Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.
","language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0130978","usgsCitation":"New, L., Bjerre, E., Millsap, B.A., Otto, M.C., and Runge, M.C., 2015, A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos: PLoS ONE, v. 10, no. 7, p. 1-12, https://doi.org/10.1371/journal.pone.0130978.","productDescription":"12 p.","startPage":"1","endPage":"12","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049300","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471954,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0130978","text":"Publisher Index Page"},{"id":305581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-02","publicationStatus":"PW","scienceBaseUri":"559ba6a8e4b0b94a640170c5","contributors":{"authors":[{"text":"New, Leslie lnew@usgs.gov","contributorId":145484,"corporation":false,"usgs":true,"family":"New","given":"Leslie","email":"lnew@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":564175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bjerre, Emily","contributorId":44451,"corporation":false,"usgs":true,"family":"Bjerre","given":"Emily","affiliations":[],"preferred":false,"id":564176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Millsap, Brian A.","contributorId":75841,"corporation":false,"usgs":true,"family":"Millsap","given":"Brian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":564177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Otto, Mark C.","contributorId":6307,"corporation":false,"usgs":true,"family":"Otto","given":"Mark","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":564178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":564174,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70155220,"text":"70155220 - 2015 - Social living mitigates the costs of a chronic illness in a cooperative carnivore","interactions":[],"lastModifiedDate":"2018-08-09T12:52:14","indexId":"70155220","displayToPublicDate":"2015-07-01T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Social living mitigates the costs of a chronic illness in a cooperative carnivore","docAbstract":"Infection risk is assumed to increase with social group size, and thus be a cost of group living. We assess infection risk and costs with respect to group size using data from an epidemic of sarcoptic mange (Sarcoptes scabiei) among grey wolves (Canis lupus). We demonstrate that group size does not predict infection risk and that individual costs of infection, in terms of reduced survival, can be entirely offset by having sufficient numbers of pack-mates. Infected individuals experience increased mortality hazards with increasing proportions of infected pack-mates, but healthy individuals remain unaffected. The social support of group hunting and territory defence are two possible mechanisms mediating infection costs. This is likely a common phenomenon among other social species and chronic infections, but difficult to detect in systems where infection status cannot be measured continuously over time.
","language":"English","publisher":"Blackwell Science","publisherLocation":"Oxford","doi":"10.1111/ele.12444","usgsCitation":"Almberg, E., Cross, P.C., Dobson, A.P., Smith, D.W., Metz, M., Stahler, D.R., and Hudson, P., 2015, Social living mitigates the costs of a chronic illness in a cooperative carnivore: Ecology Letters, v. 18, no. 7, p. 660-667, https://doi.org/10.1111/ele.12444.","productDescription":"8 p.","startPage":"660","endPage":"667","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062430","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":471957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.12444","text":"Publisher Index Page"},{"id":306321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-18","publicationStatus":"PW","scienceBaseUri":"55c090b5e4b033ef521042b7","chorus":{"doi":"10.1111/ele.12444","url":"http://dx.doi.org/10.1111/ele.12444","publisher":"Wiley-Blackwell","authors":"Almberg E. 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Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/jwmg.894","usgsCitation":"Fieberg, J., and Johnson, D.H., 2015, MMI: Multimodel inference or models with management implications?: Journal of Wildlife Management, v. 79, no. 5, p. 708-718, https://doi.org/10.1002/jwmg.894.","productDescription":"11 p.","startPage":"708","endPage":"718","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059993","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471958,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.894","text":"Publisher Index Page"},{"id":308443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-25","publicationStatus":"PW","scienceBaseUri":"5603cd4ee4b03bc34f544b25","contributors":{"authors":[{"text":"Fieberg, J.","contributorId":106070,"corporation":false,"usgs":true,"family":"Fieberg","given":"J.","affiliations":[],"preferred":false,"id":572697,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":572696,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70154854,"text":"70154854 - 2015 - Climate-water quality relationships in Texas reservoirs","interactions":[],"lastModifiedDate":"2015-12-21T13:19:21","indexId":"70154854","displayToPublicDate":"2015-07-01T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Climate-water quality relationships in Texas reservoirs","docAbstract":"Water temperature, dissolved oxygen, and concentrations of salts in surface water bodies can be affected by the natural environment, local human activities such as surface and ground water withdrawals, land use, and energy extraction, and variability and long-term trends in atmospheric conditions including temperature and precipitation. Here, we quantify the relationship between 121 indicators of mean and extreme temperature and precipitation and 24 water quality parameters in 57 Texas reservoirs using observational data records covering the period 1960 to 2010. We find that water temperature, dissolved oxygen, pH, specific conductance, chloride, sulfate, and phosphorus all show consistent correlations with atmospheric predictors, including high and low temperature extremes, dry days, heavy precipitation events, and mean temperature and precipitation over time scales ranging from one week to two years. Based on this analysis and published future projections for this region, we expect climate change to increase water temperatures, decrease dissolved oxygen levels, decrease pH, increase specific conductance, and increase levels of sulfate, chloride in Texas reservoirs. Over decadal time scales, this may affect aquatic ecosystems in the reservoirs, including altering the risk of conditions conducive to algae occurrence, as well as affecting the quality of water available for human consumption and recreation.
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Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were re-projected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process.
","language":"English","publisher":"American Society of Photogrammetry","publisherLocation":"Falls Church, VA","doi":"10.14358/PERS.81.7.573","usgsCitation":"Nelson, K., and Steinwand, D.R., 2015, A landsat data tiling and compositing approach optimized for change detection in the conterminous United States: Photogrammetric Engineering and Remote Sensing, v. 81, no. 7, p. 573-586, https://doi.org/10.14358/PERS.81.7.573.","productDescription":"14 p.","startPage":"573","endPage":"586","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055870","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488385,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.81.7.573","text":"Publisher Index Page"},{"id":306782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n","volume":"81","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55d305aae4b0518e35468cd1","contributors":{"authors":[{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":566669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steinwand, Daniel R. steinwand@usgs.gov","contributorId":3224,"corporation":false,"usgs":true,"family":"Steinwand","given":"Daniel","email":"steinwand@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":566670,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155974,"text":"70155974 - 2015 - Observational data on the effects of infection by the copepod Salmincola californiensis on the short- and long-term viability of juvenile Chinook salmon (Oncorhynchus tshawytscha) implanted with telemetry tags","interactions":[],"lastModifiedDate":"2015-08-21T10:11:16","indexId":"70155974","displayToPublicDate":"2015-07-01T11:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Observational data on the effects of infection by the copepod Salmincola californiensis on the short- and long-term viability of juvenile Chinook salmon (Oncorhynchus tshawytscha) implanted with telemetry tags","docAbstract":"Fish movements are often studied using radio or acoustic tags assuming the handling and tagging procedures have little effect on the behavior of the animal. Indeed, many studies provide guidelines for acceptable methods. However, these studies generally assume the fish are otherwise healthy but this may not always be the case. One example is the infection of juvenile salmon in the western USA by the naturally-occurring parasitic copepod Salmincola californiensis, for which little is known about the effects on results from tagged animals. We report on observational data from juvenile Chinook salmon (Oncorhynchus tshawytscha) surgically implanted with telemetry tags relative to the numbers of S. californiensis within their branchial cavities and on their bodies to determine if the intensity of infection resulted in differences in mortality shortly after tagging or post-release activity in a reservoir over a period of about 4 months.
\nThe data indicate a negative effect of copepods in the branchial cavities on short-term mortality (within 24 h of tagging) and suggest negative effects on movements after release into the reservoir. Short-term mortalities were infrequent and, due to the observational nature of the data, few tagged fish had more than three copepods in their branchial cavities, although surveys of fish in the reservoir indicate much greater infection intensities are common. Copepod numbers on the body did not appear to be associated with short-term mortality or movements after release. The number of copepods on the body was unrelated to the number within the branchial cavities, indicating site-specific counts are needed to assess the infection.
\nInfection with Salmincola californiensis is common in juvenile Chinook salmon in western USA reservoirs and may affect the viability of fish used in studies of telemetered animals. Our limited assessment suggests infection by Salmincola californiensis affects the short-term morality of tagged fish and may affect long-term viability of tagged fish after release; however, the intensity of infection in the sample population did not represent the source population due to the observational nature of the data. We suggest these results warrant further study into the effects of infection bySalmincola californiensis on the results obtained through active telemetry and perhaps other methods requiring handling of infected fish.
","language":"English","publisher":"Biomed Central","publisherLocation":"London","doi":"10.1186/s40317-015-0056-5","usgsCitation":"Beeman, J.W., Hansen, A.C., and Sprando, J.M., 2015, Observational data on the effects of infection by the copepod Salmincola californiensis on the short- and long-term viability of juvenile Chinook salmon (Oncorhynchus tshawytscha) implanted with telemetry tags: Animal Biotelemetry, v. 3, no. 20, p. 1-7, https://doi.org/10.1186/s40317-015-0056-5.","productDescription":"7 p.","startPage":"1","endPage":"7","numberOfPages":"7","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064010","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":471965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-015-0056-5","text":"Publisher Index Page"},{"id":307104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"20","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-16","publicationStatus":"PW","scienceBaseUri":"55d84bbae4b0518e3546f02a","contributors":{"authors":[{"text":"Beeman, John W. jbeeman@usgs.gov","contributorId":2646,"corporation":false,"usgs":true,"family":"Beeman","given":"John","email":"jbeeman@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Amy C. 0000-0002-0298-9137 achansen@usgs.gov","orcid":"https://orcid.org/0000-0002-0298-9137","contributorId":4350,"corporation":false,"usgs":true,"family":"Hansen","given":"Amy","email":"achansen@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sprando, Jamie M. jsprando@usgs.gov","contributorId":4005,"corporation":false,"usgs":true,"family":"Sprando","given":"Jamie","email":"jsprando@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155153,"text":"70155153 - 2015 - Temperature profile around a basaltic sill intruded into wet sediments","interactions":[],"lastModifiedDate":"2018-11-08T16:23:59","indexId":"70155153","displayToPublicDate":"2015-07-01T11:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Temperature profile around a basaltic sill intruded into wet sediments","docAbstract":"The transfer of heat into wet sediments from magmatic intrusions or lava flows is not well constrained from field data. Such field constraints on numerical models of heat transfer could significantly improve our understanding of water–lava interactions. We use experimentally calibrated pollen darkening to measure the temperature profile around a basaltic sill emplaced into wet lakebed sediments. It is well known that, upon heating, initially transparent palynomorphs darken progressively through golden, brown, and black shades before being destroyed; however, this approach to measuring temperature has not been applied to volcanological questions. We collected sediment samples from established Miocene fossil localities at Clarkia, Idaho. Fossils in the sediments include pollen from numerous tree and shrub species. We experimentally calibrated changes in the color of Clarkia sediment pollen and used this calibration to determine sediment temperatures around a Miocene basaltic sill emplaced in the sediments. Results indicated a flat temperature profile above and below the sill, with T > 325 °C within 1 cm of the basalt-sediment contact, near 300 °C at 1–2 cm from the contact, and ~ 250 °C at 1 m from the sill contact. This profile suggests that heat transport in the sediments was hydrothermally rather than conductively controlled. This information will be used to test numerical models of heat transfer in wet sediments on Earth and Mars.
","language":"English","publisher":"Elsevier Science","publisherLocation":"Amsterdam","doi":"10.1016/j.jvolgeores.2015.06.012","usgsCitation":"Baker, L., Bernard, A., Rember, W.C., Milazzo, M.P., Dundas, C.M., Abramov, O., and Keszthelyi, L.P., 2015, Temperature profile around a basaltic sill intruded into wet sediments: Journal of Volcanology and Geothermal Research, v. 302, p. 81-86, https://doi.org/10.1016/j.jvolgeores.2015.06.012.","productDescription":"6 p.","startPage":"81","endPage":"86","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062688","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":471963,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2015.06.012","text":"Publisher Index Page"},{"id":306281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"302","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55bc9c2ee4b033ef52100f3b","contributors":{"authors":[{"text":"Baker, Leslie","contributorId":145650,"corporation":false,"usgs":false,"family":"Baker","given":"Leslie","email":"","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":564903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernard, Andrew","contributorId":146264,"corporation":false,"usgs":false,"family":"Bernard","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":566911,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rember, William C.","contributorId":107748,"corporation":false,"usgs":true,"family":"Rember","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":566912,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milazzo, Moses P. 0000-0002-9101-2191 moses@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-2191","contributorId":4811,"corporation":false,"usgs":true,"family":"Milazzo","given":"Moses","email":"moses@usgs.gov","middleInitial":"P.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":564906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dundas, Colin M. 0000-0003-2343-7224 cdundas@usgs.gov","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":2937,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin","email":"cdundas@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":564905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abramov, Oleg oabramov@usgs.gov","contributorId":604,"corporation":false,"usgs":true,"family":"Abramov","given":"Oleg","email":"oabramov@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":564904,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keszthelyi, Laszlo P. 0000-0003-1879-4331 laz@usgs.gov","orcid":"https://orcid.org/0000-0003-1879-4331","contributorId":227,"corporation":false,"usgs":true,"family":"Keszthelyi","given":"Laszlo","email":"laz@usgs.gov","middleInitial":"P.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":564902,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70147394,"text":"sir20155057 - 2015 - Chloride concentrations, loads, and yields in four watersheds along Interstate 95, southeastern Connecticut, 2008-11: factors that affect peak chloride concentrations during winter storms","interactions":[],"lastModifiedDate":"2021-09-23T14:47:13.203498","indexId":"sir20155057","displayToPublicDate":"2015-07-01T10:30:00","publicationYear":"2015","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":"2015-5057","title":"Chloride concentrations, loads, and yields in four watersheds along Interstate 95, southeastern Connecticut, 2008-11: factors that affect peak chloride concentrations during winter storms","docAbstract":"Chloride (Cl-) concentrations and loads and other water chemistry characteristics were assessed to evaluate potential effects of road-deicer applications on streamwater quality in four watersheds along Interstate 95 (I–95) in southeastern Connecticut from November 1, 2008, through September 30, 2011. Streamflow and water quality were studied in the Four Mile River, Oil Mill Brook, Stony Brook, and Jordan Brook watersheds, where developed land ranged from 9 to 32 percent. Water-quality samples were collected and specific conductance was measured continuously at paired water-quality monitoring sites, upstream and downstream from I–95. Specific conductance values were related to Cl- concentrations to assist in determining the effects of road-deicing operations on the levels of Cl-in the streams. Streamflow and water-quality data were compared with weather data and with the timing, amount, and composition of deicers applied to State highways. Grab samples were collected during winter stormwater-runoff events, such as winter storms or periods of rain or warm temperatures in which melting takes place. Grab samples were also collected periodically during the spring and summer and during base-flow conditions.
\nThe estimated Cl- concentrations at the eight water-quality monitoring sites during winter storms peaked as high as 270 milligrams per liter (mg/L) and were well below the U.S. Environmental Protection Agency (EPA) recommended acute chloride toxicity criterion of 860 mg/L and the chronic 4-day average toxicity criterion of 230 mg/L. Cl- concentrations in streams, particularly at sites downstream from I–95, peaked during increased streamflow in the winter and early spring as a result of deicers applied to roads and washed off by stormwater or meltwater. Cl- concentrations during most of the nonwinter seasons decreased during increases in streamflow because storm runoff was more dilute than base flow. However, peaks in specific conductance and estimated chloride concentrations at streams in more urbanized watersheds corresponded to peaks in streamflow well after winter snow or ice events; these delayed peaks in Cl- concentration likely resulted from deicer residue that remained in melting snow piles and on roadsides and (or) that were flushed from soils and shallow groundwater, then discharged downstream.
\nEstimated peak Cl- concentrations varied with the type of winter storm event and were highest during or after winter storms of frozen precipitation and rain, in which the rain or meltwater effectively washed off the deicers. Estimated peak Cl- concentrations correlated positively with the duration of deicer application but generally not with streamflow. Estimated peak Cl-concentrations during the winter season were highest during low streamflow at most sites.
\nChloride concentrations varied considerably in shallow groundwater as a result of land-use differences. Cl- concentrations were very high (as high as 800 mg/L) in shallow groundwater downstream from I–95 at the Four Mile River site. Chloride/bromide mass concentration ratios and the proximity of a former landfill and sewage lagoon upstream indicate a likely source of Cl- is landfill leachate and possibly sewage leachate.
\nCl- loads in streams generally were highest in the winter and early spring. The estimated daily Cl- yield for the four monitoring sites downstream from I–95 ranged from 0.0004 ton per day per square mile for one of the least developed watersheds to 0.052 ton per day per square mile for the watershed with the highest percentage of urban development and impervious area. The estimated median contribution of Cl- load from atmospheric deposition was small and ranged from 0.07 percent of Cl- load at the Jordan Brook watershed to 0.57 percent at the Oil Mill Brook watershed. The Cl- loads in streams (outputs) were compared with Cl- load inputs, which include atmospheric deposition and deicer applications; Cl- load inputs were slightly larger than the Cl- load outputs at most of the sites during most years but do not account for the Cl- load in groundwater leaving the watersheds.
\nA multiple linear regression model was developed to describe the variability of the natural log of peak specific conductance, as well as estimated Cl- concentrations. Five significant variables best explained the variability in the natural log of the peak specific conductance after deicing events: (1) snow on ground before deicing event; (2) winter precipitation with rain; (3) specific conductance in base flow; (4) State-operated road lane miles divided by watershed area; and (5) amount of Cl- from deicers applied to State-operated roads per lane mile. In this report, winter precipitation is defined as any type of precipitation, including frozen precipitation and rain, that occurs during the active deicing season, typically November through March. Frozen precipitation is defined here as snow, sleet, freezing rain, or any winter precipitation except rain.
\nThe addition of a lane mile in both directions on I–95 would result in an estimate of approximately 2 to 11 percent increase in Cl- input from deicers applied to I–95 and other roads maintained by Connecticut Department of Transportation. The largest estimated increase in Cl- load was in the watersheds with the greatest number miles of I–95 corridor relative to the total lane miles maintained by Connecticut Department of Transportation. On the basis of these estimates and the estimated peak Cl- concentrations during the study period, it is unlikely that the increased use of deicers on the additional lanes would lead to Cl- concentrations that exceed the aquatic habitat criteria.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155057","collaboration":"Prepared in cooperation with the Federal Highway Administration and the Connecticut Department of Transportation","usgsCitation":"Brown, C.J., Mullaney, J.R., Morrison, Jonathan, Martin, J.W., and Trombley, T.J., 2015, Chloride concentrations, loads, and yields in four watersheds along Interstate 95, southeastern Connecticut, 2008–11— Factors that affect peak chloride concentrations during winter storms: U.S. Geological Survey Scientific Investigations Report 2015–5057, 68 p., https://dx.doi.org/10.3133/sir20155057.","productDescription":"Report: x, 68 p.; Appendix; Tables","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-054199","costCenters":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"links":[{"id":305518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155057.jpg"},{"id":305516,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2015/5057/attachments/sir2015-5057_table10.xlsx","text":"Table 10","size":"186 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 10","linkHelpText":"Storm characteristics, weather data, and peak chloride concentrations related to deicing and melting events."},{"id":305513,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5057/"},{"id":305517,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5057/attachments/sir2015-5057_appendix1.xlsx","text":"Appendix 1","size":"198 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 1"},{"id":305515,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2015/5057/attachments/sir2015-5057_table5.xlsx","text":"Table 5","size":"200 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 5","linkHelpText":"Description of the applications of deicing materials to State-operated roads during winter storms."},{"id":305514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5057/pdf/sir2015-5057.pdf","text":"Report","size":"8.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Connecticut","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.26806640624999,\n 41.3177863571168\n ],\n [\n -72.26806640624999,\n 41.32835758409141\n ],\n [\n -72.2512435913086,\n 41.32835758409141\n ],\n [\n -72.2512435913086,\n 41.3177863571168\n ],\n [\n -72.26806640624999,\n 41.3177863571168\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.18978881835936,\n 41.35593783017404\n ],\n [\n -72.18978881835936,\n 41.40900335304861\n ],\n [\n -72.14309692382811,\n 41.40900335304861\n ],\n [\n -72.14309692382811,\n 41.35593783017404\n ],\n [\n -72.18978881835936,\n 41.35593783017404\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Or visit our Web site at:
http://ct.water.usgs.gov
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native communities, and encompasses unique habitats of global significance. Coastal erosion along the Arctic coast is chronic, widespread, and may be accelerating, which threatens defense- and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change Project (hereafter referred to as the \"National Assessment project\";http://coastal.er.usgs.gov/shoreline-change/). A comprehensive database of digital vector shorelines and rates of shoreline change for Alaska, from the U.S.-Canadian border to Icy Cape, is presented in this report as part of the National Assessment project.
\nThere is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner. A detailed report on shoreline change for the north coast of Alaska that contains a discussion of the data presented here is available and cited in section, \"Geospatial Data.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151030","usgsCitation":"Gibbs, A.E., Ohman, K.A., and Richmond, B.M., 2015, National assessment of shoreline change: a GIS compilation of vector shorelines and associated shoreline change data for the north coast of Alaska, U.S.-Canadian border to Icy Cape: U.S. Geological Survey Open-File Report 2015-1030, HTML Document, https://doi.org/10.3133/ofr20151030.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-053360","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":305511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151030.jpg"},{"id":305509,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1030/html/ofr2015-1030_title.html","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Report"},{"id":305510,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2015/1048/","text":"Open-File Report 2015-1048","description":"Open-File Report 2015-1048"},{"id":305494,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1030/"}],"country":"Canada, United States","state":"Alaska","otherGeospatial":"Icy Cape","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.94921875,\n 69.4575536150494\n ],\n [\n -162.94921875,\n 71.45515260247822\n ],\n [\n -141.0205078125,\n 71.45515260247822\n ],\n [\n -141.0205078125,\n 69.4575536150494\n ],\n [\n -162.94921875,\n 69.4575536150494\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55950122e4b0b6d21dd6cbb8","contributors":{"authors":[{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":563999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ohman, Karen A.","contributorId":139262,"corporation":false,"usgs":false,"family":"Ohman","given":"Karen","email":"","middleInitial":"A.","affiliations":[{"id":12712,"text":"Michael Baker International","active":true,"usgs":false}],"preferred":false,"id":564000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richmond, Bruce M. 0000-0002-0056-5832 brichmond@usgs.gov","orcid":"https://orcid.org/0000-0002-0056-5832","contributorId":2459,"corporation":false,"usgs":true,"family":"Richmond","given":"Bruce","email":"brichmond@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":564001,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70150412,"text":"ofr20151122 - 2015 - Trench logs, terrestrial lidar system imagery, and radiocarbon data from the kilometer-62 site on the Greenville Fault, southeastern Alameda County, California, 2014","interactions":[],"lastModifiedDate":"2015-07-01T09:14:40","indexId":"ofr20151122","displayToPublicDate":"2015-07-01T10:00:00","publicationYear":"2015","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":"2015-1122","title":"Trench logs, terrestrial lidar system imagery, and radiocarbon data from the kilometer-62 site on the Greenville Fault, southeastern Alameda County, California, 2014","docAbstract":"In 2014, we investigated an abrupt 8.5-meter (m), right-laterally deflected stream channel located near the Greenville Fault in southeastern Alameda County, California (-121.56224° E, 37.53430° N) that we discovered using 0.5-m resolution, 2011 aerial lidar imagery flown along the active fault trace. Prior to trenching we surveyed the site using a terrestrial lidar system (TLS) to document the exact geomorphic expression of this deflected stream channel before excavating a trench adjacent to it. We trenched perpendicular to the fault hoping to document the prehistoric history of earthquake ruptures along the fault. However, the alluvial stratigraphy that we document in these trench logs shows conclusively that this trench did not expose any active fault trace. Using other local geomorphic evidence for the fault location, a straight fault scarp immediately north of this stream projects slightly upslope of the west end of our trench and may be the actual location of the active fault trace. Five radiocarbon samples establish age control for the alluvial sequence documented in the trench, which may in the future be useful in constraining the long-term slip rate of the Greenville Fault. The deflection had been caused by an abrupt nontectonic termination of unit u30, a relatively thick (0.15–0.35 m) silt that is more erosion resistant than the adjacent cohesionless sand and gravel.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151122","usgsCitation":"Lienkaemper, J.J., DeLong, S.B., Avdievitch, N.N., Pickering, A.J., and Guilderson, T.P., 2015, Trench logs, terrestrial lidar system imagery, and radiocarbon data from the kilometer-62 site on the Greenville Fault, southeastern Alameda County, California, 2014: U.S. Geological Survey Open-File Report 2015-1122, 1 Sheet, https://doi.org/10.3133/ofr20151122.","productDescription":"1 Sheet","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2014-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-065482","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":305505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151122.gif"},{"id":305472,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1122/"},{"id":305504,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1122/pdf/ofr20151122.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"California","county":"Alameda County","otherGeospatial":"Greenville Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.7449951171875,\n 37.41816326969145\n ],\n [\n -121.7449951171875,\n 37.64468458716586\n ],\n [\n -121.3604736328125,\n 37.64468458716586\n ],\n [\n -121.3604736328125,\n 37.41816326969145\n ],\n [\n -121.7449951171875,\n 37.41816326969145\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55950124e4b0b6d21dd6cbc6","contributors":{"authors":[{"text":"Lienkaemper, James J. 0000-0002-7578-7042 jlienk@usgs.gov","orcid":"https://orcid.org/0000-0002-7578-7042","contributorId":1941,"corporation":false,"usgs":true,"family":"Lienkaemper","given":"James","email":"jlienk@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":563988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeLong, Stephen B. 0000-0002-0945-2172 sdelong@usgs.gov","orcid":"https://orcid.org/0000-0002-0945-2172","contributorId":5240,"corporation":false,"usgs":true,"family":"DeLong","given":"Stephen","email":"sdelong@usgs.gov","middleInitial":"B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":563989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avdievitch, Nikita N.","contributorId":143693,"corporation":false,"usgs":false,"family":"Avdievitch","given":"Nikita","email":"","middleInitial":"N.","affiliations":[{"id":15304,"text":"University of Tubingen, Wilhelmstrasse 56, Tugingen, GER 72076","active":true,"usgs":false}],"preferred":false,"id":563990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pickering, Alexandra J. 0000-0002-1281-6117 apickering@usgs.gov","orcid":"https://orcid.org/0000-0002-1281-6117","contributorId":5990,"corporation":false,"usgs":true,"family":"Pickering","given":"Alexandra","email":"apickering@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":563991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guilderson, Thomas P.","contributorId":59121,"corporation":false,"usgs":true,"family":"Guilderson","given":"Thomas","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":563992,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70159788,"text":"70159788 - 2015 - Hydraulic fracturing water use variability in the United States and potential environmental implications","interactions":[],"lastModifiedDate":"2015-11-23T10:26:47","indexId":"70159788","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Hydraulic fracturing water use variability in the United States and potential environmental implications","docAbstract":"Until now, up-to-date, comprehensive, spatial, national-scale data on hydraulic fracturing water volumes have been lacking. Water volumes used (injected) to hydraulically fracture over 263,859 oil and gas wells drilled between 2000 and 2014 were compiled and used to create the first U.S. map of hydraulic fracturing water use. Although median annual volumes of 15,275 m3 and 19,425 m3 of water per well was used to hydraulically fracture individual horizontal oil and gas wells, respectively, in 2014, about 42% of wells were actually either vertical or directional, which required less than 2600 m3 water per well. The highest average hydraulic fracturing water usage (10,000−36,620 m3 per well) in watersheds across the United States generally correlated with shale-gas areas (versus coalbed methane, tight oil, or tight gas) where the greatest proportion of hydraulically fractured wells were horizontally drilled, reflecting that the natural reservoir properties influence water use. This analysis also demonstrates that many oil and gas resources within a given basin are developed using a mix of horizontal, vertical, and some directional wells, explaining why large volume hydraulic fracturing water usage is not widespread. This spatial variability in hydraulic fracturing water use relates to the potential for environmental impacts such as water availability, water quality, wastewater disposal, and possible wastewater injection-induced earthquakes.
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Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r2 = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r2 = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2015.03.002","usgsCitation":"Prabhakara, K., Hively, W., and McCarty, G.W., 2015, Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States: International Journal of Applied Earth Observation and Geoinformation, v. 39, p. 88-102, https://doi.org/10.1016/j.jag.2015.03.002.","productDescription":"15 p.","startPage":"88","endPage":"102","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062027","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":488361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2015.03.002","text":"Publisher Index Page"},{"id":305541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Beltsville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.88652992248535,\n 39.02681869722843\n ],\n [\n -76.88652992248535,\n 39.0351862510659\n ],\n [\n -76.87356948852539,\n 39.0351862510659\n ],\n [\n -76.87356948852539,\n 39.02681869722843\n ],\n [\n -76.88652992248535,\n 39.02681869722843\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55950121e4b0b6d21dd6cbb4","contributors":{"authors":[{"text":"Prabhakara, Kusuma","contributorId":6313,"corporation":false,"usgs":true,"family":"Prabhakara","given":"Kusuma","email":"","affiliations":[],"preferred":false,"id":556699,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hively, W. 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By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.
","language":"English","publisher":"AIMS Press","doi":"10.3934/environsci.2015.3.668","usgsCitation":"Sleeter, R., Acevedo, W., Soulard, C.E., and Sleeter, B.M., 2015, Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model: AIMS Environmental Science, v. 2, no. 3, p. 668-693, https://doi.org/10.3934/environsci.2015.3.668.","productDescription":"26 p.","startPage":"668","endPage":"693","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064560","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/environsci.2015.3.668","text":"Publisher Index Page"},{"id":308154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55fa92c3e4b05d6c4e501aab","contributors":{"authors":[{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":556922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acevedo, William wacevedo@usgs.gov","contributorId":2689,"corporation":false,"usgs":true,"family":"Acevedo","given":"William","email":"wacevedo@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":556923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":556924,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":556925,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70182467,"text":"70182467 - 2015 - Tree mortality predicted from drought-induced vascular damage","interactions":[],"lastModifiedDate":"2017-02-23T12:46:12","indexId":"70182467","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Tree mortality predicted from drought-induced vascular damage","docAbstract":"The projected responses of forest ecosystems to warming and drying associated with twenty-first-century climate change vary widely from resiliency to widespread tree mortality1, 2, 3. Current vegetation models lack the ability to account for mortality of overstorey trees during extreme drought owing to uncertainties in mechanisms and thresholds causing mortality4, 5. Here we assess the causes of tree mortality, using field measurements of branch hydraulic conductivity during ongoing mortality in Populus tremuloides in the southwestern United States and a detailed plant hydraulics model. We identify a lethal plant water stress threshold that corresponds with a loss of vascular transport capacity from air entry into the xylem. We then use this hydraulic-based threshold to simulate forest dieback during historical drought, and compare predictions against three independent mortality data sets. The hydraulic threshold predicted with 75% accuracy regional patterns of tree mortality as found in field plots and mortality maps derived from Landsat imagery. In a high-emissions scenario, climate models project that drought stress will exceed the observed mortality threshold in the southwestern United States by the 2050s. Our approach provides a powerful and tractable way of incorporating tree mortality into vegetation models to resolve uncertainty over the fate of forest ecosystems in a changing climate.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/NGEO2400","usgsCitation":"Anderegg, W.R., Flint, A.L., Huang, C., Flint, L.E., Berry, J.A., Davis, F., Sperry, J.S., and Field, C.B., 2015, Tree mortality predicted from drought-induced vascular damage: Nature Geoscience, v. 8, p. 367-371, https://doi.org/10.1038/NGEO2400.","productDescription":"5 p.","startPage":"367","endPage":"371","ipdsId":"IP-059538","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":336106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-30","publicationStatus":"PW","scienceBaseUri":"58b002c7e4b01ccd54fb27d1","contributors":{"authors":[{"text":"Anderegg, William R.L.","contributorId":147089,"corporation":false,"usgs":false,"family":"Anderegg","given":"William","email":"","middleInitial":"R.L.","affiliations":[{"id":16784,"text":"Princeton U.","active":true,"usgs":false}],"preferred":false,"id":671207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":671208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Cho-ying","contributorId":182348,"corporation":false,"usgs":false,"family":"Huang","given":"Cho-ying","email":"","affiliations":[],"preferred":false,"id":671209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":671206,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berry, Joseph A.","contributorId":182349,"corporation":false,"usgs":false,"family":"Berry","given":"Joseph","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":671210,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Frank W.","contributorId":127849,"corporation":false,"usgs":false,"family":"Davis","given":"Frank W.","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":671211,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sperry, John S.","contributorId":182350,"corporation":false,"usgs":false,"family":"Sperry","given":"John","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":671212,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Field, Christopher B.","contributorId":182351,"corporation":false,"usgs":false,"family":"Field","given":"Christopher","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":671213,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70176520,"text":"70176520 - 2015 - Use of structured decision making to identify monitoring variables and management priorities for salt marsh ecosystems","interactions":[],"lastModifiedDate":"2017-05-03T13:12:54","indexId":"70176520","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Use of structured decision making to identify monitoring variables and management priorities for salt marsh ecosystems","docAbstract":"Most salt marshes in the USA have been degraded by human activities, and coastal managers are faced with complex choices among possible actions to restore or enhance ecosystem integrity. We applied structured decision making (SDM) to guide selection of monitoring variables and management priorities for salt marshes within the National Wildlife Refuge System in the northeastern USA. In general, SDM is a systematic process for decomposing a decision into its essential elements. We first engaged stakeholders in clarifying regional salt marsh decision problems, defining objectives and attributes to evaluate whether objectives are achieved, and developing a pool of alternative management actions for achieving objectives. Through this process, we identified salt marsh attributes that were applicable to monitoring National Wildlife Refuges on a regional scale and that targeted management needs. We then analyzed management decisions within three salt marsh units at Prime Hook National Wildlife Refuge, coastal Delaware, as a case example of prioritizing management alternatives. Values for salt marsh attributes were estimated from 2 years of baseline monitoring data and expert opinion. We used linear value modeling to aggregate multiple attributes into a single performance score for each alternative, constrained optimization to identify alternatives that maximized total management benefits subject to refuge-wide cost constraints, and used graphical analysis to identify the optimal set of alternatives for the refuge. SDM offers an efficient, transparent approach for integrating monitoring into management practice and improving the quality of management decisions.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-014-9822-5","usgsCitation":"Neckles, H.A., Lyons, J., Guntenspergen, G.R., Shriver, W.G., and Adamowicz, S.C., 2015, Use of structured decision making to identify monitoring variables and management priorities for salt marsh ecosystems: Estuaries and Coasts, v. 38, no. 4, p. 1215-1232, https://doi.org/10.1007/s12237-014-9822-5.","productDescription":"18 p.","startPage":"1215","endPage":"1232","ipdsId":"IP-050872","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"4","noUsgsAuthors":false,"publicationDate":"2014-05-20","publicationStatus":"PW","scienceBaseUri":"57f7ee36e4b0bc0bec09e915","contributors":{"authors":[{"text":"Neckles, Hilary A. 0000-0002-5662-2314 hneckles@usgs.gov","orcid":"https://orcid.org/0000-0002-5662-2314","contributorId":3821,"corporation":false,"usgs":true,"family":"Neckles","given":"Hilary","email":"hneckles@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":649085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, James E.","contributorId":35461,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[],"preferred":false,"id":649086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":649087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shriver, W. Gregory","contributorId":174711,"corporation":false,"usgs":false,"family":"Shriver","given":"W.","email":"","middleInitial":"Gregory","affiliations":[],"preferred":false,"id":649088,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adamowicz, Susan C.","contributorId":174712,"corporation":false,"usgs":false,"family":"Adamowicz","given":"Susan","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":649089,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176119,"text":"70176119 - 2015 - Exposure and food web transfer of pharmaceuticals in ospreys (Pandion haliaetus): Predictive model and empirical data","interactions":[],"lastModifiedDate":"2018-09-04T15:59:22","indexId":"70176119","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Exposure and food web transfer of pharmaceuticals in ospreys (Pandion haliaetus): Predictive model and empirical data","docAbstract":"The osprey (Pandion haliaetus) is a well-known sentinel of environmental contamination, yet no studies have traced pharmaceuticals through the water–fish–osprey food web. A screening-level exposure assessment was used to evaluate the bioaccumulation potential of 113 pharmaceuticals and metabolites, and an artificial sweetener in this food web. Hypothetical concentrations in water reflecting “wastewater effluent dominated” or “dilution dominated” scenarios were combined with pH-specific bioconcentration factors (BCFs) to predict uptake in fish. Residues in fish and osprey food intake rate were used to calculate the daily intake (DI) of compounds by an adult female osprey. Fourteen pharmaceuticals and a drug metabolite with a BCF greater than 100 and a DI greater than 20 µg/kg were identified as being most likely to exceed the adult human therapeutic dose (HTD). These 15 compounds were also evaluated in a 40 day cumulative dose exposure scenario using first-order kinetics to account for uptake and elimination. Assuming comparable absorption to humans, the half-lives (t1/2) for an adult osprey to reach the HTD within 40 days were calculated. For 3 of these pharmaceuticals, the estimated t1/2 in ospreys was less than that for humans, and thus an osprey might theoretically reach or exceed the HTD in 3 to 7 days. To complement the exposure model, 24 compounds were quantified in water, fish plasma, and osprey nestling plasma from 7 potentially impaired locations in Chesapeake Bay. Of the 18 analytes detected in water, 8 were found in fish plasma, but only 1 in osprey plasma (the antihypertensive diltiazem). Compared to diltiazem detection rate and concentrations in water (10/12 detects, <method detection limits [MDL]–173 ng/L), there was a lower detection frequency in fish (31/233 detects, <MDL–2400 ng/L); however when present in fish, all values exceeded the maximum diltiazem concentration found in water. Diltiazem was found in all 69 osprey plasma samples (540–8630 ng/L), with 41% of these samples exceeding maximum concentrations found in fish. Diltiazem levels in fish and osprey plasma were below the human therapeutic plasma concentration (30 000 ng/L). Effect thresholds for diltiazem are unknown in ospreys at this time, and there is no evidence to suggest adverse effects. This screening-level exposure model can help identify those compounds that warrant further investigation in high-trophic level species.
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.1570","usgsCitation":"Lazarus, R.S., Rattner, B.A., Du, B., McGowan, P.C., Blazer, V., and Ottinger, M.A., 2015, Exposure and food web transfer of pharmaceuticals in ospreys (Pandion haliaetus): Predictive model and empirical data: Integrated Environmental Assessment and Management, v. 11, no. 1, p. 118-129, https://doi.org/10.1002/ieam.1570.","productDescription":"12 p.","startPage":"118","endPage":"129","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057952","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":327898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-08-01","publicationStatus":"PW","scienceBaseUri":"57c16836e4b0f2f0ceb907db","contributors":{"authors":[{"text":"Lazarus, Rebecca S. 0000-0003-1731-6469 rlazarus@usgs.gov","orcid":"https://orcid.org/0000-0003-1731-6469","contributorId":5594,"corporation":false,"usgs":true,"family":"Lazarus","given":"Rebecca","email":"rlazarus@usgs.gov","middleInitial":"S.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":647180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":647181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Du, Bowen","contributorId":149285,"corporation":false,"usgs":false,"family":"Du","given":"Bowen","email":"","affiliations":[{"id":16605,"text":"Department of Environmental Science and the Center for Reservoir and Aquatic Systems Research (CRASR), Baylor University, Waco, TX","active":true,"usgs":false}],"preferred":false,"id":647182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGowan, Peter C.","contributorId":13867,"corporation":false,"usgs":false,"family":"McGowan","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":647183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":647184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ottinger, Mary Ann","contributorId":26422,"corporation":false,"usgs":false,"family":"Ottinger","given":"Mary","email":"","middleInitial":"Ann","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":647185,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70155184,"text":"70155184 - 2015 - Can orchards help connect Mediterranean ecosystems? Animal movement data alter conservation priorities","interactions":[],"lastModifiedDate":"2015-08-05T11:12:12","indexId":"70155184","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":737,"text":"American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Can orchards help connect Mediterranean ecosystems? Animal movement data alter conservation priorities","docAbstract":"As natural habitats become fragmented by human activities, animals must increasingly move through human-dominated systems, particularly agricultural landscapes. Mapping areas important for animal movement has therefore become a key part of conservation planning. Models of landscape connectivity are often parameterized using expert opinion and seldom distinguish between the risks and barriers presented by different crop types. Recent research, however, suggests different crop types, such as row crops and orchards, differ in the degree to which they facilitate or impede species movements. Like many mammalian carnivores, bobcats (Lynx rufus) are sensitive to fragmentation and loss of connectivity between habitat patches. We investigated how distinguishing between different agricultural land covers might change conclusions about the relative conservation importance of different land uses in a Mediterranean ecosystem. Bobcats moved relatively quickly in row crops but relatively slowly in orchards, at rates similar to those in natural habitats of woodlands and scrub. We found that parameterizing a connectivity model using empirical data on bobcat movements in agricultural lands and other land covers, instead of parameterizing the model using habitat suitability indices based on expert opinion, altered locations of predicted animal movement routes. These results emphasize that differentiating between types of agriculture can alter conservation planning outcomes.
","language":"English","publisher":"University of Notre Dame","doi":"10.1674/0003-0031-174.1.105","usgsCitation":"Nogeire, T.M., Davis, F., Crooks, K.R., McRae, B.H., Lyren, L.M., and Boydston, E.E., 2015, Can orchards help connect Mediterranean ecosystems? Animal movement data alter conservation priorities: American Midland Naturalist, v. 174, no. 1, p. 105-116, https://doi.org/10.1674/0003-0031-174.1.105.","productDescription":"12 p.","startPage":"105","endPage":"116","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052372","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471968,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/0ps3x1b0","text":"External Repository"},{"id":306426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Orange County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.72262573242188,\n 33.592887216626245\n ],\n [\n -117.72262573242188,\n 33.71862851510573\n ],\n [\n -117.57843017578126,\n 33.71862851510573\n ],\n [\n -117.57843017578126,\n 33.592887216626245\n ],\n [\n -117.72262573242188,\n 33.592887216626245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"174","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55c333aae4b033ef52106a81","contributors":{"authors":[{"text":"Nogeire, Theresa M.","contributorId":83434,"corporation":false,"usgs":true,"family":"Nogeire","given":"Theresa","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":565002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Frank W.","contributorId":36894,"corporation":false,"usgs":true,"family":"Davis","given":"Frank W.","affiliations":[],"preferred":false,"id":565003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crooks, Kevin R.","contributorId":51137,"corporation":false,"usgs":false,"family":"Crooks","given":"Kevin","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":565004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McRae, Brad H.","contributorId":145697,"corporation":false,"usgs":false,"family":"McRae","given":"Brad","email":"","middleInitial":"H.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":565005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lyren, Lisa M. llyren@usgs.gov","contributorId":2398,"corporation":false,"usgs":true,"family":"Lyren","given":"Lisa","email":"llyren@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":565001,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boydston, Erin E. 0000-0002-8452-835X eboydston@usgs.gov","orcid":"https://orcid.org/0000-0002-8452-835X","contributorId":1705,"corporation":false,"usgs":true,"family":"Boydston","given":"Erin","email":"eboydston@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":565000,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191255,"text":"70191255 - 2015 - The distribution of selected elements and minerals in soil of the conterminous United States","interactions":[],"lastModifiedDate":"2025-05-14T19:08:03.94424","indexId":"70191255","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"The distribution of selected elements and minerals in soil of the conterminous United States","docAbstract":"In 2007, the U.S. Geological Survey initiated a low-density (1 site per 1600 km2, 4857 sites) geochemical and mineralogical survey of soil of the conterminous United States as part of the North American Soil Geochemical Landscapes Project. Three soil samples were collected, if possible, from each site; (1) a sample from a depth of 0 to 5 cm, (2) a composite of the soil A-horizon, and (3) a deeper sample from the soil C-horizon or, if the top of the C-horizon was at a depth greater than 100 cm, from a depth of approximately 80–100 cm. The < 2 mm fraction of each sample was analysed for a suite of 45 major and trace elements following near-total multi-acid digestion. The major mineralogical components in samples from the soil A- and C-horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. Sampling ended in 2010 and chemical and mineralogical analyses were completed in May 2013. Maps of the conterminous United States showing predicted element and mineral concentrations were interpolated from actual soil data for each soil sample type by an inverse distance weighted (IDW) technique using ArcGIS software. Regional- and national-scale map patterns for selected elements and minerals apparent in interpolated maps are described here in the context of soil-forming factors and possible human inputs. These patterns can be related to (1) soil parent materials, for example, in the distribution of quartz, (2) climate impacts, for example, in the distribution of feldspar and kaolinite, (3) soil age, for example, in the distribution of carbonate in young glacial deposits, and (4) possible anthropogenic loading of phosphorus (P) and lead (Pb) to surface soil. This new geochemical and mineralogical data set for the conterminous United States represents a major step forward from prior national-scale soil geochemistry data and provides a robust soil data framework for the United States now and into the future.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2015.01.006","usgsCitation":"Woodruff, L.G., Cannon, W.F., Smith, D.B., and Solano, F., 2015, The distribution of selected elements and minerals in soil of the conterminous United States: Journal of Geochemical Exploration, v. 154, p. 49-60, https://doi.org/10.1016/j.gexplo.2015.01.006.","productDescription":"12 p.","startPage":"49","endPage":"60","ipdsId":"IP-054587","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":346320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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The spatiotemporal relationships of ET with rainfall and vegetation dynamics in the Nile Basin during 2002–2011 were analyzed using satellite-derived data. Non-parametric statistics were used to quantify ET-rainfall interactions and trends across land cover types and subbasins. We found that 65% of the study area (2.5 million km2) showed significant (p < 0.05) positive correlations between monthly ET and rainfall, whereas 7% showed significant negative correlations. As expected, positive ET-rainfall correlations were observed over natural vegetation, mixed croplands/natural vegetation, and croplands, with a few subbasin-specific exceptions. In particular, irrigated croplands, wetlands and some forests exhibited negative correlations. Trend tests revealed spatial clusters of statistically significant trends in ET (6% of study area was negative; 12% positive), vegetation greenness (24% negative; 12% positive) and rainfall (11% negative; 1% positive) during 2002–2011. The Nile Delta, Ethiopian highlands and central Uganda regions showed decline in ET while central parts of Sudan, South Sudan, southwestern Ethiopia and northeastern Uganda showed increases. Except for a decline in ET in central Uganda, the detected changes in ET (both positive and negative) were not associated with corresponding changes in rainfall. Detected declines in ET in the Nile delta and Ethiopian highlands were found to be attributable to anthropogenic land degradation, while the ET decline in central Uganda is likely caused by rainfall reduction.
","language":"English","publisher":"MDPI","doi":"10.3390/w7094914","usgsCitation":"Alemu, H., Kaptue, A.T., Senay, G., Wimberly, M.C., and Henebry, G.M., 2015, Evapotranspiration in the Nile Basin: Identifying dynamics and drivers, 2002–2011: Water, v. 7, no. 9, p. 4914-1931, https://doi.org/10.3390/w7094914.","productDescription":"18 p.","startPage":"4914","endPage":"1931","ipdsId":"IP-066148","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471985,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w7094914","text":"Publisher Index Page"},{"id":337517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Nile Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 32.431640625,\n 31.42866311735861\n ],\n [\n 30.8935546875,\n 31.952162238024975\n ],\n [\n 29.091796875,\n 31.015278981711266\n ],\n [\n 27.9931640625,\n 21.94304553343818\n ],\n [\n 23.6865234375,\n 8.624472107633936\n ],\n [\n 27.2021484375,\n 5.134714634014467\n ],\n [\n 30.629882812499996,\n 3.601142320158735\n ],\n [\n 30.761718749999996,\n -3.90809888189411\n ],\n [\n 34.9365234375,\n -3.46955730306146\n ],\n [\n 37.0458984375,\n 4.083452772038619\n ],\n [\n 40.7373046875,\n 14.817370620155254\n ],\n [\n 32.431640625,\n 31.42866311735861\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-09","publicationStatus":"PW","scienceBaseUri":"58c90128e4b0849ce97abcf3","contributors":{"authors":[{"text":"Alemu, Henok","contributorId":124527,"corporation":false,"usgs":false,"family":"Alemu","given":"Henok","email":"","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":684251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaptue, Armel T.","contributorId":189254,"corporation":false,"usgs":false,"family":"Kaptue","given":"Armel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":684252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":683981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wimberly, Michael C.","contributorId":167855,"corporation":false,"usgs":false,"family":"Wimberly","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":684253,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henebry, Geoffrey M.","contributorId":124528,"corporation":false,"usgs":false,"family":"Henebry","given":"Geoffrey","email":"","middleInitial":"M.","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":684254,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70193232,"text":"70193232 - 2015 - Statistical analysis of soil geochemical data to identify pathfinders associated with mineral deposits: An example from the Coles Hill uranium deposit, Virginia, USA","interactions":[],"lastModifiedDate":"2022-10-31T16:53:41.817888","indexId":"70193232","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Statistical analysis of soil geochemical data to identify pathfinders associated with mineral deposits: An example from the Coles Hill uranium deposit, Virginia, USA","docAbstract":"Soil geochemical anomalies can be used to identify pathfinders in exploration for ore deposits. In this study, compositional data analysis is used with multivariate statistical methods to analyse soil geochemical data collected from the Coles Hill uranium deposit, Virginia, USA, to identify pathfinders associated with this deposit. Elemental compositions and relationships were compared between the collected Coles Hill soil and reference soil samples extracted from a regional subset of a national-scale geochemical survey. Results show that pathfinders for the Coles Hill deposit include light rare earth elements (La and Ce), which, when normalised by their Al content, are correlated with U/Al, and elevated Th/Al values, which are not correlated with U/Al, supporting decoupling of U from Th during soil generation. These results can be used in genetic and weathering models of the Coles Hill deposit, and can also be applied to future prospecting for similar U deposits in the eastern United States, and in regions with similar geological/climatic conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2014.12.012","usgsCitation":"Levitan, D.M., Zipper, C.E., Donovan, P., Schreiber, M.E., Seal, R.R., Engle, M.A., Chermak, J.A., Bodnar, R.J., Johnson, D.K., and Aylor, J.G., 2015, Statistical analysis of soil geochemical data to identify pathfinders associated with mineral deposits: An example from the Coles Hill uranium deposit, Virginia, USA: Journal of Geochemical Exploration, v. 154, p. 238-251, https://doi.org/10.1016/j.gexplo.2014.12.012.","productDescription":"14 p.","startPage":"238","endPage":"251","ipdsId":"IP-056717","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":349148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Coles Hill uranium deposit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.3333,\n 36.9\n ],\n [\n -79.3333,\n 36.85\n ],\n [\n -79.2667,\n 36.85\n ],\n [\n -79.2667,\n 36.9\n ],\n [\n -79.3333,\n 36.9\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"154","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fe80e4b06e28e9c25307","contributors":{"authors":[{"text":"Levitan, Denise M.","contributorId":199138,"corporation":false,"usgs":false,"family":"Levitan","given":"Denise","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":718298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zipper, Carl E.","contributorId":198104,"corporation":false,"usgs":false,"family":"Zipper","given":"Carl","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":718299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donovan, Patricia","contributorId":199139,"corporation":false,"usgs":false,"family":"Donovan","given":"Patricia","email":"","affiliations":[],"preferred":false,"id":718300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schreiber, Madeline E.","contributorId":138959,"corporation":false,"usgs":false,"family":"Schreiber","given":"Madeline","email":"","middleInitial":"E.","affiliations":[{"id":12594,"text":"Department of Geosciences, Virginia Tech, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":718301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seal, Robert R. 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":193011,"corporation":false,"usgs":true,"family":"Seal","given":"Robert","email":"rseal@usgs.gov","middleInitial":"R.","affiliations":[{"id":250,"text":"Eastern Water Science Field Team","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":718297,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engle, Mark A. 0000-0001-5258-7374 engle@usgs.gov","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":584,"corporation":false,"usgs":true,"family":"Engle","given":"Mark","email":"engle@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":722887,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chermak, John A.","contributorId":199140,"corporation":false,"usgs":false,"family":"Chermak","given":"John","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":718302,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bodnar, Robert J.","contributorId":199141,"corporation":false,"usgs":false,"family":"Bodnar","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":718303,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Johnson, Daniel K.","contributorId":200614,"corporation":false,"usgs":false,"family":"Johnson","given":"Daniel","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":722888,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Aylor, Joseph G. Jr.","contributorId":199142,"corporation":false,"usgs":false,"family":"Aylor","given":"Joseph","suffix":"Jr.","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":718304,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70171446,"text":"70171446 - 2015 - Habitat quality from individual- and population-level perspectives and implications for management","interactions":[],"lastModifiedDate":"2016-06-01T09:44:53","indexId":"70171446","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Habitat quality from individual- and population-level perspectives and implications for management","docAbstract":"Many wildlife management prescriptions are either implicitly or explicitly designed to improve habitat quality for a focal species, but habitat quality is often difficult to quantify. Depending upon the approach used to define and identify high-quality habitat, management decisions may differ widely. Although individual-level measures of habitat quality based on per capita reproduction (e.g., average nesting success, number of young produced per pair) are most common in the literature, they may not align with population-level measures that reflect number of young produced within a defined area. Using data on the cerulean warbler (Setophaga cerulea) collected in the Cumberland Mountains (Tennessee, USA; 2008–2010) as an example, we illustrate how lack of concordance between individual- and population-level measures of habitat quality can have real-world management implications.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/wsb.528","usgsCitation":"Boves, T.J., Rodewald, A.D., Wood, P.B., Buehler, D.A., Larkin, J.L., Wigley, T.B., and Keyser, P.D., 2015, Habitat quality from individual- and population-level perspectives and implications for management: Wildlife Society Bulletin, v. 39, no. 2, p. 443-447, https://doi.org/10.1002/wsb.528.","productDescription":"5 p.","startPage":"443","endPage":"447","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056027","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":500063,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/9d3a5c06f84a4a4290d5b7038a311ac5","text":"External Repository"},{"id":321977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-18","publicationStatus":"PW","scienceBaseUri":"57500766e4b0ee97d51bb63c","contributors":{"authors":[{"text":"Boves, Than J.","contributorId":169750,"corporation":false,"usgs":false,"family":"Boves","given":"Than","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":631187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodewald, Amanda D.","contributorId":169748,"corporation":false,"usgs":false,"family":"Rodewald","given":"Amanda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":631188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Petra Bohall pbwood@usgs.gov","contributorId":1791,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","middleInitial":"Bohall","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":630998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buehler, David A.","contributorId":169746,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":631189,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larkin, Jeffrey L.","contributorId":169747,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":34542,"text":"Department of Biology. Indiana University of Pennsylvania","active":true,"usgs":false},{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false}],"preferred":false,"id":631190,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wigley, T. 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At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km2). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.
","language":"English","publisher":"BioMed Central","doi":"10.1186/s13742-015-0067-4","usgsCitation":"Soranno, P.A., Bissell, E., Cheruvelil, K.S., Christel, S.T., Collins, S.M., Fergus, C.E., Filstrup, C.T., Lapierre, J., Lotting, N.R., Oliver, S., Scott, C.E., Smith, N.J., Stopyak, S., Yuan, S., Bremigan, M.T., Downing, J., Gries, C., Henry, E.N., Skaff, N.K., Stanley, E.H., Stow, C., Tan, P., Wagner, T., and Webster, K.E., 2015, Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science through data reuse: GigaScience, v. 4, no. 28, https://doi.org/10.1186/s13742-015-0067-4.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062339","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":471979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13742-015-0067-4","text":"Publisher Index Page"},{"id":324012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Illinois, Indiana, Iowa, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.3388671875,\n 49.35375571830993\n ],\n [\n -94.7900390625,\n 36.421282443649496\n ],\n [\n -89.296875,\n 35.99578538642032\n ],\n [\n -88.330078125,\n 37.19533058280065\n ],\n [\n -87.3193359375,\n 37.64903402157866\n ],\n [\n -84.5947265625,\n 38.71980474264239\n ],\n [\n -82.6171875,\n 38.272688535980976\n ],\n [\n -80.6396484375,\n 39.707186656826565\n ],\n [\n -75.9375,\n 39.774769485295465\n ],\n [\n -74.8388671875,\n 38.8225909761771\n ],\n [\n -67.1044921875,\n 43.73935207915473\n ],\n [\n -66.357421875,\n 45.398449976304086\n ],\n [\n -68.15917968749999,\n 47.90161354142077\n ],\n [\n -77.7392578125,\n 45.85941212790755\n ],\n [\n -86.220703125,\n 49.410973199695846\n ],\n [\n -97.3388671875,\n 49.35375571830993\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"28","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-01","publicationStatus":"PW","scienceBaseUri":"576913b1e4b07657d19fefae","contributors":{"authors":[{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":639828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bissell, E.G.","contributorId":88823,"corporation":false,"usgs":true,"family":"Bissell","given":"E.G.","email":"","affiliations":[],"preferred":false,"id":639829,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cheruvelil, Kendra S.","contributorId":172029,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"Kendra","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":639830,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christel, Samuel T.","contributorId":169272,"corporation":false,"usgs":false,"family":"Christel","given":"Samuel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":639831,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Sarah M.","contributorId":172181,"corporation":false,"usgs":false,"family":"Collins","given":"Sarah","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":639832,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fergus, C. 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2015 - Parameter estimation for groundwater models under uncertain irrigation data","interactions":[],"lastModifiedDate":"2017-05-31T14:13:05","indexId":"70188037","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Parameter estimation for groundwater models under uncertain irrigation data","docAbstract":"The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12235","usgsCitation":"Demissie, Y., Valocchi, A.J., Cai, X., Brozovic, N., Senay, G., and Gebremichael, M., 2015, Parameter estimation for groundwater models under uncertain irrigation data: Groundwater, v. 53, no. 4, p. 614-625, https://doi.org/10.1111/gwat.12235.","productDescription":"12 p.","startPage":"614","endPage":"625","ipdsId":"IP-057423","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-07-12","publicationStatus":"PW","scienceBaseUri":"592fd63ee4b0e9bd0ea896fd","contributors":{"authors":[{"text":"Demissie, Yonas","contributorId":192369,"corporation":false,"usgs":false,"family":"Demissie","given":"Yonas","email":"","affiliations":[],"preferred":false,"id":696798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valocchi, Albert J.","contributorId":25062,"corporation":false,"usgs":true,"family":"Valocchi","given":"Albert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":696799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cai, Ximing","contributorId":149230,"corporation":false,"usgs":false,"family":"Cai","given":"Ximing","email":"","affiliations":[{"id":17685,"text":"University of Illinois, Champagne-Urbana","active":true,"usgs":false}],"preferred":false,"id":696800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brozovic, Nicholas","contributorId":192552,"corporation":false,"usgs":false,"family":"Brozovic","given":"Nicholas","email":"","affiliations":[],"preferred":false,"id":696801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gebremichael, Mekonnen","contributorId":147882,"corporation":false,"usgs":false,"family":"Gebremichael","given":"Mekonnen","email":"","affiliations":[],"preferred":false,"id":696802,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187146,"text":"70187146 - 2015 - Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling","interactions":[],"lastModifiedDate":"2017-04-25T16:26:23","indexId":"70187146","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3893,"text":"AIMS Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling","docAbstract":"Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation.
","language":"English","publisher":"AIMS Press","doi":"10.3934/environsci.2015.3.623","usgsCitation":"Sherba, J.T., Sleeter, B.M., Davis, A.W., and Parker, O.P., 2015, Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling: AIMS Environmental Science, v. 2, no. 3, p. 623-647, https://doi.org/10.3934/environsci.2015.3.623.","productDescription":"25 p.","startPage":"623","endPage":"647","ipdsId":"IP-063655","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471986,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/environsci.2015.3.623","text":"Publisher Index Page"},{"id":340252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006064e4b0e85db3a5dde1","contributors":{"authors":[{"text":"Sherba, Jason T. jsherba@usgs.gov","contributorId":5972,"corporation":false,"usgs":true,"family":"Sherba","given":"Jason","email":"jsherba@usgs.gov","middleInitial":"T.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":692760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":692761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Adam W. awdavis@usgs.gov","contributorId":4982,"corporation":false,"usgs":true,"family":"Davis","given":"Adam","email":"awdavis@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":true,"id":692762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Owen P.","contributorId":147263,"corporation":false,"usgs":false,"family":"Parker","given":"Owen","email":"","middleInitial":"P.","affiliations":[{"id":6785,"text":"USGS Contractor, Minerals & Environmental Resources Sci Ctr","active":true,"usgs":false}],"preferred":false,"id":692763,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168390,"text":"70168390 - 2015 - Linking degradation status with ecosystem vulnerability to environmental change","interactions":[],"lastModifiedDate":"2016-02-11T10:09:18","indexId":"70168390","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Linking degradation status with ecosystem vulnerability to environmental change","docAbstract":"Environmental change can cause regime shifts in ecosystems, potentially threatening ecosystem services. It is unclear if the degradation status of ecosystems correlates with their vulnerability to environmental change, and thus the risk of future regime shifts. We assessed resilience in acidified (degraded) and circumneutral (undegraded) lakes with long-term data (1988–2012), using time series modeling. We identified temporal frequencies in invertebrate assemblages, which identifies groups of species whose population dynamics vary at particular temporal scales. We also assessed species with stochastic dynamics, those whose population dynamics vary irregularly and unpredictably over time. We determined the distribution of functional feeding groups of invertebrates within and across the temporal scales identified, and in those species with stochastic dynamics, and assessed attributes hypothesized to contribute to resilience. Three patterns of temporal dynamics, consistent across study lakes, were identified in the invertebrates. The first pattern was one of monotonic change associated with changing abiotic lake conditions. The second and third patterns appeared unrelated to the environmental changes we monitored. Acidified and the circumneutral lakes shared similar levels and patterns of functional richness, evenness, diversity, and redundancy for species within and across the observed temporal scales and for stochastic species groups. These similar resilience characteristics suggest that both lake types did not differ in vulnerability to the environmental changes observed here. Although both lake types appeared equally vulnerable in this study, our approach demonstrates how assessing systemic vulnerability by quantifying ecological resilience can help address uncertainty in predicting ecosystem responses to environmental change across ecosystems.
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