{"pageNumber":"593","pageRowStart":"14800","pageSize":"25","recordCount":46681,"records":[{"id":70045371,"text":"70045371 - 2013 - Descriptions and characterizations of water-level data and groundwater flow for the Brewster Boulevard and Castle Hayne Aquifer Systems and the Tarawa Terrace Aquifer","interactions":[],"lastModifiedDate":"2014-06-20T14:09:30","indexId":"70045371","displayToPublicDate":"2013-01-01T10:59:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Descriptions and characterizations of water-level data and groundwater flow for the Brewster Boulevard and Castle Hayne Aquifer Systems and the Tarawa Terrace Aquifer","docAbstract":"This supplement of Chapter A (Supplement 3) summarizes results of analyses of groundwater-level data and describes corresponding elements of groundwater flow such as vertical hydraulic gradients useful for groundwater-flow model calibration. Field data as well as theoretical concepts indicate that potentiometric surfaces within the study area are shown to resemble to a large degree a subdued replica of surface topography. Consequently, precipitation that infiltrates to the water table flows laterally from highland to lowland areas and eventually discharges to streams such as Northeast and Wallace Creeks and New River. Vertically downward hydraulic gradients occur in highland areas resulting in the transfer of groundwater from shallow relatively unconfined aquifers to underlying confined or semi-confined aquifers. Conversely, in the vicinity of large streams such as Wallace and Frenchs Creeks, diffuse upward leakage occurs from underlying confined or semi-confined aquifers. Point water-level data indicating water-table altitudes, water-table altitudes estimated using a regression equation, and estimates of stream levels determined from a digital elevation model (DEM) and topographic maps were used to estimate a predevelopment water-table surface in the study area. Approximate flow lines along hydraulic gradients are shown on a predevelopment potentiometric surface map and extend from highland areas where potentiometric levels are greatest toward streams such as Wallace Creek and Northeast Creek. The distribution of potentiometric levels and corresponding groundwater-flow directions conform closely to related descriptions of the conceptual model.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Analyses and historical reconstruction of groundwater flow, contaminant fate and transport, and distribution of drinking water within the service areas of the Hadnot Point and Holcomb Boulevard Water Treatment Plants and Vicinities, U.S. Marine Corps Base Camp Lejeune, North Carolina","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"Agency for Toxic Substances and Disease Registry","publisherLocation":"Atlanta, GA","usgsCitation":"Faye, R.E., Jones, L.E., and Suárez-Soto, R., 2013, Descriptions and characterizations of water-level data and groundwater flow for the Brewster Boulevard and Castle Hayne Aquifer Systems and the Tarawa Terrace Aquifer, v, 102 p.","productDescription":"v, 102 p.","numberOfPages":"112","ipdsId":"IP-044303","costCenters":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"links":[{"id":275567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"U.S. Marine Corps Base Camp Lejeune","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -77.642065,34.449951 ], [ -77.642065,34.824047 ], [ -77.065869,34.824047 ], [ -77.065869,34.449951 ], [ -77.642065,34.449951 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51f8e061e4b0cecbe8fa9864","contributors":{"authors":[{"text":"Faye, Robert E.","contributorId":92221,"corporation":false,"usgs":true,"family":"Faye","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":477309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, L. Elliott 0000-0002-7394-2053 lejones@usgs.gov","orcid":"https://orcid.org/0000-0002-7394-2053","contributorId":44569,"corporation":false,"usgs":true,"family":"Jones","given":"L.","email":"lejones@usgs.gov","middleInitial":"Elliott","affiliations":[],"preferred":false,"id":477308,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suárez-Soto, René J.","contributorId":11101,"corporation":false,"usgs":true,"family":"Suárez-Soto","given":"René J.","affiliations":[],"preferred":false,"id":477307,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70048592,"text":"70048592 - 2013 - Pacific island landbird monitoring annual report, National Park of American Samoa, Ta‘u and Tutuila units, 2011","interactions":[],"lastModifiedDate":"2016-08-08T08:55:33","indexId":"70048592","displayToPublicDate":"2013-01-01T10:51:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":272,"text":"National Park Service Natural Resource Technical Report","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"NPS/PACN/NRTR—2013/666","title":"Pacific island landbird monitoring annual report, National Park of American Samoa, Ta‘u and Tutuila units, 2011","docAbstract":"<p>The National Park of American Samoa (NPSA) was surveyed for landbirds and habitat characteristics from June through August, 2011. This information provides the first data in the time-series of landbird monitoring for long-term trends in forest bird distribution, density, and abundance within the NPSA. The NPSA survey area was comprised of the terrestrial portions of the Ta&lsquo;u and Tutuila Units. Each Unit was surveyed using point-transect distance sampling to estimate bird abundance. Sampling was conducted using a split-panel design where legacy transects are visited during each sampling occasion and newly, randomly located transects are visited only during one sampling occasion. This design optimizes trend detection while allowing for measuring and correcting for estimator bias.</p>\n<p>A total of 2,516 birds was detected from 13 species in both Units. All species were either endemic or indigenous to the islands of American Samoa. Numbers of detections ranged from 7 to 1,111. Nearly every species detected was broadly distributed in the predominantly native forests of NPSA. Sufficient detections were made of seven species, allowing for density estimation. Densities of species were higher in the Tutuila Unit; with the exception of the Wattled Honeyeater (<i>Foulehaio carunculata</i>), which was the most abundant species in both Units. The species occurred at nearly every station sampled and had densities much higher than the Samoan Starling (<i>Aplonis atrifusca</i>), Polynesian Starling (<i>Aplonis tabuensis</i>), and Collared Kingfisher (<i>Halcyon chloris</i>) which occurred in modest densities. The remaining species detected occurred at less than 20% of stations sampled and we were only able to determine the number of birds per station and percent occurrence. The White-rumped Swiftlet (<i>Aerodramus spodiopygius</i>) and Cardinal Honeyeater (<i>Myzomela cardinalis</i>) were detected in small numbers, but both species can be difficult to detect in closed canopy forests. The Purple Swamphen (<i>Porphyrio porphyrio</i>) and Banded Rail (<i>Gallirallus philippensis</i>) were most often detected in areas close to villages and agroforestry plantations. The Blue-crowned Lorikeet (<i>Vini australis</i>) and Fiji Shrikebill (<i>Clytorhynchus vitiensis</i>) only occur in the Manu&lsquo;a Island Group. The former was detected in most survey areas and the latter was patchily distributed in the Ta&lsquo;u Unit. The Many-colored Fruit-dove (<i>Ptilinopus perousii</i>), a species of concern, was detected in very small numbers in both Units. The Spotless Crake (<i>Porzana tabuensi</i>), which is extirpated on Tutuila Island, has been incidentally detected in small numbers on Ta&lsquo;u Island. However, the species was neither seen nor heard during this survey and remains a species of concern.</p>\n<p>NPSA canopy and understory composition was predominantly native, and trees formed a dense closed canopy at nearly 90% of the stations sampled. More than half of the tree heights in both units were taller than 5 m and the majority of slopes were steeper than 20 degrees. There were no clear dominant tree species in the mixed native forests. The most common tree species documented included <i>Syzygium</i> spp., <i>Dysoxylum</i> spp., <i>Ficus</i> spp., <i>Hibiscus tiliaceus</i> and <i>Rhus taitensis</i> (among others). There were significant differences in the distribution of bird densities between legacy and random transects. Determining differences in detection probabilities cannot be definitively assessed from a single survey. We recommend both panels be sampled in the future until bias in density and abundance can be evaluated, or if sampling may be reduced.</p>","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Judge, S.W., Camp, R., Vaivai, V., and Hart, P., 2013, Pacific island landbird monitoring annual report, National Park of American Samoa, Ta‘u and Tutuila units, 2011: National Park Service Natural Resource Technical Report NPS/PACN/NRTR—2013/666, xv, 85 p.","productDescription":"xv, 85 p.","startPage":"1","endPage":"85","numberOfPages":"106","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037067","costCenters":[],"links":[{"id":279169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279168,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/App/Reference/Profile/2192630"}],"country":"United States","otherGeospatial":"American Samoa;National Park Of American Samoa","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -170.8903,-14.42 ], [ -170.8903,-14.1396 ], [ -169.3821,-14.1396 ], [ -169.3821,-14.42 ], [ -170.8903,-14.42 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528c96b5e4b0c629af44ddd4","contributors":{"authors":[{"text":"Judge, Seth W.","contributorId":8718,"corporation":false,"usgs":true,"family":"Judge","given":"Seth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":485154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J.","contributorId":27392,"corporation":false,"usgs":true,"family":"Camp","given":"Richard J.","affiliations":[],"preferred":false,"id":485155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaivai, Visa","contributorId":96992,"corporation":false,"usgs":true,"family":"Vaivai","given":"Visa","email":"","affiliations":[],"preferred":false,"id":485157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick J.","contributorId":79750,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick J.","affiliations":[],"preferred":false,"id":485156,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70140352,"text":"70140352 - 2013 - Expression of terrain and surface geology in high-resolution helicopter-borne gravity gradient (AGG) data: examples from Great Sand Dunes National Park, Rio Grande Rift, Colorado","interactions":[],"lastModifiedDate":"2015-02-09T09:33:20","indexId":"70140352","displayToPublicDate":"2013-01-01T10:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3568,"text":"The Leading Edge","active":true,"publicationSubtype":{"id":10}},"title":"Expression of terrain and surface geology in high-resolution helicopter-borne gravity gradient (AGG) data: examples from Great Sand Dunes National Park, Rio Grande Rift, Colorado","docAbstract":"<p>Airborne gravity gradient (AGG) data are rapidly becoming standard components of geophysical mapping programs, due to their advantages in cost, access, and resolution advantages over measurements of the gravity field on the ground. Unlike conventional techniques that measure the gravity field, AGG methods measure derivatives of the gravity field. This means that effects of terrain and near-surface geology are amplified in AGG data, and that proper terrain corrections are critically important for AGG data processing. However, terrain corrections require reasonable estimates of density for the rocks and sediments that make up the terrain. A recommended philosophical approach is to use the terrain and surface geology, with their strong expression in AGG data, to the interpreter&rsquo;s advantage. An example of such an approach is presented here for an area with very difficult ground access and little ground gravity data. Nettleton-style profiling is used with AGG data to estimate the densities of the sand dunefield and adjacent Precambrian rocks from the area of Great Sand Dunes National Park in southern Colorado. Processing of the AGG data using the density estimate for the dunefield allows buried structures, including a hypothesized buried basement bench, to be mapped beneath the sand dunes.</p>","language":"English","publisher":"Society of Exploration Geophysicists","publisherLocation":"Tulsa, OK","doi":"10.1190/tle32080924.1","usgsCitation":"Drenth, B.J., 2013, Expression of terrain and surface geology in high-resolution helicopter-borne gravity gradient (AGG) data: examples from Great Sand Dunes National Park, Rio Grande Rift, Colorado: The Leading Edge, v. 32, no. 8, p. 924-930, https://doi.org/10.1190/tle32080924.1.","productDescription":"7 p.","startPage":"924","endPage":"930","numberOfPages":"7","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044714","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":297830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":297829,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://tle.geoscienceworld.org/content/32/8/924.abstract"}],"country":"United States","state":"Colorado","otherGeospatial":"Great Sand Dunes National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.05029296875,\n              36.99377838872517\n            ],\n            [\n              -109.05029296875,\n              41.0130657870063\n            ],\n            [\n              -102.030029296875,\n              41.0130657870063\n            ],\n            [\n              -102.030029296875,\n              36.99377838872517\n            ],\n            [\n              -109.05029296875,\n              36.99377838872517\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"32","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2b9be4b08de9379b3425","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":539998,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70112474,"text":"70112474 - 2013 - Harmonizing multiple methods for reconstructing historical potential and reference evapotranspiration","interactions":[],"lastModifiedDate":"2014-07-28T08:47:26","indexId":"70112474","displayToPublicDate":"2013-01-01T10:35:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Harmonizing multiple methods for reconstructing historical potential and reference evapotranspiration","docAbstract":"Potential evapotranspiration (PET) and reference evapotranspiration (RET) data are usually critical components of hydrologic analysis. Many different equations are available to estimate PET and RET. Most of these equations, such as the Priestley-Taylor and Penman- Monteith methods, rely on detailed meteorological data collected at ground-based weather stations. Few weather stations collect enough data to estimate PET or RET using one of the more complex evapotranspiration equations. Currently, satellite data integrated with ground meteorological data are used with one of these evapotranspiration equations to accurately estimate PET and RET. However, earlier than the last few decades, historical reconstructions of PET and RET needed for many hydrologic analyses are limited by the paucity of satellite data and of some types of ground data. Air temperature stands out as the most generally available meteorological ground data type over the last century. Temperature-based approaches used with readily available historical temperature data offer the potential for long period-of-record PET and RET historical reconstructions. A challenge is the inconsistency between the more accurate, but more data intensive, methods appropriate for more recent periods and the less accurate, but less data intensive, methods appropriate to the more distant past. In this study, multiple methods are harmonized in a seamless reconstruction of historical PET and RET by quantifying and eliminating the biases of the simple Hargreaves-Samani method relative to the more complex and accurate Priestley-Taylor and Penman-Monteith methods. This harmonization process is used to generate long-term, internally consistent, spatiotemporal databases of PET and RET.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Hydrologic Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Civil Engineers","publisherLocation":"New York, NY","doi":"10.1061/(ASCE)HE.1943-5584.0000935","usgsCitation":"Belaineh, G., Sumner, D., Carter, E., and Clapp, D., 2013, Harmonizing multiple methods for reconstructing historical potential and reference evapotranspiration: Journal of Hydrologic Engineering, v. 19, no. 8, 8 p., https://doi.org/10.1061/(ASCE)HE.1943-5584.0000935.","productDescription":"8 p.","numberOfPages":"8","ipdsId":"IP-039256","costCenters":[{"id":285,"text":"Florida Water Science Center","active":false,"usgs":true}],"links":[{"id":288621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288619,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000935"}],"country":"United States","state":"Florida","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -84.0,27.0 ], [ -84.0,31.0 ], [ -80.0,31.0 ], [ -80.0,27.0 ], [ -84.0,27.0 ] ] ] } } ] }","volume":"19","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7736e4b0abf75cf2c0a7","contributors":{"authors":[{"text":"Belaineh, Getachew","contributorId":37262,"corporation":false,"usgs":true,"family":"Belaineh","given":"Getachew","email":"","affiliations":[],"preferred":false,"id":494756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sumner, David","contributorId":63731,"corporation":false,"usgs":true,"family":"Sumner","given":"David","affiliations":[],"preferred":false,"id":494758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, Edward","contributorId":49714,"corporation":false,"usgs":true,"family":"Carter","given":"Edward","email":"","affiliations":[],"preferred":false,"id":494757,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clapp, David","contributorId":10338,"corporation":false,"usgs":true,"family":"Clapp","given":"David","email":"","affiliations":[],"preferred":false,"id":494755,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70039019,"text":"70039019 - 2013 - Using habitat suitability models to target invasive plant species surveys","interactions":[],"lastModifiedDate":"2014-01-15T10:37:19","indexId":"70039019","displayToPublicDate":"2013-01-01T10:30:01","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Using habitat suitability models to target invasive plant species surveys","docAbstract":"Managers need new tools for detecting the movement and spread of nonnative, invasive species. Habitat suitability models are a popular tool for mapping the potential distribution of current invaders, but the ability of these models to prioritize monitoring efforts has not been tested in the field. We tested the utility of an iterative sampling design (i.e., models based on field observations used to guide subsequent field data collection to improve the model), hypothesizing that model performance would increase when new data were gathered from targeted sampling using criteria based on the initial model results. We also tested the ability of habitat suitability models to predict the spread of invasive species, hypothesizing that models would accurately predict occurrences in the field, and that the use of targeted sampling would detect more species with less sampling effort than a nontargeted approach. We tested these hypotheses on two species at the state scale (<i>Centaurea stoebe</i> and <i>Pastinaca sativa</i>) in Wisconsin (USA), and one genus at the regional scale (<i>Tamarix</i>) in the western United States. These initial data were merged with environmental data at 30-m<sup>2</sup> resolution for Wisconsin and 1-km<sup>2</sup> resolution for the western United States to produce our first iteration models. We stratified these initial models to target field sampling and compared our models and success at detecting our species of interest to other surveys being conducted during the same field season (i.e., nontargeted sampling). Although more data did not always improve our models based on correct classification rate (CCR), sensitivity, specificity, kappa, or area under the curve (AUC), our models generated from targeted sampling data always performed better than models generated from nontargeted data. For Wisconsin species, the model described actual locations in the field fairly well (kappa = 0.51, 0.19, P < 0.01), and targeted sampling did detect more species than nontargeted sampling with less sampling effort (χ<sup>2</sup>) = 47.42, P < 0.01). From these findings, we conclude that habitat suitability models can be highly useful tools for guiding invasive species monitoring, and we support the use of an iterative sampling design for guiding such efforts.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Applications","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","publisherLocation":"Tempe, AZ","doi":"10.1890/12-0465.1","usgsCitation":"Crall, A.W., Jarnevich, C.S., Panke, B., Young, N., Renz, M., and Morisette, J., 2013, Using habitat suitability models to target invasive plant species surveys: Ecological Applications, v. 23, no. 1, p. 60-72, https://doi.org/10.1890/12-0465.1.","productDescription":"13 p.","startPage":"60","endPage":"72","numberOfPages":"13","ipdsId":"IP-039050","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":281074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281073,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/12-0465.1"}],"country":"United States","state":"Wisconsin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.88,25.85 ], [ -124.88,49.04 ], [ -86.76,49.04 ], [ -86.76,25.85 ], [ -124.88,25.85 ] ] ] } } ] }","volume":"23","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd7acae4b0b2908510db58","contributors":{"authors":[{"text":"Crall, Alycia W.","contributorId":60123,"corporation":false,"usgs":true,"family":"Crall","given":"Alycia","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":465451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":465448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panke, Brendon","contributorId":22244,"corporation":false,"usgs":true,"family":"Panke","given":"Brendon","email":"","affiliations":[],"preferred":false,"id":465449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Nick","contributorId":28489,"corporation":false,"usgs":true,"family":"Young","given":"Nick","email":"","affiliations":[],"preferred":false,"id":465450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renz, Mark","contributorId":89440,"corporation":false,"usgs":true,"family":"Renz","given":"Mark","affiliations":[],"preferred":false,"id":465452,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morisette, Jeffrey","contributorId":100739,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","affiliations":[],"preferred":false,"id":465453,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70143942,"text":"70143942 - 2013 - Home range and use of habitat of western yellow-billed cuckoos on the middle Rio Grande, New Mexico","interactions":[],"lastModifiedDate":"2018-01-05T12:39:06","indexId":"70143942","displayToPublicDate":"2013-01-01T10:30:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3451,"text":"Southwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Home range and use of habitat of western yellow-billed cuckoos on the middle Rio Grande, New Mexico","docAbstract":"<p>The western yellow-billed cuckoo (Coccyzus americanus occidentalis) is a Distinct Population Segment that has been proposed for listing under the Endangered Species Act, yet very little is known about its spatial use on the breeding grounds. We implemented a study, using radio telemetry, of home range and use of habitat for breeding cuckoos along the Middle Rio Grande in central New Mexico in 2007 and 2008. Nine of 13 cuckoos were tracked for sufficient time to generate estimates of home range. Overall size of home ranges for the 2 years was 91 ha for a minimum-convex-polygon estimate and 62 ha for a 95%-kernel-home-range estimate. Home ranges varied considerably among individuals, highlighting variability in spatial use by cuckoos. Additionally, use of habitat differed between core areas and overall home ranges, but the differences were nonsignificant. Home ranges calculated for western yellow-billed cuckoos on the Middle Rio Grande are larger than those in other southwestern riparian areas. Based on calculated home ranges and availability of riparian habitat in the study area, we estimate that the study area is capable of supporting 82-99 nonoverlapping home ranges of cuckoos. Spatial data from this study should contribute to the understanding of the requirements of area and habitat of this species for management of resources and help facilitate recovery if a listing occurs.</p>","language":"English","publisher":"Southwestern Association of Naturalists","publisherLocation":"Dallas, TX","doi":"10.1894/0038-4909-58.4.411","usgsCitation":"Sechrist, J., Ahlers, D., Potak Zehfuss, K., Doster, R., Paxton, E., and Ryan, V.M., 2013, Home range and use of habitat of western yellow-billed cuckoos on the middle Rio Grande, New Mexico: Southwestern Naturalist, v. 58, no. 4, p. 411-419, https://doi.org/10.1894/0038-4909-58.4.411.","productDescription":"9 p.","startPage":"411","endPage":"419","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-029723","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":298889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.61407470703125,\n              35.282621700715175\n            ],\n            [\n              -106.59896850585938,\n              35.240011164750456\n            ],\n            [\n              -106.64978027343749,\n              35.2108436808834\n            ],\n            [\n              -106.70196533203125,\n              35.13675607652669\n            ],\n            [\n              -106.710205078125,\n              35.0873268458165\n            ],\n            [\n              -106.66488647460936,\n          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Darrell","contributorId":139821,"corporation":false,"usgs":false,"family":"Ahlers","given":"Darrell","affiliations":[{"id":13285,"text":"U.S. Bureau of Reclamation, Denver Technical Svc Center","active":true,"usgs":false}],"preferred":false,"id":543117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Potak Zehfuss, Katherine","contributorId":139823,"corporation":false,"usgs":false,"family":"Potak Zehfuss","given":"Katherine","email":"","affiliations":[{"id":13286,"text":"North Wind Inc, Denver Tech Svc Center, Denver","active":true,"usgs":false}],"preferred":false,"id":543119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doster, Robert","contributorId":139824,"corporation":false,"usgs":false,"family":"Doster","given":"Robert","affiliations":[{"id":13287,"text":"U.S. Fish and Wildlife Svc, Pacific SW Region, Willows, CA","active":true,"usgs":false}],"preferred":false,"id":543120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":false,"id":543116,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ryan, Vicky M.","contributorId":65742,"corporation":false,"usgs":true,"family":"Ryan","given":"Vicky","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":543121,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70125648,"text":"70125648 - 2013 - Comparing mechanisms of host manipulation across host and parasite taxa","interactions":[],"lastModifiedDate":"2017-06-30T15:14:15","indexId":"70125648","displayToPublicDate":"2013-01-01T10:22:27","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2275,"text":"Journal of Experimental Biology","active":true,"publicationSubtype":{"id":10}},"title":"Comparing mechanisms of host manipulation across host and parasite taxa","docAbstract":"Parasites affect host behavior in several ways. They can alter activity, microhabitats or both. For trophically transmitted parasites (the focus of our study), decreased activity might impair the ability of hosts to respond to final-host predators, and increased activity and altered microhabitat choice might increase contact rates between hosts and final-host predators. In an analysis of trophically transmitted parasites, more parasite groups altered activity than altered microhabitat choice. Parasites that infected vertebrates were more likely to impair the host’s reaction to predators, whereas parasites that infected invertebrates were more likely to increase the host’s contact with predators. The site of infection might affect how parasites manipulate their hosts. For instance, parasites in the central nervous system seem particularly suited to manipulating host behavior. Manipulative parasites commonly occupy the body cavity, muscles and central nervous systems of their hosts. Acanthocephalans in the data set differed from other taxa in that they occurred exclusively in the body cavity of invertebrates. In addition, they were more likely to alter microhabitat choice than activity. Parasites in the body cavity (across parasite types) were more likely to be associated with increased host contact with predators. Parasites can manipulate the host through energetic drain, but most parasites use more sophisticated means. For instance, parasites target four physiological systems that shape behavior in both invertebrates and vertebrates: neural, endocrine, neuromodulatory and immunomodulatory. The interconnections between these systems make it difficult to isolate specific mechanisms of host behavioral manipulation.","language":"English","publisher":"The Company of Biologists","doi":"10.1242/jeb.073668","usgsCitation":"Lafferty, K.D., and Shaw, J., 2013, Comparing mechanisms of host manipulation across host and parasite taxa: Journal of Experimental Biology, v. 216, p. 56-66, https://doi.org/10.1242/jeb.073668.","productDescription":"11 p.","startPage":"56","endPage":"66","numberOfPages":"11","ipdsId":"IP-038578","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":474005,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1242/jeb.073668","text":"Publisher Index Page"},{"id":294117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294034,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1242/jeb.073668"}],"volume":"216","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541bf421e4b0e96537ddf66f","contributors":{"authors":[{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaw, Jenny C.","contributorId":7196,"corporation":false,"usgs":true,"family":"Shaw","given":"Jenny C.","affiliations":[],"preferred":false,"id":501536,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70048595,"text":"70048595 - 2013 - Pacific Island landbird monitoring annual report, Haleakalā National Park, 2012","interactions":[],"lastModifiedDate":"2014-06-20T14:14:19","indexId":"70048595","displayToPublicDate":"2013-01-01T10:16:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":272,"text":"National Park Service Natural Resource Technical Report","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"NPS/PACN/NRTR—2013/740","title":"Pacific Island landbird monitoring annual report, Haleakalā National Park, 2012","docAbstract":"<p>Haleakalā National Park (HALE) was surveyed for landbirds and habitat characteristics from March 20 through July 26, 2012. This information provides data in the time-series of landbird monitoring for long-term trends in forest bird distribution, density, and abundance. The Kīpahulu District of eastern Haleakalā Volcano was surveyed using point-transect distance sampling to estimate bird abundance. We surveyed 160 stations and detected a total of 2,830 birds from 12 species. Half of the species were native and half were non-native. Numbers of detections per species ranged from 1 to 849. There were sufficient detections of seven species to allow density estimation. Āpapane (<i>Himatione sanguinea</i>) was the most widely distributed and abundant native species detected in the survey. ‘Alauahio (<i>Paroreomyza montana newtoni</i>), Maui ‘Amakihi (<i>Hemignathus virens wilsoni</i>), and I‘iwi (<i>Vestiaria coccinea</i>) were widespread and occurred in relatively modest densities. Only eight Kiwikiu (<i>Pseudonestor xanthophrys</i>) and 20 ‘Ākohekohe (<i>Palmeria dolei</i>) were detected and were restricted to high elevation wet forest. We estimated an abundance of 495 ± 261individuals of Kiwikiu in a 2,036 ha inference area which likely includes the entire suitable habitat for this species in HALE. For ‘Ākohekohe, we estimated an abundance of 1,150 ± 389 individuals in the 1,458 ha inference area. There was a strong representation of non-native landbirds in the survey area. The Japanese White-eye (<i>Zosterops japonicus</i>), Japanese Bush-warbler (<i>Cettia diphone</i>), and Red-billed Leiothrix (<i>Leiothrix lutea</i>) accounted for nearly half of all landbird detections. Each species was common in predominantly native forests.</p>\n<br/>\n<p>Vegetation and topographic characteristics were recorded on 160 landbird monitoring stations. HALE canopy and understory composition was predominantly native, especially at elevations above 1,100 m. Much of the forest canopy was comprised of `ohi`a (<i>Metrosideros polymorpha</i>) interspersed with mature olapa (<i>Cheirodendron platyphyllum</i>). This canopy class occurred at 92.5% of the stations surveyed. More than three-quarters (77.5%) of the monitoring stations had a dense canopy with most crowns interlocking (> 60% cover). More than half (52%) of the stations surveyed had trees taller than 10 m, while almost a third (31%) had trees 5-10 m. Only 17% of the stations had a canopy shorter than 5 m. The native shrubs <i>Vaccinium calycinum</i>, <i>Broussaisia arguta</i>, and <i>Leptecophylla tameiameae</i> were the most common understory plants recorded, occurring at more than 30% of the stations sampled. Native mosses and ferns were also common at stations, occurring at more than 90% of the stations sampled. The invasive <i>Psidium cattleainum</i>, <i>Clidemia hirta</i>, and <i>Hedychium gardnerianum</i> occurred at approximately 14% of the stations sampled, predominantly at elevations below 1,100 m.</p>","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Judge, S.W., Camp, R., and Hart, P., 2013, Pacific Island landbird monitoring annual report, Haleakalā National Park, 2012: National Park Service Natural Resource Technical Report NPS/PACN/NRTR—2013/740, ix, 82 p.","productDescription":"ix, 82 p.","numberOfPages":"96","ipdsId":"IP-044651","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":279162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":279174,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/App/Reference/Profile/2195246"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Haleakala National Park;Maui","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -156.275743,20.586349 ], [ -156.275743,20.795098 ], [ -156.020951,20.795098 ], [ -156.020951,20.586349 ], [ -156.275743,20.586349 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528c96b5e4b0c629af44ddd1","contributors":{"authors":[{"text":"Judge, Seth W.","contributorId":8718,"corporation":false,"usgs":true,"family":"Judge","given":"Seth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":485169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J.","contributorId":27392,"corporation":false,"usgs":true,"family":"Camp","given":"Richard J.","affiliations":[],"preferred":false,"id":485170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Patrick J.","contributorId":79750,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick J.","affiliations":[],"preferred":false,"id":485171,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70113284,"text":"70113284 - 2013 - SPARROW models used to understand nutrient sources in the Mississippi/Atchafalaya River Basin","interactions":[],"lastModifiedDate":"2018-02-06T12:25:58","indexId":"70113284","displayToPublicDate":"2013-01-01T10:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"SPARROW models used to understand nutrient sources in the Mississippi/Atchafalaya River Basin","docAbstract":"Nitrogen (N) and phosphorus (P) loading from the Mississippi/Atchafalaya River Basin (MARB) has been linked to hypoxia in the Gulf of Mexico. To describe where and from what sources those loads originate, SPAtially Referenced Regression On Watershed attributes (SPARROW) models were constructed for the MARB using geospatial datasets for 2002, including inputs from wastewater treatment plants (WWTPs), and calibration sites throughout the MARB. Previous studies found that highest N and P yields were from the north-central part of the MARB (Corn Belt). Based on the MARB SPARROW models, highest N yields were still from the Corn Belt but centered over Iowa and Indiana, and highest P yields were widely distributed throughout the center of the MARB. Similar to that found in other studies, agricultural inputs were found to be the largest N and P sources throughout most of the MARB: farm fertilizers were the largest N source, whereas farm fertilizers, manure, and urban inputs were dominant P sources. The MARB models enable individual N and P sources to be defined at scales ranging from SPARROW catchments (∼50 km<sup>2</sup>) to the entire area of the MARB. Inputs of P from WWTPs and urban areas were more important than found in most other studies. Information from this study will help to reduce nutrient loading from the MARB by providing managers with a description of where each of the sources of N and P are most important, thus providing a basis for prioritizing management actions and ultimately reducing the extent of Gulf hypoxia.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Environmental Quality","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Agronomy","doi":"10.2134/jeq2013.02.0066","usgsCitation":"Robertson, D.M., and Saad, D.A., 2013, SPARROW models used to understand nutrient sources in the Mississippi/Atchafalaya River Basin: Journal of Environmental Quality, v. 42, no. 5, p. 1422-1440, https://doi.org/10.2134/jeq2013.02.0066.","productDescription":"19 p.","startPage":"1422","endPage":"1440","numberOfPages":"19","ipdsId":"IP-043684","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":474009,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2013.02.0066","text":"Publisher Index Page"},{"id":288956,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288911,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2134/jeq2013.02.0066"}],"country":"United States","otherGeospatial":"Mississippi/atchafalaya River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.05,29.63 ], [ -116.05,49.0 ], [ -76.27,49.0 ], [ -76.27,29.63 ], [ -116.05,29.63 ] ] ] } } ] }","volume":"42","issue":"5","noUsgsAuthors":false,"publicationDate":"2013-09-01","publicationStatus":"PW","scienceBaseUri":"53ae7818e4b0abf75cf2c9cc","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":495040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saad, David A. dasaad@usgs.gov","contributorId":121,"corporation":false,"usgs":true,"family":"Saad","given":"David","email":"dasaad@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":495041,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70114667,"text":"70114667 - 2013 - The influence of precipitation, vegetation and soil properties on the ecohydrology of sagebrush steppe rangelands on the INL site","interactions":[],"lastModifiedDate":"2014-07-03T09:55:45","indexId":"70114667","displayToPublicDate":"2013-01-01T09:52:42","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"The influence of precipitation, vegetation and soil properties on the ecohydrology of sagebrush steppe rangelands on the INL site","docAbstract":"<p>The INL Site and other landscapes having sagebrush steppe vegetation are experiencing a simultaneous change in climate and floristics that result from increases in exotic species. Determining the separate and combined/interactive effects of climate and vegetation change is important for assessing future changes on the landscape and for hydrologic processes.</p>\n<br/>\n<p>This research uses the 72 experimental plots established and initially maintained for many years as the “Protective Cap Biobarrier Experiment” by Dr. Jay Anderson and the Stoller ESER program, and the experiment is also now referred to as the “INL Site Ecohydrology Study.” We are evaluating long-term impacts of different plant communities commonly found throughout Idaho subject to different precipitation regimes and to different soil depths. Treatments of amount and timing of precipitation (irrigation), soil depth, and either native/perennial or exotic grass vegetation allow researchers to investigate how vegetation, precipitation and soil interact to influence soil hydrology and ecosystem biogeochemistry. This information will be used to improve a variety of models, as well as provide data for these models.</p>","language":"English","publisher":"National Laboratory Site Enviromental Surveillance, Education, and Research Program","publisherLocation":"Broomfield, CO","usgsCitation":"Germino, M., 2013, The influence of precipitation, vegetation and soil properties on the ecohydrology of sagebrush steppe rangelands on the INL site, 1 p.","productDescription":"1 p.","numberOfPages":"1","ipdsId":"IP-053875","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":289094,"type":{"id":15,"text":"Index Page"},"url":"https://www.gsseser.com/LandManagement/ecohydrology2012.html"},{"id":289416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b67b84e4b014fc094d5477","contributors":{"authors":[{"text":"Germino, Matthew J.","contributorId":50029,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[],"preferred":false,"id":495400,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70125273,"text":"70125273 - 2013 - A natural resource condition assessment for Sequoia and Kings Canyon National Parks: Appendix 22: climatic change","interactions":[],"lastModifiedDate":"2014-09-25T09:56:39","indexId":"70125273","displayToPublicDate":"2013-01-01T09:52:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SEKI/NRR--2013/665.22","title":"A natural resource condition assessment for Sequoia and Kings Canyon National Parks: Appendix 22: climatic change","docAbstract":"<p>Climate is a master controller of the structure, composition, and function of biotic communities, \naffecting them both directly, through physiological effects, and indirectly, by mediating biotic \ninteractions and by influencing disturbance regimes. Sequoia and Kings Canyon National Park’s \n(SEKI’s) dramatic elevational changes in biotic communities -- from warm mediterranean to \ncold alpine -- are but one manifestation of climate’s overarching importance in shaping SEKI’s \nlandscape. </p>\n<br>\n<p>Yet humans are now altering the global climate, with measurable effects on ecosystems (IPCC \n2007). Over the last few decades across the western United States, human-induced climatic \nchanges have likely contributed to observed declines in fraction of precipitation falling as snow \nand snowpack water content (Mote et al. 2005, Knowles et al. 2006), advance in spring \nsnowmelt (Stewart et al. 2005, Barnett et al. 2008), and consequent increase in area burned in \nwildfires (Westerling et al. 2006). In the Sierra Nevada, warming temperatures have likely \ncontributed to observed glacial recession (Basagic 2008), uphill migration of small mammals \n(Moritz et al. 2008), and increasing tree mortality rates (van Mantgem and Stephenson 2007, van \nMantgem et al. 2009). More substantial changes can be expected for the future (e.g., IPCC \n2007).</p>\n<br>\n<p>Given the central importance of climate and climatic changes, we sought to describe long-term \ntrends in temperature and precipitation at SEKI. Time and budget constraints limited us to \nanalyses of mean annual temperature and mean annual precipitation, using readily-available data. \nIf funds become available in the future, further analyses will be needed to analyze trends by \nseason, trends in daily minimum and maximum temperatures, and so on.</p>\n<br>\n<p>We chose to analyze data from individual weather stations rather than use interpolated climatic \ndata from sources such as PRISM (http://www.prism.oregonstate.edu/). In topographically \ncomplex mountainous regions with few weather stations, like SEKI, the addition or subtraction \nof even a single weather station through time has the potential to significantly bias trends in \ninterpolated data. In particular, this analysis was motivated by our questioning of some PRISM \nresults presented in Appendix 1 (Landscape Context) that compared temperature averages \nbetween two 30-year periods of the 20th Century. Figures 6 and 11 of Appendix 1 indicate that \nrecent (1971-2000) temperatures in northern Kings Canyon National Park averaged some 2° C \ncooler than those of 1911-1940. This would represent a truly profound and persistent cooling, \nand seems to be at odds both with the glacial retreats observed in the area over the century \n(Basagic 2008), and with the reported PRISM warming of nearly 2° C just to the west of the \ncooling (see Figs. 6 and 11 in Appendix 1). We suspect that the extreme localized Kings Canyon \ncooling reported by PRISM is an artifact of sparsely-distributed weather stations in the region \nbeing added and discontinued over the span of the 20th Century. For example, data from the \nWestern Regional Climate Center (http://www.wrcc.dri.edu/coopmap/) suggest that for the \nperiod 1911 through 1924 PRISM must interpolate northern Kings Canyon temperatures based \non a few low-elevation stations -- separated by hundreds of kilometers -- in Nevada and \nCalifornia’s San Joaquin Valley. In contrast, by 1970 PRISM interpolations will be dominated \nby closer, higher-elevation stations (see this report). The single weather station closest to \nnorthern Kings Canyon that has a temperature record at least partly spanning Appendix 1’s two\n30-year time periods -- the Independence station, with a relatively continuous temperature record \nstarting in 1925 -- shows a modest warming, not a cooling, between 1925-1940 and 1971-2000, \nfurther casting doubt on the Kings Canyon cooling shown in Figs. 6 and 11 of Appendix 1. If \nfunds become available, it will be useful to more formally analyze potential PRISM biases in \nlong-term SEKI climatic trends. Until then, the analyses of individual weather station records \npresented here (effectively an analysis of source data that PRISM uses) are meant to provide a \nrobust summary of climatic changes in SEKI.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"A natural resource condition assessment for Sequoia and Kings Canyon National Parks","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"Das, A., and Stephenson, N.L., 2013, A natural resource condition assessment for Sequoia and Kings Canyon National Parks: Appendix 22: climatic change: Natural Resource Report NPS/SEKI/NRR--2013/665.22, v, 28 p.","productDescription":"v, 28 p.","numberOfPages":"36","ipdsId":"IP-039290","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":294467,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294466,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/App/Reference/Profile/2195963"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park;Sequoia National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -118.983208,36.118448 ], [ -118.983208,37.237613 ], [ -118.020777,37.237613 ], [ -118.020777,36.118448 ], [ -118.983208,36.118448 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54252e99e4b0e641df8a6e1c","contributors":{"authors":[{"text":"Das, Adrian J. 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":3842,"corporation":false,"usgs":true,"family":"Das","given":"Adrian J.","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501081,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70121475,"text":"70121475 - 2013 - Monitoring vegetation response to episodic disturbance events by using multitemporal vegetation indices","interactions":[],"lastModifiedDate":"2019-07-01T11:46:55","indexId":"70121475","displayToPublicDate":"2013-01-01T09:51:00","publicationYear":"2013","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":"Monitoring vegetation response to episodic disturbance events by using multitemporal vegetation indices","docAbstract":"<p><span>Normalized Difference Vegetation Index (NDVI) derived from MODerate-resolution Imaging Spectroradiometer (MODIS) satellite imagery and land/water assessments from Landsat Thematic Mapper (TM) imagery were used to quantify the extent and severity of damage and subsequent recovery after Hurricanes Katrina and Rita of 2005 within the vegetation communities of Louisiana's coastal wetlands. Field data on species composition and total live cover were collected from 232 unique plots during multiple time periods to corroborate changes in NDVI values over time. Aprehurricane 5-year baseline time series clearly identified NDVI values by habitat type, suggesting the sensitivity of NDVI to assess and monitor phenological changes in coastal wetland habitats. Monthly data from March 2005 to November 2006 were compared to the baseline average to create a departure from average statistic. Departures suggest that over 33% (4,714 km</span><sup>2</sup><span>) of the prestorm, coastal wetlands experienced a substantial decline in the density and vigor of vegetation by October 2005 (poststorm), mostly in the east and west regions, where landfalls of Hurricanes Katrina and Rita occurred. The percentage of area of persistent vegetation damage due to long-lasting formation of new open water was 91.8% in the east and 81.0% and 29.0% in the central and west regions, respectively. Although below average NDVI values were observed in most marsh communities through November 2006, recovery of vegetation was evident. Results indicated that impacts and recovery from large episodic disturbance events that influence multiple habitat types can be accurately determined using NDVI, especially when integrated with assessments of physical landscape changes and field verifications.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/SI63-011.1","usgsCitation":"Steyer, G.D., Couvillion, B.R., and Barras, J., 2013, Monitoring vegetation response to episodic disturbance events by using multitemporal vegetation indices: Journal of Coastal Research, no. 63, p. 118-130, https://doi.org/10.2112/SI63-011.1.","productDescription":"13 p.","startPage":"118","endPage":"130","numberOfPages":"13","ipdsId":"IP-035355","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":292831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.0434,28.9254 ], [ -94.0434,30.5829 ], [ -88.8162,30.5829 ], [ -88.8162,28.9254 ], [ -94.0434,28.9254 ] ] ] } } ] }","issue":"63","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53f85975e4b03f038c5c1872","contributors":{"authors":[{"text":"Steyer, Gregory D. 0000-0001-7231-0110 steyerg@usgs.gov","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":2856,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","email":"steyerg@usgs.gov","middleInitial":"D.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":499102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Couvillion, Brady R. 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":3829,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","middleInitial":"R.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":499101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barras, John A. jbarras@usgs.gov","contributorId":2425,"corporation":false,"usgs":true,"family":"Barras","given":"John A.","email":"jbarras@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":499103,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70138950,"text":"70138950 - 2013 - The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data","interactions":[],"lastModifiedDate":"2015-01-26T09:31:15","indexId":"70138950","displayToPublicDate":"2013-01-01T09:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data","docAbstract":"<p>The movement of animals is strongly influenced by external factors in their surrounding environment such as weather, habitat types, and human land use. With advances in positioning and sensor technologies, it is now possible to capture animal locations at high spatial and temporal granularities. Likewise, scientists have an increasing access to large volumes of environmental data. Environmental data are heterogeneous in source and format, and are usually obtained at different spatiotemporal scales than movement data. Indeed, there remain scientific and technical challenges in developing linkages between the growing collections of animal movement data and the large repositories of heterogeneous remote sensing observations, as well as in the developments of new statistical and computational methods for the analysis of movement in its environmental context. These challenges include retrieval, indexing, efficient storage, data integration, and analytical techniques.</p>","language":"English","publisher":"Minerva Center for Movement Ecology","publisherLocation":"London","doi":"10.1186/2051-3933-1-3","usgsCitation":"Dodge, S., Bohrer, G., Weinzierl, R.P., Davidson, S.C., Kays, R., Douglas, D.C., Cruz, S., Han, J., Brandes, D., and Wikelski, M., 2013, The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data: Movement Ecology, v. 1, no. 3, p. 1-14, https://doi.org/10.1186/2051-3933-1-3.","productDescription":"14 p.","startPage":"1","endPage":"14","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044477","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":474012,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/2051-3933-1-3","text":"Publisher Index Page"},{"id":297505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":297497,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1186/2051-3933-1-3"}],"volume":"1","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2013-07-03","publicationStatus":"PW","scienceBaseUri":"54dd2c6ce4b08de9379b37d2","contributors":{"authors":[{"text":"Dodge, Somayeh","contributorId":138916,"corporation":false,"usgs":false,"family":"Dodge","given":"Somayeh","email":"","affiliations":[],"preferred":false,"id":539210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohrer, Gil","contributorId":66569,"corporation":false,"usgs":true,"family":"Bohrer","given":"Gil","affiliations":[],"preferred":false,"id":539211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weinzierl, Rolf P.","contributorId":74687,"corporation":false,"usgs":true,"family":"Weinzierl","given":"Rolf","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":539212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davidson, Sarah C.","contributorId":31651,"corporation":false,"usgs":true,"family":"Davidson","given":"Sarah","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":539213,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kays, Roland","contributorId":83815,"corporation":false,"usgs":true,"family":"Kays","given":"Roland","affiliations":[],"preferred":false,"id":539214,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":539215,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cruz, Sebastian","contributorId":26987,"corporation":false,"usgs":true,"family":"Cruz","given":"Sebastian","affiliations":[],"preferred":false,"id":539216,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Han, J.","contributorId":52442,"corporation":false,"usgs":true,"family":"Han","given":"J.","affiliations":[],"preferred":false,"id":539217,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brandes, David","contributorId":138917,"corporation":false,"usgs":false,"family":"Brandes","given":"David","email":"","affiliations":[{"id":35653,"text":"Lafayette College, Easton, PA","active":true,"usgs":false}],"preferred":false,"id":539218,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wikelski, Martin","contributorId":76451,"corporation":false,"usgs":true,"family":"Wikelski","given":"Martin","affiliations":[],"preferred":false,"id":539219,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70121460,"text":"70121460 - 2013 - Marsh collapse thresholds for coastal Louisiana estimated using elevation and vegetation index data","interactions":[],"lastModifiedDate":"2014-08-22T09:46:03","indexId":"70121460","displayToPublicDate":"2013-01-01T09:42:00","publicationYear":"2013","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":"Marsh collapse thresholds for coastal Louisiana estimated using elevation and vegetation index data","docAbstract":"<p>Forecasting marsh collapse in coastal Louisiana as a result of changes in sea-level rise, subsidence, and accretion deficits necessitates an understanding of thresholds beyond which inundation stress impedes marsh survival. The variability in thresholds at which different marsh types cease to occur (i.e., marsh collapse) is not well understood. We utilized remotely sensed imagery, field data, and elevation data to help gain insight into the relationships between vegetation health and inundation. A Normalized Difference Vegetation Index (NDVI) dataset was calculated using remotely sensed data at peak biomass (August) and used as a proxy for vegetation health and productivity. Statistics were calculated for NDVI values by marsh type for intermediate, brackish, and saline marsh in coastal Louisiana. Marsh-type specific NDVI values of 1.5 and 2 standard deviations below the mean were used as upper and lower limits to identify conditions indicative of collapse. As marshes seldom occur beyond these values, they are believed to represent a range within which marsh collapse is likely to occur. Inundation depth was selected as the primary candidate for evaluation of marsh collapse thresholds. Elevation relative to mean water level (MWL) was calculated by subtracting MWL from an elevation dataset compiled from multiple data types including light detection and ranging (lidar) and bathymetry. A polynomial cubic regression was used to examine a random subset of pixels to determine the relationship between elevation (relative to MWL) and NDVI. The marsh collapse uncertainty range values were found by locating the intercept of the regression line with the 1.5 and 2 standard deviations below the mean NDVI value for each marsh type. Results indicate marsh collapse uncertainty ranges of 30.7–35.8 cm below MWL for intermediate marsh, 20–25.6 cm below MWL for brackish marsh, and 16.9–23.5 cm below MWL for saline marsh. These values are thought to represent the ranges of inundation depths within which marsh collapse is probable.</p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Coastal Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/SI63-006.1","usgsCitation":"Couvillion, B., and Beck, H., 2013, Marsh collapse thresholds for coastal Louisiana estimated using elevation and vegetation index data: Journal of Coastal Research, p. 58-67, https://doi.org/10.2112/SI63-006.1.","productDescription":"10 p.","startPage":"58","endPage":"67","numberOfPages":"10","ipdsId":"IP-035354","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":292826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":292823,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2112/SI63-006.1"}],"country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.0434,28.9254 ], [ -94.0434,30.6491 ], [ -88.8162,30.6491 ], [ -88.8162,28.9254 ], [ -94.0434,28.9254 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53f8596ae4b03f038c5c1847","contributors":{"authors":[{"text":"Couvillion, Brady R. 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":98834,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady R.","affiliations":[],"preferred":false,"id":499081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, Holly 0000-0002-0567-9329","orcid":"https://orcid.org/0000-0002-0567-9329","contributorId":54714,"corporation":false,"usgs":true,"family":"Beck","given":"Holly","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":499080,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70124603,"text":"70124603 - 2013 - Flying with the wind: Scale dependency of speed and direction measurements in modelling wind support in avian flight","interactions":[],"lastModifiedDate":"2017-08-30T10:29:43","indexId":"70124603","displayToPublicDate":"2013-01-01T09:38:23","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Flying with the wind: Scale dependency of speed and direction measurements in modelling wind support in avian flight","docAbstract":"<p><strong>Background</strong>: Understanding how environmental conditions, especially wind, influence birds' flight speeds is a prerequisite for understanding many important aspects of bird flight, including optimal migration strategies, navigation, and compensation for wind drift. Recent developments in tracking technology and the increased availability of data on large-scale weather patterns have made it possible to use path annotation to link the location of animals to environmental conditions such as wind speed and direction. However, there are various measures available for describing not only wind conditions but also the bird's flight direction and ground speed, and it is unclear which is best for determining the amount of wind support (the length of the wind vector in a bird’s flight direction) and the influence of cross-winds (the length of the wind vector perpendicular to a bird’s direction) throughout a bird's journey.</p><p><strong>Results</strong>: We compared relationships between cross-wind, wind support and bird movements, using path annotation derived from two different global weather reanalysis datasets and three different measures of direction and speed calculation for 288 individuals of nine bird species. Wind was a strong predictor of bird ground speed, explaining 10-66% of the variance, depending on species. Models using data from different weather sources gave qualitatively similar results; however, determining flight direction and speed from successive locations, even at short (15 min intervals), was inferior to using instantaneous GPS-based measures of speed and direction. Use of successive location data significantly underestimated the birds' ground and airspeed, and also resulted in mistaken associations between cross-winds, wind support, and their interactive effects, in relation to the birds' onward flight.</p><p><strong>Conclusions</strong>: Wind has strong effects on bird flight, and combining GPS technology with path annotation of weather variables allows us to quantify these effects for understanding flight behaviour. The potentially strong influence of scaling effects must be considered and implemented in developing sampling regimes and data analysis.</p>","language":"English","publisher":"BioMed Central","doi":"10.1186/2051-3933-1-4","usgsCitation":"Safi, K., Kranstauber, B., Weinzierl, R.P., Griffin, L., Reese, E.C., Cabot, D., Cruz, S., Proaño, C., Takekawa, J.Y., Newman, S.H., Waldenstrom, J., Bengtsson, D., Kays, R., Wikelski, M., and Bohrer, G., 2013, Flying with the wind: Scale dependency of speed and direction measurements in modelling wind support in avian flight: Movement Ecology, v. 1, no. 4, 13 p., https://doi.org/10.1186/2051-3933-1-4.","productDescription":"13 p.","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-046325","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":474015,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/2051-3933-1-4","text":"Publisher Index Page"},{"id":293800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"4","noUsgsAuthors":false,"publicationDate":"2013-07-03","publicationStatus":"PW","scienceBaseUri":"54140b1fe4b082fed288b912","contributors":{"authors":[{"text":"Safi, Kamran","contributorId":83036,"corporation":false,"usgs":true,"family":"Safi","given":"Kamran","affiliations":[],"preferred":false,"id":519464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranstauber, Bart","contributorId":66610,"corporation":false,"usgs":true,"family":"Kranstauber","given":"Bart","affiliations":[],"preferred":false,"id":519461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weinzierl, Rolf P.","contributorId":74687,"corporation":false,"usgs":true,"family":"Weinzierl","given":"Rolf","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":519462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffin, Larry","contributorId":108038,"corporation":false,"usgs":true,"family":"Griffin","given":"Larry","email":"","affiliations":[],"preferred":false,"id":519467,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reese, Eileen C.","contributorId":30157,"corporation":false,"usgs":true,"family":"Reese","given":"Eileen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":519457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cabot, David","contributorId":13160,"corporation":false,"usgs":true,"family":"Cabot","given":"David","email":"","affiliations":[],"preferred":false,"id":519454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cruz, Sebastian","contributorId":26987,"corporation":false,"usgs":true,"family":"Cruz","given":"Sebastian","affiliations":[],"preferred":false,"id":519455,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Proaño, Carolina","contributorId":28180,"corporation":false,"usgs":true,"family":"Proaño","given":"Carolina","affiliations":[],"preferred":false,"id":519456,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":519453,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Newman, Scott H.","contributorId":101372,"corporation":false,"usgs":true,"family":"Newman","given":"Scott","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":519466,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Waldenstrom, Jonas","contributorId":42891,"corporation":false,"usgs":true,"family":"Waldenstrom","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":519458,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bengtsson, Daniel","contributorId":56168,"corporation":false,"usgs":true,"family":"Bengtsson","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":519459,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kays, Roland","contributorId":83815,"corporation":false,"usgs":true,"family":"Kays","given":"Roland","affiliations":[],"preferred":false,"id":519465,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wikelski, Martin","contributorId":76451,"corporation":false,"usgs":true,"family":"Wikelski","given":"Martin","affiliations":[],"preferred":false,"id":519463,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Bohrer, Gil","contributorId":66569,"corporation":false,"usgs":true,"family":"Bohrer","given":"Gil","affiliations":[],"preferred":false,"id":519460,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}}
,{"id":70095678,"text":"70095678 - 2013 - The magnetic tides of Honolulu","interactions":[],"lastModifiedDate":"2014-03-10T09:26:40","indexId":"70095678","displayToPublicDate":"2013-01-01T09:20:00","publicationYear":"2013","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"The magnetic tides of Honolulu","docAbstract":"We review the phenomenon of time-stationary, periodic quiet-time geomagnetic tides. These are generated by the ionospheric and oceanic dynamos, and, to a lesser-extent, by the quiet-time magnetosphere, and they are affected by currents induced in the Earth's electrically conducting interior. We examine historical time series of hourly magnetic-vector measurements made at the Honolulu observatory. We construct high-resolution, frequency-domain Lomb-periodogram and maximum-entropy power spectra that reveal a panorama of stationary harmonics across periods from 0.1 to 10000.0-d, including harmonics that result from amplitude and phase modulation. We identify solar-diurnal tides and their annual and solar-cycle sideband modulations, lunar semi-diurnal tides and their solar-diurnal sidebands, and tides due to precession of lunar eccentricity and nodes. We provide evidence that a method intended for separating the ionospheric and oceanic dynamo signals by midnight subsampling of observatory data time series is prone to frequency-domain aliasing. The tidal signals we summarize in this review can be used to test our fundamental understanding of the dynamics of the quiet-time ionosphere and magnetosphere, induction in the ocean and in the electrically conducting interior of the Earth, and they are useful for defining a quiet-time baseline against which magnetospheric-storm intensity is measured.","largerWorkTitle":"Progress in EM Induction Studies of Crust and Mantle From Land, Sea, Air, and Space lll Posters","language":"English","publisher":"American Geophysical Union","usgsCitation":"Love, J.J., and Rigler, E.J., 2013, The magnetic tides of Honolulu, <i>in</i> Progress in EM Induction Studies of Crust and Mantle From Land, Sea, Air, and Space lll Posters.","ipdsId":"IP-055292","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":283503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":283502,"type":{"id":1,"text":"Abstract"},"url":"https://abstractsearch.agu.org/meetings/2013/FM/sections/GP/sessions/GP23A/abstracts/GP23A-0983.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd7830e4b0b2908510bfb4","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":491338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigler, Erin Joshua","contributorId":85502,"corporation":false,"usgs":true,"family":"Rigler","given":"Erin","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":491339,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70104965,"text":"70104965 - 2013 - Estimating abundance of the Southern Hudson Bay polar bear subpopulation using aerial surveys, 2011 and 2012","interactions":[],"lastModifiedDate":"2016-07-12T15:11:31","indexId":"70104965","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":5138,"text":"Wildlife Research Series","active":true,"publicationSubtype":{"id":4}},"seriesNumber":"2013-01","title":"Estimating abundance of the Southern Hudson Bay polar bear subpopulation using aerial surveys, 2011 and 2012","docAbstract":"<p>The Southern Hudson Bay (SH) polar bear subpopulation occurs at the southern extent of the species&rsquo; range. Although capture-recapture studies indicate that abundance remained stable between 1986 and 2005, declines in body condition and survival were documented during the period, possibly foreshadowing a future decrease in abundance. To obtain a current estimate of abundance, we conducted a comprehensive line transect aerial survey of SH during 2011&ndash;2012. We stratified the study site by anticipated densities and flew coastal contour transects and systematically spaced inland transects in Ontario and on Akimiski Island and large offshore islands in 2011. Data were collected with double observer and distance sampling protocols. We also surveyed small islands in Hudson Bay and James Bay and flew a comprehensive transect along the Qu&eacute;bec coastline in 2012. We observed 667 bears in Ontario and on Akimiski Island and nearby islands in 2011, and we sighted 80 bears on offshore islands during 2012. Mark-recapture distance sampling and sightresight models yielded a model-averaged estimate of 868 (SE: 177) for the 2011 study area. Our estimate of abundance for the entire SH subpopulation (951; SE: 177) suggests that abundance has remained unchanged. However, this result should be interpreted cautiously because of the methodological differences between historical studies (physical capture) and this survey. A conservative management approach is warranted given the previous increases in the duration of the ice-free season, which are predicted to continue in the future, and previously documented declines in body condition and vital rates.</p>","language":"English","publisher":"Ontario Ministry of Natural Resources, Science and Research Branch","publisherLocation":"Peterborough, Ontario, Canada","isbn":"978-1-4606-3321-2","usgsCitation":"Obbard, M.E., Middel, K.R., Stapleton, S.P., Thibault, I., Brodeur, V., and Jutras, C., 2013, Estimating abundance of the Southern Hudson Bay polar bear subpopulation using aerial surveys, 2011 and 2012: Wildlife Research Series 2013-01, 33 p.","productDescription":"33 p.","numberOfPages":"35","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052644","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":325121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"Hudson Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.857421875,\n              50.14874640066278\n            ],\n            [\n              -88.857421875,\n              60.174306261926034\n            ],\n            [\n              -72.6416015625,\n              60.174306261926034\n            ],\n            [\n              -72.6416015625,\n              50.14874640066278\n            ],\n            [\n              -88.857421875,\n              50.14874640066278\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"579dcfdee4b0589fa1cbd7f5","contributors":{"authors":[{"text":"Obbard, Martyn E.","contributorId":108002,"corporation":false,"usgs":false,"family":"Obbard","given":"Martyn","email":"","middleInitial":"E.","affiliations":[{"id":6780,"text":"Ontario Ministry of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":548211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middel, Kevin R.","contributorId":141065,"corporation":false,"usgs":false,"family":"Middel","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":548212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stapleton, Seth P. sstapleton@usgs.gov","contributorId":3979,"corporation":false,"usgs":true,"family":"Stapleton","given":"Seth","email":"sstapleton@usgs.gov","middleInitial":"P.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":518862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thibault, Isabelle","contributorId":141066,"corporation":false,"usgs":false,"family":"Thibault","given":"Isabelle","email":"","affiliations":[],"preferred":false,"id":548213,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brodeur, Vincent","contributorId":141067,"corporation":false,"usgs":false,"family":"Brodeur","given":"Vincent","email":"","affiliations":[],"preferred":false,"id":548214,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jutras, Charles","contributorId":141068,"corporation":false,"usgs":false,"family":"Jutras","given":"Charles","email":"","affiliations":[],"preferred":false,"id":548215,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70048335,"text":"70048335 - 2013 - Generalized additive regression models of discharge and mean velocity associated with direct-runoff conditions in Texas: Utility of the U.S. Geological Survey discharge measurement database","interactions":[],"lastModifiedDate":"2017-04-25T13:04:35","indexId":"70048335","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Generalized additive regression models of discharge and mean velocity associated with direct-runoff conditions in Texas: Utility of the U.S. Geological Survey discharge measurement database","docAbstract":"<p><span>A database containing more than 17,700 discharge values and ancillary hydraulic properties was assembled from summaries of discharge measurement records for 424 U.S. Geological Survey streamflow-gauging stations (stream gauges) in Texas. Each discharge exceeds the 90th-percentile daily mean streamflow as determined by period-of-record, stream-gauge-specific, flow-duration curves. Each discharge therefore is assumed to represent discharge measurement made during direct-runoff conditions. The hydraulic properties of each discharge measurement included concomitant cross-sectional flow area, water-surface top width, and reported mean velocity. Systematic and statewide investigation of these data in pursuit of regional models for the estimation of discharge and mean velocity has not been previously attempted. Generalized additive regression modeling is used to develop readily implemented procedures by end-users for estimation of discharge and mean velocity from select predictor variables at ungauged stream locations. The discharge model uses predictor variables of cross-sectional flow area, top width, stream location, mean annual precipitation, and a generalized terrain and climate index (OmegaEM) derived for a previous flood-frequency regionalization study. The mean velocity model uses predictor variables of discharge, top width, stream location, mean annual precipitation, and OmegaEM. The discharge model has an adjusted R-squared value of about 0.95 and a residual standard error (RSE) of about 0.22 base-10 logarithm (cubic meters per second); the mean velocity model has an adjusted R-squared value of about 0.67 and an RSE of about 0.063 fifth root (meters per second). Example applications and computations using both regression models are provided. - See more at: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000635#sthash.jhGyPxgZ.dpuf</span></p>","publisher":"American Society of Civil Engineers","doi":"10.1061/(ASCE)HE.1943-5584.0000635","usgsCitation":"Asquith, W.H., Herrmann, G.R., and Cleveland, T., 2013, Generalized additive regression models of discharge and mean velocity associated with direct-runoff conditions in Texas: Utility of the U.S. Geological Survey discharge measurement database: Journal of Hydrologic Engineering, v. 18, no. 10, p. 1331-1348, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000635.","productDescription":"18 p.","startPage":"1331","endPage":"1348","ipdsId":"IP-039500","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":340267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006066e4b0e85db3a5de0b","contributors":{"authors":[{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":518200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrmann, George R.","contributorId":191361,"corporation":false,"usgs":false,"family":"Herrmann","given":"George","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":692815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cleveland, Theodore G.","contributorId":88029,"corporation":false,"usgs":true,"family":"Cleveland","given":"Theodore G.","affiliations":[],"preferred":false,"id":692816,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70047654,"text":"70047654 - 2013 - Capture-recapture methodology","interactions":[],"lastModifiedDate":"2015-01-16T15:15:59","indexId":"70047654","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Capture-recapture methodology","docAbstract":"<p>Capture-recapture methods were initially developed to estimate human population abundance, but since that time have seen widespread use for fish and wildlife populations to estimate and model various parameters of population, metapopulation, and disease dynamics. Repeated sampling of marked animals provides information for estimating abundance and tracking the fate of individuals in the face of imperfect detection. Mark types have evolved from clipping or tagging to use of noninvasive methods such as photography of natural markings and DNA collection from feces. Survival estimation has been emphasized more recently as have transition probabilities between life history states and/or geographical locations, even where some states are unobservable or uncertain. Sophisticated software has been developed to handle highly parameterized models, including environmental and individual covariates, to conduct model selection, and to employ various estimation approaches such as maximum likelihood and Bayesian approaches. With these user-friendly tools, complex statistical models for studying population dynamics have been made available to ecologists. The future will include a continuing trend toward integrating data types, both for tagged and untagged individuals, to produce more precise and robust population models.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Environmetrics","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9780470057339.vac002.pub2","usgsCitation":"Gould, W., and Kendall, W.L., 2013, Capture-recapture methodology, chap. <i>of</i> Encyclopedia of Environmetrics, https://doi.org/10.1002/9780470057339.vac002.pub2.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037243","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":276696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2013-01-15","publicationStatus":"PW","scienceBaseUri":"520f3bd2e4b0fc50304bc478","contributors":{"authors":[{"text":"Gould, William R.","contributorId":63780,"corporation":false,"usgs":true,"family":"Gould","given":"William R.","affiliations":[],"preferred":false,"id":482638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. wkendall@usgs.gov","contributorId":406,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"wkendall@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":482637,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70148073,"text":"70148073 - 2013 - Demography and population status of polar bears in western Hudson Bay","interactions":[],"lastModifiedDate":"2016-08-16T14:07:46","indexId":"70148073","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Demography and population status of polar bears in western Hudson Bay","docAbstract":"<ul>\n<li>We evaluated the demography and population status of the Western Hudson Bay (WH) polar bear subpopulation for the period 1984-2011, using live-recapture data from research studies and management actions, and dead-recovery data from polar bears harvested for subsistence purposes or removed during human-bear conflicts.</li>\n<li>We used a Bayesian implementation of multistate capture-recapture models, coupled with a matrix-based demographic projection model, to integrate several types of data and to incorporate sampling uncertainty, and demographic and environmental stochasticity across the polar bear life cycle. This approach allowed for estimation of a suite of vital rates, including survival and reproduction. These vital rates were used to parameterize a Bayesian population model to evaluate population trends and project potential population outcomes under different environmental scenarios.</li>\n<li>Survival of female polar bears of all age classes was significantly correlated with sea ice conditions; particularly with the timing of sea ice break-up in the spring and formation in the fall and the interaction of the two. This is consistent with previous findings linking body condition and survival of WH polar bears to environmental changes associated with climatic warming and supports the ecological dependence of polar bears on the availability of sea ice.</li>\n<li>Survival of male polar bears was not correlated with sea ice conditions. This was likely because a higher proportion of mortality for males was caused by humans rather than by natural factors. For example, approximately 73% of mortality for young male bears (i.e., 5-9 years old) was due to direct human-caused removals, largely because of sex selectivity in the subsistence harvest.</li>\n<li>The declining trend in size of the WH subpopulation over the period 1987-2004 was similar to a previous analysis (Regehr et al. 2007), suggesting consistency between the two demographic evaluations. Point estimates of abundance were somewhat lower using the updated statistical approach. It is important to recognize that the analyzed data were not collected in a manner that is optimal for estimating abundance and that the goal of the current analysis was to estimate vital rates and demographic trends.</li>\n<li>Estimates of population growth rate were also derived using a Bayesian population model based on estimated survival and reproductive rates from the multistate capture-recapture model. For the recent decade 2001-2011, the growth rate of the female segment of the population was 1.02 (95% CI = 0.98-1.06). Apparently stable to positive population growth for females may be due in large part to nonlinearity (i.e., short-term stability) in the long-term observed and forecasted trend toward earlier sea ice break-up in western Hudson Bay.</li>\n<li>The 2011 abundance estimate from this analysis was 806 bears with a 95% Bayesian credible interval of 653-984. This is lower than, but broadly consistent with, the abundance estimate of 1,030 (95% confidence interval = 745-1406) from a 2011 aerial survey (Stapleton et al. 2014). The capture-recapture and aerial survey approaches have different spatial and temporal coverage of the WH subpopulation and, consequently, the effective study population considered by each approach is different.</li>\n</ul>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Research Report","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Environment Canada","usgsCitation":"Lunn, N., Regher, E.V., Servanty, S., Converse, S.J., Richardson, E.S., and Stirling, I., 2013, Demography and population status of polar bears in western Hudson Bay, 50 p.","productDescription":"50 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058521","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":326582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b43942e4b03bcb01039fa5","contributors":{"authors":[{"text":"Lunn, Nicholas J.","contributorId":78421,"corporation":false,"usgs":true,"family":"Lunn","given":"Nicholas J.","affiliations":[],"preferred":false,"id":547162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regher, Eric V","contributorId":140838,"corporation":false,"usgs":false,"family":"Regher","given":"Eric","email":"","middleInitial":"V","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":547165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Servanty, Sabrina","contributorId":53296,"corporation":false,"usgs":true,"family":"Servanty","given":"Sabrina","affiliations":[],"preferred":false,"id":547164,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":3513,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":547163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, Evan S.","contributorId":139901,"corporation":false,"usgs":false,"family":"Richardson","given":"Evan","email":"","middleInitial":"S.","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":547166,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stirling, Ian","contributorId":72079,"corporation":false,"usgs":false,"family":"Stirling","given":"Ian","email":"","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":547167,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70136386,"text":"70136386 - 2013 - Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model","interactions":[],"lastModifiedDate":"2015-01-05T09:46:02","indexId":"70136386","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model","docAbstract":"<p><span>Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay Watershed (CBW), which is located in the Mid-Atlantic US, winter cover crop use has been emphasized and federal and state cost-share programs are available to farmers to subsidize the cost of winter cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops at the watershed scale and to identify critical source areas of high nitrate export. A physically-based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data and satellite-based estimates of winter cover crop species performance to simulate hydrological processes and nutrient cycling over the period of 1991&ndash;2000. Multiple scenarios were developed to obtain baseline information on nitrate loading without winter cover crops planted and to investigate how nitrate loading could change with different winter cover crop planting scenarios, including different species, planting times, and implementation areas. The results indicate that winter cover crops had a negligible impact on water budget, but significantly reduced nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading was approximately 14 kg ha</span><sup>&minus;1</sup><span>, but it decreased to 4.6&ndash;10.1 kg ha</span><sup>&minus;1</sup><span>&nbsp;with winter cover crops resulting in a reduction rate of 27&ndash;67% at the watershed scale. Rye was most effective, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of winter cover crops (~30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~2 kg ha</span><sup>&minus;1</sup><span>&nbsp;when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of winter cover crop implementation. Agricultural fields with well-drained soils and those that were more frequently used to grow corn had a higher potential for nitrate leaching and export to the waterways. This study supports the effective implement of winter cover crop programs, in part by helping to target critical pollution source areas for winter cover crop implementation.</span></p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hessd-10-14229-2013","collaboration":"Department of Geographical Sciences, University of Maryland, College Park, MD; USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD; Dream it Do it Western New York, Jamestown, NY; USDA Forest Service, Northern Research Station, Beltsville, MD","usgsCitation":"Yeo, I., Lee, S., Sadeghi, A.M., Beeson, P.C., Hively, W., McCarty, G.W., and Lang, M.W., 2013, Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model: Hydrology and Earth System Sciences, v. 10, no. 11, p. 14229-14263, https://doi.org/10.5194/hessd-10-14229-2013.","productDescription":"35 p.","startPage":"14229","endPage":"14263","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056041","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":474018,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hessd-10-14229-2013","text":"Publisher Index Page"},{"id":296981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay Watershed","volume":"10","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2b3ce4b08de9379b32bf","contributors":{"authors":[{"text":"Yeo, In-Young","contributorId":131145,"corporation":false,"usgs":false,"family":"Yeo","given":"In-Young","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":537473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Sangchui","contributorId":131146,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchui","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":537474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadeghi, Ali M.","contributorId":131147,"corporation":false,"usgs":false,"family":"Sadeghi","given":"Ali","email":"","middleInitial":"M.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":537475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beeson, Peter C.","contributorId":131148,"corporation":false,"usgs":false,"family":"Beeson","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":7263,"text":"Dream it Do it Western New York, Jamestown, NY 14701","active":true,"usgs":false}],"preferred":false,"id":537476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hively, W. Dean whively@usgs.gov","contributorId":4919,"corporation":false,"usgs":true,"family":"Hively","given":"W. Dean","email":"whively@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":537472,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCarty, Greg W.","contributorId":131149,"corporation":false,"usgs":false,"family":"McCarty","given":"Greg","email":"","middleInitial":"W.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":537477,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lang, Megan W.","contributorId":131150,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":7264,"text":"USDA Forest Service, Northern Research Station, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":537478,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70138191,"text":"70138191 - 2013 - Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?","interactions":[],"lastModifiedDate":"2015-01-15T11:45:59","indexId":"70138191","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?","docAbstract":"<p><span>In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.</span><span><br /></span></p>","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-17-1575-2013","usgsCitation":"Archfield, S.A., Pugliese, A., Castellarin, A., Skoien, J.O., and Kiang, J.E., 2013, Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?: Hydrology and Earth System Sciences, v. 17, p. 1575-1588, https://doi.org/10.5194/hess-17-1575-2013.","productDescription":"14 p.","startPage":"1575","endPage":"1588","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-041594","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":474174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-17-1575-2013","text":"Publisher Index Page"},{"id":297289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -171.73828125,\n              17.97873309555617\n            ],\n            [\n              -171.73828125,\n              71.35706654962706\n            ],\n            [\n              -66.26953125,\n              71.35706654962706\n            ],\n            [\n              -66.26953125,\n              17.97873309555617\n            ],\n            [\n              -171.73828125,\n              17.97873309555617\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"17","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2013-04-23","publicationStatus":"PW","scienceBaseUri":"54dd2c72e4b08de9379b3803","contributors":{"authors":[{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":538597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pugliese, Alessio","contributorId":138746,"corporation":false,"usgs":false,"family":"Pugliese","given":"Alessio","email":"","affiliations":[{"id":12516,"text":"Dept. DICAM, Sch of CE, U of Bol, Italy","active":true,"usgs":false}],"preferred":false,"id":538598,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castellarin, Attilio","contributorId":138747,"corporation":false,"usgs":false,"family":"Castellarin","given":"Attilio","email":"","affiliations":[{"id":12516,"text":"Dept. DICAM, Sch of CE, U of Bol, Italy","active":true,"usgs":false}],"preferred":false,"id":538599,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skoien, Jon O.","contributorId":138748,"corporation":false,"usgs":false,"family":"Skoien","given":"Jon","email":"","middleInitial":"O.","affiliations":[{"id":12517,"text":"Inst for Env & Sust, JRC, EC, Italy","active":true,"usgs":false}],"preferred":false,"id":538600,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kiang, Julie E. 0000-0003-0653-4225 jkiang@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-4225","contributorId":2179,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","email":"jkiang@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":538601,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70159359,"text":"70159359 - 2013 - Metadata squared: enhancing its usability for volunteered geographic information and the GeoWeb","interactions":[],"lastModifiedDate":"2015-10-22T17:58:21","indexId":"70159359","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Metadata squared: enhancing its usability for volunteered geographic information and the GeoWeb","docAbstract":"<p><span>The Internet has brought many changes to the way geographic information is created and shared. One aspect that has not changed is metadata. Static spatial data quality descriptions were standardized in the mid-1990s and cannot accommodate the current climate of data creation where nonexperts are using mobile phones and other location-based devices on a continuous basis to contribute data to Internet mapping platforms. The usability of standard geospatial metadata is being questioned by academics and neogeographers alike. This chapter analyzes current discussions of metadata to demonstrate how the media shift that is occurring has affected requirements for metadata. Two case studies of metadata use are presented&mdash;online sharing of environmental information through a regional spatial data infrastructure in the early 2000s, and new types of metadata that are being used today in OpenStreetMap, a map of the world created entirely by volunteers. Changes in metadata requirements are examined for usability, the ease with which metadata supports coproduction of data by communities of users, how metadata enhances findability, and how the relationship between metadata and data has changed. We argue that traditional metadata associated with spatial data infrastructures is inadequate and suggest several research avenues to make this type of metadata more interactive and effective in the GeoWeb.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Crowdsourcing geographic knowledge volunteered geographic information (VGI) in theory and practice","language":"English","publisher":"Springer","publisherLocation":"Dordrecht; New York","doi":"10.1007/978-94-007-4587-2_4","usgsCitation":"Poore, B.S., and Wolf, E.B., 2013, Metadata squared: enhancing its usability for volunteered geographic information and the GeoWeb, chap. <i>of</i> Crowdsourcing geographic knowledge volunteered geographic information (VGI) in theory and practice, p. 43-64, https://doi.org/10.1007/978-94-007-4587-2_4.","productDescription":"21 p.","startPage":"43","endPage":"64","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":310571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2012-06-29","publicationStatus":"PW","scienceBaseUri":"562a08d9e4b011227bf1fd91","contributors":{"editors":[{"text":"Sui, Daniel Z.","contributorId":149381,"corporation":false,"usgs":false,"family":"Sui","given":"Daniel","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":578216,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Elwood, Sarah","contributorId":149382,"corporation":false,"usgs":false,"family":"Elwood","given":"Sarah","email":"","affiliations":[],"preferred":false,"id":578217,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Goodchild, Michael F.","contributorId":149383,"corporation":false,"usgs":false,"family":"Goodchild","given":"Michael","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":578218,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Poore, Barbara S. bspoore@usgs.gov","contributorId":2541,"corporation":false,"usgs":true,"family":"Poore","given":"Barbara","email":"bspoore@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":578214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolf, Eric B. ebwolf@usgs.gov","contributorId":4535,"corporation":false,"usgs":true,"family":"Wolf","given":"Eric","email":"ebwolf@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":true,"id":578215,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70154988,"text":"70154988 - 2013 - Spring migratory pathways and migration chronology of Canada geese (<i>Branta canadensis interior</i>) wintering at the Santee National Wildlife Refuge, South Carolina","interactions":[],"lastModifiedDate":"2020-12-23T14:14:31.829768","indexId":"70154988","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1163,"text":"Canadian Field-Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Spring migratory pathways and migration chronology of Canada geese (<i>Branta canadensis interior</i>) wintering at the Santee National Wildlife Refuge, South Carolina","docAbstract":"<p><span>We assessed the migratory pathways, migration chronology, and breeding ground affiliation of Canada Geese (</span><i>Branta canadensis interior</i><span>) that winter in and adjacent to the Santee National Wildlife Refuge in Summerton, South Carolina, United States. Satellite transmitters were fitted to eight Canada Geese at Santee National Wildlife Refuge during the winter of 2009–2010. Canada Geese departed Santee National Wildlife Refuge between 5 and 7 March 2010. Six Canada Geese followed a route that included stopovers in northeastern North Carolina and western New York, with three of those birds completing spring migration to breeding grounds associated with the Atlantic Population (AP). The mean distance between stopover sites along this route was 417 km, the mean total migration distance was 2838 km, and the Canada Geese arrived on AP breeding grounds on the eastern shore of Hudson Bay between 20 and 24 May 2010. Two Canada Geese followed a different route from that described above, with stopovers in northeastern Ohio, prior to arriving on the breeding grounds on 9 June 2010. Mean distance between stopover sites was 402 and 365 km for these two birds, and total migration distance was 4020 and 3650 km. These data represent the first efforts to track migratory Canada Geese from the southernmost extent of their current wintering range in the Atlantic Flyway. We did not track any Canada Geese to breeding grounds associated with the Southern James Bay Population. Caution should be used in the interpretation of this finding, however, because of the small sample size. We demonstrated that migratory Canada Geese wintering in South Carolina use at least two migratory pathways and that an affiliation with the Atlantic Population breeding ground exists.</span></p>","language":"English","publisher":"The Canadian Field-Naturalist","doi":"10.22621/cfn.v127i1.1402","usgsCitation":"Giles, M.M., Jodice, P.G., Baldwin, R.F., Stanton, J.D., and Epstein, M., 2013, Spring migratory pathways and migration chronology of Canada geese (<i>Branta canadensis interior</i>) wintering at the Santee National Wildlife Refuge, South Carolina: Canadian Field-Naturalist, v. 127, no. 1, p. 17-25, https://doi.org/10.22621/cfn.v127i1.1402.","productDescription":"9 p.","startPage":"17","endPage":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-038246","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":474026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.22621/cfn.v127i1.1402","text":"Publisher Index Page"},{"id":381611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Santee National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.53665161132812,\n              33.4738357141558\n            ],\n            [\n              -80.53665161132812,\n              33.56199537293026\n            ],\n            [\n              -80.4473876953125,\n              33.56199537293026\n            ],\n            [\n              -80.4473876953125,\n              33.4738357141558\n            ],\n            [\n              -80.53665161132812,\n              33.4738357141558\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"127","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2013-07-14","publicationStatus":"PW","scienceBaseUri":"55b0beafe4b09a3b01b530a5","contributors":{"authors":[{"text":"Giles, Molly M.","contributorId":145797,"corporation":false,"usgs":false,"family":"Giles","given":"Molly","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":565331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X pjodice@usgs.gov","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":1119,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","email":"pjodice@usgs.gov","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":564467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldwin, Robert F.","contributorId":96415,"corporation":false,"usgs":true,"family":"Baldwin","given":"Robert","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":565332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanton, John D.","contributorId":145798,"corporation":false,"usgs":false,"family":"Stanton","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":565333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Epstein, Marc","contributorId":145799,"corporation":false,"usgs":false,"family":"Epstein","given":"Marc","email":"","affiliations":[],"preferred":false,"id":565334,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70142504,"text":"70142504 - 2013 - NDVI saturation adjustment: a new approach for improving cropland performance estimates in the Greater Platte River Basin, USA","interactions":[],"lastModifiedDate":"2017-01-18T11:51:16","indexId":"70142504","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"NDVI saturation adjustment: a new approach for improving cropland performance estimates in the Greater Platte River Basin, USA","docAbstract":"<p><span>In this study, we developed a new approach that adjusted normalized difference vegetation index (NDVI) pixel values that were near saturation to better characterize the cropland performance (CP) in the Greater Platte River Basin (GPRB), USA. The relationship between NDVI and the ratio vegetation index (RVI) at high NDVI values was investigated, and an empirical equation for estimating saturation-adjusted NDVI (NDVI</span><sub>sat</sub><span>_</span><sub>adjust</sub><span>) based on RVI was developed. A 10-year (2000&ndash;2009) NDVI</span><sub>sat</sub><span>_</span><sub>adjust</sub><span>&nbsp;data set was developed using 250-m 7-day composite historical eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. The growing season averaged NDVI (GSN), which is a proxy for ecosystem performance, was estimated and long-term NDVI non-saturation- and saturation-adjusted cropland performance (CP</span><sub>non</sub><span>_</span><sub>sat</sub><span>_</span><sub>adjust</sub><span>, CP</span><sub>sat</sub><span>_</span><sub>adjust</sub><span>) maps were produced over the GPRB. The final CP maps were validated using National Agricultural Statistics Service (NASS) crop yield data. The relationship between CP</span><sub>sat</sub><span>_</span><sub>adjust</sub><span>&nbsp;and the NASS average corn yield data (</span><i>r</i><span>&nbsp;=&nbsp;0.78, 113 samples) is stronger than the relationship between CP</span><sub>non</sub><span>_</span><sub>sat</sub><span>_</span><sub>adjust</sub><span>&nbsp;and the NASS average corn yield data (</span><i>r</i><span>&nbsp;=&nbsp;0.67, 113 samples), indicating that the new CP</span><sub>sat</sub><span>_</span><sub>adjust</sub><span>&nbsp;map reduces the NDVI saturation effects and is in good agreement with the corn yield ground observations. Results demonstrate that the NDVI saturation adjustment approach improves the quality of the original GSN map and better depicts the actual vegetation conditions of the GPRB cropland systems.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2013.01.041","usgsCitation":"Gu, Y., Wylie, B.K., Howard, D., Phuyal, K.P., and Ji, L., 2013, NDVI saturation adjustment: a new approach for improving cropland performance estimates in the Greater Platte River Basin, USA: Ecological Indicators, v. 30, p. 1-6, https://doi.org/10.1016/j.ecolind.2013.01.041.","productDescription":"6 p.","startPage":"1","endPage":"6","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-038440","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":298320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Nebraska, South Dakota, Wyoming","otherGeospatial":"Greater Platte River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.8193359375,\n              38.71980474264239\n            ],\n            [\n              -109.8193359375,\n              43.992814500489914\n            ],\n            [\n              -95.9326171875,\n              43.992814500489914\n            ],\n            [\n              -95.9326171875,\n              38.71980474264239\n            ],\n            [\n              -109.8193359375,\n              38.71980474264239\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"30","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54faddbae4b02419550db6dd","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":409,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":541929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":541930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Daniel M. 0000-0002-7563-7538 dhoward@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":4431,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel M.","email":"dhoward@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":541931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phuyal, Khem P.","contributorId":28517,"corporation":false,"usgs":true,"family":"Phuyal","given":"Khem","email":"","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":541932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":2832,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":541933,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
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