Soil samples were collected throughout Connecticut (CT) to determine the relationship of soil chemistry with the underlying geology and to better understand background concentrations of major and trace elements in soils. Soil samples were collected (1) from the upper 5 cm of surficial soil at 100 sites, (2) from the A horizon at 86 of these sites, and (3) from the deeper horizon, typically the C horizon, at 79 of these sites. The <2-millimeter fraction of each sample was analyzed for 44 elements by methods that yield the total or near-total elemental content. Sample sites were characterized by glacial setting, underlying bedrock geology, and soil type. These spatial data were used with element concentrations in the C-horizon to relate geologic factors to soil chemistry.
Concentrations of elements in C-horizon soils varied with grain size in surficial glacial materials and with underlying rock types, as determined using nonparametric statistical procedures. Concentrations of most elements in C-horizon soils showed a positive correlation with silt and (or) clay content and were higher in surficial materials mapped as till, thick till, and (or) fines. Element concentrations in C-horizon soils showed significant differences among the underlying geologic provinces and were highest overlying the Grenville Belt and (or) the Grenville Shelf Sequence Provinces in western CT. These rocks consist mainly of carbonates and the relatively high element concentrations in overlying soils likely result from less influence of dilution by quartz compared to other provinces. Element concentrations in C-horizon soils in CT were compared with those in samples from other New England states overlying similar lithologic bedrock types. The upper range of As concentrations in C-horizon soils overlying the New Hampshire-Maine (NH-ME) Sequence in CT was 15 mg/kg, lower than the upper range of 24 mg/kg in C-horizon soils overlying the same sequence in ME. In CT, U concentration means were significantly higher in C-horizon soils overlying Avalonian granites, and U concentrations ranged as high as 14 mg/kg, compared to those in C-horizon soil samples collected from other New England states, which ranged as high as 6.1 mg/kg in a sample in NH overlying the NH-ME Sequence.
Element concentrations in C-horizon soils in CT were compared with those in samples collected from shallower depths. Concentrations of most major elements were highest in C-horizon soil samples, including Al, Ca, Fe, K, Na, and Ti, but element concentrations showed a relatively similar pattern in A-horizon and surficial soil samples among the underlying geologic provinces. Trace element concentrations, including Ba, W, Ga, Ni, Cs, Rb, Sr, Th, Sc, and U, also were higher in C-horizon soil samples than in overlying soil samples. Concentrations of Mg, and several trace elements, including Mn, P, As, Nb, Sn, Be, Bi, Hg, Se, Sb, La, Co, Cr, Pb, V, Y, Cu, Pb, and Zn were highest in some A-horizon or surficial soils, and indicate possible contributions from anthropogenic sources. Because element concentrations in soils above the C horizon are more likely to be affected by anthropogenic factors, concentration ranges in C-horizon soils and their spatially varying geologic associations should be considered when estimating background concentrations of elements in CT soils.
Surface-wave dispersion analysis is useful for estimating near-surface shear-wave velocity models, designing receiver arrays, and suppressing surface waves. Here, we analyze whether passive seismic noise generated during hydraulic-fracturing operations can be used to extract surface-wave dispersion characteristics. Applying seismic interferometry to noise measurements, we extract surface waves by cross-correlating several minutes of passive records; this approach is distinct from previous studies that used hours or days of passive records for cross-correlation. For comparison, we also perform dispersion analysis for an active-source array that has some receivers in common with the passive array. The active and passive data show good agreement in the dispersive character of the fundamental-mode surface-waves. For the higher mode surface waves, however, active and passive data resolve the dispersive properties at different frequency ranges. To demonstrate an application of dispersion analysis, we invert the observed surface-wave dispersion characteristics to determine the near-surface, one-dimensional shear-wave velocity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jappgeo.2014.09.008","usgsCitation":"Forghani-Arani, F., Willis, M., Snieder, R., Haines, S.S., Behura, J., Batzle, M., and Davidson, M., 2014, Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations: Journal of Applied Geophysics, v. 111, p. 129-134, https://doi.org/10.1016/j.jappgeo.2014.09.008.","productDescription":"6 p.","startPage":"129","endPage":"134","ipdsId":"IP-058038","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":473305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1556315","text":"Publisher Index Page"},{"id":345987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c4cf97e4b017cf313d3cb8","contributors":{"authors":[{"text":"Forghani-Arani, Farnoush","contributorId":196642,"corporation":false,"usgs":false,"family":"Forghani-Arani","given":"Farnoush","email":"","affiliations":[{"id":34665,"text":"Microseismic Inc.","active":true,"usgs":false}],"preferred":false,"id":710974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willis, Mark","contributorId":196643,"corporation":false,"usgs":false,"family":"Willis","given":"Mark","email":"","affiliations":[{"id":34662,"text":"Halliburton","active":true,"usgs":false}],"preferred":false,"id":710975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snieder, Roel","contributorId":196644,"corporation":false,"usgs":false,"family":"Snieder","given":"Roel","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":710976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":710973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Behura, Jyoti","contributorId":196645,"corporation":false,"usgs":false,"family":"Behura","given":"Jyoti","email":"","affiliations":[{"id":34663,"text":"Seismic Science LLC","active":true,"usgs":false}],"preferred":false,"id":710977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Batzle, Mike","contributorId":196646,"corporation":false,"usgs":false,"family":"Batzle","given":"Mike","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":710978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davidson, Michael","contributorId":196647,"corporation":false,"usgs":false,"family":"Davidson","given":"Michael","email":"","affiliations":[{"id":17916,"text":"ConocoPhillips","active":true,"usgs":false}],"preferred":false,"id":710979,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70187708,"text":"70187708 - 2014 - Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","interactions":[],"lastModifiedDate":"2017-05-31T16:12:20","indexId":"70187708","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","docAbstract":"Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.08.014","usgsCitation":"Hansen, M., Egorov, A., Potapov, P., Stehman, S., Tyukavina, A., Turubanova, S., Roy, D.P., Goetz, S., Loveland, T., Ju, J., Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T., 2014, Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD): Remote Sensing of Environment, v. 140, p. 466-484, https://doi.org/10.1016/j.rse.2013.08.014.","productDescription":"19 p.","startPage":"466","endPage":"484","ipdsId":"IP-046262","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"140","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591c0fcce4b0a7fdb43ddf00","contributors":{"authors":[{"text":"Hansen, M.C.","contributorId":69690,"corporation":false,"usgs":false,"family":"Hansen","given":"M.C.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Egorov, Alexey","contributorId":81719,"corporation":false,"usgs":false,"family":"Egorov","given":"Alexey","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":695303,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Potapov, P.V.","contributorId":19677,"corporation":false,"usgs":false,"family":"Potapov","given":"P.V.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stehman, S.V.","contributorId":91974,"corporation":false,"usgs":false,"family":"Stehman","given":"S.V.","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":695306,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyukavina, A.","contributorId":19872,"corporation":false,"usgs":false,"family":"Tyukavina","given":"A.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695307,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turubanova, S.A.","contributorId":108388,"corporation":false,"usgs":false,"family":"Turubanova","given":"S.A.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695308,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roy, David P.","contributorId":54761,"corporation":false,"usgs":false,"family":"Roy","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false},{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false},{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695309,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goetz, S.J.","contributorId":55186,"corporation":false,"usgs":false,"family":"Goetz","given":"S.J.","email":"","affiliations":[{"id":25456,"text":"Woods Hole Research Center, Falmouth, MA, United States","active":true,"usgs":false}],"preferred":false,"id":695310,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":106125,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":695311,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ju, J.","contributorId":85801,"corporation":false,"usgs":false,"family":"Ju","given":"J.","email":"","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":695312,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kommareddy, A.","contributorId":105638,"corporation":false,"usgs":false,"family":"Kommareddy","given":"A.","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695317,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kovalskyy, Valeriy","contributorId":192062,"corporation":false,"usgs":false,"family":"Kovalskyy","given":"Valeriy","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695318,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Forsyth, C.","contributorId":192034,"corporation":false,"usgs":false,"family":"Forsyth","given":"C.","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":695319,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bents, T.","contributorId":139577,"corporation":false,"usgs":false,"family":"Bents","given":"T.","email":"","affiliations":[{"id":33302,"text":"University of Kansas, Lawrence","active":true,"usgs":false}],"preferred":false,"id":695320,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70192195,"text":"70192195 - 2014 - Hydroclimatic regimes: a distributed water-balance framework for hydrologic assessment, classification, and management","interactions":[],"lastModifiedDate":"2018-04-03T11:40:25","indexId":"70192195","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","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":"Hydroclimatic regimes: a distributed water-balance framework for hydrologic assessment, classification, and management","docAbstract":"Runoff-based indicators of terrestrial water availability are appropriate for humid regions, but have tended to limit our basic hydrologic understanding of drylands – the dry-subhumid, semiarid, and arid regions which presently cover nearly half of the global land surface. In response, we introduce an indicator framework that gives equal weight to humid and dryland regions, accounting fully for both vertical (precipitation + evapotranspiration) and horizontal (groundwater + surface-water) components of the hydrologic cycle in any given location – as well as fluxes into and out of landscape storage. We apply the framework to a diverse hydroclimatic region (the conterminous USA) using a distributed water-balance model consisting of 53 400 networked landscape hydrologic units. Our model simulations indicate that about 21% of the conterminous USA either generated no runoff or consumed runoff from upgradient sources on a mean-annual basis during the 20th century. Vertical fluxes exceeded horizontal fluxes across 76% of the conterminous area. Long-term-average total water availability (TWA) during the 20th century, defined here as the total influx to a landscape hydrologic unit from precipitation, groundwater, and surface water, varied spatially by about 400 000-fold, a range of variation ~100 times larger than that for mean-annual runoff across the same area. The framework includes but is not limited to classical, runoff-based approaches to water-resource assessment. It also incorporates and reinterprets the green- and blue-water perspective now gaining international acceptance. Implications of the new framework for several areas of contemporary hydrology are explored, and the data requirements of the approach are discussed in relation to the increasing availability of gridded global climate, land-surface, and hydrologic data sets.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-18-3855-2014","usgsCitation":"Weiskel, P.K., Wolock, D.M., Zarriello, P.J., Vogel, R.M., Levin, S.B., and Lent, R.M., 2014, Hydroclimatic regimes: a distributed water-balance framework for hydrologic assessment, classification, and management: Hydrology and Earth System Sciences, v. 18, p. 3855-3872, https://doi.org/10.5194/hess-18-3855-2014.","productDescription":"18 p.","startPage":"3855","endPage":"3872","ipdsId":"IP-044838","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":473320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-18-3855-2014","text":"Publisher Index Page"},{"id":347118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2014-10-01","publicationStatus":"PW","scienceBaseUri":"59eeffade4b0220bbd988fd1","contributors":{"authors":[{"text":"Weiskel, Peter K. pweiskel@usgs.gov","contributorId":1099,"corporation":false,"usgs":true,"family":"Weiskel","given":"Peter","email":"pweiskel@usgs.gov","middleInitial":"K.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714681,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":714680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zarriello, Phillip J. 0000-0001-9598-9904 pzarriel@usgs.gov","orcid":"https://orcid.org/0000-0001-9598-9904","contributorId":1868,"corporation":false,"usgs":true,"family":"Zarriello","given":"Phillip","email":"pzarriel@usgs.gov","middleInitial":"J.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714682,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vogel, Richard M.","contributorId":66811,"corporation":false,"usgs":true,"family":"Vogel","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":714684,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Levin, Sara B. 0000-0002-2448-3129 slevin@usgs.gov","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":1870,"corporation":false,"usgs":true,"family":"Levin","given":"Sara","email":"slevin@usgs.gov","middleInitial":"B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714685,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lent, Robert M. rmlent@usgs.gov","contributorId":284,"corporation":false,"usgs":true,"family":"Lent","given":"Robert","email":"rmlent@usgs.gov","middleInitial":"M.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714683,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192200,"text":"70192200 - 2014 - Integrating recent land cover mapping efforts to update the National Gap Analysis Program's species habitat map","interactions":[],"lastModifiedDate":"2018-12-20T12:53:56","indexId":"70192200","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"seriesTitle":{"id":5650,"text":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","onlineIssn":"2194-9034","printIssn":"1682-1750","active":true,"publicationSubtype":{"id":19}},"title":"Integrating recent land cover mapping efforts to update the National Gap Analysis Program's species habitat map","docAbstract":"Over the past decade, great progress has been made to develop national extent land cover mapping products to address natural resource issues. One of the core products of the GAP Program is range-wide species distribution models for nearly 2000 terrestrial vertebrate species in the U.S. We rely on deductive modeling of habitat affinities using these products to create models of habitat availability. That approach requires that we have a thematically rich and ecologically meaningful map legend to support the modeling effort. In this work, we tested the integration of the Multi-Resolution Landscape Characterization Consortium's National Land Cover Database 2011 and LANDFIRE's Disturbance Products to update the 2001 National GAP Vegetation Dataset to reflect 2011 conditions. The revised product can then be used to update the species models.
We tested the update approach in three geographic areas (Northeast, Southeast, and Interior Northwest). We used the NLCD product to identify areas where the cover type mapped in 2011 was different from what was in the 2001 land cover map. We used Google Earth and ArcGIS base maps as reference imagery in order to label areas identified as \"changed\" to the appropriate class from our map legend. Areas mapped as urban or water in the 2011 NLCD map that were mapped differently in the 2001 GAP map were accepted without further validation and recoded to the corresponding GAP class. We used LANDFIRE's Disturbance products to identify changes that are the result of recent disturbance and to inform the reassignment of areas to their updated thematic label. We ran species habitat models for three species including Lewis's Woodpecker (Melanerpes lewis) and the White-tailed Jack Rabbit (Lepus townsendii) and Brown Headed nuthatch (Sitta pusilla). For each of three vertebrate species we found important differences in the amount and location of suitable habitat between the 2001 and 2011 habitat maps. Specifically, Brown headed nuthatch habitat in 2011 was −14% of the 2001 modeled habitat, whereas Lewis's Woodpecker increased by 4%. The white-tailed jack rabbit (Lepus townsendii) had a net change of −1% (11% decline, 10% gain). For that species we found the updates related to opening of forest due to burning and regenerating shrubs following harvest to be the locally important main transitions. In the Southeast updates related to timber management and urbanization are locally important.
Potential for large prairie remnants to provide habitat for grassland-obligate wildlife may be compromised by nonsustainable range-management practices. In 1979–1980, high nesting densities of 3 species of hawks in the genus Buteo—Ferruginous Hawk (Buteo regalis), Red-tailed Hawk (B. jamaicensis), and Swainson's Hawk (B. swainsoni)—were documented on the Zumwalt Prairie and surrounding agricultural areas (34,361 ha) in northeastern Oregon, USA. This area has been managed primarily as livestock summer range since it was homesteaded. Unlike in other prairie remnants, land management on the Zumwalt Prairie was consistent over the past several decades; thus, we predicted that territory occupancy of these 3 species would be stable. We also predicted that territory occupancy would be positively related to local availability of nesting structures within territories. We evaluated these hypotheses using a historical dataset, current survey and habitat data, and occupancy models. In support of our predictions, territory occupancy of all 3 species has not changed over the study period of ∼25 yr, which suggests that local range-management practices are not negatively affecting these taxa. Probability of Ferruginous Hawk occupancy increased with increasing area of aspen, an important nest structure for this species in grasslands. Probability of Swainson's Hawk occupancy increased with increasing area of large shrubs, and probability of Red-tailed Hawk occupancy was weakly associated with area of conifers. In the study area, large shrubs and conifers are commonly used as nesting structures by Swainson's Hawks and Red-tailed Hawks, respectively. Availability of these woody species is changing (increases in conifers and large shrubs, and decline in aspen) throughout the west, and these changes may result in declines in Ferruginous Hawk occupancy and increases in Swainson's Hawk and Red-tailed Hawk occupancy in the future.
","language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-12-174.1","usgsCitation":"Kennedy, P.L., Bartuszevige, A.M., Houle, M., Humphrey, A.B., Dugger, K.M., and Williams, J., 2014, Stable occupancy by breeding hawks (Buteo spp.) over 25 years on a privately managed bunchgrass prairie in northeastern Oregon, USA: The Condor, v. 116, no. 3, p. 435-445, https://doi.org/10.1650/CONDOR-12-174.1.","productDescription":"11 p.","startPage":"435","endPage":"445","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-042226","costCenters":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":325089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"579dd03be4b0589fa1cbde45","contributors":{"authors":[{"text":"Kennedy, Patricia L.","contributorId":172826,"corporation":false,"usgs":false,"family":"Kennedy","given":"Patricia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":642201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartuszevige, Anne M.","contributorId":172827,"corporation":false,"usgs":false,"family":"Bartuszevige","given":"Anne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":642202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Houle, Marcy","contributorId":172828,"corporation":false,"usgs":false,"family":"Houle","given":"Marcy","email":"","affiliations":[],"preferred":false,"id":642203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphrey, Ann B.","contributorId":172829,"corporation":false,"usgs":false,"family":"Humphrey","given":"Ann","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":642204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dugger, Katie M. 0000-0002-4148-246X","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":36037,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":518124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, John","contributorId":23842,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"","affiliations":[],"preferred":false,"id":642205,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189095,"text":"70189095 - 2014 - Multielevation calibration of frequency-domain electromagnetic data","interactions":[],"lastModifiedDate":"2017-06-29T14:58:22","indexId":"70189095","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Multielevation calibration of frequency-domain electromagnetic data","docAbstract":"Systematic calibration errors must be taken into account because they can substantially impact the accuracy of inverted subsurface resistivity models derived from frequency-domain electromagnetic data, resulting in potentially misleading interpretations. We have developed an approach that uses data acquired at multiple elevations over the same location to assess calibration errors. A significant advantage is that this method does not require prior knowledge of subsurface properties from borehole or ground geophysical data (though these can be readily incorporated if available), and is, therefore, well suited to remote areas. The multielevation data were used to solve for calibration parameters and a single subsurface resistivity model that are self consistent over all elevations. The deterministic and Bayesian formulations of the multielevation approach illustrate parameter sensitivity and uncertainty using synthetic- and field-data examples. Multiplicative calibration errors (gain and phase) were found to be better resolved at high frequencies and when data were acquired over a relatively conductive area, whereas additive errors (bias) were reasonably resolved over conductive and resistive areas at all frequencies. The Bayesian approach outperformed the deterministic approach when estimating calibration parameters using multielevation data at a single location; however, joint analysis of multielevation data at multiple locations using the deterministic algorithm yielded the most accurate estimates of calibration parameters. Inversion results using calibration-corrected data revealed marked improvement in misfit, lending added confidence to the interpretation of these models.
This is the fifty-first in a series of annual reports that describe groundwater conditions in Utah. Reports in this series, published cooperatively by the U.S. Geological Survey and the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality, provide data to enable interested parties to maintain awareness of changing groundwater conditions.
This report, like the others in the series, contains information on well construction, groundwater withdrawal from wells, water-level changes, precipitation, streamflow, and chemical quality of water. Information on well construction included in this report refers only to wells constructed for new appropriations of groundwater. Supplementary data are included in reports of this series only for those years or areas that are important to a discussion of changing groundwater conditions and for which applicable data are available.
This report includes individual discussions of selected significant areas of groundwater development in the State for calendar year 2013. Most of the reported data were collected by the U.S. Geological Survey in cooperation with the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality. This report is also available online at http://www.waterrights.utah.gov/techinfo/ and http://ut.water. usgs.gov/publications/GW2014.pdf. Groundwater conditions in Utah for calendar year 2012 are reported in Burden and others (2013) and are available online at http://ut.water.usgs. gov/publications/GW2013.pdf
","language":"English","publisher":"Utah Department of Natural Resources","usgsCitation":"Burden, C.B., 2014, Groundwater conditions in Utah, spring of 2014: Cooperative Investigations Report 55, x, 118 p.","productDescription":"x, 118 p.","numberOfPages":"132","ipdsId":"IP-056622","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":350085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364083,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://waterrights.utah.gov/techinfo/wwwpub/GW2014.pdf"}],"country":"United 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\"}}]}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100c9e4b06e28e9c2541d","contributors":{"authors":[{"text":"Burden, Carole B. cburden@usgs.gov","contributorId":852,"corporation":false,"usgs":true,"family":"Burden","given":"Carole","email":"cburden@usgs.gov","middleInitial":"B.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718032,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70117792,"text":"70117792 - 2014 - Predicting Impacts of tropical cyclones and sea-Level rise on beach mouse habitat","interactions":[],"lastModifiedDate":"2015-01-16T16:27:23","indexId":"70117792","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","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":"Predicting Impacts of tropical cyclones and sea-Level rise on beach mouse habitat","docAbstract":"Alabama beach mouse (ABM) (Peromyscus polionotus ammobates) is an important component of the coastal dune ecosystem along the Gulf of Mexico. Due to habitat loss and degradation, ABM is federally listed as an endangered species. In this study, we examined the impacts of storm surge and wind waves, which are induced by hurricanes and sea-level rise (SLR), on the ABM habitat on Fort Morgan Peninsula, Alabama, using advanced storm surge and wind wave models and spatial analysis tools in geographic information systems (GIS). Statistical analyses of the long-term historical data enabled us to predict the extreme values of winds, wind waves, and water levels in the study area at different return periods. We developed a series of nested domains for both wave and surge modeling and validated the models using field observations of surge hydrographs and high watermarks of Hurricane Ivan (2004). We then developed wave atlases and flood maps corresponding to the extreme wind, surge and waves without SLR and with a 0.5 m of SLR by coupling the wave and surge prediction models. The flood maps were then merged with a map of ABM habitat to determine the extent and location of habitat impacted by the 100-year storm with and without SLR. Simulation results indicate that more than 82% of ABM habitat would be inundated in such an extreme storm event, especially under SLR, making ABM populations more vulnerable to future storm damage. These results have aided biologists, community planners, and other stakeholders in the identification, restoration and protection of key beach mouse habitat in Alabama. Methods outlined in this paper could also be used to assist in the conservation and recovery of imperiled coastal species elsewhere.
","doi":"10.2112/SI68-002.1","usgsCitation":"Chen, Q., Wang, H., Wang, L., Tawes, R., and Rollman, D., 2014, Predicting Impacts of tropical cyclones and sea-Level rise on beach mouse habitat: Journal of Coastal Research, v. 68, p. 12-19, https://doi.org/10.2112/SI68-002.1.","productDescription":"8 p.","startPage":"12","endPage":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057038","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":297361,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Fort Morgan Peninsula","volume":"68","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2c29e4b08de9379b3679","contributors":{"authors":[{"text":"Chen, Q. 0000-0002-6540-8758","orcid":"https://orcid.org/0000-0002-6540-8758","contributorId":56532,"corporation":false,"usgs":false,"family":"Chen","given":"Q.","affiliations":[{"id":38331,"text":"Northeastern University","active":true,"usgs":false}],"preferred":true,"id":519117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":4421,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":519116,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Lixia","contributorId":118439,"corporation":false,"usgs":false,"family":"Wang","given":"Lixia","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":519120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tawes, Robert","contributorId":116795,"corporation":false,"usgs":false,"family":"Tawes","given":"Robert","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":519118,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rollman, Drew","contributorId":117214,"corporation":false,"usgs":false,"family":"Rollman","given":"Drew","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":519119,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178389,"text":"70178389 - 2014 - Water quality monitoring protocol for wadeable streams and rivers in the Northern Great Plains Network","interactions":[],"lastModifiedDate":"2018-02-12T13:26:08","indexId":"70178389","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","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/NGPN/NRR—2014/868","title":"Water quality monitoring protocol for wadeable streams and rivers in the Northern Great Plains Network","docAbstract":"Preserving the national parks unimpaired for the enjoyment of future generations is a fundamental purpose of the National Park Service (NPS). To address growing concerns regarding the overall physical, chemical, and biological elements and processes of park ecosystems, the NPS implemented science-based management through “Vital Signs” monitoring in 270 national parks (NPS 2007). The Northern Great Plains Network (NGPN) is among the 32 National Park Service Networks participating in this monitoring effort. The NGPN will develop protocols over the next several years to determine the overall health or condition of resources within 13 parks located in Nebraska, North Dakota, South Dakota, and Wyoming.\nThe NGPN identified water resources as a Vital Sign to monitor because water quality and quantity are important aspects of ecological processes that operate across multiple temporal and spatial scales. In the semi-arid region of the Northern Great Plains, surface-water resources within the NGPN are ecologically important. The 13 parks within the NGPN are diverse and vary greatly in size, visitation, and water resources. For example, the measured surface area of the Badlands National Park is about 243,000 acres, which represents nearly one-half of the combined acreage of all 13 NGPN park units; however, water resources within the park are scarce and the majority of streams are intermittent. The Badlands National Park annually hosts nearly 860,000 visitors. Mount Rushmore National Memorial also has limited water resources but hosts nearly 3 million visitors per year within its 1,278 acres. The Missouri National Recreational River contains the greatest portion of waterbodies within the NGPN, consisting of 139 rivers and streams within an areal extent of about 69,000 acres. Although water resources and acreage of the NGPN parks are varied, unifying factors among the parks include the relatively low population density within the Great Plains area and the strong emphasis on agrarian land use throughout the region.\nTo address the diverse water quality concerns, NGPN received input from park staff and conducted pilot studies in 2009 and 2010. These factors, in combination with the NGPN budget allocations, resulted in development of the NGPN’s water quality monitoring protocol. This protocol will provide a context to aid park resource managers in their day-to-day decisions and allow the assessment of the status (current conditions) and trends (directional changes across time) of streams/rivers within selected NGPN parks. Data collected from integrating water resource monitoring, in combination with the inventory of additional Vital Signs, can be used to assess resources and to aid in sound managerial decisions by the NGPN parks.\nAs recommended by Oakley et al. (2003), this protocol provides a narrative and the rationale for selection of streams and rivers within the NGPN that will be measured for water quality, including dissolved oxygen, pH, specific conductivity, and temperature. Standard operating procedures (SOPs) that detail the steps to collect, manage, and disseminate the NGPN water quality data are in an accompanying document. The sampling design documented in this protocol may be updated as monitoring information is collected and interpreted, and as refinement of methodologies develop through time. In addition, evaluation of data and refinement of the program may necessitate potential changes of program objectives. Changes to the NGPN water quality protocols and SOPs will be carefully documented in a revision history log.","language":"English","publisher":"National Park Service","usgsCitation":"Wilson, M.H., Rowe, B.L., Gitzen, R.A., Wilson, S.K., and Paintner-Green, K.J., 2014, Water quality monitoring protocol for wadeable streams and rivers in the Northern Great Plains Network: Natural Resource Report NPS/NGPN/NRR—2014/868, xxi., 52p.","productDescription":"xxi., 52p.","ipdsId":"IP-042869","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":332301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331056,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2216799"}],"country":"United States","state":"Colorado, Montana, Nebraska, North Dakota, South Dakota","otherGeospatial":"Northern Great Plains ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.21826171874999,\n 42.27730877423709\n ],\n [\n -100.5029296875,\n 40.17887331434696\n ],\n [\n -103.22753906249999,\n 39.87601941962116\n ],\n [\n -105.09521484375,\n 40.48038142908172\n ],\n [\n -106.2158203125,\n 42.73087427928485\n ],\n [\n -106.06201171875,\n 45.79816953017265\n ],\n [\n -106.10595703125,\n 48.1367666796927\n ],\n [\n -105.75439453125,\n 49.023461463214126\n ],\n [\n -97.31689453125,\n 49.023461463214126\n ],\n [\n -97.1630859375,\n 48.67645370777654\n ],\n [\n -97.09716796875,\n 47.88688085106901\n ],\n [\n -96.8115234375,\n 47.12995075666307\n ],\n [\n -96.61376953125,\n 46.210249600187225\n ],\n [\n -96.85546875,\n 45.66012730272194\n ],\n [\n -96.416015625,\n 45.336701909968134\n ],\n [\n -96.48193359375,\n 43.34116005412307\n ],\n [\n -96.50390625,\n 42.601619944327965\n ],\n [\n -96.328125,\n 42.374778361114195\n ],\n [\n -96.21826171874999,\n 42.27730877423709\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5859000ae4b03639a6025e37","contributors":{"authors":[{"text":"Wilson, Marcia H.","contributorId":6149,"corporation":false,"usgs":true,"family":"Wilson","given":"Marcia","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":653915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rowe, Barbara L. blrowe@usgs.gov","contributorId":2673,"corporation":false,"usgs":true,"family":"Rowe","given":"Barbara","email":"blrowe@usgs.gov","middleInitial":"L.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gitzen, Robert A.","contributorId":75498,"corporation":false,"usgs":true,"family":"Gitzen","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":653916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Stephen K.","contributorId":191011,"corporation":false,"usgs":false,"family":"Wilson","given":"Stephen","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":653917,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paintner-Green, Kara J.","contributorId":176899,"corporation":false,"usgs":false,"family":"Paintner-Green","given":"Kara","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":653914,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188320,"text":"70188320 - 2014 - Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data","interactions":[],"lastModifiedDate":"2017-06-06T14:00:55","indexId":"70188320","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data","docAbstract":"Various best management practices (BMPs) have been implemented on rangelands with the goals of controlling nonpoint source pollution, reducing the impact of livestock in ecologically important riparian areas, and improving grazing distribution. Providing off-stream water sources to livestock in pastures, cross-fencing, and rotational grazing are common rangeland BMPs that have demonstrated success in drawing livestock grazing pressure away from streams. We evaluated the effects of rangeland BMP implementation with six commercial-scale pastures in the northern mixed-grass prairie. Four pastures received a BMP suite consisting of off-stream water, cross-fencing, and deferred-rotation grazing, and two pastures did not receive BMPs. We hypothesized that the BMPs increased the quantity of riparian vegetation cover relative to the conditions in these pastures during the pre-BMP period and to the two pastures that did not receive BMPs. We used a series of 30-m Landsat normalized difference vegetation index (NDVI) images to track the spatial and temporal changes (1984–2010, n = 24) in vegetation cover, to which NDVI has been well correlated. Validation indicated that the remotely sensed signal from in-channel vegetation was representative of ground conditions. The BMP suite was associated with a 15% increase in the in-channel NDVI (0–30 m from stream centerline) and 18% increase in the riparian NDVI (30–180 m from stream center line). Conversely, the in-channel and riparian NDVI of non-BMP pastures declined 30% and 18% over the study period. The majority of change occurred within 2 yr of BMP implementation. The patterns of in-channel NDVI among pastures suggested that BMP implementation likely altered grazing distribution by decreasing the preferential use of riparian and in-channel areas. We demonstrated that satellite imagery time series are useful in retrospectively evaluating the efficacy of conservation practices, providing critical information to guide adaptive management and decision makers.
","language":"English","publisher":"Elsevier","doi":"10.2111/REM-D-12-00185.1","usgsCitation":"Rigge, M.B., Smart, A., Wylie, B.K., and de Van Kamp, K., 2014, Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data: Rangeland Ecology and Management, v. 67, no. 1, p. 1-8, https://doi.org/10.2111/REM-D-12-00185.1.","productDescription":"8 p.","startPage":"1","endPage":"8","ipdsId":"IP-035745","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":342160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2fe4b0f6c2d0d9c781","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":697194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smart, Alexander","contributorId":24262,"corporation":false,"usgs":true,"family":"Smart","given":"Alexander","affiliations":[],"preferred":false,"id":697310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":697311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Van Kamp, Kendall","contributorId":192662,"corporation":false,"usgs":false,"family":"de Van Kamp","given":"Kendall","email":"","affiliations":[],"preferred":false,"id":697312,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189074,"text":"70189074 - 2014 - Spectroscopy from Space","interactions":[],"lastModifiedDate":"2020-11-05T16:48:04.612491","indexId":"70189074","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3281,"text":"Reviews in Mineralogy and Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Spectroscopy from Space","docAbstract":"This chapter reviews detection of materials on solid and liquid (lakes and ocean) surfaces in the solar system using ultraviolet to infrared spectroscopy from space, or near space (high altitude aircraft on the Earth), or in the case of remote objects, earth-based and earth-orbiting telescopes. Point spectrometers and imaging spectrometers have been probing the surfaces of our solar system for decades. Spacecraft carrying imaging spectrometers are currently in orbit around Mercury, Venus, Earth, Mars, and Saturn, and systems have recently visited Jupiter, comets, asteroids, and one spectrometer-carrying spacecraft is on its way to Pluto. Together these systems are providing a wealth of data that will enable a better understanding of the composition of condensed matter bodies in the solar system.
Minerals, ices, liquids, and other materials have been detected and mapped on the Earth and all planets and/or their satellites where the surface can be observed from space, with the exception of Venus whose thick atmosphere limits surface observation. Basaltic minerals (e.g., pyroxene and olivine) have been detected with spectroscopy on the Earth, Moon, Mars and some asteroids. The greatest mineralogic diversity seen from space is observed on the Earth and Mars. The Earth, with oceans, active tectonic and hydrologic cycles, and biological processes, displays the greatest material diversity including the detection of amorphous and crystalline inorganic materials, organic compounds, water and water ice.
Water ice is a very common mineral throughout the Solar System and has been unambiguously detected or inferred in every planet and/or their moon(s) where good spectroscopic data has been obtained.
In addition to water ice, other molecular solids have been observed in the solar system using spectroscopic methods. Solid carbon dioxide is found on all systems beyond the Earth except Pluto, although CO2 sometimes appears to be trapped in other solids rather than as an ice on some objects. The largest deposits of carbon dioxide ice are found on Mars. Sulfur dioxide ice is found in the Jupiter system. Nitrogen and methane ices are common beyond the Uranian system.
Saturn’s moon Titan probably has the most complex active extra-terrestrial surface chemistry involving organic compounds. Some of the observed or inferred compounds include ices of benzene (C6H6), cyanoacetylene (HC3N), toluene (C7H8), cyanogen (C2N2), acetonitrile (CH3CN), water (H2O), carbon dioxide (CO2), and ammonia (NH3). Confirming compounds on Titan is hampered by its thick smoggy atmosphere, where in relative terms the atmospheric interferences that hamper surface characterization lie between that of Venus and Earth.
In this chapter we exclude discussion of the planets Jupiter, Saturn, Uranus, and Neptune because their thick atmospheres preclude observing the surface, even if surfaces exist. However, we do discuss spectroscopic observations on a number of the extra-terrestrial satellite bodies. Ammonia was predicted on many icy moons but is notably absent among the definitively detected ices with possible exceptions on Charon and possible trace amounts on some of the Saturnian satellites. Comets, storehouses of many compounds that could exist as ices in their nuclei, have only had small amounts of water ice definitively detected on their surfaces from spectroscopy. Only two asteroids have had a direct detection of surface water ice, although its presence can be inferred in others.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2138/rmg.2014.78.10","usgsCitation":"Clark, R.N., Swayze, G.A., Carlson, R.R., Grundy, W., and Noll, K., 2014, Spectroscopy from Space: Reviews in Mineralogy and Geochemistry, v. 78, no. 1, p. 399-446, https://doi.org/10.2138/rmg.2014.78.10.","productDescription":"48 p.","startPage":"399","endPage":"446","ipdsId":"IP-036673","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":343176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-02-27","publicationStatus":"PW","scienceBaseUri":"595611b9e4b0d1f9f0506772","contributors":{"authors":[{"text":"Clark, Roger N. 0000-0002-7021-1220 rclark@usgs.gov","orcid":"https://orcid.org/0000-0002-7021-1220","contributorId":515,"corporation":false,"usgs":true,"family":"Clark","given":"Roger","email":"rclark@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":702780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swayze, Gregg A. 0000-0002-1814-7823 gswayze@usgs.gov","orcid":"https://orcid.org/0000-0002-1814-7823","contributorId":518,"corporation":false,"usgs":true,"family":"Swayze","given":"Gregg","email":"gswayze@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":702779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlson, Robert R.","contributorId":71944,"corporation":false,"usgs":true,"family":"Carlson","given":"Robert","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":702931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grundy, Will","contributorId":156333,"corporation":false,"usgs":false,"family":"Grundy","given":"Will","email":"","affiliations":[],"preferred":false,"id":702932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noll, Keith","contributorId":193877,"corporation":false,"usgs":false,"family":"Noll","given":"Keith","email":"","affiliations":[],"preferred":false,"id":702933,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187420,"text":"70187420 - 2014 - Normative standards for land use in Vermont: Implications for biodiversity","interactions":[],"lastModifiedDate":"2017-05-02T12:52:14","indexId":"70187420","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Normative standards for land use in Vermont: Implications for biodiversity","docAbstract":"The conversion of natural lands to developed uses poses a great threat to global terrestrial biodiversity. Natural resource managers, tasked with managing wildlife as a public trust, require techniques for predicting how much and where wildlife habitat is likely to be converted in the future. Here, we develop a methodology to estimate the “social carrying capacity for development” – SKd – for 251 towns across the state of Vermont, USA. SKd represents town residents’ minimum acceptable human population size and level of development within town boundaries. To estimate SKd across towns within the state of Vermont (USA), as well as the average state-wide SKd, we administered a visual preference survey (n = 1505 responses) to Vermont residents, and asked respondents to rate alternative landuse scenarios in a fictional Vermont town on a scale of +4 (highly acceptable) to −4 (highly unacceptable). We additionally collected demographic data such as age and income, as well as ancillary information such as participation in town-planning meetings and location of residence. We used model selection and AIC to fit a cubic function to the response data, allowing us to estimate SKd at a town scale based on town demographic characteristics. On average, Vermonters had a SKd of 9.1% development on the landscape; this estimate is 68% higher than year 2000 levels for development (5.4%). Respondents indicated that management action to curb development was appropriate at 9.4% development (roughly the statewide SKd average). Management by local, regional, and state levels were considered acceptable for curbing development while federal level management of development was considered unacceptable. Given a scenario where development levels were at SKd, we predicted a 16,753 km2 reduction in forested land (−11.16%) and a 1038 km2 reduction in farmland (−60.45%). Such changes would dramatically alter biodiversity patterns state-wide. In a companion paper, we estimate how these changes would affect the distribution of wildlife species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2013.07.009","usgsCitation":"Bettigole, C.A., Donovan, T., Manning, R., and Austin, J., 2014, Normative standards for land use in Vermont: Implications for biodiversity: Biological Conservation, v. 169, p. 392-400, https://doi.org/10.1016/j.biocon.2013.07.009.","productDescription":"9 p.","startPage":"392","endPage":"400","ipdsId":"IP-040027","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Philippines","docAbstract":"Findings are presented of a comprehensive analysis of time series catch and effort data from 2000 to 2006 collected from a multi-species, multi-gear and two-sector (municipal and commercial) capture fisheries in Cuyo East Pass, Philippines. Multivariate techniques were used to determine temporal variation in species composition and gear selectivity that corresponded with annual trends in catch and effort. Distinct annual variation in species composition was found for five fisheries classified according to sector-gear combination, corresponding decline in catch diversity, noted shifts in gears used, and an erratic CPUE trend as a result of catch variation. These patterns and trends illustrate the occurrence of ecosystem overfishing for Cuyo East Pass. Our approach provided a holistic representation of the fishing situation, condition of the fisheries and corresponding implications to the ecosystem, fitting well within the context of the ecosystem approach to fisheries management.
","language":"English","publisher":"International Journal of Fisheries and Aquatic Studies","usgsCitation":"San Diego, T.A., and Fisher, W.L., 2014, Trends in the capture fisheries in Cuyo East Pass, Philippines: International Journal of Fisheries and Aquatic Studies, v. 1, no. 3, p. 57-72.","productDescription":"16 p.","startPage":"57","endPage":"72","ipdsId":"IP-014204","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347854,"type":{"id":15,"text":"Index Page"},"url":"https://www.fisheriesjournal.com/vol1issue3/14.1.html"}],"country":"Philippines","otherGeospatial":"Cuyo East Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 120.80017089843749,\n 10.055402736564236\n ],\n [\n 122.10205078125,\n 10.055402736564236\n ],\n [\n 122.10205078125,\n 11.689893557325728\n ],\n [\n 120.80017089843749,\n 11.689893557325728\n ],\n [\n 120.80017089843749,\n 10.055402736564236\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100c8e4b06e28e9c25419","contributors":{"authors":[{"text":"San Diego, Tee-Jay A.","contributorId":200421,"corporation":false,"usgs":false,"family":"San Diego","given":"Tee-Jay","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":722259,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, William L. wfisher@usgs.gov","contributorId":1229,"corporation":false,"usgs":true,"family":"Fisher","given":"William","email":"wfisher@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":718467,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191985,"text":"70191985 - 2014 - Preliminary testing of flow-ecology hypotheses developed for the GCP LCC region","interactions":[],"lastModifiedDate":"2018-01-23T14:21:46","indexId":"70191985","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-108-2014","title":"Preliminary testing of flow-ecology hypotheses developed for the GCP LCC region","docAbstract":"The Ecological Limits of Hydrological Alteration (ELOHA) framework calls for the development of flow-ecology hypotheses to support protection of the flow regime from ecologically harmful alteration due to human activities. As part of a larger instream flow project for the Gulf Coast Prairie Landscape Conservation Cooperative (GCP LCC), regional flow-ecology hypotheses were developed for fish, mussels, birds, and riparian vegetation (Davis and Brewer 20141
). The objective of this study was to assess the usefulness of existing ecological and hydrological data to test these hypotheses or others that may be developed in the future. Several databases related to biological collections and hydrologic data from Oklahoma, Texas, and Louisiana were compiled. State fish-community data from Oklahoma and Louisiana were summarized and paired with existing USGS gage data having at least a 40-year period of record that could be separated into reference and current conditions for comparison. The objective of this study was not to conduct exhaustive analyses of these data, the hypotheses, or analyses interpretation, but rather to use these data to determine if existing data were adequate to statistically test the regional flow-ecology hypotheses. The regional flow-ecology hypotheses were developed for the GCP LCC by a committee chaired by Shannon Brewer and Mary Davis (Davis and Brewer 2014). Existing data were useful for informing the hypotheses and suggest support for some hypotheses, but also highlight the need for additional testing and development as some results contradicted hypotheses. Results presented here suggest existing data are adequate to support some flow-ecology hypotheses; however, lack of sampling effort reported with the fish collections and the need for ecoregion-specific analyses suggest more data would be beneficial to analyses in some ecoregions. Additional fish sampling data from Texas and Louisiana will be available for future analyses and may ameliorate some of the data concerns and improve hypothesis interpretation. If the regional hydrologic model currently under development by the U.S. Geological Survey for the South-Central Climate Science Center is improved to produce daily hydrographs, it will enable use of fish data at ungaged locations. In future efforts, exhaustive analyses using these data, in addition to the development of more complex multivariate hypotheses, would be beneficial to understanding data gaps, particularly as relevant to species of conservation concern.
Ecosystems may occupy functionally distinct alternative states, some of which are more or less desirable from a management standpoint. Transitions from state to state are usually associated with a particular trigger or sequence of triggers, such as the addition or subtraction of a disturbance. Transitions are often not linear, rather it is common to see an abrupt transition come about even though the trigger increases only incrementally; these are examples of threshold behaviors. An ideal monitoring program, such as the National Park Service’s Inventory and Monitoring Program, would quantify triggers, and be able to inform managers when measurements of a trigger are approaching a threshold so that management action can avoid an unwanted state transition. Unfortunately, both triggers and the threshold points at which state transitions occur are generally only partially known. Using case studies, we advance a general procedure to help identify triggers and estimate where threshold dynamics may occur. Our procedure is as follows: (1) Operationally define the ecosystem type being considered; we suggest that the ecological site concept of the Natural Resource Conservation Service is a useful system, (2) Using all available a priori knowledge to develop a state-and-transition model (STM), which defines possible ecosystem states, plausible transitions among them and likely triggers, (3) Validate the STM by verifying the existence of its states to the greatest degree possible, (4) Use the STM model to identify transitions and triggers likely to be detectable by a monitoring program, and estimate to the greatest degree possible the value of a measurable indicator of a trigger at the point that a state transition is imminent (tipping point), and values that may indicate when management intervention should be considered (assessment points). We illustrate two different methods for attaining these goals using a data-rich case study in Canyonlands National Park, and a data-poor case study in Wupatki National Monument. In the data-rich case, STMs are validated and revised, and tipping and assessment points are estimated using statistical analysis of data. In the data-poor case, we develop an iterative expert opinion survey approach to validate the degree of confidence in an STM, revise the model, identify lack of confidence in specific model components, and create reasonable first approximations of tipping and assessment points, which can later be refined when more data are available. Our goal should be to develop the best set of models possible given the level of information available to support decisions, which is often not much. The approach presented here offers a flexible means of achieving this goal, and determining specific research areas in need of study.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Application of threshold concepts in natural resource decision making","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-1-4899-8041-0_7","isbn":"978-1-4899-8040-3","usgsCitation":"Bowker, M.A., Miller, M.E., Garman, S.L., and Belote, T., 2014, Applying threshold concepts to conservation management of dryland ecosystems: Case studies on the Colorado Plateau, chap. of Application of threshold concepts in natural resource decision making, p. 101-130, https://doi.org/10.1007/978-1-4899-8041-0_7.","productDescription":"30 p.","startPage":"101","endPage":"130","ipdsId":"IP-022672","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":340726,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2014-02-08","publicationStatus":"PW","scienceBaseUri":"59099ab1e4b0fc4e44915812","contributors":{"editors":[{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":693915,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Bowker, Matthew A. mbowker@usgs.gov","contributorId":2875,"corporation":false,"usgs":true,"family":"Bowker","given":"Matthew","email":"mbowker@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":693911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Mark E.","contributorId":91580,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":6959,"text":"National Park Service Southeast Utah Group","active":true,"usgs":false}],"preferred":false,"id":693912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garman, Steven L. 0000-0002-9032-9074 slgarman@usgs.gov","orcid":"https://orcid.org/0000-0002-9032-9074","contributorId":3741,"corporation":false,"usgs":true,"family":"Garman","given":"Steven","email":"slgarman@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":693913,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belote, Travis","contributorId":191702,"corporation":false,"usgs":false,"family":"Belote","given":"Travis","email":"","affiliations":[],"preferred":false,"id":693914,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70156247,"text":"70156247 - 2014 - Modeling structured population dynamics using data from unmarked individuals","interactions":[],"lastModifiedDate":"2017-02-13T15:08:48","indexId":"70156247","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling structured population dynamics using data from unmarked individuals","docAbstract":"The study of population dynamics requires unbiased, precise estimates of abundance and vital rates that account for the demographic structure inherent in all wildlife and plant populations. Traditionally, these estimates have only been available through approaches that rely on intensive mark–recapture data. We extended recently developed N-mixture models to demonstrate how demographic parameters and abundance can be estimated for structured populations using only stage-structured count data. Our modeling framework can be used to make reliable inferences on abundance as well as recruitment, immigration, stage-specific survival, and detection rates during sampling. We present a range of simulations to illustrate the data requirements, including the number of years and locations necessary for accurate and precise parameter estimates. We apply our modeling framework to a population of northern dusky salamanders (Desmognathus fuscus) in the mid-Atlantic region (USA) and find that the population is unexpectedly declining. Our approach represents a valuable advance in the estimation of population dynamics using multistate data from unmarked individuals and should additionally be useful in the development of integrated models that combine data from intensive (e.g., mark–recapture) and extensive (e.g., counts) data sources.
Conservation efforts for threatened or endangered species are challenging because the multi-scale factors that relate to their decline or inhibit their recovery are often unknown. To further exacerbate matters, the perceptions associated with the mechanisms of species decline are often viewed myopically rather than across the entire species range. We used over 80 years of fish presence data collected from the Great Plains and associated ecoregions of the United States, to investigate the relative influence of changing environmental factors on the historic and current truncated distributions of the Arkansas River shiner Notropis girardi. Arkansas River shiner represent a threatened reproductive ecotype considered especially well adapted to the harsh environmental extremes of the Great Plains. Historic (n = 163 records) and current (n = 47 records) species distribution models were constructed using a vector-based approach in MaxEnt by splitting the available data at a time when Arkansas River shiner dramatically declined. Discharge and stream order were significant predictors in both models; however, the shape of the relationship between the predictors and species presence varied between time periods. Drift distance (river fragment length available for ichthyoplankton downstream drift before meeting a barrier) was a more important predictor in the current model and indicated river segments 375–780 km had the highest probability of species presence. Performance for the historic and current models was high (area under the curve; AUC > 0.95); however, forecasting and backcasting to alternative time periods suggested less predictive power. Our results identify fragments that could be considered refuges for endemic plains fish species and we highlight significant environmental factors (e.g., discharge) that could be manipulated to aid recovery.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.12329","usgsCitation":"Worthington, T.A., Brewer, S.K., Grabowski, T.B., and Mueller, J., 2014, Backcasting the decline of a vulnerable Great Plains reproductive ecotype: identifying threats and conservation priorities: Global Change Biology, v. 20, no. 1, p. 89-102, https://doi.org/10.1111/gcb.12329.","productDescription":"14 p.","startPage":"89","endPage":"102","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045497","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":300250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.9736328125,\n 34.052659421375964\n ],\n [\n -107.9736328125,\n 40.245991504199026\n ],\n [\n -91.49414062499999,\n 40.245991504199026\n ],\n [\n -91.49414062499999,\n 34.052659421375964\n ],\n [\n -107.9736328125,\n 34.052659421375964\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2013-10-16","publicationStatus":"PW","scienceBaseUri":"554dde29e4b082ec54129f19","contributors":{"authors":[{"text":"Worthington, Thomas A.","contributorId":140662,"corporation":false,"usgs":false,"family":"Worthington","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":546500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":546501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":546502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mueller, Julia","contributorId":140663,"corporation":false,"usgs":false,"family":"Mueller","given":"Julia","affiliations":[],"preferred":false,"id":546503,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70123296,"text":"fs20143090 - 2014 - Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2011–13","interactions":[],"lastModifiedDate":"2017-10-30T11:20:08","indexId":"fs20143090","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-3090","title":"Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2011–13","docAbstract":"The U.S. Geological Survey (USGS) monitors water quality and suspended-sediment transport in the San Francisco Bay. The San Francisco Bay area is home to millions of people, and the bay teems with both resident and migratory wildlife, plants, and fish. Fresh water mixes with salt water in the bay, which is subject both to riverine and marine (tides, waves, influx of salt water) influences. To understand this environment, the USGS, along with its partners, has been monitoring the bay’s waters continuously since 1988. Several water-quality variables are of particular importance to State and Federal resource managers and are monitored at key locations throughout the bay. Salinity, which indicates the relative mixing of fresh and ocean waters in the bay, is derived from specific conductance measurements. Water temperature, along with salinity, affects the density of water, which causes gravity driven circulation patterns and stratification in the water column. Turbidity is measured using light-scattering from suspended solids in water, and is used as a surrogate for suspended-sediment concentration (SSC). Suspended sediment often carries adsorbed contaminants; attenuates sunlight in the water column; deposits on tidal marsh and intertidal mudflats, which can help sustain these habitats as sea level rises; and deposits in ports and shipping channels, which can necessitate dredging. Dissolved oxygen, which is essential to a healthy ecosystem, is a fundamental indicator of water quality, and its concentration is affected by water temperature, salinity, ecosystem metabolism, tidal currents, and wind. Tidal currents in the bay reverse four times a day, and wind direction and intensity typically change on a daily cycle: consequently, salinity, water temperature, suspendedsediment concentration, and dissolvedoxygen concentration vary spatially and temporally throughout the bay, and continuous measurements are needed to observe these changes. The purpose of this fact sheet is to inform the public and resource managers of the availability of these water-quality data.\r\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20143090","usgsCitation":"Buchanan, P.A., Downing-Kunz, M.A., Schoellhamer, D., Shellenbarger, G., and Weidich, K., 2014, Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2011–13: U.S. Geological Survey Fact Sheet 2014-3090, 4 p., https://doi.org/10.3133/fs20143090.","productDescription":"4 p.","ipdsId":"IP-050934","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":294438,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2014/3090/"},{"id":347671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":347670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2014/3090/pdf/fs2014-3090.pdf"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.01391601562499,\n 37.29153547292737\n ],\n [\n -121.33300781249999,\n 37.29153547292737\n ],\n [\n -121.33300781249999,\n 38.35027253825765\n ],\n [\n -123.01391601562499,\n 38.35027253825765\n ],\n [\n -123.01391601562499,\n 37.29153547292737\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5423cf09e4b037b608f9d3b9","contributors":{"authors":[{"text":"Buchanan, Paul A. 0000-0002-4796-4734 buchanan@usgs.gov","orcid":"https://orcid.org/0000-0002-4796-4734","contributorId":1018,"corporation":false,"usgs":true,"family":"Buchanan","given":"Paul","email":"buchanan@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":519347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Downing-Kunz, Maureen A. 0000-0002-4879-0318 mdowning-kunz@usgs.gov","orcid":"https://orcid.org/0000-0002-4879-0318","contributorId":3690,"corporation":false,"usgs":true,"family":"Downing-Kunz","given":"Maureen","email":"mdowning-kunz@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":519349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schoellhamer, David H. 0000-0001-9488-7340 dschoell@usgs.gov","orcid":"https://orcid.org/0000-0001-9488-7340","contributorId":631,"corporation":false,"usgs":true,"family":"Schoellhamer","given":"David H.","email":"dschoell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":519346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shellenbarger, Gregory gshellen@usgs.gov","contributorId":1133,"corporation":false,"usgs":true,"family":"Shellenbarger","given":"Gregory","email":"gshellen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":519348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weidich, Kurt kweidich@usgs.gov","contributorId":5922,"corporation":false,"usgs":true,"family":"Weidich","given":"Kurt","email":"kweidich@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":519350,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70147911,"text":"70147911 - 2014 - Development of a multimetric index for fish assemblages in a cold tailwater in Tennessee","interactions":[],"lastModifiedDate":"2015-05-08T11:03:49","indexId":"70147911","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Development of a multimetric index for fish assemblages in a cold tailwater in Tennessee","docAbstract":"Tailwaters downstream of hypolimnetic-release hydropeaking dams exhibit a unique combination of stressors that affects the structure and function of resident fish assemblages. We developed a statistically and biologically defensible multimetric index of fish assemblages for the Caney Fork River below Center Hill Dam, Tennessee. Fish assemblages were sampled at five sites using boat-mounted and backpack electrofishing gear from fall 2009 through summer 2011. A multivariate statistical approach was used to select metrics that best reflected the downstream gradients in abiotic variables. Five metrics derived from boat electrofishing samples and four metrics derived from backpack electrofishing samples were selected for incorporation into the index based on their high correlation with environmental data. The nine metrics demonstrated predictable patterns of increase or decrease with increasing distance downstream of the dam. The multimetric index generally exhibited a pattern of increasing scores with increasing distance from the dam, indicating a downstream recovery gradient in fish assemblage composition. The index can be used to monitor anticipated changes in the fish communities of the Caney Fork River when repairs to Center Hill Dam are completed later this decade, resulting in altered dam operations.
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The Root Zone Water Quality Model (RZWQM) has been tested under a variety of conditions. However, the current model's ability to simulate pesticide transport to subsurface drain flow over a long term period under different tillage systems and application rates is not clear. Therefore, we calibrated and tested RZWQM using six years of data from Nashua, Iowa. In this experiment, atrazine was spring applied at 2.8 (1990–1992) and 0.6 kg/ha/yr (1993–1995) to two 0.4 ha plots with different tillage (till and no-till). The observed and simulated average annual flow weighted atrazine concentrations (FWAC) in subsurface drain flow from the no-till plot were 3.7 and 3.2 μg/L, respectively for the period with high atrazine application rates, and 0.8 and 0.9 μg/L, respectively for the period with low application rates. The 1990–1992 observed average annual FWAC difference between the no-till and tilled plot was 2.4 μg/L while the simulated difference was 2.1 μg/L. These observed and simulated differences for 1993–1995 were 0.1 and 0.1 μg/L, respectively. The Nash–Sutcliffe model performance statistic (EF) for cumulative atrazine flux to subsurface drain flow was 0.93 for the no-till plot testing years (1993–1995), which is comparable to other recent model tests. The value of EF is 1.0 when simulated data perfectly match observed data. The order of selected parameter sensitivity for RZWQM simulated FWAC was atrazine partition coefficient > number of macropores > atrazine half life in soil > soil hydraulic conductivity. Simulations from 1990 to 1995 with four different atrazine application rates applied at a constant rate throughout the simulation period showed concentrations in drain flow for the no-till plot to be twice those of the tilled plot. The differences were more pronounced in the early simulation period (1990–1992), partly because of the characteristics of macropore flow during large storms. The results suggest that RZWQM is a promising tool to study pesticide transport to subsurface drain flow under different tillage systems and application rates over several years, the concentrations of atrazine in drain flow can be higher with no-till than tilled soil over a range of atrazine application rates, and atrazine concentrations in drain flow are sensitive to the macropore flow characteristics under different tillage systems and rainfall timing and intensity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2013.09.009","usgsCitation":"Malone, R.W., Nolan, B.T., Ma, L., Kanwar, R.S., Pederson, C.H., and Heilman, P., 2014, Effects of tillage and application rate on atrazine transport to subsurface drainage: Evaluation of RZWQM using a six-year field study: Agricultural Water Management, v. 132, p. 10-22, https://doi.org/10.1016/j.agwat.2013.09.009.","productDescription":"13 p.","startPage":"10","endPage":"22","ipdsId":"IP-041818","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":473447,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1945&context=abe_eng_pubs","text":"External Repository"},{"id":348670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"132","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100d5e4b06e28e9c2542e","contributors":{"authors":[{"text":"Malone, Robert W.","contributorId":10347,"corporation":false,"usgs":false,"family":"Malone","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":717284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":721747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ma, Liwang","contributorId":6751,"corporation":false,"usgs":false,"family":"Ma","given":"Liwang","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":721748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanwar, Rameshwar S.","contributorId":143671,"corporation":false,"usgs":false,"family":"Kanwar","given":"Rameshwar","email":"","middleInitial":"S.","affiliations":[{"id":15296,"text":"Iowa State University, Ames, IA, USA","active":true,"usgs":false}],"preferred":false,"id":721749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pederson, Carl H.","contributorId":143672,"corporation":false,"usgs":false,"family":"Pederson","given":"Carl","email":"","middleInitial":"H.","affiliations":[{"id":15296,"text":"Iowa State University, Ames, IA, USA","active":true,"usgs":false}],"preferred":false,"id":721750,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heilman, Philip","contributorId":169768,"corporation":false,"usgs":false,"family":"Heilman","given":"Philip","email":"","affiliations":[{"id":25585,"text":"USDA-ARS Southwest Watershed Research Center, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":721751,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191008,"text":"70191008 - 2014 - Integrating research tools to support the management of social-ecological systems under climate change","interactions":[],"lastModifiedDate":"2017-09-20T14:52:32","indexId":"70191008","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Integrating research tools to support the management of social-ecological systems under climate change","docAbstract":"Developing resource management strategies in the face of climate change is complicated by the considerable uncertainty associated with projections of climate and its impacts and by the complex interactions between social and ecological variables. The broad, interconnected nature of this challenge has resulted in calls for analytical frameworks that integrate research tools and can support natural resource management decision making in the face of uncertainty and complex interactions. We respond to this call by first reviewing three methods that have proven useful for climate change research, but whose application and development have been largely isolated: species distribution modeling, scenario planning, and simulation modeling. Species distribution models provide data-driven estimates of the future distributions of species of interest, but they face several limitations and their output alone is not sufficient to guide complex decisions for how best to manage resources given social and economic considerations along with dynamic and uncertain future conditions. Researchers and managers are increasingly exploring potential futures of social-ecological systems through scenario planning, but this process often lacks quantitative response modeling and validation procedures. Simulation models are well placed to provide added rigor to scenario planning because of their ability to reproduce complex system dynamics, but the scenarios and management options explored in simulations are often not developed by stakeholders, and there is not a clear consensus on how to include climate model outputs. We see these strengths and weaknesses as complementarities and offer an analytical framework for integrating these three tools. We then describe the ways in which this framework can help shift climate change research from useful to usable.
","language":"English","publisher":"Ecology and Society","doi":"10.5751/ES-06813-190341","usgsCitation":"Miller, B.W., and Morisette, J.T., 2014, Integrating research tools to support the management of social-ecological systems under climate change: Ecology and Society, v. 19, no. 3, p. 1-12, https://doi.org/10.5751/ES-06813-190341.","productDescription":"Article 41; 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-056771","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":473287,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-06813-190341","text":"Publisher Index Page"},{"id":345942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c37e3be4b091459a631706","contributors":{"authors":[{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":710905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":710904,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70161758,"text":"70161758 - 2014 - Estimating structural collapse fragility of generic building typologies using expert judgment","interactions":[],"lastModifiedDate":"2021-10-13T16:26:50.090642","indexId":"70161758","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Estimating structural collapse fragility of generic building typologies using expert judgment","docAbstract":"The structured expert elicitation process proposed by Cooke (1991), \nhereafter referred to as Cooke’s approach, is applied for the first time \nin the realm of structural collapse-fragility assessment for selected generic \nconstruction types. Cooke’s approach works on the principle of objective \ncalibration scoring of judgments coupled with hypothesis testing used in classical\n statistics. The performance-based scoring system reflects the combined \nmeasure of an expert’s informativeness about variables in the problem area \nunder consideration, and their ability to enumerate, in a statistically accurate \nway through expressing their true beliefs, the quantitative uncertainties \nassociated with their assessments. We summarize the findings of an expert \nelicitation workshop in which a dozen earthquake-engineering professionals\n from around the world were engaged to estimate seismic collapse fragility for\n generic construction types. Development of seismic collapse fragility \nfunctions was accomplished by combining their judgments using weights \nderived from Cooke’s method. Although substantial effort was needed to\n elicit the inputs of these experts successfully, we anticipate that the elicitation\n strategy described here will gain momentum in a wide variety of earthquake \nseismology and engineering hazard and risk analyses where physical model \nand data limitations are inherent and objective professional judgment can fill \ngaps.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Safety, reliability, risk, and life-cycle performance of structures and infrastructures","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"1th International Conference on Structural Safety and Reliability (ICOSSAR2013)","conferenceDate":"June 16-20, 2013","conferenceLocation":"New York, NY","language":"English","publisher":"CRC Press","doi":"10.1201/b16387-130","isbn":"9781138000865","usgsCitation":"Jaiswal, K., Perkins, D., Wald, D., Aspinall, W.P., and Kiremidjian, A.S., 2014, Estimating structural collapse fragility of generic building typologies using expert judgment, in Safety, reliability, risk, and life-cycle performance of structures and infrastructures, New York, NY, June 16-20, 2013, p. 879-886, https://doi.org/10.1201/b16387-130.","productDescription":"8 p.","startPage":"879","endPage":"886","ipdsId":"IP-045829","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":340113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-01-13","publicationStatus":"PW","scienceBaseUri":"58fdbd18e4b0074928294487","contributors":{"editors":[{"text":"Deodatis, George","contributorId":191242,"corporation":false,"usgs":false,"family":"Deodatis","given":"George","email":"","affiliations":[],"preferred":false,"id":692458,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Ellingwood, Bruce R.","contributorId":44446,"corporation":false,"usgs":true,"family":"Ellingwood","given":"Bruce","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":692459,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Frangopol, Dan M.","contributorId":191243,"corporation":false,"usgs":false,"family":"Frangopol","given":"Dan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":692460,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Jaiswal, Kishor S. kjaiswal@usgs.gov","contributorId":145925,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor S.","email":"kjaiswal@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":692461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, D.J. 0000-0002-1454-4514","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":43809,"corporation":false,"usgs":true,"family":"Wald","given":"D.J.","affiliations":[],"preferred":false,"id":692462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, D.","contributorId":83589,"corporation":false,"usgs":true,"family":"Perkins","given":"D.","affiliations":[],"preferred":false,"id":692463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aspinall, W. P.","contributorId":82077,"corporation":false,"usgs":true,"family":"Aspinall","given":"W.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":692464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kiremidjian, Anne S.","contributorId":60649,"corporation":false,"usgs":true,"family":"Kiremidjian","given":"Anne","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":692465,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191982,"text":"70191982 - 2014 - Niche restriction and conservatism in a neotropical psittacine: the case of the Puerto Rican parrot","interactions":[],"lastModifiedDate":"2018-01-25T11:17:58","indexId":"70191982","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Niche restriction and conservatism in a neotropical psittacine: the case of the Puerto Rican parrot","docAbstract":"The factors which govern species‘ distribution and abundance are myriad, and together constitute the ecological niche of a given species. Because abiotic factors are arguably the most profound of the factors influencing niche boundaries and thus, species distributions, substantial changes in either climatic or habitat-related parameters can be expected to produce interrelated and profound niche shifts. Habitat loss and degradation can also effectively induce a de facto climate change by forcing populations to relocate to environmentally suboptimal habitats. Populations experiencing niche shifts due to range restrictions and geographic isolation become subject to a suite of factors that may act synergistically to amplify deleterious ecological effects of habitat loss. These factors tend to exert a greater influence on populations of rare or endemic species with inherently restricted ranges. The Puerto Rican parrot (Amazona vittata) is an example of a tropical, insular, endemic and critically-endangered species that has suffered from extensive habitat loss and degradation over the past century, resulting in a single relict wild population restricted for more than 70 years to the montane rainforest of the Luquillo Mountains in northeastern Puerto Rico. In this chapter, we examine the current ecological situation of this geographically and demographically isolated parrot population by reviewing the history of landscape-level changes in and around the Luquillo Mountains, and concurrent biotic and abiotic limiting factors in relation to both historical population trajectory and current prognosis for species recovery. We used a decade (2000-2009) of empirical data on parrot fledgling survival together with long-term climatological data to model effects of local climate on fledgling survival and gain insights into its influence on population growth. We also modeled hypothetical survival of parrot fledglings in the lowlands surrounding the Luquillo Mountains, areas currently deforested but previously occupied by parrots, to illustrate both quantitative and qualitative losses of reproductive habitat for the species. We illustrate and systematically discuss how progressive and sustained changes in landscape composition and associated limiting factors have effectively shifted and restricted the ecological niche of this species, and how this complex suite of ecological processes affects the Puerto Rican parrot in the Luquillo Mountains. Our niche restriction hypothesis is supported by the demographic response of Puerto Rican parrots recently (2006-2009) reintroduced in the lower elevation karst forest of northwestern Puerto Rico. Based on our findings, we present conservation strategies aimed at promoting the recovery of the species both in the Luquillo Mountains and elsewhere in Puerto Rico. Finally, we address the relevance of our findings to conservation of other endangered species, particularly those threatened by both habitat loss and climate change.
","largerWorkType":{"id":4,"text":"Book"},"language":"English","publisher":"Nova Science Publishers","publisherLocation":"Habitat loss: Causes, impacts on biodiversity and reduction strategies","isbn":"978-1-63117-231-1","usgsCitation":"White, T.H., Collazo, J., Dinsmore, S., and Llerandi-Roman, I.C., 2014, Niche restriction and conservatism in a neotropical psittacine: the case of the Puerto Rican parrot, p. 1-84.","productDescription":"84 p.","startPage":"1","endPage":"84","ipdsId":"IP-052674","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":350601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350600,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.novapublishers.com/catalog/product_info.php?products_id=49029"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6afac8e4b06e28e9c9a91b","contributors":{"authors":[{"text":"White, Thomas H. Jr.","contributorId":201474,"corporation":false,"usgs":false,"family":"White","given":"Thomas","suffix":"Jr.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":725798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":713809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dinsmore, Stephen J.","contributorId":61718,"corporation":false,"usgs":true,"family":"Dinsmore","given":"Stephen J.","affiliations":[],"preferred":false,"id":725799,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Llerandi-Roman, I. C.","contributorId":67324,"corporation":false,"usgs":true,"family":"Llerandi-Roman","given":"I.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":725800,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}