We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. 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":70157072,"text":"70157072 - 2015 - Scale dependence of disease impacts on quaking aspen (Populus tremuloides) mortality in the southwestern United States","interactions":[],"lastModifiedDate":"2015-09-09T11:13:12","indexId":"70157072","displayToPublicDate":"2015-07-01T12:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3835,"text":"Ecology, Evolution, and Systematics","active":true,"publicationSubtype":{"id":10}},"title":"Scale dependence of disease impacts on quaking aspen (Populus tremuloides) mortality in the southwestern United States","docAbstract":"Depending on how disease impacts tree exposure to risk, both the prevalence of disease and disease effects on survival may contribute to patterns of mortality risk across a species' range. Disease may accelerate tree species' declines in response to global change factors, such as drought, biotic interactions, such as competition, or functional traits, such as allometry. To assess the role of disease in mediating mortality risk in quaking aspen (Populus tremuloides), we developed hierarchical Bayesian models for both disease prevalence in live aspen stems and the resulting survival rates of healthy and diseased aspen near the species' southern range limit using 5088 individual trees on 281 United States Forest Service Forest Inventory and Analysis plots in the southwestern United States.
\nWe found that disease prevalence depended primarily on tree size, tree allometry, and spatial variation in precipitation, while mortality depended on tree size, allometry, competition, spatial variation in summer temperature, and both temporal and spatial variation in summer precipitation. Disease prevalence was highest in large trees with low slenderness found on dry sites. For healthy trees, mortality decreased with diameter, slenderness, and temporal variation in summer precipitation, but increased with competition and spatial variation in summer temperature. Mortality of diseased trees decreased with diameter and aspen relative basal area and increased with mean summer temperature and precipitation. Disease infection increased aspen mortality, especially in trees of intermediate size and trees on plots at climatic extremes (i.e., cool, wet and warm, dry climates).
\nBy examining variation in disease prevalence, mortality of healthy trees, and mortality of diseased trees, we showed that the role of disease in aspen tree mortality depended on the scale of inference. For variation among individuals in diameter, disease tended to expose intermediate-size trees experiencing moderate risk to greater risk. For spatial variation in summer temperature, disease exposed lower risk populations to greater mortality probabilities, but the magnitude of this exposure depended on summer precipitation. Furthermore, the importance of diameter and slenderness in mediating responses to climate supports the increasing emphasis on trait variation in studies of ecological responses to global change.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Brooklyn, NY","doi":"10.1890/14-1184.1","usgsCitation":"Bell, D.M., Bradford, J.B., and Lauenroth, W.K., 2015, Scale dependence of disease impacts on quaking aspen (Populus tremuloides) mortality in the southwestern United States: Ecology, Evolution, and Systematics, v. 96, no. 7, p. 1835-1845, https://doi.org/10.1890/14-1184.1.","productDescription":"11 p.","startPage":"1835","endPage":"1845","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059614","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":307999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55f15832e4b0dacf699eb976","contributors":{"authors":[{"text":"Bell, David M.","contributorId":34423,"corporation":false,"usgs":true,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":571502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":571501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":571503,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155283,"text":"70155283 - 2015 - Summer declines in activity and body temperature offer polar bears limited energy savings","interactions":[],"lastModifiedDate":"2017-08-29T18:11:00","indexId":"70155283","displayToPublicDate":"2015-07-01T12:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Summer declines in activity and body temperature offer polar bears limited energy savings","docAbstract":"Polar bears (Ursus maritimus) summer on the sea ice or, where it melts, on shore. Although the physiology of “ice” bears in summer is unknown, “shore” bears purportedly minimize energy losses by entering a hibernation-like state when deprived of food. Such a strategy could partially compensate for the loss of on-ice foraging opportunities caused by climate change. However, here we report gradual, moderate declines in activity and body temperature of both shore and ice bears in summer, resembling energy expenditures typical of fasting, nonhibernating mammals. Also, we found that to avoid unsustainable heat loss while swimming, bears employed unusual heterothermy of the body core. Thus, although well adapted to seasonal ice melt, polar bears appear susceptible to deleterious declines in body condition during the lengthening period of summer food deprivation.
","language":"English","publisher":"American Association for the Advancement of Science","publisherLocation":"New York, NY","doi":"10.1126/science.aaa8623","usgsCitation":"Whiteman, J., Harlow, H., Durner, G.M., Anderson-Sprecher, R., Albeke, S.E., Regehr, E.V., Amstrup, S.C., and Ben-David, M., 2015, Summer declines in activity and body temperature offer polar bears limited energy savings: Science, v. 349, no. 6245, p. 295-298, https://doi.org/10.1126/science.aaa8623.","productDescription":"4 p.","startPage":"295","endPage":"298","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063276","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":306491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"349","issue":"6245","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7eef3e4b0bc0bec09ee16","contributors":{"authors":[{"text":"Whiteman, J.P.","contributorId":107549,"corporation":false,"usgs":true,"family":"Whiteman","given":"J.P.","email":"","affiliations":[],"preferred":false,"id":567545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harlow, H.J.","contributorId":20178,"corporation":false,"usgs":true,"family":"Harlow","given":"H.J.","email":"","affiliations":[],"preferred":false,"id":567546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":565494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson-Sprecher, R.","contributorId":146357,"corporation":false,"usgs":false,"family":"Anderson-Sprecher","given":"R.","affiliations":[],"preferred":false,"id":567547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Albeke, Shannon E.","contributorId":81781,"corporation":false,"usgs":true,"family":"Albeke","given":"Shannon","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":567548,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":567549,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amstrup, Steven C.","contributorId":67034,"corporation":false,"usgs":false,"family":"Amstrup","given":"Steven","email":"","middleInitial":"C.","affiliations":[{"id":13182,"text":"Polar Bears International","active":true,"usgs":false}],"preferred":false,"id":567550,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ben-David, M.","contributorId":11563,"corporation":false,"usgs":true,"family":"Ben-David","given":"M.","email":"","affiliations":[],"preferred":false,"id":567551,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70157378,"text":"70157378 - 2015 - Self-similar rupture implied by scaling properties of volcanic earthquakes occurring during the 2004-2008 eruption of Mount St. Helens, Washington","interactions":[],"lastModifiedDate":"2015-09-23T10:55:39","indexId":"70157378","displayToPublicDate":"2015-07-01T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Self-similar rupture implied by scaling properties of volcanic earthquakes occurring during the 2004-2008 eruption of Mount St. Helens, Washington","docAbstract":"We analyze a group of 6073 low-frequency earthquakes recorded during a week-long temporary deployment of broadband seismometers at distances of less than 3 km from the crater at Mount St. Helens in September of 2006. We estimate the seismic moment (M0) and spectral corner frequency (f0) using a spectral ratio approach for events with a high signal-to-noise (SNR) ratio that have a cross-correlation coefficient of 0.8 or greater with at least five other events. A cluster analysis of cross-correlation values indicates that the group of 421 events meeting the SNR and cross-correlation criteria forms eight event families that exhibit largely self-similar scaling. We estimate the M0 and f0 values of the 421 events and calculate their static stress drop and scaled energy (ER/M0) values. The estimated values suggest self-similar scaling within families, as well as between five of eight families (i.e., ![\"inline](\"http://api.onlinelibrary.wiley.com/asset/v1/doi/10.1002%2F2014JB011744/asset/equation%2Fjgrb51149-math-0001.png?l=SkaBT8QEx2qAil3ITBtkuHTOQ1pnqowGQTmpw5QHnx2U2cn3oXAM090hrHXYlyZSX7%2Bmk1m%2BpFCe%0AAIvE%2FSocLg%3D%3D\")
and ![\"inline](\"http://api.onlinelibrary.wiley.com/asset/v1/doi/10.1002%2F2014JB011744/asset/equation%2Fjgrb51149-math-0002.png?l=SkaBT8QEx2qAil3ITBtkuHTOQ1pnqowGQTmpw5QHnx2U2cn3oXAM090hrHXYlyZSlKraTIzJaq1Q%0ASI5N7VQPag%3D%3D\")
constant). We speculate that differences in scaled energy values for the two families with variable scaling may result from a lack of resolution in the velocity model. The observation of self-similar scaling is the first of its kind for such a large group of low-frequency volcanic tectonic events occurring during a single active dome extrusion eruption.
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":70154858,"text":"70154858 - 2015 - Novel associations between contaminant body burdens and biomarkers of reproductive condition in male Common Carp along multiple gradients of contaminant exposure in Lake Mead National Recreation Area, USA","interactions":[],"lastModifiedDate":"2015-07-17T12:54:29","indexId":"70154858","displayToPublicDate":"2015-07-01T11:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1738,"text":"General and Comparative Endocrinology","active":true,"publicationSubtype":{"id":10}},"title":"Novel associations between contaminant body burdens and biomarkers of reproductive condition in male Common Carp along multiple gradients of contaminant exposure in Lake Mead National Recreation Area, USA","docAbstract":"Adult male Common Carp were sampled in 2007/08 over a full reproductive cycle at Lake Mead National Recreation Area. Sites sampled included a stream dominated by treated wastewater effluent, a lake basin receiving the streamflow, an upstream lake basin (reference), and a site below Hoover Dam. Individual body burdens for 252 contaminants were measured, and biological variables assessed included physiological [plasma vitellogenin (VTG), estradiol-17β (E2), 11-ketotestosterone (11KT)] and organ [gonadosomatic index (GSI)] endpoints. Patterns in contaminant composition and biological condition were determined by Principal Component Analysis, and their associations modeled by Principal Component Regression. Three spatially distinct but temporally stable gradients of contaminant distribution were recognized: a contaminant mixture typical of wastewaters (PBDEs, methyl triclosan, galaxolide), PCBs, and DDTs. Two spatiotemporally variable patterns of biological condition were recognized: a primary pattern consisting of reproductive condition variables (11KT, E2, GSI), and a secondary pattern including general condition traits (condition factor, hematocrit, fork length). VTG was low in all fish, indicating low estrogenic activity of water at all sites. Wastewater contaminants associated negatively with GSI, 11KT and E2; PCBs associated negatively with GSI and 11KT; and DDTs associated positively with GSI and 11KT. Regression of GSI on sex steroids revealed a novel, nonlinear association between these variables. Inclusion of sex steroids in the GSI regression on contaminants rendered wastewater contaminants nonsignificant in the model and reduced the influence of PCBs and DDTs. Thus, the influence of contaminants on GSI may have been partially driven by organismal modes-of-action that include changes in sex steroid production. The positive association of DDTs with 11KT and GSI suggests that lifetime, sub-lethal exposures to DDTs have effects on male carp opposite of those reported by studies where exposure concentrations were relatively high. Lastly, this study highlighted advantages of multivariate/multiple regression approaches for exploring associations between complex contaminant mixtures and gradients and reproductive condition in wild fishes.
","language":"English","publisher":"Academic Press","publisherLocation":"New York, NY","doi":"10.1016/j.ygcen.2014.12.013","usgsCitation":"Patino, R., VanLandeghem, M., Goodbred, S.L., Orsak, E., Jenkins, J.A., Echols, K.R., Rosen, M.R., and Torres, L., 2015, Novel associations between contaminant body burdens and biomarkers of reproductive condition in male Common Carp along multiple gradients of contaminant exposure in Lake Mead National Recreation Area, USA: General and Comparative Endocrinology, v. 219, p. 112-124, https://doi.org/10.1016/j.ygcen.2014.12.013.","productDescription":"13 p.","startPage":"112","endPage":"124","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059505","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":305643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"219","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55a0ecb2e4b0183d66e43046","contributors":{"authors":[{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"VanLandeghem, Matthew M.","contributorId":143728,"corporation":false,"usgs":false,"family":"VanLandeghem","given":"Matthew M.","affiliations":[],"preferred":false,"id":564279,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goodbred, Steven L. sgoodbred@usgs.gov","contributorId":497,"corporation":false,"usgs":true,"family":"Goodbred","given":"Steven","email":"sgoodbred@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":564280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orsak, Erik","contributorId":92763,"corporation":false,"usgs":true,"family":"Orsak","given":"Erik","affiliations":[],"preferred":false,"id":564281,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenkins, Jill A. 0000-0002-5087-0894 jenkinsj@usgs.gov","orcid":"https://orcid.org/0000-0002-5087-0894","contributorId":2710,"corporation":false,"usgs":true,"family":"Jenkins","given":"Jill","email":"jenkinsj@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":564282,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Echols, Kathy R. 0000-0003-2631-9143 kechols@usgs.gov","orcid":"https://orcid.org/0000-0003-2631-9143","contributorId":2799,"corporation":false,"usgs":true,"family":"Echols","given":"Kathy","email":"kechols@usgs.gov","middleInitial":"R.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":564283,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564284,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Torres, Leticia","contributorId":143738,"corporation":false,"usgs":false,"family":"Torres","given":"Leticia","email":"","affiliations":[],"preferred":false,"id":564285,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70154761,"text":"70154761 - 2015 - Similarities and differences in 13C and 15N stable isotope ratios in two non-lethal tissue types from shovelnose sturgeon Scaphirhynchus platorynchus (Rafinesque, 1820)","interactions":[],"lastModifiedDate":"2015-07-01T10:13:52","indexId":"70154761","displayToPublicDate":"2015-07-01T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2166,"text":"Journal of Applied Ichthyology","active":true,"publicationSubtype":{"id":10}},"title":"Similarities and differences in 13C and 15N stable isotope ratios in two non-lethal tissue types from shovelnose sturgeon Scaphirhynchus platorynchus (Rafinesque, 1820)","docAbstract":"We tested the hypothesis that δ13C and δ15N signatures of pectoral spines would provide measures of δ13C and δ15N similar to those obtained from fin clips for adult shovelnose sturgeon Scaphirhynchus platorynchus. Thirty-two shovelnose sturgeon (fork length [FL] = 500–724 mm) were sampled from the lower Mississippi River, USA on 23 February 2013. Isotopic relationships between the two tissue types were analyzed using mixed model analysis of covariance. Tissue types differed significantly for both δ13C (P < 0.01; spine: mean = −23.83, SD = 0.62; fin clip: mean = −25.74, SD = 0.97) and δ15N (P = 0.01; spine: mean = 17.01, SD = 0.51; fin clip: mean = 17.19, SD = 0.62). Neither FL nor the FL × tissue type interaction had significant (P > 0.05) effects on δ13C. Fin clip δ13C values were highly variable and weakly correlated (r = 0.16, P = 0.40) with those from pectoral spines. We found a significant FL-tissue type interaction for δ15N, reflecting increasing δ15N with FL for spines and decreasing δ15N with FL for fin clips. These results indicate that spines are not a substitute for fin clip tissue for measuring δ13C and δ15N for shovelnose sturgeon in the lower Mississippi River, but the two tissues have different turnover rates they may provide complementary information for assessing trophic position at different time scales.
","language":"English","publisher":"Wiley","doi":"10.1111/jai.12708","usgsCitation":"DeVries, R.J., and Schramm, H.L., 2015, Similarities and differences in 13C and 15N stable isotope ratios in two non-lethal tissue types from shovelnose sturgeon Scaphirhynchus platorynchus (Rafinesque, 1820): Journal of Applied Ichthyology, v. 31, no. 3, p. 474-478, https://doi.org/10.1111/jai.12708.","productDescription":"5 p.","startPage":"474","endPage":"478","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053396","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":471966,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.12708","text":"Publisher Index Page"},{"id":305521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.14669799804688,\n 33.36264966025664\n ],\n [\n -91.14669799804688,\n 33.43086829665599\n ],\n [\n -91.05949401855469,\n 33.43086829665599\n ],\n [\n -91.05949401855469,\n 33.36264966025664\n ],\n [\n -91.14669799804688,\n 33.36264966025664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-21","publicationStatus":"PW","scienceBaseUri":"55950123e4b0b6d21dd6cbbe","chorus":{"doi":"10.1111/jai.12708","url":"http://dx.doi.org/10.1111/jai.12708","publisher":"Wiley-Blackwell","authors":"DeVries R. J., Schramm H. L.","journalName":"Journal of Applied Ichthyology","publicationDate":"3/21/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"DeVries, R. J.","contributorId":145428,"corporation":false,"usgs":false,"family":"DeVries","given":"R.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":564011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schramm, Harold L. Jr. hschramm@usgs.gov","contributorId":145424,"corporation":false,"usgs":true,"family":"Schramm","given":"Harold","suffix":"Jr.","email":"hschramm@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":563982,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155515,"text":"70155515 - 2015 - Genetic diversity is related to climatic variation and vulnerability in threatened bull trout","interactions":[],"lastModifiedDate":"2015-08-10T10:03:50","indexId":"70155515","displayToPublicDate":"2015-07-01T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Genetic diversity is related to climatic variation and vulnerability in threatened bull trout","docAbstract":"Understanding how climatic variation influences ecological and evolutionary processes is crucial for informed conservation decision-making. Nevertheless, few studies have measured how climatic variation influences genetic diversity within populations or how genetic diversity is distributed across space relative to future climatic stress. Here, we tested whether patterns of genetic diversity (allelic richness) were related to climatic variation and habitat features in 130 bull trout (Salvelinus confluentus) populations from 24 watersheds (i.e., ~4–7th order river subbasins) across the Columbia River Basin, USA. We then determined whether bull trout genetic diversity was related to climate vulnerability at the watershed scale, which we quantified on the basis of exposure to future climatic conditions (projected scenarios for the 2040s) and existing habitat complexity. We found a strong gradient in genetic diversity in bull trout populations across the Columbia River Basin, where populations located in the most upstream headwater areas had the greatest genetic diversity. After accounting for spatial patterns with linear mixed models, allelic richness in bull trout populations was positively related to habitat patch size and complexity, and negatively related to maximum summer temperature and the frequency of winter flooding. These relationships strongly suggest that climatic variation influences evolutionary processes in this threatened species and that genetic diversity will likely decrease due to future climate change. Vulnerability at a watershed scale was negatively correlated with average genetic diversity (r = −0.77;P < 0.001); watersheds containing populations with lower average genetic diversity generally had the lowest habitat complexity, warmest stream temperatures, and greatest frequency of winter flooding. Together, these findings have important conservation implications for bull trout and other imperiled species. Genetic diversity is already depressed where climatic vulnerability is highest; it will likely erode further in the very places where diversity may be most needed for future persistence.
","language":"English","publisher":"Blackwell Science","publisherLocation":"Oxford, England","doi":"10.1111/gcb.12850","usgsCitation":"Kovach, R., Muhlfeld, C.C., Wade, A., Hand, B.K., Whited, D.C., DeHaan, P.W., Al-Chokhachy, R.K., and Luikart, G., 2015, Genetic diversity is related to climatic variation and vulnerability in threatened bull trout: Global Change Biology, v. 21, no. 7, p. 2510-2524, https://doi.org/10.1111/gcb.12850.","productDescription":"15 p.","startPage":"2510","endPage":"2524","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060882","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":306527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-06","publicationStatus":"PW","scienceBaseUri":"55c9cb34e4b08400b1fdb70c","contributors":{"authors":[{"text":"Kovach, Ryan 0000-0001-5402-2123 rkovach@usgs.gov","orcid":"https://orcid.org/0000-0001-5402-2123","contributorId":145914,"corporation":false,"usgs":true,"family":"Kovach","given":"Ryan","email":"rkovach@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":565647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":565648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wade, Alisa A.","contributorId":145917,"corporation":false,"usgs":false,"family":"Wade","given":"Alisa A.","affiliations":[{"id":16296,"text":"University of Montana, Polson Montana 59860 USA","active":true,"usgs":false}],"preferred":false,"id":565651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hand, Brian K.","contributorId":145915,"corporation":false,"usgs":false,"family":"Hand","given":"Brian","email":"","middleInitial":"K.","affiliations":[{"id":16296,"text":"University of Montana, Polson Montana 59860 USA","active":true,"usgs":false}],"preferred":false,"id":565649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whited, Diane C.","contributorId":145916,"corporation":false,"usgs":false,"family":"Whited","given":"Diane","email":"","middleInitial":"C.","affiliations":[{"id":16296,"text":"University of Montana, Polson Montana 59860 USA","active":true,"usgs":false}],"preferred":false,"id":565650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeHaan, Patrick W.","contributorId":145918,"corporation":false,"usgs":false,"family":"DeHaan","given":"Patrick","email":"","middleInitial":"W.","affiliations":[{"id":16297,"text":"USFWS Abernathy Fish Technology Center, Longview, WA 98632","active":true,"usgs":false}],"preferred":false,"id":565652,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Al-Chokhachy, Robert K. 0000-0002-2136-5098 ral-chokhachy@usgs.gov","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":1674,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","email":"ral-chokhachy@usgs.gov","middleInitial":"K.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":565654,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":565653,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70154764,"text":"70154764 - 2015 - The Effect of modeled recharge distribution on simulated groundwater availability and capture","interactions":[],"lastModifiedDate":"2015-07-01T10:06:41","indexId":"70154764","displayToPublicDate":"2015-07-01T11:00:00","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":"The Effect of modeled recharge distribution on simulated groundwater availability and capture","docAbstract":"Simulating groundwater flow in basin-fill aquifers of the semiarid southwestern United States commonly requires decisions about how to distribute aquifer recharge. Precipitation can recharge basin-fill aquifers by direct infiltration and transport through faults and fractures in the high-elevation areas, by flowing overland through high-elevation areas to infiltrate at basin-fill margins along mountain fronts, by flowing overland to infiltrate along ephemeral channels that often traverse basins in the area, or by some combination of these processes. The importance of accurately simulating recharge distributions is a current topic of discussion among hydrologists and water managers in the region, but no comparative study has been performed to analyze the effects of different recharge distributions on groundwater simulations. This study investigates the importance of the distribution of aquifer recharge in simulating regional groundwater flow in basin-fill aquifers by calibrating a groundwater-flow model to four different recharge distributions, all with the same total amount of recharge. Similarities are seen in results from steady-state models for optimized hydraulic conductivity values, fit of simulated to observed hydraulic heads, and composite scaled sensitivities of conductivity parameter zones. Transient simulations with hypothetical storage properties and pumping rates produce similar capture rates and storage change results, but differences are noted in the rate of drawdown at some well locations owing to the differences in optimized hydraulic conductivity. Depending on whether the purpose of the groundwater model is to simulate changes in groundwater levels or changes in storage and capture, the distribution of aquifer recharge may or may not be of primary importance.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12210","usgsCitation":"Tillman, F., Pool, D.R., and Leake, S.A., 2015, The Effect of modeled recharge distribution on simulated groundwater availability and capture: Groundwater, v. 53, no. 3, p. 378-388, https://doi.org/10.1111/gwat.12210.","productDescription":"11 p.","startPage":"378","endPage":"388","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051913","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":305519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Detrital Valley, Hualapai Valley, Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.334716796875,\n 36.00467348670187\n ],\n [\n -113.97216796875,\n 36.20882309283712\n ],\n [\n -113.90625,\n 36.06686213257888\n ],\n [\n -113.64257812499999,\n 36.00467348670187\n ],\n [\n -113.280029296875,\n 35.746512259918504\n ],\n [\n -113.21411132812499,\n 35.35321610123821\n ],\n [\n -113.5986328125,\n 35.092945313732635\n ],\n [\n -113.466796875,\n 34.542762387234845\n ],\n [\n -113.785400390625,\n 34.334364487026306\n ],\n [\n -114.202880859375,\n 34.50655662164561\n ],\n [\n -114.32373046875,\n 34.71452466170392\n ],\n [\n -114.06005859375,\n 34.858890491257824\n ],\n [\n -114.554443359375,\n 35.98689628443789\n ],\n [\n -114.334716796875,\n 36.00467348670187\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"53","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-05-19","publicationStatus":"PW","scienceBaseUri":"55950123e4b0b6d21dd6cbc2","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":1629,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred D.","email":"ftillman@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":564002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pool, Donald R. drpool@usgs.gov","contributorId":1121,"corporation":false,"usgs":true,"family":"Pool","given":"Donald","email":"drpool@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leake, Stanley A. 0000-0003-3568-2542 saleake@usgs.gov","orcid":"https://orcid.org/0000-0003-3568-2542","contributorId":1846,"corporation":false,"usgs":true,"family":"Leake","given":"Stanley","email":"saleake@usgs.gov","middleInitial":"A.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":564004,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"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
Beach erosion is a persistent problem along most open-ocean shores of the United States. Along the Arctic coast of Alaska, coastal erosion is widespread, may be accelerating, and is threatening defense and energy-related infrastructure, coastal habitats, and Native communities. As coastal populations continue to expand and infrastructure and habitat are increasingly threatened by erosion, there is increased demand for accurate information regarding past and present trends and rates of shoreline movement. There also is a need for a comprehensive analysis of shoreline change with metrics that are consistent from one coastal region to another. To meet these national needs, the U.S. Geological Survey is conducting an analysis of historical shoreline changes along the open-ocean sandy shores of the conterminous United States and parts of Hawaii, Alaska, and the Great Lakes. One purpose of this work is to develop standard, repeatable methods for mapping and analyzing shoreline change so that periodic, systematic, and internally consistent updates regarding coastal erosion and land loss can be made nationally.
\nThis report on shoreline change along the north coast of Alaska, between the U.S.-Canadian border and Icy Cape, is one in a series of regionally focused reports on historical shoreline change. Previous investigations include analyses and descriptive reports for the coasts of the U.S. Gulf of Mexico, the Southeast Atlantic, California, the New England and Mid-Atlantic, portions of Hawaii, and the Pacific Northwest coasts of Oregon and Washington.
\nSimilar to the earlier reports in this series, this report summarizes the methods of analysis, documents and describes the results of the analysis, and explains historical trends and rates of shoreline change. This Alaska shoreline change assessment differs from previously published shoreline change assessments in that: (1) only two historical shorelines (from the 1940s and 2000s eras) were available for the Alaska study area whereas four or more shorelines (from 1850 to 2002) were available for the other assessments and, thus, only end-point rates for one long-term analysis period are reported here, compared to a combination of long-term and short-term rates as reported in other studies; (2) modern (2000s era) shorelines in this study represent a visually derived land-water interface position versus an elevation based, tidally referenced shoreline position; and (3) both exposed open-ocean and sheltered mainland-lagoon shorelines and rates of change are included in this study compared to other locations where only exposed open-ocean sandy shorelines or bluff edges were evaluated. No distinction was made between sand or gravel beaches, and the base of the unconsolidated coastal bluff was considered the shoreline where no fronting beach existed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151048","usgsCitation":"Gibbs, A.E., and Richmond, B.M., 2015, National assessment of shoreline change: historical change along the north coast of Alaska, U.S.-Canadian border to Icy Cape: U.S. Geological Survey Open-File Report 2015-1048, ix, 96 p., https://doi.org/10.3133/ofr20151048.","productDescription":"ix, 96 p.","numberOfPages":"110","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-050947","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":305508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151048.jpg"},{"id":305506,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1048/pdf/ofr2015-1048.pdf","text":"Report","size":"14.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":305507,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2015/1030/","text":"Open-File Report 2015-1030","description":"Open-File Report 2015-1030"},{"id":305493,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1048/"}],"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":"55950122e4b0b6d21dd6cbba","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":563998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":563997,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148718,"text":"70148718 - 2015 - Global volcanic hazards and risk","interactions":[],"lastModifiedDate":"2021-02-05T21:22:53.188537","indexId":"70148718","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Global volcanic hazards and risk","docAbstract":"An estimated 800 million people live within 100 km of an active volcano in 86 countries and additional overseas territories worldwide [see Chapter 4 and Appendix B]1. Volcanoes are compelling evidence that the Earth is a dynamic planet characterised by endless change and renewal. Humans have always found volcanic activity fascinating and have often chosen to live close to volcanoes, which commonly provide favourable environments for life. Volcanoes bring many benefits to society: eruptions fertilise soils; elevated topography provides good sites for infrastructure (e.g. telecommunications on elevated ground); water resources are commonly plentiful; volcano tourism can be lucrative; and volcanoes can acquire spiritual, aesthetic or religious significance. Some volcanoes are also associated with geothermal resources, making them a target for exploration and a potential energy resource.
Much of the time volcanoes are not a threat because they erupt very infrequently or because communities have become resilient to frequently erupting volcanoes. However, there is an everpresent danger of a long-dormant volcano re-awakening or of volcanoes producing anomalously large or unexpected eruptions. Volcanic eruptions can cause loss of life and livelihoods in exposed communities, damage or disrupt critical infrastructure and add stress to already fragile environments. Their impacts can be both short-term, e.g. physical damage, and long-term, e.g. sustained or permanent displacement of populations. The risk from volcanic eruptions and their attendant hazards is often underestimated beyond areas within the immediate proximity of a volcano. For example, volcanic ash hazards can have effects hundreds of kilometres away from the vent and have an adverse impact on human and animal health, infrastructure, transport, agriculture and horticulture, the environment and economies. The products of volcanism and their impacts can extend beyond country borders, to be regional and even global in scale.
Although known historical loss of life from volcanic eruptions (since 1600 AD about 280,000 fatalities are recorded, Auker et al. (2013)) is modest compared to other major natural hazards, volcanic eruptions can be catastrophic for exposed communities. In 1985 the town of Armero in Colombia was buried by lahars (volcanic mudflows) with more than 21,000 fatalities due to relatively small explosive eruptions at the summit of Nevado del Ruiz volcano that partially melted a glacier (Voight, 1990).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Global Volcanic Hazards and Risk","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Cambridge University Press","doi":"10.1017/CBO9781316276273.004","isbn":"9781316276273","usgsCitation":"Brown, S.K., Loughlin, S.C., Sparks, R.S., Vye-Brown, C., Barclay, J., Calder, E., Cottrell, E., Jolly, G., Komorowski, J., Mandeville, C., Newhall, C., Palma, J., Potter, S., and Valentine, G., 2015, Global volcanic hazards and risk, chap. of Global Volcanic Hazards and Risk, p. 81-173, https://doi.org/10.1017/CBO9781316276273.004.","productDescription":"93 p.","startPage":"81","endPage":"173","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065048","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"links":[{"id":310875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56349579e4b048076347fd7d","contributors":{"editors":[{"text":"Loughlin, S. C.","contributorId":149548,"corporation":false,"usgs":false,"family":"Loughlin","given":"S.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":578808,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Sparks, R. S. J.","contributorId":46686,"corporation":false,"usgs":false,"family":"Sparks","given":"R.","email":"","middleInitial":"S. J.","affiliations":[],"preferred":false,"id":578810,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Brown, S. K.","contributorId":149551,"corporation":false,"usgs":false,"family":"Brown","given":"S.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":578811,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Jenkins, S. F.","contributorId":149564,"corporation":false,"usgs":false,"family":"Jenkins","given":"S.","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":578829,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Vye-Brown, C.","contributorId":149549,"corporation":false,"usgs":false,"family":"Vye-Brown","given":"C.","email":"","affiliations":[],"preferred":false,"id":578873,"contributorType":{"id":2,"text":"Editors"},"rank":5}],"authors":[{"text":"Brown, S. K.","contributorId":149551,"corporation":false,"usgs":false,"family":"Brown","given":"S.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":578874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loughlin, S. C.","contributorId":149548,"corporation":false,"usgs":false,"family":"Loughlin","given":"S.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":578875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sparks, R. S. J.","contributorId":46686,"corporation":false,"usgs":false,"family":"Sparks","given":"R.","email":"","middleInitial":"S. J.","affiliations":[],"preferred":false,"id":578876,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vye-Brown, C.","contributorId":149549,"corporation":false,"usgs":false,"family":"Vye-Brown","given":"C.","email":"","affiliations":[],"preferred":false,"id":578877,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barclay, J.","contributorId":41168,"corporation":false,"usgs":true,"family":"Barclay","given":"J.","email":"","affiliations":[],"preferred":false,"id":578878,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Calder, E.","contributorId":149552,"corporation":false,"usgs":false,"family":"Calder","given":"E.","email":"","affiliations":[],"preferred":false,"id":578879,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cottrell, E.","contributorId":149553,"corporation":false,"usgs":false,"family":"Cottrell","given":"E.","email":"","affiliations":[],"preferred":false,"id":578880,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jolly, G.","contributorId":149554,"corporation":false,"usgs":false,"family":"Jolly","given":"G.","email":"","affiliations":[],"preferred":false,"id":578881,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Komorowski, J.C.","contributorId":82071,"corporation":false,"usgs":true,"family":"Komorowski","given":"J.C.","affiliations":[],"preferred":false,"id":578882,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mandeville, Charlie 0000-0002-8485-3689 cmandeville@usgs.gov","orcid":"https://orcid.org/0000-0002-8485-3689","contributorId":753,"corporation":false,"usgs":true,"family":"Mandeville","given":"Charlie","email":"cmandeville@usgs.gov","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":549086,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Newhall, C.","contributorId":16557,"corporation":false,"usgs":true,"family":"Newhall","given":"C.","affiliations":[],"preferred":false,"id":578884,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Palma, J.","contributorId":149556,"corporation":false,"usgs":false,"family":"Palma","given":"J.","email":"","affiliations":[],"preferred":false,"id":578885,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Potter, S.","contributorId":149557,"corporation":false,"usgs":false,"family":"Potter","given":"S.","email":"","affiliations":[],"preferred":false,"id":578886,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Valentine, G.","contributorId":149558,"corporation":false,"usgs":false,"family":"Valentine","given":"G.","email":"","affiliations":[],"preferred":false,"id":578887,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"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":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":70155921,"text":"70155921 - 2015 - Tectonic and sedimentary linkages between the Belt-Purcell basin and southwestern Laurentia during the Mesoproterozoic ca. 1.60-1.40 Ga","interactions":[],"lastModifiedDate":"2018-06-19T19:20:17","indexId":"70155921","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2626,"text":"Lithosphere","active":true,"publicationSubtype":{"id":10}},"title":"Tectonic and sedimentary linkages between the Belt-Purcell basin and southwestern Laurentia during the Mesoproterozoic ca. 1.60-1.40 Ga","docAbstract":"Mesoproterozoic sedimentary basins in western North America provide key constraints on pre-Rodinia craton positions and interactions along the western rifted margin of Laurentia. One such basin, the Belt-Purcell basin, extends from southern Idaho into southern British Columbia and contains a >18-km-thick succession of siliciclastic sediment deposited ca. 1.47–1.40 Ga. The ca. 1.47–1.45 Ga lower part of the succession contains abundant distinctive non-Laurentian 1.61–1.50 Ga detrital zircon populations derived from exotic cratonic sources. Contemporaneous metasedimentary successions in the southwestern United States–the Trampas and Yankee Joe basins in Arizona and New Mexico–also contain abundant 1.61–1.50 Ga detrital zircons. Similarities in depositional age and distinctive non-Laurentian detrital zircon populations suggest that both the Belt-Purcell and southwestern successions record sedimentary and tectonic linkages between western Laurentia and one or more cratons including North Australia, South Australia, and (or) East Antarctica. At ca. 1.45 Ga, both the Belt-Purcell and southwest successions underwent major sedimentological changes, with a pronounced shift to Laurentian provenance and the disappearance of the 1.61–1.50 Ga detrital zircon. Upper Belt-Purcell strata contain strongly unimodal ca. 1.73 Ga detrital zircon age populations that match the detrital zircon signature of Paleoproterozoic metasedimentary rocks of the Yavapai province to the south and southeast. We propose that the shift at ca. 1.45 Ga records the onset of orogenesis in southern Laurentia coeval with rifting along its northwestern margin. Bedrock uplift associated with orogenesis and widespread, coeval magmatism caused extensive exhumation and erosion of the Yavapai province ca. 1.45–1.36 Ga, providing a voluminous and areally extensive sediment source–with suitable zircon ages–during upper Belt deposition. This model provides a comprehensive and integrated view of the Mesoproterozoic tectonic evolution of western Laurentia and its position within the supercontinent Columbia as it evolved into Rodinia.
","language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","doi":"10.1130/L438.1","usgsCitation":"Jones, J.V., Dainel, C.G., and Doe, M., 2015, Tectonic and sedimentary linkages between the Belt-Purcell basin and southwestern Laurentia during the Mesoproterozoic ca. 1.60-1.40 Ga: Lithosphere, v. 7, no. 4, p. 465-472, https://doi.org/10.1130/L438.1.","productDescription":"8 p.","startPage":"465","endPage":"472","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058162","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":471981,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/l438.1","text":"Publisher Index Page"},{"id":306643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-21","publicationStatus":"PW","scienceBaseUri":"55cdbfbde4b08400b1fe143f","contributors":{"authors":[{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":566869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dainel, Christohper G","contributorId":146260,"corporation":false,"usgs":false,"family":"Dainel","given":"Christohper","email":"","middleInitial":"G","affiliations":[{"id":16651,"text":"Bucknell University","active":true,"usgs":false}],"preferred":false,"id":566870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doe, Michael F","contributorId":146261,"corporation":false,"usgs":false,"family":"Doe","given":"Michael F","affiliations":[{"id":16652,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":566871,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"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":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research 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":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":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":250,"text":"Eastern Water Science Field Team","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":70188037,"text":"70188037 - 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":70187424,"text":"70187424 - 2015 - Early Holocene Great Salt Lake","interactions":[],"lastModifiedDate":"2017-05-02T14:51:22","indexId":"70187424","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Early Holocene Great Salt Lake","docAbstract":"Shorelines and surficial deposits (including buried forest-floor mats and organic-rich wetland sediments) show that Great Salt Lake did not rise higher than modern lake levels during the earliest Holocene (11.5–10.2 cal ka BP; 10–9 14C ka BP). During that period, finely laminated, organic-rich muds (sapropel) containing brine-shrimp cysts and pellets and interbedded sodium-sulfate salts were deposited on the lake floor. Sapropel deposition was probably caused by stratification of the water column — a freshwater cap possibly was formed by groundwater, which had been stored in upland aquifers during the immediately preceding late-Pleistocene deep-lake cycle (Lake Bonneville), and was actively discharging on the basin floor. A climate characterized by low precipitation and runoff, combined with local areas of groundwater discharge in piedmont settings, could explain the apparent conflict between evidence for a shallow lake (a dry climate) and previously published interpretations for a moist climate in the Great Salt Lake basin of the eastern Great Basin.
","language":"English","publisher":"Cambridge University Press","doi":"10.1016/j.yqres.2015.05.001","usgsCitation":"Oviatt, C., Madsen, D.B., Miller, D., Thompson, R.S., and McGeehin, J.P., 2015, Early Holocene Great Salt Lake: Quaternary Research, v. 84, no. 1, p. 57-68, https://doi.org/10.1016/j.yqres.2015.05.001.","productDescription":"12 p.","startPage":"57","endPage":"68","ipdsId":"IP-064151","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":340752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Great Salt Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.20037841796875,\n 40.60978237983301\n ],\n [\n -111.8023681640625,\n 40.60978237983301\n ],\n [\n -111.8023681640625,\n 41.73033005046653\n ],\n [\n -113.20037841796875,\n 41.73033005046653\n ],\n [\n -113.20037841796875,\n 40.60978237983301\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"59099aafe4b0fc4e449157f8","contributors":{"authors":[{"text":"Oviatt, Charles G.","contributorId":13503,"corporation":false,"usgs":true,"family":"Oviatt","given":"Charles G.","affiliations":[],"preferred":false,"id":694001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madsen, David B.","contributorId":191727,"corporation":false,"usgs":false,"family":"Madsen","given":"David","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":694002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David M. 0000-0003-3711-0441 dmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":140769,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","email":"dmiller@usgs.gov","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":694000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Robert S. 0000-0001-9287-2954 rthompson@usgs.gov","orcid":"https://orcid.org/0000-0001-9287-2954","contributorId":891,"corporation":false,"usgs":true,"family":"Thompson","given":"Robert","email":"rthompson@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":694003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGeehin, John P. 0000-0002-5320-6091 mcgeehin@usgs.gov","orcid":"https://orcid.org/0000-0002-5320-6091","contributorId":130967,"corporation":false,"usgs":true,"family":"McGeehin","given":"John","email":"mcgeehin@usgs.gov","middleInitial":"P.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":694004,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156876,"text":"70156876 - 2015 - Soil surface organic layers in Arctic Alaska: spatial distribution, rates of formation, and microclimatic effects","interactions":[],"lastModifiedDate":"2018-04-04T16:07:37","indexId":"70156876","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Soil surface organic layers in Arctic Alaska: spatial distribution, rates of formation, and microclimatic effects","docAbstract":"Organic layers of living and dead vegetation cover the ground surface in many permafrost landscapes and play important roles in ecosystem processes. These soil surface organic layers (SSOLs) store large amounts of carbon and buffer the underlying permafrost and its contained carbon from changes in aboveground climate. Understanding the dynamics of SSOLs is a prerequisite for predicting how permafrost and carbon stocks will respond to warming climate. Here we ask three questions about SSOLs in a representative area of the Arctic Foothills region of northern Alaska: (1) What environmental factors control the thickness of SSOLs and the carbon they store? (2) How long do SSOLs take to develop on newly stabilized point bars? (3) How do SSOLs affect temperature in the underlying ground? Results show that SSOL thickness and distribution correlate with elevation, drainage area, vegetation productivity, and incoming solar radiation. A multiple regression model based on these correlations can simulate spatial distribution of SSOLs and estimate the organic carbon stored there. SSOLs develop within a few decades after a new, sandy, geomorphic surface stabilizes but require 500–700 years to reach steady state thickness. Mature SSOLs lower the growing season temperature and mean annual temperature of the underlying mineral soil by 8 and 3°C, respectively. We suggest that the proximate effects of warming climate on permafrost landscapes now covered by SSOLs will occur indirectly via climate's effects on the frequency, extent, and severity of disturbances like fires and landslides that disrupt the SSOLs and interfere with their protection of the underlying permafrost.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015JG002983","usgsCitation":"Baughman, C., Mann, D., Verbyla, D.L., and Kunz, M.L., 2015, Soil surface organic layers in Arctic Alaska: spatial distribution, rates of formation, and microclimatic effects: Journal of Geophysical Research: Biogeosciences, v. 120, no. 6, p. 1150-1164, https://doi.org/10.1002/2015JG002983.","productDescription":"15 p.","startPage":"1150","endPage":"1164","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064795","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":471970,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jg002983","text":"Publisher Index Page"},{"id":307786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.466796875,\n 67.19551751715585\n ],\n [\n -158.466796875,\n 69.12344255014861\n ],\n [\n -155.01708984375,\n 69.12344255014861\n ],\n [\n -155.01708984375,\n 67.19551751715585\n ],\n [\n -158.466796875,\n 67.19551751715585\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-06-30","publicationStatus":"PW","scienceBaseUri":"55e6cc37e4b05561fa20a02b","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":570920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mann, Daniel H.","contributorId":97441,"corporation":false,"usgs":true,"family":"Mann","given":"Daniel H.","affiliations":[],"preferred":false,"id":570921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Verbyla, David L.","contributorId":84611,"corporation":false,"usgs":true,"family":"Verbyla","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":570922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kunz, Michael L.","contributorId":50820,"corporation":false,"usgs":true,"family":"Kunz","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":570923,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70162083,"text":"70162083 - 2015 - Predicting redox conditions in groundwater at a regional scale","interactions":[],"lastModifiedDate":"2016-03-07T12:03:19","indexId":"70162083","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Predicting redox conditions in groundwater at a regional scale","docAbstract":"Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.5b01869","usgsCitation":"Tesoriero, A., Terziotti, S., and Abrams, D.B., 2015, Predicting redox conditions in groundwater at a regional scale: Environmental Science & Technology, v. 49, no. 16, p. 9657-9664, https://doi.org/10.1021/acs.est.5b01869.","productDescription":"8 p.","startPage":"9657","endPage":"9664","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064245","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":438691,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78C9TC5","text":"USGS data release","linkHelpText":"Depth to 50 percent probability of oxic conditions, Chesapeake Bay Watershed"},{"id":314322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New York, Pennsylvania, Virginia","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.0693359375,\n 36.8092847020594\n ],\n [\n -76.66259765625,\n 36.914764288955936\n ],\n [\n -77.431640625,\n 37.020098201368114\n ],\n [\n -79.21142578125,\n 37.19533058280065\n ],\n [\n -80.09033203125,\n 37.33522435930641\n ],\n [\n -80.7275390625,\n 37.42252593456307\n ],\n [\n -80.26611328125,\n 37.54457732085582\n ],\n [\n -80.0244140625,\n 38.03078569382294\n ],\n [\n -79.65087890624999,\n 38.5825261593533\n ],\n [\n -79.2333984375,\n 38.444984668894705\n ],\n [\n -79.453125,\n 39.30029918615029\n ],\n [\n -78.79394531249999,\n 39.67337039176558\n ],\n [\n -78.42041015625,\n 40.48038142908172\n ],\n [\n -78.134765625,\n 41.32732632036622\n ],\n [\n -77.49755859375,\n 42.04929263868686\n ],\n [\n -77.3876953125,\n 42.42345651793833\n ],\n [\n -77.431640625,\n 42.89206418807337\n ],\n [\n -76.66259765625,\n 43.068887774169625\n ],\n [\n -76.09130859375,\n 42.924251753870685\n ],\n [\n -74.99267578125,\n 42.87596410238254\n ],\n [\n -74.70703125,\n 43.29320031385282\n ],\n [\n -73.8720703125,\n 43.42100882994726\n ],\n [\n -73.828125,\n 42.71473218539458\n ],\n [\n -74.68505859374999,\n 42.16340342422401\n ],\n [\n -75.21240234375,\n 41.820455096140314\n ],\n [\n -75.498046875,\n 41.52502957323801\n ],\n [\n -75.498046875,\n 41.3108238809182\n ],\n [\n -75.87158203125,\n 40.713955826286046\n ],\n [\n -76.552734375,\n 40.49709237269567\n ],\n [\n -76.552734375,\n 40.094882122321174\n ],\n [\n -76.1572265625,\n 39.707186656826565\n ],\n [\n -75.87158203125,\n 39.06184913429154\n ],\n [\n -75.56396484375,\n 38.47939467327645\n ],\n [\n -75.3662109375,\n 38.09998264736481\n ],\n [\n -75.82763671875,\n 37.579412513438385\n ],\n [\n -76.0693359375,\n 36.8092847020594\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"16","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-31","publicationStatus":"PW","scienceBaseUri":"5698d4d0e4b0fbd3f7fa4c5a","chorus":{"doi":"10.1021/acs.est.5b01869","url":"http://dx.doi.org/10.1021/acs.est.5b01869","publisher":"American Chemical Society (ACS)","authors":"Tesoriero Anthony J., Terziotti Silvia, Abrams Daniel B.","journalName":"Environmental Science & Technology","publicationDate":"8/18/2015"},"contributors":{"authors":[{"text":"Tesoriero, Anthony J.","contributorId":40207,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":588481,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terziotti, Silvia 0000-0003-3559-5844 seterzio@usgs.gov","orcid":"https://orcid.org/0000-0003-3559-5844","contributorId":1613,"corporation":false,"usgs":true,"family":"Terziotti","given":"Silvia","email":"seterzio@usgs.gov","affiliations":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abrams, Daniel B.","contributorId":45985,"corporation":false,"usgs":true,"family":"Abrams","given":"Daniel","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":588483,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70174152,"text":"70174152 - 2015 - Linking state-and-transition simulation and timber supply models for forest biomass production scenarios","interactions":[],"lastModifiedDate":"2018-12-20T12:54:49","indexId":"70174152","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":"Linking state-and-transition simulation and timber supply models for forest biomass production scenarios","docAbstract":"We linked state-and-transition simulation models (STSMs) with an economics-based timber supply model to examine landscape dynamics in North Carolina through 2050 for three scenarios of forest biomass production. Forest biomass could be an important source of renewable energy in the future, but there is currently much uncertainty about how biomass production would impact landscapes. In the southeastern US, if forests become important sources of biomass for bioenergy, we expect increased land-use change and forest management. STSMs are ideal for simulating these landscape changes, but the amounts of change will depend on drivers such as timber prices and demand for forest land, which are best captured with forest economic models. We first developed state-and-transition model pathways in the ST-Sim software platform for 49 vegetation and land-use types that incorporated each expected type of landscape change. Next, for the three biomass production scenarios, the SubRegional Timber Supply Model (SRTS) was used to determine the annual areas of thinning and harvest in five broad forest types, as well as annual areas converted among those forest types, agricultural, and urban lands. The SRTS output was used to define area targets for STSMs in ST-Sim under two scenarios of biomass production and one baseline, business-as-usual scenario. We show that ST-Sim output matched SRTS targets in most cases. Landscape dynamics results indicate that, compared with the baseline scenario, forest biomass production leads to more forest and, specifically, more intensively managed forest on the landscape by 2050. Thus, the STSMs, informed by forest economics models, provide important information about potential landscape effects of bioenergy production.
","language":"English","publisher":"AIMS Press","doi":"10.3934/environsci.2015.2.180","usgsCitation":"Costanza, J., Abt, R.C., McKerrow, A., and Collazo, J., 2015, Linking state-and-transition simulation and timber supply models for forest biomass production scenarios: AIMS Environmental Science, v. 2, no. 2, p. 180-202, https://doi.org/10.3934/environsci.2015.2.180.","productDescription":"23 p.","startPage":"180","endPage":"202","ipdsId":"IP-063156","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":38315,"text":"GAP Analysis Project","active":true,"usgs":true}],"links":[{"id":471975,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/environsci.2015.2.180","text":"Publisher Index Page"},{"id":328360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57d28bade4b0571647d0f934","contributors":{"authors":[{"text":"Costanza, Jennifer","contributorId":74689,"corporation":false,"usgs":true,"family":"Costanza","given":"Jennifer","affiliations":[],"preferred":false,"id":648330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abt, Robert C.","contributorId":174475,"corporation":false,"usgs":false,"family":"Abt","given":"Robert","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":648331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":648332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime jaime_collazo@usgs.gov","contributorId":2613,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"jaime_collazo@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":640999,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173444,"text":"70173444 - 2015 - Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science through data reuse","interactions":[],"lastModifiedDate":"2016-06-20T14:07:39","indexId":"70173444","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5091,"text":"GigaScience","active":true,"publicationSubtype":{"id":10}},"title":"Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science through data reuse","docAbstract":"Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. 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.
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Southern flounder (Paralichthys lethostigma) is a popular commercial and recreational species along the southeast Atlantic coast and Gulf of Mexico, USA. Recent studies have provided genetic and otolith morphology evidence that the Gulf of Mexico and Atlantic Ocean stocks differ. Using age and growth data from four states (Texas, Alabama, South Carolina, and North Carolina) we expanded upon the traditional von Bertalanffy model in order to compare growth rates of putative geographic stocks of southern flounder. We improved the model fitting process by adding a hierarchical Bayesian framework to allow each parameter to vary spatially or temporally as a random effect, as well as log transforming the three model parameters (L∞, K, andt0). Multiple comparisons of parameters showed that growth rates varied (even within states) for females, but less for males. Growth rates were also consistent through time, when long-term data were available. 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