Understanding the distribution of coal-bearing geologic units in Antarctica provides information that can be used in sedimentary, geomorphological, paleontological, and climatological studies. This report is a digital compilation of information on Antarctica’s coal-bearing geologic units found in the literature. It is intended to be used in small-scale spatial geographic information system (GIS) investigations and as a visual aid in the discussion of Antarctica’s coal resources or in other coal-based geologic investigations. Instead of using spatially insignificant point markers to represent large coal-bearing areas, this dataset uses polygons to represent actual coal-bearing lithologic units. Specific locations of coal deposits confirmed from the literature are provided in the attribution for the coal-bearing unit polygons. Coal-sample-location data were used to confirm some reported coal-bearing geology. The age and extent of the coal deposits indicated in the literature were checked against geologic maps ranging from local scale at 1:50,000 to Antarctic continental scale at 1:5,000,000; if satisfactory, the map boundaries were used to generate the polygons for the coal-bearing localities.
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http://energy.usgs.gov/
The NWW-striking North Qilian Orogenic Belt records the Paleozoic accretion–collision processes in NW China, and hosts Paleozoic Cu–Pb–Zn mineralization that was temporally and spatially related to the closure of the Paleo Qilian-Qinling Ocean. The Wangdian Cu deposit is located in the eastern part of the North Qilian Orogenic Belt, NW China. Copper mineralization is spatially associated with an altered early Paleozoic porphyritic granodiorite, which intruded tonalites and volcaniclastic rocks. Alteration zones surrounding the mineralization progress outward from a potassic to a feldspar-destructive phyllic assemblage. Mineralization consists mainly of quartz-sulfide stockworks and disseminated sulfides, with ore minerals chalcopyrite, pyrite, molybdenite, and minor galena and sphalerite. Gangue minerals include quartz, orthoclase, biotite, sericite, and K-feldspar. Zircon LA-ICPMS U–Pb dating of the ore-bearing porphyritic granodiorite yielded a mean 206Pb/238U age of 444.6 ± 7.8 Ma, with a group of inherited zircons yielding a mean U–Pb age of 485 ± 12 Ma, consistent with the emplacement age (485.3 ± 6.2 Ma) of the barren precursor tonalite. Rhenium and osmium analyses of molybdenite grains returned model ages of 442.9 ± 6.8 Ma and 443.3 ± 6.2 Ma, indicating mineralization was coeval with the emplacement of the host porphyritic granodiorite. Rhenium concentrations in molybdenite (208.9–213.2 ppm) suggest a mantle Re source. The tonalities are medium-K calc-alkaline. They are characterized by enrichment of light rare-earth elements (LREEs) and large-ion lithophile elements (LILEs), depletion of heavy rare-earth elements (HREEs) and high-field-strength elements (HFSEs), and minor negative Eu anomalies. They have εHf(t) values in the range of +3.6 to +11.1, with two-stage Hf model ages of 0.67–1.13 Ga, suggesting that the ca. 485 Ma barren tonalites were products of arc magmatism incorporating melts from the mantle wedge and the lithosphere. In contrast, the 40-m.y.-younger ore-bearing porphyritic granodiorite is sub-alkaline and peraluminous. They are enriched in LREEs and LILEs, depleted in HFSEs, and show weak negative Eu anomalies. They displayεHf(t) values of captured or inherited zircons in the range of +8.5 to +10.0, and younger two-stage Hf model ages of 0.78 Ga and 0.86 Ga, similar to those of ca. 485 Ma tonalite. The ca. 445 Ma zircons have εHf(t) values of −2.1 to +9.9, with two-stage Hf model ages of 0.75–1.27 Ga. Moreover, they have relatively high oxygen fugacity than that of the precursor barren tonalite. The ca. 445 Ma magmas at Wangdian thus formed in a subduction setting, and incorporated melts from the subduction-modified lithosphere that had previously been enriched by additions of chalcophile and siderophile element-rich materials by the earlier magmatism and metasomatism during the Paleo Qilian-Qinling Ocean subduction event.
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2016 - Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region","interactions":[],"lastModifiedDate":"2016-06-22T15:40:36","indexId":"70173767","displayToPublicDate":"2016-03-11T07:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region","docAbstract":"A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyze simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models and compare them with observations from 268 Russian stations. There are large across-model differences as expressed by simulated differences between near-surface soil and air temperatures, (ΔT), of 3 to 14 K, in the gradients between soil and air temperatures (0.13 to 0.96°C/°C), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, and hence guide improvements to the model’s conceptual structure and process parameterizations. Models with better performance apply multi-layer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (12–16 million km2). However, there is not a simple relationship between the quality of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, likely because several other factors such as differences in the treatment of soil organic matter, soil hydrology, surface energy calculations, and vegetation also provide important controls on simulated permafrost distribution.
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2016 - Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6","interactions":[],"lastModifiedDate":"2018-03-05T16:49:57","indexId":"70168922","displayToPublicDate":"2016-03-11T03:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"title":"Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6","docAbstract":"
Floodplains can be viewed as complex adaptive systems (Levin, 1998) because they are comprised of many different biophysical components, such as morphological features, soil groups and vegetation communities as well as being sites of key biogeochemical processing (Stanford et al., 2005). Interactions and feedbacks among the biophysical components often result in additional phenomena occuring over a range of scales, often in the absence of any controlling factors (sensu Hallet, 1990). This emergence of new biophysical features and rates of processing can lead to alternative stable states which feed back into floodplain adaptive cycles (cf. Hughes, 1997; Stanford et al., 2005). Interactions between different biophysical components, feedbacks, self emergence and scale are all key properties of complex adaptive systems (Levin, 1998; Phillips, 2003; Murray et al., 2014) and therefore will influence the manner in which we study and view spatial patterns. Measuring the spatial patterns of floodplain biophysical components is a prerequisite to examining and understanding these ecosystems as complex adaptive systems. Elucidating relationships between pattern and process, which are intrinsically linked within floodplains (Ward et al., 2002), is dependent upon an understanding of spatial pattern. This knowledge can help river scientists determine the major drivers, controllers and responses of floodplain structure and function, as well as the consequences of altering those drivers and controllers (Hughes and Cass, 1997; Whited et al., 2007). Interactions and feedbacks between physical, chemical and biological components of floodplain ecosystems create and maintain a structurally diverse and dynamic template (Stanford et al., 2005). This template influences subsequent interactions between components that consequently affect system trajectories within floodplains (sensu Bak et al., 1988). Constructing and evaluating models used to predict floodplain ecosystem responses to natural and anthropogenic disturbances therefore require quantification of spatial pattern (Asselman and Middelkoop, 1995; Walling and He, 1998). Quantifying these patterns also provides insights into the spatial and temporal domains of structuring processes as well as enabling the detection of self-emergent phenomena, environmental constraints or anthropogenic interference (Turner et al., 1990; Holling, 1992; De Jager and Rohweder, 2012). Thus, quantifying spatial pattern is an important building block on which to examine floodplains as complex adaptive systems (Levin, 1998). Approaches to measuring spatial pattern in floodplains must be cognisant of scale, self-emergent phenomena, spatial organisation, and location. Fundamental problems may arise when patterns observed at a site or transect scale are scaled-up to infer processes and patterns over entire floodplain surfaces (Wiens, 2002; Thorp et al., 2008). Likewise, patterns observed over the entire spatial extent of a landscape can mask important variation and detail at finer scales (Riitters et al., 2002). Indeed, different patterns often emerge at different scales (Turner et al., 1990) because of hierarchical structuring processes (O'Neill et al., 1991). Categorising data into discrete, homogeneous and predefined spatial units at a particular scale (e.g. polygons) creates issues and errors associated with scale and subjective classification (McGarigal et al., 2009; Cushman et al., 2010). These include, loss of information within classified ‘patches’, as well as the ability to detect the emergence of new features that do not fit the original classification scheme. Many of these issues arise because floodplains are highly heterogeneous and have complex spatial organizations (Carbonneau et al., 2012; Legleiter, 2013). As a result, the scale and location at which measurements are made can influence the observed spatial patterns; and patterns may not be scale independent or applicable in different geomorp
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River science: Research and management for the 21st century","language":"English","publisher":"John Wiley & Sons, Ltd","doi":"10.1002/9781118643525.ch6","isbn":"978-1-119-99434-3","usgsCitation":"Scown, M.W., Thoms, M.C., and De Jager, N.R., 2016, Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6, chap. of River science: Research and management for the 21st century, p. 103-131, https://doi.org/10.1002/9781118643525.ch6.","productDescription":"29 p.","startPage":"103","endPage":"131","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056619","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":321675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5746ccbee4b07e28b662dcf0","contributors":{"editors":[{"text":"Gilvear, David J.","contributorId":169613,"corporation":false,"usgs":false,"family":"Gilvear","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630282,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Greenwood, Malcolm T.","contributorId":169614,"corporation":false,"usgs":false,"family":"Greenwood","given":"Malcolm","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":630283,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":630284,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Wood, Paul J.","contributorId":169615,"corporation":false,"usgs":false,"family":"Wood","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630285,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Scown, Murray W.","contributorId":145709,"corporation":false,"usgs":false,"family":"Scown","given":"Murray","email":"","middleInitial":"W.","affiliations":[{"id":24492,"text":"Riverine Landscapes Research Laboratory, University of New England, Armidale, Australia","active":true,"usgs":false}],"preferred":false,"id":622119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":622120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":622118,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184235,"text":"70184235 - 2016 - Illuminating wildfire erosion and deposition patterns with repeat terrestrial lidar","interactions":[],"lastModifiedDate":"2017-03-06T10:51:56","indexId":"70184235","displayToPublicDate":"2016-03-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Illuminating wildfire erosion and deposition patterns with repeat terrestrial lidar","docAbstract":"Erosion following a wildfire is much greater than background erosion in forests because of wildfire-induced changes to soil erodibility and water infiltration. While many previous studies have documented post-wildfire erosion with point and small plot-scale measurements, the spatial distribution of post-fire erosion patterns at the watershed scale remains largely unexplored. In this study lidar surveys were collected periodically in a small, first-order drainage basin over a period of 2 years following a wildfire. The study site was relatively steep with slopes ranging from 17° to > 30°. During the study period, several different types of rain storms occurred on the site including low-intensity frontal storms (2.4 mm h−1) and high-intensity convective thunderstorms (79 mm h−1). These storms were the dominant drivers of erosion. Erosion resulting from dry ravel and debris flows was notably absent at the site. Successive lidar surveys were subtracted from one another to obtain digital maps of topographic change between surveys. The results show an evolution in geomorphic response, such that the erosional response after rain storms was strongly influenced by the previous erosional events and pre-fire site morphology. Hillslope and channel roughness increased over time, and the watershed armored as coarse cobbles and boulders were exposed. The erosional response was spatially nonuniform; shallow erosion from hillslopes (87% of the study area) contributed 3 times more sediment volume than erosion from convergent areas (13% of the study area). However, the total normalized erosion depth (volume/area) was highest in convergent areas. From a detailed understanding of the spatial locations of erosion, we made inferences regarding the processes driving erosion. It appears that hillslope erosion is controlled by rain splash (for detachment) and overland flow (for transport and quasi-channelized erosion), with the sites of highest erosion corresponding to locations with the lowest roughness. By contrast, in convergent areas we found erosion caused by overland flow. Soil erosion was locally interrupted by immobile objects such as boulders, bedrock, or tree trunks, resulting in a patchy erosion network with increasing roughness over time.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1002/2015JF003600","usgsCitation":"Rengers, F.K., Tucker, G., Moody, J., and Ebel, B., 2016, Illuminating wildfire erosion and deposition patterns with repeat terrestrial lidar: Journal of Geophysical Research F: Earth Surface, v. 121, no. 3, p. 588-608, https://doi.org/10.1002/2015JF003600.","productDescription":"21 p.","startPage":"588","endPage":"608","ipdsId":"IP-068620","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jf003600","text":"Publisher Index Page"},{"id":336854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.403611,\n 40.030833\n ],\n [\n -105.402222,\n 40.030833\n ],\n [\n -105.402222,\n 40.032222\n ],\n [\n -105.403611,\n 40.032222\n ],\n [\n -105.403611,\n 40.030833\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-11","publicationStatus":"PW","scienceBaseUri":"58be8339e4b014cc3a3a99e5","contributors":{"authors":[{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":680682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, G.E.","contributorId":150423,"corporation":false,"usgs":false,"family":"Tucker","given":"G.E.","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":680683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moody, J. A.","contributorId":187515,"corporation":false,"usgs":false,"family":"Moody","given":"J. A.","affiliations":[],"preferred":false,"id":680684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ebel, Brian","contributorId":187516,"corporation":false,"usgs":false,"family":"Ebel","given":"Brian","affiliations":[],"preferred":false,"id":680685,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169027,"text":"ofr20161036 - 2016 - Assessing the socioeconomic impact and value of open geospatial information","interactions":[],"lastModifiedDate":"2016-05-23T09:05:39","indexId":"ofr20161036","displayToPublicDate":"2016-03-10T18:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1036","title":"Assessing the socioeconomic impact and value of open geospatial information","docAbstract":"The production and accessibility of geospatial information including Earth observation is changing greatly both technically and in terms of human participation. Advances in technology have changed the way that geospatial data are produced and accessed, resulting in more efficient processes and greater accessibility than ever before. Improved technology has also created opportunities for increased participation in the gathering and interpretation of data through crowdsourcing and citizen science efforts. Increased accessibility has resulted in greater participation in the use of data as prices for Government-produced data have fallen and barriers to access have been reduced.
\nThe increase in participation in the production and in the use of data, defined as data democracy for this workshop, are having great impacts on economics and more generally on society.
\nThere is also a strong drive by governments around the world, as shown by the G8 Declaration in June 2013, to make public sector information and scientific data more widely accessible. These are respectively termed “open data” and “open research data.”
\nThis report summarizes discussion at the Workshop on Assessing the Impact and Value of Open Geospatial Information held at George Washington University in Washington, D.C. in October 2014. Workshop participants examined the consequences of expanding data democracy with a focus on its socioeconomic impacts. Evaluations were presented of state-of-the-art methods to assess these socioeconomic impacts, which included position papers and remarks by discussants. The workshop included discussions about the following topics: (1) increased and expanded information sources; (2) societal impacts, including approaches to economics assessments; (3) constraints to open access, including the demands for return on investment, specifications of intellectual property rights, and privacy issues; and (4) learning from the experiences of other data-rich domains, such as environmental management, internet businesses, health, and transportation.
\nThe workshop was a working meeting with strong participant engagement, leading to recommendations for action. The meeting included five topic-driven sessions and keynote presentations. Precirculated position papers for each panel session facilitated preparation and remarks by discussants. After the position papers are updated following the discussants’ remarks, it is planned to submit them for publication.
\nThe workshop included 68 participants coming from international organizations, the U.S. public and private sectors, nongovernmental organizations, and academia. Participants included policy makers and analysts, financial analysts, economists, information scientists, geospatial practitioners, and other discipline experts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161036","collaboration":"Prepared in cooperation with the Socioeconomic Benefits Community","usgsCitation":"Pearlman, Francoise, Pearlman, Jay, Bernknopf, Richard, Coote, Andrew, Craglia, Massimo, Friedl, Lawrence, Gallo, Jason, Hertzfeld, Henry, Jolly, Claire, Macauley, Molly, Shapiro, Carl, and Smart, Alan, 2016, Assessing the socioeconomic impact and value of open geospatial information: U.S. Geological Survey Open-File Report 2016–1036, 36 p., https://dx.doi.org/10.3133/ofr20161036.","productDescription":"vi, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1036/coverthb.jpg"},{"id":318803,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1036/ofr20161036.pdf","text":"Report","size":"5.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1036"}],"otherGeospatial":"Global","contact":"U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
http://www.usgs.gov
Well-informed river management decisions rely on an explicit statement of objectives, repeatable analyses, and a transparent system for assessing trade-offs. These components may then be applied to compare alternative operational regimes for water resource infrastructure (e.g., diversions, locks, and dams). Intra- and inter-annual hydrologic variability further complicates these already complex environmental flow decisions. Effective discharge analysis (developed in studies of geomorphology) is a powerful tool for integrating temporal variability of flow magnitude and associated ecological consequences. Here, we adapt the effectiveness framework to include multiple elements of the natural flow regime (i.e., timing, duration, and rate-of-change) as well as two flow variables. We demonstrate this analytical approach using a case study of environmental flow management based on long-term (60 years) daily discharge records in the Middle Oconee River near Athens, GA, USA. Specifically, we apply an existing model for estimating young-of-year fish recruitment based on flow-dependent metrics to an effective discharge analysis that incorporates hydrologic variability and multiple focal taxa. We then compare three alternative methods of environmental flow provision. Percentage-based withdrawal schemes outcompete other environmental flow methods across all levels of water withdrawal and ecological outcomes.
","language":"English","publisher":"Springer-Verlag","publisherLocation":"New York","doi":"10.1007/s00267-016-0684-4","usgsCitation":"McKay, S.K., Freeman, M., and Covich, A., 2016, Application of effective discharge analysis to environmental flow decision-making: Environmental Management, v. 575, no. 6, p. 1153-1165, https://doi.org/10.1007/s00267-016-0684-4.","productDescription":"13 p.","startPage":"1153","endPage":"1165","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073347","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":320849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"575","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"57287a2be4b0b13d391865af","contributors":{"authors":[{"text":"McKay, S. Kyle","contributorId":169086,"corporation":false,"usgs":false,"family":"McKay","given":"S.","email":"","middleInitial":"Kyle","affiliations":[],"preferred":false,"id":628390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Covich, A.P.","contributorId":14965,"corporation":false,"usgs":true,"family":"Covich","given":"A.P.","email":"","affiliations":[],"preferred":false,"id":628392,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170765,"text":"70170765 - 2016 - Study of biological communities subject to imperfect detection: Bias and precision of community N-mixture abundance models in small-sample situations","interactions":[],"lastModifiedDate":"2016-05-02T15:06:29","indexId":"70170765","displayToPublicDate":"2016-03-10T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1461,"text":"Ecological Research","active":true,"publicationSubtype":{"id":10}},"title":"Study of biological communities subject to imperfect detection: Bias and precision of community N-mixture abundance models in small-sample situations","docAbstract":"Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance (λ¯: 0.1, 0.5, 1, 5), detection probability (p¯: 0.1, 0.2, 0.5), and number of sampling sites (n site : 10, 20, 40) and visits (n visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables (λ¯, p¯, n site , n visit ) increased. Detection probability p¯ was most important for the estimates of mean abundance, while λ¯ was most influential for covariate effect and species richness estimates. For all parameters, increasing n site was more beneficial than increasing n visit . Minimal conditions for obtaining adequate performance of community abundance models were n site ≥ 20, p¯ ≥ 0.2, and λ¯ ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
","language":"English","publisher":"Blackwell Science","doi":"10.1007/s11284-016-1340-4","collaboration":"Yuichi Yamaura;\nMarc Kery","usgsCitation":"Yamaura, Y., Kery, M., and Royle, A., 2016, Study of biological communities subject to imperfect detection: Bias and precision of community N-mixture abundance models in small-sample situations: Ecological Research, v. 31, no. 3, p. 289-305, https://doi.org/10.1007/s11284-016-1340-4.","productDescription":"17 p.","startPage":"289","endPage":"305","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072120","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471158,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11284-016-1340-4","text":"Publisher Index Page"},{"id":320845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"57287a33e4b0b13d391865dd","contributors":{"authors":[{"text":"Yamaura, Yuichi","contributorId":169067,"corporation":false,"usgs":false,"family":"Yamaura","given":"Yuichi","affiliations":[{"id":25402,"text":"Hokkaido Univ.","active":true,"usgs":false}],"preferred":false,"id":628332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":628333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628331,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169872,"text":"70169872 - 2016 - Prebreeding survival of Roseate Terns Sterna dougallii varies with sex, hatching order and hatching date","interactions":[],"lastModifiedDate":"2016-03-28T14:22:47","indexId":"70169872","displayToPublicDate":"2016-03-10T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Prebreeding survival of Roseate Terns Sterna dougallii varies with sex, hatching order and hatching date","docAbstract":"Unequal sex ratios can reduce the productivity of animal populations and are especially prevalent among endangered species. A cohort of 333 Roseate Tern Sterna dougallii chicks at a site where the adult sex ratio was skewed towards females was sexed at hatching and followed through fledging and return to the breeding area, and subsequently during adulthood. The entire regional metapopulation was sampled for returning birds. Prebreeding survival (from fledging to age 3 years) was lower in males than in females, but only among B-chicks (second in hatching order). Prebreeding survival also declined with hatching date. The proportion of females in this cohort increased from 54.6% at hatching to 56.2% at fledging and to an estimated 58.0% among survivors at age 3 years. This was more than sufficient to explain the degree of skew in the sex ratio of the adult population, but changes in this degree of skew during the study period make it difficult to identify the influence of a single cohort of recruits. Many studies of prebreeding survival in other bird species have identified effects of sex, hatching order or hatching date, but no previous study has tested for effects of all three factors simultaneously.
","language":"English","publisher":"Academic Press","publisherLocation":"London","doi":"10.1111/ibi.12359","collaboration":"Ian Nisbet; David Monticelli; Jeffrey Spendelow; Patricia Szczys","usgsCitation":"Nisbet, I.C., Monticelli, D., Spendelow, J.A., and Szczys, P., 2016, Prebreeding survival of Roseate Terns Sterna dougallii varies with sex, hatching order and hatching date: Ibis, v. 158, no. 2, p. 327-334, https://doi.org/10.1111/ibi.12359.","productDescription":"8 p.","startPage":"327","endPage":"334","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072951","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":319556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"158","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"56fa560be4b0a6037df0ab5a","contributors":{"authors":[{"text":"Nisbet, Ian C. T.","contributorId":54866,"corporation":false,"usgs":true,"family":"Nisbet","given":"Ian","email":"","middleInitial":"C. T.","affiliations":[],"preferred":false,"id":625408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monticelli, David","contributorId":168304,"corporation":false,"usgs":false,"family":"Monticelli","given":"David","email":"","affiliations":[{"id":25244,"text":"Marine and Environmental Science Centre, Universidade de Coimbra, Coimbra, Portugal","active":true,"usgs":false}],"preferred":false,"id":625406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spendelow, Jeffrey A. 0000-0001-8167-0898 jspendelow@usgs.gov","orcid":"https://orcid.org/0000-0001-8167-0898","contributorId":4355,"corporation":false,"usgs":true,"family":"Spendelow","given":"Jeffrey","email":"jspendelow@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Szczys, Patricia","contributorId":35613,"corporation":false,"usgs":true,"family":"Szczys","given":"Patricia","email":"","affiliations":[],"preferred":false,"id":625407,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169085,"text":"70169085 - 2016 - Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (Tamias palmeri): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA","interactions":[],"lastModifiedDate":"2016-12-16T11:08:53","indexId":"70169085","displayToPublicDate":"2016-03-10T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (Tamias palmeri): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA","docAbstract":"Although montane sky islands surrounded by desert scrub and shrub steppe comprise a large part of the biological diversity of the Basin and Range Province of southwestern North America, comprehensive ecological and population demographic studies for high-elevation small mammals within these areas are rare. Here, we examine the ecology and population parameters of the Palmer’s chipmunk (Tamias palmeri) in the Spring Mountains of southern Nevada, and present a predictive GIS-based distribution and probability of occurrence model at both home range and geographic spatial scales. Logistic regression analyses and Akaike Information Criterion model selection found variables of forest type, slope, and distance to water sources as predictive of chipmunk occurrence at the geographic scale. At the home range scale, increasing population density, decreasing overstory canopy cover, and decreasing understory canopy cover contributed to increased survival rates.
","language":"English","publisher":"American Society of Mammalogists","publisherLocation":"Lawrence, KS","doi":"10.1093/jmammal/gyw026","usgsCitation":"Lowrey, C.E., Longshore, K.M., Riddle, B., and Mantooth, S., 2016, Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (Tamias palmeri): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA: Journal of Mammalogy, v. 97, no. 4, p. 1033-1043, https://doi.org/10.1093/jmammal/gyw026.","productDescription":"11 p.","startPage":"1033","endPage":"1043","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-028807","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471159,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyw026","text":"Publisher Index Page"},{"id":318914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Spring Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.08428955078125,\n 36.53832942872816\n ],\n [\n -116.070556640625,\n 36.45884507478879\n ],\n [\n -115.99090576171875,\n 36.34610265300638\n ],\n [\n -115.95520019531249,\n 36.27085020723905\n ],\n [\n -115.9002685546875,\n 36.24870331653198\n ],\n [\n -115.81512451171876,\n 36.16448788632064\n ],\n [\n -115.74920654296874,\n 36.06686213257888\n ],\n [\n -115.67230224609374,\n 36.01133890448606\n ],\n [\n -115.59814453125001,\n 35.96022296929667\n ],\n [\n -115.56243896484374,\n 35.88237433729238\n ],\n [\n -115.4168701171875,\n 35.84230806912384\n ],\n [\n -115.32073974609375,\n 35.82226734114509\n ],\n [\n -115.33172607421876,\n 35.89795019335754\n ],\n [\n -115.33721923828125,\n 35.96022296929667\n ],\n [\n -115.33447265625,\n 36.06464195517141\n ],\n [\n -115.31249999999999,\n 36.16005298551354\n ],\n [\n -115.29052734375,\n 36.24648828212654\n ],\n [\n -115.29602050781249,\n 36.33725319397006\n ],\n [\n -115.3839111328125,\n 36.348314860643015\n ],\n [\n -115.3839111328125,\n 36.416862115300304\n ],\n [\n -115.39764404296875,\n 36.485348924361425\n ],\n [\n -115.49652099609375,\n 36.48314061639213\n ],\n [\n -115.5706787109375,\n 36.416862115300304\n ],\n [\n -115.6475830078125,\n 36.405810193726765\n ],\n [\n -115.72174072265626,\n 36.41465185677698\n ],\n [\n -115.76843261718751,\n 36.50301312197295\n ],\n [\n -115.84259033203124,\n 36.52950186333475\n ],\n [\n -115.93872070312499,\n 36.52950186333475\n ],\n [\n -116.01562499999999,\n 36.542742833547834\n ],\n [\n -116.08428955078125,\n 36.53832942872816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"97","issue":"4","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"56ea83ace4b0f59b85d90ce2","chorus":{"doi":"10.1093/jmammal/gyw026","url":"http://dx.doi.org/10.1093/jmammal/gyw026","publisher":"Oxford University Press (OUP)","authors":"Lowrey Christopher, Longshore Kathleen, Riddle Brett, Mantooth Stacy","journalName":"Journal of Mammalogy","publicationDate":"3/10/2016"},"contributors":{"authors":[{"text":"Lowrey, Chris E. 0000-0001-5084-7275 clowrey@usgs.gov","orcid":"https://orcid.org/0000-0001-5084-7275","contributorId":3225,"corporation":false,"usgs":true,"family":"Lowrey","given":"Chris","email":"clowrey@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Longshore, Kathleen M. 0000-0001-6621-1271 longshore@usgs.gov","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":2677,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"longshore@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":622835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riddle, Brett R.","contributorId":93016,"corporation":false,"usgs":true,"family":"Riddle","given":"Brett R.","affiliations":[],"preferred":false,"id":622837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mantooth, Stacy","contributorId":167608,"corporation":false,"usgs":false,"family":"Mantooth","given":"Stacy","email":"","affiliations":[{"id":24777,"text":"Nevada State College","active":true,"usgs":false}],"preferred":false,"id":622838,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169044,"text":"70169044 - 2016 - Prioritizing avian species for their risk of population-level consequences from wind energy development","interactions":[],"lastModifiedDate":"2016-03-14T13:05:13","indexId":"70169044","displayToPublicDate":"2016-03-10T14:00:00","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":"Prioritizing avian species for their risk of population-level consequences from wind energy development","docAbstract":"Recent growth in the wind energy industry has increased concerns about its impacts on wildlife populations. Direct impacts of wind energy include bird and bat collisions with turbines whereas indirect impacts include changes in wildlife habitat and behavior. Although many species may withstand these effects, species that are long-lived with low rates of reproduction, have specialized habitat preferences, or are attracted to turbines may be more prone to declines in population abundance. We developed a prioritization system to identify the avian species most likely to experience population declines from wind facilities based on their current conservation status and their expected risk from turbines. We developed 3 metrics of turbine risk that incorporate data on collision fatalities at wind facilities, population size, life history, species’ distributions relative to turbine locations, number of suitable habitat types, and species’ conservation status. We calculated at least 1 measure of turbine risk for 428 avian species that breed in the United States. We then simulated 100,000 random sets of cutoff criteria (i.e., the metric values used to assign species to different priority categories) for each turbine risk metric and for conservation status. For each set of criteria, we assigned each species a priority score and calculated the average priority score across all sets of criteria. Our prioritization system highlights both species that could potentially experience population decline caused by wind energy and species at low risk of population decline. For instance, several birds of prey, such as the long-eared owl, ferruginous hawk, Swainson’s hawk, and golden eagle, were at relatively high risk of population decline across a wide variety of cutoff values, whereas many passerines were at relatively low risk of decline. This prioritization system is a first step that will help researchers, conservationists, managers, and industry target future study and management activity.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS One","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0150813","usgsCitation":"Beston, J.A., Diffendorfer, J., Loss, S., and Johnson, D.H., 2016, Prioritizing avian species for their risk of population-level consequences from wind energy development: PLoS ONE, v. 11, no. 3, https://doi.org/10.1371/journal.pone.0150813.","startPage":"Article e0150813","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057769","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471160,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0150813","text":"Publisher Index Page"},{"id":318849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"56e7e0c0e4b0f59b85d6aabc","contributors":{"authors":[{"text":"Beston, Julie A. jbeston@usgs.gov","contributorId":5673,"corporation":false,"usgs":true,"family":"Beston","given":"Julie","email":"jbeston@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":622671,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":622672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loss, Scott","contributorId":131107,"corporation":false,"usgs":false,"family":"Loss","given":"Scott","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":622673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":622674,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169123,"text":"70169123 - 2016 - Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management","interactions":[],"lastModifiedDate":"2016-08-25T10:26:16","indexId":"70169123","displayToPublicDate":"2016-03-10T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management","docAbstract":"Mangrove forest rehabilitation should begin much sooner than at the point of catastrophic loss. We describe the need for “mangrove forest heart attack prevention”, and how that might be accomplished in a general sense by embedding plot and remote sensing monitoring within coastal management plans. The major cause of mangrove stress at many sites globally is often linked to reduced tidal flows and exchanges. Blocked water flows can reduce flushing not only from the seaward side, but also result in higher salinity and reduced sediments when flows are blocked landward. Long-term degradation of function leads to acute mortality prompted by acute events, but created by a systematic propensity for long-term neglect of mangroves. Often, mangroves are lost within a few years; however, vulnerability is re-set decades earlier when seemingly innocuous hydrological modifications are made (e.g., road construction, blocked tidal channels), but which remain undetected without reasonable large-scale monitoring.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Pollution Bulletin","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.marpolbul.2016.03.006","usgsCitation":"Lewis, R.R., Milbrandt, E.C., Brown, B., Krauss, K.W., Rovai, A.S., Beever, J.W., and Flynn, L., 2016, Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management: Marine Pollution Bulletin, v. 109, no. 2, p. 764-771, https://doi.org/10.1016/j.marpolbul.2016.03.006.","productDescription":"8 p.","startPage":"764","endPage":"771","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070524","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":319080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Worldwide","volume":"109","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f11b70e4b0f59b85ddc517","contributors":{"authors":[{"text":"Lewis, Roy R","contributorId":167668,"corporation":false,"usgs":false,"family":"Lewis","given":"Roy","email":"","middleInitial":"R","affiliations":[{"id":24798,"text":"Coastal Resources Group, Salt Springs, FL","active":true,"usgs":false}],"preferred":false,"id":623077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milbrandt, Eric C","contributorId":167669,"corporation":false,"usgs":false,"family":"Milbrandt","given":"Eric","email":"","middleInitial":"C","affiliations":[{"id":24799,"text":"Sanibel-Captiva Conservation Foundation, Sanibel, FL","active":true,"usgs":false}],"preferred":false,"id":623078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Benjamin","contributorId":167670,"corporation":false,"usgs":false,"family":"Brown","given":"Benjamin","email":"","affiliations":[{"id":24800,"text":"Charles Darwin University, Research Institute for Environment and Livelihoolds, AUS","active":true,"usgs":false}],"preferred":false,"id":623079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":623076,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rovai, Andre S.","contributorId":167671,"corporation":false,"usgs":false,"family":"Rovai","given":"Andre","email":"","middleInitial":"S.","affiliations":[{"id":24801,"text":"Federal University of Santa Catarina, Dept. Ecology and Zoology, Brazil","active":true,"usgs":false}],"preferred":false,"id":623080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beever, James W.","contributorId":167672,"corporation":false,"usgs":false,"family":"Beever","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":24802,"text":"Southwest Florida Regional Planning Council, Fort Myers, FL","active":true,"usgs":false}],"preferred":false,"id":623081,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flynn, Laura L","contributorId":167673,"corporation":false,"usgs":false,"family":"Flynn","given":"Laura L","affiliations":[{"id":24798,"text":"Coastal Resources Group, Salt Springs, FL","active":true,"usgs":false}],"preferred":false,"id":623082,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70169007,"text":"70169007 - 2016 - Online induction heating for determination of isotope composition of woody stem water with laser spectrometry: A methods assessment","interactions":[],"lastModifiedDate":"2017-11-22T17:39:15","indexId":"70169007","displayToPublicDate":"2016-03-10T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2114,"text":"Isotopes in Environmental and Health Studies","active":true,"publicationSubtype":{"id":10}},"title":"Online induction heating for determination of isotope composition of woody stem water with laser spectrometry: A methods assessment","docAbstract":"Application of stable isotopes of water to studies of plant–soil interactions often requires a substantial preparatory step of extracting water from samples without fractionating isotopes. Online heating is an emerging approach for this need, but is relatively untested and major questions of how to best deliver standards and assess interference by organics have not been evaluated. We examined these issues in our application of measuring woody stem xylem of sagebrush using a Picarro laser spectrometer with online induction heating. We determined (1) effects of cryogenic compared to induction-heating extraction, (2) effects of delivery of standards on filter media compared to on woody stem sections, and (3) spectral interference from organic compounds for these approaches (and developed a technique to do so). Our results suggest that matching sample and standard media improves accuracy, but that isotopic values differ with the extraction method in ways that are not due to spectral interference from organics.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10256016.2016.1141205","usgsCitation":"Lazarus, B.E., Germino, M., and Vander Veen, J.L., 2016, Online induction heating for determination of isotope composition of woody stem water with laser spectrometry: A methods assessment: Isotopes in Environmental and Health Studies, v. 52, no. 3, p. 309-325, https://doi.org/10.1080/10256016.2016.1141205.","productDescription":"17 p.","startPage":"309","endPage":"325","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063959","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":318824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"56e3fa58e4b0f59b85d4946d","contributors":{"authors":[{"text":"Lazarus, Brynne E. 0000-0002-6352-486X blazarus@usgs.gov","orcid":"https://orcid.org/0000-0002-6352-486X","contributorId":4901,"corporation":false,"usgs":true,"family":"Lazarus","given":"Brynne","email":"blazarus@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":622488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":622487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vander Veen, Jessica L.","contributorId":167500,"corporation":false,"usgs":false,"family":"Vander Veen","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":24728,"text":"USGS FRESC","active":true,"usgs":false}],"preferred":false,"id":622489,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70168923,"text":"70168923 - 2016 - Does water chemistry limit the distribution of New Zealand mud snails in Redwood National Park?","interactions":[],"lastModifiedDate":"2016-06-02T11:02:21","indexId":"70168923","displayToPublicDate":"2016-03-10T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Does water chemistry limit the distribution of New Zealand mud snails in Redwood National Park?","docAbstract":"New Zealand mud snails (NZMS) are exotic mollusks present in many waterways of the western United States. In 2009, NZMS were detected in Redwood Creek in Redwood National Park, CA. Although NZMS are noted for their ability to rapidly increase in abundance and colonize new areas, after more than 5 years in Redwood Creek, their distribution remains limited to a ca. 300 m reach. Recent literature suggests that low specific conductivity and environmental calcium can limit NZMS distribution. We conducted laboratory experiments, exposing NZMS collected from Redwood Creek to both natural waters and artificial treatment solutions, to determine if low conductivity and calcium concentration limit the distribution of NZMS in Redwood National Park. For natural water exposures, we held NZMS in water from their source location (conductivity 135 μS/cm, calcium 13 mg/L) or water from four other locations in the Redwood Creek watershed encompassing a range of conductivity (77–158 μS/cm) and calcium concentration (<5–13 mg/L). For exposures in treatment solutions, we manipulated both conductivity (range 20–200 μS/cm) and calcium concentration (range <5–17.5 mg/L) in a factorial design. Response variables measured included mortality and reproductive output. Adult NZMS survived for long periods (>4 months) in the lowest conductivity waters from Redwood Creek and all but the lowest-conductivity treatment solutions, regardless of calcium concentration. However, reproductive output was very low in all natural waters and all low-calcium treatment solutions. Our results suggest that water chemistry may inhibit the spread of NZMS in Redwood National Park by reducing their reproductive output.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-016-1098-1","usgsCitation":"Vazquez, R., Ward, D.M., and Sepulveda, A.J., 2016, Does water chemistry limit the distribution of New Zealand mud snails in Redwood National Park?: Biological Invasions, v. 18, no. 6, p. 1523-1531, https://doi.org/10.1007/s10530-016-1098-1.","productDescription":"9 p.","startPage":"1523","endPage":"1531","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070980","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":318785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Redwood Creek, Redwood National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.05349731445312,\n 41.10470834043821\n ],\n [\n -124.05349731445312,\n 41.30411857136123\n ],\n [\n -123.91891479492186,\n 41.30411857136123\n ],\n [\n -123.91891479492186,\n 41.10470834043821\n ],\n [\n -124.05349731445312,\n 41.10470834043821\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-05","publicationStatus":"PW","scienceBaseUri":"56e29aade4b0f59b85d32753","contributors":{"authors":[{"text":"Vazquez, Ryan","contributorId":167388,"corporation":false,"usgs":false,"family":"Vazquez","given":"Ryan","email":"","affiliations":[{"id":24705,"text":"Department of Fisheries Biology, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":622122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ward, Darren M.","contributorId":167389,"corporation":false,"usgs":false,"family":"Ward","given":"Darren","email":"","middleInitial":"M.","affiliations":[{"id":24705,"text":"Department of Fisheries Biology, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":622123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sepulveda, Adam J. 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":150628,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":622121,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70168968,"text":"70168968 - 2016 - The differing biogeochemical and microbial signatures of glaciers and rock glaciers","interactions":[],"lastModifiedDate":"2018-02-22T11:30:49","indexId":"70168968","displayToPublicDate":"2016-03-10T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2319,"text":"Journal of Geophysical Research G: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"The differing biogeochemical and microbial signatures of glaciers and rock glaciers","docAbstract":"Glaciers and rock glaciers supply water and bioavailable nutrients to headwater mountain lakes and streams across all regions of the American West. Here we present a comparative study of the metal, nutrient, and microbial characteristics of glacial and rock glacial influence on headwater ecosystems in three mountain ranges of the contiguous U.S.: The Cascade Mountains, Rocky Mountains, and Sierra Nevada. Several meltwater characteristics (water temperature, conductivity, pH, heavy metals, nutrients, complexity of dissolved organic matter (DOM), and bacterial richness and diversity) differed significantly between glacier and rock glacier meltwaters, while other characteristics (Ca2+, Fe3+, SiO2 concentrations, reactive nitrogen, and microbial processing of DOM) showed distinct trends between mountain ranges regardless of meltwater source. Some characteristics were affected both by glacier type and mountain range (e.g. temperature, ammonium (NH4+) and nitrate (NO3- ) concentrations, bacterial diversity). Due to the ubiquity of rock glaciers and the accelerating loss of the low latitude glaciers our results point to the important and changing influence that these frozen features place on headwater ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/2015JG003236","usgsCitation":"Fegel, T.S., Baron, J., Fountain, A.G., Johnson, G.F., and Hall, E.K., 2016, The differing biogeochemical and microbial signatures of glaciers and rock glaciers: Journal of Geophysical Research G: Biogeosciences, v. 121, no. 3, p. 919-932, https://doi.org/10.1002/2015JG003236.","productDescription":"14 p.","startPage":"919","endPage":"932","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069738","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471161,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jg003236","text":"Publisher Index Page"},{"id":318781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado, Oregon, Washington, Wyoming","otherGeospatial":"Cascade Mountains, Sierra Nevada, Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.8046875,\n 34.08906131584996\n ],\n [\n -124.8046875,\n 49.03786794532644\n ],\n [\n -105.46875,\n 49.03786794532644\n ],\n [\n -105.46875,\n 34.08906131584996\n ],\n [\n -124.8046875,\n 34.08906131584996\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-29","publicationStatus":"PW","scienceBaseUri":"56e29aafe4b0f59b85d3275b","contributors":{"authors":[{"text":"Fegel, Timothy S.","contributorId":167462,"corporation":false,"usgs":false,"family":"Fegel","given":"Timothy","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":622415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baron, Jill 0000-0002-5902-6251 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":194124,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":622414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fountain, Andrew G.","contributorId":10410,"corporation":false,"usgs":false,"family":"Fountain","given":"Andrew","email":"","middleInitial":"G.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":622416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Gunnar F.","contributorId":167464,"corporation":false,"usgs":false,"family":"Johnson","given":"Gunnar","email":"","middleInitial":"F.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":622417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hall, Edward K. ehall@usgs.gov","contributorId":4837,"corporation":false,"usgs":true,"family":"Hall","given":"Edward","email":"ehall@usgs.gov","middleInitial":"K.","affiliations":[],"preferred":true,"id":622418,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70168967,"text":"70168967 - 2016 - Using science-policy integration to improve ecosystem science and inform decision-making: Lessons from U.S. LTERs","interactions":[],"lastModifiedDate":"2018-02-22T11:28:37","indexId":"70168967","displayToPublicDate":"2016-03-10T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1121,"text":"Bulletin of the Ecological Society of America","active":true,"publicationSubtype":{"id":10}},"title":"Using science-policy integration to improve ecosystem science and inform decision-making: Lessons from U.S. LTERs","docAbstract":"This Special Session took place on 12 August 2015 at the 100th Meeting of the Ecological Society of America in Baltimore, Maryland, and was conceived of and coordinated by the Science Policy Exchange. The Science Policy Exchange (SPE) is a boundary- spanning organization established to work at the interface of science and policy to confront pressing environmental challenges . SPE was created as a collaborative of six research institutions to increase the impact of science on environmental decisions. This session was organized by Marissa Weiss and co- organized by Pamela Templer, Kathleen Fallon Lambert, Jill Baron, Charles Driscoll, and David Foster. Along the theme of ESA ’ s Centennial meeting, the group of presenters represented collectively more than 100 years of experience in integration of science, policy, and outreach.
","language":"English","publisher":"Wiley","doi":"10.1002/bes2.1206","usgsCitation":"Templer, P.H., Lambert, K.F., Weiss, M., Baron, J., Driscoll, C.T., and Foster, D.R., 2016, Using science-policy integration to improve ecosystem science and inform decision-making: Lessons from U.S. LTERs: Bulletin of the Ecological Society of America, v. 97, no. 1, p. 123-128, https://doi.org/10.1002/bes2.1206.","productDescription":"6 p.","startPage":"123","endPage":"128","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070047","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/bes2.1206","text":"Publisher Index Page"},{"id":318774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-14","publicationStatus":"PW","scienceBaseUri":"56e29aafe4b0f59b85d3275d","contributors":{"authors":[{"text":"Templer, Pamela H.","contributorId":167457,"corporation":false,"usgs":false,"family":"Templer","given":"Pamela","email":"","middleInitial":"H.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":622409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lambert, Kathleen Fallon","contributorId":167458,"corporation":false,"usgs":false,"family":"Lambert","given":"Kathleen","email":"","middleInitial":"Fallon","affiliations":[{"id":24712,"text":"Harvard Forest","active":true,"usgs":false}],"preferred":false,"id":622410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weiss, Marissa","contributorId":167459,"corporation":false,"usgs":false,"family":"Weiss","given":"Marissa","email":"","affiliations":[{"id":24712,"text":"Harvard Forest","active":true,"usgs":false}],"preferred":false,"id":622411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baron, Jill 0000-0002-5902-6251 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":194124,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":622408,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Driscoll, Charles T.","contributorId":35418,"corporation":false,"usgs":true,"family":"Driscoll","given":"Charles","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":622412,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Foster, David R.","contributorId":167461,"corporation":false,"usgs":false,"family":"Foster","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":24712,"text":"Harvard Forest","active":true,"usgs":false}],"preferred":false,"id":622413,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169000,"text":"70169000 - 2016 - Organic contaminants in Great Lakes tributaries: Prevalence and potential aquatic toxicity","interactions":[],"lastModifiedDate":"2016-03-10T09:59:17","indexId":"70169000","displayToPublicDate":"2016-03-10T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Organic contaminants in Great Lakes tributaries: Prevalence and potential aquatic toxicity","docAbstract":"Organic compounds used in agriculture, industry, and households make their way into surface waters through runoff, leaking septic-conveyance systems, regulated and unregulated discharges, and combined sewer overflows, among other sources. Concentrations of these organic waste compounds (OWCs) in some Great Lakes tributaries indicate a high potential for adverse impacts on aquatic organisms. During 2010–13, 709 water samples were collected at 57 tributaries, together representing approximately 41% of the total inflow to the lakes. Samples were collected during runoff and low-flow conditions and analyzed for 69 OWCs, including herbicides, insecticides, polycyclic aromatic hydrocarbons, plasticizers, antioxidants, detergent metabolites, fire retardants, non-prescription human drugs, flavors/fragrances, and dyes. Urban-related land cover characteristics were the most important explanatory variables of concentrations of many OWCs. Compared to samples from nonurban watersheds (< 15% urban land cover) samples from urban watersheds (> 15% urban land cover) had nearly four times the number of detected compounds and four times the total sample concentration, on average. Concentration differences between runoff and low-flow conditions were not observed, but seasonal differences were observed in atrazine, metolachlor, DEET, and HHCB concentrations. Water quality benchmarks for individual OWCs were exceeded at 20 sites, and at 7 sites benchmarks were exceeded by a factor of 10 or more. The compounds with the most frequent water quality benchmark exceedances were the PAHs benzo[a]pyrene, pyrene, fluoranthene, and anthracene, the detergent metabolite 4-nonylphenol, and the herbicide atrazine. Computed estradiol equivalency quotients (EEQs) using only nonsteroidal endocrine-active compounds indicated medium to high risk of estrogenic effects (intersex or vitellogenin induction) at 10 sites. EEQs at 3 sites were comparable to values reported in effluent. This multifaceted study is the largest, most comprehensive assessment of the occurrence and potential effects of OWCs in the Great Lakes Basin to date.
","language":"English","doi":"10.1016/j.scitotenv.2016.02.137","usgsCitation":"Baldwin, A.K., Corsi, S., De Cicco, L., Lenaker, P.L., Lutz, M.A., Sullivan, D.J., and Richards, K.D., 2016, Organic contaminants in Great Lakes tributaries: Prevalence and potential aquatic toxicity: Science of the Total Environment, v. 554-555, p. 42-52, https://doi.org/10.1016/j.scitotenv.2016.02.137.","productDescription":"11 p.","startPage":"42","endPage":"52","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073075","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":471162,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.02.137","text":"Publisher Index Page"},{"id":318776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.63671875,\n 41.44272637767212\n ],\n [\n -92.63671875,\n 48.951366470947725\n ],\n [\n -75.76171875,\n 48.951366470947725\n ],\n [\n -75.76171875,\n 41.44272637767212\n ],\n [\n -92.63671875,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"554-555","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56e29aaee4b0f59b85d32759","contributors":{"authors":[{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. srcorsi@usgs.gov","contributorId":511,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":622454,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Cicco, Laura A. 0000-0002-3915-9487 ldecicco@usgs.gov","orcid":"https://orcid.org/0000-0002-3915-9487","contributorId":4814,"corporation":false,"usgs":true,"family":"De Cicco","given":"Laura A.","email":"ldecicco@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":622455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lenaker, Peter L. 0000-0002-9469-6285 plenaker@usgs.gov","orcid":"https://orcid.org/0000-0002-9469-6285","contributorId":5572,"corporation":false,"usgs":true,"family":"Lenaker","given":"Peter","email":"plenaker@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lutz, Michelle A. malutz@usgs.gov","contributorId":167259,"corporation":false,"usgs":true,"family":"Lutz","given":"Michelle","email":"malutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sullivan, Daniel J. 0000-0003-2705-3738 djsulliv@usgs.gov","orcid":"https://orcid.org/0000-0003-2705-3738","contributorId":1703,"corporation":false,"usgs":true,"family":"Sullivan","given":"Daniel","email":"djsulliv@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":622458,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Richards, Kevin D. krichard@usgs.gov","contributorId":280,"corporation":false,"usgs":true,"family":"Richards","given":"Kevin","email":"krichard@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":622459,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70169001,"text":"70169001 - 2016 - Application of lime (CaCO3) to promote forest recovery from severe acidification increases potential for earthworm invasion","interactions":[],"lastModifiedDate":"2016-08-17T11:06:43","indexId":"70169001","displayToPublicDate":"2016-03-10T10:15:00","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":"Application of lime (CaCO3) to promote forest recovery from severe acidification increases potential for earthworm invasion","docAbstract":"The application of lime (calcium carbonate) may be a cost-effective strategy to promote forest ecosystem recovery from acid impairment, under contemporary low levels of acidic deposition. However, liming acidified soils may create more suitable habitat for invasive earthworms that cause significant damage to forest floor communities and may disrupt ecosystem processes. We investigated the potential effects of liming in acidified soils where earthworms are rare in conjunction with a whole-ecosystem liming experiment in the chronically acidified forests of the western Adirondacks (USA). Using a microcosm experiment that replicated the whole-ecosystem treatment, we evaluated effects of soil liming on Lumbricus terrestris survivorship and biomass growth. We found that a moderate lime application (raising pH from 3.1 to 3.7) dramatically increased survival and biomass of L. terrestris, likely via increases in soil pH and associated reductions in inorganic aluminum, a known toxin. Very few L. terrestris individuals survived in unlimed soils, whereas earthworms in limed soils survived, grew, and rapidly consumed leaf litter. We supplemented this experiment with field surveys of extant earthworm communities along a gradient of soil pH in Adirondack hardwood forests, ranging from severely acidified (pH < 3) to well-buffered (pH > 5). In the field, no earthworms were observed where soil pH < 3.6. Abundance and species richness of earthworms was greatest in areas where soil pH > 4.4 and human dispersal vectors, including proximity to roads and public fishing access, were most prevalent. Overall our results suggest that moderate lime additions can be sufficient to increase earthworm invasion risk where dispersal vectors are present.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2016.03.002","usgsCitation":"Homan, C., Beirer, C.M., McCay, T.S., and Lawrence, G.B., 2016, Application of lime (CaCO3) to promote forest recovery from severe acidification increases potential for earthworm invasion: Forest Ecology and Management, v. 368, p. 39-44, https://doi.org/10.1016/j.foreco.2016.03.002.","productDescription":"6 p.","startPage":"39","endPage":"44","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071766","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":471165,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2016.03.002","text":"Publisher Index Page"},{"id":318768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.87268447875977,\n 43.50423881694708\n ],\n [\n -74.87268447875977,\n 43.53672718543221\n ],\n [\n -74.79852676391602,\n 43.53672718543221\n ],\n [\n -74.79852676391602,\n 43.50423881694708\n ],\n [\n -74.87268447875977,\n 43.50423881694708\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"368","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56e29aaae4b0f59b85d3274d","chorus":{"doi":"10.1016/j.foreco.2016.03.002","url":"http://dx.doi.org/10.1016/j.foreco.2016.03.002","publisher":"Elsevier BV","authors":"Homan Caitlin, Beier Colin, McCay Timothy, Lawrence Gregory","journalName":"Forest Ecology and Management","publicationDate":"5/2016"},"contributors":{"authors":[{"text":"Homan, Caitlin","contributorId":167484,"corporation":false,"usgs":false,"family":"Homan","given":"Caitlin","email":"","affiliations":[{"id":24722,"text":"Graduate Student, SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":622462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beirer, Colin M","contributorId":167485,"corporation":false,"usgs":false,"family":"Beirer","given":"Colin","email":"","middleInitial":"M","affiliations":[{"id":24723,"text":"Associate Professor, Forest & Natural Resources, SUNY College of ESF","active":true,"usgs":false}],"preferred":false,"id":622463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCay, Timothy S","contributorId":167486,"corporation":false,"usgs":false,"family":"McCay","given":"Timothy","email":"","middleInitial":"S","affiliations":[{"id":24724,"text":"Professor of Biology & Environmental Studies, Colgate University","active":true,"usgs":false}],"preferred":false,"id":622464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622461,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70157168,"text":"ds69GG - 2016 - Assessment of undiscovered hydrocarbon resources of sub-Saharan Africa","interactions":[],"lastModifiedDate":"2016-06-08T09:28:17","indexId":"ds69GG","displayToPublicDate":"2016-03-10T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"69","chapter":"GG","title":"Assessment of undiscovered hydrocarbon resources of sub-Saharan Africa","docAbstract":"The main objective of the U.S. Geological Survey’s (USGS) National and Global Petroleum Assessment Project is to assess the potential for undiscovered, technically recoverable oil and natural gas resources of the United States and the world (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). The USGS updated assessments that were completed during the USGS World Petroleum Assessment 2000 (U.S. Geological Survey World Energy Assessment Team, 2000) and conducted new assessments in areas around the world that were not previously examined (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). These assessments used the latest geology-based assessment methodology for conventional oil and gas resources. The new assessments are available at the USGS website, (http://energy.usgs.gov/OilGas/AssessmentsData/WorldPetroleumAssessment.aspx).
\nAs part of this project, the USGS assessed 13 geologic provinces located in sub-Saharan Africa (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). Coastal provinces were extended offshore to water depths ranging from 2,000 to 4,000 meters (m). Within these 13 geologic provinces 18 assessment units (figs. 1, 2) were identified.
\nThe west Africa provinces are (1) the Senegal, containing the passive-margin Senegal Basin of Middle Jurassic to Holocene age; (2) the West African Coastal, characterized by rift, passive-margin, and transform tectonics; (3) the Gulf of Guinea, characterized by transform tectonics; (4) the Niger Delta, containing more than 9,100 m of sedimentary rock and recent sediments; (5) the West-Central Coastal, which contains the Aptian salt basin, is dominated by both rift and sag tectonics, and includes the Congo Basin; and (6) the Orange River Coastal, containing more than 7,000 m of syn-rift and post-rift sedimentary rock. The West African Coastal Province was assessed for the first time, whereas the other five west Africa provinces were reassessed for the 2012 World Oil and Wandrey Gas Resource Assessment (fig. 1 of U.S. Geological Survey World Conventional Resources Assessment Team, 2012). More than 275 new oil and gas fields have been discovered in the six west Africa provinces (IHS Energy, 2008, 2009) since the USGS World Petroleum Assessment in 2000 (U.S. Geological Survey World Energy Assessment Team, 2000). These provinces were assessed because of increased energy exploration activity and new oil and gas discoveries within the provinces.
\nSeven provinces not assessed as part of the World Petroleum Assessment 2000 (U.S. Geological Survey World Energy Assessment Team, 2000) were assessed by the USGS as part of the World Assessment 2012 (U.S. Geological Survey World Conventional Resources Assessment Team, 2012). These provinces are (1) the Chad Province, containing Cretaceous and Cenozoic-age lacustrine, continental, and minor marine rocks; (2) the Sud Province, containing Cretaceous and Paleogene age lacustrine, continental, and minor marine rocks; (3) the South Africa Coastal Province, which contains rift, transform, and passive-margin rocks; (4) the Mozambique Coastal Province, containing rift, drift, and passive-margin rocks; (5) the Morondava Province, which contains failed rift, drift, and passive-margin rocks; (6) the Tanzania Coastal Province, containing rift, drift, and passive-margin rocks; and (7) the Seychelles Province, which contains rift and drift rocks. At the time of this assessment 157 oil and gas fields had been discovered in the seven provinces (IHS Energy, 2009). These provinces were assessed because of increased interest and new oil and gas discoveries within the provinces.
\nThe assessment was geology-based and used the total petroleum system (TPS) concept. The geologic elements of a TPS are hydrocarbon source rocks (source rock maturation and hydrocarbon generation and migration), reservoir rocks (quality and distribution), and traps where hydrocarbon accumulates. Using these geologic criteria, 16 conventional total petroleum systems and 18 assessment units in the 13 provinces were defined. The undiscovered, technically recoverable oil and gas resources were assessed for all assessment units.
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We investigated ecosystem effects resulting from introductions of Rangifer tarandus (hereafter “Rangifer”) to native mammalian predator- and herbivore-free islands in the Aleutian archipelago of Alaska. We hypothesized that the maritime tundra of these islands would experience either: (1) accelerated ecosystem processes mediated by positive feedbacks between increased graminoid production and rapid nitrogen cycling; or (2) decelerated processes mediated by herbivory that stimulated shrub domination and lowered soil fertility. We measured summer plant and soil properties across three islands representing a chronosequence of elapsed time post-Rangifer introduction (Atka: ~100 yr; Adak: ~50; Kagalaska: ~0), with distinct stages of irruptive population dynamics of Rangifer nested within each island (Atka: irruption, K-overshoot, decline, K-re-equilibration; Adak: irruption, K-overshoot; Kagalaska: initial introduction). We also measured Rangifer spatial use within islands (indexed by pellet group counts) to determine how ecosystem processes responded to spatial variation in herbivory. Vegetation community response to herbivory varied with temporal and spatial scale. When comparing temporal effects using the island chronosequence, increased time since herbivore introduction led to more graminoids and fewer dwarf-shrubs, lichens, and mosses. Slow-growingCladonia lichens that are highly preferred winter forage were decimated on both long-termRangifer-occupied islands. In addition, linear relations between more concentrated Rangifer spatial use and reductions in graminoid and forb biomass within islands added spatial heterogeneity to long-term patterns identified by the chronosequence. These results support, in part, the hypothesis that Rangifer population persistence on islands is facilitated by successful exploitation of graminoid biomass as winter forage after palatable lichens are decimated. However, the shift from shrubs to graminoids was expected to enhance rates of nitrogen cycling, yet rates of net N-mineralization, NH4+ pools, and soil δ15N declined markedly along the chronosequence and were weakly associated with spatial use within islands. Overall plant and soil patterns were disrupted but responded differently to intermediate (50 yr) and long-term (100 yr) herbivory, and were correlated with distinct stages of irruptive population dynamics.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/ecs2.1219","collaboration":"USFWS","usgsCitation":"Ricca, M.A., Miles, A.K., Van Vuren, D., and Eviner, V.T., 2016, Impacts of introduced Rangifer on ecosystem processes of maritime tundra on subarctic islands: Ecosphere, v. 7, no. 3, https://doi.org/10.1002/ecs2.1219.","productDescription":"23 p.","startPage":"Article e01219","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068619","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471166,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1219","text":"Publisher Index Page"},{"id":318832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian archipelago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -177.01171875,\n 51.5429188223739\n ],\n [\n -177.01171875,\n 52.449314140869696\n ],\n [\n -173.924560546875,\n 52.449314140869696\n ],\n [\n -173.924560546875,\n 51.5429188223739\n ],\n [\n -177.01171875,\n 51.5429188223739\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"56e7e0b9e4b0f59b85d6aa79","contributors":{"authors":[{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miles, A. Keith 0000-0002-3108-808X keith_miles@usgs.gov","orcid":"https://orcid.org/0000-0002-3108-808X","contributorId":196,"corporation":false,"usgs":true,"family":"Miles","given":"A.","email":"keith_miles@usgs.gov","middleInitial":"Keith","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Vuren, Dirk H.","contributorId":89408,"corporation":false,"usgs":true,"family":"Van Vuren","given":"Dirk H.","affiliations":[],"preferred":false,"id":622654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eviner, Valerie T.","contributorId":167553,"corporation":false,"usgs":false,"family":"Eviner","given":"Valerie","email":"","middleInitial":"T.","affiliations":[{"id":24746,"text":"Department of Plant Sciences, UC Davis, CA","active":true,"usgs":false}],"preferred":false,"id":622655,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169297,"text":"70169297 - 2016 - The role of competition – colonization tradeoffs and spatial heterogeneity in promoting trematode coexistence","interactions":[],"lastModifiedDate":"2016-12-16T11:22:24","indexId":"70169297","displayToPublicDate":"2016-03-10T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The role of competition – colonization tradeoffs and spatial heterogeneity in promoting trematode coexistence","docAbstract":"Competition – colonization tradeoffs occur in many systems, and theory predicts that they can strongly promote species coexistence. However, there is little empirical evidence that observed competition – colonization tradeoffs are strong enough to maintain diversity in natural systems. This is due in part to a mismatch between theoretical assumptions and biological reality in some systems. We tested whether a competition – colonization tradeoff explains how a diverse trematode guild coexists in California horn snail populations, a system that meets the requisite criteria for the tradeoff to promote coexistence. A field experiment showed that subordinate trematode species tended to have higher colonization rates than dominant species. This tradeoff promoted coexistence in parameterized models but did not fully explain trematode diversity and abundance, suggesting a role of additional diversity maintenance mechanisms. Spatial heterogeneity is an alternative way to promote coexistence if it isolates competing species. We used scale transition theory to expand the competition – colonization tradeoff model to include spatial variation. The parameterized model showed that spatial variation in trematode prevalence did not isolate most species sufficiently to explain the overall high diversity, but could benefit some rare species. Together, the results suggest that several mechanisms combine to maintain diversity, even when a competition – colonization tradeoff occurs.
","language":"English","publisher":"The Ecological Society of America","doi":"10.1890/15-0753.1","usgsCitation":"Mordecai, E., Jaramillo, A.G., Ashford, J.E., Hechinger, R., and Lafferty, K.D., 2016, The role of competition – colonization tradeoffs and spatial heterogeneity in promoting trematode coexistence: Ecology, v. 97, no. 6, p. 1484-1496, https://doi.org/10.1890/15-0753.1.","productDescription":"13 p.","startPage":"1484","endPage":"1496","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067090","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":319334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f50fd3e4b0f59b85e1ebd8","contributors":{"authors":[{"text":"Mordecai, Erin A.","contributorId":9113,"corporation":false,"usgs":true,"family":"Mordecai","given":"Erin A.","affiliations":[],"preferred":false,"id":623479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaramillo, Alejandra G.","contributorId":149800,"corporation":false,"usgs":false,"family":"Jaramillo","given":"Alejandra","email":"","middleInitial":"G.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":623480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ashford, Jacob E.","contributorId":149801,"corporation":false,"usgs":false,"family":"Ashford","given":"Jacob","email":"","middleInitial":"E.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":623481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hechinger, Ryan F.","contributorId":73730,"corporation":false,"usgs":true,"family":"Hechinger","given":"Ryan F.","affiliations":[],"preferred":false,"id":623482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623478,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70174947,"text":"70174947 - 2016 - Changing regional emissions of airborne pollutants reflected in the chemistry of snowpacks and wetfall in the Rocky Mountain region, USA, 1993–2012","interactions":[],"lastModifiedDate":"2018-02-13T10:27:49","indexId":"70174947","displayToPublicDate":"2016-03-10T02:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3728,"text":"Water, Air, & Soil Pollution","onlineIssn":"1573-2932","printIssn":"0049-6979","active":true,"publicationSubtype":{"id":10}},"title":"Changing regional emissions of airborne pollutants reflected in the chemistry of snowpacks and wetfall in the Rocky Mountain region, USA, 1993–2012","docAbstract":"Wintertime precipitation sample data from 55 Snowpack sites and 17 National Atmospheric Deposition Program (NADP)/National Trends Network Wetfall sites in the Rocky Mountain region were examined to identify long-term trends in chemical concentration, deposition, and precipitation using Regional and Seasonal Kendall tests. The Natural Resources Conservation Service snow-telemetry (SNOTEL) network provided snow-water-equivalent data from 33 sites located near Snowpack- and NADP Wetfall-sampling sites for further comparisons. Concentration and deposition of ammonium, calcium, nitrate, and sulfate were tested for trends for the period 1993–2012. Precipitation trends were compared between the three monitoring networks for the winter seasons and downward trends were observed for both Snowpack and SNOTEL networks, but not for the NADP Wetfall network. The dry-deposition fraction of total atmospheric deposition, relative to wet deposition, was shown to be considerable in the region. Potential sources of regional airborne pollutant emissions were identified from the U.S. Environmental Protection Agency 2011 National Emissions Inventory, and from long-term emissions data for the period 1996–2013. Changes in the emissions of ammonia, nitrogen oxides, and sulfur dioxide were reflected in significant trends in snowpack and wetfall chemistry. In general, ammonia emissions in the western USA showed a gradual increase over the past decade, while ammonium concentrations and deposition in snowpacks and wetfall showed upward trends. Emissions of nitrogen oxides and sulfur dioxide declined while regional trends in snowpack and wetfall concentrations and deposition of nitrate and sulfate were downward.
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Basins intercepting the main flow and external P sources had highest P and CHL and shallowest SD. Internal loading in shallow, polymictic basins caused P and CHL to increase and SD to decrease as summer progressed. Winter aeration used to eliminate winterkill increased summer internal P loading and decreased water quality, while reductions in upstream beaver impoundments had little effect on water quality. Variations in air temperature and precipitation affected each basin differently. Warmer air temperatures increased productivity throughout the lake and decreased clarity in less eutrophic basins. Increased precipitation increased P in the basins intercepting the main flow but had little effect on the isolated deep West Bay. These relations are used to project effects of future climatic changes on LSG and other temperate lakes.
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earthquake (magnitude 7.8) on 25 April 2015 and later aftershocks struck South Asia, killing ~9000 people and damaging a large region. Supported by a large campaign of responsive satellite data acquisitions over the earthquake disaster zone, our team undertook a satellite image survey of the earthquakes’ induced geohazards in Nepal and China and an assessment of the geomorphic, tectonic, and lithologic controls on quake-induced landslides. Timely analysis and communication aided response and recovery and informed decision-makers. We mapped 4312 coseismic and postseismic landslides. We also surveyed 491 glacier lakes for earthquake damage but found only nine landslide-impacted lakes and no visible satellite evidence of outbursts. Landslide densities correlate with slope, peak ground acceleration, surface downdrop, and specific metamorphic lithologies and large plutonic intrusions.
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Seismic‐hazard analysis usually relies on ground‐motion prediction equations (GMPEs); within this framework site response is modeled statistically with simplified site parameters that include the time‐averaged shear‐wave velocity to 30 m (VS30) and basin depth parameters. Because VS30 is not known in most locations, it must be interpolated or inferred through secondary information such as geology or topography. In this article, we analyze a subset of stations for which VS30 has been measured to address effects of VS30 proxies on the uncertainty in the ground motions as modeled by GMPEs. The stations we analyze also include multiple recordings, which allow us to compute the repeatable site effects (or empirical amplification factors [EAFs]) from the ground motions. Although all methods exhibit similar bias, the proxy methods only reduce the ground‐motion standard deviations at long periods when compared to GMPEs without a site term, whereas measured VS30 values reduce the standard deviations at all periods. The standard deviation of the ground motions are much lower when the EAFs are used, indicating that future refinements of the site term in GMPEs have the potential to substantially reduce the overall uncertainty in the prediction of ground motions by GMPEs.
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