{"pageNumber":"459","pageRowStart":"11450","pageSize":"25","recordCount":40783,"records":[{"id":70178810,"text":"70178810 - 2016 - Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades","interactions":[],"lastModifiedDate":"2016-12-08T09:30:34","indexId":"70178810","displayToPublicDate":"2016-12-08T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades","docAbstract":"<p><span>Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH</span><sub>4</sub><span>) emissions in China is important for improving our knowledge on CH</span><sub>4</sub><span> budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH</span><sub>4</sub><span> model to quantify the human and climate change induced contributions to natural wetland CH</span><sub>4</sub><span> emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH</span><sub>4</sub><span> emissions reduction (0.92 TgCH</span><sub>4</sub><span>), and climate change contributed 20.4% to the CH</span><sub>4</sub><span> emissions increase (0.31 TgCH</span><sub>4</sub><span>), suggesting that decreasing CH</span><sub>4</sub><span> emissions due to human-induced wetland reductions has offset the increasing climate-driven CH</span><sub>4</sub><span> emissions. With climate change only, temperature was a dominant controlling factor for wetland CH</span><sub>4</sub><span> emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH</span><sub>4&nbsp;</sub><span>emissions estimation.</span></p>","language":"English","publisher":"Macmillan Journals Ltd.","publisherLocation":"London","doi":"10.1038/srep38020","usgsCitation":"Zhu, Q., Peng, C., Liu, J., Jiang, H., Fang, X., Chen, H., Niu, Z., Gong, P., Lin, G., Wang, M., Yang, Y., Chang, J., Ge, Y., Xiang, W., Deng, X., and He, J., 2016, Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades: Scientific Reports, v. 6, 38020; 7 p., https://doi.org/10.1038/srep38020.","productDescription":"38020; 7 p.","ipdsId":"IP-067732","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470334,"rank":0,"type":{"id":40,"text":"Open Access 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Jin-Sheng","contributorId":177302,"corporation":false,"usgs":false,"family":"He","given":"Jin-Sheng","email":"","affiliations":[],"preferred":false,"id":655241,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70178803,"text":"70178803 - 2016 - Response comment: Carbon sequestration on Mars","interactions":[],"lastModifiedDate":"2016-12-08T09:15:40","indexId":"70178803","displayToPublicDate":"2016-12-08T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Response comment: Carbon sequestration on Mars","docAbstract":"<p>Martian atmospheric pressure has important implications for the past and present habitability of the planet, including the timing and causes of environmental change. The ancient Martian surface is strewn with evidence for early water bound in minerals (e.g., Ehlmann and Edwards, 2014) and recorded in surface features such as large catastrophically created outflow channels (e.g., Carr, 1979), valley networks (Hynek et al., 2010; Irwin et al., 2005), and crater lakes (e.g., Fassett and Head, 2008). Using orbital spectral data sets coupled with geologic maps and a set of numerical spectral analysis models, Edwards and Ehlmann (2015) constrained the amount of atmospheric sequestration in early Martian rocks and found that the majority of this sequestration occurred prior to the formation of the early Hesperian/late Noachian valley networks (Fassett and Head, 2011; Hynek et al., 2010), thus implying the atmosphere was already thin by the time these surface-water-related features were formed.</p>","language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","doi":"10.1130/G37984Y.1","usgsCitation":"Edwards, C., and Ehlmann, B.L., 2016, Response comment: Carbon sequestration on Mars: Geology, v. 44, no. 6, e389; 1 p., https://doi.org/10.1130/G37984Y.1.","productDescription":"e389; 1 p.","ipdsId":"IP-075232","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":462001,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g37984y.1","text":"Publisher Index Page"},{"id":331672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"44","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-23","publicationStatus":"PW","scienceBaseUri":"584a7f7de4b07e29c706dd35","contributors":{"authors":[{"text":"Edwards, Christopher cedwards@usgs.gov","contributorId":147768,"corporation":false,"usgs":true,"family":"Edwards","given":"Christopher","email":"cedwards@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":655155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ehlmann, Bethany L. 0000-0002-2745-3240","orcid":"https://orcid.org/0000-0002-2745-3240","contributorId":147154,"corporation":false,"usgs":false,"family":"Ehlmann","given":"Bethany","email":"","middleInitial":"L.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":655156,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70178758,"text":"70178758 - 2016 - The Carolina Sandhills: Quaternary eolian sand sheets and dunes along the updip margin of the Atlantic Coastal Plain province, southeastern United States","interactions":[],"lastModifiedDate":"2016-12-07T11:15:30","indexId":"70178758","displayToPublicDate":"2016-12-07T00:00:00","publicationYear":"2016","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":"The Carolina Sandhills: Quaternary eolian sand sheets and dunes along the updip margin of the Atlantic Coastal Plain province, southeastern United States","docAbstract":"<p><span>The Carolina Sandhills is a physiographic region of the Atlantic Coastal Plain province in the southeastern United States. In Chesterfield County (South Carolina), the surficial sand of this region is the Pinehurst Formation, which is interpreted as eolian sand derived from the underlying Cretaceous Middendorf Formation. This sand has yielded three clusters of optically stimulated luminescence ages: (1) 75 to 37 thousand years ago (ka), coincident with growth of the Laurentide Ice Sheet; (2) 28 to 18 ka, coincident with the last glacial maximum (LGM); and (3) 12 to 6 ka, mostly coincident with the Younger Dryas through final collapse of the Laurentide Ice Sheet. Relict dune morphologies are consistent with winds from the west or northwest, coincident with modern and inferred LGM January wind directions. Sand sheets are more common than dunes because of effects of coarse grain size (mean range: 0.35–0.59&nbsp;mm) and vegetation. The coarse grain size would have required LGM wind velocities of at least 4–6&nbsp;m/sec, accounting for effects of colder air temperatures on eolian sand transport. The eolian interpretation of the Carolina Sandhills is consistent with other evidence for eolian activity in the southeastern United States during the last glaciation.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.yqres.2016.08.007","usgsCitation":"Swezey, C.S., Fitzwater, B.A., Whittecar, G.R., Mahan, S.A., Garrity, C.P., Aleman-Gonzalez, W.B., and Dobbs, K.M., 2016, The Carolina Sandhills: Quaternary eolian sand sheets and dunes along the updip margin of the Atlantic Coastal Plain province, southeastern United States: Quaternary Research, v. 86, no. 3, p. 271-286, https://doi.org/10.1016/j.yqres.2016.08.007.","productDescription":"16 p.","startPage":"271","endPage":"286","ipdsId":"IP-072092","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":331620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"86","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"58492df1e4b06d80b7b0939c","contributors":{"authors":[{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":655055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzwater, Bradley A.","contributorId":177211,"corporation":false,"usgs":false,"family":"Fitzwater","given":"Bradley","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":655056,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whittecar, G. Richard","contributorId":177212,"corporation":false,"usgs":false,"family":"Whittecar","given":"G.","email":"","middleInitial":"Richard","affiliations":[],"preferred":false,"id":655057,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":655058,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrity, Christopher P. 0000-0002-5565-1818 cgarrity@usgs.gov","orcid":"https://orcid.org/0000-0002-5565-1818","contributorId":644,"corporation":false,"usgs":true,"family":"Garrity","given":"Christopher","email":"cgarrity@usgs.gov","middleInitial":"P.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":655059,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aleman-Gonzalez, Wilma B. 0000-0003-3156-0126 waleman@usgs.gov","orcid":"https://orcid.org/0000-0003-3156-0126","contributorId":2530,"corporation":false,"usgs":true,"family":"Aleman-Gonzalez","given":"Wilma","email":"waleman@usgs.gov","middleInitial":"B.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":655060,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dobbs, Kerby M.","contributorId":177220,"corporation":false,"usgs":false,"family":"Dobbs","given":"Kerby","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":655061,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70178727,"text":"70178727 - 2016 - Animal movement in the absence of predation: environmental drivers of movement strategies in a partial migration system","interactions":[],"lastModifiedDate":"2017-07-03T09:36:15","indexId":"70178727","displayToPublicDate":"2016-12-07T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Animal movement in the absence of predation: environmental drivers of movement strategies in a partial migration system","docAbstract":"<p><span>Animal movement strategies including migration, dispersal, nomadism, and residency are shaped by broad-scale spatial-temporal structuring of the environment, including factors such as the degrees of spatial variation, seasonality and inter-annual predictability. Animal movement strategies, in turn, interact with the characteristics of individuals and the local distribution of resources to determine local patterns of resource selection with complex and poorly understood implications for animal fitness. Here we present a multi-scale investigation of animal movement strategies and resource selection. We consider the degree to which spatial variation, seasonality, and inter-annual predictability in resources drive migration patterns among different taxa and how movement strategies in turn shape local resource selection patterns. We focus on adult Galapagos giant tortoises </span><i>Chelonoidis</i><span> spp. as a model system since they display many movement strategies and evolved in the absence of predators of adults. Specifically, our analysis is based on 63 individuals among four taxa tracked on three islands over six years and almost 10</span><sup>6</sup><span> tortoise re-locations. Tortoises displayed a continuum of movement strategies from migration to sedentarism that were linked to the spatio-temporal scale and predictability of resource distributions. Movement strategies shaped patterns of resource selection. Specifically, migratory individuals displayed stronger selection toward areas where resources were more predictable among years than did non-migratory individuals, which indicates a selective advantage for migrants in seasonally structured, more predictable environments. Our analytical framework combines large-scale predictions for movement strategies, based on environmental structuring, with finer-scale analysis of space-use. Integrating different organizational levels of analysis provides a deeper understanding of the eco-evolutionary dynamics at play in the emergence and maintenance of migration and the critical role of resource predictability. Our results highlight that assessing the potential benefits of differential behavioral responses first requires an understanding of the interactions among movement strategies, resource selection and individual characteristics.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/oik.03928","usgsCitation":"Bastille-Rousseau, G., Gibbs, J.P., Yackulic, C.B., Frair, J.L., Cabrera, F., and Rousseau, L., 2016, Animal movement in the absence of predation: environmental drivers of movement strategies in a partial migration system: Oikos, v. 126, no. 7, p. 1004-1019, https://doi.org/10.1111/oik.03928.","productDescription":"16 p.","startPage":"1004","endPage":"1019","ipdsId":"IP-077124","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470339,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-0-397949","text":"External Repository"},{"id":331621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-23","publicationStatus":"PW","scienceBaseUri":"58492df1e4b06d80b7b0939e","contributors":{"authors":[{"text":"Bastille-Rousseau, Guillaume","contributorId":169986,"corporation":false,"usgs":false,"family":"Bastille-Rousseau","given":"Guillaume","affiliations":[{"id":25645,"text":"State Uni. of New York","active":true,"usgs":false}],"preferred":false,"id":655080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gibbs, James P.","contributorId":102418,"corporation":false,"usgs":false,"family":"Gibbs","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":12623,"text":"State University of New York College of Environmental Science and Forestry","active":true,"usgs":false}],"preferred":false,"id":655081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":655082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frair, Jacqueline L.","contributorId":140184,"corporation":false,"usgs":false,"family":"Frair","given":"Jacqueline","email":"","middleInitial":"L.","affiliations":[{"id":13404,"text":"SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":655083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cabrera, Fredy","contributorId":139278,"corporation":false,"usgs":false,"family":"Cabrera","given":"Fredy","affiliations":[{"id":12718,"text":"Charles Darwin Foundation","active":true,"usgs":false}],"preferred":false,"id":655084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rousseau, Louis-Philippe","contributorId":177206,"corporation":false,"usgs":false,"family":"Rousseau","given":"Louis-Philippe","email":"","affiliations":[],"preferred":false,"id":655085,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70178701,"text":"70178701 - 2016 - Byproduct metal requirements for U.S. wind and solar photovoltaic electricity generation up to the year 2040 under various Clean Power Plan scenarios","interactions":[],"lastModifiedDate":"2016-12-06T12:34:47","indexId":"70178701","displayToPublicDate":"2016-12-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":832,"text":"Applied Energy","active":true,"publicationSubtype":{"id":10}},"title":"Byproduct metal requirements for U.S. wind and solar photovoltaic electricity generation up to the year 2040 under various Clean Power Plan scenarios","docAbstract":"<p><span>The United States has and will likely continue to obtain an increasing share of its electricity from solar photovoltaics (PV) and wind power, especially under the Clean Power Plan (CPP). The need for additional solar PV modules and wind turbines will, among other things, result in greater demand for a number of minor metals that are produced mainly or only as byproducts. In this analysis, the quantities of 11 byproduct metals (Ag, Cd, Te, In, Ga, Se, Ge, Nd, Pr, Dy, and Tb) required for wind turbines with rare-earth permanent magnets and four solar PV technologies are assessed through the year 2040. Three key uncertainties (electricity generation capacities, technology market shares, and material intensities) are varied to develop 42 scenarios for each byproduct metal. The results indicate that byproduct metal requirements vary significantly across technologies, scenarios, and over time. In certain scenarios, the requirements are projected to become a significant portion of current primary production. This is especially the case for Te, Ge, Dy, In, and Tb under the more aggressive scenarios of increasing market share and conservative material intensities. Te and Dy are, perhaps, of most concern given their substitution limitations. In certain years, the differences in byproduct metal requirements between the technology market share and material intensity scenarios are greater than those between the various CPP and No CPP scenarios. Cumulatively across years 2016–2040, the various CPP scenarios are estimated to require 15–43% more byproduct metals than the No CPP scenario depending on the specific byproduct metal and scenario. Increasing primary production via enhanced recovery rates of the byproduct metals during the beneficiation and enrichment operations, improving end-of-life recycling rates, and developing substitutes are important strategies that may help meet the increased demand for these byproduct metals.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.apenergy.2016.08.062","usgsCitation":"Nassar, N., Wilburn, D.R., and Goonan, T.G., 2016, Byproduct metal requirements for U.S. wind and solar photovoltaic electricity generation up to the year 2040 under various Clean Power Plan scenarios: Applied Energy, v. 183, p. 1209-1226, https://doi.org/10.1016/j.apenergy.2016.08.062.","productDescription":"18 p.","startPage":"1209","endPage":"1226","ipdsId":"IP-078635","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":331545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"183","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5847dc7be4b06d80b7af6aa7","contributors":{"authors":[{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":177175,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal T.","email":"nnassar@usgs.gov","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":654872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilburn, David R. 0000-0002-5371-7617 wilburn@usgs.gov","orcid":"https://orcid.org/0000-0002-5371-7617","contributorId":1755,"corporation":false,"usgs":true,"family":"Wilburn","given":"David","email":"wilburn@usgs.gov","middleInitial":"R.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":654873,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goonan, Thomas G. goonan@usgs.gov","contributorId":2761,"corporation":false,"usgs":true,"family":"Goonan","given":"Thomas","email":"goonan@usgs.gov","middleInitial":"G.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":654874,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70175362,"text":"70175362 - 2016 - Survival estimates for reintroduced populations of the Chiricahua Leopard Frog (<i>Lithobates chiricahuensis</i>)","interactions":[],"lastModifiedDate":"2016-12-06T10:24:46","indexId":"70175362","displayToPublicDate":"2016-12-06T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1337,"text":"Copeia","active":true,"publicationSubtype":{"id":10}},"title":"Survival estimates for reintroduced populations of the Chiricahua Leopard Frog (<i>Lithobates chiricahuensis</i>)","docAbstract":"<p><span>Global amphibian declines have been attributed to a number of factors including disease, invasive species, habitat degradation, and climate change. Reintroduction is one management action that is commonly used with the goal of recovering imperiled species. The success of reintroductions varies widely, and evaluating their efficacy requires estimates of population viability metrics, such as underlying vital rates and trends in abundance. Although rarely quantified, assessing vital rates for recovering populations provides a more mechanistic understanding of population growth than numerical trends in population occupancy or abundance. We used three years of capture-mark-recapture data from three breeding ponds and a Cormack-Jolly-Seber model to estimate annual apparent survival for reintroduced populations of the federally threatened Chiricahua Leopard Frog (</span><i>Lithobates chiricahuensis</i><span>) at the Buenos Aires National Wildlife Refuge (BANWR), in the Altar Valley, Arizona, USA. To place our results in context, we also compiled published survival estimates for other ranids. Average apparent survival of Chiricahua Leopard Frogs at BANWR was 0.27 (95% CI [0.07, 0.74]) and average individual capture probability was 0.02 (95% CI [0, 0.05]). Our apparent survival estimate for Chiricahua Leopard Frogs is lower than for most other ranids and is not consistent with recent research that showed metapopulation viability in the Altar Valley is high. We suggest that low apparent survival may be indicative of high emigration rates. We recommend that future research should estimate emigration rates so that actual, rather than apparent, survival can be quantified to improve population viability assessments of threatened species following reintroduction efforts.</span></p>","language":"English","publisher":"The American Society of Ichthyologists and Herpetologists","doi":"10.1643/CE-16-406","usgsCitation":"Howell, P., Hossack, B.R., Muths, E.L., Sigafus, B.H., and Chandler, R.B., 2016, Survival estimates for reintroduced populations of the Chiricahua Leopard Frog (<i>Lithobates chiricahuensis</i>): Copeia, v. 104, no. 4, p. 824-830, https://doi.org/10.1643/CE-16-406.","productDescription":"7 p.","startPage":"824","endPage":"830","ipdsId":"IP-072948","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":331507,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"104","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5847dc7ce4b06d80b7af6aab","contributors":{"authors":[{"text":"Howell, Paige E.","contributorId":173495,"corporation":false,"usgs":false,"family":"Howell","given":"Paige E.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":644889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":644888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":644890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":644891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chandler, Richard B. rchandler@usgs.gov","contributorId":63524,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":644892,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70178689,"text":"70178689 - 2016 - Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate","interactions":[],"lastModifiedDate":"2016-12-05T11:08:21","indexId":"70178689","displayToPublicDate":"2016-12-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate","docAbstract":"<p><span>Methane is a powerful greenhouse gas, and atmospheric concentrations have risen 2.5 times since the beginning of the Industrial age. While much of this increase is attributed to anthropogenic sources, natural sources, which contribute between 35% and 50% of global methane emissions, are thought to have a role in the atmospheric methane increase, in part due to human influences. Methane emissions from many natural sources are sensitive to climate, and positive feedbacks from climate change and cultural eutrophication may promote increased emissions to the atmosphere. These natural sources include aquatic environments such as wetlands, freshwater lakes, streams and rivers, and estuarine, coastal, and marine systems. Furthermore, there are significant marine sediment stores of methane in the form of clathrates that are vulnerable to mobilization and release to the atmosphere from climate feedbacks, and subsurface thermogenic gas which in exceptional cases may be released following accidents and disasters (North Sea blowout and </span><i>Deepwater Horizon</i><span> Spill respectively). Understanding of natural sources, key processes, and controls on emission is continually evolving as new measurement and modeling capabilities develop, and different sources and processes are revealed. This special issue of </span><i>Limnology and Oceanography</i><span> gathers together diverse studies on methane production, consumption, and emissions from freshwater, estuarine, and marine systems, and provides a broad view of the current science on methane dynamics of aquatic ecosystems. Here, we provide a general overview of aquatic methane sources, their contribution to the global methane budget, and key uncertainties. We then briefly summarize the contributions to and highlights of this special issue.</span></p>","language":"English","publisher":"ASLO","doi":"10.1002/lno.10449","usgsCitation":"Hamdan, L.J., and Wickland, K.P., 2016, Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate: Limnology and Oceanography, v. 61, no. S1, p. S3-S12, https://doi.org/10.1002/lno.10449.","productDescription":"10 p.","startPage":"S3","endPage":"S12","ipdsId":"IP-079689","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":462003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10449","text":"Publisher Index Page"},{"id":331455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"S1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-10","publicationStatus":"PW","scienceBaseUri":"58468ae8e4b04fc80e5236c1","contributors":{"authors":[{"text":"Hamdan, Leila J.","contributorId":177155,"corporation":false,"usgs":false,"family":"Hamdan","given":"Leila","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":654819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":654818,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70192557,"text":"70192557 - 2016 - Interactive effects between nest microclimate and nest vegetation structure confirm microclimate thresholds for Lesser Prairie-Chicken nest survival","interactions":[],"lastModifiedDate":"2017-12-04T14:32:13","indexId":"70192557","displayToPublicDate":"2016-12-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Interactive effects between nest microclimate and nest vegetation structure confirm microclimate thresholds for Lesser Prairie-Chicken nest survival","docAbstract":"<p><span>The range of Lesser Prairie-Chickens (</span><i><i>Tympanuchus pallidicinctus</i></i><span>) spans 4 unique ecoregions along 2 distinct environmental gradients. The Sand Shinnery Oak Prairie ecoregion of the Southern High Plains of New Mexico and Texas is environmentally isolated, warmer, and more arid than the Short-Grass, Sand Sagebrush, and Mixed-Grass Prairie ecoregions in Colorado, Kansas, Oklahoma, and the northeast panhandle of Texas. Weather is known to influence Lesser Prairie-Chicken nest survival in the Sand Shinnery Oak Prairie ecoregion; regional variation may also influence nest microclimate and, ultimately, survival during incubation. To address this question, we placed data loggers adjacent to nests during incubation to quantify temperature and humidity distribution functions in 3 ecoregions. We developed a suite of a priori nest survival models that incorporated derived microclimate parameters and visual obstruction as covariates in Program MARK. We monitored 49 nests in Mixed-Grass, 22 nests in Sand Shinnery Oak, and 30 nests in Short-Grass ecoregions from 2010 to 2014. Our findings indicated that (1) the Sand Shinnery Oak Prairie ecoregion was hotter and drier during incubation than the Mixed- and Short-Grass ecoregions; (2) nest microclimate varied among years within ecoregions; (3) visual obstruction was positively associated with nest survival; but (4) daily nest survival probability decreased by 10% every half-hour when temperature was greater than 34°C and vapor pressure deficit was less than −23 mmHg during the day (about 0600–2100 hours). Our major finding confirmed microclimate thresholds for nest survival under natural conditions across the species' distribution, although Lesser Prairie-Chickens are more likely to experience microclimate conditions that result in nest failures in the Sand Shinnery Oak Prairie ecoregion. The species would benefit from identification of thermal landscapes and management actions that promote cooler, more humid nest microclimates.</span></p>","language":"English","doi":"10.1650/CONDOR-16-38.1","usgsCitation":"Grisham, B.A., Godar, A.J., Boal, C.W., and Haukos, D.A., 2016, Interactive effects between nest microclimate and nest vegetation structure confirm microclimate thresholds for Lesser Prairie-Chicken nest survival: The Condor, v. 118, no. 4, p. 728-746, https://doi.org/10.1650/CONDOR-16-38.1.","productDescription":"19 p.","startPage":"728","endPage":"746","ipdsId":"IP-043669","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-16-38.1","text":"Publisher Index Page"},{"id":349658,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"118","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-19","publicationStatus":"PW","scienceBaseUri":"5a60fc7ce4b06e28e9c23eff","contributors":{"authors":[{"text":"Grisham, Blake A.","contributorId":75419,"corporation":false,"usgs":true,"family":"Grisham","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Godar, Alixandra J.","contributorId":201107,"corporation":false,"usgs":false,"family":"Godar","given":"Alixandra","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":724362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716190,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":724361,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70178691,"text":"70178691 - 2016 - Towards simplification of hydrologic modeling:  Identification of dominant processes","interactions":[],"lastModifiedDate":"2016-12-05T11:04:39","indexId":"70178691","displayToPublicDate":"2016-12-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Towards simplification of hydrologic modeling:  Identification of dominant processes","docAbstract":"<p>The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. </p><p>The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many</p>","language":"English","publisher":"Europen Geosciences Union","doi":"10.5194/hess-20-4655-2016","usgsCitation":"Markstrom, S.L., Hay, L.E., and Clark, M., 2016, Towards simplification of hydrologic modeling:  Identification of dominant processes: Hydrology and Earth System Sciences, v. 20, p. 4655-4671, https://doi.org/10.5194/hess-20-4655-2016.","productDescription":"17 p.","startPage":"4655","endPage":"4671","ipdsId":"IP-076154","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470341,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-20-4655-2016","text":"Publisher Index Page"},{"id":331454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"58468ae8e4b04fc80e5236bf","contributors":{"authors":[{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":654822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":654823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Martyn P.","contributorId":21445,"corporation":false,"usgs":true,"family":"Clark","given":"Martyn P.","affiliations":[],"preferred":false,"id":654824,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70178688,"text":"70178688 - 2016 - Potential effects of drought on carrying capacity for wintering waterfowl in the Central Valley of California","interactions":[],"lastModifiedDate":"2016-12-06T10:01:53","indexId":"70178688","displayToPublicDate":"2016-12-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Potential effects of drought on carrying capacity for wintering waterfowl in the Central Valley of California","docAbstract":"We used the bioenergetics model TRUEMET to evaluate potential effects of California's recent drought on food supplies for waterfowl wintering in the Central Valley under a range of habitat and waterfowl population scenarios. In nondrought years in the current Central Valley landscape, food supplies are projected to be adequate for waterfowl from fall through early spring (except late March) even if waterfowl populations reach North American Waterfowl Management Plan goals. However, in all drought scenarios that we evaluated, food supplies were projected to be exhausted for ducks by mid- to late winter and by late winter or early spring for geese. For ducks, these results were strongly related to projected declines in winter-flooded rice fields that provide 45% of all the food energy available to ducks in the Central Valley in nondrought water years. Delayed flooding of some managed wetlands may help alleviate food shortages by providing wetland food resources better timed with waterfowl migration and abundance patterns in the Central Valley, as well as reducing the amount of water needed to manage these habitats. However, future research is needed to evaluate the impacts of delayed flooding on waterfowl hunting, and whether California's existing water delivery system would make delayed flooding feasible. Securing adequate water supplies for waterfowl and other wetland-dependent birds is among the greatest challenges facing resource managers in coming years, especially in the increasingly arid western United States.","doi":"10.3996/082015-JFWM-082","usgsCitation":"Petrie, M.J., Fleskes, J., Wolder, M.A., Isola, C.R., Yarris, G., and Skalos, D.A., 2016, Potential effects of drought on carrying capacity for wintering waterfowl in the Central Valley of California: Journal of Fish and Wildlife Management, v. 7, no. 2, p. 408-422, https://doi.org/10.3996/082015-JFWM-082.","productDescription":"15 p.","startPage":"408","endPage":"422","onlineOnly":"N","ipdsId":"IP-073695","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488586,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/082015-jfwm-082","text":"Publisher Index Page"},{"id":331451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-01","publicationStatus":"PW","scienceBaseUri":"58468ae8e4b04fc80e5236c3","contributors":{"authors":[{"text":"Petrie, Mark J.","contributorId":89655,"corporation":false,"usgs":true,"family":"Petrie","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":654838,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleskes, Joseph P. joe_fleskes@usgs.gov","contributorId":138999,"corporation":false,"usgs":true,"family":"Fleskes","given":"Joseph P.","email":"joe_fleskes@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":654839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wolder, Mike A.","contributorId":6403,"corporation":false,"usgs":true,"family":"Wolder","given":"Mike","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":654840,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isola, Craig R.","contributorId":177166,"corporation":false,"usgs":false,"family":"Isola","given":"Craig","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":654841,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yarris, Gregory S.","contributorId":115361,"corporation":false,"usgs":true,"family":"Yarris","given":"Gregory S.","affiliations":[],"preferred":false,"id":654842,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Skalos, Daniel A.","contributorId":64123,"corporation":false,"usgs":true,"family":"Skalos","given":"Daniel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":654843,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70217795,"text":"70217795 - 2016 - Decadal shifts in grass and woody plant cover are driven by prolonged drying and modified by topo‐edaphic properties","interactions":[],"lastModifiedDate":"2022-04-22T14:28:03.732724","indexId":"70217795","displayToPublicDate":"2016-12-01T15:33:19","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Decadal shifts in grass and woody plant cover are driven by prolonged drying and modified by topo‐edaphic properties","docAbstract":"<p><span>Woody plant encroachment and overall declines in perennial vegetation in dryland regions can alter ecosystem properties and indicate land degradation, but the causes of these shifts remain controversial. Determining how changes in the abundance and distribution of grass and woody plants are influenced by conditions that regulate water availability at a regional scale provides a baseline to compare how management actions alter the composition of these vegetation types at a more local scale and can be used to predict future shifts under climate change. Using a remote‐sensing‐based approach, we assessed the balance between grasses and woody plants and how climate and topo‐edaphic conditions affected their abundances across the northern Sonoran Desert from 1989 to 2009. Despite widespread woody plant encroachment in this region over the last 150&nbsp;years, we found that leguminous trees, including mesquite (</span><i>Prosopis</i><span>&nbsp;spp.), declined in cover in areas with prolonged drying conditions during the early 21st century. Creosote bush (</span><i>Larrea tridentata</i><span>) also had moderate decreases with prolonged drying but was buffered from changes on soils with low clay that promote infiltration and high available water capacity that allows for retention of water at depth. Perennial grasses have expanded and contracted over the last two decades in response to summer precipitation and were especially dynamic on shallow soils with high clay that have large fluctuations in water availability. Our results suggest that topo‐edaphic properties can amplify or ameliorate climate‐induced changes in woody plants and perennial grasses. Understanding these relationships has important implications for ecosystem function under climate change in the southwestern USA and can inform management efforts to regulate grass and woody plant abundances.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1389","usgsCitation":"Munson, S.M., Sankey, T.T., Xian, G.Z., Villarreal, M.L., and Homer, C.G., 2016, Decadal shifts in grass and woody plant cover are driven by prolonged drying and modified by topo‐edaphic properties: Ecological Applications, v. 26, no. 8, p. 2480-2494, https://doi.org/10.1002/eap.1389.","productDescription":"15 p.","startPage":"2480","endPage":"2494","ipdsId":"IP-071685","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":382903,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.95,\n              31.7\n            ],\n            [\n              -111.3,\n              31.7\n            ],\n            [\n              -111.3,\n              33.79\n            ],\n            [\n              -112.95,\n              33.79\n            ],\n            [\n              -112.95,\n              31.7\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"26","issue":"8","noUsgsAuthors":false,"publicationDate":"2016-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":809742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":809743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":809744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":809745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":809746,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70188831,"text":"70188831 - 2016 - Mineral thermometry and fluid inclusion studies of the Pea Ridge iron oxide-apatite–rare earth element deposit, Mesoproterozoic St. Francois Mountains Terrane, southeast Missouri, USA","interactions":[],"lastModifiedDate":"2018-08-07T14:42:35","indexId":"70188831","displayToPublicDate":"2016-12-01T14:42:28","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Mineral thermometry and fluid inclusion studies of the Pea Ridge iron oxide-apatite–rare earth element deposit, Mesoproterozoic St. Francois Mountains Terrane, southeast Missouri, USA","docAbstract":"<p>Mineral thermometry and fluid inclusion studies were conducted on variably altered and mineralized samples from the Mesoproterozoic Pea Ridge iron oxide-apatite (IOA)-rare earth element (REE) deposit in order to constrain P-T conditions, fluid chemistry, and the source of salt and volatiles during early magnetite and later REE mineralization.</p><p>Scanning electron microscopy (SEM)-cathodoluminescence and SEM-backscatter electron images show that quartz and rutile precipitated before, during, and after magnetite and REE mineral growth. Ti-in-quartz and Zr-in-rutile equilibration temperatures range from ≤350° to 750°C in the amphibole, magnetite, hematite, and silicified zones where T increased during magnetite and quartz growth and dropped precipitously after fracturing and brecciation. Late drusy quartz cements within a REE-rich breccia pipe record the lowest T (≤315°–400°C).</p><p>Liquid-, vapor-rich, and hypersaline (±hematite, calcite) fluid inclusions are common and liquid CO<sub>2</sub><span>&nbsp;</span>is present locally. Salinities define three populations: saline (10–27 wt % NaCl equiv), hypersaline (34–&gt;60 wt % NaCl equiv), and dilute (0–10 wt % NaCl equiv ). The wide range of eutectic melting temperatures (−67° to −19°C) suggests that saline inclusions trapped variable proportions of a CaCl-MgCl-FeCl-bearing fluid end member and an NaCl-KCl fluid end member. Homogenization temperatures and pressures of these saline inclusions suggest they were trapped when fluids unmixed into brine and vapor at T &lt;350°C, P &lt;15 MPa, and a depth of ~1.5 km. Hypersaline inclusions were trapped at low T and P (~200°C and ~1 MPa) along the V + L + H curve when the system vented to the paleosurface. Data for dilute inclusions in late drusy quartz from the REE-rich breccia pipe are indicative of a boiling epithermal environment.</p><p>The Na/Cl, Na/K, and Cl/Br ratios of fluid inclusion extracts provide evidence for mixtures of magmatic hydrothermal fluids and evaporated seawater. Extracts from magnetite, hematite, and pyrite plot in the magmatic-hydrothermal field, indicating that Fe was derived from a magmatic source. Their enrichments in Mg and Ca are consistent with a mafic magmatic source. The positive correlation between Na/Mg and Na/Ca ratios may be due to halite saturation or albitization of igneous rocks. Extracts from barite in the REE-rich breccia pipes are enriched in Na and Br and plot near the seawater evaporation trend.</p><p>He is highly enriched relative to Ne and Ar in fluid inclusion extracts, which precludes air as a source of He. Although the He is mostly of crustal origin, pyrite with a<span>&nbsp;</span><sup>3</sup>He/<sup>4</sup>He (R/R<sub>A</sub>) of 0.1 contains up to 12% mantle He. Many extracts have low<span>&nbsp;</span><sup>20</sup>Ne/<sup>22</sup>Ne ratios due to nucleogenic production of<span>&nbsp;</span><sup>22</sup>Ne in high F/O minerals such as fluorapatite or F biotite. The arrays of data for<span>&nbsp;</span><sup>3</sup>He/<sup>4</sup>He (R/R<sub>A</sub>) and<span>&nbsp;</span><sup>22</sup>Ne/<sup>20</sup>Ne suggest that volatiles were derived from two sources, a moderate F mafic magma containing mantle He and a high F silicic magma with crustal He.</p><p>Together with other evidence cited in this report, these data (1) support a magmatic hydrothermal origin for the Mesoproterozoic magnetite-apatite deposit with ore fluids derived from a concealed mafic to intermediate-composition intrusion, (2) suggest that the REE minerals in breccia pipes were either derived from apatite or precipitated in response to decompression and cooling during breccia pipe formation, (3) provide evidence for the influx of basinal brine, magmatic fluids from granitic intrusions, and meteoric water after breccia pipe formation, and (4) show that Pea Ridge was relatively unaffected by the late Paleozoic Mississippi Valley-type (MVT) Pb-Zn system in overlying Cambrian sedimentary rocks.</p>","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.111.8.1985","usgsCitation":"Hofstra, A.H., Meighan, C.J., Song, X., Samson, I., Marsh, E.E., Lowers, H.A., Emsbo, P., and Hunt, A.G., 2016, Mineral thermometry and fluid inclusion studies of the Pea Ridge iron oxide-apatite–rare earth element deposit, Mesoproterozoic St. Francois Mountains Terrane, southeast Missouri, USA: Economic Geology, v. 111, no. 8, p. 1985-2016, https://doi.org/10.2113/econgeo.111.8.1985.","productDescription":"32 p.","startPage":"1985","endPage":"2016","ipdsId":"IP-076706","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":356299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","volume":"111","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"5b6fc800e4b0f5d57878ec07","contributors":{"authors":[{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meighan, Corey J. 0000-0002-5668-1621 cmeighan@usgs.gov","orcid":"https://orcid.org/0000-0002-5668-1621","contributorId":5892,"corporation":false,"usgs":true,"family":"Meighan","given":"Corey","email":"cmeighan@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Song, Xinyu","contributorId":193465,"corporation":false,"usgs":false,"family":"Song","given":"Xinyu","email":"","affiliations":[],"preferred":false,"id":700539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Samson, Iain","contributorId":193466,"corporation":false,"usgs":false,"family":"Samson","given":"Iain","affiliations":[],"preferred":false,"id":700540,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marsh, Erin E. 0000-0001-5245-9532 emarsh@usgs.gov","orcid":"https://orcid.org/0000-0001-5245-9532","contributorId":1250,"corporation":false,"usgs":true,"family":"Marsh","given":"Erin","email":"emarsh@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":700542,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Emsbo, Poul 0000-0001-9421-201X pemsbo@usgs.gov","orcid":"https://orcid.org/0000-0001-9421-201X","contributorId":997,"corporation":false,"usgs":true,"family":"Emsbo","given":"Poul","email":"pemsbo@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":700543,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":700544,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70200475,"text":"70200475 - 2016 - Bitumen prices and structural changes in North American crude oil markets","interactions":[],"lastModifiedDate":"2018-10-22T13:40:37","indexId":"70200475","displayToPublicDate":"2016-12-01T13:40:31","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Bitumen prices and structural changes in North American crude oil markets","docAbstract":"<p><span>In an earlier report, changes in bitumen prices at Hardesty, Alberta, Canada, were modeled as the responses to changes in monthly prices of Hardesty light/medium crude oil for the period 2000–2006 with a simple error correction econometric model. This note re-examines that price relationship for the period 2009–2014. Over the period 2006–2014, there was also rapid growth in North American light oil production from low-permeability carbonate, sandstone, and shale reservoirs. During that period, Canadian raw bitumen production grew by more than 12% per year and there was significant geographical diversification in its markets. Results of the statistical analysis showed that the change in the dynamic relationships between bitumen prices and Hardesty light oil prices probably reflected, in part, the maturation of bitumen markets and closer integration with North American light oil markets. The analysis also examines the dynamic relationships between bitumen prices and West Texas Intermediate and Brent international benchmark crude oil prices. Ideally, if bitumen prices are found to be closely related to a widely traded benchmark crude oil, the benchmark crude oil price forecasts could be used as a basis for predicting bitumen prices. However, neither of international benchmark crude oils tested had high explanatory power.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11053-016-9298-z","usgsCitation":"Attanasi, E., 2016, Bitumen prices and structural changes in North American crude oil markets: Natural Resources Research, v. 25, no. 4, p. 487-496, https://doi.org/10.1007/s11053-016-9298-z.","productDescription":"10 p.","startPage":"487","endPage":"496","ipdsId":"IP-060092","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":358624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-29","publicationStatus":"PW","scienceBaseUri":"5c10ad37e4b034bf6a7e718c","contributors":{"authors":[{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":749055,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70180251,"text":"70180251 - 2016 - Climate change and the Delta","interactions":[],"lastModifiedDate":"2018-09-13T16:10:50","indexId":"70180251","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and the Delta","docAbstract":"<p>Anthropogenic climate change amounts to a rapidly approaching, “new” stressor in the Sacramento–San Joaquin Delta system. In response to California’s extreme natural hydroclimatic variability, complex water-management systems have been developed, even as the Delta’s natural ecosystems have been largely devastated. Climate change is projected to challenge these management and ecological systems in different ways that are characterized by different levels of uncertainty. For example, there is high certainty that climate will warm by about 2°C more (than late-20th-century averages) by mid-century and about 4°C by end of century, if greenhouse-gas emissions continue their current rates of acceleration. Future precipitation changes are much less certain, with as many climate models projecting wetter conditions as drier. However, the same projections agree that precipitation will be more intense when storms do arrive, even as more dry days will separate storms. Warmer temperatures will likely enhance evaporative demands and raise water temperatures. Consequently, climate change is projected to yield both more extreme flood risks and greater drought risks. Sea level rise (SLR) during the 20th century was about 22cm, and is projected to increase by at least 3-fold this century. SLR together with land subsidence threatens the Delta with greater vulnerabilities to inundation and salinity intrusion. Effects on the Delta ecosystem that are traceable to warming include SLR, reduced snowpack, earlier snowmelt and larger storm-driven streamflows, warmer and longer summers, warmer summer water temperatures, and water-quality changes. These changes and their uncertainties will challenge the operations of water projects and uses throughout the Delta’s watershed and delivery areas. Although the effects of climate change on Delta ecosystems may be profound, the end results are difficult to predict, except that native species will fare worse than invaders. Successful preparation for the coming changes will require greater integration of monitoring, modeling, and decision making across time, variables, and space than has been historically normal.</p>","language":"English","publisher":"University of California","doi":"10.15447/sfews.2016v14iss3art5","usgsCitation":"Dettinger, M.D., Anderson, J., Anderson, M.L., Brown, L.R., Cayan, D., and Maurer, E., 2016, Climate change and the Delta: San Francisco Estuary and Watershed Science, v. 14, no. 3, p. 1-26, https://doi.org/10.15447/sfews.2016v14iss3art5.","productDescription":"Article 5; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-077659","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":470395,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2016v14iss3art5","text":"Publisher Index Page"},{"id":334064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-09","publicationStatus":"PW","scienceBaseUri":"588b1977e4b0ad67323f97e2","contributors":{"authors":[{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":149896,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael","email":"mddettin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":660928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Jamie","contributorId":178769,"corporation":false,"usgs":false,"family":"Anderson","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":660929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Michael L.","contributorId":149932,"corporation":false,"usgs":false,"family":"Anderson","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":660930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":660931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cayan, Daniel drcayan@usgs.gov","contributorId":149912,"corporation":false,"usgs":true,"family":"Cayan","given":"Daniel","email":"drcayan@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":660932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maurer, Edwin P.","contributorId":13129,"corporation":false,"usgs":true,"family":"Maurer","given":"Edwin P.","affiliations":[],"preferred":false,"id":660933,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192677,"text":"70192677 - 2016 - Development of a Kemp’s ridley sea turtle stock assessment model","interactions":[],"lastModifiedDate":"2020-12-21T14:45:16.380494","indexId":"70192677","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1873,"text":"Gulf of Mexico Science","active":true,"publicationSubtype":{"id":10}},"title":"Development of a Kemp’s ridley sea turtle stock assessment model","docAbstract":"<p><span>We developed a Kemp’s ridley (</span><i>Lepidochelys kempii</i><span>) stock assessment model to evaluate the relative contributions of conservation efforts and other factors toward this critically endangered species’ recovery. The Kemp’s ridley demographic model developed by the Turtle Expert Working Group (TEWG) in 1998 and 2000 and updated for the binational recovery plan in 2011 was modified for use as our base model. The TEWG model uses indices of the annual reproductive population (number of nests) and hatchling recruitment to predict future annual numbers of nests on the basis of a series of assumptions regarding age and maturity, remigration interval, sex ratios, nests per female, juvenile mortality, and a putative ‘‘turtle excluder device effect’’ multiplier starting in 1990. This multiplier was necessary to fit the number of nests observed in 1990 and later. We added the effects of shrimping effort directly, modified by habitat weightings, as a proxy for all sources of anthropogenic mortality. Additional data included in our model were incremental growth of Kemp’s ridleys marked and recaptured in the Gulf of Mexico, and the length frequency of stranded Kemp’s ridleys. We also added a 2010 mortality factor that was necessary to fit the number of nests for 2010 and later (2011 and 2012). Last, we used an empirical basis for estimating natural mortality, on the basis of a Lorenzen mortality curve and growth estimates. Although our model generated reasonable estimates of annual total turtle deaths attributable to shrimp trawling, as well as additional deaths due to undetermined anthropogenic causes in 2010, we were unable to provide a clear explanation for the observed increase in the number of stranded Kemp’s ridleys in recent years, and subsequent disruption of the species’ exponential growth since the 2009 nesting season. Our consensus is that expanded data collection at the nesting beaches is needed and of high priority, and that 2015 be targeted for the next stock assessment to evaluate the 2010 event using more recent nesting and in-water data.</span></p>","language":"English","publisher":"Gulf of Mexico Science","doi":"10.18785/goms.3302.03","usgsCitation":"Gallaway, B.J., Gazey, W., Caillouet, C.W., Plotkin, P.T., Abreu Grobois, F.A., Amos, A.F., Burchfield, P.M., Carthy, R.R., Castro Martinez, M.A., Cole, J.G., Coleman, A.T., Cook, M., DiMarco, S.F., Epperly, S.P., Fujiwara, M., Gamez, D.G., Graham, G.L., Griffin, W.L., Illescas Martinez, F., Lamont, M.M., Lewison, R.L., Lohmann, K.J., Nance, J.M., Pitchford, J., Putman, N.F., Raborn, S.W., Rester, J.K., Rudloe, J.J., Sarti Martinez, L., Schexnayder, M., Schmid, J.R., Shaver, D.J., Slay, C., Tucker, A.D., Tumlin, M., Wibbels, T., and Zapata Najera, B.M., 2016, Development of a Kemp’s ridley sea turtle stock assessment model: Gulf of Mexico 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Thane","contributorId":200839,"corporation":false,"usgs":false,"family":"Wibbels","given":"Thane","email":"","affiliations":[],"preferred":false,"id":723523,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Zapata Najera, Blanca M.","contributorId":200840,"corporation":false,"usgs":false,"family":"Zapata Najera","given":"Blanca","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":723524,"contributorType":{"id":1,"text":"Authors"},"rank":37}]}}
,{"id":70193341,"text":"70193341 - 2016 - Assessing Brook Trout populations in headwater streams of the Adirondack Mountains using environmental DNA -- Summary report","interactions":[],"lastModifiedDate":"2017-12-21T10:14:14","indexId":"70193341","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5590,"text":"NYSERDA Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"17-02","title":"Assessing Brook Trout populations in headwater streams of the Adirondack Mountains using environmental DNA -- Summary report","docAbstract":"<p>This project evaluated standard fish-survey and environmental DNA (eDNA) sampling methods to determine the ability of eDNA to accurately predict the presence and abundance of resident Brook Trout populations in 40 headwater streams mainly in the western Adirondack Mountains during 2014–2015 (Figure 2). Standard 3-pass electrofishing surveys found that Brook Trout were absent from about 25 percent of study sites, and at low densities in 25 percent of sites, moderate densities in 25 percent of sites, and high densities in 25 percent of sites. Environmental DNA results correctly predicted the presence/absence of Brook Trout in 85.0 to 92.5 percent of study sites and explained 44.0 percent of the variability in density and 24 percent of the variability in biomass of their populations. The findings indicate that eDNA surveys will enable researchers to effectively characterize the presence as well as the abundance of Brook Trout and other species populations in headwater streams across the Adirondack Mountains and elsewhere.</p>","language":"English","publisher":"New York State Energy Research and Development Authority","usgsCitation":"Baldigo, B.P., George, S.D., Sporn, L.A., and Ball, J., 2016, Assessing Brook Trout populations in headwater streams of the Adirondack Mountains using environmental DNA -- Summary report: NYSERDA Report 17-02, iv, 13 p.","productDescription":"iv, 13 p.","ipdsId":"IP-084709","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":350150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347922,"type":{"id":11,"text":"Document"},"url":"https://www.nyserda.ny.gov/-/media/Files/Publications/Research/Environmental/17-02-Environmental-DNA-Brook-Trout.pdf","text":"Report","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.5,\n              43.2\n            ],\n            [\n              -73.8,\n              43.2\n            ],\n            [\n              -73.8,\n              44.4\n            ],\n            [\n              -75.5,\n              44.4\n            ],\n            [\n              -75.5,\n              43.2\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc7de4b06e28e9c23f0c","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":725310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sporn, Lee Ann","contributorId":199350,"corporation":false,"usgs":false,"family":"Sporn","given":"Lee","email":"","middleInitial":"Ann","affiliations":[],"preferred":false,"id":718757,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ball, Jacob","contributorId":177389,"corporation":false,"usgs":false,"family":"Ball","given":"Jacob","email":"","affiliations":[],"preferred":false,"id":725311,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192566,"text":"70192566 - 2016 - Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams","interactions":[],"lastModifiedDate":"2017-10-26T14:43:51","indexId":"70192566","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1550,"text":"Environmental Modeling & Assessment","onlineIssn":" 1573-296","printIssn":"1420-2026","active":true,"publicationSubtype":{"id":10}},"title":"Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams","docAbstract":"<p>Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (&lt;585&nbsp;km<sup>2</sup>) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76&nbsp;% of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10666-016-9525-3","usgsCitation":"Kronholm, S.C., Capel, P.D., and Terziotti, S., 2016, Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams: Environmental Modeling & Assessment, v. 21, no. 6, p. 681-690, https://doi.org/10.1007/s10666-016-9525-3.","productDescription":"10 p.","startPage":"681","endPage":"690","ipdsId":"IP-076954","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":438501,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TX3CGB","text":"USGS data release","linkHelpText":"Data on annual total nitrogen loads and watershed characteristics used to develop a method to estimate the total nitrogen loads in small streams"},{"id":347496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-26","publicationStatus":"PW","scienceBaseUri":"5a07e98de4b09af898c8cc26","contributors":{"authors":[{"text":"Kronholm, Scott C.","contributorId":184190,"corporation":false,"usgs":false,"family":"Kronholm","given":"Scott","email":"","middleInitial":"C.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":716220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":716219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716221,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70193163,"text":"70193163 - 2016 - Bayesian analysis of Jolly-Seber type models","interactions":[],"lastModifiedDate":"2017-11-20T15:57:21","indexId":"70193163","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1573,"text":"Environmental and Ecological Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian analysis of Jolly-Seber type models","docAbstract":"<p><span>We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We demonstrate this approach using a data set of migrating semipalmated sandpipers (</span><i class=\"EmphasisTypeItalic \">Calidris pussila</i><span>) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. We demonstrate the use of reversible jump MCMC methods to derive posterior distributions for the model parameters and the models, simultaneously. The algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10651-016-0352-0","usgsCitation":"Matechou, E., Nicholls, G.K., Morgan, B.J., Collazo, J., and Lyons, J.E., 2016, Bayesian analysis of Jolly-Seber type models: Environmental and Ecological Statistics, v. 23, no. 4, p. 531-547, https://doi.org/10.1007/s10651-016-0352-0.","productDescription":"17 p.","startPage":"531","endPage":"547","ipdsId":"IP-057563","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10651-016-0352-0","text":"Publisher Index Page"},{"id":349161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-04","publicationStatus":"PW","scienceBaseUri":"5a60fc7de4b06e28e9c23f0f","contributors":{"authors":[{"text":"Matechou, Eleni","contributorId":200631,"corporation":false,"usgs":false,"family":"Matechou","given":"Eleni","email":"","affiliations":[],"preferred":false,"id":722930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicholls, Geoff K.","contributorId":200632,"corporation":false,"usgs":false,"family":"Nicholls","given":"Geoff","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":722931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morgan, Byron J. T.","contributorId":200633,"corporation":false,"usgs":false,"family":"Morgan","given":"Byron","email":"","middleInitial":"J. T.","affiliations":[],"preferred":false,"id":722932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":718111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lyons, James E. 0000-0002-9810-8751 jelyons@usgs.gov","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":177546,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"jelyons@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":722933,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70193660,"text":"70193660 - 2016 - Structured decision making as a framework for large-scale wildlife harvest management decisions","interactions":[],"lastModifiedDate":"2017-11-05T21:40:27","indexId":"70193660","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Structured decision making as a framework for large-scale wildlife harvest management decisions","docAbstract":"<p>Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer (<i>Odocoileus virginianus</i>) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.</p>","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1613","usgsCitation":"Robinson, K., Fuller, A.K., Hurst, J.E., Swift, B.L., Kirsch, A., Farquhar, J.F., Decker, D.J., and Siemer, W.F., 2016, Structured decision making as a framework for large-scale wildlife harvest management decisions: Ecosphere, v. 7, no. 12, Article e01613; 14 p., https://doi.org/10.1002/ecs2.1613.","productDescription":"Article e01613; 14 p.","ipdsId":"IP-078686","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470346,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1613","text":"Publisher Index Page"},{"id":348207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-20","publicationStatus":"PW","scienceBaseUri":"5a003151e4b0531197b5a750","contributors":{"authors":[{"text":"Robinson, Kelly F.","contributorId":140157,"corporation":false,"usgs":false,"family":"Robinson","given":"Kelly F.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false},{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false},{"id":473,"text":"New York Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":719792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurst, Jeremy E.","contributorId":177504,"corporation":false,"usgs":false,"family":"Hurst","given":"Jeremy","email":"","middleInitial":"E.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swift, Bryan L.","contributorId":11433,"corporation":false,"usgs":false,"family":"Swift","given":"Bryan","email":"","middleInitial":"L.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirsch, Arthur","contributorId":199698,"corporation":false,"usgs":false,"family":"Kirsch","given":"Arthur","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Farquhar, James F.","contributorId":150969,"corporation":false,"usgs":false,"family":"Farquhar","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719796,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Decker, Daniel J.","contributorId":166906,"corporation":false,"usgs":false,"family":"Decker","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":719797,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Siemer, William F.","contributorId":192551,"corporation":false,"usgs":false,"family":"Siemer","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":719798,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70178570,"text":"70178570 - 2016 - Graphical function mapping as a new way to explore cause-and-effect chains","interactions":[],"lastModifiedDate":"2018-02-28T14:36:31","indexId":"70178570","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"Graphical function mapping as a new way to explore cause-and-effect chains","docAbstract":"<p><span>Graphical function mapping provides a simple method for improving communication within interdisciplinary research teams and between scientists and nonscientists. This article introduces graphical function mapping using two examples and discusses its usefulness. Function mapping projects the outcome of one function into another to show the combined effect. Using this mathematical property in a simpler, even cartoon-like, graphical way allows the rapid combination of multiple information sources (models, empirical data, expert judgment, and guesses) in an intuitive visual to promote further discussion, scenario development, and clear communication.</span></p>","language":"English","publisher":"American Fisheries Society","publisherLocation":"Bethesda, MD","doi":"10.1080/03632415.2016.1221404","usgsCitation":"Evans, M.A., 2016, Graphical function mapping as a new way to explore cause-and-effect chains: Fisheries, v. 41, no. 11, p. 638-643, https://doi.org/10.1080/03632415.2016.1221404.","productDescription":"6 p.","startPage":"638","endPage":"643","ipdsId":"IP-060085","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":331372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"11","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"584144dde4b04fc80e50737f","contributors":{"authors":[{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":4883,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":654409,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70187209,"text":"70187209 - 2016 - Weather as a proximate explanation for fission–fusion dynamics in female northern long-eared bats","interactions":[],"lastModifiedDate":"2017-04-26T12:50:55","indexId":"70187209","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":770,"text":"Animal Behaviour","active":true,"publicationSubtype":{"id":10}},"title":"Weather as a proximate explanation for fission–fusion dynamics in female northern long-eared bats","docAbstract":"<p><span>Fission–fusion dynamics appear common among temperate bats where females form roost groups that change in size and composition, as females switch roosts almost daily. One hypothesis for frequent roost switching is that females move to find suitable thermal conditions as ambient conditions change. Tests of this hypothesis have, however, been conducted mostly at roosts in artificial structures where microclimate is relatively stable. The goal of our study was to determine whether roost switching and roost use by northern long-eared bats, </span><i>Myotis septentrionalis</i><span>, that roost in trees are related to ambient conditions. We used generalized linear fixed effects models to explore the influence of roost characteristics and changes in ambient conditions on the likelihood of roost switching. We used canonical correlation analyses to examine the relationship between ambient conditions and roost characteristics. Roost switching was indeed linked to ambient conditions together with characteristics of roosts on the previous day; the best descriptors of roost switching differed between the two geographical regions we analysed. In Nova Scotia, females were less likely to switch roosts when it rained, particularly if they were in roosts below surrounding canopy whereas they were more likely to switch roosts when they were in roosts of high decay. Females roosted in shorter trees in earlier decay classes on warm days, as well as on windy and rainy days. In Kentucky, females were more likely to switch roosts at high temperatures, particularly when they were in roosts in high decay. Females roosted in shorter, decayed trees on warm days, and in less decayed trees with small diameter on windy and rainy days. Our results suggest bats switch roosts in response to changes in ambient conditions to select suitable roosting conditions, which may explain some of the proximate factors shaping fission–fusion dynamics of bats.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.anbehav.2016.09.022","usgsCitation":"Patriquin, K.J., Leonard, M.L., Broders, H.G., Ford, W.M., Britzke, E.R., and Silvis, A., 2016, Weather as a proximate explanation for fission–fusion dynamics in female northern long-eared bats: Animal Behaviour, v. 122, p. 47-57, https://doi.org/10.1016/j.anbehav.2016.09.022.","productDescription":"11 p.","startPage":"47","endPage":"57","ipdsId":"IP-071165","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5901b1bbe4b0c2e071a99b9a","contributors":{"authors":[{"text":"Patriquin, Krista J.","contributorId":191434,"corporation":false,"usgs":false,"family":"Patriquin","given":"Krista","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":693039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leonard, Marty L.","contributorId":191435,"corporation":false,"usgs":false,"family":"Leonard","given":"Marty","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broders, Hugh G.","contributorId":191436,"corporation":false,"usgs":false,"family":"Broders","given":"Hugh","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":693041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark wford@usgs.gov","contributorId":3858,"corporation":false,"usgs":true,"family":"Ford","given":"W.","email":"wford@usgs.gov","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":693037,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britzke, Eric R.","contributorId":8327,"corporation":false,"usgs":true,"family":"Britzke","given":"Eric","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":693042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":693043,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70190306,"text":"70190306 - 2016 - Re-Occupancy of Breeding Territories by Ferruginous Hawks in Wyoming: Relationships to Environmental and Anthropogenic Factors","interactions":[],"lastModifiedDate":"2017-08-28T13:35:41","indexId":"70190306","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Re-Occupancy of Breeding Territories by Ferruginous Hawks in Wyoming: Relationships to Environmental and Anthropogenic Factors","docAbstract":"<p><span>Grassland and shrubland birds are declining globally due in part to anthropogenic habitat modification. Because population performance of these species is also influenced by non-anthropogenic factors, it is important to incorporate all relevant ecological drivers into demographic models. We used design-based sampling and occupancy models to test relationships of environmental factors that influence raptor demographics with re-occupancy of breeding territories by ferruginous hawks (</span><i>Buteo regalis</i><span>) across Wyoming, USA, 2011–2013. We also tested correlations of territory re-occupancy with oil and gas infrastructure—a leading cause of habitat modification throughout the range of this species of conservation concern. Probability of re-occupancy was not related to any covariates we investigated in 2011, had a strong negative relationship with cover of sagebrush (</span><i>Artemisia</i><span><span>&nbsp;</span>spp.) in 2012, was slightly higher for territories with artificial platforms than other nest substrates in 2013, and had a positive relationship with abundance of ground squirrels (</span><i>Urocitellus</i><span><span>&nbsp;</span>spp.) that was strong in 2012 and weak in 2013. Associations with roads were weak and varied by year, road-type, and scale: in 2012, re-occupancy probability had a weak positive correlation with density of roads not associated with oil and gas fields at the territory-scale; however, in 2013 re-occupancy had a very weak negative correlation with density of oil and gas field roads near nest sites (≤500 m). Although our results indicate re-occupancy of breeding territories by ferruginous hawks was compatible with densities of anthropogenic infrastructure in our study area, the lack of relationships between oil and gas well density and territory re-occupancy may have occurred because pre-treatment data were unavailable. We used probabilistic sampling at a broad spatial extent, methods to account for imperfect detection, and conducted extensive prey sampling; nonetheless, future research using before-after-control-impact designs is needed to fully assess impacts of oil and gas development on ferruginous hawks.</span></p>","language":"English","publisher":"PLOS ONE","doi":"10.1371/journal.pone.0152977","usgsCitation":"Wallace, Z.P., Kennedy, P.L., Squires, J.R., Oakleaf, R.J., Olson, L.E., and Dugger, K.M., 2016, Re-Occupancy of Breeding Territories by Ferruginous Hawks in Wyoming: Relationships to Environmental and Anthropogenic Factors: PLoS ONE, v. 11, no. 4, e0152977; 16 p., https://doi.org/10.1371/journal.pone.0152977.","productDescription":"e0152977; 16 p.","ipdsId":"IP-059418","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470387,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0152977","text":"Publisher Index Page"},{"id":345193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-110.048476,40.997555],[-110.121639,40.997101],[-110.125709,40.99655],[-110.237848,40.995427],[-110.250709,40.996089],[-110.375714,40.994947],[-110.500718,40.994746],[-110.539819,40.996346],[-110.715026,40.996347],[-110.750727,40.996847],[-111.046723,40.997959],[-111.046551,41.251716],[-111.0466,41.360692],[-111.046264,41.377731],[-111.045789,41.565571],[-111.045818,41.579845],[-111.046689,42.001567],[-111.047109,42.142497],[-111.047107,42.148971],[-111.047058,42.182672],[-111.047097,42.194773],[-111.047074,42.280787],[-111.04708,42.34942],[-111.046801,42.504946],[-111.046719,42.513118],[-111.046017,42.582723],[-111.043564,42.722624],[-111.044135,42.874924],[-111.043959,42.96445],[-111.043957,42.969482],[-111.043924,42.975063],[-111.044129,43.018702],[-111.044156,43.020052],[-111.044206,43.022614],[-111.044034,43.024581],[-111.044034,43.024844],[-111.044033,43.026411],[-111.044094,43.02927],[-111.043997,43.041415],[-111.044058,43.04464],[-111.044063,43.046302],[-111.044086,43.054819],[-111.044117,43.060309],[-111.04415,43.066172],[-111.044162,43.068222],[-111.044143,43.072364],[-111.044235,43.177121],[-111.044266,43.177236],[-111.044232,43.18444],[-111.044168,43.189244],[-111.044229,43.195579],[-111.044617,43.31572],[-111.045205,43.501136],[-111.045706,43.659112],[-111.04588,43.681033],[-111.046118,43.684902],[-111.046051,43.685812],[-111.04611,43.687848],[-111.046421,43.722059],[-111.046435,43.726545],[-111.04634,43.726957],[-111.046715,43.815832],[-111.046515,43.908376],[-111.046917,43.974978],[-111.047064,43.983467],[-111.047349,43.999921],[-111.049077,44.020072],[-111.048751,44.060403],[-111.048751,44.060838],[-111.048633,44.062903],[-111.048452,44.114831],[-111.049119,44.124923],[-111.049695,44.353626],[-111.049148,44.374925],[-111.049216,44.435811],[-111.049194,44.438058],[-111.048974,44.474072],[-111.055208,44.624927],[-111.055333,44.666263],[-111.055511,44.725343],[-111.056416,44.749928],[-111.056888,44.866658],[-111.055629,44.933578],[-111.056207,44.935901],[-111.055199,45.001321],[-111.044275,45.001345],[-110.785008,45.002952],[-110.761554,44.999934],[-110.750767,44.997948],[-110.705272,44.992324],[-110.552433,44.992237],[-110.547165,44.992459],[-110.48807,44.992361],[-110.402927,44.99381],[-110.362698,45.000593],[-110.342131,44.999053],[-110.324441,44.999156],[-110.28677,44.99685],[-110.199503,44.996188],[-110.110103,45.003905],[-110.026347,45.003665],[-110.025544,45.003602],[-109.99505,45.003174],[-109.875735,45.003275],[-109.798687,45.002188],[-109.75073,45.001605],[-109.663673,45.002536],[-109.574321,45.002631],[-109.386432,45.004887],[-109.375713,45.00461],[-109.269294,45.005283],[-109.263431,45.005345],[-109.103445,45.005904],[-109.08301,44.99961],[-109.062262,44.999623],[-108.621313,45.000408],[-108.578484,45.000484],[-108.565921,45.000578],[-108.500679,44.999691],[-108.271201,45.000251],[-108.249345,44.999458],[-108.238139,45.000206],[-108.218479,45.000541],[-108.14939,45.001062],[-108.000663,45.001223],[-107.997353,45.001565],[-107.911743,45.001292],[-107.750654,45.000778],[-107.608854,45.00086],[-107.607824,45.000929],[-107.49205,45.00148],[-107.351441,45.001407],[-107.13418,45.000109],[-107.125633,44.999388],[-107.105685,44.998734],[-107.084939,44.996599],[-107.074996,44.997004],[-107.050801,44.996424],[-106.892875,44.995947],[-106.888773,44.995885],[-106.263586,44.993788],[-106.024814,44.993688],[-105.928184,44.993647],[-105.914258,44.999986],[-105.913382,45.000941],[-105.848065,45.000396],[-105.076607,45.000347],[-105.038405,45.000345],[-105.025266,45.00029],[-105.019284,45.000329],[-105.01824,45.000437],[-104.765063,44.999183],[-104.759855,44.999066],[-104.72637,44.999518],[-104.665171,44.998618],[-104.663882,44.998869],[-104.470422,44.998453],[-104.470117,44.998453],[-104.250145,44.99822],[-104.057698,44.997431],[-104.055914,44.874986],[-104.056496,44.867034],[-104.055963,44.768236],[-104.055963,44.767962],[-104.055934,44.72372],[-104.05587,44.723422],[-104.055777,44.700466],[-104.055938,44.693881],[-104.05581,44.691343],[-104.055877,44.571016],[-104.055892,44.543341],[-104.055927,44.51773],[-104.055389,44.249983],[-104.054487,44.180381],[-104.054562,44.141081],[-104.05495,43.93809],[-104.055077,43.936535],[-104.055488,43.853477],[-104.055488,43.853476],[-104.055138,43.750421],[-104.055133,43.747105],[-104.054902,43.583852],[-104.054885,43.583512],[-104.05484,43.579368],[-104.055032,43.558603],[-104.054787,43.503328],[-104.054786,43.503072],[-104.054779,43.477815],[-104.054766,43.428914],[-104.054614,43.390949],[-104.054403,43.325914],[-104.054218,43.30437],[-104.053884,43.297047],[-104.053876,43.289801],[-104.053127,43.000585],[-104.052863,42.754569],[-104.052809,42.749966],[-104.052583,42.650062],[-104.052741,42.633982],[-104.052586,42.630917],[-104.052773,42.611766],[-104.052775,42.61159],[-104.052775,42.610813],[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 \"}}]}","volume":"11","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-06","publicationStatus":"PW","scienceBaseUri":"59a52bd5e4b0fa5ae7c7483b","contributors":{"authors":[{"text":"Wallace, Zachary P.","contributorId":195900,"corporation":false,"usgs":false,"family":"Wallace","given":"Zachary","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":708633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennedy, Patricia L.","contributorId":172826,"corporation":false,"usgs":false,"family":"Kennedy","given":"Patricia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":708634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Squires, John R.","contributorId":195901,"corporation":false,"usgs":false,"family":"Squires","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":708635,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oakleaf, Robert J.","contributorId":195902,"corporation":false,"usgs":false,"family":"Oakleaf","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":708636,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olson, Lucretia E.","contributorId":195903,"corporation":false,"usgs":false,"family":"Olson","given":"Lucretia","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":708637,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dugger, Katie M. 0000-0002-4148-246X","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":36037,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":708363,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70182236,"text":"70182236 - 2016 - The relative contributions of disease and insects in the decline of a long-lived tree: a stochastic demographic model of whitebark pine (Pinus albicaulis)","interactions":[],"lastModifiedDate":"2017-02-22T16:01:15","indexId":"70182236","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"The relative contributions of disease and insects in the decline of a long-lived tree: a stochastic demographic model of whitebark pine (Pinus albicaulis)","docAbstract":"<p><span>Pathogens and insect pests have become increasingly important drivers of tree mortality in forested ecosystems. Unfortunately, understanding the relative contributions of multiple mortality agents to the population decline of trees is difficult, because it requires frequent measures of tree survival, growth, and recruitment, as well as the incidence of mortality agents. We present a population model of whitebark pine (</span><i>Pinus albicaulis</i><span>), a high-elevation tree undergoing rapid decline in western North America. The loss of whitebark pine is thought to be primarily due to an invasive pathogen (white pine blister rust; </span><i>Cronartium ribicola</i><span>) and a native insect (mountain pine beetle; </span><i>Dendroctonus ponderosae</i><span>). We utilized seven plots in Crater Lake National Park (Oregon, USA) where 1220 trees were surveyed for health and the presence of blister rust and beetle activity annually from 2003–2014, except 2008. We constructed size-based projection matrices for nine years and calculated the deterministic growth rate (λ) using an average matrix and the stochastic growth rate (λ</span><sub>s</sub><span>) by simulation for whitebark pine in our study population. We then assessed the roles of blister rust and beetles by calculating λ and λ</span><sub>s</sub><span>using matrices in which we removed trees with blister rust and, separately, trees with beetles. We also conducted life-table response experiments (LTRE) to determine which demographic changes contributed most to differences in λ between ambient conditions and the two other scenarios. The model suggests that whitebark pine in our plots are currently declining 1.1% per year (λ&nbsp;=&nbsp;0.9888, λ</span><sub>s</sub><span>&nbsp;=&nbsp;0.9899). Removing blister rust from the models resulted in almost no increase in growth (λ&nbsp;=&nbsp;0.9916, λ</span><sub>s</sub><span>&nbsp;=&nbsp;0.9930), while removing beetles resulted in a larger increase in growth (λ&nbsp;=&nbsp;1.0028, λ</span><sub>s</sub><span>&nbsp;=&nbsp;1.0045). The LTRE demonstrated that reductions in stasis of the three largest size classes due to beetles contributed most to the smaller λ in the ambient condition. Our work demonstrates a method for assessing the relative effects of different mortality agents on declining tree populations, and it shows that the effects of insects and pathogens can be markedly different from one another. In our study, beetle activity significantly reduced tree population growth while a pathogen had minimal effect, thus management actions to stabilize our study population will likely need to include reducing beetle activity.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2016.09.022","usgsCitation":"Jules, E., Jackson, J.I., van Mantgem, P.J., Beck, J.S., Murray, M.P., and Sahara, E.A., 2016, The relative contributions of disease and insects in the decline of a long-lived tree: a stochastic demographic model of whitebark pine (Pinus albicaulis): Forest Ecology and Management, v. 381, p. 144-156, https://doi.org/10.1016/j.foreco.2016.09.022.","productDescription":"13 p.","startPage":"144","endPage":"156","ipdsId":"IP-075247","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470354,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2016.09.022","text":"Publisher Index Page"},{"id":336015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"381","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58aeb13be4b01ccd54f9ee18","chorus":{"doi":"10.1016/j.foreco.2016.09.022","url":"http://dx.doi.org/10.1016/j.foreco.2016.09.022","publisher":"Elsevier BV","authors":"Jules Erik S., Jackson Jenell I., van Mantgem Phillip J., Beck Jennifer S., Murray Michael P., Sahara E. April","journalName":"Forest Ecology and Management","publicationDate":"12/2016"},"contributors":{"authors":[{"text":"Jules, Erik S","contributorId":181945,"corporation":false,"usgs":false,"family":"Jules","given":"Erik S","affiliations":[],"preferred":false,"id":670110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, Jenell I.","contributorId":181946,"corporation":false,"usgs":false,"family":"Jackson","given":"Jenell","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":670111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422 pvanmantgem@usgs.gov","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":2838,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip","email":"pvanmantgem@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":670109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beck, Jennifer S.","contributorId":181947,"corporation":false,"usgs":false,"family":"Beck","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":670112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murray, Michael P.","contributorId":181948,"corporation":false,"usgs":false,"family":"Murray","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":670113,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sahara, E. April","contributorId":181949,"corporation":false,"usgs":false,"family":"Sahara","given":"E.","email":"","middleInitial":"April","affiliations":[],"preferred":false,"id":670114,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70189332,"text":"70189332 - 2016 - Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity","interactions":[],"lastModifiedDate":"2017-07-11T13:16:58","indexId":"70189332","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity","docAbstract":"<p><span>Climate change is rapidly warming aquatic ecosystems including lakes and reservoirs. However, variability in lake characteristics can modulate how lakes respond to climate. Water clarity is especially important both because it influences the depth range over which heat is absorbed, and because it is changing in many lakes. Here, we show that simulated long-term water clarity trends influence how both surface and bottom water temperatures of lakes and reservoirs respond to climate change. Clarity changes can either amplify or suppress climate-induced warming, depending on lake depth and the direction of clarity change. Using a process-based model to simulate 1894 north temperate lakes from 1979 to 2012, we show that a scenario of decreasing clarity at a conservative yet widely observed rate of 0.92% yr</span><sup>−1</sup><span><span>&nbsp;</span>warmed surface waters and cooled bottom waters at rates comparable in magnitude to climate-induced warming. For lakes deeper than 6.5 m, decreasing clarity was sufficient to fully offset the effects of climate-induced warming on median whole-lake mean temperatures. Conversely, a scenario increasing clarity at the same rate cooled surface waters and warmed bottom waters relative to baseline warming rates. Furthermore, in 43% of lakes, increasing clarity more than doubled baseline bottom temperature warming rates. Long-term empirical observations of water temperature in lakes with and without clarity trends support these simulation results. Together, these results demonstrate that water clarity trends may be as important as rising air temperatures in determining how waterbodies respond to climate change.</span></p>","language":"English","publisher":"ASLO","doi":"10.1002/lol2.10027","usgsCitation":"Rose, K.C., Winslow, L.A., Read, J.S., and Hansen, G.J., 2016, Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity: Limnology and Oceanography Letters, v. 1, no. 1, p. 44-53, https://doi.org/10.1002/lol2.10027.","productDescription":"10 p.","startPage":"44","endPage":"53","ipdsId":"IP-070817","costCenters":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"links":[{"id":470371,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10027","text":"Publisher Index Page"},{"id":438499,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7028PN4","text":"USGS data release","linkHelpText":"Climate warming of Wisconsin lakes can be either amplified or suppressed by trends in water clarity"},{"id":343576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"1","noUsgsAuthors":false,"publicationDate":"2016-10-24","publicationStatus":"PW","scienceBaseUri":"5965b26be4b0d1f9f05b37f1","contributors":{"authors":[{"text":"Rose, Kevin C.","contributorId":174809,"corporation":false,"usgs":false,"family":"Rose","given":"Kevin","email":"","middleInitial":"C.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":704200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winslow, Luke A. 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":5919,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":false,"id":704201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":704202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Gretchen J. A.","contributorId":131099,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen","email":"","middleInitial":"J. A.","affiliations":[{"id":7242,"text":"Wisconsin Department of Natural Resources, Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":704203,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70178358,"text":"70178358 - 2016 - The 2016 groundwater flow model for Dane County, Wisconsin","interactions":[],"lastModifiedDate":"2017-01-03T14:13:59","indexId":"70178358","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":242,"text":"Bulletin","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"110","title":"The 2016 groundwater flow model for Dane County, Wisconsin","docAbstract":"<p>A new groundwater flow model for Dane County, Wisconsin, replaces an earlier model developed in the 1990s by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS). This modeling study was conducted cooperatively by the WGNHS and the USGS with funding from the Capital Area Regional Planning Commission (CARPC). Although the overall conceptual model of the groundwater system remains largely unchanged, the incorporation of newly acquired high-quality datasets, recent research findings, and improved modeling and calibration techniques have led to the development of a more detailed and sophisticated model representation of the groundwater system. The new model is three-dimensional and transient, and conceptualizes the county’s hydrogeology as a 12-layer system including all major unlithified and bedrock hydrostratigraphic units and two high-conductivity horizontal fracture zones. </p><p>Beginning from the surface down, the model represents the unlithified deposits as two distinct model layers (1 and 2). A single layer (3) simulates the Ordovician sandstone and dolomite of the Sinnipee, Ancell, and Prairie du Chien Groups. Sandstone of the Jordan Formation (layer 4) and silty dolostone of the St. Lawrence Formation (layer 5) each comprise separate model layers. The underlying glauconitic sandstone of the Tunnel City Group makes up three distinct layers: an upper aquifer (layer 6), a fracture feature (layer 7), and a lower aquifer (layer 8). The fracture layer represents a network of horizontal bedding-plane fractures that serve as a preferential pathway for groundwater flow. The model simulates the sandstone of the Wonewoc Formation as an upper aquifer (layer 9) with a bedding-plane fracture feature (layer 10) at its base. The Eau Claire aquitard (layer 11) includes shale beds within the upper portion of the Eau Claire Formation. This layer, along with overlying bedrock units, is mostly absent in the preglacially eroded valleys along the Yahara River valley and in northeastern Dane County. Layer 12 represents the Mount Simon sandstone as the lowermost model layer. It directly overlies the Precambrian crystalline basement rock, whose top surface forms the lower boundary of the model. </p><p>The model uses the USGS MODFLOW-NWT finite-difference code, a standalone version of MODFLOW-2005 that incorporates the Newton (NWT) solver. MODFLOW-NWT improves the handling of unconfined conditions by smoothing the transition from wet to dry cells. The model explicitly simulates groundwater–surface-water interaction with streamflow routing and lake-level fluctuation. Model input included published and unpublished hydrogeologic data from recent estimates of aquifer hydraulic conductivities. A spatial groundwater recharge distribution was obtained from a recent GIS-based, soil-water-balance model for Dane County. Groundwater withdrawals from pumping were simulated for 572 wells across the entire model domain, which includes Dane County and portions of seven neighboring counties—Columbia, Dodge, Green, Iowa, Jefferson, Lafayette, and Rock. These wells withdrew an average of 60 million gallons per day (mgd) over the 5-year period from 2006 through 2010. Within Dane County, 385 wells were simulated with an average withdrawal rate of 52 mgd.</p><p>Model calibration used the parameter estimation code PEST, and calibration targets included heads, stream and spring flows, lake levels, and borehole flows. Steady-state calibration focused on the period 2006 through 2010; the transient calibration focused on the 7-week drought period from late May through July 2012. </p><p>This model represents a significant step forward from previous work because of its finer grid resolution, improved hydrostratigraphic discretization, transient capabilities, and more sophisticated representation of surface-water features and multi-aquifer wells.</p><p>Potential applications of the model include evaluation of potential sites for and impacts of new high-capacity wells, development of wellhead protection plans, evaluating the effects of changing land use and climate on groundwater, and quantifying the relationships between groundwater and surface water.</p>","language":"English","publisher":"Wisconsin Geological and Natural History Survey","isbn":"978-0-88169-992-0","usgsCitation":"Parsen, M.J., Bradbury, K.R., Hunt, R.J., and Feinstein, D.T., 2016, The 2016 groundwater flow model for Dane County, Wisconsin: Bulletin 110, 56 p.","productDescription":"56 p.","numberOfPages":"64","ipdsId":"IP-071783","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":332790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330992,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.uwex.edu/dane-county-groundwater-model/"}],"country":"United States","state":"Wisconsin","county":"Dane County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.0094,43.286],[-89.0084,43.2555],[-89.0094,43.2],[-89.01,43.1131],[-89.0109,43.0849],[-89.0107,43.0271],[-89.0132,42.9353],[-89.013,42.8762],[-89.0119,42.8471],[-89.132,42.8479],[-89.2488,42.8478],[-89.3689,42.8484],[-89.3688,42.8575],[-89.4832,42.858],[-89.6026,42.8575],[-89.7196,42.8587],[-89.8377,42.8598],[-89.8375,42.9471],[-89.8386,43.0317],[-89.8384,43.1181],[-89.8394,43.205],[-89.8325,43.2123],[-89.825,43.2187],[-89.8175,43.226],[-89.8125,43.2342],[-89.8088,43.2369],[-89.8012,43.2365],[-89.7874,43.2356],[-89.771,43.237],[-89.7579,43.2379],[-89.7529,43.2443],[-89.7485,43.2507],[-89.7391,43.2548],[-89.7259,43.2644],[-89.7171,43.2739],[-89.714,43.2821],[-89.7165,43.2867],[-89.7235,43.2935],[-89.7209,43.2935],[-89.6008,43.2932],[-89.4819,43.2942],[-89.3617,43.2954],[-89.3624,43.2832],[-89.246,43.2834],[-89.1271,43.2827],[-89.0094,43.286]]]},\"properties\":{\"name\":\"Dane\",\"state\":\"WI\"}}]}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc695e4b0f5ce109fa953","contributors":{"authors":[{"text":"Parsen, Michael J.","contributorId":176845,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":657411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradbury, Kenneth R.","contributorId":49419,"corporation":false,"usgs":true,"family":"Bradbury","given":"Kenneth","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":657412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657414,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
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