Most environmental bio-monitoring methods using the species composition of marine faunas define the Ecological Quality Status of soft bottom ecosystems based on the relative proportions of species assigned to a limited number of ecological categories. In this study we analyse the distribution patterns of benthic foraminifera in the Mediterranean as a function of organic carbon gradients on the basis of 15 publications and assign the individual species to five ecological categories. Our categories (of sensitive, indifferent and 3rd, 2nd and 1st order opportunists) are very similar to the ecological categories commonly used for macrofauna, but show some minor differences. In the 15 analysed publications, we considered the numerical data of 493 taxa, of which 199 could be assigned. In all 79 taxa were classified as sensitive, 60 as indifferent, 46 as 3rd order, 12 as 2nd order and 2 as 1st order opportunists. The remaining 294 taxa are all accessory, and will only marginally contribute to biotic indices based on relative species proportions. In this paper we wanted also to explain the methodology we used for these species assignments, paying particular attention to all complications and problems encountered. We think that the species list proposed here will constitute a highly useful tool for foraminiferal bio-monitoring of soft bottoms in the Mediterranean Sea, which can be used in different ecological indices (Foram-AMBI and similar methods). With additional information becoming available in the next few years, it will be possible to expand the list, and, if necessary, to apply some minor corrections. As a next step, we intend to test this species list using several biotic indices, in a number of independent data sets, as soon as these will become available.
Tributyltin (TBT) has been recognized as an endocrine disrupting chemical (EDC) for several decades. However, only in the last decade, was its primary endocrine mechanism of action (MeOA) elucidated—interactions with the nuclear retinoid-X receptor (RXR), peroxisome proliferator-activated receptor γ (PPARγ), and their heterodimers. This molecular initiating event (MIE) alters a range of reproductive, developmental, and metabolic pathways at the organism level. It is noteworthy that a variety of MeOAs have been proposed over the years for the observed endocrine-type effects of TBT; however, convincing data for the MIE was provided only recently and now several researchers have confirmed and refined the information on this MeOA. One of the most important lessons learned from years of research on TBT concerns apparent species sensitivity. Several aspects such as the rates of uptake and elimination, chemical potency, and metabolic capacity are all important for identifying the most sensitive species for a given chemical, including EDCs. For TBT, much of this was discovered by trial and error, hence important relationships and important sensitive taxa were not identified until several decades after its introduction to the environment. As recognized for many years, TBT-induced responses are known to occur at very low concentrations for molluscs, a fact that has more recently also been observed in fish species. This review explores the MeOA and effects of TBT in different species (aquatic molluscs and other invertebrates, fish, amphibians, birds, and mammals) according to the OECD Conceptual Framework for Endocrine Disruptor Testing and Assessment (CFEDTA). The information gathered on biological effects that are relevant for populations of aquatic animals was used to construct Species Sensitivity Distributions (SSDs) based on No Observed Effect Concentrations (NOECs) and Lowest Observed Effect Concentrations (LOECs). Fish appear at the lower end of these distributions, showing that they are as sensitive as molluscs, and for some species, even more sensitive. Concentrations in the range of 1 ng/L for water exposure (10 ng/g for whole-body burden) have been shown to elicit endocrine-type responses, whereas mortality occurs at water concentrations ten times higher. Current screening and assessment methodologies as compiled in the OECD CFEDTA are able to identify TBT as a potent endocrine disruptor with a high environmental risk for the original use pattern. If those approaches had been available when TBT was introduced to the market, it is likely that its use would have been regulated sooner, thus avoiding the detrimental effects on marine gastropod populations and communities as documented over several decades.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reviews of environmental contamination and toxicology Volume 245","language":"English","publisher":"Springer","doi":"10.1007/398_2017_8","usgsCitation":"Lagadic, L., Katsiadaki, I., Biever, R.C., Guiney, P., Karouna-Renier, N., Schwarz, T., and Meador, J., 2018, Tributyltin: Advancing the science on assessing endocrine disruption with an unconventional endocrine-disrupting compound, chap. of Reviews of environmental contamination and toxicology Volume 245, v. 245, p. 65-127, https://doi.org/10.1007/398_2017_8.","productDescription":"63 p.","startPage":"65","endPage":"127","ipdsId":"IP-075961","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":352685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"245","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-09","publicationStatus":"PW","scienceBaseUri":"5afee6fae4b0da30c1bfc006","contributors":{"authors":[{"text":"Lagadic, Laurent","contributorId":200679,"corporation":false,"usgs":false,"family":"Lagadic","given":"Laurent","email":"","affiliations":[],"preferred":false,"id":731400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katsiadaki, Ioanna","contributorId":200653,"corporation":false,"usgs":false,"family":"Katsiadaki","given":"Ioanna","email":"","affiliations":[],"preferred":false,"id":731401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biever, Ronald C.","contributorId":200660,"corporation":false,"usgs":false,"family":"Biever","given":"Ronald","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":731402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guiney, Patrick","contributorId":193148,"corporation":false,"usgs":false,"family":"Guiney","given":"Patrick","affiliations":[],"preferred":false,"id":731403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":731399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarz, Tamar","contributorId":200733,"corporation":false,"usgs":false,"family":"Schwarz","given":"Tamar","email":"","affiliations":[],"preferred":false,"id":731404,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meador, James P.","contributorId":174075,"corporation":false,"usgs":false,"family":"Meador","given":"James P.","affiliations":[],"preferred":false,"id":731405,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196120,"text":"70196120 - 2018 - Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change","interactions":[],"lastModifiedDate":"2018-10-23T17:05:30","indexId":"70196120","displayToPublicDate":"2018-03-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3510,"text":"Systematic Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change","title":"Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change","docAbstract":"Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/sysbio/syy017","usgsCitation":"Maguire, K.C., Shinneman, D.J., Potter, K.M., and Hipkins, V.D., 2018, Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change: Systematic Biology, v. 67, no. 6, p. 965-978, https://doi.org/10.1093/sysbio/syy017.","productDescription":"14 p.","startPage":"965","endPage":"978","ipdsId":"IP-088076","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468902,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/sysbio/syy017","text":"Publisher Index Page"},{"id":352679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5afee6f9e4b0da30c1bfc000","contributors":{"authors":[{"text":"Maguire, Kaitlin C. 0000-0001-8193-2384","orcid":"https://orcid.org/0000-0001-8193-2384","contributorId":203419,"corporation":false,"usgs":true,"family":"Maguire","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":731442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","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":731443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Potter, Kevin M.","contributorId":167660,"corporation":false,"usgs":false,"family":"Potter","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":24794,"text":"Department of Forestry and Environmental Resources, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":731444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hipkins, Valerie D.","contributorId":167661,"corporation":false,"usgs":false,"family":"Hipkins","given":"Valerie","email":"","middleInitial":"D.","affiliations":[{"id":24795,"text":"National Forest Genetics Laboratory, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":731445,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195978,"text":"sir20185006 - 2018 - Nitrogen concentrations and loads for the Connecticut River at Middle Haddam, Connecticut, computed with the use of autosampling and continuous measurements of water quality for water years 2009 to 2014","interactions":[],"lastModifiedDate":"2018-03-21T15:01:13","indexId":"sir20185006","displayToPublicDate":"2018-03-20T16:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5006","title":"Nitrogen concentrations and loads for the Connecticut River at Middle Haddam, Connecticut, computed with the use of autosampling and continuous measurements of water quality for water years 2009 to 2014","docAbstract":"The daily and annual loads of nitrate plus nitrite and total nitrogen for the Connecticut River at Middle Haddam, Connecticut, were determined for water years 2009 to 2014. The analysis was done with a combination of methods, which included a predefined rating curve method for nitrate plus nitrite and total nitrogen for water years 2009 to 2011 and a custom rating curve method that included sensor measurements of nitrate plus nitrite nitrogen concentration and turbidity along with mean daily flow to determine total nitrogen loads for water years 2011 to 2014. Instantaneous concentrations of total nitrogen were estimated through the use of a regression model based on sensor measurements at 15-minute intervals of nitrate plus nitrite nitrogen and turbidity for water years 2011 to 2014.
Annual total nitrogen loads at the Connecticut River at Middle Haddam ranged from 12,900 to 19,200 metric tons, of which about 42 to 49 percent was in the form of nitrate plus nitrite. The mean 95-percent prediction intervals on daily total nitrogen load estimates were smaller from the custom model, which used sensor data, than those calculated by the predefined model.
Annual total nitrogen load estimates at the Connecticut River at Middle Haddam were compared with the upstream load estimates at the Connecticut River at Thompsonville, Conn. Annual gains in total nitrogen loads between the two stations ranged from 3,430 to 6,660 metric tons. These increases between the two stations were attributed to the effects of increased urbanization and to combined annual discharges of 1,540 to 2,090 metric tons of nitrogen from 24 wastewater treatment facilities in the drainage area between the two stations. The contribution of total nitrogen from wastewater discharge between the two stations had declined substantially before the beginning of this study and accounted for from 31 to 52 percent of the gain in nitrogen load between the Thompsonville and Middle Haddam sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185006","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Mullaney, J.R., Martin, J.W., and Morrison, J., 2018, Nitrogen concentrations and loads for the Connecticut River at Middle Haddam, Connecticut, computed with the use of autosampling and continuous measurements of water quality for water years 2009 to 2014: U.S. Geological Survey Scientific Investigations Report 2018–5006, 22 p., https://doi.org/10.3133/sir20185006.","productDescription":"Report: vii, 22 p.; Data release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091217","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5006/sir20185006.pdf","text":"Report","size":"4.79 MB ","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5006"},{"id":352631,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ31WT","text":"USGS data release","description":"USGS data release","linkHelpText":"Nitrogen Concentrations and Loads for the Connecticut River at Middle Haddam, Connecticut, Computed With the Use of Autosampling and Continuous Measurements of Water Quality for Water Years 2009 to 2014"},{"id":352409,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5006/coverthb.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Connecticut River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.94097900390625,\n 41.34691753986531\n ],\n [\n -72.18154907226562,\n 41.34691753986531\n ],\n [\n -72.18154907226562,\n 42.04011410708205\n ],\n [\n -72.94097900390625,\n 42.04011410708205\n ],\n [\n -72.94097900390625,\n 41.34691753986531\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Assessment of the “Bird or Animal Deformities or Reproductive Problems” Beneficial Use Impairment (BUI) can be accomplished by (1) comparing tissue concentrations to established background and Lowest Observable Effect Level (LOEL) for reproductive effects, or (2) directly measuring reproductive success at Areas of Concern (AOCs) and statistically comparing those rates to minimally impacted reference locations (non-AOCs). Results from recent tree swallow (Tachycineta bicolor) publications were used to evaluate this BUI based on both approaches. For both endpoints, a 95-percent confidence interval (CI) was used to test for significant differences. Additional information on BUIs, AOCs, and the program in general can be found in the Great Lakes Water Quality Agreement (2012).
For the first metric, there are good background and reproductive effect threshold LOELs for tree swallow egg concentrations for polychlorinated biphenyls (PCBs), dioxins and furans (PCDD/Fs), and mercury, as well as, for some other organic and inorganic contaminants. For the second assessment, comparisons were made between AOC and non-AOC sites for reproductive success, which was measured as the daily probability of egg failure and the percentage of eggs laid that hatched. Multistate modeling was used to assess whether there was an association between the daily probability of egg failure and a suite of contaminants, including PCBs, but also whether there was an association with ecological variables, such as female age and date within season. Both of these ecological variables are known to affect hatching success in birds. The objective of this report is to synthesize the previously published information to assist in the assessment of the “Bird or Animal Deformities or Reproductive Problems” BUI at 16 sites within the 5 Wisconsin AOCs (table 1). The logic behind this interpretation is applicable to other AOCs as well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181032","usgsCitation":"Custer, C.M., Custer, T.W., and Dummer, P.M., 2018, Synthesis of tree swallow (Tachycineta bicolor) data for Beneficial Use Impairment (BUI) assessment at Wisconsin Areas of Concern: U.S. Geological Survey Open-File Report 2018–1032, 8 p., https://doi.org/10.3133/ofr20181032.","productDescription":"iv, 8 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092682","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":352653,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1032/ofr20181032.pdf","text":"Report","size":"111 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1032"},{"id":352652,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1032/coverthb.jpg"}],"country":"United States","state":"Michigan, Minnesota, Wisconsin","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Cross, WI 54603
Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km2 landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines.
","language":"English","publisher":"MDPI","doi":"10.3390/s18030880","usgsCitation":"Gallant, A.L., Sadinski, W.J., Brown, J.F., Senay, G., and Roth, M.F., 2018, Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes: Sensors, v. 18, no. 3, p. 1-38, https://doi.org/10.3390/s18030880.","productDescription":"Article 880; 38 p.","startPage":"1","endPage":"38","ipdsId":"IP-094477","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468903,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s18030880","text":"Publisher Index Page"},{"id":437982,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QF8S3H","text":"USGS data release","linkHelpText":"Data files supporting the paper titled "Complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate lessons from temperate wetland-upland landscapes""},{"id":352650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-16","publicationStatus":"PW","scienceBaseUri":"5afee6fce4b0da30c1bfc012","contributors":{"authors":[{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":731314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sadinski, Walter J. wsadinski@usgs.gov","contributorId":3287,"corporation":false,"usgs":true,"family":"Sadinski","given":"Walter","email":"wsadinski@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":731315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":3241,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":731316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":152206,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel B.","email":"senay@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":731317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, Mark F. 0000-0001-5095-1865 mroth@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-1865","contributorId":3286,"corporation":false,"usgs":true,"family":"Roth","given":"Mark","email":"mroth@usgs.gov","middleInitial":"F.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":731318,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196104,"text":"70196104 - 2018 - Spatial and temporal variation in sources of atmospheric nitrogen deposition in the Rocky Mountains using nitrogen isotopes","interactions":[],"lastModifiedDate":"2018-03-20T09:03:28","indexId":"70196104","displayToPublicDate":"2018-03-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":924,"text":"Atmospheric Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variation in sources of atmospheric nitrogen deposition in the Rocky Mountains using nitrogen isotopes","docAbstract":"Variation in source areas and source types of atmospheric nitrogen (N) deposition to high-elevation ecosystems in the Rocky Mountains were evaluated using spatially and temporally distributed N isotope data from atmospheric deposition networks for 1995-2016. This unique dataset links N in wet deposition and snowpack to mobile and stationary emissions sources, and enhances understanding of the impacts of anthropogenic activities and environmental policies that mitigate effects of accelerated N cycling across the Rocky Mountain region. δ15N−NO3− at 50 U.S. Geological Survey Rocky Mountain Snowpack (Snowpack) sites ranged from −3.3‰ to +6.5‰, with a mean value of +1.4‰. At 15 National Atmospheric Deposition Program (NADP)/National Trends Network wet deposition (NADP Wetfall) sites, summer δ15N−NO3− is significantly lower ranging from −7.6‰ to −1.3‰ while winter δ15N−NO3− ranges from −2.6‰ to +5.5‰, with a mean value of +0.7‰ during the cool season. The strong seasonal difference in NADP Wetfall δ15N−NO3− is due in part to variation in the proportion of N originating from source regions at different times of the year due to seasonal changes in weather patterns. Snowpack NO3− and δ15N−NO3− are significantly related to NADP Wetfall (fall and winter) suggesting that bulk snowpack samples provide a reliable estimate at high elevations. Spatial trends show higher NO3−concentrations and δ15N−NO3− in the Southern Rocky Mountains located near larger anthropogenic N emission sources compared to the Northern Rocky Mountains. NADP Wetfall δ15N−NH4+ ranged from −10‰ to 0‰, with no observed spatial pattern. However, the lowest δ15N−NH4+(−9‰), and the highest NH4+ concentration (35 μeq/L) were observed at a Utah site dominated by local agricultural activities, whereas the higher δ15N−NH4+observed in Colorado and Wyoming are likely due to mixed sources, including fossil fuel combustion and agricultural sources. These findings show spatial and seasonal variation in N isotope data that reflect differences in sources of anthropogenic N deposition to high-elevation ecosystems and have important implications for environmental policy across the Rocky Mountain region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.atmosenv.2017.12.023","usgsCitation":"Nanus, L., Campbell, D.H., Lehmann, C.M., and Mast, M.A., 2018, Spatial and temporal variation in sources of atmospheric nitrogen deposition in the Rocky Mountains using nitrogen isotopes: Atmospheric Environment, v. 176, p. 110-119, https://doi.org/10.1016/j.atmosenv.2017.12.023.","productDescription":"10 p.","startPage":"110","endPage":"119","ipdsId":"IP-088572","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":468904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.atmosenv.2017.12.023","text":"Publisher Index Page"},{"id":352648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Rocky Mountains","volume":"176","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6fbe4b0da30c1bfc010","contributors":{"authors":[{"text":"Nanus, Leora","contributorId":27930,"corporation":false,"usgs":true,"family":"Nanus","given":"Leora","email":"","affiliations":[],"preferred":false,"id":731365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Donald H. dhcampbe@usgs.gov","contributorId":1670,"corporation":false,"usgs":true,"family":"Campbell","given":"Donald","email":"dhcampbe@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":731366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lehmann, Christopher M.B.","contributorId":84859,"corporation":false,"usgs":true,"family":"Lehmann","given":"Christopher","email":"","middleInitial":"M.B.","affiliations":[],"preferred":false,"id":731367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mast, M. Alisa 0000-0001-6253-8162 mamast@usgs.gov","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":827,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"mamast@usgs.gov","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731364,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196107,"text":"70196107 - 2018 - Population estimates of the Endangered Hawaiʻi ʻĀkepa (Loxops coccineus) in different habitats on windward Mauna Loa","interactions":[],"lastModifiedDate":"2018-03-20T08:59:44","indexId":"70196107","displayToPublicDate":"2018-03-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Population estimates of the Endangered Hawaiʻi ʻĀkepa (Loxops coccineus) in different habitats on windward Mauna Loa","title":"Population estimates of the Endangered Hawaiʻi ʻĀkepa (Loxops coccineus) in different habitats on windward Mauna Loa","docAbstract":"Endangered Hawai‘i ʻĀkepas (Loxops coccineus) are endemic to Hawai‘i island, where they occur in five spatially distinct populations. Data concerning the status and population trends of these unique Hawaiian honeycreepers are crucial for assessing the effectiveness of recovery and management actions. In 2016, we used point‐transect distance sampling to estimate the abundance of Hawai‘i ʻĀkepas in portions of Hawai‘i Volcanoes National Park (HAVO) and the Kaʻū Forest Reserve (KFR) on Mauna Loa volcano. We then compiled the survey data from four other populations to provide a global population estimate. In our HAVO and KFR study area, we mapped habitat classes to determine the population densities in each habitat. Densities were highest (1.03 birds/ha) in open‐canopy montane ʻōhiʻa (Metrosideros polymorpha) woodland. In contrast, densities of the largest ʻĀkepa population on Mauna Kea volcano were highest in closed‐canopy ʻōhiʻa and koa (Acacia koa) forest where the species is dependent on nest cavities in tall (> 15 m), large (> 50‐cm diameter at breast height) trees. We surveyed potential nesting habitat in HAVO and KFR and found only one cavity in the short‐stature montane ʻōhiʻa woodland and five cavities in the tall‐stature forest. Differences in densities between the Mauna Kea and Mauna Loa populations suggest that Hawai‘i ʻĀkepas may exhibit different foraging and nesting behaviors in the two habitats. The estimated overall population density in the HAVO and KFR study area was 0.52 birds/ha, which equates to 3663 (95% CI 1725–6961) birds in their 11,377‐ha population range. We calculated a global population of 16,428 (95% CI 10,065–25,198) birds, which is similar to an estimate of 13,892 (95% CI 10,315–17,469) birds made in 1986. Our results suggest that populations are stable to increasing in the two largest populations, but the three other populations are smaller (range = 77–1443 birds) and trends for those populations are unknown.
","language":"English","publisher":"Wiley","doi":"10.1111/jofo.12243","usgsCitation":"Judge, S.W., Camp, R.J., Hart, P.J., and Kichman, S.T., 2018, Population estimates of the Endangered Hawaiʻi ʻĀkepa (Loxops coccineus) in different habitats on windward Mauna Loa: Journal of Field Ornithology, v. 89, no. 1, p. 11-21, https://doi.org/10.1111/jofo.12243.","productDescription":"11 p.","startPage":"11","endPage":"21","ipdsId":"IP-094596","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":437983,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7S181SV","text":"USGS data release","linkHelpText":"HAVO Montane Ohia Diameter and Cavity Data 2017"},{"id":352647,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Mauna Loa","volume":"89","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-15","publicationStatus":"PW","scienceBaseUri":"5afee6fbe4b0da30c1bfc00e","contributors":{"authors":[{"text":"Judge, Seth W.","contributorId":8718,"corporation":false,"usgs":true,"family":"Judge","given":"Seth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":731375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":116175,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":false,"id":731376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":731377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kichman, Scott T.","contributorId":203396,"corporation":false,"usgs":false,"family":"Kichman","given":"Scott","email":"","middleInitial":"T.","affiliations":[{"id":36609,"text":"NPS, Pacific Island Inventory and Monitoring Program","active":true,"usgs":false}],"preferred":false,"id":731378,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191483,"text":"sir20175088 - 2018 - Hydrologic assessment of the Edwin B. Forsythe National Wildlife Refuge","interactions":[],"lastModifiedDate":"2018-03-19T16:50:38","indexId":"sir20175088","displayToPublicDate":"2018-03-19T12:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5088","title":"Hydrologic assessment of the Edwin B. Forsythe National Wildlife Refuge","docAbstract":"The Edwin B. Forsythe National Wildlife Refuge (hereafter Forsythe refuge or the refuge) is situated along the central New Jersey coast and provides a mixture of freshwater and saltwater habitats for numerous bird, wildlife, and plant species. Little data and information were previously available regarding the freshwater dynamics that support the refuge’s ecosystems. In cooperation with the U.S. Fish and Wildlife Service, the U.S. Geological Survey conducted an assessment of the hydrologic resources and processes in the refuge and surrounding areas to provide baseline information for evaluating restoration projects and future changes in the hydrologic system associated with climate change and other anthropogenic stressors.
During spring 2015, water levels were measured at groundwater and surface-water sites in and near the Forsythe refuge. These water-level measurements, along with surface-water elevations obtained from digital elevation models, were used to construct water-table-elevation and depth-to-water maps of the refuge and surrounding areas. Water-table elevations in the refuge ranged from sea level to approximately 65 feet above sea level; in most of the refuge, the water-table elevation was within 3 feet of sea level. The water-table-elevation map indicates that the direction of shallow groundwater flow at the regional scale is generally from west to east (much of it from the northwest to the southeast), and groundwater moves downgradient from the uplands toward major groundwater discharge areas consisting of coastal streams and wetlands. The depth to water is estimated to be less than 2 feet for approximately 86 percent of the refuge, which coincides closely with the percentage of wetland area in the refuge. Depth to water in excess of 20 feet below land surface is limited to higher elevation areas of the refuge.
Streamflow data collected at continuous-record streamgages and partial-record stations within the Mullica-Toms Basin were summarized. Hydrograph separation of streamflow data for eight streamgages (2004–13) reveals that base flow accounts for 68–94 percent of streamflow in basins upstream from the refuge. The high base-flow inputs underscore the importance of groundwater as a source of freshwater that supports both the streams that flow into the refuge and the hydroecology of the contributing basins. Mean annual flow typically ranged from 1.7 to 2.1 cubic feet per second per square mile at the streamgages (2004–13) and between 1.2 and 2.3 cubic feet per second per square mile at the partial-record stations (1965–2015) but was notably greater or lower than these ranges at several stations.
Mean annual water budgets were estimated for multiple regions of the refuge for 2004–13 using data compiled from nearby meteorological stations and groundwater flows derived from previously calibrated groundwater-flow models. Precipitation, groundwater recharge, and evapotranspiration were estimated from available data; direct runoff was calculated as the residual component of the water balance. Groundwater recharge rates were greatest in the upland-dominated areas of the refuge with estimates of 14.4 to 18.9 inches per year, which are equivalent to 30 to 40 percent of precipitation. Groundwater recharge rates were nearly zero in the central coastal areas because these areas are major groundwater discharge zones, the water table is near land surface, the subsurface is close to saturation and cannot accept much recharge, and much of the area is underlain by thick marsh deposits likely with low permeability. Estimates of evapotranspiration varied from about 26 inches per year in the upland-dominated areas to more than 35 inches per year in the coastal wetlands, equivalent to 55–79 percent of mean annual precipitation, indicating that it is a major component of the hydrodynamics of the Forsythe refuge.
On the basis of output from previously calibrated groundwater-flow models, nearly all of the groundwater exiting the surficial aquifer system in the central coastal areas of the refuge is discharged to wetlands, which highlights the importance of groundwater discharge in supporting the ecosystems of the Forsythe refuge. In the central coastal areas, horizontal flow contributes more than 90 percent of the groundwater flow to the surficial system, indicating that the upbasin areas are a substantial source of water that ultimately discharges to streams and wetlands in the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175088","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Wieben, C.M., and Chepiga, M.M., 2018, Hydrologic assessment of the Edwin B. Forsythe National Wildlife Refuge, New Jersey: U.S. Geological Survey Scientific Investigations Report 2017–5088, 38 p., https://doi.org/10.3133/sir20175088.\n","productDescription":"Report: viii, 38 p.; 2 Plates: 24.0 x 36.0 inches; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079840","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":352411,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5088/sir20175088.pdf","text":"Report","size":"25.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5088"},{"id":352410,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5088/coverthb.jpg"},{"id":352412,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JMN","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-table elevation contours and depth-to-water grid for the Edwin B. Forsythe National Wildlife Refuge, New Jersey, and vicinity, spring 2015"},{"id":352535,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5088/sir20175088_plate02.pdf","text":"Plate 2","size":"4.15 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Water-Table Elevation in and near the Southern Part of the Edwin B. Forsythe National Wildlife Refuge, New Jersey, Spring 2015"},{"id":352426,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20175135","text":"Scientific Investigations Report 2017–5135","linkHelpText":"- Hydrogeology of, Simulation of Groundwater Flow in, and Potential Effects of Sea-Level Rise on the Kirkwood-Cohansey Aquifer System in the Vicinity of Edwin B. Forsythe National Wildlife Refuge, New Jersey"},{"id":352534,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2017/5088/sir20175088_plate01.pdf","text":"Plate 1 ","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Water-Table Elevation in and near the Northern Part of the Edwin B. Forsythe National Wildlife Refuge, New Jersey, Spring 2015"}],"country":"United States","state":"New Jersey","otherGeospatial":"Edwin B. Forsythe National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74,\n 39.4167\n ],\n [\n -74,\n 40.07807142745009\n ],\n [\n -74.5,\n 40.07807142745009\n ],\n [\n -74.5,\n 39.4167\n ],\n [\n -74,\n 39.4167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Late-winter lake ice regimes are controlled by water depth relative to maximum ice thickness (MIT). When MIT exceeds maximum water depth, lakes freeze to the bottom with bedfast ice (BI) and when MIT is less than maximum water depth lakes have floating ice (FI). Both airborne radar and space-borne synthetic aperture radar (SAR) imagery (Ku-, X-, C-, and L-band) have been used previously to determine whether lakes have a BI or FI regime in a given year, across a number of years, or across large regions. In this study, we use a combination of ERS-1/2, RADARSAT-2, Envisat, and Sentinel-1 SAR imagery for seven lake-rich regions in Arctic Alaska to analyze lake ice regime extents and dynamics over a 25-year period (1992–2016). Our interactive threshold classification method determines a unique statistic-based intensity threshold for each SAR scene, allowing for the comparison of classification results from C-band SAR data acquired with different polarizations and incidence angles. Additionally, our novel method accommodates declining signal strength in aging extended-mission satellite SAR instruments. Comparison of SAR ice regime classifications with extensive field measurements from six years yielded a 93% accuracy. Significant declines in BI regimes were only observed in the Fish Creek area with 3% of lakes exhibiting transitional ice regimes—lakes that switch from BI to FI during this 25-year period. This analysis suggests that the potential conversion from BI to FI regimes is primarily a function of lake depth distributions in addition to regional differences in climate variability. Remote sensing of lake ice regimes with C-band SAR is a useful tool to monitor the associated thermal impacts on permafrost, since lake ice regimes can be used as a proxy for of sub-lake permafrost thaw, considered by the Global Climate Observing System as an Essential Climate Variable (ECV). Continued winter warming and variable snow conditions in the Arctic are expected and our long-term analysis provides a valuable baseline for predicting where potential future lake ice regimes shifts will be most pronounced.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2018.02.022","usgsCitation":"Engram, M., Arp, C.D., Jones, B.M., Ajadi, O.A., and Meyer, F.J., 2018, Analyzing floating and bedfast lake ice regimes across Arctic Alaska using 25 years of space-borne SAR imagery: Remote Sensing of Environment, v. 209, p. 660-676, https://doi.org/10.1016/j.rse.2018.02.022.","productDescription":"17 p.","startPage":"660","endPage":"676","ipdsId":"IP-090354","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":468905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2018.02.022","text":"Publisher Index Page"},{"id":408647,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.5263144061035,\n 71.43267111072981\n ],\n [\n -159.5263144061035,\n 68.42639425141334\n ],\n [\n -146.22562492863324,\n 68.42639425141334\n ],\n [\n -146.22562492863324,\n 71.43267111072981\n ],\n [\n -159.5263144061035,\n 71.43267111072981\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.58954484767287,\n 66.64438234906729\n ],\n [\n -166.61500668033133,\n 66.64438234906729\n ],\n [\n -166.61500668033133,\n 65.88496852258822\n ],\n [\n -163.58954484767287,\n 65.88496852258822\n ],\n [\n -163.58954484767287,\n 66.64438234906729\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"209","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Engram, Melanie","contributorId":191062,"corporation":false,"usgs":false,"family":"Engram","given":"Melanie","email":"","affiliations":[],"preferred":false,"id":855648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":855649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":855650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ajadi, Olaniyi A","contributorId":298461,"corporation":false,"usgs":false,"family":"Ajadi","given":"Olaniyi","email":"","middleInitial":"A","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":855651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Franz J","contributorId":298463,"corporation":false,"usgs":false,"family":"Meyer","given":"Franz","email":"","middleInitial":"J","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":855652,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196105,"text":"ofr20171164 - 2018 - Construction and analysis of a giant gartersnake (Thamnophis gigas) population projection model","interactions":[],"lastModifiedDate":"2018-03-21T10:52:15","indexId":"ofr20171164","displayToPublicDate":"2018-03-19T00:00:00","publicationYear":"2018","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":"2017-1164","displayTitle":"Construction and analysis of a giant gartersnake (Thamnophis gigas) population projection model","title":"Construction and analysis of a giant gartersnake (Thamnophis gigas) population projection model","docAbstract":"The giant gartersnake (Thamnophis gigas) is a state and federally threatened species precinctive to California. The range of the giant gartersnake has contracted in the last century because its wetland habitat has been drained for agriculture and development. As a result of this habitat alteration, giant gartersnakes now largely persist in and near rice agriculture in the Sacramento Valley, because the system of canals that conveys water for rice growing approximates historical wetland habitat. Many aspects of the demography of giant gartersnakes are unknown, including how individuals grow throughout their life, how size influences reproduction, and how survival varies over time and among populations. We studied giant gartersnakes throughout the Sacramento Valley of California from 1995 to 2016 using capture-mark-recapture to study the growth, reproduction, and survival of this threatened species. We then use these data to construct an Integral Projection Model, and analyze this demographic model to understand which vital rates contribute most to the growth rate of giant gartersnake populations. We find that giant gartersnakes exhibit indeterminate growth; growth slows as individuals’ age. Fecundity, probability of reproduction, and survival all increase with size, although survival may decline for the largest female giant gartersnakes. The population growth rate of giant gartersnakes is most influenced by the survival and growth of large adult females, and the size at which 1 year old recruits enter the population. Our results indicate that management actions benefitting these influential demographic parameters will have the greatest positive effect on giant gartersnake population growth rates, and therefore population persistence. This study informs the conservation and management of giant gartersnakes and their habitat, and illustrates the effectiveness of hierarchical Bayesian models for the study of rare and elusive species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171164","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Rose, J.P., Ersan, J.S.M., Wylie, G.D., Casazza, M.L., and Halstead, B.J., 2018, Construction and analysis of a giant gartersnake (Thamnophis gigas) population projection model: U.S. Geological Survey Open-File Report 2017–1164, 98 p., https://doi.org/10.3133/ofr20171164.","productDescription":"viii, 98 p.","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-090465","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":352644,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1164/ofr20171164.pdf","text":"Report","size":"8.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1164"},{"id":352643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1164/coverthb.jpg"}],"contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The magmatic degassing history of newly erupted volcanic glass is recorded in its remaining volatile content. However, this history is subsequently overprinted by post-depositional (secondary) hydration, the rates and origins of which are not yet adequately constrained. Here, we present the results of a natural experiment using products of the 1980 eruptions of Mount St. Helens. We measured water concentration, δDglass, and δ18OBSG (δ18O of the bulk silicate glass) of samples collected during the dry summer months of 1980 and compared them with material resampled in 2015 from the same deposits. Samples collected from the subsurface near gas escape pipes show elevated water concentrations (near 2.0 wt%), and these are associated with lower δDglass (− 110 to − 130‰) and δ18OBSG (6.0 to 6.6‰) values than the 1980 glass (− 70 to − 100‰ and 6.8 to 6.9‰, respectively). Samples collected in 2015 from the surface to 10-cm subsurface of the 1980 summer deposits have a small increase in average water contents of 0.1–0.2 wt% but similar δ18OBSG (6.8–6.9‰) values compared to the 1980 glass values. These samples, however, show 15‰ higher δDglass values; exchange with meteoric water is expected to yield lower δDglass values. We attribute higher δDglass values in the upper portion of the 1980 deposits collected in 2015 to rehydration by higher δD waters that were degassed for several months to a year from the hot underlying deposits, which hydrated the overlying deposits with relatively high δD gases. Our data also contribute to magmatic degassing of crystal-rich volcanoes. Using the 1980 samples, our reconstructed δD-H2O trends for the dacitic Mount St. Helens deposits with rhyolitic groundmass yield a trend that overlaps with the degassing trend for crystal-poor rhyolitic eruptions studied previously elsewhere, suggesting similar behavior of volatiles upon exsolution from magma. Furthermore, our data support previous studies proposing that exsolved volatiles were trapped within a rapidly rising magma and started degassing only at shallow depths during the 1980 eruptions.
We investigated the fate of Sb and As downstream of the abandoned Su Suergiu mine (Sardinia, Italy) and surrounding areas. The mined area is a priority in the Sardinian remediation plan for contaminated sites due to the high concentrations of Sb and As in the mining-related wastes, which may impact the Flumendosa River that supplies water for agriculture and domestic uses. Hydrogeochemical surveys conducted from 2005 to 2015 produced time-series data and downstream profiles of water chemistry at 46 sites. Water was sampled at: springs and streams unaffected by mining; adits and streams in the mine area; drainage from the slag heaps; stream water downstream of the slag drainages; and the Flumendosa River downstream from the confluence of the contaminated waters. At specific sites, water sampling was repeated under different flow conditions, resulting in a total of 99 samples. The water samples were neutral to slightly alkaline. Elevated Sb (up to 30 mg L−1) and As (up to 16 mg L−1) concentrations were observed in water flowing from the slag materials from where the Sb ore was processed. These slag materials were the main Sb and As source at Su Suergiu. A strong base, Na-carbonate, from the foundry wastes, had a major influence on mobilizing Sb and As. Downstream contamination can be explained by considering that: (1) the predominant aqueous species, Sb(OH)6 − and HAsO4 −2, are not favored in sorption processes at the observed pH conditions; (2) precipitation of Sb- and As-bearing solid phases was not observed, which is consistent with modeling results indicating undersaturation; and (3) the main decrease in dissolved Sb and As concentrations was by dilution. Dissolved As concentrations in the Flumendosa River did not generally exceed the EU limit of 10 µg L−1, whereas dissolved Sb in the river downstream of the contamination source always exceeded the EU limit of 5 µg L−1. Recent actions aimed at retaining runoff from the slag heaps are apparently not sufficiently mitigating contamination in the Flumendosa River.
","language":"English","publisher":"Springer","doi":"10.1007/s10230-017-0479-8","usgsCitation":"Cidu, R., Dore, E., Biddau, R., and Nordstrom, D.K., 2018, Fate of antimony and arsenic in contaminated waters at the abandoned Su Suergiu mine (Sardinia, Italy): Mine Water and the Environment, v. 37, no. 1, p. 151-165, https://doi.org/10.1007/s10230-017-0479-8.","productDescription":"15 p.","startPage":"151","endPage":"165","ipdsId":"IP-071489","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":352624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","state":"Sardinia","otherGeospatial":"Su Suergiu mine","volume":"37","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-31","publicationStatus":"PW","scienceBaseUri":"5afee6fce4b0da30c1bfc020","contributors":{"authors":[{"text":"Cidu, Rosa","contributorId":194017,"corporation":false,"usgs":false,"family":"Cidu","given":"Rosa","affiliations":[{"id":36605,"text":"University of Cagliari, Cagliari, Sardinia","active":true,"usgs":false}],"preferred":false,"id":731269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dore, Elisabetta","contributorId":203363,"corporation":false,"usgs":false,"family":"Dore","given":"Elisabetta","email":"","affiliations":[{"id":36605,"text":"University of Cagliari, Cagliari, Sardinia","active":true,"usgs":false}],"preferred":false,"id":731271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biddau, Riccardo","contributorId":203362,"corporation":false,"usgs":false,"family":"Biddau","given":"Riccardo","email":"","affiliations":[{"id":36605,"text":"University of Cagliari, Cagliari, Sardinia","active":true,"usgs":false}],"preferred":false,"id":731270,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":731268,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249425,"text":"70249425 - 2018 - Attribution analysis of the Ethiopian drought of 2015","interactions":[],"lastModifiedDate":"2023-10-06T14:09:55.757687","indexId":"70249425","displayToPublicDate":"2018-03-15T09:01:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2216,"text":"Journal of Climate","active":true,"publicationSubtype":{"id":10}},"title":"Attribution analysis of the Ethiopian drought of 2015","docAbstract":"In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JCLI-D-17-0274.1","usgsCitation":"Philip, S., Kew, S.F., van Oldenborgh, G.J., Otto, F., O’Keefe, S., Haustein, K., King, A.L., Zegeye, A., Eshetu, Z., Hailemariam, K., Singh, R., Jjemba, E., Funk, C., and Cullen, H., 2018, Attribution analysis of the Ethiopian drought of 2015: Journal of Climate, v. 31, no. 6, p. 2465-2486, https://doi.org/10.1175/JCLI-D-17-0274.1.","productDescription":"22 p.","startPage":"2465","endPage":"2486","ipdsId":"IP-091117","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468909,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://ora.ox.ac.uk/objects/uuid:f057b9f9-2a54-4a67-9c5f-492b38cdb84d","text":"External 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Ethiopia","active":true,"usgs":false}],"preferred":false,"id":885570,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Singh, Roop","contributorId":330677,"corporation":false,"usgs":false,"family":"Singh","given":"Roop","email":"","affiliations":[{"id":78962,"text":"Red Cross Red Crescent Climate Centre","active":true,"usgs":false}],"preferred":false,"id":885571,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jjemba, Eddie","contributorId":330678,"corporation":false,"usgs":false,"family":"Jjemba","given":"Eddie","email":"","affiliations":[{"id":78962,"text":"Red Cross Red Crescent Climate Centre","active":true,"usgs":false}],"preferred":false,"id":885572,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885573,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cullen, Heidi","contributorId":330679,"corporation":false,"usgs":false,"family":"Cullen","given":"Heidi","email":"","affiliations":[{"id":78963,"text":"Climate Central, Princeton, US","active":true,"usgs":false}],"preferred":false,"id":885574,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70195496,"text":"sir20185027 - 2018 - Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development","interactions":[],"lastModifiedDate":"2018-09-25T06:18:31","indexId":"sir20185027","displayToPublicDate":"2018-03-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5027","title":"Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development","docAbstract":"As the demand for energy increases in the United States, so does the demand for water used to produce many forms of that energy. Technological advances, limited access to conventional oil and gas accumulations, and the rise of oil and gas prices resulted in increased development of unconventional oil and gas (UOG) accumulations. Unconventional oil and gas is developed using a method that combines directional drilling and hydraulic fracturing techniques, allowing for greater oil and gas production from previously unrecoverable reservoirs. Quantification of the water resources required for UOG development and production is difficult because of disparate data sources, variable reporting requirements across boundaries (local, State, and national), and incomplete or proprietary datasets.
A topical study was started in 2015 under the U.S. Geological Survey’s Water Availability and Use Science Program, as part of the directive in the Secure Water Act for the U.S. Geological Survey to conduct a National Water Census, to better understand the relation between production of UOG resources for energy and the amount of water needed to produce and sustain this type of energy development in the United States. The Water Availability and Use Science Program goal for this topical study is to develop and apply a statistical model to better estimate the water use associated with UOG development, regardless of the location and target geologic formation. As a first step, a conceptual model has been developed to characterize the life cycle of water use in areas of UOG development.
Categories of water use and the way water-use data are collected might change over time; therefore, a generic approach was used in developing the conceptual model to allow for greater flexibility in adapting to future changes or newly available data. UOG development can be summarized into four stages: predrilling construction, drilling, hydraulic fracturing, and ongoing production. The water used in UOG production can be categorized further as direct, indirect, or ancillary water use. Direct water use is defined as the water used for drilling and hydraulic fracturing a well and for maintaining the well during ongoing production. Indirect water use is defined as the water used at or near a well pad. The water used for dust abatement also is considered an indirect use but may be applied away from the well pad. Ancillary water use is defined as the additional local or regional water use resulting from a change (for example, population) directly related to UOG development throughout the life cycle that is not used directly in the well or indirectly for any other purpose at the well pad.
The conceptual model presented in this report consists of five elements: (1) input data, (2) processes, (3) decisions, (4) output data, and (5) outcomes. The input data requirements for estimating water use associated with UOG development are somewhat onerous, and obtaining suitable datasets can be challenging because local, State, and Federal agencies do not collect data similarly. The quality of a water-use assessment that uses the conceptual model presented in this report is dependent on the quality and quantity of data that are available for a UOG play. The conceptual model can be used for an assessment with sparse data; however, having sparse data likely will result in greater uncertainty in the water-use estimates.
The conceptual model presented in this report is designed to be robust to characterize and simulate the data processing to estimate water use associated with UOG development. Although the results of an analysis that includes missing data have greater uncertainty, the analysis still can be insightful because it can establish a baseline estimate of UOG water use that may be refined further as more data become available. Analysis of models that include missing data also could aid in identifying the data most needed for future water-use estimates. Characterizing individual model limitations is important because the conceptual model can be used in future water-use studies to facilitate data compiling, data processing, estimating, and assessing UOG activities regardless of location.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185027","collaboration":"U.S. Geological Survey Water Availability and Use Science Program","usgsCitation":"Valder, J.F., McShane, R.R., Barnhart, T.B., Sando, R., Carter, J.M., and Lundgren, R.F., 2018, Conceptual model to assess water use associated with the life cycle of unconventional oil and gas development: U.S. Geological Survey Scientific Investigations Report 2018–5027, 22 p., https://doi.org/10.3133/sir20185027.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-092881","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":352571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5027/coverthb2.jpg"},{"id":352572,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5027/sir20185027.pdf","text":"Report","size":"3.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5027"},{"id":352573,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/wausp/","text":"Water Availability and Use Science Program"}],"contact":"Director, Dakota Water Science Center, South Dakota Office
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
Rock samples have been collected, analyzed, and interpreted from drilling and mining operations at the Nevada National Security Site for over one-half of a century. Records containing geologic and hydrologic analyses and interpretations have been compiled into a series of databases. Rock samples have been photographed and thin sections scanned. Records and images are preserved and available for public viewing and downloading at the U.S. Geological Survey ScienceBase, Mercury Core Library and Data Center Web site at https://www.sciencebase.gov/mercury/ and documented in U.S. Geological Survey Data Series 297. Example applications of these data and images are provided in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181011","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Site Office, Office of Environmental Management under Interagency Agreement DE-AI52-12NA30865/DE-NA0001654","usgsCitation":"Wood, D.B., 2018, Hydrogeologic applications for historical records and images from rock samples collected at the Nevada National Security Site and vicinity, Nye County, Nevada—A supplement to Data Series 297: U.S. Geological Survey Open-File Report 2018–1011, 13 p., https://doi.org/10.3133/ofr20181011.","productDescription":"Report: iv, 13 p.; 2 Figures","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092236","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":352503,"rank":5,"type":{"id":13,"text":"Illustration"},"url":"https://pubs.usgs.gov/of/2018/1011/ofr20181011_figure04.pdf","text":"Figure 4","size":"415 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1011 Figure 4"},{"id":352500,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds297","text":"Data Series 297","description":"Data Series 297"},{"id":352502,"rank":4,"type":{"id":13,"text":"Illustration"},"url":"https://pubs.usgs.gov/of/2018/1011/ofr20181011_figure03.pdf","text":"Figure 3","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1011 Figure 3"},{"id":352496,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1011/coverthb.jpg"},{"id":352497,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1011/ofr20181011.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1011"}],"country":"United States","state":"Nevada","county":"Nye County","otherGeospatial":"Nevada National Security Site","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.75,36.5 ], [ -116.75,37.5 ], [ -115.75,37.5 ], [ -115.75,36.5 ], [ -116.75,36.5 ] ] ] } } ] }","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
The Piper diagram has been a staple for the analysis of water chemistry data since its introduction in 1944. It was conceived to be a method for water classification, determination of potential water mixing between end-members, and to aid in the identification of chemical reactions controlling a sample set. This study uses the information gleaned over the years since the release of the Piper diagram and proposes an alternative to it, capturing the strengths of the original diagram while adding new ideas to increase its robustness. The new method uses compositional data analysis to create 4 isometric log-ratio coordinates for the 6 major chemical species analyzed in the Piper diagram and transforms the data to a 4-field bi-plot, the ilr-ion plot. This ilr-ion plot conveys all of the information in the Piper diagram (water mixing, water types, and chemical reactions) while also visualizing additional data, the ability to examine Ca2+/Mg2+ versus Cl-/SO42−. The Piper and the ilr-ion plot were also compared using multiple synthetic and real datasets in order to illustrate the caveats and the advantages of using either diagram to analyze water chemistry data. Although there are challenges with using the ilr-ion plot (e.g., missing or zero values zeros in the dataset must be imputed by positive real numbers), it appears that the use of compositional data analysis coupled with the ilr-ion plot provides a more in-depth and complete analysis of water quality data compared to the original Piper diagram.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2018.03.003","usgsCitation":"Shelton, J., Engle, M.A., Buccianti, A., and Blondes, M., 2018, The isometric log-ratio (ilr)-ion plot: A proposed alternative to the Piper diagram: Journal of Geochemical Exploration, v. 190, p. 130-141, https://doi.org/10.1016/j.gexplo.2018.03.003.","productDescription":"12 p.","startPage":"130","endPage":"141","ipdsId":"IP-090414","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":468916,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gexplo.2018.03.003","text":"Publisher Index Page"},{"id":352510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"190","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6fee4b0da30c1bfc03a","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":731070,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engle, Mark A. 0000-0001-5258-7374 engle@usgs.gov","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":584,"corporation":false,"usgs":true,"family":"Engle","given":"Mark","email":"engle@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":731071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buccianti, Antonella","contributorId":198657,"corporation":false,"usgs":false,"family":"Buccianti","given":"Antonella","email":"","affiliations":[],"preferred":false,"id":731072,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":731073,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196250,"text":"70196250 - 2018 - Restricted access Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering","interactions":[],"lastModifiedDate":"2018-03-28T13:04:41","indexId":"70196250","displayToPublicDate":"2018-03-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3173,"text":"Proceedings of the Royal Society B","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Restricted access Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering","title":"Restricted access Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering","docAbstract":"Foundation species define the ecosystems they live in, but ecologists have often characterized dominant plants as foundational without supporting evidence. Giant kelp has long been considered a marine foundation species due to its complex structure and high productivity; however, there is little quantitative evidence to evaluate this. Here, we apply structural equation modelling to a 15-year time series of reef community data to evaluate how giant kelp affects the reef community. Although species richness was positively associated with giant kelp biomass, most direct paths did not involve giant kelp. Instead, the foundational qualities of giant kelp were driven mostly by indirect effects attributed to its dominant physical structure and associated engineering influence on the ecosystem, rather than by its use as food by invertebrates and fishes. Giant kelp structure has indirect effects because it shades out understorey algae that compete with sessile invertebrates. When released from competition, sessile species in turn increase the diversity of mobile predators. Sea urchin grazing effects could have been misinterpreted as kelp effects, because sea urchins can overgraze giant kelp, understorey algae and sessile invertebrates alike. Our results confirm the high diversity and biomass associated with kelp forests, but highlight how species interactions and habitat attributes can be misconstrued as direct consequences of a foundation species like giant kelp.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2017.2571","usgsCitation":"Miller, R.J., Lafferty, K.D., Lamy, T., Kui, L., Rassweiler, A., and Reed, D.C., 2018, Restricted access Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering: Proceedings of the Royal Society B, v. 285, no. 1874, Article 20172571, https://doi.org/10.1098/rspb.2017.2571.","productDescription":"Article 20172571","ipdsId":"IP-087899","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468917,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1098/rspb.2017.2571","text":"External Repository"},{"id":352838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"285","issue":"1874","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5afee6fee4b0da30c1bfc034","contributors":{"authors":[{"text":"Miller, Robert J.","contributorId":176277,"corporation":false,"usgs":false,"family":"Miller","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":731870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":731869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamy, Thomas","contributorId":203605,"corporation":false,"usgs":false,"family":"Lamy","given":"Thomas","email":"","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":731871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kui, Li","contributorId":194515,"corporation":false,"usgs":false,"family":"Kui","given":"Li","email":"","affiliations":[],"preferred":false,"id":731872,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rassweiler, Andrew 0000-0002-8760-3888","orcid":"https://orcid.org/0000-0002-8760-3888","contributorId":203606,"corporation":false,"usgs":false,"family":"Rassweiler","given":"Andrew","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":731873,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Daniel C.","contributorId":203607,"corporation":false,"usgs":false,"family":"Reed","given":"Daniel","email":"","middleInitial":"C.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":731874,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195979,"text":"70195979 - 2018 - Spatial capture–recapture with partial identity: An application to camera traps","interactions":[],"lastModifiedDate":"2018-03-12T11:06:43","indexId":"70195979","displayToPublicDate":"2018-03-12T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":787,"text":"Annals of Applied Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Spatial capture–recapture with partial identity: An application to camera traps","docAbstract":"Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and, while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM outperforms other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and, in the ocelot example, individual sex is determined from photographs used to further resolve partial identities—one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard two camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.
","language":"English","publisher":"IMS","doi":"10.1214/17-AOAS1091","usgsCitation":"Augustine, B.C., Royle, J., Kelly, M.J., Satter, C.B., Alonso, R.S., Boydston, E.E., and Crooks, K.R., 2018, Spatial capture–recapture with partial identity: An application to camera traps: Annals of Applied Statistics, v. 12, no. 1, p. 67-95, https://doi.org/10.1214/17-AOAS1091.","productDescription":"29 p.","startPage":"67","endPage":"95","ipdsId":"IP-088130","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468923,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1214/17-aoas1091","text":"Publisher Index Page"},{"id":352404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6ffe4b0da30c1bfc048","contributors":{"authors":[{"text":"Augustine, Ben C.","contributorId":203257,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":730769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":138865,"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":false,"id":730768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, Marcella J.","contributorId":179348,"corporation":false,"usgs":false,"family":"Kelly","given":"Marcella","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Satter, Christopher B.","contributorId":203259,"corporation":false,"usgs":false,"family":"Satter","given":"Christopher","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":730771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alonso, Robert S.","contributorId":93739,"corporation":false,"usgs":false,"family":"Alonso","given":"Robert","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":730772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boydston, Erin E. 0000-0002-8452-835X eboydston@usgs.gov","orcid":"https://orcid.org/0000-0002-8452-835X","contributorId":1705,"corporation":false,"usgs":true,"family":"Boydston","given":"Erin","email":"eboydston@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":730773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crooks, Kevin R.","contributorId":51137,"corporation":false,"usgs":false,"family":"Crooks","given":"Kevin","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":730774,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190866,"text":"ofr20161179 - 2018 - Collection methods, data compilation, and lessons learned from a study of stream geomorphology associated with riparian cattle grazing along the Fever River, University of Wisconsin- Platteville Pioneer Farm, Wisconsin, 2004–11","interactions":[],"lastModifiedDate":"2018-03-09T11:14:49","indexId":"ofr20161179","displayToPublicDate":"2018-03-09T11:15:00","publicationYear":"2018","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":"2016-1179","title":"Collection methods, data compilation, and lessons learned from a study of stream geomorphology associated with riparian cattle grazing along the Fever River, University of Wisconsin- Platteville Pioneer Farm, Wisconsin, 2004–11","docAbstract":"Stream geomorphic characteristics were monitored along a 0.8-mile reach of the Fever River in the Driftless Area of southwestern Wisconsin from 2004 to 2011 where cattle grazed in paddocks along the riverbank at the University of Wisconsin-Platteville’s Pioneer Farm. The study reach encompassed seven paddocks that covered a total of 30 acres on both sides of the river. Monitoring data included channel crosssection surveys, eroding bank measurements and photograph points, erosion-pin measurements, longitudinal profile surveys, measurements of the volume of soft sediment in the channel, and repeated time-lapse photographs. Characteristics were summarized into subreaches by use of a geographic information system. From 2004 to 2007, baseline monitoring was done to identify geomorphic conditions prior to evaluating the effects of management alternatives for riparian grazing. Subsequent to the full-scale baseline monitoring, additional data were collected from 2007 to 2011. Samples of eroding bank and in-channel soft sediment were collected and analyzed for dry bulk density in 2008 for use in a sediment budget. One of the pastures was excluded from cattle grazing in the fall of 2007; in 2009 channel cross sections, longitudinal profiles, erosion-pin measurements, photographs, and a soft sediment survey were again collected along the full 0.8-mile reach for a comparison to baseline monitoring data. Channel cross sections were surveyed a final time in 2011. Lessons learned from bank monitoring with erosion pins were most numerous and included the need for consistent tracking of each pin and whether there was deposition or erosion, timing of measurements and bank conditions during measurements (frozen, postflood), and awareness of pins loosening in place. Repeated freezing and thawing of banks and consequential mass wasting and jointing enhance fluvial erosion. Monitoring equipment in the paddocks was kept flush to the ground or located high on posts to avoid injuring the cattle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161179","collaboration":"Prepared in cooperation with the University of Wisconsin-Platteville Pioneer Farm Program","usgsCitation":"Peppler, M.C., and Fitzpatrick, F.A., 2018, Collection methods, data compilation, and lessons learned from a study of stream geomorphology associated with riparian cattle grazing along the Fever River, University of Wisconsin- Platteville Pioneer Farm, Wisconsin, 2004–11: U.S. Geological Survey Open-File Report 2016–1179, 23 p., https://doi.org/10.3133/ofr20161179.","productDescription":"Report: viii, 21 p.; Appendixes 1-9; Readme","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069094","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":352034,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix1.zip","text":"Appendix 1","size":"39.1 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Channel CrossSection Surveys"},{"id":352035,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix2.zip","text":"Appendix 2","size":"2.10 GB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Eroding Bank Measurements and Photograph Points"},{"id":352036,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix3.zip","text":"Appendix 3","size":"320 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Erosion Pin Measurements"},{"id":352038,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix5.zip","text":"Appendix 5","size":"30 MB ","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- In-Channel Soft Sediment Depth and Volume"},{"id":352037,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix4.zip","text":"Appendix 4","size":"128 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Longitudinal Profile Surveys"},{"id":351479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1179/ofr20161179.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1179"},{"id":352039,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix6.zip","text":"Appendix 6","size":"545 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Geographic Information System Analyses of Reach Characteristics"},{"id":352040,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix7.zip","text":"Appendix 7","size":"28 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Sediment Bulk Density"},{"id":352041,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix8.zip","text":"Appendix 8","size":"73.2 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Time Lapse Photographs"},{"id":351478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1179/coverthb.jpg","text":"Report"},{"id":351870,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/appendixes-readme.pdf","text":"Appendix Readme","size":"22 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":352042,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1179/appendix/ofr20161179_appendix9.zip","text":"Appendix 9","size":"611. MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Miscellaneous Photographs and Field Notes"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fever River, University of Wisconsin-Platteville Pioneer Agricultural Stewardship Farm","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.40331840515137,\n 42.71124784262846\n ],\n [\n -90.393168926239,\n 42.71124784262846\n ],\n [\n -90.393168926239,\n 42.71984009899354\n ],\n [\n -90.40331840515137,\n 42.71984009899354\n ],\n [\n -90.40331840515137,\n 42.71124784262846\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
How well seabirds compensate for variability in prey abundance and composition near their breeding colonies influences their distribution and reproductive success. We used tufted puffins (Fratercula cirrhata) as forage fish samplers to study marine food webs from the western Aleutian Islands (53°N, 173°E) to Kodiak Island (57°N, 153°W), Alaska, during August 2012–2014. Around each colony we obtained data on: environmental characteristics (sea surface temperature and salinity, seafloor depth and slope, tidal range, and chlorophyll-a), relative forage fish biomass (hydroacoustic backscatter), and seabird community composition and density at-sea. On colonies, we collected puffin chick-meals to characterize forage communities and determine meal energy density, and measured chicks to obtain a body condition index. There were distinct environmental gradients from west to east, and environmental variables differed by ecoregions: the (1) Western-Central Aleutians, (2) Eastern Aleutians, and, (3) Alaska Peninsula. Forage fish biomass, species richness, and community composition all differed markedly between ecoregions. Forage biomass was strongly correlated with environmental gradients, and environmental gradients and forage biomass accounted for ~ 50% of the variability in at-sea density of tufted puffins and all seabird taxa combined. Despite the local and regional variability in marine environments and forage, the mean biomass of prey delivered to puffin chicks did not differ significantly between ecoregions, nor did chick condition or puffin density at-sea. We conclude that puffins can adjust their foraging behavior to produce healthy chicks across a wide range of environmental conditions. This extraordinary flexibility enables their overall success and wide distribution across the North Pacific Ocean.
","language":"English","publisher":"Springer","doi":"10.1007/s00227-018-3304-4","usgsCitation":"Schoen, S.K., Piatt, J.F., Arimitsu, M.L., Heflin, B., Madison, E.N., Drew, G.S., Renner, M., Rojek, N.A., Douglas, D.C., and DeGange, A.R., 2018, Avian predator buffers against variability in marine habitats with flexible foraging behavior: Marine Biology, v. 165, p. 1-14, https://doi.org/10.1007/s00227-018-3304-4.","productDescription":"Article 47; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-090458","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":437987,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TQ60GV","text":"USGS data release","linkHelpText":"Marine ecology near Tufted Puffin colonies across the Aleutian Archipelago and Alaska Peninsula, 2012-2014"},{"id":352355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Aleutian Islands, Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -188.525390625,\n 50.84757295365389\n ],\n [\n -151.6552734375,\n 50.84757295365389\n ],\n [\n -151.6552734375,\n 57.657157596582984\n ],\n [\n -188.525390625,\n 57.657157596582984\n ],\n [\n -188.525390625,\n 50.84757295365389\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"165","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-15","publicationStatus":"PW","scienceBaseUri":"5afee701e4b0da30c1bfc050","contributors":{"authors":[{"text":"Schoen, Sarah K. 0000-0002-5685-5185 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0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":730653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heflin, Brielle 0000-0002-4836-9187 bheflin@usgs.gov","orcid":"https://orcid.org/0000-0002-4836-9187","contributorId":198164,"corporation":false,"usgs":true,"family":"Heflin","given":"Brielle","email":"bheflin@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":730654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madison, Erica N. 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Martin","contributorId":198248,"corporation":false,"usgs":false,"family":"Renner","given":"Martin","email":"","affiliations":[],"preferred":false,"id":730657,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rojek, Nora A.","contributorId":201046,"corporation":false,"usgs":false,"family":"Rojek","given":"Nora","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":730658,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":730659,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"DeGange, Anthony R. tdegange@usgs.gov","contributorId":203210,"corporation":false,"usgs":false,"family":"DeGange","given":"Anthony","email":"tdegange@usgs.gov","middleInitial":"R.","affiliations":[{"id":36582,"text":"Former USGS ASC employee","active":true,"usgs":false}],"preferred":false,"id":730660,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70195620,"text":"ofr20181026 - 2018 - Preliminary stage and streamflow data at selected U.S. Geological Survey streamgages in Maine and New Hampshire for the flood of October 30–31, 2017","interactions":[],"lastModifiedDate":"2018-03-08T12:26:26","indexId":"ofr20181026","displayToPublicDate":"2018-03-08T11:15:00","publicationYear":"2018","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":"2018-1026","title":"Preliminary stage and streamflow data at selected U.S. Geological Survey streamgages in Maine and New Hampshire for the flood of October 30–31, 2017","docAbstract":"Rainfall from a storm on October 24–27, 2017, and Tropical Storm Philippe on October 29–30, created conditions that led to flooding across portions of New Hampshire and western Maine. On the basis of streamflow data collected at 30 selected U.S. Geological Survey (USGS) streamgages in the Androscoggin River, Connecticut River, Merrimack River, and Saco River Basins, the storms caused minor to moderate flooding in those basins on October 30–31, 2017. During the storms, the USGS deployed hydrographers to take discrete measurements of streamflow. The measurements were used to confirm the stage-to-streamflow relation (rating curve) at the selected USGS streamgages. Following the storms, hydrographers documented high-water marks in support of indirect measurements of streamflow. Seven streamgages with greater than 50 years of streamflow data recorded preliminary streamflow peaks within the top five for the periods of record. Twelve streamgages recorded preliminary peak streamflows greater than an estimate of the 100-year streamflow based on drainage area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181026","usgsCitation":"Kiah, R.G, and Stasulis, N.W., 2018, Preliminary stage and streamflow data at selected U.S. Geological Survey streamgages in Maine and New Hampshire for the flood of October 30–31, 2017: U.S. Geological Survey Open-File Report 2018–1026, 12 p., https://doi.org/10.3133/ofr20181026.","productDescription":"iv, 12 p.","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092894","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1026/ofr20181026.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1026"},{"id":352269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1026/coverthb.jpg"}],"country":"United States","state":"Maine, New Hampshire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72,\n 42.75\n ],\n [\n -69,\n 42.75\n ],\n [\n -69,\n 46\n ],\n [\n -72,\n 46\n ],\n [\n -72,\n 42.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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331 Commerce Way, Suite 2
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We analyze the response of Kenyan maize yields to near-term climate change and explore potential mitigation options. We model county level yields as a function of rainfall and temperature during a period of increased regional warming and drying (1989–2008). We then do a counter factual analysis by comparing existing maize yields from 2000 to 2008 to what yields might have been if observed warming and drying trends had not occurred. We also examine maize yields based on projected 2026–2040 climate trends. Without the observed warming and drying trends, Eastern Kenya would have had an 8% increase in maize yields, which in turn would have led to a net production increase of 500,000 metric tons. In Western Kenya, the magnitude of change is higher but the relative changes in predicted values are smaller. If warming and drying trends continue, we expect future maize yields to decline by 11% in Eastern Kenya (vs. 7% in Western Kenya). We also examine whether these future losses might be offset through agricultural development. For that analysis, we use a household panel dataset (2000, 2005) with measurements of individual farm plot yields, inputs, and outputs. We find that under a scenario of aggressive adoption of hybrid seeds and fertilizer usage coupled with warming and drying trends, yields in Western Kenya might increase by 6% while those in Eastern Kenya could increase by 14%. This increase in yields might be larger if there is a corresponding increase in usage of drought-tolerant hybrids. However, wide prediction intervals across models highlight the uncertainty in these outcomes and scenarios.
The U.S. Geological Survey (USGS) monitors water quality and suspended-sediment transport in the San Francisco Bay (bay) as part of a multi-agency effort to address management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people, and the bay teems both with resident and with migratory wildlife, plants, and fish. Freshwater mixes with salt water in the bay, which is subject both to riverine influences (floods, droughts, managed reservoir releases and freshwater diversions) and to marine influences (tides, waves, effects of salt water). To understand this environment, the USGS, along with its partners (see “Acknowledgements”), has been monitoring the bay’s waters continuously since 1988. Several water-quality variables are of particular importance to State and Federal resource managers and are monitored at key locations throughout the bay (fig. 1). Salinity, which indicates the relative mixing of fresh and ocean waters in the bay, is derived from specific conductance measurements. Water temperature, along with salinity, affects the density of water, which controls gravity-driven circulation patterns and stratification in the water column. Turbidity, a measure of light scattered from suspended particles in the water, is used to estimate suspended-sediment concentration (SSC). Suspended sediment affects the bay in multiple ways: attenuation of sunlight in the water column, affecting phytoplankton growth; deposition on tidal marsh and intertidal mudflats, which can help sustain these habitats as sea level rises; deposition in ports and shipping channels, which can necessitate dredging; and often, adsorption of contaminants, affecting their distribution and concentrations in the environment. Dissolved oxygen concentration, essential to a healthy ecosystem and a fundamental indicator of water quality, is affected by water temperature, salinity, ecosystem metabolism, tidal currents, and wind. Tidal currents in the bay reverse four times a day, and wind direction and intensity typically vary on a daily cycle. Consequently, salinity, water temperature, SSC, and dissolved-oxygen concentration vary spatially and temporally throughout the bay. Therefore, continuous measurements are needed to observe these changes. The purpose of this fact sheet is to provide information about these variables, as well as internet links to access these continuous water-quality data collected by the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183013","usgsCitation":"Buchanan, P.A., Downing-Kunz, M.A., Schoellhamer, D.H., and Livsey, D.N., 2018, Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2014–15 (ver. 1.1, May 2018): U.S. Geological Survey Fact Sheet 2018–3013, 5 p., https://doi.org/10.3133/fs20183013.","productDescription":"5 p.","ipdsId":"IP-062545","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":354204,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2018/3013/fs20183013_versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"Fact Sheet 2018-3013 Version History"},{"id":352337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3013/coverthb.jpg"},{"id":352338,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3013/fs20183013_v1.1.pdf","text":"Report","size":"650 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3013"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5689697265625,\n 37.399619108675594\n ],\n [\n -121.76834106445311,\n 37.399619108675594\n ],\n [\n -121.76834106445311,\n 38.19825933797085\n ],\n [\n -122.5689697265625,\n 38.19825933797085\n ],\n [\n -122.5689697265625,\n 37.399619108675594\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 8, 2018; Version 1.1: May 15, 2018","contact":"Director,
California Water Science Center
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6000 J Street, Placer Hall
Sacramento, CA 95819