Quantifying physical heterogeneity is essential for meaningful ecological research and effective resource management. Spatial patterns of multiple, co-occurring physical features are rarely quantified across a seascape because of methodological challenges. Here, we identified approaches that measured total site-specific heterogeneity, an often overlooked aspect of estuarine ecosystems. Specifically, we examined 23 metrics that quantified four types of common physical features: (1) river and creek confluences, (2) bathymetric variation including underwater drop-offs, (3) land features such as islands/sandbars, and (4) major underwater channel networks. Our research at 40 sites throughout Plum Island Estuary (PIE) provided solutions to two problems. The first problem was that individual metrics that measured heterogeneity of a single physical feature showed different regional patterns. We solved this first problem by combining multiple metrics for a single feature using a within-physical feature cluster analysis. With this approach, we identified sites with four different types of confluences and three different types of underwater drop-offs. The second problem was that when multiple physical features co-occurred, new patterns of total site-specific heterogeneity were created across the seascape. This pattern of total heterogeneity has potential ecological relevance to structure-oriented predators. To address this second problem, we identified sites with similar types of total physical heterogeneity using an across-physical feature cluster analysis. Then, we calculated an additive heterogeneity index, which integrated all physical features at a site. Finally, we tested if site-specific additive heterogeneity index values differed for across-physical feature clusters. In PIE, the sites with the highest additive heterogeneity index values were clustered together and corresponded to sites where a fish predator, adult striped bass (Morone saxatilis), aggregated in a related acoustic tracking study. In summary, we have shown general approaches to quantifying site-specific heterogeneity.
","language":"English","publisher":"Springer International","doi":"10.1007/s12237-016-0207-9","usgsCitation":"Kennedy, C.G., Mather, M.E., and Smith, J.M., 2017, Quantifying site-specific physical heterogeneity within an estuarine seascape: Estuaries and Coasts, v. 40, no. 5, p. 1385-1397, https://doi.org/10.1007/s12237-016-0207-9.","productDescription":"13 p.","startPage":"1385","endPage":"1397","ipdsId":"IP-070125","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.85855484008789,\n 42.69088969601617\n ],\n [\n -70.76482772827148,\n 42.69088969601617\n ],\n [\n -70.76482772827148,\n 42.76780873017273\n ],\n [\n -70.85855484008789,\n 42.76780873017273\n ],\n [\n -70.85855484008789,\n 42.69088969601617\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"5","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"5a0425bce4b0dc0b45b453b6","contributors":{"authors":[{"text":"Kennedy, Cristina G.","contributorId":200162,"corporation":false,"usgs":false,"family":"Kennedy","given":"Cristina","email":"","middleInitial":"G.","affiliations":[{"id":18918,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA, 01003, USA","active":true,"usgs":false}],"preferred":false,"id":721224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph M.","contributorId":106712,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false},{"id":17855,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":721225,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70179658,"text":"70179658 - 2017 - Timescales of carbon turnover in soils with mixed crystalline mineralogies","interactions":[],"lastModifiedDate":"2017-01-10T15:30:06","indexId":"70179658","displayToPublicDate":"2017-01-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5259,"text":"SOIL","active":true,"publicationSubtype":{"id":10}},"title":"Timescales of carbon turnover in soils with mixed crystalline mineralogies","docAbstract":"Organic matter–mineral associations stabilize much of the carbon (C) stored globally in soils. Metastable short-range-order (SRO) minerals such as allophane and ferrihydrite provide one mechanism for long-term stabilization of organic matter in young soil. However, in soils with few SRO minerals and a predominance of crystalline aluminosilicate or Fe (and Al) oxyhydroxide, C turnover should be governed by chemisorption with those minerals. Here, we correlate mineral composition from soils containing small amounts of SRO minerals with mean turnover time (TT) of C estimated from radiocarbon (14C) in bulk soil, free light fraction and mineral-associated organic matter. We varied the mineral amount and composition by sampling ancient soils formed on different lithologies in arid to subhumid climates in Kruger National Park (KNP), South Africa. Mineral contents in bulk soils were assessed using chemical extractions to quantify Fe oxyhydroxides and SRO minerals. Because of our interest in the role of silicate clay mineralogy, particularly smectite (2 : 1) and kaolinite (1 : 1), we separately quantified the mineralogy of the clay-sized fraction using X-ray diffraction (XRD) and measured 14C on the same fraction.
Density separation demonstrated that mineral associated C accounted for 40–70 % of bulk soil organic C in A and B1 horizons for granite, nephelinite and arid-zone gabbro soils, and > 80 % in other soils. Organic matter strongly associated with the isolated clay-sized fraction represented only 9–47 % of the bulk soil C. The mean TT of C strongly associated with the clay-sized fraction increased with the amount of smectite (2 : 1 clays); in samples with > 40 % smectite it averaged 1020 ± 460 years. The C not strongly associated with clay-sized minerals, including a combination of low-density C, the C associated with minerals of sizes between 2 µm and 2 cm (including Fe oxyhydroxides as coatings), and C removed from clay-sized material by 2 % hydrogen peroxide had TTs averaging 190 ± 190 years in surface horizons. Summed over the bulk soil profile, we found that smectite content correlated with the mean TT of bulk soil C across varied lithologies. The SRO mineral content in KNP soils was generally very low, except for the soils developed on gabbros under more humid climate that also had very high Fe and C contents with a surprisingly short, mean C TTs. In younger landscapes, SRO minerals are metastable and sequester C for long timescales. We hypothesize that in the KNP, SRO minerals represent a transient stage of mineral evolution and therefore lock up C for a shorter time.
Overall, we found crystalline Fe-oxyhydroxides (determined as the difference between Fe in dithionate citrate and oxalate extractions) to be the strongest predictor for soil C content, while the mean TT of soil C was best predicted from the amount of smectite, which was also related to more easily measured bulk properties such as cation exchange capacity or pH. Combined with previous research on C turnover times in 2 : 1 vs. 1 : 1 clays, our results hold promise for predicting C inventory and persistence based on intrinsic timescales of specific carbon–mineral interactions.
The western population of the yellow-billed cuckoo (Coccyzus americanus) was recently listed as threatened under the federal Endangered Species Act. Yellow-billed cuckoo conservation efforts require the identification of features and area requirements associated with high quality, riparian forest habitat at spatial scales that range from nest microhabitat to landscape, as well as lower-suitability areas that can be enhanced or restored. Spatially explicit models inform conservation efforts by increasing ecological understanding of a target species, especially at landscape scales. Previous yellow-billed cuckoo modelling efforts derived plant-community maps from aerial photography, an expensive and oftentimes inconsistent approach. Satellite models can remotely map vegetation features (e.g., vegetation density, heterogeneity in vegetation density or structure) across large areas with near perfect repeatability, but they usually cannot identify plant communities. We used aerial photos and satellite imagery, and a hierarchical spatial scale approach, to identify yellow-billed cuckoo breeding habitat along the Lower Colorado River and its tributaries. Aerial-photo and satellite models identified several key features associated with yellow-billed cuckoo breeding locations: (1) a 4.5 ha core area of dense cottonwood-willow vegetation, (2) a large native, heterogeneously dense forest (72 ha) around the core area, and (3) moderately rough topography. The odds of yellow-billed cuckoo occurrence decreased rapidly as the amount of tamarisk cover increased or when cottonwood-willow vegetation was limited. We achieved model accuracies of 75–80% in the project area the following year after updating the imagery and location data. The two model types had very similar probability maps, largely predicting the same areas as high quality habitat. While each model provided unique information, a dual-modelling approach provided a more complete picture of yellow-billed cuckoo habitat requirements and will be useful for management and conservation activities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2016.12.010","usgsCitation":"Johnson, M.J., Hatten, J.R., Holmes, J.A., and Shafroth, P.B., 2017, Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach: Ecological Modelling, v. 347, p. 50-62, https://doi.org/10.1016/j.ecolmodel.2016.12.010.","productDescription":"13 p.","startPage":"50","endPage":"62","ipdsId":"IP-075673","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":470144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2016.12.010","text":"Publisher Index Page"},{"id":333009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"347","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58760114e4b04eac8e0746d3","contributors":{"authors":[{"text":"Johnson, Matthew J. mjjohnson@usgs.gov","contributorId":167197,"corporation":false,"usgs":false,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","middleInitial":"J.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":658079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":658078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Jennifer A.","contributorId":178159,"corporation":false,"usgs":false,"family":"Holmes","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":658080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":658081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179076,"text":"sim3371 - 2017 - Geologic map of the Fittstown 7.5΄ quadrangle, Pontotoc and Johnston Counties, Oklahoma","interactions":[],"lastModifiedDate":"2017-02-10T11:33:48","indexId":"sim3371","displayToPublicDate":"2017-01-09T13:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3371","title":"Geologic map of the Fittstown 7.5΄ quadrangle, Pontotoc and Johnston Counties, Oklahoma","docAbstract":"This 1:24,000-scale geologic map includes new geologic mapping as well as compilation and revision of previous geologic maps in the area. Field investigations were carried out during 2009–2011 that included mapping and investigations of the geology and hydrology of the Chickasaw National Recreation Area, Oklahoma, west of the map area.
The Fittstown quadrangle is in Pontotoc and Johnston Counties in south-central Oklahoma, which is in the northeastern part of the Arbuckle Mountains. The Arbuckle Mountains are composed of a thick sequence of Paleozoic sedimentary rocks that overlie Lower Cambrian and Precambrian igneous rocks; these latter rocks are not exposed in the quadrangle. From Middle to Late Pennsylvanian time, the Arbuckle Mountains region was folded, faulted, and uplifted. Periods of erosion followed these Pennsylvanian mountain-building events, beveling this region and ultimately developing the current subtle topography that includes hills and incised uplands. The southern and northwestern parts of the Fittstown quadrangle are directly underlain by Lower Ordovician dolomite of the Arbuckle Group that has eroded to form an extensive, stream-incised upland containing the broad, gently southeast-plunging, Pennsylvanian-age Hunton anticline. The northeastern part of the map area is underlain by Middle Ordovician to Pennsylvanian limestone, shale, and sandstone units that predominantly dip northeast and form the northeastern limb of the Hunton anticline; this limb is cut by steeply dipping, northwest-southeast striking faults of the Franks fault zone. This limb and the Franks fault zone define the southwestern margin of the Franks graben, which is underlain by Pennsylvanian rocks in the northeast part of the map area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3371","usgsCitation":"Lidke, D.J., and Blome, C.D., 2017, Geologic map of the Fittstown 7.5′ quadrangle, Pontotoc and Johnston Counties, Oklahoma: U.S. Geological Survey Scientific Investigations Map 3371, 14 p., 1 sheet, scale 1:24,000, https://doi.org/10.3133/sim3371.","productDescription":"Pamphlet: iv, 14 p.; 1 Sheet: 34.09 x 34.35 inches; Read Me; Spatial Data","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073124","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":438453,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77P8WJN","text":"USGS data release","linkHelpText":"Geologic map of the Fittstown 7 1/2' quadrangle, Pontotoc and Johnston Counties, Oklahoma"},{"id":332521,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3371/sim3371__map.pdf","text":"Map","size":"31.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3371 Map"},{"id":332522,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3371/sim3371__map_geo.pdf","text":"Georeferenced Map","size":"187 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3371 Georeferenced Map"},{"id":332520,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3371/sim3371_ReadMe_v2.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3371 Read Me"},{"id":332523,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://doi.org/10.5066/F77P8WJN","text":"Data Release","description":"SIM 3371 Data Release"},{"id":332519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3371/sim3371_pamphlet.pdf","text":"Pamphlet","size":"3.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3371 Pamphlet"},{"id":332518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3371/coverthb_map2.jpg"}],"country":"United States","state":"Oklahoma","county":"Johnson County. Pontotoc County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.75,\n 34.625\n ],\n [\n -96.75,\n 34.5\n ],\n [\n -96.625,\n 34.5\n ],\n [\n -96.625,\n 34.625\n ],\n [\n -96.75,\n 34.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, Mail Stop 980
Denver, CO 80225
Lake Whitefish Coregonus clupeaformis is the most commercially valuable species in Lake Huron. The fishery for this species has historically been managed based on 25 management units (17 in Canada, 8 in the USA). However, congruence between the contemporary population structure of Lake Whitefish and management units is poorly understood. We used stable isotopes of carbon (δ13C) and nitrogen (δ15N), food web markers that reflect patterns in resource use (i.e., prey, location, habitat), to assess the population structure of spawning-phase Lake Whitefish collected from 32 sites (1,474 fish) across Lake Huron. We found large isotopic variation among fish from different sites (ranges: δ13C = 10.2‰, δ15N = 5.5‰) and variable niche size and levels of overlap (standard ellipse area = 1.0–4.3‰2). Lake Huron contained spawning-phase fish from four major isotopic clusters largely defined by extensive variation in δ13C, and the isotopic composition of fish sampled was spatially structured both within and between lake basins. Based on cluster compositions, we identified six putative regional groups, some of which represented sites of high diversity (three to four clusters) and others with less (one to two clusters). Analysis of isotopic values from Lake Whitefish collected from summer feeding locations and baseline prey items showed similar isotopic variation and established spatial linkage between spawning-phase and summer fish. Our results show that summer feeding location contributes strongly to the isotopic structure we observed in spawning-phase fish. One of the regional groups we identified in northern Georgian Bay is highly distinct based on isotopic composition and possibly ecologically unique within Lake Huron. Our findings are congruent with several previous studies using different markers (genetics, mark–recapture), and we conclude that current management units are generally too small and numerous to reflect the population structure of Lake Whitefish in Lake Huron.
","language":"English","publisher":"Taylor and Franis","doi":"10.1080/02755947.2016.1245225","usgsCitation":"Eberts, R.L., Wissel, B., Simpson, G.L., Crawford, S.S., Stott, W., Hanner, R.H., Manzon, R.G., Wilson, J.Y., Boreham, D.R., and Somers, C.M., 2017, Isotopic structure of Lake Whitefish in Lake Huron: Evidence for regional and local populations based on resource use: North American Journal of Fisheries Management, v. 37, no. 1, p. 133-148, https://doi.org/10.1080/02755947.2016.1245225.","productDescription":"16 p.","startPage":"133","endPage":"148","ipdsId":"IP-076083","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":470146,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Isotopic_Structure_of_Lake_Whitefish_in_Lake_Huron_Evidence_for_Regional_and_Local_Populations_Based_on_Resource_Use/4515455","text":"External Repository"},{"id":332981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.72656249999999,\n 42.924251753870685\n ],\n [\n -84.72656249999999,\n 46.33175800051563\n ],\n [\n -79.6728515625,\n 46.33175800051563\n ],\n [\n -79.6728515625,\n 42.924251753870685\n ],\n [\n -84.72656249999999,\n 42.924251753870685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-03","publicationStatus":"PW","scienceBaseUri":"5874b0aae4b0a829a320bb5b","contributors":{"authors":[{"text":"Eberts, Rebecca L.","contributorId":178137,"corporation":false,"usgs":false,"family":"Eberts","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":658016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wissel, Bjorn","contributorId":178138,"corporation":false,"usgs":false,"family":"Wissel","given":"Bjorn","email":"","affiliations":[],"preferred":false,"id":658017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simpson, Gavin L.","contributorId":178139,"corporation":false,"usgs":false,"family":"Simpson","given":"Gavin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":658018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crawford, Stephen S.","contributorId":178140,"corporation":false,"usgs":false,"family":"Crawford","given":"Stephen","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":658019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stott, Wendylee wstott@usgs.gov","contributorId":3763,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":658015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hanner, Robert H.","contributorId":178141,"corporation":false,"usgs":false,"family":"Hanner","given":"Robert","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":658020,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manzon, Richard G.","contributorId":178142,"corporation":false,"usgs":false,"family":"Manzon","given":"Richard","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":658021,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wilson, Joanna Y.","contributorId":178143,"corporation":false,"usgs":false,"family":"Wilson","given":"Joanna","email":"","middleInitial":"Y.","affiliations":[],"preferred":false,"id":658022,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Boreham, Douglas R.","contributorId":178144,"corporation":false,"usgs":false,"family":"Boreham","given":"Douglas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":658023,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Somers, Christopher M.","contributorId":178145,"corporation":false,"usgs":false,"family":"Somers","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":658024,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70179643,"text":"70179643 - 2017 - A comparison of honey bee-collected pollen from working agricultural lands using light microscopy and ITS metabarcoding","interactions":[],"lastModifiedDate":"2021-03-22T15:28:43.415971","indexId":"70179643","displayToPublicDate":"2017-01-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1536,"text":"Environmental Entomology","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of honey bee-collected pollen from working agricultural lands using light microscopy and ITS metabarcoding","docAbstract":"Taxonomic identification of pollen has historically been accomplished via light microscopy but requires specialized knowledge and reference collections, particularly when identification to lower taxonomic levels is necessary. Recently, next-generation sequencing technology has been used as a cost-effective alternative for identifying bee-collected pollen; however, this novel approach has not been tested on a spatially or temporally robust number of pollen samples. Here, we compare pollen identification results derived from light microscopy and DNA sequencing techniques with samples collected from honey bee colonies embedded within a gradient of intensive agricultural landscapes in the Northern Great Plains throughout the 2010–2011 growing seasons. We demonstrate that at all taxonomic levels, DNA sequencing was able to discern a greater number of taxa, and was particularly useful for the identification of infrequently detected species. Importantly, substantial phenological overlap did occur for commonly detected taxa using either technique, suggesting that DNA sequencing is an appropriate, and enhancing, substitutive technique for accurately capturing the breadth of bee-collected species of pollen present across agricultural landscapes. We also show that honey bees located in high and low intensity agricultural settings forage on dissimilar plants, though with overlap of the most abundantly collected pollen taxa. We highlight practical applications of utilizing sequencing technology, including addressing ecological issues surrounding land use, climate change, importance of taxa relative to abundance, and evaluating the impact of conservation program habitat enhancement efforts.
","language":"English","publisher":"Oxford University Journals","doi":"10.1093/ee/nvw159","usgsCitation":"Smart, M., Cornman, R.S., Iwanowicz, D.D., McDermott-Kubeczko, M., Pettis, J.S., Spivak, M., and Otto, C., 2017, A comparison of honey bee-collected pollen from working agricultural lands using light microscopy and ITS metabarcoding: Environmental Entomology, v. 46, no. 1, p. 38-49, https://doi.org/10.1093/ee/nvw159.","productDescription":"12 p.","startPage":"38","endPage":"49","ipdsId":"IP-078924","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470145,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvw159","text":"Publisher Index Page"},{"id":332984,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-06","publicationStatus":"PW","scienceBaseUri":"5874b0a9e4b0a829a320bb59","contributors":{"authors":[{"text":"Smart, Matthew 0000-0003-0711-3035 msmart@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-3035","contributorId":174424,"corporation":false,"usgs":true,"family":"Smart","given":"Matthew","email":"msmart@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":658031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":658032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":658033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDermott-Kubeczko, Margaret","contributorId":178147,"corporation":false,"usgs":false,"family":"McDermott-Kubeczko","given":"Margaret","email":"","affiliations":[],"preferred":false,"id":658034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pettis, Jeff S","contributorId":178149,"corporation":false,"usgs":false,"family":"Pettis","given":"Jeff","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":658036,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spivak, Marla S","contributorId":178148,"corporation":false,"usgs":false,"family":"Spivak","given":"Marla S","affiliations":[],"preferred":false,"id":658035,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":658037,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178812,"text":"sir20165168 - 2017 - Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015","interactions":[],"lastModifiedDate":"2017-01-25T12:54:21","indexId":"sir20165168","displayToPublicDate":"2017-01-09T00:00:00","publicationYear":"2017","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":"2016-5168","title":"Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015","docAbstract":"Cyanobacterial harmful algal blooms (CyanoHABs) tend to be spatially variable vertically in the water column and horizontally across the lake surface because of in-lake and weather-driven processes and can vary by orders of magnitude in concentration across relatively short distances (meters or less). Extreme spatial variability in cyanobacteria and associated compounds poses unique challenges to collecting representative samples for scientific study and public-health protection. The objective of this study was to assess the spatial variability of cyanobacteria and microcystin in Milford Lake, Kansas, using data collected on July 27 and August 31, 2015. Spatially dense near-surface data were collected by the U.S. Geological Survey, nearshore data were collected by the Kansas Department of Health and Environment, and open-water data were collected by U.S. Army Corps of Engineers. CyanoHABs are known to be spatially variable, but that variability is rarely quantified. A better understanding of the spatial variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with CyanoHAB issues throughout the Nation.
The CyanoHABs in Milford Lake during July and August 2015 displayed the extreme spatial variability characteristic of cyanobacterial blooms. The phytoplankton community was almost exclusively cyanobacteria (greater than 90 percent) during July and August. Cyanobacteria (measured directly by cell counts and indirectly by regression-estimated chlorophyll) and microcystin (measured directly by enzyme-linked immunosorbent assay [ELISA] and indirectly by regression estimates) concentrations varied by orders of magnitude throughout the lake. During July and August 2015, cyanobacteria and microcystin concentrations decreased in the downlake (towards the outlet) direction.
Nearshore and open-water surface grabs were collected and analyzed for microcystin as part of this study. Samples were collected in the uplake (Zone C), midlake (Zone B), and downlake (Zone A) parts of the lake. Overall, no consistent pattern was indicated as to which sample location (nearshore or open water) had the highest microcystin concentrations. In July, the maximum microcystin concentration observed in each zone was detected at a nearshore site, and in August, maximum microcystin concentrations in each zone were detected at an open-water site.
The Kansas Department of Health and Environment uses two guidance levels (a watch and a warning level) to issue recreational public-health advisories for CyanoHABs in Kansas lakes. The levels are based on concentrations of microcystin and numbers of cyanobacteria. In July and August, discrete water-quality samples were predominantly indicative of warning status in Zone C, watch status in Zone B, and no advisories in Zone A. Regression-estimated microcystin concentrations, which provided more thorough coverage of Milford Lake (n=683–720) than discrete samples (n=21–24), generally indicated the same overall pattern. Regardless of the individual agencies sampling approach, the overall public-health advisory status of each zone in Milford Lake was similar according to the Kansas Department of Health and Environment guidance levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165168","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment and the U.S. Army Corps of Engineers, Kansas City District","usgsCitation":"Foster, G.M., Graham, J.L., Stiles, T.C., Boyer, M.G., King, L.R., and Loftin, K.A., 2017, Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015: U.S. Geological Survey Scientific Investigations Report 2016–5168, 45 p., https://doi.org/10.3133/sir20165168.","productDescription":"Report: v, 45 p.; Data Releases","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078303","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":333877,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69GRH","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality data from two sites on Milford Lake, Kansas, July 26-27 and August 30-31, 2015"},{"id":332912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5168/coverthb.jpg"},{"id":333876,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5168/sir20165168.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5168 Report PDF"},{"id":333878,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WQ01ZW","text":"USGS data release","description":"USGS data release","linkHelpText":" Milford Lake, Kansas, spatial water-quality data, July 27 and August 31, 2015"},{"id":333879,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9971","text":"USGS data release","description":"USGS data release","linkHelpText":"Phytoplankton data for Milford Lake, Kansas, July 27 and August 31, 2015"}],"country":"United States","state":"Kansas","otherGeospatial":"Milford Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 38.982897808179985\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Comprehensive sampling of peat, underlying lakebed sediments, and coexisting waters of a naturally uraniferous montane wetland are combined with hydrologic measurements to define the important controls on uranium (U) supply and uptake. The major source of U to the wetland is groundwater flowing through locally fractured and faulted granite gneiss of Proterozoic age. Dissolved U concentrations in four springs and one seep ranged from 20 to 83 ppb (μg/l). Maximum U concentrations are ∼300 ppm (mg/kg) in lakebed sediments and >3000 ppm in peat. Uranium in lakebed sediments is primarily stratabound in the more organic-rich layers, but samples of similar organic content display variable U concentrations. Post-depositional modifications include variable additions of U delivered by groundwater. Uranium distribution in peat is heterogeneous and primarily controlled by proximity to groundwater-fed springs and seeps that act as local point sources of U, and by proximity to groundwater directed along the peat/lakebeds contact. Uranium is initially sorbed on various organic components of peat as oxidized U(VI) present in groundwater. Selective extractions indicate that the majority of sorbed U remains as the oxidized species despite reducing conditions that should favor formation of U(IV). Possible explanations are kinetic hindrances related to strong complex formation between uranyl and humic substances, inhibition of anaerobic bacterial activity by low supply of dissolved iron and sulfate, and by cold temperatures.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.01.001","usgsCitation":"Schumann, R.R., Zielinski, R.A., Otton, J.K., Pantea, M.P., and Orem, W.H., 2017, Uranium delivery and uptake in a montane wetland, north-central Colorado, USA: Applied Geochemistry, v. 78, no. 3, p. 363-379, https://doi.org/10.1016/j.apgeochem.2017.01.001.","productDescription":"17 p.","startPage":"363","endPage":"379","ipdsId":"IP-074221","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470147,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.01.001","text":"Publisher Index Page"},{"id":335164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335496,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70Z71DQ","text":"Stratigraphic, geochemical, and hydrologic data for the Boston Peak wetland, Larimer County, CO, USA"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 38.805470223177466\n ],\n [\n -107.5341796875,\n 41.0130657870063\n ],\n [\n -103.4912109375,\n 41.0130657870063\n ],\n [\n -103.4912109375,\n 38.805470223177466\n ],\n [\n -107.5341796875,\n 38.805470223177466\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"589ffedfe4b099f50d3e0434","contributors":{"authors":[{"text":"Schumann, R. Randall 0000-0001-8158-6960 rschumann@usgs.gov","orcid":"https://orcid.org/0000-0001-8158-6960","contributorId":1569,"corporation":false,"usgs":true,"family":"Schumann","given":"R.","email":"rschumann@usgs.gov","middleInitial":"Randall","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":663365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zielinski, Robert A. 0000-0002-4047-5129 rzielinski@usgs.gov","orcid":"https://orcid.org/0000-0002-4047-5129","contributorId":1593,"corporation":false,"usgs":true,"family":"Zielinski","given":"Robert","email":"rzielinski@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":663366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Otton, James K. jkotton@usgs.gov","contributorId":1170,"corporation":false,"usgs":true,"family":"Otton","given":"James","email":"jkotton@usgs.gov","middleInitial":"K.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":663367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pantea, Michael P. mpantea@usgs.gov","contributorId":1549,"corporation":false,"usgs":true,"family":"Pantea","given":"Michael","email":"mpantea@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":663368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":663369,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188657,"text":"70188657 - 2017 - Evaluation of diffuse and preferential flow pathways of infiltratedprecipitation and irrigation using oxygen and hydrogen isotopes","interactions":[],"lastModifiedDate":"2017-06-20T14:29:28","indexId":"70188657","displayToPublicDate":"2017-01-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of diffuse and preferential flow pathways of infiltratedprecipitation and irrigation using oxygen and hydrogen isotopes","docAbstract":"Subsurface-water flow pathways in three different land-use areas (non-irrigated grassland, poplar forest, and irrigated arable land) in the central North China Plain were investigated using oxygen (18O) and hydrogen (2H) isotopes in samples of precipitation, soils, and groundwater. Soil water in the top 10 cm was significantly affected by both evaporation and infiltration. Water at 10–40 cm depth in the grassland and arable land, and 10–60 cm in poplar forest, showed a relatively short residence time, as a substantial proportion of antecedent soil water was mixed with a 92-mm storm infiltration event, whereas below those depths (down to 150 cm), depleted δ18O spikes suggested that some storm water bypassed the shallow soil layers. Significant differences, in soil-water content and δ18O values, within a small area, suggested that the proportion of immobile soil water and water flowing in subsurface pathways varies depending on local vegetation cover, soil characteristics and irrigation applications. Soil-water δ18O values revealed that preferential flow and diffuse flow coexist. Preferential flow was active within the root zone, independent of antecedent soil-water content, in both poplar forest and arable land, whereas diffuse flow was observed in grassland. The depleted δ18O spikes at 20–50 cm depth in the arable land suggested the infiltration of irrigation water during the dry season. Temporal isotopic variations in precipitation were subdued in the shallow groundwater, suggesting more complete mixing of different input waters in the unsaturated zone before reaching the shallow groundwater.
","language":"English","publisher":"SpringerLink","doi":"10.1007/s10040-016-1525-5","usgsCitation":"Ma, B., Liang, X., Liu, S., Jin, M., Nimmo, J.R., and Li, J., 2017, Evaluation of diffuse and preferential flow pathways of infiltratedprecipitation and irrigation using oxygen and hydrogen isotopes: Hydrogeology Journal, v. 25, no. 3, p. 675-688, https://doi.org/10.1007/s10040-016-1525-5.","productDescription":"14 p. 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Previous work by the U.S. Geological Survey South Atlantic Water Science Center at the site involved the collection of borehole geophysical log data in 15 wells, in addition to surface geologic mapping and passive diffusion bag sampling. In a continued effort to assist the U.S. Environmental Protection Agency in developing a conceptual groundwater model to assess current contaminant distribution and future migration of contaminants, more than 900 subsurface features (primarily fracture orientations) in 10 open borehole wells were delineated and continuous water-level data information from 14 monitoring wells within close proximity of the initially drilled boreholes was collected to observe any induced water-level fluctuations during drilling operations
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1022","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Region 4 Superfund Section","usgsCitation":"Antolino, D.J., and Chapman, M.J., 2017, Continued geophysical logging near the GMH Electronics National Priorities List Superfund site near Roxboro, North Carolina: U.S. Geological Survey Data Series 1022, 37 p., https://doi.org/10.3133/ds1022.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076902","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":438455,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX77JP","text":"USGS data release","linkHelpText":"Continued Geophysical Logging in the vicinity of the GMH Electronics Superfund Site near Roxboro, North 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U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.
","language":"English","publisher":"Informa UK Limited","doi":"10.1080/10402381.2016.1263694","usgsCitation":"Harris, T.D., and Graham, J., 2017, Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset: Lake and Reservoir Management, no. 33, 17 p., https://doi.org/10.1080/10402381.2016.1263694.","productDescription":"17 p.","ipdsId":"IP-078030","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":335169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.94174194335936,\n 37.666429212090605\n ],\n [\n -97.94174194335936,\n 37.845037026243425\n ],\n [\n -97.72270202636717,\n 37.845037026243425\n ],\n [\n -97.72270202636717,\n 37.666429212090605\n ],\n [\n -97.94174194335936,\n 37.666429212090605\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"33","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-06","publicationStatus":"PW","scienceBaseUri":"589ffedfe4b099f50d3e0436","contributors":{"authors":[{"text":"Harris, Ted D.","contributorId":149758,"corporation":false,"usgs":false,"family":"Harris","given":"Ted","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":663305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":663304,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179610,"text":"70179610 - 2017 - Editor’s note","interactions":[],"lastModifiedDate":"2017-01-19T13:41:37","indexId":"70179610","displayToPublicDate":"2017-01-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1103,"text":"Bulletin of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Editor’s note","docAbstract":"Heavy metal contamination at shooting ranges is well documented (e.g., Heier et al. 2009; Islam et al. 2016). Primarily lead, but also copper, zinc, and antimony often occur at high concentrations in shooting range soils; cadmium, nickel, silver, and arsenic may also be present (Cao et al. 2003; Islam et al. 2016). These metals represent a potential threat to human health and wildlife. Although much of the lead and other metals remains in the soil (Clausen et al. 2011), some metals can also contaminate groundwater and surface water and thereby threaten aquatic life (Heier et al. 2009). Results of a study published in the current issue of the Bulletin of Environmental Contamination and Toxicology (Stauffer et al. 2017) indicate that mercury contamination may also be an issue at shooting ranges, which has not been previously reported.
","language":"English","publisher":"Springer","doi":"10.1007/s00128-016-2003-4","usgsCitation":"Schmitt, C.J., 2017, Editor’s note: Bulletin of Environmental Contamination and Toxicology, v. 98, no. 1, p. 1-1, https://doi.org/10.1007/s00128-016-2003-4.","productDescription":"1 p.","startPage":"1","endPage":"1","ipdsId":"IP-081601","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":470148,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00128-016-2003-4","text":"Publisher Index Page"},{"id":332941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-28","publicationStatus":"PW","scienceBaseUri":"58772077e4b0315b4c11fe28","contributors":{"authors":[{"text":"Schmitt, Christopher J. 0000-0001-6804-2360 cjschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6804-2360","contributorId":491,"corporation":false,"usgs":true,"family":"Schmitt","given":"Christopher","email":"cjschmitt@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":657895,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70179601,"text":"70179601 - 2017 - Simulated mussel mortality thresholds as a function of mussel biomass and nutrient loading","interactions":[],"lastModifiedDate":"2017-01-05T10:53:43","indexId":"70179601","displayToPublicDate":"2017-01-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Simulated mussel mortality thresholds as a function of mussel biomass and nutrient loading","docAbstract":"A freshwater “mussel mortality threshold” was explored as a function of porewater ammonium (NH4+) concentration, mussel biomass, and total nitrogen (N) utilizing a numerical model calibrated with data from mesocosms with and without mussels. A mortality threshold of 2 mg-N L−1 porewater NH4+ was selected based on a study that estimated 100% mortality of juvenile Lampsilis mussels exposed to 1.9 mg-N L−1NH4+ in equilibrium with 0.18 mg-N L−1 NH3. At the highest simulated mussel biomass (560 g m−2) and the lowest simulated influent water “food” concentration (0.1 mg-N L−1), the porewater NH4+ concentration after a 2,160 h timespan without mussels was 0.5 mg-N L−1 compared to 2.25 mg-N L−1 with mussels. Continuing these simulations while varying mussel biomass and N content yielded a mortality threshold contour that was essentially linear which contradicted the non-linear and non-monotonic relationship suggested by Strayer (2014). Our model suggests that mussels spatially focus nutrients from the overlying water to the sediments as evidenced by elevated porewater NH4+ in mesocosms with mussels. However, our previous work and the model utilized here show elevated concentrations of nitrite and nitrate in overlying waters as an indirect consequence of mussel activity. Even when the simulated overlying water food availability was quite low, the mortality threshold was reached at a mussel biomass of about 480 g m−2. At a food concentration of 10 mg-N L−1, the mortality threshold was reached at a biomass of about 250 g m−2. Our model suggests the mortality threshold for juvenile Lampsilis species could be exceeded at low mussel biomass if exposed for even a short time to the highly elevated total N loadings endemic to the agricultural Midwest.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.2838","usgsCitation":"Bril, J.S., Langenfeld, K., Just, C.L., Spak, S.N., and Newton, T., 2017, Simulated mussel mortality thresholds as a function of mussel biomass and nutrient loading: PeerJ, v. 5, e2838; 17 p., https://doi.org/10.7717/peerj.2838.","productDescription":"e2838; 17 p.","ipdsId":"IP-072131","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":470149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.2838","text":"Publisher Index Page"},{"id":332924,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-04","publicationStatus":"PW","scienceBaseUri":"586f69a2e4b01a71ba0bc8fb","contributors":{"authors":[{"text":"Bril, Jeremy S.","contributorId":178035,"corporation":false,"usgs":false,"family":"Bril","given":"Jeremy","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":657826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langenfeld, Kathryn","contributorId":178036,"corporation":false,"usgs":false,"family":"Langenfeld","given":"Kathryn","email":"","affiliations":[],"preferred":false,"id":657827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Just, Craig L.","contributorId":178037,"corporation":false,"usgs":false,"family":"Just","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":657828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spak, Scott N.","contributorId":178038,"corporation":false,"usgs":false,"family":"Spak","given":"Scott","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":657829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newton, Teresa 0000-0001-9351-5852 tnewton@usgs.gov","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":150098,"corporation":false,"usgs":true,"family":"Newton","given":"Teresa","email":"tnewton@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":657825,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179570,"text":"70179570 - 2017 - A refined electrofishing technique for collecting Silver Carp: Implications for management","interactions":[],"lastModifiedDate":"2017-02-08T14:25:01","indexId":"70179570","displayToPublicDate":"2017-01-04T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"A refined electrofishing technique for collecting Silver Carp: Implications for management","docAbstract":"Detecting nuisance species at low abundance or in newly established areas is critical to developing pest management strategies. Due to their sensitivity to disturbance and erratic jumping behavior, Silver Carp Hypophthalmichthys molitrix can be difficult to collect with traditional sampling methods. We compared catch per unit effort (CPUE) of all species from a Long Term Resource Monitoring (LTRM) electrofishing protocol to an experimental electrofishing technique designed to minimize Silver Carp evasion through tactical boat maneuvering and selective application of power. Differences in CPUE between electrofishing methods were detected for 2 of 41 species collected across 2 years of sampling at 20 sites along the Illinois River. The mean catch rate of Silver Carp using the experimental technique was 2.2 times the mean catch rate of the LTRM electrofishing technique; the increased capture efficiency at low relative abundance emphasizes the utility of this method for early detection. The experimental electrofishing also collected slightly larger Silver Carp (mean: 510.7 mm TL versus 495.2 mm TL), and nearly four times as many Silver Carp independently jumped into the boat during experimental transects. Novel sampling approaches, such as the experimental electrofishing technique used in this study, should be considered to increase probability of detection for aquatic nuisance species.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02755947.2016.1240122","usgsCitation":"Bouska, W.W., Glover, D.C., Bouska, K.L., and Garvey, J.E., 2017, A refined electrofishing technique for collecting Silver Carp: Implications for management: North American Journal of Fisheries Management, v. 37, no. 1, p. 101-107, https://doi.org/10.1080/02755947.2016.1240122.","productDescription":"7 p.","startPage":"101","endPage":"107","ipdsId":"IP-076352","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":332904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335022,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7S46Q4W","text":"A refined electrofishing technique for collecting Silver Carp: Implications for management. Supporting data"}],"volume":"37","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-03","publicationStatus":"PW","scienceBaseUri":"586e181ce4b0f5ce109fcad1","contributors":{"authors":[{"text":"Bouska, Wesley W.","contributorId":143724,"corporation":false,"usgs":false,"family":"Bouska","given":"Wesley","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":657770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glover, David C.","contributorId":178006,"corporation":false,"usgs":false,"family":"Glover","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":657771,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":657769,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garvey, James E.","contributorId":178007,"corporation":false,"usgs":false,"family":"Garvey","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":657772,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179508,"text":"70179508 - 2017 - Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus","interactions":[],"lastModifiedDate":"2023-06-21T15:04:52.97662","indexId":"70179508","displayToPublicDate":"2017-01-04T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus","title":"Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus","docAbstract":"Since the introduction of West Nile virus (WNV) into North America in 1999 a number of passerine bird species have been found to play a role in the amplification of the virus. Arbovirus surveillance, observational studies and experimental studies have implicated passerine birds (songbirds, e.g., crows, American robins, house sparrows, and house finches) as significant reservoirs of WNV in North America, yet we lack a tractable passerine animal model for controlled studies of the virus. The zebra finch (Taeniopygia guttata) serves as a model system across a diversity of fields, and here we develop the zebra finch a songbird model for WNV. Like many natural hosts of WNV, we found that zebra finches developed sufficient viremia to serve as a competent host, yet in general resisted mortality from infection. In the Australian zebra finch (AZF) T. g. castanotis, we detected WNV in the majority of sampled tissues by 4 days post injection (dpi). However, WNV was not detected in tissues of sacrificed birds at 14 dpi, shortly after the development of detectable anti-WNV antibodies in the majority of birds indicating successful viral clearance. We compared susceptibility between the two zebra finch subspecies AZF and Timor zebra finch (TZF) T. g. guttata. Compared to AZF, WNV RNA was detected in a larger proportion of challenged TZF and molecular detection of virus in the serum of TZF was significantly higher than in AZF. Given the observed moderate host competence and disease susceptibility, we suggest that zebra finches are appropriate as models for the study of WNV and although underutilized in this respect, may be ideal models for the study of the many diseases carried and transmitted by songbirds.
","language":"English","publisher":"PLOS One","doi":"10.1371/journal.pone.0167876","usgsCitation":"Hofmeister, E.K., Lund, M., Shearn-Bochsler, V.I., and Balakrishnan, C.N., 2017, Susceptibility and antibody response of the laboratory model zebra finch (Taeniopygia guttata) to West Nile Virus: PLoS ONE, v. 12, no. 1, e0167876; 17 p.; Data Release, https://doi.org/10.1371/journal.pone.0167876.","productDescription":"e0167876; 17 p.; Data Release","ipdsId":"IP-075765","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":470150,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0167876","text":"Publisher Index Page"},{"id":332816,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418291,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7707ZM3"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-03","publicationStatus":"PW","scienceBaseUri":"586e181ee4b0f5ce109fcad3","contributors":{"authors":[{"text":"Hofmeister, Erik K. 0000-0002-6360-3912 ehofmeister@usgs.gov","orcid":"https://orcid.org/0000-0002-6360-3912","contributorId":3230,"corporation":false,"usgs":true,"family":"Hofmeister","given":"Erik","email":"ehofmeister@usgs.gov","middleInitial":"K.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lund, Melissa 0000-0003-4577-2015 mlund@usgs.gov","orcid":"https://orcid.org/0000-0003-4577-2015","contributorId":177923,"corporation":false,"usgs":true,"family":"Lund","given":"Melissa","email":"mlund@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shearn-Bochsler, Valerie I. 0000-0002-5590-6518 vbochsler@usgs.gov","orcid":"https://orcid.org/0000-0002-5590-6518","contributorId":3234,"corporation":false,"usgs":true,"family":"Shearn-Bochsler","given":"Valerie","email":"vbochsler@usgs.gov","middleInitial":"I.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Balakrishnan, Christopher N.","contributorId":177924,"corporation":false,"usgs":false,"family":"Balakrishnan","given":"Christopher","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":657508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179503,"text":"70179503 - 2017 - Prevalence and distribution of Wellfleet Bay virus exposure in the Common Eider (Somateria mollissima)","interactions":[],"lastModifiedDate":"2017-01-10T13:06:07","indexId":"70179503","displayToPublicDate":"2017-01-04T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Prevalence and distribution of Wellfleet Bay virus exposure in the Common Eider (Somateria mollissima)","docAbstract":"Between 1998 and 2014, recurrent mortality events were reported in the Dresser's subspecies of the Common Eider (Somateria mollissima dresseri) on Cape Cod, Massachusetts, USA near Wellfleet Harbor. The early die-offs were attributed to parasitism and emaciation, but beginning in 2006 a suite of distinct lesions was observed concomitant with the isolation of a previously unknown RNA virus. This novel pathogen was identified as an orthomyxovirus in the genus Quaranjavirus and was named Wellfleet Bay virus (WFBV). To assess evidence of exposure to this virus in Common Eiders, we conducted a longitudinal study of the prevalence of WFBV antibodies at multiple locations from 2004–14; we collected 2,258 serum samples from six locations and analyzed each using a microneutralization assay. Results corroborate the emergence of WFBV in 2006 based on the first detection of antibodies in that year. Significantly higher prevalence was detected in Common Eiders sampled in Massachusetts compared to those in Maine, Nova Scotia, and Québec. For birds breeding and wintering in Massachusetss, viral exposure varied by age, sex, and season of sampling, and prevalence by season and sex were highly interrelated with greater numbers of antibody-positive males in the autumn and females in the spring. No evidence of viral exposure was detected in the Northern subspecies (Somateria mollissima borealis). Among the locations sampled, Massachusetts appears to be the epicenter of Common Eider exposure to WFBV. Further research is warranted to understand the factors controlling the epidemiology of WFBV in Massachussetts, including those that may be limiting geographic expansion of this virus.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/2016-01-019","usgsCitation":"Ballard, J.R., Mickley, R.M., Gibbs, S., Dwyer, C.P., Soos, C., Harms, N.J., Gilchrist, H.G., Hall, J.S., Franson, J.C., Milton, G.R., Parsons, G., Allen, B., Giroux, J., Lair, S., Mead, D.G., and Fischer, J.R., 2017, Prevalence and distribution of Wellfleet Bay virus exposure in the Common Eider (Somateria mollissima): Journal of Wildlife Diseases, v. 53, no. 1, p. 81-90, https://doi.org/10.7589/2016-01-019.","productDescription":"10 p.","startPage":"81","endPage":"90","ipdsId":"IP-077502","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":461795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7589/2016-01-019","text":"Publisher Index Page"},{"id":332811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586e181fe4b0f5ce109fcad5","contributors":{"authors":[{"text":"Ballard, Jennifer R.","contributorId":127726,"corporation":false,"usgs":false,"family":"Ballard","given":"Jennifer","email":"","middleInitial":"R.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":657478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mickley, Randall M.","contributorId":127738,"corporation":false,"usgs":false,"family":"Mickley","given":"Randall","email":"","middleInitial":"M.","affiliations":[{"id":7124,"text":"United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, 644 Bayfield Street, Suite 215, St Paul, Minnesota, 55107, USA","active":true,"usgs":false}],"preferred":false,"id":657479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, Samantha E.J.","contributorId":127739,"corporation":false,"usgs":false,"family":"Gibbs","given":"Samantha E.J.","affiliations":[{"id":7128,"text":"Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 01536, USA.","active":true,"usgs":false}],"preferred":false,"id":657480,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dwyer, Chris P.","contributorId":127734,"corporation":false,"usgs":false,"family":"Dwyer","given":"Chris","email":"","middleInitial":"P.","affiliations":[{"id":7131,"text":"United States Department of the Interior, United States Fish and Wildlife Service, Northeast Region, Division of Migratory Birds, Hadley, MA 01035, USA.","active":true,"usgs":false}],"preferred":false,"id":657481,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soos, Catherine","contributorId":177909,"corporation":false,"usgs":false,"family":"Soos","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":657482,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harms, N. Jane","contributorId":177910,"corporation":false,"usgs":false,"family":"Harms","given":"N.","email":"","middleInitial":"Jane","affiliations":[],"preferred":false,"id":657483,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gilchrist, H. Grant","contributorId":177911,"corporation":false,"usgs":false,"family":"Gilchrist","given":"H.","email":"","middleInitial":"Grant","affiliations":[],"preferred":false,"id":657484,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, Jeffrey S. 0000-0001-5599-2826 jshall@usgs.gov","orcid":"https://orcid.org/0000-0001-5599-2826","contributorId":2254,"corporation":false,"usgs":true,"family":"Hall","given":"Jeffrey","email":"jshall@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657477,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Franson, J. Christian 0000-0002-0251-4238 jfranson@usgs.gov","orcid":"https://orcid.org/0000-0002-0251-4238","contributorId":177499,"corporation":false,"usgs":true,"family":"Franson","given":"J.","email":"jfranson@usgs.gov","middleInitial":"Christian","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":657485,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Milton, G. 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Ecologists have long assumed that climatic drivers (i.e., temperature and rainfall regimes) govern the global distribution, structure, and function of mangrove forests. However, data constraints have hindered the quantification of direct climate-mangrove linkages in many parts of the world. Recently, the quality and availability of global-scale climate and mangrove data have been improving. Here, we used these data to better understand the influence of air temperature and rainfall regimes upon the distribution, abundance, and species richness of mangrove forests. Although our analyses identify global-scale relationships and thresholds, we show that the influence of climatic drivers is best characterized via regional range limit-specific analyses. We quantified climatic controls across targeted gradients in temperature and/or rainfall within 14 mangrove distributional range limits. Climatic thresholds for mangrove presence, abundance, and species richness differed among the 14 studied range limits. We identified minimum temperature-based thresholds for range limits in eastern North America, eastern Australia, New Zealand, eastern Asia, eastern South America, and southeast Africa. We identified rainfall-based thresholds for range limits in western North America, western Gulf of Mexico, western South America, western Australia, Middle East, northwest Africa, east central Africa, and west central Africa. Our results show that in certain range limits (e.g., eastern North America, western Gulf of Mexico, eastern Asia), winter air temperature extremes play an especially important role. We conclude that rainfall and temperature regimes are both important in western North America, western Gulf of Mexico, and western Australia. With climate change, alterations in temperature and rainfall regimes will affect the global distribution, abundance, and diversity of mangrove forests. In general, warmer winter temperatures are expected to allow mangroves to expand poleward at the expense of salt marshes. However, dispersal and habitat availability constraints may hinder expansion near certain range limits. Along arid and semi-arid coasts, decreases or increases in rainfall are expected to lead to mangrove contraction or expansion, respectively. Collectively, our analyses quantify climate-mangrove linkages and improve our understanding of the expected global- and regional-scale effects of climate change upon mangrove forests.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1248","usgsCitation":"Osland, M.J., Feher, L.C., Griffith, K., Cavanaugh, K.C., Enwright, N.M., Day, R.H., Stagg, C.L., Krauss, K.W., Howard, R.J., Grace, J.B., and Rogers, K., 2017, Climatic controls on the global distribution, abundance, and species richness of mangrove forests: Ecological Monographs, v. 87, no. 2, p. 341-359, https://doi.org/10.1002/ecm.1248.","productDescription":"19 p.","startPage":"341","endPage":"359","ipdsId":"IP-076270","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470151,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Climatic_controls_on_the_global_distribution_abundance_and_species_richness_of_mangrove_forests/27744399","text":"External Repository"},{"id":438457,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78C9TDM","text":"USGS data release","linkHelpText":"Climatic controls on the global distribution, abundance, and species richness of mangrove forests"},{"id":332731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"586cc689e4b0f5ce109fa937","contributors":{"authors":[{"text":"Osland, Michael J. 0000-0001-9902-8692 mosland@usgs.gov","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":3080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","email":"mosland@usgs.gov","middleInitial":"J.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research 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dayr@usgs.gov","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":2427,"corporation":false,"usgs":true,"family":"Day","given":"Richard","email":"dayr@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":657264,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stagg, Camille L. 0000-0002-1125-7253 staggc@usgs.gov","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":4111,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","email":"staggc@usgs.gov","middleInitial":"L.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":657265,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":657266,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Howard, Rebecca J. 0000-0001-7264-4364 howardr@usgs.gov","orcid":"https://orcid.org/0000-0001-7264-4364","contributorId":2429,"corporation":false,"usgs":true,"family":"Howard","given":"Rebecca","email":"howardr@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research 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Australia","active":true,"usgs":false}],"preferred":false,"id":657269,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70179446,"text":"70179446 - 2017 - Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar","interactions":[],"lastModifiedDate":"2017-01-03T11:42:07","indexId":"70179446","displayToPublicDate":"2017-01-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar","docAbstract":"In recent years, lightweight, inexpensive, vessel-mounted ‘recreational grade’ sonar systems have rapidly grown in popularity among aquatic scientists, for swath imaging of benthic substrates. To promote an ongoing ‘democratization’ of acoustical imaging of shallow water environments, methods to carry out geometric and radiometric correction and georectification of sonar echograms are presented, based on simplified models for sonar-target geometry and acoustic backscattering and attenuation in shallow water. Procedures are described for automated removal of the acoustic shadows, identification of bed-water interface for situations when the water is too turbid or turbulent for reliable depth echosounding, and for automated bed substrate classification based on singlebeam full-waveform analysis. These methods are encoded in an open-source and freely-available software package, which should further facilitate use of recreational-grade sidescan sonar, in a fully automated and objective manner. The sequential correction, mapping, and analysis steps are demonstrated using a data set from a shallow freshwater environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2016.12.003","usgsCitation":"Buscombe, D.D., 2017, Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar: Environmental Modelling and Software, p. 1-18, https://doi.org/10.1016/j.envsoft.2016.12.003.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-073207","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470156,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://eartharxiv.org/gfxa6/","text":"External Repository"},{"id":332729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"89","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc68ce4b0f5ce109fa93b","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":657254,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70179447,"text":"70179447 - 2017 - Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques","interactions":[],"lastModifiedDate":"2017-05-18T11:00:47","indexId":"70179447","displayToPublicDate":"2017-01-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3369,"text":"Sedimentology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques","docAbstract":"Most techniques for estimating settling velocities of natural particles have been developed for siliciclastic sediments. Therefore, to understand how these techniques apply to bioclastic environments, measured settling velocities of bioclastic sedimentary deposits sampled from a nearshore fringing reef in Western Australia were compared with settling velocities calculated using results from several common grain-size analysis techniques (sieve, laser diffraction and image analysis) and established models. The effects of sediment density and shape were also examined using a range of density values and three different models of settling velocity. Sediment density was found to have a significant effect on calculated settling velocity, causing a range in normalized root-mean-square error of up to 28%, depending upon settling velocity model and grain-size method. Accounting for particle shape reduced errors in predicted settling velocity by 3% to 6% and removed any velocity-dependent bias, which is particularly important for the fastest settling fractions. When shape was accounted for and measured density was used, normalized root-mean-square errors were 4%, 10% and 18% for laser diffraction, sieve and image analysis, respectively. The results of this study show that established models of settling velocity that account for particle shape can be used to estimate settling velocity of irregularly shaped, sand-sized bioclastic sediments from sieve, laser diffraction, or image analysis-derived measures of grain size with a limited amount of error. Collectively, these findings will allow for grain-size data measured with different methods to be accurately converted to settling velocity for comparison. This will facilitate greater understanding of the hydraulic properties of bioclastic sediment which can help to increase our general knowledge of sediment dynamics in these environments.
","language":"English","publisher":"International Association of Sedimentologists","publisherLocation":"Oxford, United Kingdom","doi":"10.1111/sed.12338","usgsCitation":"Cuttler, M.V., Lowe, R.J., Falter, J.L., and Buscombe, D.D., 2017, Estimating the settling velocity of bioclastic sediment using common grain-size analysis techniques: Sedimentology, v. 64, no. 4, p. 987-1004, https://doi.org/10.1111/sed.12338.","productDescription":"18 p.","startPage":"987","endPage":"1004","ipdsId":"IP-073934","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470155,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://admin.research-repository.uwa.edu.au/en/publications/fe5b1cde-8ee4-4296-ba73-13808106388b","text":"External Repository"},{"id":332804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"64","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-29","publicationStatus":"PW","scienceBaseUri":"586cc68ce4b0f5ce109fa939","contributors":{"authors":[{"text":"Cuttler, Michael V. W.","contributorId":177844,"corporation":false,"usgs":false,"family":"Cuttler","given":"Michael","email":"","middleInitial":"V. W.","affiliations":[],"preferred":false,"id":657256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Ryan J.","contributorId":152265,"corporation":false,"usgs":false,"family":"Lowe","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":657257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Falter, James L.","contributorId":177846,"corporation":false,"usgs":false,"family":"Falter","given":"James","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":657258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":657255,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179438,"text":"70179438 - 2017 - Spatial and temporal patterns of dissolved organic matter quantity and quality in the Mississippi River Basin, 1997–2013","interactions":[],"lastModifiedDate":"2017-02-15T15:39:56","indexId":"70179438","displayToPublicDate":"2017-01-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal patterns of dissolved organic matter quantity and quality in the Mississippi River Basin, 1997–2013","docAbstract":"Recent studies have found insignificant or decreasing trends in time-series dissolved organic carbon (DOC) datasets, questioning the assumption that long-term DOC concentrations in surface waters are increasing in response to anthropogenic forcing, including climate change, land use, and atmospheric acid deposition. We used the weighted regressions on time, discharge, and season (WRTDS) model to estimate annual flow-normalized concentrations and fluxes to determine if changes in DOC quantity and quality signal anthropogenic forcing at 10 locations in the Mississippi River Basin. Despite increases in agriculture and urban development throughout the basin, net increases in DOC concentration and flux were significant at only 3 of 10 sites from 1997 to 2013 and ranged between −3.5% to +18% and −0.1 to 19%, respectively. Positive shifts in DOC quality, characterized by increasing specific ultraviolet absorbance at 254 nm, ranged between +8% and +45%, but only occurred at one of the sites with significant DOC quantity increases. Basinwide reductions in atmospheric sulfate deposition did not result in large increases in DOC either, likely because of the high buffering capacity of the soil. Hydroclimatic factors including annual discharge, precipitation, and temperature did not significantly change during the 17-year timespan of this study, which contrasts with results from previous studies showing significant increases in precipitation and discharge over a century time scale. Our study also contrasts with those from smaller catchments, which have shown stronger DOC responses to climate, land use, and acidic deposition. This temporal and spatial analysis indicated that there was a potential change in DOC sources in the Mississippi River Basin between 1997 and 2013. However, the overall magnitude of DOC trends was not large, and the pattern in quantity and quality increases for the 10 study sites was not consistent throughout the basin.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11072","usgsCitation":"Stackpoole, S.M., Stets, E., Clow, D.W., Burns, D.A., Aiken, G.R., Aulenbach, B.T., Creed, I., Hirsch, R.M., Laudon, H., Pellerin, B., and Striegl, R.G., 2017, Spatial and temporal patterns of dissolved organic matter quantity and quality in the Mississippi River Basin, 1997–2013: Hydrological Processes, v. 31, no. 4, p. 902-915, https://doi.org/10.1002/hyp.11072.","productDescription":"14 p.","startPage":"902","endPage":"915","ipdsId":"IP-066770","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":470153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11072","text":"Publisher Index Page"},{"id":332738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-11","publicationStatus":"PW","scienceBaseUri":"586cc68ee4b0f5ce109fa93d","contributors":{"authors":[{"text":"Stackpoole, Sarah M. 0000-0002-5876-4922 sstackpoole@usgs.gov","orcid":"https://orcid.org/0000-0002-5876-4922","contributorId":3784,"corporation":false,"usgs":true,"family":"Stackpoole","given":"Sarah","email":"sstackpoole@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - 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Understanding the impacts of this encroachment on behavioral decisions, distributions, and population dynamics of greater sage-grouse (Centrocercus urophasianus) and other sagebrush obligate species could help benefit sagebrush ecosystem management actions. We employed a novel two-stage Bayesian model that linked avoidance across different levels of pinyon-juniper cover to sage-grouse survival. Our analysis relied on extensive telemetry data collected across 6 yr and seven subpopulations within the Bi-State Distinct Population Segment (DPS), on the border of Nevada and California. The first model stage indicated avoidance behavior for all canopy cover classes on average, but individual grouse exhibited a high degree of heterogeneity in avoidance behavior of the lowest cover class (e.g., scattered isolated trees). The second stage modeled survival as a function of estimated avoidance parameters and indicated increased survival rates for individuals that exhibited avoidance of the lowest cover class. A post hoc frailty analysis revealed the greatest increase in hazard (i.e., mortality risk) occurred in areas with scattered isolated trees consisting of relatively high primary plant productivity. Collectively, these results provide clear evidence that local sage-grouse distributions and demographic rates are influenced by pinyon-juniper, especially in habitats with higher primary productivity but relatively low and seemingly benign tree cover. Such areas may function as ecological traps that convey attractive resources but adversely affect population vital rates. To increase sage-grouse survival, our model predictions support reducing actual pinyon-juniper cover as low as 1.5%, which is lower than the published target of 4.0%. These results may represent effects of pinyon-juniper cover in areas with similar ecological conditions to those of the Bi-State DPS, where populations occur at relatively high elevations and pinyon-juniper is abundant and widespread.
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Benjamin 0000-0003-3530-0372 kgustafson@usgs.gov","orcid":"https://orcid.org/0000-0003-3530-0372","contributorId":166818,"corporation":false,"usgs":true,"family":"Gustafson","given":"K.","email":"kgustafson@usgs.gov","middleInitial":"Benjamin","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ziegler, Pilar T.","contributorId":175033,"corporation":false,"usgs":false,"family":"Ziegler","given":"Pilar","email":"","middleInitial":"T.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":657066,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":657067,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188468,"text":"70188468 - 2017 - Fitful and protracted magma assembly leading to a giant eruption, Youngest Toba Tuff, Indonesia","interactions":[],"lastModifiedDate":"2017-06-29T11:48:37","indexId":"70188468","displayToPublicDate":"2017-01-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Fitful and protracted magma assembly leading to a giant eruption, Youngest Toba Tuff, Indonesia","docAbstract":"The paroxysmal eruption of the 74 ka Youngest Toba Tuff (YTT) of northern Sumatra produced an extraordinary 2800 km3 of non-welded to densely welded ignimbrite and co-ignimbrite ash-fall. We report insights into the duration of YTT magma assembly obtained from ion microprobe U-Th and U-Pb dates, including continuous age spectra over >50% of final zircon growth, for pumices and a welded tuff spanning the compositional range of the YTT. A relatively large subpopulation of zircon crystals nucleated before the penultimate caldera-related eruption at 501 ka, but most zircons yielded interior dates 100-300 ka thereafter. Zircon nucleation and growth was likely episodic and from diverse conditions over protracted time intervals of >100 to >500 ka. Final zircon growth is evident as thin rim plateaus that are in Th/U chemical equilibrium with hosts, and that give crystallization ages within tens of ka of eruption. The longevity and chemical characteristics of the YTT zircons, as well as evidence for intermittent zircon isolation and remobilization associated with magma recharge, is especially favored at the cool and wet eutectoid conditions that characterize at least half of the YTT, wherein heat fluxes could dissolve major phases but have only a minor effect on larger zircon crystals. Repeated magma recharge may have contributed to the development of compositional zoning in the YTT but, considered together with limited allanite, quartz, and other mineral dating and geospeedometry, regular perturbations to the magma reservoir over >400 ka did not lead to eruption until 74 ka ago.
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Typical structure of young redwood stands impedes the recovery of old-growth conditions, such as dominance of redwood (Sequoia sempervirens (D. Don) Endl.), distinct canopy layers and diverse understory vegetation. Young forests are commonly comprised of dense, even-aged Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and redwood stump sprouts, with simple canopy structure and little understory development. Moreover, many of these young stands are believed to be vulnerable to disturbance in the form of drought, disease and fire. Silvicultural practices are increasingly being employed by conservation agencies to restore degraded forests throughout the coast redwood range; however, prescribed fire treatments are less common and potentially under-utilized as a restoration tool. We present an early synthesis from three separate management-scale prescribed fire projects at Redwood National Park spanning 1to 7 years post-treatment. Low intensity prescribed fire had minimal effect on overstory structure, with some mortality observed in trees smaller than 30 cm diameter. Moderate to high intensity fire may be required to reduce densities of larger Douglas-fir, the primary competitor of redwood in the Park’s second growth forests. Fine woody surface fuels fully recovered by 7 years post-burn, while recruitment of larger surface fuels was quite variable. Managers of coastal redwood ecosystems will benefit by having a variety of tools at their disposal for forest restoration and management.
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