{"pageNumber":"264","pageRowStart":"6575","pageSize":"25","recordCount":68827,"records":[{"id":70240961,"text":"70240961 - 2020 - Geoacoustic inversion for a New England mud patch sediment using the silt-suspension theory of marine mud","interactions":[],"lastModifiedDate":"2023-03-02T16:34:20.050478","indexId":"70240961","displayToPublicDate":"2019-09-13T10:28:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1941,"text":"IEEE Journal of Oceanic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Geoacoustic inversion for a New England mud patch sediment using the silt-suspension theory of marine mud","docAbstract":"<p><span>This article provides an application of the silt-suspension theory to a Bayesian-inference inversion for the geo-acoustic parameters in marine mud. The theory, with consequences that have been developed recently, postulates a suspension of water and clay mineral card-houses that supports moderately dilute concentrations of silt particles. The approach is an example of a physically based model inversion, in which parameters representing physical mud-layer properties are obtained by inversion and used to produce estimates of geoacoustic properties, including their frequency dependence. The acoustic data are from a combustive source signal propagated along a track, located over several meters of fine-grained mud in the New England Mud Patch, to a single hydrophone on a receiver array during the 2017 Seabed Characterization Experiment. Data extracted from a nearby piston core inform the physical modeling, with selections of inversion parameters guided by both sensitivity analyses and bounds from archival and core measurements. Results show the feasibility of this inversion approach. The estimates of mud density and sound speed are close to values obtained independently. The frequency dependence of attenuation is estimated over the full low-frequency source band and has an approximate power exponent of 1.72.</span></p>","language":"English","publisher":"IEEE","doi":"10.1109/JOE.2019.2934604","usgsCitation":"Brown, E.M., Lin, Y., Chaytor, J., and Siegmann, W.L., 2020, Geoacoustic inversion for a New England mud patch sediment using the silt-suspension theory of marine mud: IEEE Journal of Oceanic Engineering, v. 45, no. 1, p. 144-160, https://doi.org/10.1109/JOE.2019.2934604.","productDescription":"17 p.","startPage":"144","endPage":"160","ipdsId":"IP-106813","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":413624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brown, Elisabeth M.","contributorId":302803,"corporation":false,"usgs":false,"family":"Brown","given":"Elisabeth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":865499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Ying-Tsong","contributorId":302804,"corporation":false,"usgs":false,"family":"Lin","given":"Ying-Tsong","email":"","affiliations":[],"preferred":false,"id":865500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chaytor, Jason 0000-0001-8135-8677 jchaytor@usgs.gov","orcid":"https://orcid.org/0000-0001-8135-8677","contributorId":140095,"corporation":false,"usgs":true,"family":"Chaytor","given":"Jason","email":"jchaytor@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":865501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siegmann, William L.","contributorId":302805,"corporation":false,"usgs":false,"family":"Siegmann","given":"William","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":865502,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70218268,"text":"70218268 - 2020 - Spatial variability of phytoplankton in a shallow tidal freshwater system reveals complex controls on abundance and community structure","interactions":[],"lastModifiedDate":"2021-02-23T13:23:17.461665","indexId":"70218268","displayToPublicDate":"2019-09-13T07:20:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variability of phytoplankton in a shallow tidal freshwater system reveals complex controls on abundance and community structure","docAbstract":"<div id=\"ab015\" class=\"abstract author\" lang=\"en\"><div id=\"as015\"><p id=\"sp0015\">Estuaries worldwide are undergoing changes to patterns of aquatic productivity because of human activities that alter flow, impact sediment delivery and thus the light field, and contribute nutrients and contaminants like pesticides and metals. These changes can influence phytoplankton communities, which in turn can alter estuarine food webs. We used multiple approaches-including high-resolution water quality mapping, synoptic sampling, productivity and nitrogen uptake rates, Lagrangian parcel tracking, enclosure experiments and bottle incubations-over a short time period to take a “spatial snapshot” of conditions in the northern region of the San Francisco Estuary (California, USA) to examine how environmental drivers like light availability, nutrients, water residence time, and contaminants affect phytoplankton abundance and community attributes like size distribution, taxonomic structure, and nutrient uptake rates. Zones characterized by longer residence time (15–60 days) had higher chlorophyll-a concentrations (9 ± 4 µg L<sup>−1</sup>) and were comprised primarily of small phytoplankton cells (&lt;5 µm, 74 ± 8%), lower ammonium concentrations (1 ± 0.8 µM), higher nitrate uptake rates, and higher rates of potential carbon productivity. Conversely, zones characterized by shorter residence time (1–14 days) had higher ammonium concentration (13 ± 5 µM) and lower chlorophyll-a concentration (5 ± 1 µg L<sup>−1</sup>) with diatoms making up a larger percent contribution. Longer residence time, however, did not result in the accumulation of large (&gt;5 µm) cells considered important to pelagic food webs. Rather, longer residence time zones had a phytoplankton community comprised primarily of small cells, particularly picocyanobacteria that made up 38 ± 17% of the chlorophyll-a – nearly double the concentration seen in shorter residence time zones (22 ± 7% picocyanobacterial of chlorophyll-a). Our results suggest that water residence time in estuaries may have an effect as large or larger than that experimentally demonstrated for light, contaminants, or nutrients.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.134392","usgsCitation":"Stumpner, E.B., Bergamaschi, B.A., Kraus, T.E., Parker, A., Wilkerson, F., Downing, B.D., Dugdale, R., Murrell, M.T., Carpenter, K.D., Orlando, J., and Kendall, C., 2020, Spatial variability of phytoplankton in a shallow tidal freshwater system reveals complex controls on abundance and community structure: Science of the Total Environment, v. 700, 134392, 17 p., https://doi.org/10.1016/j.scitotenv.2019.134392.","productDescription":"134392, 17 p.","ipdsId":"IP-070979","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":458659,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.134392","text":"Publisher Index Page"},{"id":383591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.1787109375,\n              37.16031654673677\n            ],\n            [\n              -121.55273437499999,\n              37.16031654673677\n            ],\n            [\n              -121.55273437499999,\n              38.685509760012\n            ],\n            [\n              -123.1787109375,\n              38.685509760012\n            ],\n            [\n              -123.1787109375,\n              37.16031654673677\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"700","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stumpner, Elizabeth B. 0000-0003-2356-2244 estumpner@usgs.gov","orcid":"https://orcid.org/0000-0003-2356-2244","contributorId":181854,"corporation":false,"usgs":true,"family":"Stumpner","given":"Elizabeth","email":"estumpner@usgs.gov","middleInitial":"B.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Alexander","contributorId":181853,"corporation":false,"usgs":false,"family":"Parker","given":"Alexander","affiliations":[],"preferred":false,"id":810781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilkerson, Francis","contributorId":251891,"corporation":false,"usgs":false,"family":"Wilkerson","given":"Francis","email":"","affiliations":[{"id":50414,"text":"Romberg Tiburon Center","active":true,"usgs":false}],"preferred":false,"id":810782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Downing, Bryan D. 0000-0002-2007-5304 bdowning@usgs.gov","orcid":"https://orcid.org/0000-0002-2007-5304","contributorId":1449,"corporation":false,"usgs":true,"family":"Downing","given":"Bryan","email":"bdowning@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810783,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dugdale, Richard","contributorId":196463,"corporation":false,"usgs":false,"family":"Dugdale","given":"Richard","email":"","affiliations":[],"preferred":false,"id":810784,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Murrell, Michael T.","contributorId":189408,"corporation":false,"usgs":false,"family":"Murrell","given":"Michael","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":810785,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carpenter, Kurt D. 0000-0002-6231-8335 kdcar@usgs.gov","orcid":"https://orcid.org/0000-0002-6231-8335","contributorId":127442,"corporation":false,"usgs":true,"family":"Carpenter","given":"Kurt","email":"kdcar@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810786,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Orlando, James 0000-0002-0099-7221","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":208413,"corporation":false,"usgs":true,"family":"Orlando","given":"James","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810787,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kendall, Carol 0000-0002-0247-3405 ckendall@usgs.gov","orcid":"https://orcid.org/0000-0002-0247-3405","contributorId":1462,"corporation":false,"usgs":true,"family":"Kendall","given":"Carol","email":"ckendall@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":810788,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70205560,"text":"70205560 - 2020 - Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","interactions":[],"lastModifiedDate":"2020-01-20T12:22:35","indexId":"70205560","displayToPublicDate":"2019-09-09T10:19:04","publicationYear":"2020","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":"Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","docAbstract":"Streamwater quality can be affected by climate-related variability in hydrologic state, which controls flow paths and affects biogeochemical processes. Thirty-one years of input/output solute fluxes at Panola Mountain Research Watershed, a small, forested, seasonally water-limited watershed near Atlanta, Georgia, were used to quantify the effects of climatic-related variability in storage on streamwater solute concentrations and fluxes. Streamwater fluxes were estimated for ten solutes from weekly and event sample concentrations using regression-based methods. The most pertinent storage attribute (current or antecedent watershed, shallow, and deep storage) for each solute was determined by fitting separate concentration relationships. The concentration-discharge relationships varied more for reactive solutes such as potassium, sulfate, and DOC and less for weathering products (base cations and dissolved silica) and conservative chloride. Many solutes exhibited higher concentrations when storage levels were lower or wetting up, which was likely the result of the concentrating effects of evapotranspiration and/or the buildup and flushing of weathering products associated with longer residence times. The impacts of storage modeling on annual fluxes varied by solute, ranging from about 5% (magnesium) to 52% (nitrate) as relative standard deviations, and sufficiently removed climate-related patterns observed in streamwater concentrations. Sulfate was particularly mobilized following growing season droughts but only if deep storage was sufficiently recharged, possibly indicating that sulfides in the deep storage pool were oxidized to sulfate during droughts and mobilized when re-wetted. The lack of streamwater sulfate response to 61% declines in atmospheric deposition indicates the importance of watershed biogeochemical processes on controls of streamwater export of sulfate. The approach of explicitly incorporating storage in the streamwater concentration modeling elucidated the effects of climate on streamwater water-quality and may provide insight into the effects of climatic change on future fluxes.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13589","usgsCitation":"Aulenbach, B.T., 2020, Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States: Hydrological Processes, v. 34, no. 2, p. 189-208, https://doi.org/10.1002/hyp.13589.","productDescription":"20 p.","startPage":"189","endPage":"208","ipdsId":"IP-104585","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":367690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Panola Mountain Research Watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -84.18994903564453,\n              33.61976556057674\n            ],\n            [\n              -84.13021087646484,\n              33.61976556057674\n            ],\n            [\n              -84.13021087646484,\n              33.64627826509988\n            ],\n            [\n              -84.18994903564453,\n              33.64627826509988\n            ],\n            [\n              -84.18994903564453,\n              33.61976556057674\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"34","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771652,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70245421,"text":"70245421 - 2020 - Body shape is related to the attempt rate and passage success of brook trout at in-stream barriers","interactions":[],"lastModifiedDate":"2023-06-23T12:25:51.987001","indexId":"70245421","displayToPublicDate":"2019-09-09T07:24:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Body shape is related to the attempt rate and passage success of brook trout at in-stream barriers","docAbstract":"<ol class=\"\"><li>The ability to move between habitats has important implications for fitness in many species. In-stream barriers such as culverts can impede movements of riverine fishes and thus reduce connectivity between habitats. The ability of fish to overcome barriers is related to the features of the environment and the barrier itself, but also to physiological, morphological and behavioural traits of the fish. Among these, body shape varies among and within species, and influences swimming ability, a key component of passage performance through culverts.</li><li>We conducted an experimental study on wild brook trout (<i>Salvelinus fontinalis</i>) to assess the effects of individual body shape on attempt rate and passage success through culverts on six streams.</li><li>A more streamlined body shape was associated with an increased motivation to enter and ascend the culverts, and, to a lesser extent, with the&nbsp;probability of successful passage once an attempt was staged. Motivation and successful passage were also influenced by the density of conspecifics below the culvert, time of day, fish body size and water velocity.</li><li><i>Policy implications.</i><span>&nbsp;</span>While fish body shape is expected to influence swimming performance, our research shows the most important effect of body shape to be on an individual's motivation to stage passage attempts at culverts. This study points to an important connection between behaviour and morphological traits that influence passage success and suggests that in-stream barriers may be an important agent of selection on behaviour and morphology in wild fish populations.</li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13497","usgsCitation":"Goerig, E., Wasserman, B.A., Castro-Santos, T.R., and Palkovacs, E., 2020, Body shape is related to the attempt rate and passage success of brook trout at in-stream barriers: Journal of Applied Ecology, v. 57, no. 1, p. 91-100, https://doi.org/10.1111/1365-2664.13497.","productDescription":"10 p.","startPage":"91","endPage":"100","ipdsId":"IP-098538","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":458668,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13497","text":"Publisher Index Page"},{"id":418397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Goerig, Elsa","contributorId":261644,"corporation":false,"usgs":false,"family":"Goerig","given":"Elsa","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":876088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wasserman, Ben A.","contributorId":311227,"corporation":false,"usgs":false,"family":"Wasserman","given":"Ben","email":"","middleInitial":"A.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":876089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":876090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palkovacs, Eric P.","contributorId":311228,"corporation":false,"usgs":false,"family":"Palkovacs","given":"Eric P.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":876091,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70206074,"text":"70206074 - 2020 - Historical changes in fish communities in urban streams of the southeastern U.S. and the relative importance of water-quality stressors","interactions":[],"lastModifiedDate":"2020-01-05T14:01:25","indexId":"70206074","displayToPublicDate":"2019-09-04T10:54:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Historical changes in fish communities in urban streams of the southeastern U.S. and the relative importance of water-quality stressors","docAbstract":"A total of 71 stream sites representing a gradient of urban land use was sampled across the Piedmont of the southeastern U.S. in 2014.  Fish data collected (observed) at each site were compared to an expected community based on georeferenced historical (~1950 - ~1990) species occurrence records for stream segments (1:100,000 scale) containing the sampled stream sites.  Loss of expected fish species (percent of fish species expected to occur but not observed) and homogenization (difference in Jaccard’s similarity of the fish community among sites observed and expected) were determined.  On average, there was a 13.2% increase in the similarity of fish communities across sites, demonstrating evidence of community homogenization.  Occurrence of Redbreast Sunfish (Lepomis auritus), Green Sunfish (L. cyanellus), and Bluegill (L. macrochirus) increased more than 50% over time (between observed and expected).  Species loss increased significantly with urbanization whereas homogenization was not related to urbanization.  Random forest analysis indicated that herbicides, insecticides, and centrarchid species richness were significant predictors of species loss.  Of these, generalized additive model regression indicated that herbicides represented the most parsimonious model based on a single predictor.  Stream base flow, elevation, and total nitrogen were significant predictors of homogenization.  Generalized additive model regression indicated that decreased stream base flow was the single most important factor associated with increased homogenization.  Chemical contaminants and associated ecosystem alteration and changes in stream flow may represent important regional influences on changes in fish communities in urban streams in the southeastern U.S.","language":"English","publisher":"Wiley","doi":"10.1111/eff.12503","usgsCitation":"Meador, M.R., 2020, Historical changes in fish communities in urban streams of the southeastern U.S. and the relative importance of water-quality stressors: Ecology of Freshwater Fish, v. 29, no. 1, p. 156-169, https://doi.org/10.1111/eff.12503.","productDescription":"14 p.","startPage":"156","endPage":"169","ipdsId":"IP-092961","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":368448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States ","state":"Alabama, Georgia, North Carolina, South Carolina, Tennessee, Virginia, Washington DC.","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -78.431396484375,\n              39.487084981687495\n            ],\n            [\n              -79.266357421875,\n              38.70265930723801\n            ],\n            [\n              -79.771728515625,\n              38.634036452919226\n            ],\n            [\n              -80.343017578125,\n              37.90953361677018\n            ],\n            [\n              -80.628662109375,\n              37.58811876638322\n            ],\n            [\n              -81.090087890625,\n              37.49229399862877\n            ],\n            [\n              -81.89208984375,\n              37.52715361723378\n            ],\n            [\n              -84.1552734375,\n              36.65079252503471\n            ],\n            [\n              -85.60546875,\n              34.994003757575776\n            ],\n            [\n              -86.5283203125,\n              33.137551192346145\n            ],\n            [\n              -87.29736328125,\n              32.0639555946604\n            ],\n            [\n              -85.10009765625,\n              31.690781806136822\n            ],\n            [\n              -83.78173828125,\n              32.1570124860701\n            ],\n            [\n              -81.9580078125,\n              33.46810795527896\n            ],\n            [\n              -80.947265625,\n              33.797408767572485\n            ],\n            [\n              -80.04638671875,\n              34.17999758688084\n            ],\n            [\n              -79.60693359375,\n              34.867904962568716\n            ],\n            [\n              -78.22265625,\n              35.7286770448517\n            ],\n            [\n              -77.113037109375,\n              36.54494944148322\n            ],\n            [\n              -77.16796875,\n              37.96152331396614\n            ],\n            [\n              -76.849365234375,\n              38.92522904714054\n            ],\n            [\n              -77.18994140625,\n              39.06184913429154\n            ],\n            [\n              -77.991943359375,\n              38.496593518947584\n            ],\n            [\n              -77.82714843749999,\n              39.13006024213511\n            ],\n            [\n              -78.431396484375,\n              39.487084981687495\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"29","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Meador, Michael R. 0000-0001-5956-3340 mrmeador@usgs.gov","orcid":"https://orcid.org/0000-0001-5956-3340","contributorId":219878,"corporation":false,"usgs":true,"family":"Meador","given":"Michael","email":"mrmeador@usgs.gov","middleInitial":"R.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":773484,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70263567,"text":"70263567 - 2020 - Regional Global Navigation Satellite System networks for crustal deformation monitoring","interactions":[],"lastModifiedDate":"2025-02-13T16:47:47.502631","indexId":"70263567","displayToPublicDate":"2019-09-04T10:45:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Regional Global Navigation Satellite System networks for crustal deformation monitoring","docAbstract":"<p><span>Regional networks of Global Navigation Satellite System (GNSS) stations cover seismically and volcanically active areas throughout the United States. Data from these networks have been used to produce high‐precision, three‐component velocity fields covering broad geographic regions as well as position time series that track time‐varying crustal deformation. This information has contributed to assessing interseismic strain accumulation and related seismic hazard, revealed previously unknown occurrences of aseismic fault slip, constrained coseismic slip estimates, and enabled monitoring of volcanic unrest and postseismic deformation. In addition, real‐time GNSS data are now widely available. Such observations proved invaluable for tracking the rapidly evolving eruption of Kīlauea in 2018. Real‐time earthquake source modeling using GNSS data is being incorporated into tsunami warning systems, and a vigorous research effort is focused on quantifying the contribution that real‐time GNSS can make to improve earthquake early warnings as part of the Advanced National Seismic System ShakeAlert system. Real‐time GNSS data can also aid in the tracking of ionospheric disturbances and precipitable water vapor for weather forecasting. Although regional GNSS and seismic networks generally have been established independently, their spatial footprints often overlap, and in some cases the same institution operates both types of networks. Further integration of GNSS and seismic networks would promote joint use of the two data types to better characterize earthquake sources and ground motion as well as offer opportunities for more efficient network operations. Looking ahead, upgrading network stations to leverage new GNSS technology could enable more precise positioning and robust real‐time operations. New computational approaches such as machine learning have the potential to enable full utilization of the large amounts of data generated by continuous GNSS networks. Development of seafloor Global Positioning System‐acoustic networks would provide unique information for fundamental and applied research on subduction zone seismic hazard and, potentially, monitoring.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190113","usgsCitation":"Murray, J.R., Bartlow, N., Bock, Y., Brooks, B.A., Foster, J.H., Freymueller, J.T., Hammond, W.C., Hodgkinson, K., Johanson, I.A., Lopez-Venegas, A., Mann, D., Mattioli, G., Melbourne, T., Mencin, D., Montgomery-Brown, E.K., Murray, M.H., Smalley, R., and Thomas, V., 2020, Regional Global Navigation Satellite System networks for crustal deformation monitoring: Seismological Research Letters, v. 91, no. 2A, p. 552-572, https://doi.org/10.1785/0220190113.","productDescription":"21 p.","startPage":"552","endPage":"572","ipdsId":"IP-108083","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482039,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"2A","noUsgsAuthors":false,"publicationDate":"2019-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartlow, Noel 0000-0002-9961-5608","orcid":"https://orcid.org/0000-0002-9961-5608","contributorId":242895,"corporation":false,"usgs":false,"family":"Bartlow","given":"Noel","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":927349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Yehuda 0000-0001-8296-6623","orcid":"https://orcid.org/0000-0001-8296-6623","contributorId":350938,"corporation":false,"usgs":false,"family":"Bock","given":"Yehuda","affiliations":[{"id":83883,"text":"University of California San Diego Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":927350,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927351,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Foster, James H.","contributorId":244553,"corporation":false,"usgs":false,"family":"Foster","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":48939,"text":"Hawaii Institute of Geophysics and Planetology, University of Hawaii at Manoa, HI, USA","active":true,"usgs":false}],"preferred":false,"id":927352,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Freymueller, Jeffery T. 0000-0003-0614-0306","orcid":"https://orcid.org/0000-0003-0614-0306","contributorId":244609,"corporation":false,"usgs":false,"family":"Freymueller","given":"Jeffery","email":"","middleInitial":"T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":927353,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hammond, William C.","contributorId":73735,"corporation":false,"usgs":true,"family":"Hammond","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":927354,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hodgkinson, Kathleen 0000-0001-8529-0913","orcid":"https://orcid.org/0000-0001-8529-0913","contributorId":209915,"corporation":false,"usgs":false,"family":"Hodgkinson","given":"Kathleen","email":"","affiliations":[{"id":38024,"text":"UNAVCO Inc.","active":true,"usgs":false}],"preferred":false,"id":927355,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Johanson, Ingrid A. 0000-0002-6049-2225","orcid":"https://orcid.org/0000-0002-6049-2225","contributorId":215613,"corporation":false,"usgs":true,"family":"Johanson","given":"Ingrid","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927356,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lopez-Venegas, Alberto 0000-0002-3413-0546","orcid":"https://orcid.org/0000-0002-3413-0546","contributorId":350939,"corporation":false,"usgs":false,"family":"Lopez-Venegas","given":"Alberto","affiliations":[{"id":34129,"text":"University of Puerto Rico Mayaguez","active":true,"usgs":false}],"preferred":false,"id":927357,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mann, Doerte 0000-0002-8789-5219","orcid":"https://orcid.org/0000-0002-8789-5219","contributorId":350940,"corporation":false,"usgs":false,"family":"Mann","given":"Doerte","affiliations":[{"id":83886,"text":"UNAVCO, Inc.","active":true,"usgs":false}],"preferred":false,"id":927358,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mattioli, Glen 0000-0002-9117-7471","orcid":"https://orcid.org/0000-0002-9117-7471","contributorId":350941,"corporation":false,"usgs":false,"family":"Mattioli","given":"Glen","affiliations":[{"id":83886,"text":"UNAVCO, Inc.","active":true,"usgs":false}],"preferred":false,"id":927359,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Melbourne, Timothy 0000-0003-1870-3962","orcid":"https://orcid.org/0000-0003-1870-3962","contributorId":209916,"corporation":false,"usgs":false,"family":"Melbourne","given":"Timothy","email":"","affiliations":[{"id":26935,"text":"Central Washington University","active":true,"usgs":false}],"preferred":false,"id":927360,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mencin, David 0000-0001-9984-6724","orcid":"https://orcid.org/0000-0001-9984-6724","contributorId":328836,"corporation":false,"usgs":false,"family":"Mencin","given":"David","email":"","affiliations":[{"id":5114,"text":"UNAVCO","active":true,"usgs":false}],"preferred":false,"id":927361,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927362,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Murray, Mark Hunter 0000-0003-4862-5547","orcid":"https://orcid.org/0000-0003-4862-5547","contributorId":300982,"corporation":false,"usgs":true,"family":"Murray","given":"Mark","email":"","middleInitial":"Hunter","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927363,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Smalley, Robert Jr.","contributorId":244558,"corporation":false,"usgs":false,"family":"Smalley","given":"Robert","suffix":"Jr.","email":"","affiliations":[{"id":48941,"text":"Center for Earthquake Research and Information, University of Memphis, Memphis, TN, USA","active":true,"usgs":false}],"preferred":false,"id":927364,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Thomas, Valerie 0000-0001-6170-5563","orcid":"https://orcid.org/0000-0001-6170-5563","contributorId":222022,"corporation":false,"usgs":true,"family":"Thomas","given":"Valerie","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927365,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}}
,{"id":70206562,"text":"70206562 - 2020 - De facto water reuse: Bioassay suite approach delivers depth and breadth in endocrine active compound detection","interactions":[],"lastModifiedDate":"2019-11-08T08:48:22","indexId":"70206562","displayToPublicDate":"2019-09-04T08:44:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"De facto water reuse: Bioassay suite approach delivers depth and breadth in endocrine active compound detection","docAbstract":"Although endocrine disrupting compounds (EDCs) have been detected in wastewater and surface waters worldwide using a variety of in vitro effects-based screening tools, e.g. bioassays, few have examined potential attenuation of environmental contaminants by both natural (sorption, degradation, etc) and anthropogenic (water treatment practices) processes. This study used several bioassays and quantitative chemical analyses to assess residence-time weighted samples at six sites along a river in the northeastern United States beginning upstream of a waste water treatment plant (WWTP) outfall and proceeding downstream along the stream reach to a drinking water treatment plant (DWTP). Known steroidal estrogens were quantified and changes in signaling pathway molecular initiating events (activation of estrogen, androgen, glucocorticoid, peroxisome proliferator-activated, pregnane X receptor, and aryl hydrocarbon receptor signaling networks) were identified in water extracts. In initial multi-endpoint assays geographic and receptor-specific endocrine activity patterns in transcription factor signatures and nuclear receptor activation were discovered. In subsequent single endpoint receptor-specific bioassays, estrogen (16 of 18 samples; 0.01 to 28 ng estradiol equivalents [E2Eqs]/L) glucocorticoid (3 of 18 samples; 1.8 to 21 ng dexamethasone equivalents [DexEqs]/L), and androgen (2 of 18 samples; 0.95 to 2.1 ng dihydrotestosterone equivalents [DHTEqs]/L) receptor transcriptional activation occurred above respective assay method detection limits (0.04 ng E2Eqs/L, 1.2 ng DexEqs/L, and 0.77 ng DHTEqs/L) in multiple sampling events. Estrogen activity, the most often detected, correlated well with measured concentrations of known steroidal estrogens (R2 = 0.890). Overall, activity indicative of multiple types of endocrine active compounds was highest in wastewater effluent samples, while activity downstream was progressively lower, and negligible in (unfinished) treated water. This multiple bioassay approach, in conjunction with targeted analytical chemistry methods, has gained acceptance among water quality screening programs. Not only was estrogenic and glucocorticoid activity confirmed in the effluent by utilizing multiple methods concurrently, but other activated signaling networks that historically received less attention (i.e. peroxisome proliferator-activated receptor) were also detected.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.134297","usgsCitation":"Medlock Kakaley, E.K., Blackwell, B., Cardon, M.C., Conley, J.M., Evans, N., Feifarek, D.J., Furlong, E., Glassmeyer, S.T., Gray, L.E., Hartig, P.C., Kolpin, D., Mills, M.A., Rosenblum, L., Villeneuve, D.L., and Wilson, V.S., 2020, De facto water reuse: Bioassay suite approach delivers depth and breadth in endocrine active compound detection: Science of the Total Environment, v. 699, https://doi.org/10.1016/j.scitotenv.2019.134297.","productDescription":"134297, 12 p.","startPage":"1-12","ipdsId":"IP-111008","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":458674,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1703348","text":"Publisher Index Page"},{"id":369079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"699","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Medlock Kakaley, Elizabeth K","contributorId":220449,"corporation":false,"usgs":false,"family":"Medlock Kakaley","given":"Elizabeth","email":"","middleInitial":"K","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":774944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blackwell, Brett R.","contributorId":173601,"corporation":false,"usgs":false,"family":"Blackwell","given":"Brett R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":774945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cardon, Mary C.","contributorId":190792,"corporation":false,"usgs":false,"family":"Cardon","given":"Mary","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":774946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conley, Justin M.","contributorId":184086,"corporation":false,"usgs":false,"family":"Conley","given":"Justin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":774947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Evans, Nicola","contributorId":184087,"corporation":false,"usgs":false,"family":"Evans","given":"Nicola","email":"","affiliations":[],"preferred":false,"id":774948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Feifarek, David J.","contributorId":198057,"corporation":false,"usgs":false,"family":"Feifarek","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774949,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":774950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Glassmeyer, Susan T.","contributorId":184135,"corporation":false,"usgs":false,"family":"Glassmeyer","given":"Susan","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":774951,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gray, L. Earl","contributorId":220450,"corporation":false,"usgs":false,"family":"Gray","given":"L.","email":"","middleInitial":"Earl","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":774952,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hartig, Phillip C.","contributorId":190793,"corporation":false,"usgs":false,"family":"Hartig","given":"Phillip","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":774953,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":205652,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774943,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":774954,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rosenblum, Laura","contributorId":184089,"corporation":false,"usgs":false,"family":"Rosenblum","given":"Laura","email":"","affiliations":[],"preferred":false,"id":774955,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Villeneuve, Daniel L.","contributorId":141084,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":774956,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wilson, Vickie S. 0000-0003-1661-8481","orcid":"https://orcid.org/0000-0003-1661-8481","contributorId":184092,"corporation":false,"usgs":false,"family":"Wilson","given":"Vickie","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":774957,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}}
,{"id":70211191,"text":"70211191 - 2020 - Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic","interactions":[],"lastModifiedDate":"2020-07-16T18:49:40.296564","indexId":"70211191","displayToPublicDate":"2019-09-03T13:44:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic","docAbstract":"<p><span>Permafrost thaw in the Arctic is mobilizing old carbon (C) from soils to aquatic ecosystems and the atmosphere. Little is known, however, about the assimilation of old C by aquatic food webs in Arctic watersheds. Here, we used C isotopes (δ</span><sup>13</sup><span>C, Δ</span><sup>14</sup><span>C) to quantify C assimilation by biota across 12 streams in arctic Alaska. Streams spanned watersheds with varying permafrost hydrology, from ice-poor bedrock to ice-rich loess (that is, yedoma). We measured isotopic content of (1) C sources including dissolved organic C (DOC), dissolved inorganic C (DIC), and soil C, and (2) stream biota, including benthic biofilm and macroinvertebrates, and resident fish species (Arctic Grayling (</span><i>Thymallus arcticus</i><span>) and Dolly Varden (</span><i>Salvelinus malma</i><span>)). Findings document the assimilation of old C by stream biota, with depleted Δ</span><sup>14</sup><span>C values observed at multiple trophic levels, including benthic biofilm (</span><sup>14</sup><span>C ages = 5255 to 265&nbsp;years before present (y BP)), macroinvertebrates (4490 y BP to modern), and fish (3195 y BP to modern). Mixing model results indicate that DOC and DIC contribute to benthic biofilm composition, with relative contributions differing across streams draining ice-poor and ice-rich terrain. DOC originates primarily from old terrestrial C sources, including deep peat horizons (39–47%; 530 y BP) and near-surface permafrost (12–19%; 5490 y BP). DOC also accounts for approximately half of fish isotopic composition. Analyses suggest that as the contribution of old C to fish increases, fish growth and nutritional status decline. We anticipate increases in old DOC delivery to streams under projected warming, which may further alter food web function in Arctic watersheds.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10021-019-00413-6","usgsCitation":"O'Donnell, J., Carey, M.P., Koch, J.C., Xu, X., Poulin, B., Walker, J., and Zimmerman, C.E., 2020, Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic: Ecosystems, v. 23, p. 435-453, https://doi.org/10.1007/s10021-019-00413-6.","productDescription":"19 p.","startPage":"435","endPage":"453","ipdsId":"IP-102831","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":437218,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NAUIQR","text":"USGS data release","linkHelpText":"Carbon Isotope Concentrations in Stream Food Webs of the Arctic Network National Parks, Alaska, 2014-2016"},{"id":376449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Land Bridge and Noatak National Preserves","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -165.58593749999997,\n              65.4217295985527\n            ],\n            [\n              -156.09375,\n              65.4217295985527\n            ],\n            [\n              -156.09375,\n              68.12248241161676\n            ],\n            [\n              -165.58593749999997,\n              68.12248241161676\n            ],\n            [\n              -165.58593749999997,\n              65.4217295985527\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"O'Donnell, Jonathon A 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":222968,"corporation":false,"usgs":false,"family":"O'Donnell","given":"Jonathon A","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":793044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":793045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":793046,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xu, Xiaomei","contributorId":139915,"corporation":false,"usgs":false,"family":"Xu","given":"Xiaomei","email":"","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":793047,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulin, Brett 0000-0002-5555-7733 bpoulin@usgs.gov","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":194253,"corporation":false,"usgs":true,"family":"Poulin","given":"Brett","email":"bpoulin@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":793048,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walker, Jennifer","contributorId":201558,"corporation":false,"usgs":false,"family":"Walker","given":"Jennifer","affiliations":[],"preferred":false,"id":793049,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":793050,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70211842,"text":"70211842 - 2020 - The influence of sample matrix on the accuracy of nitrite N and O isotope ratio analyses with the azide method","interactions":[],"lastModifiedDate":"2020-08-07T20:58:04.651731","indexId":"70211842","displayToPublicDate":"2019-08-31T15:55:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3233,"text":"Rapid Communications in Mass Spectrometry","active":true,"publicationSubtype":{"id":10}},"title":"The influence of sample matrix on the accuracy of nitrite N and O isotope ratio analyses with the azide method","docAbstract":"<h3 id=\"rcm8569-sec-0001-title\" class=\"article-section__sub-title section1\">Rationale</h3><p>The isotope ratios of nitrogen (<sup>15</sup>N/<sup>14</sup>N) and oxygen (<sup>18</sup>O/<sup>16</sup>O) in nitrite (NO<sub>2</sub><sup>−</sup>) can be measured by conversion of the nitrite into nitrous oxide (N<sub>2</sub>O) with azide, followed by mass spectrometric analysis of N<sub>2</sub>O by gas chromatography isotope ratio mass spectrometry (GC/IRMS). While applying this method to brackish samples, we noticed that the N and O isotope ratio measurements of NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>are highly sensitive to sample salinity and to the pH at which samples are preserved.</p><h3 id=\"rcm8569-sec-0002-title\" class=\"article-section__sub-title section1\">Methods</h3><p>We investigated the influence of sample salinity and sample preservation pH on the N and O isotope ratios of the N<sub>2</sub>O produced from the reaction of NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>with azide. The N<sub>2</sub>O isotope ratios were measured by GC/IRMS.</p><h3 id=\"rcm8569-sec-0003-title\" class=\"article-section__sub-title section1\">Results</h3><p>Under the experimental reaction conditions, the conversion of NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>into N<sub>2</sub>O was less complete in lower salinity solutions, resulting in respective N and O isotopic offsets of +2.5‰ and −14.0‰ compared with seawater solutions. Differences in salinity were also associated with differences in the fraction of O atoms exchanged between NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>and water during the reaction. Similarly, aqueous NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>samples preserved at elevated pH values resulted in the incomplete conversion of NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>into N<sub>2</sub>O by azide, and consequent pH‐dependent isotopic offsets, as well as differences in the fraction of O atoms exchanged with water. The addition of sodium chloride to the reaction matrix of samples and standards largely mitigated salinity‐dependent isotopic offsets in the N<sub>2</sub>O product, and nearly homogenized the fraction of O atom exchange among samples of different salinity. A test of the hypobromite–azide method to measure N isotope ratios of ammonium by conversion into NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>then N<sub>2</sub>O revealed no influence of sample salinity on the N isotope ratios of the N<sub>2</sub>O product.</p><h3 id=\"rcm8569-sec-0004-title\" class=\"article-section__sub-title section1\">Conclusions</h3><p>We outline recommendations to mitigate potential matrix effects among samples and standards, to improve the accuracy of N and O isotope ratios in NO<sub>2</sub><sup>−</sup><span>&nbsp;</span>measured with the azide method.</p>","language":"English","publisher":"Wiley","doi":"10.1002/rcm.8569","usgsCitation":"Granger, J., Boshers, D.S., Bohlke, J., Yu, D., Chen, N., and Tobias, C.R., 2020, The influence of sample matrix on the accuracy of nitrite N and O isotope ratio analyses with the azide method: Rapid Communications in Mass Spectrometry, v. 34, e8569, 12 p., https://doi.org/10.1002/rcm.8569.","productDescription":"e8569, 12 p.","ipdsId":"IP-110163","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":377206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","noUsgsAuthors":false,"publicationDate":"2020-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Granger, Julie","contributorId":214194,"corporation":false,"usgs":false,"family":"Granger","given":"Julie","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":795339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boshers, Danielle S.","contributorId":214193,"corporation":false,"usgs":false,"family":"Boshers","given":"Danielle","email":"","middleInitial":"S.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":795340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":795341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yu, Dan","contributorId":237802,"corporation":false,"usgs":false,"family":"Yu","given":"Dan","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":795342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chen, Nengwang","contributorId":237803,"corporation":false,"usgs":false,"family":"Chen","given":"Nengwang","email":"","affiliations":[{"id":47617,"text":"Xiamen University, China","active":true,"usgs":false}],"preferred":false,"id":795343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tobias, Craig R.","contributorId":191283,"corporation":false,"usgs":false,"family":"Tobias","given":"Craig","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":795344,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70206445,"text":"70206445 - 2020 - A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades","interactions":[],"lastModifiedDate":"2020-08-05T13:58:56.739844","indexId":"70206445","displayToPublicDate":"2019-08-28T15:41:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades","docAbstract":"<p><span>The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45 kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67 kg/m³) or 20–30° (0.94 kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75 kg/m³), medium (&gt;0.75 to &lt;1.10 kg/m³), and high CWP (≥1.10 kg/m³). For corn the global CWP categories were low (≤1.25 kg/m³), medium (&gt;1.25 to ≤1.75 kg/m³), and high (&gt;1.75 kg/m³). For rice the global CWP categories were low (≤0.70 kg/m³), medium (&gt;0.70 to ≤1.25 kg/m³), and high (&gt;1.25 kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India’s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100 m³ in volume that leads to many benefits such as acting as ‘water banks’ for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/17538947.2019.1651912","usgsCitation":"Daniel J. Foley, Thenkabail, P., Aneece, I., Pardhasaradhi Teluguntla, and Oliphant, A., 2020, A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades: International Journal of Digital Earth, v. 13, no. 8, p. 939-975, https://doi.org/10.1080/17538947.2019.1651912.","productDescription":"37 p.","startPage":"939","endPage":"975","ipdsId":"IP-105160","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":458691,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/17538947.2019.1651912","text":"Publisher Index Page"},{"id":368938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Daniel J. Foley 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":220240,"corporation":false,"usgs":false,"family":"Daniel J. Foley","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":774571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774570,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aneece, Itiya 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":220241,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardhasaradhi Teluguntla 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":214457,"corporation":false,"usgs":false,"family":"Pardhasaradhi Teluguntla","affiliations":[{"id":39046,"text":"Bay Area Environmental Research Institute at USGS","active":true,"usgs":false}],"preferred":false,"id":774573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774574,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70206262,"text":"70206262 - 2020 - Species-specific responses to wetland mitigation among amphibians in the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2020-02-06T10:56:46","indexId":"70206262","displayToPublicDate":"2019-08-26T06:49:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Species-specific responses to wetland mitigation among amphibians in the Greater Yellowstone Ecosystem","docAbstract":"<p><span>Habitat loss and degradation are leading causes of biodiversity declines, therefore assessing the capacity of created mitigation wetlands to replace habitat for wildlife has become a management priority. We used single season occupancy models to compare the occurrence of larvae of four species of pond‐breeding amphibians in wetlands created for mitigation, wetlands impacted by road construction, and unimpacted reference wetlands along a highway corridor in the Greater Yellowstone Ecosystem, United States. Created wetlands were shallow and had less aquatic vegetation and surface area than impacted and reference wetlands. Occupancy of barred tiger salamander (</span><i>Ambystoma mavortium</i><span>) and boreal chorus frog (</span><i>Pseudacris maculata</i><span>) larvae was similar across wetland types, whereas boreal toads (</span><i>Anaxyrus boreas</i><span>) occurred more often in created wetlands than reference and impacted wetlands. However, the majority of created wetlands (&gt;80%) dried partially or completely before amphibian metamorphosis occurred in both years of our study, resulting in heavy mortality of larvae and, we suspect, little to no recruitment. Columbia spotted frogs (</span><i>Rana luteiventris</i><span>), which require emergent vegetation that is not common in newly created wetlands, occurred commonly in impacted and reference wetlands but were found in only one created wetland. Our results show that shallow created wetlands with little aquatic vegetation may be attractive breeding areas for some amphibians, but may result in high mortality and little recruitment if they fail to hold water for the entire larval period.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/rec.13031","usgsCitation":"Swartz, L., Lowe, W., Muths, E.L., and Hossack, B.R., 2020, Species-specific responses to wetland mitigation among amphibians in the Greater Yellowstone Ecosystem: Restoration Ecology, v. 28, no. 1, p. 206-214, https://doi.org/10.1111/rec.13031.","productDescription":"9 p.","startPage":"206","endPage":"214","ipdsId":"IP-103888","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":368638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Yellowstone","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.7694091796875,\n              43.40903821777055\n            ],\n            [\n              -108.9129638671875,\n              43.40903821777055\n            ],\n            [\n              -108.9129638671875,\n              45.32897866218559\n            ],\n            [\n              -111.7694091796875,\n              45.32897866218559\n            ],\n            [\n              -111.7694091796875,\n              43.40903821777055\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Swartz, LK","contributorId":220046,"corporation":false,"usgs":false,"family":"Swartz","given":"LK","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":773968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, WH","contributorId":220047,"corporation":false,"usgs":false,"family":"Lowe","given":"WH","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":773969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":773970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":773967,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204933,"text":"70204933 - 2020 - Maximum entropy derived statistics of sound speed structure in a fine-grained sediment inferred from sparse broadband acoustic measurements on the New England continental shelf","interactions":[],"lastModifiedDate":"2020-01-20T12:25:57","indexId":"70204933","displayToPublicDate":"2019-08-23T10:55:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1941,"text":"IEEE Journal of Oceanic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Maximum entropy derived statistics of sound speed structure in a fine-grained sediment inferred from sparse broadband acoustic measurements on the New England continental shelf","docAbstract":"Marginal probability distributions for parameters representing an effective sound-speed structure of a fine-grained sediment are inferred from a data ensemble maximum entropy method that utilizes a sparse spatially distributed set of received pressure time series resulting from multiple explosive sources in a shallow-water ocean environment possessing significant spatial variability of the seabed. A remote sensing seabed acoustics experiment undertaken in March 2017 off the New England Shelf was designed so that multiple independent analyses could infer the statistical properties of the seabed. The current analysis incorporates the measured horizontal variability from interpretations of a subbottom profiling survey of the experimental area. An idealized range- and azimuth-dependent parameterization of the seabed is derived from identification of horizons within the seabed that define multiple sediment layers. A sparse set of explosive charges were deployed on circular tracks with radii of about 2, 4, and 6.5 km with an acoustic array at the center to correlate a set of random measurements to physical acoustic processes that characterize the seabed. The mean values of a surface sound speed ratio and a linear sound speed gradient for the fine-grained sediment layer derived from 12 data samples processed in the 25–275-Hz band provide an estimate of the effective sound-speed structure in a 130-km $^2$ area. The inferred sediment sound speed values are evaluated by predicting measured time series data not used in the statistical inference, and are also compared to historical measurements. Finally, the low-frequency maximum entropy estimate of the sediment sound speed along with physical measurements derived from piston core measurements are utilized to estimate the sediment grain bulk modulus.\npredictions made by the viscous grain shearing model.","language":"English","publisher":"IEEE","doi":"10.1109/JOE.2019.2922717","usgsCitation":"Knobles, D.P., Wilson, P.S., Goff, J., Wan, L., Buckingham, M., Chaytor, J., and Badiey, M., 2020, Maximum entropy derived statistics of sound speed structure in a fine-grained sediment inferred from sparse broadband acoustic measurements on the New England continental shelf: IEEE Journal of Oceanic Engineering, v. 45, no. 1, p. 161-173, https://doi.org/10.1109/JOE.2019.2922717.","productDescription":"9 p.","startPage":"161","endPage":"173","ipdsId":"IP-102085","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":366851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, Vermont,  New Hampshire, Massachusetts, Rhode Island, 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 \"}}]}","volume":"45","issue":"1","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knobles, David P.","contributorId":218392,"corporation":false,"usgs":false,"family":"Knobles","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":769154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Preston S.","contributorId":139561,"corporation":false,"usgs":false,"family":"Wilson","given":"Preston","email":"","middleInitial":"S.","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. Current address:  TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":false,"id":769155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goff, J.A.","contributorId":17004,"corporation":false,"usgs":true,"family":"Goff","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":769156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wan, L.","contributorId":218393,"corporation":false,"usgs":false,"family":"Wan","given":"L.","email":"","affiliations":[],"preferred":false,"id":769157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buckingham, M.J.","contributorId":28772,"corporation":false,"usgs":true,"family":"Buckingham","given":"M.J.","email":"","affiliations":[],"preferred":false,"id":769158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chaytor, Jason 0000-0001-8135-8677 jchaytor@usgs.gov","orcid":"https://orcid.org/0000-0001-8135-8677","contributorId":140095,"corporation":false,"usgs":true,"family":"Chaytor","given":"Jason","email":"jchaytor@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":769159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Badiey, Mohsen","contributorId":218394,"corporation":false,"usgs":false,"family":"Badiey","given":"Mohsen","email":"","affiliations":[],"preferred":false,"id":769160,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70208109,"text":"70208109 - 2020 - Social attraction used to establish Caspian tern nesting colonies in San Francisco Bay","interactions":[],"lastModifiedDate":"2020-01-27T19:22:21","indexId":"70208109","displayToPublicDate":"2019-08-14T19:21:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Social attraction used to establish Caspian tern nesting colonies in San Francisco Bay","docAbstract":"Conservation of colonial waterbird breeding populations often includes restoring historic nesting habitat or establishing new nesting habitat in protected areas. However, colonization of new or restored nesting habitat may be hindered by the lack of social cues from nesting conspecifics to attract prospecting birds. Social attraction, whereby decoys and colony sound recordings are used to mimic active nesting colonies, has been used successfully to establish waterbird nesting colonies throughout the world. We constructed islands, modified the substrate so that it was attractive to nesting Caspian terns (Hydroprogne caspia), and then used social attraction to establish nesting colonies within two managed ponds in San Francisco Bay, California where Caspian terns had not previously nested. During the 2015–2017 breeding seasons, we deployed decoys of adult Caspian terns, broadcasted colony sound recordings, and monitored Caspian tern response. Caspian terns formed nesting colonies within weeks of social attraction deployment at each of the two ponds in 2015, and the size of these colonies increased in each subsequent year of the study. In 2017, the final year of the study, we estimated a minimum of 501 breeding pairs between the two colonies, making them two of the three largest Caspian tern colonies in the San Francisco Bay estuary. In total, these two colonies produced 1343 nests and 531 fledglings over the three-year study period. Nest densities were low (mean: 0.29 nests/m2 of active colony area) compared to other studies, and greater than 80% of the modified island habitat remained unused by nesting Caspian terns in 2017, suggesting that there is additional space for future colony growth. The successful establishment of two of the largest Caspian tern nesting colonies in the San Francisco Bay estuary in just three years demonstrates the potential of using island construction and habitat modifications, combined with social attraction measures to establish waterbird nesting colonies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2019.e00757","usgsCitation":"Hartman, C.A., Ackerman, J., Herzog, M.P., Strong, C., and Trachtenbarg, D.A., 2020, Social attraction used to establish Caspian tern nesting colonies in San Francisco Bay: Global Ecology and Conservation, v. 20, e00757, https://doi.org/10.1016/j.gecco.2019.e00757.","productDescription":"e00757","ipdsId":"IP-110956","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458697,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2019.e00757","text":"Publisher Index Page"},{"id":371616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California ","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.04687499999999,\n              37.21283151445594\n            ],\n            [\n              -121.6845703125,\n              37.21283151445594\n            ],\n            [\n              -121.6845703125,\n              38.30718056188316\n            ],\n            [\n              -123.04687499999999,\n              38.30718056188316\n            ],\n            [\n              -123.04687499999999,\n              37.21283151445594\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"20","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":780495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strong, Cheryl","contributorId":149428,"corporation":false,"usgs":false,"family":"Strong","given":"Cheryl","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":780498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Trachtenbarg, David A","contributorId":146351,"corporation":false,"usgs":false,"family":"Trachtenbarg","given":"David","email":"","middleInitial":"A","affiliations":[{"id":16680,"text":"U.S. Army Corps of Engineers, Walla Walla District, Walla Walla, WA 99362","active":true,"usgs":false}],"preferred":false,"id":780499,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208318,"text":"70208318 - 2020 - Global change-driven use of onshore habitat impacts polar bear faecal microbiota","interactions":[],"lastModifiedDate":"2020-02-04T12:26:26","indexId":"70208318","displayToPublicDate":"2019-08-05T12:20:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1956,"text":"ISME Journal","active":true,"publicationSubtype":{"id":10}},"title":"Global change-driven use of onshore habitat impacts polar bear faecal microbiota","docAbstract":"The gut microbiota plays a critical role in host health, yet remains poorly studied in wild species.  Polar bears (Ursus maritimus), key indicators of Arctic ecosystem health and environmental change, are currently affected by rapid shifts in habitat that may alter gut homeostasis. Declining sea ice has led to a divide in the southern Beaufort Sea polar bear subpopulation such that an increasing proportion of individuals now inhabit onshore coastal regions during the open-water period (‘onshore bears’) while others continue to exhibit their typical behaviour of remaining on the ice (‘offshore bears’). We propose that bears that have altered their habitat selection in response to climate change will exhibit a distinct gut microbiota diversity and composition, which may ultimately have important consequences for their health. Here, we perform the first assessment of abundance and diversity in the faecal microbiota of wild polar bears using 16S rRNA Illumina technology. We find that bacterial diversity is significantly higher in onshore bears compared to offshore bears. The majority of unique bacterial taxa for onshore bears belonged to the phyla Proteobacteria (a proposed indicator of poor health in adult humans), whereas for offshore bears, Firmicutes (associated with adiposity) dominated. We conclude that climate-driven changes in polar bear land use are associated with distinct microbial communities. In doing so, we present the first case of global change mediated alterations in the gut microbiota of a free-roaming wild animal.","language":"English","publisher":"Nature","doi":"10.1038/s41396-019-0480-2","usgsCitation":"Watson, S., Hauffe, H., Bull, M., Atwood, T.C., McKinney, M., Pindo, M., and Perkins, S., 2020, Global change-driven use of onshore habitat impacts polar bear faecal microbiota: ISME Journal, v. 13, p. 2916-2926, https://doi.org/10.1038/s41396-019-0480-2.","productDescription":"11 p.","startPage":"2916","endPage":"2926","ipdsId":"IP-098794","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":458708,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41396-019-0480-2","text":"Publisher Index Page"},{"id":372013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Southern Beaufort Sea","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -141.064453125,\n              69.09993967425089\n            ],\n            [\n              -140.888671875,\n              70.22974449563027\n            ],\n            [\n              -155.65429687499997,\n              73.09941313082075\n            ],\n            [\n              -164.443359375,\n              72.50172235139388\n            ],\n            [\n              -163.388671875,\n              69.68761843185617\n            ],\n            [\n              -158.466796875,\n              70.05059634999759\n            ],\n            [\n              -141.064453125,\n              69.09993967425089\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Watson, Sophie","contributorId":222143,"corporation":false,"usgs":false,"family":"Watson","given":"Sophie","email":"","affiliations":[{"id":17940,"text":"Cardiff University","active":true,"usgs":false}],"preferred":false,"id":781391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hauffe, Heidi","contributorId":222144,"corporation":false,"usgs":false,"family":"Hauffe","given":"Heidi","email":"","affiliations":[{"id":40495,"text":"Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":781392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bull, Matthew","contributorId":222145,"corporation":false,"usgs":false,"family":"Bull","given":"Matthew","email":"","affiliations":[{"id":17940,"text":"Cardiff University","active":true,"usgs":false}],"preferred":false,"id":781393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":781390,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKinney, Melissa","contributorId":222146,"corporation":false,"usgs":false,"family":"McKinney","given":"Melissa","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":781394,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pindo, Massimo","contributorId":222147,"corporation":false,"usgs":false,"family":"Pindo","given":"Massimo","email":"","affiliations":[{"id":40495,"text":"Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":781395,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perkins, Sarah","contributorId":222148,"corporation":false,"usgs":false,"family":"Perkins","given":"Sarah","email":"","affiliations":[{"id":17940,"text":"Cardiff University","active":true,"usgs":false}],"preferred":false,"id":781396,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70204664,"text":"70204664 - 2020 - Using carbon isotope ratios to verify predictions of a model simulating the interaction between coastal plant communities and their effect on ground water salinity","interactions":[],"lastModifiedDate":"2020-06-04T16:34:54.718128","indexId":"70204664","displayToPublicDate":"2019-07-31T13:28:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Using carbon isotope ratios to verify predictions of a model simulating the interaction between coastal plant communities and their effect on ground water salinity","docAbstract":"As sea level rises in low-lying coastal islands, salt-tolerant (halophytic) coastal vegetation communities may be able to migrate inland, replacing the freshwater vegetation that is unable to tolerate salt stress. The pace of such shifts may be accelerated by a self-reinforcing feedback between the halophytic vegetation and salinity, as well as by frequent and intensified salinity pulses associated with the increasing impact of storm surges as a consequence of sea-level rise. We used a modification of a previously published spatially explicit individual-based model that simulates impacts on upland freshwater hammock communities from sea-level rise and storm surge to predict the interaction between three coastal communities: mangroves, hammocks, and pinelands. The model simulation predicted two qualitative characteristics regarding the interaction between these three different coastal communities: (1) mangroves and hammock communities tend to have ground water with high salinities, while at the same time pineland ground water salinity is low, and (2) pineland located at lower elevation relative to adjacent hammock will be negatively influenced by higher ground water salinities in hammocks, as it flows toward the lower elevation pineland. We tested these predictions using foliar δ13C of Conocarpus erectus collected from Big Pine Key as a proxy for ground water salinity. Measurements of ground water salinity via this proxy confirmed the two predictions of the model. Our approach provides an approximation of the impacts of sea-level rise on terrestrial vegetation communities, including threatened pineland communities, and can be used as a tool for management decisions.","language":"English","publisher":"Springer","doi":"10.1007/s10021-019-00423-4","usgsCitation":"Subedi, S.C., Sternberg, L., DeAngelis, D.L., Ross, M.S., and Ogarcak, D., 2020, Using carbon isotope ratios to verify predictions of a model simulating the interaction between coastal plant communities and their effect on ground water salinity: Ecosystems, v. 23, p. 570-585, https://doi.org/10.1007/s10021-019-00423-4.","productDescription":"16 p.","startPage":"570","endPage":"585","ipdsId":"IP-101860","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437219,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QO2Y7J","text":"USGS data release","linkHelpText":"Carbon-13 values in tree leaves in Florida (2018)"},{"id":366394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Subedi, Suresh C. 0000-0001-8689-0689","orcid":"https://orcid.org/0000-0001-8689-0689","contributorId":217984,"corporation":false,"usgs":false,"family":"Subedi","given":"Suresh","email":"","middleInitial":"C.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":767973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sternberg, Leonel","contributorId":217985,"corporation":false,"usgs":false,"family":"Sternberg","given":"Leonel","affiliations":[],"preferred":false,"id":767974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":767972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ross, Michael S.","contributorId":202431,"corporation":false,"usgs":false,"family":"Ross","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":36434,"text":"Florida International University, Miami, FL","active":true,"usgs":false}],"preferred":false,"id":767975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ogarcak, Danielle","contributorId":217987,"corporation":false,"usgs":false,"family":"Ogarcak","given":"Danielle","email":"","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":767976,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70206828,"text":"70206828 - 2020 - Using full and partial unmixing algorithms to estimate the inundation extent of small, isolated stock ponds in an arid landscape","interactions":[],"lastModifiedDate":"2020-08-27T15:29:37.417951","indexId":"70206828","displayToPublicDate":"2019-07-30T06:48:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Using full and partial unmixing algorithms to estimate the inundation extent of small, isolated stock ponds in an arid landscape","docAbstract":"<p><span>Many natural wetlands around the world have disappeared or been replaced, resulting in the dependence of many wildlife species on small, artificial earthen stock ponds. These ponds provide critical wildlife habitat, such that the accurate detection of water and assessment of inundation extent is required. We applied a full (linear spectral mixture analysis; LSMA) and partial (matched filtering; MF) spectral unmixing algorithm to a 2007 Landsat 5 and a 2014 Landsat 8 satellite image to determine the ability of a time-intensive (i.e., more spectral input; LSMA) vs. a more efficient (less spectral input; MF) spectral unmixing approach to detect and estimate surface water area of stock ponds in southern Arizona, USA and northern Sonora, Mexico. Spearman rank correlations (</span><i>r</i><sub>s</sub><span>) between modeled and actual inundation areas less than a single Landsat pixel (&lt; 900 m</span><sup>2</sup><span>) were low for both techniques (</span><i>r</i><sub>s</sub><span>&nbsp;range = 0.22 to 0.62), but improved for inundation areas &gt;900&nbsp;m</span><sup>2</sup><span>&nbsp;(</span><i>r</i><sub>s</sub><span>&nbsp;range = 0.34 to 0.70). Our results demonstrate that the MF approach can model ranked inundation extent of known pond locations with results comparable to or better than LSMA, but further refinement is required for estimating absolute inundation areas and mapping wetlands &lt;1 Landsat pixel.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s13157-019-01201-7","usgsCitation":"Jarchow, C., Sigafus, B.H., Muths, E.L., and Hossack, B.R., 2020, Using full and partial unmixing algorithms to estimate the inundation extent of small, isolated stock ponds in an arid landscape: Wetlands, v. 40, p. 563-575, https://doi.org/10.1007/s13157-019-01201-7.","productDescription":"13 p.","startPage":"563","endPage":"575","ipdsId":"IP-092489","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437220,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95ZFPT1","text":"USGS data release","linkHelpText":"Surface water data for isolated stock ponds in southern Arizona, USA and northern Sonora, Mexico"},{"id":369519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":775953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":775952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":775954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":775955,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227115,"text":"70227115 - 2020 - Harvest–release decisions in recreational fisheries","interactions":[],"lastModifiedDate":"2021-12-30T16:41:44.505518","indexId":"70227115","displayToPublicDate":"2019-07-29T10:37:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Harvest–release decisions in recreational fisheries","docAbstract":"<p><span>Most fishery regulations aim to control angler harvest. Yet, we lack a basic understanding of what actually determines the angler’s decision to harvest or release fish caught. We used XGBoost, a machine learning algorithm, to develop a predictive angler harvest–release model by taking advantage of an extensive recreational fishery data set (24 water bodies, 9 years, and 193 523 fish). We were able to successfully predict the harvest–release outcome for 99% of fish caught in the training data set and 96% of fish caught in the test data set. Unsuccessful predictions were mostly attributed to predicting harvest of fish that were released. Fish length was the most essential feature examined for predicting angler harvest. Other important predictive harvest–release features included the number of individuals of the same species caught, geographic location of an angler’s residence, distance traveled, and time spent fishing. The XGBoost algorithm was able to effectively predict the harvest–release decision and revealed hidden and intricate relationships that are often unaccounted for with classical analysis techniques. Exposing and accounting for these angler–fish intricacies is critical for fisheries conservation and management.</span></p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0119","usgsCitation":"Kaemingk, M.A., Hurley, K.L., Chizinski, C.J., and Pope, K.L., 2020, Harvest–release decisions in recreational fisheries: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 1, p. 194-201, https://doi.org/10.1139/cjfas-2019-0119.","productDescription":"8 p.","startPage":"194","endPage":"201","ipdsId":"IP-107097","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":500808,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/96908","text":"External Repository"},{"id":393653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70208348,"text":"70208348 - 2020 - Light exposure along particle flowpaths in large rivers","interactions":[],"lastModifiedDate":"2020-02-05T16:23:00","indexId":"70208348","displayToPublicDate":"2019-07-26T16:06:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Light exposure along particle flowpaths in large rivers","docAbstract":"<p><span>Sunlight is a critical resource in aquatic systems driving photosynthesis, photodegradation of organic matter and contaminants, animal behavior, and the activity of human pathogens. In rivers, solutes, materials, and organisms are turbulently mixed across the water column during downstream transport and exposed to highly variable sunlight. However, there are no measurements of suspended particles' sunlight exposure during downstream transport to characterize this variability, and it is unclear if current measurement approaches and optical theory capture the light exposure of suspended particles. We deployed neutrally buoyant drifters and stationary buoys in the Upper Mississippi (WI, U.S.A.) and Neuse Rivers (NC, U.S.A.) to measure underwater sunlight from the perspective of suspended particles. In our study sites, underwater sunlight varied more along flowpaths measured by drifters than over time measured by fixed‐site buoys; sunlight exposure along flowpaths was dominated by bursts of light (sunflecks) that accounted for 62–99% of the cumulative sunlight exposure; and modeled sunlight exposure using optical theory was consistently 56–1700% higher than measured sunlight exposure along flowpaths. Our results suggested that suspended particles in the study reaches experienced darker conditions than predicted and have important implications for how to quantify underwater sunlight in rivers.</span></p>","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.11256","usgsCitation":"Gardner, J.R., Ensign, S.H., Houser, J.N., and Doyle, M.W., 2020, Light exposure along particle flowpaths in large rivers: Limnology and Oceanography, v. 65, no. 1, p. 128-142, https://doi.org/10.1002/lno.11256.","productDescription":"15 p.","startPage":"128","endPage":"142","ipdsId":"IP-102904","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":372097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Wisconsin","otherGeospatial":"Neuse River, Upper Missisiisppi River Pool 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28557","active":true,"usgs":false}],"preferred":false,"id":781531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":781529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doyle, Martin W.","contributorId":202217,"corporation":false,"usgs":false,"family":"Doyle","given":"Martin","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":781532,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70208680,"text":"70208680 - 2020 - In situ benthic nutrient flux and sediment oxygen demand in Barnegat Bay, New Jersey","interactions":[],"lastModifiedDate":"2020-02-24T19:17:26","indexId":"70208680","displayToPublicDate":"2019-07-14T19:13:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"In situ benthic nutrient flux and sediment oxygen demand in Barnegat Bay, New Jersey","docAbstract":"The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, measured sediment oxygen demand (SOD) and benthic nutrient fluxes throughout Barnegat Bay, New Jersey. SOD was determined in situ using chambers equipped with optical dissolved oxygen sensors. The benthic nutrient fluxes of ammonia (NH3), nitrite + nitrate (plus  ions; here, referred to as NO32), soluble reactive phosphorous (SRP), and dissolved silica (SiO2) were measured with in situ equilibrium dialysis samplers. Measurements were made at nine stations around the periphery and at three mid-Bay locations from August 2012 to October 2013. The SOD ranged from −1.5 to −8.4 g of oxygen (O2) m−2 d−1. The SOD rates varied as a function of water temperature and followed the van't Hoff rate equation for change in reaction rate with temperature, with a temperature coefficient (Θ) that varied among sites and averaged 1.083. The highest SOD rates in the bay were measured near the mouth of the Toms River embayment. Concentrations in the upper 1 m of sediment pore water were found up to 23 mg N L−1 for NH4+ and 6.7 mg P L−1 for SRP. Maximum measured fluxes into the overlying water were 3.0 × 10−2 g NH3–N m−2 d−1, 7.0 × 10−4 g NO32–N m−2 d−1, 1.9 × 10−3 g P m−2 d−1, and 3.6 × 10−3g SiO2 m−2 d−1. Using the measured benthic N and P fluxes, daily nutrient inputs derived from sediment recycling are shown to be comparable in scale to freshwater tributary inputs to the bay.","language":"English","publisher":"Coastal Education and Research Foundation, Inc","doi":"10.2112/SI78-005.1","usgsCitation":"Wilson, T., and DePaul, V.T., 2020, In situ benthic nutrient flux and sediment oxygen demand in Barnegat Bay, New Jersey: Journal of Coastal Research, v. 78, p. 46-59, https://doi.org/10.2112/SI78-005.1.","productDescription":"14 p.","startPage":"46","endPage":"59","ipdsId":"IP-069751","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":437221,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1WC3","text":"USGS data release","linkHelpText":"Benthic pore water and sediment data Barnegat Bay, New Jersey, 2012-2013"},{"id":372593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.22019958496094,\n              39.665970875883175\n            ],\n            [\n              -74.1796875,\n              39.6458824809188\n            ],\n            [\n              -74.09660339355469,\n              39.768436410838426\n            ],\n            [\n              -74.08561706542969,\n              39.92132255884663\n            ],\n            [\n              -74.10621643066406,\n              39.95185892663005\n            ],\n            [\n              -74.12200927734375,\n              39.93817189499188\n            ],\n            [\n              -74.13848876953125,\n              39.8992015115692\n            ],\n            [\n              -74.13986206054688,\n              39.86969567045658\n            ],\n            [\n              -74.14398193359375,\n              39.85072092501597\n            ],\n            [\n              -74.17076110839844,\n              39.82224896999684\n            ],\n            [\n              -74.18792724609375,\n              39.79482037706643\n            ],\n            [\n              -74.1961669921875,\n              39.7631584037253\n            ],\n            [\n              -74.19548034667969,\n              39.74837783143156\n            ],\n            [\n              -74.1796875,\n              39.73676229957947\n            ],\n            [\n              -74.17625427246094,\n              39.72461669561139\n            ],\n            [\n              -74.17556762695312,\n              39.71035608240133\n            ],\n            [\n              -74.19479370117188,\n              39.69345079688953\n            ],\n            [\n              -74.21401977539062,\n              39.67706985250899\n            ],\n            [\n              -74.22225952148438,\n              39.67072779849953\n            ],\n            [\n              -74.22019958496094,\n              39.665970875883175\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"78","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Timothy P. 0000-0003-1914-6344","orcid":"https://orcid.org/0000-0003-1914-6344","contributorId":219174,"corporation":false,"usgs":true,"family":"Wilson","given":"Timothy P.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DePaul, Vincent T. 0000-0002-7977-5217 vdepaul@usgs.gov","orcid":"https://orcid.org/0000-0002-7977-5217","contributorId":2778,"corporation":false,"usgs":true,"family":"DePaul","given":"Vincent","email":"vdepaul@usgs.gov","middleInitial":"T.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782979,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70208877,"text":"70208877 - 2020 - Investigating bedload transport under asymmetrical waves using a coupled ocean-wave model","interactions":[],"lastModifiedDate":"2020-03-04T16:45:11","indexId":"70208877","displayToPublicDate":"2019-06-30T16:39:32","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Investigating bedload transport under asymmetrical waves using a coupled ocean-wave model","docAbstract":"Transport by asymmetrical wave motions plays a key role in cross-shore movement of sand, which is important for bar migration, exchange through tidal inlets, and beach recovery after storms. We have implemented a modified version of the SANTOSS formulation in the three-dimensional open-source Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling framework. The calculation of bedload transport requires inputs that include: water depth, bulk wave statistics (significant wave height, wave period, wave orbital velocity), current velocity at the edge of the wave boundary layer, and sediment density and grain size. While the coupled ocean-wave model computes water density, depth, and bulk wave statistics, we implement a method to calculate current velocity assuming a log profile and using the Madsen formulations for wave-current bottom boundary layer flows. We investigate the sensitivity of the calculation of near-bottom current velocity to model choices and its influence on cross-shore bedload transport. Results are compared to available numerical experiments using coupled fluid and discrete element model (CFDEM) simulations.","largerWorkType":{"id":4,"text":"Book"},"largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"World Scientific","doi":"10.1142/9789811204487_0052","usgsCitation":"Kalra, T., Sherwood, C.R., Warner, J., Rafati, Y., and Hsu, T.J., 2020, Investigating bedload transport under asymmetrical waves using a coupled ocean-wave model, p. 591-604, https://doi.org/10.1142/9789811204487_0052.","productDescription":"15 p.","startPage":"591","endPage":"604","ipdsId":"IP-105261","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":783926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rafati, Yashar","contributorId":223049,"corporation":false,"usgs":false,"family":"Rafati","given":"Yashar","email":"","affiliations":[],"preferred":false,"id":783929,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hsu, Tian Jian","contributorId":149140,"corporation":false,"usgs":false,"family":"Hsu","given":"Tian","email":"","middleInitial":"Jian","affiliations":[],"preferred":false,"id":783930,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208878,"text":"70208878 - 2020 - Modeling the morphological response of a barrier island to Hurricane Matthew","interactions":[],"lastModifiedDate":"2020-03-04T16:34:17","indexId":"70208878","displayToPublicDate":"2019-06-30T16:34:01","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Modeling the morphological response of a barrier island to Hurricane Matthew","docAbstract":"Surge and wave forcing from Hurricane Matthew caused a breach south of Matanzas Inlet (FL, USA) on a complex barrier island, including sandy dunes, hard structures (residential buildings and a highway), wetlands, and the US Intracoastal Waterway. In this paper, the skill of the XBeach model to predict hurricane-induced barrier island overwash, dune erosion, and breaching is demonstrated. The location of the breach is predicted correctly if bottom roughness based on land cover is used to calculate bed shear stresses. While the dunes are initially lowered by wave attack and surge from the ocean side, the main driver for breach formation is the water level difference between the back-barrier and nearshore, causing an ocean-directed outflow of water after the peak of the storm.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal sediments 2019: Proceedings of the 9th international conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Conference on Coastal Sediments 2019","conferenceDate":"May 27-31, 2019","conferenceLocation":"Tampa/St. Petersburg, FL","language":"English","publisher":"World Scientific","doi":"10.1142/9789811204487_0012","usgsCitation":"Quataert, E., van der Lugt, M., Sherwood, C.R., van Oormondt, M., and van Dongeran, A., 2020, Modeling the morphological response of a barrier island to Hurricane Matthew, <i>in</i> Coastal sediments 2019: Proceedings of the 9th international conference, Tampa/St. Petersburg, FL, May 27-31, 2019, p. 128-138, https://doi.org/10.1142/9789811204487_0012.","productDescription":"11 p.","startPage":"128","endPage":"138","ipdsId":"IP-105247","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Matanzas Inlet","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.19171142578125,\n              29.621818021144485\n            ],\n            [\n              -81.24629974365234,\n              29.767059606099\n            ],\n            [\n              -81.26106262207031,\n              29.806688644587382\n            ],\n            [\n              -81.28921508789062,\n              29.80609283540296\n            ],\n            [\n              -81.25179290771484,\n              29.719364794202505\n            ],\n            [\n              -81.23085021972656,\n              29.65434412369176\n            ],\n            [\n              -81.21437072753906,\n              29.608088257406806\n            ],\n            [\n              -81.19171142578125,\n              29.621818021144485\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Quataert, Ellen","contributorId":149000,"corporation":false,"usgs":false,"family":"Quataert","given":"Ellen","affiliations":[{"id":17614,"text":"Delft University of Technology","active":true,"usgs":false}],"preferred":false,"id":783921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van der Lugt, Marlies","contributorId":221148,"corporation":false,"usgs":false,"family":"van der Lugt","given":"Marlies","email":"","affiliations":[{"id":40335,"text":"Detlares","active":true,"usgs":false}],"preferred":false,"id":783922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Oormondt, Maarten","contributorId":223048,"corporation":false,"usgs":false,"family":"van Oormondt","given":"Maarten","email":"","affiliations":[],"preferred":false,"id":783924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Dongeran, Ap","contributorId":176244,"corporation":false,"usgs":false,"family":"van Dongeran","given":"Ap","email":"","affiliations":[],"preferred":false,"id":783925,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70204110,"text":"70204110 - 2020 - Hydroseeding tackifiers and dryland moss restoration potential","interactions":[],"lastModifiedDate":"2024-07-17T21:41:12.423442","indexId":"70204110","displayToPublicDate":"2019-06-17T16:45:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Hydroseeding tackifiers and dryland moss restoration potential","docAbstract":"<p><span>Tackifiers are long‐chain carbon compounds used for soil stabilization and hydroseeding and could provide a vehicle for biological soil crust restoration. We examined the sensitivity of two dryland mosses,&nbsp;</span><i>Bryum argenteum</i><span>&nbsp;and&nbsp;</span><i>Syntrichia ruralis,</i><span>&nbsp;to three common tackifiers ‐ guar, psyllium, and polyacrylamide (PAM) ‐ at 0.5x, 1.0x, and 2.0x of recommended (x) concentrations for erosion control and revegetation. We measured moss shoot, gemma, and protonema production as well as moss organic matter and bound sand masses as indicators of growth and soil holding ability. We tested sand and tackifier chemistry to investigate potential nutrient and toxicant potential on moss growth. Groups of ten fragments from field‐collected mosses were grown on sand in open petri dishes arranged in a growth chamber in replicated blocks containing each tackifier and concentration combination plus a distilled water control.&nbsp;</span><i>Bryum</i><span>&nbsp;(</span><i>n</i><span>=10) and&nbsp;</span><i>Syntrichia</i><span>&nbsp;(</span><i>n</i><span>=9) growth were measured at the end of six and five weeks, respectively. Overall model tests yielded statistically significant results (</span><i>p</i><span>&lt;0.001) for every variable in each species. When compared to water, guar tended to decrease growth, psyllium tended to increase growth, and PAM's effects were generally neutral to positive. Within tackifier types, increasing concentrations of guar tended to decrease growth, while increasing concentrations of psyllium tended to increase growth. Changes in PAM concentrations had little effect on growth. Increases in guar and psyllium lowered pH and increased P and K. Psyllium and PAM yielded promising results as potential agents of dispersal and adherence of dryland mosses in field restoration.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/rec.12997","usgsCitation":"Blankenship, W.D., Condon, L.A., and Pyke, D.A., 2020, Hydroseeding tackifiers and dryland moss restoration potential: Restoration Ecology, v. 28, no. S2, p. S127-S138, https://doi.org/10.1111/rec.12997.","productDescription":"12 p.","startPage":"S127","endPage":"S138","ipdsId":"IP-106770","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":458742,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.12997","text":"Publisher Index Page"},{"id":365318,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"S2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Blankenship, W. Dillon","contributorId":216798,"corporation":false,"usgs":false,"family":"Blankenship","given":"W.","email":"","middleInitial":"Dillon","affiliations":[{"id":39520,"text":"Oregon State University, Department of Botany and Plant Pathology","active":true,"usgs":false}],"preferred":false,"id":765561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Condon, Lea A. 0000-0002-9357-3881","orcid":"https://orcid.org/0000-0002-9357-3881","contributorId":202908,"corporation":false,"usgs":true,"family":"Condon","given":"Lea","email":"","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":765560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":765559,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70223487,"text":"70223487 - 2020 - Bridging the gap between salmon spawner abundance and marine nutrient assimilation by juvenile salmon: Seasonal cycles and landscape effects at the watershed scale","interactions":[],"lastModifiedDate":"2021-08-30T13:19:09.53628","indexId":"70223487","displayToPublicDate":"2019-06-08T08:15:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Bridging the gap between salmon spawner abundance and marine nutrient assimilation by juvenile salmon: Seasonal cycles and landscape effects at the watershed scale","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Anadromous Pacific salmon are semelparous, and resource subsidies from spawning adults (marine-derived nutrients, or MDN) benefit juvenile salmonids rearing in freshwater. However, it remains unclear how MDN assimilation relates to spawner abundance within a watershed. To address this, we examined seasonal, watershed-scale patterns of MDN assimilation in rearing coho (<i>Oncorhynchus kisutch</i>) and Chinook (<i>O. tshawytscha</i>) salmon and compared it with spawner biomass and landscape features in a western Alaska watershed with contrasting structural complexity in two sub-drainages. Adult salmon biomass density was estimated from escapement and spawner distribution data, and MDN assimilation in juvenile salmon was estimated via stable isotopes. In the North River, MDN assimilation was lowest in early summer, prior to annual spawning migrations, increased after spawning, and peaked in late winter. In the more complex mainstem Unalakleet River, MDN assimilation was higher but varied minimally from summer through fall before increasing in late fall and winter. Summer MDN assimilation, prior to salmon spawning, was primarily a function of habitat complexity, where MDN was highest in sloughs and the more complex mainstem river. After salmon spawned, fall MDN assimilation was a function of adult pink and Chinook salmon biomass as well as MDN assimilation that occurred prior to spawning (that is, summer MDN), but unrelated to total summer biomass (all salmon species biomass combined). Thus, MDN assimilation by juvenile salmon in the fall was a function of species-specific adult spawner abundance but seasonal patterns of MDN assimilation were masked in complex habitat where summer MDN assimilation remained high.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s10021-019-00406-5","usgsCitation":"Joy, P., Stricker, C.A., Ivanoff, R., Wipfli, M.S., Seitz, A., and Tyers, M., 2020, Bridging the gap between salmon spawner abundance and marine nutrient assimilation by juvenile salmon: Seasonal cycles and landscape effects at the watershed scale: Ecosystems, v. 23, p. 338-358, https://doi.org/10.1007/s10021-019-00406-5.","productDescription":"21 p.","startPage":"338","endPage":"358","ipdsId":"IP-103525","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":388655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Unalakleet River drainage","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -161.3671875,\n              63.35212928507874\n            ],\n            [\n              -156.09375,\n              63.35212928507874\n            ],\n            [\n              -156.09375,\n              65.164578884019\n            ],\n            [\n              -161.3671875,\n              65.164578884019\n            ],\n            [\n              -161.3671875,\n              63.35212928507874\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2019-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Joy, Philip J.","contributorId":264888,"corporation":false,"usgs":false,"family":"Joy","given":"Philip J.","affiliations":[{"id":54573,"text":"AK FG","active":true,"usgs":false}],"preferred":false,"id":822142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ivanoff, Renae","contributorId":264889,"corporation":false,"usgs":false,"family":"Ivanoff","given":"Renae","affiliations":[{"id":54574,"text":"norton sound","active":true,"usgs":false}],"preferred":false,"id":822144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":822145,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seitz, Andrew C.","contributorId":264890,"corporation":false,"usgs":false,"family":"Seitz","given":"Andrew C.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":822146,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tyers, Matthew","contributorId":264891,"corporation":false,"usgs":false,"family":"Tyers","given":"Matthew","email":"","affiliations":[{"id":54573,"text":"AK FG","active":true,"usgs":false}],"preferred":false,"id":822147,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70217878,"text":"70217878 - 2020 - Revisiting “An Exercise in Groundwater Model Calibration and Prediction” after 30 years: Insights and New Directions","interactions":[],"lastModifiedDate":"2021-02-09T13:19:53.893989","indexId":"70217878","displayToPublicDate":"2019-05-22T07:18:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Revisiting “An Exercise in Groundwater Model Calibration and Prediction” after 30 years: Insights and New Directions","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; the primary forecasts of interest were heads in pumping wells after a river was modified. The model was calibrated using manual trial‐and‐error approaches where a model's forecast quality was not related to how well it was calibrated. Here, we investigate whether tools widely available today obviate the shortcomings identified 30 years ago. A reconstructed version of the 1988 true model was tested using increasing parameter estimation sophistication. The parameter estimation demonstrated the inverse problem was non‐unique because only head data were available for calibration. When a flux observation was included, current parameter estimation approaches were able to overcome all calibration and forecast issues noted in 1988. The best forecasts were obtained from a highly parameterized model that used pilot points for hydraulic conductivity and was constrained with soft knowledge. Like the 1988 results, however, the best calibrated model did not produce the best forecasts due to parameter overfitting. Finally, a computationally frugal linear uncertainty analysis demonstrated that the single‐zone model was oversimplified, with only half of the forecasts falling within the calculated uncertainty bounds. Uncertainties from the highly parameterized models had all six forecasts within the calculated uncertainty. The current results outperformed those of the 1988 effort, demonstrating the value of quantitative parameter estimation and uncertainty analysis methods.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12907","usgsCitation":"Hunt, R., Fienen, M., and White, J., 2020, Revisiting “An Exercise in Groundwater Model Calibration and Prediction” after 30 years: Insights and New Directions: Groundwater, v. 58, no. 2, p. 168-182, https://doi.org/10.1111/gwat.12907.","productDescription":"15 p.","startPage":"168","endPage":"182","ipdsId":"IP-102385","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":458763,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.12907","text":"Publisher Index Page"},{"id":437225,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96A7ZC9","text":"USGS data release","linkHelpText":" MODFLOW-2005 Models for Revisiting 'An Exercise in Groundwater Model Calibration and Prediction'"},{"id":383147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":208800,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[],"preferred":true,"id":810014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":248830,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":50032,"text":"GNS New Zealand","active":true,"usgs":false}],"preferred":false,"id":810016,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70270778,"text":"70270778 - 2020 - Using environmental DNA (eDNA) to assess the presence of cavefish and cave crayfish populations in caves of the Ozark Highlands","interactions":[],"lastModifiedDate":"2025-08-27T14:58:10.15187","indexId":"70270778","displayToPublicDate":"2019-03-03T09:54:08","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-135-2020","title":"Using environmental DNA (eDNA) to assess the presence of cavefish and cave crayfish populations in caves of the Ozark Highlands","docAbstract":"<p>Many cavefishes and cave crayfishes are considered of conservation concern; however, sampling these species is inherently difficult given their occupied environments. The goal of our project was to verify the presence of select karst organisms while developing the foundation for sampling approaches that might be useful to conservation and management agencies. Our project objectives were to develop assays to amplify deoxyribonucleic acid (DNA) from several species of Ozark cavefishes and cave crayfishes and complete an initial surveillance of locations across the Ozark Highlands using environmental DNA (eDNA). Using DNA either provided by agency cooperators or that we extracted from tissue samples, we PCR amplified and then sequenced the Cytochrome Oxidase 1 (CO1) gene for cave crayfishes and the NADH Dehydrogenase Subunit 2 (ND2) gene for cavefishes. We developed species-specific primers and probes for five cave crayfishes and two cavefishes. From February 2017 to May 2017, we sampled 1–5 sampling units from 42 caves, wells, and springs (i.e., sites) using eDNA and traditional visual surveys. We measured physicochemical parameters at each sampling unit to estimate detection probability associated with both techniques. We also calculated two occupancy covariates for each site using geospatial data. We successfully amplified Troglichthys rosae DNA from the environment and detected DNA representing this species at 24 of 40 sites. At 16 of the sites where we detected T. rosae DNA, we did not visually observe the species. Although our assay for Typlichthys eigenmanni successfully amplified the target DNA from the environment, it also resulted in false absences where the species was visually confirmed. Using eDNA to detect cave crayfishes was much more difficult. The assay for Cambarus subterraneus did not work for eDNA samples and we were unable to pick up DNA from the environment, even at locations where it was visually confirmed. Alternatively, the eDNA surveys worked well for C. tartarus and we were able to amplify DNA at every site where it was visually observed. Our assay for C. aculabrum was based on a single sample obtained from GenBank, and did not amplify eDNA from field samples. Lastly, our eDNA results from samples in the known range of Orconectes stygocaneyi suggested the species may be found at an additional cave. Detection using eDNA based on our O. stygocaneyi assay was likely low because it was designed from a pseudogene; however, positive eDNA samples were sequenced to confirm species-specific DNA. Detection probability of both cavefishes and cave crayfishes varied by survey technique and was influenced by water volume, water clarity, water velocity, and substrate. Detection of cavefishes and cave crayfishes via visual surveys decreased when water volume increased, whereas detection using eDNA increased with greater water volume. Detection between taxa using either sample method was highest in habitats classified by fine substrates, except for eDNA detection of crayfishes which was greatest in coarse substrates. Detection of cavefishes increased with water clarity, but detection of cave crayfishes increased with turbidity. Detection probability of both cavefishes and crayfishes using eDNA increased slightly with water velocity, but decreased with visual surveys as water velocity increased. Occupancy by both taxa was positively related to particular geologic series. Crayfish occupancy was negatively related to fine-scale anthropogenic disturbance (i.e., 500-m buffer around the site), whereas crayfish showed no relationship with disturbance. Our results suggest possible range extensions, provide insights to factors driving detection using both sample techniques, and suggest areas where recharge zones may be shared among caves. Future efforts focused on a comprehensive evaluation of genetic diversity among cave crayfishes to improve assay design could improve detection and the applicability of eDNA as a supplemental and non-invasive sampling approach.&nbsp;</p>","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Brewer, S., Mouser, J., and Van Den Bussche, R., 2020, Using environmental DNA (eDNA) to assess the presence of cavefish and cave crayfish populations in caves of the Ozark Highlands: Cooperator Science Series CSS-135-2020, ii, 62 p.","productDescription":"ii, 62 p.","ipdsId":"IP-106157","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494683,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/using-environmental-dna-edna-assess-presence-cavefish-and-cave-crayfish-populations-caves"},{"id":494946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mouser, Joshua B.","contributorId":341406,"corporation":false,"usgs":false,"family":"Mouser","given":"Joshua B.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Den Bussche, Ronald A.","contributorId":305751,"corporation":false,"usgs":false,"family":"Van Den Bussche","given":"Ronald A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947052,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
]}