37.83798775896512\n ],\n [\n -119.871826171875,\n 37.81195385919268\n ],\n [\n -119.87594604492188,\n 37.791337175930686\n ],\n [\n -119.85260009765624,\n 37.738141282210385\n ],\n [\n -119.7784423828125,\n 37.73922729512254\n ],\n [\n -119.77981567382812,\n 37.7109857819458\n ],\n [\n -119.7674560546875,\n 37.68708070686609\n ],\n [\n -119.81277465820312,\n 37.67947293019486\n ],\n [\n -119.81277465820312,\n 37.665342132088284\n ],\n [\n -119.761962890625,\n 37.669690356567855\n ],\n [\n -119.71389770507814,\n 37.65338320128765\n ],\n [\n -119.72625732421874,\n 37.642509774448754\n ],\n [\n -119.72351074218749,\n 37.62945956107554\n ],\n [\n -119.69604492187499,\n 37.62837193983584\n ],\n [\n -119.69467163085936,\n 37.58267747761301\n ],\n [\n -119.70428466796875,\n 37.579412513438385\n ],\n [\n -119.70016479492188,\n 37.54893261064111\n ],\n [\n -119.66308593749999,\n 37.496652341233364\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-28","publicationStatus":"PW","scienceBaseUri":"57594231e4b04f417c256989","contributors":{"authors":[{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":637686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Greg M.","contributorId":88593,"corporation":false,"usgs":true,"family":"Stock","given":"Greg","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637687,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70158609,"text":"sir20155141 - 2016 - Hydrologic data and groundwater-flow simulations in the Brown Ditch Watershed, Indiana Dunes National Lakeshore, near Beverly Shores and Town of Pines, Indiana","interactions":[],"lastModifiedDate":"2016-03-18T09:27:57","indexId":"sir20155141","displayToPublicDate":"2016-03-15T09:00:00","publicationYear":"2016","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":"2015-5141","title":"Hydrologic data and groundwater-flow simulations in the Brown Ditch Watershed, Indiana Dunes National Lakeshore, near Beverly Shores and Town of Pines, Indiana","docAbstract":"The U.S. Geological Survey (USGS) collected data and simulated groundwater flow to increase understanding of the hydrology and the effects of drainage alterations on the water table in the vicinity of Great Marsh, near Beverly Shores and Town of Pines, Indiana. Prior land-management practices have modified drainage and caused changes in the distribution of open water, streams and ditches, and groundwater abundance and flow paths.
\nCollected hydrologic data indicate that the majority of water entering Great Marsh flows from the southern dune ridge beneath Town of Pines, Indiana. Groundwater flow is intercepted by Brown Ditch in the eastern portion of the study area and Derby Ditch in the western portion of the study area. A smaller amount of groundwater from the northern dune ridge beneath Beverly Shores also contributed water to Great Marsh. Continuous groundwater-level data collected indicate that the predominant north-south groundwater-flow gradients vary during the course of the year due to increased levels of precipitation or during periods of drainage obstructions. Continuous surface-water discharge and surface-water elevation were measured at three USGS streamgages, one each on Brown, Kintzele and Derby Ditches. The monthly mean discharge statistics indicate that during the period of record— June 2012 to September 2013—streamflow in Kintzele Ditch was lowest during July 2012 and highest during April 2013. In Derby Ditch, streamflow also was lowest during July 2012 and highest during April 2013.
\nPeriods of relatively high and low groundwater levels during August 1982, March 2013, and April 2014 were examined and simulated by using MODFLOW and companion software. Results from the simulation of conditions during March 2013 include that nearly 100 percent of all water entering the area simulating Town of Pines is from recharge. Of all the water simulated to enter the eastern and western portions of Great Marsh, nearly 20 and 18 percent, respectively, flows from Town of Pines to the western and eastern portions of Great Marsh. The dune ridges beneath Town of Pines and to a lesser extent beneath Beverly Shores are a major source of recharge to the surficial aquifer and Great Marsh.
\nResults from the simulation of the conditions of April 2014 include that, despite increases in the amount of water entering Great Marsh due to a beaver-dam-modified hydrologic condition, there is still virtually zero simulated groundwater flow from Great Marsh to Town of Pines. The volume of water simulated to be entering the zone representing Beverly Shores decreased by 0.43 cubic foot per second from the results of the March 2013 simulation. This simulated difference in water budgets can be attributed to increased simulated recharge in Great Marsh and Town of Pines. Effects of the inclusion of the beaver dam included the increase of the simulated water table and simulated inundated area upstream of the beaver dam due to the effects of ponding surface water.
\nResults from the simulation scenario that includes six proposed pool-riffle control structures in Brown Ditch under the hydrologic conditions of March 2013 indicate areas inundated by water are larger, including areas just to the north of the entrance of Brown Ditch into Great Marsh, and areas north of the confluence of Brown and Kintzele Ditches.
\nResults from the scenario simulating the increase of the Lake Michigan water level to the historical high of May 31, 1998, showed inundated areas of Great Marsh south of Beverly Shores enlarged on both sides of Lakeshore County Road with the greatest enlargement simulated to be southeast of the intersection of Lakeshore County Road and Beverly Drive. For the scenario simulating the decrease of the Lake Michigan water level to the historical low of December 23, 2007, results show little change from the original March 2013 inundated area.
\nThe results of this study can be used by water-resource managers to understand how surrounding ditches affect water levels in Great Marsh and other inland wetlands and residential areas. The groundwater model developed can be applied to answer questions about how alterations to the drainage system in the area affects water levels in the public and residential areas surrounding Great Marsh. The modeling methods developed in this study provide a template for other studies of groundwater flow and groundwater/surface-water interactions within the shallow surficial aquifer in northern Indiana, and in similar hydrologic settings that include surficial sand aquifers in coastal areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155141","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lampe, D.C., 2015, Hydrologic data and groundwater-flow simulations in the Brown Ditch Watershed, Indiana Dunes National Lakeshore, near Beverly Shores and Town of Pines, Indiana: U.S. Geological Survey Scientific Investigations Report 2015– 5141, 97 p., https://dx.doi.org/10.3133/sir20155141.","productDescription":"xi, 97 p.","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055857","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":318807,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5141/coverthb.jpg"},{"id":318808,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5141/sir20155141.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5141"}],"country":"United States","state":"Indiana","otherGeospatial":"Brown Ditch Watershed, Indiana Dunes National Lakeshore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1,\n 41.65\n ],\n [\n -87.1,\n 41.73\n ],\n [\n -86.9,\n 41.73\n ],\n [\n -86.9,\n 41.65\n ],\n [\n -87.1,\n 41.65\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
Phone: (317) 290-3333
http://in.water.usgs.gov/
Drought conditions during the study period of January 1, 2009, to September 30, 2013, caused a reduction in surface-water releases from water-supply storage infrastructure of the Rio Grande Project, which led to changes in surface-water and groundwater (conjunctive) use in downstream agricultural alluvial valleys. Surface water and groundwater in the agriculturally dominated alluvial Rincon and Mesilla Valleys were investigated in this study to measure the influence of drought and subsequent change in conjunctive water use on quantity and quality of these water resources. In 2013, the U.S. Geological Survey, in cooperation with the New Mexico Environment Department and the New Mexico Interstate Stream Commission, began a study to (1) calculate dissolved-solids loads over the study period at streamgages in the study area where data are available, (2) assess the temporal variability of dissolved-solids loads at and between each streamgage where data are available, and (3) relate the spatiotemporal variability of shallow groundwater data (groundwater levels and quality) within the alluvial valleys of the study area to spatiotemporal variability of surface-water data over the study period. This assessment included the calculation of surface-water dissolved-solids loads at streamgages as well as a mass-balance approach to measure the change in salt load between these streamgages. Bimodal surface-water discharge data led to a temporally-dynamic volumetric definition of release and nonrelease seasons. Continuous surface-water discharge and water-quality data from three streamgages on the Rio Grande were used to calculate daily dissolved-solids loads over the study period, and the results were aggregated annually and seasonally. Results show the majority of dissolved-solids loading occurs during release season; however, decreased duration of the release season over the 5-year study period has resulted in a decrease of the total annual loads at each streamgage. Calculation of the change of salt loads using a mass-balance approach was applied between streamgages. Results from these calculations suggest differing responses to releases in the Rincon and Mesilla Valleys over the period of study; there is a decreasing sink of salt in the Rincon Valley whereas there is an increasing sink of salt in the Mesilla Valley. Daily groundwater-level and water-quality data from shallow wells within the two alluvial valleys show spatial heterogeneity of water quality over the study period. Mass-balance salt-loading trends during the study period are similar to previous trends during the 1950s drought as well as a wet period in the 1980s. The similarity of salt-loading trends from the 1950s, 1980s, and 2000s independent of the climate indicates salt loading in this hydrologic setting may be driven by water-use practices rather than a single climatic variable.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165006","collaboration":"Prepared in cooperation with the New Mexico Environment Department and the New Mexico Interstate Stream Commission","usgsCitation":"Driscoll, J.M., and Sherson, L.R., 2016, Variability of surface-water quantity and quality and shallow groundwater levels and quality within the Rio Grande Project area, New Mexico and Texas, 2009–13: U.S. Geological Survey Scientific Investigations Report 2016–5006, 33 p., https://dx.doi.org/10.3133/sir20165006.","productDescription":"vi, 33 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065706","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":318886,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5006/coverthb.jpg"},{"id":318887,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5006/sir20165006.pdf","text":"Report","size":"1.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5006"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.46826171874999,\n 31.076460800121122\n ],\n [\n -107.46826171874999,\n 33.367237465838315\n ],\n [\n -105.6060791015625,\n 33.367237465838315\n ],\n [\n -105.6060791015625,\n 31.076460800121122\n ],\n [\n -107.46826171874999,\n 31.076460800121122\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montgomery, NE
Albuquerque, NM 87109–1311
Most of the western United States is experiencing the effects of rapid and directional climate change (Garfin et al. 2013). These effects, along with forecasts of profound changes in the future, provide strong motivation for resource managers to learn about and prepare for future changes. Climate adaptation plans are based on an understanding of historic climate variation and their effects on ecosystems and on forecasts of future climate trends. Frameworks for climate adaptation thus universally identify the importance of a summary of historical, current, and projected climates (Glick, Stein, and Edelson 2011; Cross et al. 2013; Stein et al. 2014). Trends in physical climate variables are usually the basis for evaluating the exposure component in vulnerability assessments. Thus, this chapter focuses on step 2 of the Climate-Smart Conservation framework (chap. 2): vulnerability assessment. We present analyses of historical and current observations of temperature, precipitation, and other key climate measurements to provide context and a baseline for interpreting the ecological impacts of projected climate changes.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Climate change in wildlands: Pioneering approaches to science and management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Island Press","publisherLocation":"Washington, D.C.","doi":"10.5822/978-1-61091-713-1_4","usgsCitation":"Gross, J.E., Tercek, M., Guay, K., Chang, T., Talbert, M., Thoma, D., Rodman, A., Jantz, P., and Morisette, J.T., 2016, Analyses of historical and projected climates to support climate adaptation in the northern Rocky Mountains: Chapter 4, chap. of Climate change in wildlands: Pioneering approaches to science and management, p. 55-77, https://doi.org/10.5822/978-1-61091-713-1_4.","productDescription":"23 p.","startPage":"55","endPage":"77","ipdsId":"IP-067022","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":341203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Rocky Mountains","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5916c9b5e4b044b359e4869c","contributors":{"authors":[{"text":"Gross, John E.","contributorId":106777,"corporation":false,"usgs":false,"family":"Gross","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":567988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tercek, Michael","contributorId":146469,"corporation":false,"usgs":false,"family":"Tercek","given":"Michael","affiliations":[{"id":6765,"text":"Montana State University, Department of Land Resources and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":694979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guay, Kevin","contributorId":146470,"corporation":false,"usgs":false,"family":"Guay","given":"Kevin","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":694980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chang, Tony","contributorId":191992,"corporation":false,"usgs":false,"family":"Chang","given":"Tony","email":"","affiliations":[],"preferred":false,"id":694981,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbert, Marian 0000-0003-0588-0265 mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":191730,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":false,"id":694982,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rodman, Ann","contributorId":150932,"corporation":false,"usgs":false,"family":"Rodman","given":"Ann","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":694983,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thoma, David","contributorId":190258,"corporation":false,"usgs":false,"family":"Thoma","given":"David","affiliations":[],"preferred":false,"id":694984,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jantz, Patrick","contributorId":191993,"corporation":false,"usgs":false,"family":"Jantz","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":694985,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":567987,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70188318,"text":"70188318 - 2016 - Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought","interactions":[],"lastModifiedDate":"2017-06-06T10:44:57","indexId":"70188318","displayToPublicDate":"2016-03-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought","docAbstract":"This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS).
Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6 wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4 wk), whereas corn conditions were more closely related to longer-range (8–12 wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2015.12.065","usgsCitation":"Otkin, J.A., Anderson, M.C., Hain, C., Svoboda, M., Johnson, D., Mueller, R., Tadesse, T., Wardlow, B.D., and Brown, J.F., 2016, Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought: Agricultural and Forest Meteorology, v. 218-219, p. 230-242, https://doi.org/10.1016/j.agrformet.2015.12.065.","productDescription":"13 p.","startPage":"230","endPage":"242","ipdsId":"IP-071141","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471152,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2015.12.065","text":"Publisher Index Page"},{"id":342143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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barred owls (Strix varia) is causing rapid declines in populations of northern spotted owls (Strix occidentalis caurina), and that the longterm persistence of spotted owls may be in question without additional management intervention. A pilot study in California showed that lethal removal of barred owls in combination with habitat conservation may be able to slow or even reverse population declines of spotted owls at local scales, but it remains unknown whether similar results can be obtained in larger areas with different forest conditions and where barred owls are more abundant. In 2015, we implemented a before-after-controlimpact (BACI) experimental design on two study areas in Oregon and Washington with at least 20 years of pre-treatment demographic data on spotted owls to determine if removal of barred owls can improve population trends of spatially associated spotted owls. Here we provide an overview of our research accomplishments and preliminary results in Oregon and Washington in 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161041","usgsCitation":"Wiens, J.D., Dugger, K.M., Lewicki, K.E., and Simon, D.C., 2016, Effects of experimental removal of barred owls on population demography of northern spotted owls in Washington and Oregon—2015 progress report: U.S. Geological Survey Open-File Report 2016-1041, 16 p., https://dx.doi.org/10.3133/ofr20161041.","productDescription":"iv, 16 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072911","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":318862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1041/ofr20161041.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1041"},{"id":318861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1041/coverthb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.31054687499999,\n 49.023461463214126\n ],\n [\n -117.04833984375001,\n 49.023461463214126\n ],\n [\n -117.02636718749999,\n 46.31658418182218\n ],\n [\n -116.91650390625,\n 46.164614496897094\n ],\n [\n -116.89453125,\n 46.07323062540838\n ],\n [\n -117.02636718749999,\n 45.9511496866914\n ],\n [\n -116.71874999999999,\n 45.82879925192134\n ],\n [\n -116.49902343749999,\n 45.72152152227954\n ],\n [\n -116.52099609375,\n 45.537136680398596\n ],\n [\n -116.65283203124999,\n 45.30580259943578\n ],\n [\n -116.69677734375,\n 45.1510532655634\n ],\n [\n -116.8505859375,\n 44.98034238084973\n ],\n [\n -116.8505859375,\n 44.91813929958515\n ],\n [\n -117.04833984375001,\n 44.68427737181225\n ],\n [\n -117.158203125,\n 44.55916341529184\n ],\n [\n -117.20214843749999,\n 44.38669150215206\n ],\n [\n -117.158203125,\n 44.26093725039923\n ],\n [\n -116.91650390625,\n 44.213709909702054\n ],\n [\n -116.806640625,\n 44.071800467511565\n ],\n [\n -116.93847656250001,\n 43.929549935614595\n ],\n [\n -117.04833984375001,\n 43.73935207915473\n ],\n [\n -117.04833984375001,\n 41.96765920367816\n ],\n [\n -124.25537109375,\n 41.96765920367816\n ],\n [\n -124.45312499999999,\n 42.19596877629178\n ],\n [\n -124.49707031249999,\n 42.42345651793833\n ],\n [\n -124.43115234375,\n 42.569264372193864\n ],\n [\n -124.51904296875,\n 42.68243539838623\n ],\n [\n -124.60693359374999,\n 42.81152174509788\n ],\n [\n -124.43115234375,\n 43.229195113965005\n ],\n [\n -124.51904296875,\n 43.34116005412307\n ],\n [\n -124.29931640625,\n 43.40504748787035\n ],\n [\n -124.21142578125,\n 43.73935207915473\n ],\n [\n -124.21142578125,\n 44.402391829093915\n ],\n [\n -124.12353515624999,\n 44.762336674810996\n ],\n [\n -124.01367187499999,\n 45.19752230305685\n ],\n [\n -123.99169921875,\n 45.61403741135093\n ],\n [\n -124.01367187499999,\n 45.9511496866914\n ],\n [\n -123.99169921875,\n 46.13417004624326\n ],\n [\n -124.1455078125,\n 46.240651955001695\n ],\n [\n -124.16748046874999,\n 46.66451741754235\n ],\n [\n -124.21142578125,\n 46.93526088057719\n ],\n [\n -124.25537109375,\n 47.234489635299184\n ],\n [\n -124.43115234375,\n 47.39834920035926\n ],\n [\n -124.47509765625,\n 47.635783590864854\n ],\n [\n -124.73876953125,\n 47.945786463687185\n ],\n [\n -124.73876953125,\n 48.268569112964336\n ],\n [\n -124.73876953125,\n 48.516604348867475\n ],\n [\n -124.34326171874999,\n 48.45835188280866\n ],\n [\n -123.94775390625,\n 48.268569112964336\n ],\n [\n -123.59619140625001,\n 48.25394114463431\n ],\n [\n -123.33251953125,\n 48.25394114463431\n ],\n [\n -123.1787109375,\n 48.4146186174932\n ],\n [\n -123.31054687499999,\n 48.69096039092549\n ],\n [\n -123.06884765625,\n 48.76343113791796\n ],\n [\n -123.11279296875001,\n 48.850258199721495\n ],\n [\n -123.31054687499999,\n 49.023461463214126\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov
Linking marine epizootics to a specific aetiology is notoriously difficult. Recent diagnostic successes show that marine disease diagnosis requires both modern, cutting-edge technology (e.g. metagenomics, quantitative real-time PCR) and more classic methods (e.g. transect surveys, histopathology and cell culture). Here, we discuss how this combination of traditional and modern approaches is necessary for rapid and accurate identification of marine diseases, and emphasize how sole reliance on any one technology or technique may lead disease investigations astray. We present diagnostic approaches at different scales, from the macro (environment, community, population and organismal scales) to the micro (tissue, organ, cell and genomic scales). We use disease case studies from a broad range of taxa to illustrate diagnostic successes from combining traditional and modern diagnostic methods. Finally, we recognize the need for increased capacity of centralized databases, networks, data repositories and contingency plans for diagnosis and management of marine disease.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2015.0207","usgsCitation":"Burge, C.A., Friedman, C., Getchell, R.G., House, M., Lafferty, K.D., Mydlarz, L.D., Prager, K.C., Sutherland, K.P., Renault, T., Kiryu, I., and Vega-Thurber, R., 2016, Complementary approaches to diagnosing marine diseases: a union of the modern and the classic: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 371, p. 1-11, https://doi.org/10.1098/rstb.2015.0207.","productDescription":"Article 20150207; 11 p.","startPage":"1","endPage":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071582","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2015.0207","text":"Publisher Index Page"},{"id":318858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"371","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-05","publicationStatus":"PW","scienceBaseUri":"56e7d299e4b0f59b85d64006","contributors":{"authors":[{"text":"Burge, Colleen A.","contributorId":34814,"corporation":false,"usgs":true,"family":"Burge","given":"Colleen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":622715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Carolyn S.","contributorId":13890,"corporation":false,"usgs":true,"family":"Friedman","given":"Carolyn S.","affiliations":[],"preferred":false,"id":622716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Getchell, Rodman G.","contributorId":32416,"corporation":false,"usgs":true,"family":"Getchell","given":"Rodman","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":622717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"House, Marcia","contributorId":167561,"corporation":false,"usgs":false,"family":"House","given":"Marcia","email":"","affiliations":[{"id":7059,"text":"Northwest Indian Fisheries Commission","active":true,"usgs":false}],"preferred":false,"id":622718,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622714,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mydlarz, Laura D.","contributorId":167562,"corporation":false,"usgs":false,"family":"Mydlarz","given":"Laura","email":"","middleInitial":"D.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":622719,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prager, Katherine C.","contributorId":8366,"corporation":false,"usgs":true,"family":"Prager","given":"Katherine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":622720,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sutherland, Kathryn P.","contributorId":167563,"corporation":false,"usgs":false,"family":"Sutherland","given":"Kathryn","email":"","middleInitial":"P.","affiliations":[{"id":24752,"text":"Rollins College","active":true,"usgs":false}],"preferred":false,"id":622721,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Renault, Tristan","contributorId":167564,"corporation":false,"usgs":false,"family":"Renault","given":"Tristan","email":"","affiliations":[{"id":24753,"text":"Ifremer, De´partement Ressources Biologiques et Environnement, rue de l’Ile d’Yeu","active":true,"usgs":false}],"preferred":false,"id":622722,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kiryu, Ikunari","contributorId":167565,"corporation":false,"usgs":false,"family":"Kiryu","given":"Ikunari","email":"","affiliations":[{"id":24754,"text":"National Research Institute of Aquaculture, Fisheries Research Agency","active":true,"usgs":false}],"preferred":false,"id":622723,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vega-Thurber, Rebecca","contributorId":167566,"corporation":false,"usgs":false,"family":"Vega-Thurber","given":"Rebecca","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":622724,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70169058,"text":"70169058 - 2016 - What a drag: Quantifying the global impact of chronic bottom trawling on continental shelf sediment","interactions":[],"lastModifiedDate":"2021-01-07T18:48:40.164069","indexId":"70169058","displayToPublicDate":"2016-03-14T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2381,"text":"Journal of Marine Systems","active":true,"publicationSubtype":{"id":10}},"title":"What a drag: Quantifying the global impact of chronic bottom trawling on continental shelf sediment","docAbstract":"Continental shelves worldwide are subject to intense bottom trawling that causes sediment to be resuspended. The widely used traditional concepts of modern sedimentary transport systems on the shelf rely only on estimates for naturally driven sediment resuspension such as through storm waves, bottom currents, and gravity-driven flows but they overlook a critical anthropogenic factor. The strong influence of bottom trawling on a source-to-sink sediment budget is explored on the NW Iberian shelf. Use of Automated Information System vessel tracking data provides for a high-resolution vessel track reconstruction and the accurate calculation of the spatial distribution of bottom trawling intensity and associated resuspended sediment load. The mean bottom trawling-induced resuspended sediment mass for the NW Iberian shelf is 13.50 Mt yr− 1, which leads to a six-fold increase in off-shelf sediment transport when compared to natural resuspension mechanisms. The source-to-sink budget analysis provides evidence that bottom trawling causes a rapid erosion of the fine sediment on human time scales. Combining global soft sediment distribution data of the shelves with worldwide bottom trawling intensity estimates we show that the bottom trawling-induced resuspended sediment mass amounts to approximately the same mass of all sediment entering the shelves through rivers. Spatial delineations between natural and anthropogenic sediment resuspension areas are presented to aid in marine management questions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jmarsys.2015.12.007","usgsCitation":"Oberle, F.K., Storlazzi, C.D., and Hanebuth, T.J., 2016, What a drag: Quantifying the global impact of chronic bottom trawling on continental shelf sediment: Journal of Marine Systems, v. 159, p. 109-119, https://doi.org/10.1016/j.jmarsys.2015.12.007.","productDescription":"11 p.","startPage":"109","endPage":"119","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063932","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":318859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"159","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56e7d29be4b0f59b85d6401a","contributors":{"authors":[{"text":"Oberle, Ferdinand K. J. 0000-0001-8871-3619","orcid":"https://orcid.org/0000-0001-8871-3619","contributorId":167559,"corporation":false,"usgs":false,"family":"Oberle","given":"Ferdinand","email":"","middleInitial":"K. J.","affiliations":[{"id":24749,"text":"University of Bremen","active":true,"usgs":false}],"preferred":false,"id":622712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":622711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanebuth, Till J.J.","contributorId":167560,"corporation":false,"usgs":false,"family":"Hanebuth","given":"Till","email":"","middleInitial":"J.J.","affiliations":[{"id":24750,"text":"Coastal Carolina University","active":true,"usgs":false}],"preferred":false,"id":622713,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70162298,"text":"sir20155181 - 2016 - Methods to estimate historical daily streamflow for ungaged stream locations in Minnesota","interactions":[],"lastModifiedDate":"2016-03-14T11:52:52","indexId":"sir20155181","displayToPublicDate":"2016-03-14T00:00:00","publicationYear":"2016","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":"2015-5181","title":"Methods to estimate historical daily streamflow for ungaged stream locations in Minnesota","docAbstract":"Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water; however, streamgages cannot be installed at every location where streamflow information is needed. Therefore, methods for estimating streamflow at ungaged stream locations need to be developed. This report presents a statewide study to develop methods to estimate the structure of historical daily streamflow at ungaged stream locations in Minnesota. Historical daily mean streamflow at ungaged locations in Minnesota can be estimated by transferring streamflow data at streamgages to the ungaged location using the QPPQ method. The QPPQ method uses flow-duration curves at an index streamgage, relying on the assumption that exceedance probabilities are equivalent between the index streamgage and the ungaged location, and estimates the flow at the ungaged location using the estimated flow-duration curve. Flow-duration curves at ungaged locations can be estimated using recently developed regression equations that have been incorporated into StreamStats (http://streamstats.usgs.gov/), which is a U.S. Geological Survey Web-based interactive mapping tool that can be used to obtain streamflow statistics, drainage-basin characteristics, and other information for user-selected locations on streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155181","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Lorenz, D.L., and Ziegeweid, J.R., 2016, Methods to estimate historical daily streamflow for ungaged stream locations in Minnesota: U.S. Geological Survey Scientific Investigations Report 2015–5181, 18 p., https://dx.doi.org/10.3133/sir20155181.","productDescription":"iv, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068751","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":318797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5181/coverthb.jpg"},{"id":318798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5181/sir20155181.pdf","text":"Report","size":"2.91 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\"}}]}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
The loss of historic ecosystem conditions has led forest managers to implement woodland and savanna ecosystem restoration on a landscape scale (≥10,000 ha) in the Ozark Plateau of Arkansas. Managers are attempting to restore and conserve these ecosystems through the reintroduction of disturbance, mainly short-rotation early-growing-season prescribed fire. Short-rotation early-growing season prescribed fire in the Ozarks typically occurs immediately before bud-break, through bud-break, and before leaf-out, and fire events occur on a three-to five-year interval. We examined short-rotation early-growing season prescribed fire as a restoration tool on vegetation characteristics. We collected vegetation measurements at 70 locations annually from 2011 to 2012 in and around the White Rock Ecosystem Restoration Area (WRERA), Ozark-St. Francis National Forest, Arkansas, and used generalized linear models to investigate the impact and efficacy of prescribed fire on vegetation structure. We found the number of large shrubs (>5 cm base diameter) decreased and small shrubs (<5 cm ground diameter) increased with prescribed fire severity. We found that horizontal understory cover from ground level to 1 m in height increased with time-since-prescribed-fire and woody ground cover decreased with the number of prescribed fire treatments. Using LANDFIRE datasets at the landscape scale, we found that since the initiation of a short-rotation early-growing season prescribed fire management regime, forest canopy cover has not reverted to levels characteristic of woodlands and savannas or reached restoration objectives over large areas. Without greater reductions in forest canopy cover and increases in forest-canopy cover heterogeneity, advanced regeneration will be limited in success, and woodland and savanna conditions will not return soon or to the extent desired.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Pittman, H.T., and Krementz, D.G., 2016, Efficacy of landscape scale woodland and savanna restoration at multiple spatial and temporal scales: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 3, p. 233-242.","productDescription":"10 p.","startPage":"233","endPage":"242","ipdsId":"IP-059541","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":349021,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.seafwa.org/publications/journal/?id=402058"},{"id":349022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Ozark Highlands","volume":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fd6be4b06e28e9c24d6e","contributors":{"authors":[{"text":"Pittman, H. Tyler","contributorId":200530,"corporation":false,"usgs":false,"family":"Pittman","given":"H.","email":"","middleInitial":"Tyler","affiliations":[],"preferred":false,"id":722562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718106,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70169057,"text":"70169057 - 2016 - Coherent late-Holocene climate-driven shifts in the structure of three Rocky Mountain lakes","interactions":[],"lastModifiedDate":"2016-12-16T11:07:43","indexId":"70169057","displayToPublicDate":"2016-03-11T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3562,"text":"The Holocene","active":true,"publicationSubtype":{"id":10}},"title":"Coherent late-Holocene climate-driven shifts in the structure of three Rocky Mountain lakes","docAbstract":"Large-scale atmospheric pressure centers, such as the Aleutian and Icelandic Low, have a demonstrated relationship with physical lake characteristics in contemporary monitoring studies, but the responses to these phenomena are rarely observed in lake records. We observe coherent changes in the stratification patterns of three deep (>30 m) lakes inferred from fossil diatom assemblages as a response to shifts in the location and intensity of the Aleutian Low and compare these changes with similar long-term changes observed in the δ18O record from the Yukon. Specifically, these records indicate that between 3.2 and 1.4 ka, the Aleutian Low shifted westward, resulting in an increased frequency of storm tracks across the Pacific Northwest during winter and spring. This change in atmospheric circulation ultimately produced deeper mixing in the upper waters of these three lake systems. Enhanced stratification between 4.5 and 3.3 ka and from 1.3 ka to present suggests a strengthened Aleutian Low and more meridional circulation.
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demographic consequences vary across species","docAbstract":"Behavioural responses to reduce predation risk might cause demographic ‘costs of fear’. Costs differ among species, but a conceptual framework to understand this variation is lacking. We use a life-history framework to tie together diverse traits and life stages to better understand interspecific variation in responses and costs. We used natural and experimental variation in predation risk to test phenotypic responses and associated demographic costs for 10 songbird species. Responses such as increased parental attentiveness yielded reduced development time and created benefits such as reduced predation probability. Yet, responses to increased risk also created demographic costs by reducing offspring production in the absence of direct predation. This cost of fear varied widely across species, but predictably with the probability of repeat breeding. Use of a life-history framework can aid our understanding of potential demographic costs from predation, both from responses to perceived risk and from direct predation mortality.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.12573","usgsCitation":"LaManna, J.A., and Martin, T.E., 2016, Costs of fear: Behavioral and life-history responses to risk and their demographic consequences vary across species: Ecology Letters, v. 19, no. 4, p. 403-413, https://doi.org/10.1111/ele.12573.","productDescription":"10 p.","startPage":"403","endPage":"413","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069480","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":323456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-22","publicationStatus":"PW","scienceBaseUri":"575be4abe4b04f417c27f515","contributors":{"authors":[{"text":"LaManna, Joseph A.","contributorId":171738,"corporation":false,"usgs":false,"family":"LaManna","given":"Joseph","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":638459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":638367,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70169195,"text":"70169195 - 2016 - Hydrologic controls on nitrogen cycling processes and functional gene abundance in sediments of a groundwater flow-through lake","interactions":[],"lastModifiedDate":"2018-08-07T12:10:20","indexId":"70169195","displayToPublicDate":"2016-03-11T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic controls on nitrogen cycling processes and functional gene abundance in sediments of a groundwater flow-through lake","docAbstract":"The fate and transport of inorganic nitrogen (N) is a critically important issue for human and aquatic ecosystem health because discharging N-contaminated groundwater can foul drinking water and cause algal blooms. Factors controlling N-processing were examined in sediments at three sites with contrasting hydrologic regimes at a lake on Cape Cod, MA. These factors included water chemistry, seepage rates and direction of groundwater flow, and the abundance and potential rates of activity of N-cycling microbial communities. Genes coding for denitrification, anaerobic ammonium oxidation (anammox), and nitrification were identified at all sites regardless of flow direction or groundwater dissolved oxygen concentrations. Flow direction was, however, a controlling factor in the potential for N-attenuation via denitrification in the sediments. Potential rates of denitrification varied from 6 to 4500 pmol N/g/h from the inflow to the outflow side of the lake, owing to fundamental differences in the supply of labile organic matter. The results of laboratory incubations suggested that when anoxia and limiting labile organic matter prevailed, the potential existed for concomitant anammox and denitrification. Where oxic lake water was downwelling, potential rates of nitrification at shallow depths were substantial (1640 pmol N/g/h). Rates of anammox, denitrification, and nitrification may be linked to rates of organic N-mineralization, serving to increase N-mobility and transport downgradient.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.5b06155","usgsCitation":"Stoliker, D., Repert, D.A., Smith, R.L., Song, B., LeBlanc, D.R., McCobb, T.D., Conaway, C.H., Hyun, S.P., Koh, D., Moon, H.S., and Kent, D.B., 2016, Hydrologic controls on nitrogen cycling processes and functional gene abundance in sediments of a groundwater flow-through lake: Environmental Science & Technology, v. 50, no. 7, p. 3649-3657, https://doi.org/10.1021/acs.est.5b06155.","productDescription":"9 p.","startPage":"3649","endPage":"3657","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071251","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology 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Geoscience and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":623319,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kent, Douglas B. 0000-0003-3758-8322 dbkent@usgs.gov","orcid":"https://orcid.org/0000-0003-3758-8322","contributorId":1871,"corporation":false,"usgs":true,"family":"Kent","given":"Douglas","email":"dbkent@usgs.gov","middleInitial":"B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":623320,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70168683,"text":"ofr20161026 - 2016 - Geologic assessment of undiscovered oil and gas resources in the Albian Clastic and Updip Albian Clastic Assessment Units, U.S. Gulf Coast Region","interactions":[],"lastModifiedDate":"2016-05-23T09:06:15","indexId":"ofr20161026","displayToPublicDate":"2016-03-11T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1026","title":"Geologic assessment of undiscovered oil and gas resources in the Albian Clastic and Updip Albian Clastic Assessment Units, U.S. Gulf Coast Region","docAbstract":"U.S. Geological Survey National Oil and Gas Assessments (NOGA) of Albian aged clastic reservoirs in the U.S. Gulf Coast region indicate a relatively low prospectivity for undiscovered hydrocarbon resources due to high levels of past production and exploration. Evaluation of two assessment units (AUs), (1) the Albian Clastic AU 50490125, and (2) the Updip Albian Clastic AU 50490126, were based on a geologic model incorporating consideration of source rock, thermal maturity, migration, events timing, depositional environments, reservoir rock characteristics, and production analyses built on well and field-level production histories. The Albian Clastic AU is a mature conventional hydrocarbon prospect with undiscovered accumulations probably restricted to small faulted and salt-associated structural traps that could be revealed using high resolution subsurface imaging and from targeting structures at increased drilling depths that were unproductive at shallower intervals. Mean undiscovered accumulation volumes from the probabilistic assessment are 37 million barrels of oil (MMBO), 152 billion cubic feet of gas (BCFG), and 4 million barrels of natural gas liquids (MMBNGL). Limited exploration of the Updip Albian Clastic AU reflects a paucity of hydrocarbon discoveries updip of the periphery fault zones in the northern Gulf Coastal region. Restricted migration across fault zones is a major factor behind the small discovered fields and estimation of undiscovered resources in the AU. Mean undiscovered accumulation volumes from the probabilistic assessment are 1 MMBO and 5 BCFG for the Updip Albian Clastic AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161026","usgsCitation":"Merrill, M.D., 2016, Geologic assessment of undiscovered oil and gas resources in the Albian clastic and updip Albian clastic assessment units, U.S. Gulf Coast region: U.S. Geological Survey Open-File Report 2016–1026, 31 p., https://dx.doi.org/10.3133/ofr20161026.","productDescription":"Report: v, 27; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-038743","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1026//ofr20161026.pdf","text":"Report","size":"2.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1026"},{"id":318714,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1026/ofr20161026_appendix2.pdf","text":"Appendix 2 - 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U.S. Geological Survey
12201 Sunrise Valley Drive
MS 956 National Center
Reston, VA 20192
http://energy.usgs.gov/
http://energy.usgs.gov/GeneralInfo/
ScienceCenters/Eastern.aspx
A 15-year estimated Trophic State Index (eTSI) for Michigan inland lakes is available, and it spans seven datasets, each representing 1 to 3 years of data from 1999 to 2013. On average, 3,000 inland lake eTSI values are represented in each of the datasets by a process that relates field-measured Secchi-disk transparency (SDT) to Landsat satellite imagery to provide eTSI values for unsampled inland lakes. The correlation between eTSI values and field-measured Trophic State Index (TSI) values from SDT was strong as shown by R2 values from 0.71 to 0.83. Mean eTSI values ranged from 42.7 to 46.8 units, which when converted to estimated SDT (eSDT) ranged from 8.9 to 12.5 feet for the datasets. Most eTSI values for Michigan inland lakes are in the mesotrophic TSI class. The Environmental Protection Agency (EPA) Level III Ecoregions were used to illustrate and compare the spatial distribution of eTSI classes for Michigan inland lakes. Lakes in the Northern Lakes and Forests, North Central Hardwood Forests, and Southern Michigan/Northern Indiana Drift Plains ecoregions are predominantly in the mesotrophic TSI class. The Huron/Erie Lake Plains and Eastern Corn Belt Plains ecoregions, had predominantly eutrophic class lakes and also the highest percent of hypereutrophic lakes than other ecoregions in the State. Data from multiple sampling programs—including data collected by volunteers with the Cooperative Lakes Monitoring Program (CLMP) through the Michigan Department of Environmental Quality (MDEQ), and the 2007 National Lakes Assessment (NLA)—were compiled to compare the distribution of lake TSI classes between each program. The seven eTSI datasets are available for viewing and download with eSDT from the Michigan Lake Water Clarity Interactive Map Viewer at http://mi.water.usgs.gov/projects/RemoteSensing/index.html.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165023","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Fuller, L.M., and Jodoin, R.S., 2016, Estimation of a Trophic State Index for selected inland lakes in Michigan, 1999–2013: U.S. Geological Survey Scientific Investigations Report 2016–5023, 16 p., https://dx.doi.org/10.3133/sir20165023.","productDescription":"vii, 16 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067016","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":318806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5023/sir20165023.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911-5991
http://mi.water.usgs.gov/
Information on lake sturgeon (Acipenser fulvescens) depth and thermal habitat use during non-spawning periods is unavailable due to the difficulty of observing lake sturgeon away from shallow water spawning sites. In 2002 and 2003, lake sturgeon captured in commercial trap nets near Sarnia, Ontario were implanted with archival tags and released back into southern Lake Huron. Five of the 40 tagged individuals were recaptured and were at large for 32, 57, 286, 301, and 880 days. Temperatures and depths recorded by archival tags ranged from 0 to 23.5 ºC and 0.1 to 42.4 m, respectively. For the three lake sturgeon that were at large for over 200 days, temperatures occupied emulated seasonal fluctuations. Two of these fish occupied deeper waters during winter than summer while the other occupied similar depths during non-spawning periods. This study provides important insight into depth and thermal habitat use of lake sturgeon throughout the calendar year along with exploring the feasibility of using archival tags to obtain important physical habitat attributes during non-spawning periods.
","language":"English","publisher":"Oikos Publishers","publisherLocation":"La Crosse, WI","doi":"10.1080/02705060.2016.1152321","usgsCitation":"Briggs, A., Hondorp, D.W., Quinlan, H.R., Boase, J., and Mohr, L.C., 2016, Electronic archival tags provide first glimpse of bathythermal habitat use by free-ranging adult lake sturgeon Acipenser fulvescens: Journal of Freshwater Ecology, v. 31, no. 3, p. 477-483, https://doi.org/10.1080/02705060.2016.1152321.","productDescription":"7 p.","startPage":"477","endPage":"483","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071324","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":471154,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2016.1152321","text":"Publisher Index Page"},{"id":326097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Huron","volume":"31","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-11","publicationStatus":"PW","scienceBaseUri":"57a46731e4b0ebae89b63caf","contributors":{"authors":[{"text":"Briggs, Andrew S.","contributorId":32796,"corporation":false,"usgs":true,"family":"Briggs","given":"Andrew S.","affiliations":[],"preferred":false,"id":644695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":644694,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quinlan, Henry R.","contributorId":117465,"corporation":false,"usgs":false,"family":"Quinlan","given":"Henry","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":644696,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boase, James C.","contributorId":72713,"corporation":false,"usgs":true,"family":"Boase","given":"James C.","affiliations":[],"preferred":false,"id":644697,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mohr, Lloyd C.","contributorId":77493,"corporation":false,"usgs":false,"family":"Mohr","given":"Lloyd","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":644698,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70169028,"text":"70169028 - 2016 - Paleozoic magmatism and porphyry Cu-mineralization in an evolving tectonic setting in the North Qilian Orogenic Belt, NW China","interactions":[],"lastModifiedDate":"2016-03-11T09:22:50","indexId":"70169028","displayToPublicDate":"2016-03-11T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2184,"text":"Journal of Asian Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Paleozoic magmatism and porphyry Cu-mineralization in an evolving tectonic setting in the North Qilian Orogenic Belt, NW China","docAbstract":"The NWW-striking North Qilian Orogenic Belt records the Paleozoic accretion–collision processes in NW China, and hosts Paleozoic Cu–Pb–Zn mineralization that was temporally and spatially related to the closure of the Paleo Qilian-Qinling Ocean. The Wangdian Cu deposit is located in the eastern part of the North Qilian Orogenic Belt, NW China. Copper mineralization is spatially associated with an altered early Paleozoic porphyritic granodiorite, which intruded tonalites and volcaniclastic rocks. Alteration zones surrounding the mineralization progress outward from a potassic to a feldspar-destructive phyllic assemblage. Mineralization consists mainly of quartz-sulfide stockworks and disseminated sulfides, with ore minerals chalcopyrite, pyrite, molybdenite, and minor galena and sphalerite. Gangue minerals include quartz, orthoclase, biotite, sericite, and K-feldspar. Zircon LA-ICPMS U–Pb dating of the ore-bearing porphyritic granodiorite yielded a mean 206Pb/238U age of 444.6 ± 7.8 Ma, with a group of inherited zircons yielding a mean U–Pb age of 485 ± 12 Ma, consistent with the emplacement age (485.3 ± 6.2 Ma) of the barren precursor tonalite. Rhenium and osmium analyses of molybdenite grains returned model ages of 442.9 ± 6.8 Ma and 443.3 ± 6.2 Ma, indicating mineralization was coeval with the emplacement of the host porphyritic granodiorite. Rhenium concentrations in molybdenite (208.9–213.2 ppm) suggest a mantle Re source. The tonalities are medium-K calc-alkaline. They are characterized by enrichment of light rare-earth elements (LREEs) and large-ion lithophile elements (LILEs), depletion of heavy rare-earth elements (HREEs) and high-field-strength elements (HFSEs), and minor negative Eu anomalies. They have εHf(t) values in the range of +3.6 to +11.1, with two-stage Hf model ages of 0.67–1.13 Ga, suggesting that the ca. 485 Ma barren tonalites were products of arc magmatism incorporating melts from the mantle wedge and the lithosphere. In contrast, the 40-m.y.-younger ore-bearing porphyritic granodiorite is sub-alkaline and peraluminous. They are enriched in LREEs and LILEs, depleted in HFSEs, and show weak negative Eu anomalies. They displayεHf(t) values of captured or inherited zircons in the range of +8.5 to +10.0, and younger two-stage Hf model ages of 0.78 Ga and 0.86 Ga, similar to those of ca. 485 Ma tonalite. The ca. 445 Ma zircons have εHf(t) values of −2.1 to +9.9, with two-stage Hf model ages of 0.75–1.27 Ga. Moreover, they have relatively high oxygen fugacity than that of the precursor barren tonalite. The ca. 445 Ma magmas at Wangdian thus formed in a subduction setting, and incorporated melts from the subduction-modified lithosphere that had previously been enriched by additions of chalcophile and siderophile element-rich materials by the earlier magmatism and metasomatism during the Paleo Qilian-Qinling Ocean subduction event.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Asian Earth Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.jseaes.2016.02.007","collaboration":"Kunfeng Qiu; Jun Deng; Liqiang Yang; Ryan D Taylor; Kairui Song, Yaohui Song; Quanzhong Li; Richard J Goldfarb","usgsCitation":"Qiu, K., Deng, J., Taylor, R.D., Song, K., Song, Y., Li, Q., and Goldfarb, R.J., 2016, Paleozoic magmatism and porphyry Cu-mineralization in an evolving tectonic setting in the North Qilian Orogenic Belt, NW China: Journal of Asian Earth Sciences, v. 122, p. 20-40, https://doi.org/10.1016/j.jseaes.2016.02.007.","productDescription":"21 p.","startPage":"20","endPage":"40","numberOfPages":"21","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063294","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":318811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"North Qilian Orogenic Belt","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 89.56054687499999,\n 31.203404950917395\n ],\n [\n 89.56054687499999,\n 41.37680856570233\n ],\n [\n 112.412109375,\n 41.37680856570233\n ],\n [\n 112.412109375,\n 31.203404950917395\n ],\n [\n 89.56054687499999,\n 31.203404950917395\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56e3ec2be4b0f59b85d42df1","contributors":{"authors":[{"text":"Qiu, Kun-Feng","contributorId":167527,"corporation":false,"usgs":false,"family":"Qiu","given":"Kun-Feng","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deng, Jun","contributorId":167528,"corporation":false,"usgs":false,"family":"Deng","given":"Jun","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Ryan D. 0000-0002-8845-5290 rtaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":3412,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"rtaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Song, Kai-Rui","contributorId":167530,"corporation":false,"usgs":false,"family":"Song","given":"Kai-Rui","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622596,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Song, Yao-Hui","contributorId":167531,"corporation":false,"usgs":false,"family":"Song","given":"Yao-Hui","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622597,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Quan-Zhong","contributorId":167532,"corporation":false,"usgs":false,"family":"Li","given":"Quan-Zhong","email":"","affiliations":[{"id":24738,"text":"Hefei University of Technology","active":true,"usgs":false}],"preferred":false,"id":622598,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Goldfarb, Richard J. goldfarb@usgs.gov","contributorId":1205,"corporation":false,"usgs":true,"family":"Goldfarb","given":"Richard","email":"goldfarb@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":622599,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70168729,"text":"ofr20161031 - 2016 - GIS representation of coal-bearing areas in Antarctica","interactions":[],"lastModifiedDate":"2016-05-23T09:04:55","indexId":"ofr20161031","displayToPublicDate":"2016-03-11T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1031","title":"GIS representation of coal-bearing areas in Antarctica","docAbstract":"Understanding the distribution of coal-bearing geologic units in Antarctica provides information that can be used in sedimentary, geomorphological, paleontological, and climatological studies. This report is a digital compilation of information on Antarctica’s coal-bearing geologic units found in the literature. It is intended to be used in small-scale spatial geographic information system (GIS) investigations and as a visual aid in the discussion of Antarctica’s coal resources or in other coal-based geologic investigations. Instead of using spatially insignificant point markers to represent large coal-bearing areas, this dataset uses polygons to represent actual coal-bearing lithologic units. Specific locations of coal deposits confirmed from the literature are provided in the attribution for the coal-bearing unit polygons. Coal-sample-location data were used to confirm some reported coal-bearing geology. The age and extent of the coal deposits indicated in the literature were checked against geologic maps ranging from local scale at 1:50,000 to Antarctic continental scale at 1:5,000,000; if satisfactory, the map boundaries were used to generate the polygons for the coal-bearing localities.
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U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
http://energy.usgs.gov/
A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyze simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models and compare them with observations from 268 Russian stations. There are large across-model differences as expressed by simulated differences between near-surface soil and air temperatures, (ΔT), of 3 to 14 K, in the gradients between soil and air temperatures (0.13 to 0.96°C/°C), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, and hence guide improvements to the model’s conceptual structure and process parameterizations. Models with better performance apply multi-layer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (12–16 million km2). However, there is not a simple relationship between the quality of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, likely because several other factors such as differences in the treatment of soil organic matter, soil hydrology, surface energy calculations, and vegetation also provide important controls on simulated permafrost distribution.
\n","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-2016-36","usgsCitation":"Wang, W., Rinke, A., Moore, J., Ji, D., Cui, X., Peng, S., Lawrence, D.M., McGuire, A., Burke, E.J., Chen, X., Delire, C., Koven, C., MacDougall, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, P., Decharme, B., Gouttevin, I., Hajima, T., Krinner, G., Lettenmaier, D.P., Miller, P.A., Smith, B., and Sueyoshi, T., 2016, Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region: The Cryosphere, no. 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2016 - Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6","interactions":[],"lastModifiedDate":"2018-03-05T16:49:57","indexId":"70168922","displayToPublicDate":"2016-03-11T03:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"title":"Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6","docAbstract":"
Floodplains can be viewed as complex adaptive systems (Levin, 1998) because they are comprised of many different biophysical components, such as morphological features, soil groups and vegetation communities as well as being sites of key biogeochemical processing (Stanford et al., 2005). Interactions and feedbacks among the biophysical components often result in additional phenomena occuring over a range of scales, often in the absence of any controlling factors (sensu Hallet, 1990). This emergence of new biophysical features and rates of processing can lead to alternative stable states which feed back into floodplain adaptive cycles (cf. Hughes, 1997; Stanford et al., 2005). Interactions between different biophysical components, feedbacks, self emergence and scale are all key properties of complex adaptive systems (Levin, 1998; Phillips, 2003; Murray et al., 2014) and therefore will influence the manner in which we study and view spatial patterns. Measuring the spatial patterns of floodplain biophysical components is a prerequisite to examining and understanding these ecosystems as complex adaptive systems. Elucidating relationships between pattern and process, which are intrinsically linked within floodplains (Ward et al., 2002), is dependent upon an understanding of spatial pattern. This knowledge can help river scientists determine the major drivers, controllers and responses of floodplain structure and function, as well as the consequences of altering those drivers and controllers (Hughes and Cass, 1997; Whited et al., 2007). Interactions and feedbacks between physical, chemical and biological components of floodplain ecosystems create and maintain a structurally diverse and dynamic template (Stanford et al., 2005). This template influences subsequent interactions between components that consequently affect system trajectories within floodplains (sensu Bak et al., 1988). Constructing and evaluating models used to predict floodplain ecosystem responses to natural and anthropogenic disturbances therefore require quantification of spatial pattern (Asselman and Middelkoop, 1995; Walling and He, 1998). Quantifying these patterns also provides insights into the spatial and temporal domains of structuring processes as well as enabling the detection of self-emergent phenomena, environmental constraints or anthropogenic interference (Turner et al., 1990; Holling, 1992; De Jager and Rohweder, 2012). Thus, quantifying spatial pattern is an important building block on which to examine floodplains as complex adaptive systems (Levin, 1998). Approaches to measuring spatial pattern in floodplains must be cognisant of scale, self-emergent phenomena, spatial organisation, and location. Fundamental problems may arise when patterns observed at a site or transect scale are scaled-up to infer processes and patterns over entire floodplain surfaces (Wiens, 2002; Thorp et al., 2008). Likewise, patterns observed over the entire spatial extent of a landscape can mask important variation and detail at finer scales (Riitters et al., 2002). Indeed, different patterns often emerge at different scales (Turner et al., 1990) because of hierarchical structuring processes (O'Neill et al., 1991). Categorising data into discrete, homogeneous and predefined spatial units at a particular scale (e.g. polygons) creates issues and errors associated with scale and subjective classification (McGarigal et al., 2009; Cushman et al., 2010). These include, loss of information within classified ‘patches’, as well as the ability to detect the emergence of new features that do not fit the original classification scheme. Many of these issues arise because floodplains are highly heterogeneous and have complex spatial organizations (Carbonneau et al., 2012; Legleiter, 2013). As a result, the scale and location at which measurements are made can influence the observed spatial patterns; and patterns may not be scale independent or applicable in different geomorp
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River science: Research and management for the 21st century","language":"English","publisher":"John Wiley & Sons, Ltd","doi":"10.1002/9781118643525.ch6","isbn":"978-1-119-99434-3","usgsCitation":"Scown, M.W., Thoms, M.C., and De Jager, N.R., 2016, Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6, chap. of River science: Research and management for the 21st century, p. 103-131, https://doi.org/10.1002/9781118643525.ch6.","productDescription":"29 p.","startPage":"103","endPage":"131","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056619","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":321675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5746ccbee4b07e28b662dcf0","contributors":{"editors":[{"text":"Gilvear, David J.","contributorId":169613,"corporation":false,"usgs":false,"family":"Gilvear","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630282,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Greenwood, Malcolm T.","contributorId":169614,"corporation":false,"usgs":false,"family":"Greenwood","given":"Malcolm","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":630283,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":630284,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Wood, Paul J.","contributorId":169615,"corporation":false,"usgs":false,"family":"Wood","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630285,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Scown, Murray W.","contributorId":145709,"corporation":false,"usgs":false,"family":"Scown","given":"Murray","email":"","middleInitial":"W.","affiliations":[{"id":24492,"text":"Riverine Landscapes Research Laboratory, University of New England, Armidale, Australia","active":true,"usgs":false}],"preferred":false,"id":622119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":622120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":622118,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184235,"text":"70184235 - 2016 - Illuminating wildfire erosion and deposition patterns with repeat terrestrial lidar","interactions":[],"lastModifiedDate":"2017-03-06T10:51:56","indexId":"70184235","displayToPublicDate":"2016-03-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Illuminating wildfire erosion and deposition patterns with repeat terrestrial lidar","docAbstract":"Erosion following a wildfire is much greater than background erosion in forests because of wildfire-induced changes to soil erodibility and water infiltration. While many previous studies have documented post-wildfire erosion with point and small plot-scale measurements, the spatial distribution of post-fire erosion patterns at the watershed scale remains largely unexplored. In this study lidar surveys were collected periodically in a small, first-order drainage basin over a period of 2 years following a wildfire. The study site was relatively steep with slopes ranging from 17° to > 30°. During the study period, several different types of rain storms occurred on the site including low-intensity frontal storms (2.4 mm h−1) and high-intensity convective thunderstorms (79 mm h−1). These storms were the dominant drivers of erosion. Erosion resulting from dry ravel and debris flows was notably absent at the site. Successive lidar surveys were subtracted from one another to obtain digital maps of topographic change between surveys. The results show an evolution in geomorphic response, such that the erosional response after rain storms was strongly influenced by the previous erosional events and pre-fire site morphology. Hillslope and channel roughness increased over time, and the watershed armored as coarse cobbles and boulders were exposed. The erosional response was spatially nonuniform; shallow erosion from hillslopes (87% of the study area) contributed 3 times more sediment volume than erosion from convergent areas (13% of the study area). However, the total normalized erosion depth (volume/area) was highest in convergent areas. From a detailed understanding of the spatial locations of erosion, we made inferences regarding the processes driving erosion. It appears that hillslope erosion is controlled by rain splash (for detachment) and overland flow (for transport and quasi-channelized erosion), with the sites of highest erosion corresponding to locations with the lowest roughness. By contrast, in convergent areas we found erosion caused by overland flow. Soil erosion was locally interrupted by immobile objects such as boulders, bedrock, or tree trunks, resulting in a patchy erosion network with increasing roughness over time.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1002/2015JF003600","usgsCitation":"Rengers, F.K., Tucker, G., Moody, J., and Ebel, B., 2016, Illuminating wildfire erosion and deposition patterns with repeat terrestrial lidar: Journal of Geophysical Research F: Earth Surface, v. 121, no. 3, p. 588-608, https://doi.org/10.1002/2015JF003600.","productDescription":"21 p.","startPage":"588","endPage":"608","ipdsId":"IP-068620","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jf003600","text":"Publisher Index Page"},{"id":336854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.403611,\n 40.030833\n ],\n [\n -105.402222,\n 40.030833\n ],\n [\n -105.402222,\n 40.032222\n ],\n [\n -105.403611,\n 40.032222\n ],\n [\n -105.403611,\n 40.030833\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-11","publicationStatus":"PW","scienceBaseUri":"58be8339e4b014cc3a3a99e5","contributors":{"authors":[{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":680682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, G.E.","contributorId":150423,"corporation":false,"usgs":false,"family":"Tucker","given":"G.E.","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":680683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moody, J. A.","contributorId":187515,"corporation":false,"usgs":false,"family":"Moody","given":"J. A.","affiliations":[],"preferred":false,"id":680684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ebel, Brian","contributorId":187516,"corporation":false,"usgs":false,"family":"Ebel","given":"Brian","affiliations":[],"preferred":false,"id":680685,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169027,"text":"ofr20161036 - 2016 - Assessing the socioeconomic impact and value of open geospatial information","interactions":[],"lastModifiedDate":"2016-05-23T09:05:39","indexId":"ofr20161036","displayToPublicDate":"2016-03-10T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1036","title":"Assessing the socioeconomic impact and value of open geospatial information","docAbstract":"The production and accessibility of geospatial information including Earth observation is changing greatly both technically and in terms of human participation. Advances in technology have changed the way that geospatial data are produced and accessed, resulting in more efficient processes and greater accessibility than ever before. Improved technology has also created opportunities for increased participation in the gathering and interpretation of data through crowdsourcing and citizen science efforts. Increased accessibility has resulted in greater participation in the use of data as prices for Government-produced data have fallen and barriers to access have been reduced.
\nThe increase in participation in the production and in the use of data, defined as data democracy for this workshop, are having great impacts on economics and more generally on society.
\nThere is also a strong drive by governments around the world, as shown by the G8 Declaration in June 2013, to make public sector information and scientific data more widely accessible. These are respectively termed “open data” and “open research data.”
\nThis report summarizes discussion at the Workshop on Assessing the Impact and Value of Open Geospatial Information held at George Washington University in Washington, D.C. in October 2014. Workshop participants examined the consequences of expanding data democracy with a focus on its socioeconomic impacts. Evaluations were presented of state-of-the-art methods to assess these socioeconomic impacts, which included position papers and remarks by discussants. The workshop included discussions about the following topics: (1) increased and expanded information sources; (2) societal impacts, including approaches to economics assessments; (3) constraints to open access, including the demands for return on investment, specifications of intellectual property rights, and privacy issues; and (4) learning from the experiences of other data-rich domains, such as environmental management, internet businesses, health, and transportation.
\nThe workshop was a working meeting with strong participant engagement, leading to recommendations for action. The meeting included five topic-driven sessions and keynote presentations. Precirculated position papers for each panel session facilitated preparation and remarks by discussants. After the position papers are updated following the discussants’ remarks, it is planned to submit them for publication.
\nThe workshop included 68 participants coming from international organizations, the U.S. public and private sectors, nongovernmental organizations, and academia. Participants included policy makers and analysts, financial analysts, economists, information scientists, geospatial practitioners, and other discipline experts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161036","collaboration":"Prepared in cooperation with the Socioeconomic Benefits Community","usgsCitation":"Pearlman, Francoise, Pearlman, Jay, Bernknopf, Richard, Coote, Andrew, Craglia, Massimo, Friedl, Lawrence, Gallo, Jason, Hertzfeld, Henry, Jolly, Claire, Macauley, Molly, Shapiro, Carl, and Smart, Alan, 2016, Assessing the socioeconomic impact and value of open geospatial information: U.S. Geological Survey Open-File Report 2016–1036, 36 p., https://dx.doi.org/10.3133/ofr20161036.","productDescription":"vi, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1036/coverthb.jpg"},{"id":318803,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1036/ofr20161036.pdf","text":"Report","size":"5.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1036"}],"otherGeospatial":"Global","contact":"U.S. Geological Survey
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Well-informed river management decisions rely on an explicit statement of objectives, repeatable analyses, and a transparent system for assessing trade-offs. These components may then be applied to compare alternative operational regimes for water resource infrastructure (e.g., diversions, locks, and dams). Intra- and inter-annual hydrologic variability further complicates these already complex environmental flow decisions. Effective discharge analysis (developed in studies of geomorphology) is a powerful tool for integrating temporal variability of flow magnitude and associated ecological consequences. Here, we adapt the effectiveness framework to include multiple elements of the natural flow regime (i.e., timing, duration, and rate-of-change) as well as two flow variables. We demonstrate this analytical approach using a case study of environmental flow management based on long-term (60 years) daily discharge records in the Middle Oconee River near Athens, GA, USA. Specifically, we apply an existing model for estimating young-of-year fish recruitment based on flow-dependent metrics to an effective discharge analysis that incorporates hydrologic variability and multiple focal taxa. We then compare three alternative methods of environmental flow provision. Percentage-based withdrawal schemes outcompete other environmental flow methods across all levels of water withdrawal and ecological outcomes.
","language":"English","publisher":"Springer-Verlag","publisherLocation":"New York","doi":"10.1007/s00267-016-0684-4","usgsCitation":"McKay, S.K., Freeman, M., and Covich, A., 2016, Application of effective discharge analysis to environmental flow decision-making: Environmental Management, v. 575, no. 6, p. 1153-1165, https://doi.org/10.1007/s00267-016-0684-4.","productDescription":"13 p.","startPage":"1153","endPage":"1165","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073347","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":320849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"575","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"57287a2be4b0b13d391865af","contributors":{"authors":[{"text":"McKay, S. Kyle","contributorId":169086,"corporation":false,"usgs":false,"family":"McKay","given":"S.","email":"","middleInitial":"Kyle","affiliations":[],"preferred":false,"id":628390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Covich, A.P.","contributorId":14965,"corporation":false,"usgs":true,"family":"Covich","given":"A.P.","email":"","affiliations":[],"preferred":false,"id":628392,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170765,"text":"70170765 - 2016 - Study of biological communities subject to imperfect detection: Bias and precision of community N-mixture abundance models in small-sample situations","interactions":[],"lastModifiedDate":"2016-05-02T15:06:29","indexId":"70170765","displayToPublicDate":"2016-03-10T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1461,"text":"Ecological Research","active":true,"publicationSubtype":{"id":10}},"title":"Study of biological communities subject to imperfect detection: Bias and precision of community N-mixture abundance models in small-sample situations","docAbstract":"Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance (λ¯: 0.1, 0.5, 1, 5), detection probability (p¯: 0.1, 0.2, 0.5), and number of sampling sites (n site : 10, 20, 40) and visits (n visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables (λ¯, p¯, n site , n visit ) increased. Detection probability p¯ was most important for the estimates of mean abundance, while λ¯ was most influential for covariate effect and species richness estimates. For all parameters, increasing n site was more beneficial than increasing n visit . Minimal conditions for obtaining adequate performance of community abundance models were n site ≥ 20, p¯ ≥ 0.2, and λ¯ ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
","language":"English","publisher":"Blackwell Science","doi":"10.1007/s11284-016-1340-4","collaboration":"Yuichi Yamaura;\nMarc Kery","usgsCitation":"Yamaura, Y., Kery, M., and Royle, A., 2016, Study of biological communities subject to imperfect detection: Bias and precision of community N-mixture abundance models in small-sample situations: Ecological Research, v. 31, no. 3, p. 289-305, https://doi.org/10.1007/s11284-016-1340-4.","productDescription":"17 p.","startPage":"289","endPage":"305","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072120","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471158,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11284-016-1340-4","text":"Publisher Index Page"},{"id":320845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"57287a33e4b0b13d391865dd","contributors":{"authors":[{"text":"Yamaura, Yuichi","contributorId":169067,"corporation":false,"usgs":false,"family":"Yamaura","given":"Yuichi","affiliations":[{"id":25402,"text":"Hokkaido Univ.","active":true,"usgs":false}],"preferred":false,"id":628332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":628333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628331,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}