Climate, sea level rise, and urbanization are undergoing unprecedented levels of combined change and are expected to have large effects on natural resources—particularly along the Gulf of Mexico coastline (Gulf Coast). Management decisions to address these effects (i.e., adaptation) require an understanding of the relative vulnerability of various resources to these stressors. To meet this need, the four Landscape Conservation Cooperatives along the Gulf partnered with the Gulf of Mexico Alliance to conduct this Gulf Coast Vulnerability Assessment (GCVA). Vulnerability in this context incorporates exposure and sensitivity to threats (potential impact), coupled with the adaptive capacity to mitigate those threats. Potential impact and adaptive capacity reflect natural history features of target species and ecosystems. The GCVA used an expert opinion approach to qualitatively assess the vulnerability of four ecosystems: mangrove, oyster reef, tidal emergent marsh, and barrier islands, and a suite of wildlife species that depend on them. More than 50 individuals participated in the completion of the GCVA, facilitated via Ecosystem and Species Expert Teams.
Of the species assessed, Kemp’s ridley sea turtle was identified as the most vulnerable species across the Gulf Coast. Experts identified the main threats as loss of nesting habitat to sea level rise, erosion, and urbanization. Kemp’s ridley also had an overall low adaptive capacity score due to their low genetic diversity, and higher nest site fidelity as compared to other assessed species. Tidal emergent marsh was the most vulnerable ecosystem, due in part to sea level rise and erosion. In general, avian species were more vulnerable than fish because of nesting habitat loss to sea level rise, erosion, and potential increases in storm surge.
Assessors commonly indicated a lack of information regarding impacts due to projected changes in the disturbance regime, biotic interactions, and synergistic effects in both the species and habitat assessments. Many of the assessors who focused on species also identified data gaps regarding genetic information, phenotypic plasticity, life history, and species responses to past climate change and sea level rise. Regardless of information gaps, the results from the GCVA can be used to inform Gulf-wide adaptation plans. Given the scale of climatic impacts, coordinated efforts to address Gulf-wide threats to species and ecosystems will enhance the effectiveness of management actions and also have the potential to maximize the efficacy of limited funding.
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To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.
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U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Three-part mineral resource assessment is a methodology for predicting, in a specified geographic region, both the number of undiscovered mineral deposits and the amount of mineral resources in those deposits. These predictions are based on probability calculations that are performed with computer software that is newly implemented. Compared to the previous implementation, the new implementation includes new features for the probability calculations themselves and for checks of those calculations. The development of the new implementation lead to a new understanding of the probability calculations, namely the assumptions inherent in the probability calculations. Several assumptions strongly affect the mineral resource predictions, so it is crucial that they are checked during an assessment. The evaluation of the new implementation leads to new findings about the probability calculations,namely findings regarding the precision of the computations,the computation time, and the sensitivity of the calculation results to the input.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer Programs in Book 7 Automated Data Processing and Computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C15","usgsCitation":"Ellefsen, K.J., 2017, Probability calculations for three-part mineral resource assessments: U.S. Geological Survey Techniques and Methods, book 7, chap. C15, 14 p., https://doi.org/10.3133/tm7C15.","productDescription":"Report: iv, 14 p.; Calculation Scripts: 12.0 kb txt","startPage":"1","endPage":"14","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-075806","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":342784,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c15/tm7c15.pdf","text":"Report","size":"776 kB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C15"},{"id":342783,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c15/coverthb.jpg"},{"id":342786,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c15/CalculationScripts.R","text":"Calculation Scripts","size":"12.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"TM 7-C15 Calculation Scripts"},{"id":342859,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C14","text":"Techniques and Methods 7-C14: ","linkHelpText":"User’s Guide for MapMark4—An R Package for the Probability Calculations in Three-Part Mineral Resource Assessments"},{"id":362064,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20185149","text":"Scientific Investigations Report 2018-5149: ","linkHelpText":"Effect of Size-Biased Sampling on Resource Predictions from the Three-Part Method for Quantitative Mineral Resource Assessment—A Case Study of the Gold Mines in the Timmins-Kirkland Lake Area of the Abitibi Greenstone Belt, Canada"}],"publicComments":"Chapter 15 of Section C: Computer Programs in Book 7 Automated Data Processing and Computations","contact":" Central Mineral and Environmental Resources
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
A broad study of zircons from plutonic rocks of the Sawatch and Mosquito ranges of west-central Colorado (U.S.A.) was undertaken to significantly refine the magmatic chronology and chemistry of this under-studied region of the Colorado province. This region was chosen because it lies just to the north of the suspected arc-related Gunnison-Salida volcano-plutonic terrane, which has been the subject of many recent investigations—and whose origin is still debated. Our new results provide important insights into the processes active during Proterozoic crustal evolution in this region, and they have important ramifications for broader-scope crustal evolution models for southwestern North America.
Twenty-four new U-Pb ages and sequentially acquired rare-earth element (REE), U, Th, and Hf contents of zircon have been determined using the sensitive high-resolution ion microprobe-reverse geometry (SHRIMP-RG). These zircon geochemistry data, in conjunction with whole-rock major- and trace-element data, provide important insights into zircon crystallization and melt fractionation, and they help to further constrain the tectonic environment of magma generation.
Our detailed zircon and whole-rock data support the following three interpretations:
(1) The Roosevelt Granite in the southern Sawatch Range was the oldest rock dated at 1,766 ± 7 Ma, and it intruded various metavolcanic and metasedimentary rocks. Geochemistry of both whole-rock and zircon supports the contention that this granite was produced in a magmatic arc environment and, therefore, is likely an extension of the older Dubois Greenstone Belt of the Gunnison Igneous Complex (GIC) and the Needle Mountains (1,770–1,755 Ma). Rocks of the younger Cochetopa succession of the GIC, the Salida Greenstone Belt, and the Sangre de Cristo Mountains (1,740–1,725 Ma) were not found in the Sawatch and Mosquito ranges. This observation strongly suggests that the northern edge of the Gunnison-Salida arc terrane underlies the southern portion of the Sawatch and Mosquito ranges.
(2) Calc-alkalic to alkali-calcic magmas intruded this region approximately 55 m.y. after the Roosevelt Granite with emplacement of pre-deformational plutons at ca. 1,710 Ma (e.g., Henry Mountain Granite and diorite of Denny Creek), and this continued for at least 30 m.y., ending with emplacement of post-deformational plutons at ca. 1,680 Ma (e.g., Kroenke Granodiorite, granite of Fairview Peak, and syenite of Mount Yale). The timing of deformation can be constrained to sometime after intrusion of the diorite of Denny Creek and likely before the emplacement of the undeformed granite of Fairview Peak. Geochemistry of both whole-rock and zircon indicates that the older group of ca. 1,710-Ma plutons formed at shallower depths, and then they intruded the younger group of more deeply generated, commonly peraluminous and sodic plutons. Although absent in the Sawatch and Mosquito ranges, Mazatzal-age (ca. 1,680–1,620 Ma) plutonic rocks are present regionally. Inherited zircon components of Mazatzal-age were found as cores in some 1.4-Ga Sawatch and Mosquito Range zircons, indicating the likelihood of a relatively local source. These combined data suggest the possibility that all were produced within a continental-margin magmatic arc created as a result of southward-migrating (slab rollback?), north-dipping subduction to the south of the region.
(3) Widespread Mesoproterozoic plutonism—with emplacement at various depths and exhibiting bimodal geochemistry—is recognized in 16 different samples. An older group of predominantly peraluminous, yet magnesian granitoids (e.g., granodiorite of Sayers, granite of Taylor River, and the St. Kevin Granite) were emplaced between ca. 1,450 and 1,425 Ma. These geochemical parameters suggest moderate degrees of partial melting in a low-pressure environment. Three younger metaluminous, but ferroan plutons (diorite of Grottos, diorite of Mount Elbert, and granodiorite of Mount Harvard), probably represent a final magmatic pulse at ca. 1,416 Ma.
A comprehensive treatment of zircon REE and whole-rock trace-element behavior from Proterozoic rocks is scarce. Discriminant U/Yb versus Y diagrams using zircon data show that the Sawatch and Mosquito plutons are of continental origin, not oceanic. Additional bivariate diagrams incorporating cation ratio combinations of Gd, Ce, Yb, U, Th, Hf, and Eu offer refined insight into differences in fractionation trends and depth of magma generation for the various plutons. These interpretations, on the basis of zircon trace-element data, are mirrored in the whole-rock geochemistry data.
","language":"English","publisher":"GeoScienceWorld","doi":"10.24872/rmgjournal.52.1.17","usgsCitation":"Moscati, R.J., Premo, W.R., Dewitt, E., and Wooden, J.L., 2017, U-Pb ages and geochemistry of zircon from Proterozoic plutons of the Sawatch and Mosquito ranges, Colorado, U.S.A.: Implications for crustal growth of the central Colorado province: Rocky Mountain Geology, v. 52, no. 1, p. 17-106, https://doi.org/10.24872/rmgjournal.52.1.17.","productDescription":"90 p.","startPage":"17","endPage":"106","ipdsId":"IP-054984","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":342965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Mosquito Range, Sawatch Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107,\n 39.5\n ],\n [\n -107,\n 38.7\n ],\n [\n -107.8,\n 38.7\n ],\n [\n -107.8,\n 38.0\n ],\n [\n -105.8,\n 38.0\n ],\n [\n -105.8,\n 39.5\n ],\n [\n -107,\n 39.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-26","publicationStatus":"PW","scienceBaseUri":"59536ea2e4b062508e3c7a57","contributors":{"authors":[{"text":"Moscati, Richard J. 0000-0002-0818-4401 rmoscati@usgs.gov","orcid":"https://orcid.org/0000-0002-0818-4401","contributorId":2462,"corporation":false,"usgs":true,"family":"Moscati","given":"Richard","email":"rmoscati@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":700889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":700890,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitt, Ed edewitt@usgs.gov","contributorId":193586,"corporation":false,"usgs":true,"family":"Dewitt","given":"Ed","email":"edewitt@usgs.gov","affiliations":[],"preferred":true,"id":700891,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wooden, Joseph L.","contributorId":193587,"corporation":false,"usgs":false,"family":"Wooden","given":"Joseph","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":700892,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187492,"text":"sir20175034 - 2017 - Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds","interactions":[],"lastModifiedDate":"2017-06-26T10:38:00","indexId":"sir20175034","displayToPublicDate":"2017-06-26T10:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5034","title":"Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds","docAbstract":"Quantitative estimates of base flow are necessary to address questions concerning the vulnerability and response of the Nation’s water supply to natural and human-induced change in environmental conditions. An objective of the U.S. Geological Survey National Water-Quality Assessment Project is to determine how hydrologic systems are affected by watershed characteristics, including land use, land cover, water use, climate, and natural characteristics (geology, soil type, and topography). An important component of any hydrologic system is base flow, generally described as the part of streamflow that is sustained between precipitation events, fed to stream channels by delayed (usually subsurface) pathways, and more specifically as the volumetric discharge of water, estimated at a measurement site or gage at the watershed scale, which represents groundwater that discharges directly or indirectly to stream reaches and is then routed to the measurement point.
Hydrograph separation using a recursive digital filter was applied to 225 sites in the Chesapeake Bay watershed. The recursive digital filter was chosen for the following reasons: it is based in part on the assumption that groundwater acts as a linear reservoir, and so has a physical basis; it has only two adjustable parameters (alpha, obtained directly from recession analysis, and beta, the maximum value of the base-flow index that can be modeled by the filter), which can be determined objectively and with the same physical basis of groundwater reservoir linearity, or that can be optimized by applying a chemical-mass-balance constraint. Base-flow estimates from the recursive digital filter were compared with those from five other hydrograph-separation methods with respect to two metrics: the long-term average fraction of streamflow that is base flow, or base-flow index, and the fraction of days where streamflow is entirely base flow. There was generally good correlation between the methods, with some biased slightly high and some biased slightly low compared to the recursive digital filter. There were notable differences between the days at base flow estimated by the different methods, with the recursive digital filter having a smaller range of values. This was attributed to how the different methods determine cessation of quickflow (the part of streamflow which is not base flow).
For 109 Chesapeake Bay watershed sites with available specific conductance data, the parameters of the filter were optimized using a chemical-mass-balance constraint and two different models for the time-dependence of base-flow specific conductance. Sixty-seven models were deemed acceptable and the results compared well with non-optimized results. There are a number of limitations to the optimal hydrograph-separation approach resulting from the assumptions implicit in the conceptual model, the mathematical model, and the approach taken to impose chemical mass balance (including tracer choice). These limitations may be evidenced by poor model results; conversely, poor model fit may provide an indication that two-component separation does not adequately describe the hydrologic system’s runoff response.
The results of this study may be used to address a number of questions regarding the role of groundwater in understanding past changes in stream-water quality and forecasting possible future changes, such as the timing and magnitude of land-use and management practice effects on stream and groundwater quality. Ongoing and future modeling efforts may benefit from the estimates of base flow as calibration targets or as a means to filter chemical data to model base-flow loads and trends. Ultimately, base-flow estimation might provide the basis for future work aimed at improving the ability to quantify groundwater discharge, not only at the scale of a gaged watershed, but at the scale of individual reaches as well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175034","isbn":"978-1-4113-4135-7","collaboration":"National Water Quality Program","usgsCitation":"Raffensperger, J.P., Baker, A.C., Blomquist, J.D., and Hopple, J.A., 2017, Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds: U.S. Geological Survey Scientific Investigations Report 2017–5034, 51 p., https://doi.org/10.3133/sir20175034.","productDescription":"Report: vii, 51 p.; Data Release","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-080740","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":342818,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5034/sir20175034.pdf","text":"Report","size":"2.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5034"},{"id":342817,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5034/coverthb.jpg"},{"id":342819,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F757194G","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrograph-separation results for 225 streams in the Chesapeake Bay watershed derived by using PART, HYSEP (Fixed, Local minimum, Slide), BFI, and a Recursive Digital Filter with streamflow data ranging from 1913 through 2016"}],"contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
The highly pathogenic avian influenza (H5N2) outbreak in the Midwestern United States (US) in 2015 was historic due to the number of birds and poultry operations impacted and the corresponding economic loss to the poultry industry and was the largest animal health emergency in US history. The U.S. Geological Survey (USGS), with the assistance of several state and federal agencies, aided the response to the outbreak by developing a study to determine the extent of virus transport in the environment. The study goals were to: develop the appropriate sampling methods and protocols for measuring avian influenza virus (AIV) in groundwater, provide the first baseline data on AIV and outbreak- and poultry-related contaminant occurrence and movement into groundwater, and document climatological factors that may have affected both survival and transport of AIV to groundwater during the months of the 2015 outbreak. While site selection was expedient, there were often delays in sample response times due to both relationship building between agencies, groups, and producers and logistical time constraints. This study's design and sampling process highlights the unpredictable nature of disease outbreaks and the corresponding difficulty in environmental sampling of such events. The lessons learned, including field protocols and approaches, can be used to improve future research on AIV in the environment.
Use of telemetry data to inform fisheries conservation and management is becoming increasingly common; as such, fish typically must be sedated before surgical implantation of transmitters into the coelom. Given that no widely available, immediate-release chemical sedative currently exists in North America, we investigated the feasibility of using electricity to sedate Lake Trout Salvelinus namaycush long enough for an experienced surgeon to implant an electronic transmitter (i.e., 180 s). Specifically, our study objectives were to determine (1) whether some combination of electrical waveform characteristics (i.e., duty cycle, frequency, voltage, and pulse type) could sedate Lake Trout for at least 180 s; and (2) whether Lake Trout that were sequentially exposed to continuous DC and pulsed DC had greater rates of spinal injury and short-term mortality than control fish. A Portable Electrosedation System unit was used to sedate hatchery and wild Lake Trout. Dual-frequency pulsed-DC and two-stage approaches successfully sedated Lake Trout and had similar induction and recovery times. Lake Trout sedated using the two-stage approach did not have survival rates or spinal abnormalities that were significantly different from those of control fish. We concluded that electricity was a viable alternative to chemical sedatives for sedating Lake Trout before surgical implantation of an electronic transmitter, but we suggest that Lake Trout and other closely related species (e.g., Arctic Char Salvelinus alpinus) may require morphotype-specific electrical waveforms due to their morphological diversity.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02755947.2017.1324544","usgsCitation":"Faust, M.D., Vandergoot, C., Hostnik, E.T., Binder, T., Mida Hinderer, J.L., Ives, J.T., and Krueger, C., 2017, Use of electricity to sedate Lake Trout for intracoelomic implantation of electronic transmitters: North American Journal of Fisheries Management, v. 37, no. 4, p. 768-777, https://doi.org/10.1080/02755947.2017.1324544.","productDescription":"10 p.","startPage":"768","endPage":"777","ipdsId":"IP-083769","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469732,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02755947.2017.1324544","text":"Publisher Index Page"},{"id":342877,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-01","publicationStatus":"PW","scienceBaseUri":"59521d1de4b062508e3c3644","contributors":{"authors":[{"text":"Faust, Matthew D.","contributorId":145776,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":700593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergoot, Christopher 0000-0003-4128-3329 cvandergoot@usgs.gov","orcid":"https://orcid.org/0000-0003-4128-3329","contributorId":178356,"corporation":false,"usgs":true,"family":"Vandergoot","given":"Christopher","email":"cvandergoot@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":700592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hostnik, Eric T.","contributorId":193488,"corporation":false,"usgs":false,"family":"Hostnik","given":"Eric","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":700594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Binder, Thomas R.","contributorId":21093,"corporation":false,"usgs":true,"family":"Binder","given":"Thomas R.","affiliations":[],"preferred":false,"id":700595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mida Hinderer, Julia L.","contributorId":193489,"corporation":false,"usgs":false,"family":"Mida Hinderer","given":"Julia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":700596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ives, Jessica T.","contributorId":193490,"corporation":false,"usgs":false,"family":"Ives","given":"Jessica","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":700597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":700598,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188457,"text":"fs20173045 - 2017 - US Topo—Topographic maps for the Nation","interactions":[{"subject":{"id":70048622,"text":"fs20133093 - 2013 - US Topo: topographic maps for the nation","indexId":"fs20133093","publicationYear":"2013","noYear":false,"title":"US Topo: topographic maps for the nation"},"predicate":"SUPERSEDED_BY","object":{"id":70188457,"text":"fs20173045 - 2017 - US Topo—Topographic maps for the Nation","indexId":"fs20173045","publicationYear":"2017","noYear":false,"title":"US Topo—Topographic maps for the Nation"},"id":1}],"lastModifiedDate":"2017-06-23T13:41:58","indexId":"fs20173045","displayToPublicDate":"2017-06-23T12:45:00","publicationYear":"2017","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":"2017-3045","title":"US Topo—Topographic maps for the Nation","docAbstract":"Building on the success of 125 years of mapping, the U.S. Geological Survey created US Topo, a georeferenced digital map produced from The National Map data. US Topo maps are designed to be used like the traditional 7.5-minute quadrangle paper topographic maps for which the U.S. Geological Survey is so well known. However, in contrast to paper-based maps, US Topo maps provide modern technological advantages that support faster, wider public distribution and basic, onscreen geospatial analysis, including the georeferencing capability to display the ground coordinate location as the user moves the cursor around the map.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173045","usgsCitation":"Fishburn, K.A., and Carswell, W.J., Jr., 2017, US Topo—Topographic maps for the Nation: U.S. Geological Survey Fact Sheet 2017–3045, 2 p., https://doi.org/10.3133/fs20173045. ","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-085697","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":342742,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20173048","text":"Scanning and Georeferencing Historical USGS Quadrangles"},{"id":342740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3045/coverthb.jpg"},{"id":342741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3045/fs20173045.pdf","text":"Report","size":"3.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3045"}],"contact":"National Geospatial Program
US Topo—Maps for America
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Condit Dam, at river kilometer 5.3 on the White Salmon River, Washington, was breached in 2011 and removed completely in 2012, allowing anadromous salmonids access to habitat that had been blocked for nearly 100 years. A multi-agency workgroup concluded that the preferred salmonid restoration alternative was natural recolonization with monitoring to assess efficacy, followed by a management evaluation 5 years after dam removal. Limited monitoring of salmon and steelhead spawning has occurred since 2011, but no monitoring of juveniles occurred until 2016. During 2016, we operated a rotary screw trap at river kilometer 2.3 (3 kilometers downstream of the former dam site) from late March through May and used backpack electrofishing during summer to assess juvenile salmonid distribution and abundance. The screw trap captured primarily steelhead (Oncorhynchus mykiss; smolts, parr, and fry) and coho salmon (O. kisutch; smolts and fry). We estimated the number of steelhead smolts at 3,851 (standard error = 1,454) and coho smolts at 1,093 (standard error = 412). In this document, we refer to O. mykiss caught at the screw trap as steelhead because they were actively migrating, but because we did not know migratory status of O. mykiss caught in electrofishing surveys, we simply refer to them as O. mykiss or steelhead/rainbow trout. Steelhead and coho smolts tagged with passive integrated transponder tags were subsequently detected downstream at Bonneville Dam on the Columbia River. Few Chinook salmon (O. tshawytscha) fry were captured, possibly as a result of trap location or effects of a December 2015 flood. Sampling in Mill, Buck, and Rattlesnake Creeks (all upstream of the former dam site) showed that juvenile coho were present in Mill and Buck Creeks, suggesting spawning had occurred there. We compared O. mykiss abundance data in sites on Buck and Rattlesnake Creeks to pre-dam removal data. During 2016, age-0 O. mykiss were more abundant in Buck Creek than in 2009 or 2010, though age-1 and older O. mykiss abundance was similar. In Rattlesnake Creek, age-0 O. mykiss abundance during 2016 slightly exceeded the mean abundance from 2001 through 2005, although age-1 and older O. mykiss abundance was lower than from 2001 through 2005. These sampling efforts also provided the opportunity to collect genetic samples to investigate parental and stock origin, although funding to analyze the samples was not part of this grant. Juvenile salmonid sampling efforts during 2016 have shown that natural spawning produced steelhead and coho smolts and that coho were colonizing some tributaries. The 2016 efforts also provided the first post-dam juvenile abundance estimates. We hope to continue monitoring to better understand abundance trends, distribution, and life history patterns of recolonizing salmonids in the White Salmon River to assess efficacy of natural recolonization and to inform management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171070","collaboration":"Prepared in cooperation with Mid-Columbia Fisheries Enhancement Group","usgsCitation":"Jezorek, I.G., and Hardiman, J.M., 2017, Juvenile salmonid monitoring in the White Salmon River, Washington, post-Condit Dam removal, 2016: U.S. Geological Survey Open-File Report 2017-1070, 34 p., https://doi.org/10.3133/ofr20171070.","productDescription":"iv, 34 p.","onlineOnly":"Y","ipdsId":"IP-083906","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":342774,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1070/coverthb.jpg"},{"id":342775,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1070/ofr20171070.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1070"}],"country":"United States","state":"Washington","otherGeospatial":"White Salmon River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75460815429686,\n 45.64428817847555\n ],\n [\n -121.27258300781251,\n 45.64428817847555\n ],\n [\n -121.27258300781251,\n 45.99076013759662\n ],\n [\n -121.75460815429686,\n 45.99076013759662\n ],\n [\n -121.75460815429686,\n 45.64428817847555\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
Large carnivores are depicted to shape entire ecosystems through top-down processes. Studies describing these processes are often used to support interventionist wildlife management practices, including carnivore reintroduction or lethal control programs. Unfortunately, there is an increasing tendency to ignore, disregard or devalue fundamental principles of the scientific method when communicating the reliability of current evidence for the ecological roles that large carnivores may play, eroding public confidence in large carnivore science and scientists. Here, we discuss six interrelated issues that currently undermine the reliability of the available literature on the ecological roles of large carnivores: (1) the overall paucity of available data, (2) reliability of carnivore population sampling techniques, (3) general disregard for alternative hypotheses to top-down forcing, (4) lack of applied science studies, (5) frequent use of logical fallacies, and (6) generalisation of results from relatively pristine systems to those substantially altered by humans. We first describe how widespread these issues are, and given this, show, for example, that evidence for the roles of wolves (Canis lupus) and dingoes (Canis lupus dingo) in initiating trophic cascades is not as strong as is often claimed. Managers and policy makers should exercise caution when relying on this literature to inform wildlife management decisions. We emphasise the value of manipulative experiments and discuss the role of scientific knowledge in the decision-making process. We hope that the issues we raise here prompt deeper consideration of actual evidence, leading towards an improvement in both the rigour and communication of large carnivore science.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2017.02.008","usgsCitation":"Allen, B.L., Allen, L.R., Andren, H., Ballard, G., Boitani, L., Engeman, R., Fleming, P.J., Haswell, P.M., Ford, A.T., Kowalczyk, R., Linnell, J., Mech, L.D., and Parker, D.M., 2017, Can we save large carnivores without losing large carnivore science?: Food Webs, v. 12, p. 64-75, https://doi.org/10.1016/j.fooweb.2017.02.008.","productDescription":"12 p.","startPage":"64","endPage":"75","ipdsId":"IP-081591","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469738,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.bangor.ac.uk/portal/en/researchoutputs/can-we-save-large-carnivores-without-losing-large-carnivore-science(99119441-5301-469d-a9fd-bd132f9067df).html","text":"External Repository"},{"id":342755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"594cd73de4b062508e3951c5","contributors":{"authors":[{"text":"Allen, Benjamin L.","contributorId":193210,"corporation":false,"usgs":false,"family":"Allen","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":699220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Lee R.","contributorId":193219,"corporation":false,"usgs":false,"family":"Allen","given":"Lee","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":699266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andren, Henrik","contributorId":193220,"corporation":false,"usgs":false,"family":"Andren","given":"Henrik","email":"","affiliations":[],"preferred":false,"id":699267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ballard, Guy","contributorId":193221,"corporation":false,"usgs":false,"family":"Ballard","given":"Guy","email":"","affiliations":[],"preferred":false,"id":699268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boitani, Luigi","contributorId":193211,"corporation":false,"usgs":false,"family":"Boitani","given":"Luigi","email":"","affiliations":[],"preferred":false,"id":699221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engeman, Richard M.","contributorId":39301,"corporation":false,"usgs":true,"family":"Engeman","given":"Richard M.","affiliations":[],"preferred":false,"id":699269,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fleming, Peter J.S.","contributorId":193222,"corporation":false,"usgs":false,"family":"Fleming","given":"Peter","email":"","middleInitial":"J.S.","affiliations":[],"preferred":false,"id":699270,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haswell, Peter M.","contributorId":193224,"corporation":false,"usgs":false,"family":"Haswell","given":"Peter","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":699274,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ford, Adam T.","contributorId":193223,"corporation":false,"usgs":false,"family":"Ford","given":"Adam","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":699271,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kowalczyk, Rafal","contributorId":193212,"corporation":false,"usgs":false,"family":"Kowalczyk","given":"Rafal","email":"","affiliations":[],"preferred":false,"id":699272,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":699219,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Linnell, John","contributorId":193225,"corporation":false,"usgs":false,"family":"Linnell","given":"John","email":"","affiliations":[],"preferred":false,"id":699273,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Parker, Daniel M.","contributorId":193226,"corporation":false,"usgs":false,"family":"Parker","given":"Daniel","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":699275,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70208289,"text":"70208289 - 2017 - Towards a planetary spatial data infrastructure","interactions":[],"lastModifiedDate":"2020-02-04T06:32:09","indexId":"70208289","displayToPublicDate":"2017-06-21T10:24:47","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5685,"text":"ISPRS International Journal of Geo-Information ","printIssn":"2220-9964","active":true,"publicationSubtype":{"id":10}},"title":"Towards a planetary spatial data infrastructure","docAbstract":"Planetary science is the study of planets, moons, irregular bodies such as asteroids and the processes that create and modify them. Like terrestrial sciences, planetary science research is heavily dependent on collecting, processing and archiving large quantities of spatial data to support a range of activities. To address the complexity of storing, discovering, accessing, and utilizing spatial data, the terrestrial research community has developed conceptual Spatial Data Infrastructure (SDI) models and cyberinfrastructures. The needs that these systems seek to address for terrestrial spatial data users are similar to the needs of the planetary science community: spatial data should just work for the non-spatial expert. Here we discuss a path towards a Planetary Spatial Data Infrastructure (PSDI) solution that fulfills this primary need. We first explore the linkage between SDI models and cyberinfrastructures, then describe the gaps in current PSDI concepts, and discuss the overlap between terrestrial SDIs and a new, conceptual PSDI that best serves the needs of the planetary science community.
","language":"English","publisher":"MDPI","doi":"10.3390/ijgi6060181","usgsCitation":"Laura, J., Hare, T.M., Gaddis, L.R., Fergason, R.L., Skinner, Hagerty, J., and Archinal, B., 2017, Towards a planetary spatial data infrastructure: ISPRS International Journal of Geo-Information , v. 6, no. 6, 181, 18 p., https://doi.org/10.3390/ijgi6060181.","productDescription":"181, 18 p.","ipdsId":"IP-087170","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":469739,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijgi6060181","text":"Publisher Index Page"},{"id":371916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaddis, Lisa R. 0000-0001-9953-5483 lgaddis@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":2817,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa","email":"lgaddis@usgs.gov","middleInitial":"R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fergason, Robin L. 0000-0002-2044-1714","orcid":"https://orcid.org/0000-0002-2044-1714","contributorId":206167,"corporation":false,"usgs":true,"family":"Fergason","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skinner, Jr. 0000-0002-3644-7010","orcid":"https://orcid.org/0000-0002-3644-7010","contributorId":222125,"corporation":false,"usgs":true,"family":"Skinner","suffix":"Jr.","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hagerty, Justin 0000-0003-3800-7948 jhagerty@usgs.gov","orcid":"https://orcid.org/0000-0003-3800-7948","contributorId":911,"corporation":false,"usgs":true,"family":"Hagerty","given":"Justin","email":"jhagerty@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Archinal, Brent A. 0000-0002-6654-0742","orcid":"https://orcid.org/0000-0002-6654-0742","contributorId":206341,"corporation":false,"usgs":true,"family":"Archinal","given":"Brent A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781276,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70186976,"text":"ds1046 - 2017 - The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama","interactions":[],"lastModifiedDate":"2017-06-20T12:58:19","indexId":"ds1046","displayToPublicDate":"2017-06-20T10:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1046","title":"The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama","docAbstract":"Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center collected 303 surface sediment samples from Dauphin Island, Alabama, and the surrounding water bodies in August 2015. These sediments were processed to determine physical characteristics such as organic content, bulk density, and grain-size. The environments where the sediments were collected include high and low salt marshes, washover deposits, dunes, beaches, sheltered bays, and open water. Sampling by the USGS was part of a larger study to assess the feasibility and sustainability of proposed restoration efforts for Dauphin Island, Alabama, and assess the island’s resilience to rising sea level and storm events. The data presented in this publication can be used by modelers to attempt validation of hindcast models and create predictive forecast models for both baseline conditions and storms. This study was funded by the National Fish and Wildlife Foundation, via the Gulf Environmental Benefit Fund.
This report serves as an archive for sedimentological data derived from surface sediments. Downloadable data are available as Excel spreadsheets, JPEG files, and formal Federal Geographic Data Committee metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1046","usgsCitation":"Ellis, A.M., Marot, M.E., Smith, C.G., and Wheaton, C.J., 2017, The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama: U.S. Geological Survey Data Series 1046, https://doi.org/10.3133/ds1046.\n\n","productDescription":"HTML document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078883","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1046","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1046"},{"id":342207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1046/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":" Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.37127685546875,\n 30.25076353594852\n ],\n [\n -88.34518432617188,\n 30.209234673510014\n ],\n [\n -88.29299926757812,\n 30.20211367909724\n ],\n [\n -88.18450927734375,\n 30.206861065952626\n ],\n [\n -88.08837890625,\n 30.212794977500614\n ],\n [\n -88.04580688476562,\n 30.244831915307145\n ],\n [\n -88.05816650390625,\n 30.278044377800153\n ],\n [\n -88.0609130859375,\n 30.295832146790442\n ],\n [\n -88.08151245117188,\n 30.324285866937423\n ],\n [\n -88.165283203125,\n 30.312431154103745\n ],\n [\n -88.29711914062499,\n 30.292274851024256\n ],\n [\n -88.36715698242186,\n 30.28634573802957\n ],\n [\n -88.37127685546875,\n 30.25076353594852\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
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After Hurricane Sandy made landfall along the northeastern Atlantic coast of the United States on October 29, 2012, the U.S. Geological Survey (USGS) carried out scientific investigations to assist with protecting coastal communities and resources from future flooding. The work included development and implementation of the Surge, Wave, and Tide Hydrodynamics (SWaTH) network consisting of more than 900 monitoring stations. The SWaTH network was designed to greatly improve the collection and timely dissemination of information related to storm surge and coastal flooding. The network provides a significant enhancement to USGS data-collection capabilities in the region impacted by Hurricane Sandy and represents a new strategy for observing and monitoring coastal storms, which should result in improved understanding, prediction, and warning of storm-surge impacts and lead to more resilient coastal communities.
As innovative as it is, SWaTH evolved from previous USGS efforts to collect storm-surge data needed by others to improve storm-surge modeling, warning, and mitigation. This report discusses the development and implementation of the SWaTH network, and some of the regional stories associated with the landfall of Hurricane Sandy, as well as some previous events that informed the SWaTH development effort. Additional discussions on the mechanics of inundation and how the USGS is working with partners to help protect coastal communities from future storm impacts are also included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1431","isbn":"ISBN 978-1-4113-4149-4","usgsCitation":"Verdi, R.J., Lotspeich, R.R., Robbins, J.C., Busciolano, R.J., Mullaney, J.R., Massey, A.J., Banks, W.S., Roland, M.A., Jenter, H.L., Peppler, M.C., Suro, T.P., Schubert, C.E., and Nardi, M.R., 2017, The surge, wave, and tide hydrodynamics (SWaTH) network of the U.S. Geological Survey—Past and future implementation of storm-response monitoring, data collection, and data delivery: U.S. Geological Survey Circular 1431, 35 p., https://dx.doi.org/10.3133/cir1431.","productDescription":"iv, 35 p. 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U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Groundwater quality in the 112-square-mile Bear Valley and Lake Arrowhead Watershed (BEAR) study unit was investigated as part of the Priority Basin Project (PBP) of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit comprises two study areas (Bear Valley and Lake Arrowhead Watershed) in southern California in San Bernardino County. The GAMA-PBP is conducted by the California State Water Resources Control Board (SWRCB) in cooperation with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory.
The GAMA BEAR study was designed to provide a spatially balanced, robust assessment of the quality of untreated (raw) groundwater from the primary aquifer systems in the two study areas of the BEAR study unit. The assessment is based on water-quality collected by the USGS from 38 sites (27 grid and 11 understanding) during 2010 and on water-quality data from the SWRCB-Division of Drinking Water (DDW) database. The primary aquifer system is defined by springs and the perforation intervals of wells listed in the SWRCB-DDW water-quality database for the BEAR study unit.
This study included two types of assessments: (1) a status assessment, which characterized the status of the quality of the groundwater resource as of 2010 by using data from samples analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements, and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting the groundwater quality. The assessments were intended to characterize the quality of groundwater resources in the primary aquifer system of the BEAR study unit, not the treated drinking water delivered to consumers. Bear Valley study area and the Lake Arrowhead Watershed study area were also compared statistically on the basis of water-quality results and factors potentially affecting the groundwater quality.
Relative concentrations (RCs), which are sample concentration of a particular constituent divided by its associated health- or aesthetic-based benchmark concentrations, were used for evaluating the groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. An RC greater than 1.0 indicates a concentration greater than a benchmark. Organic (volatile organic compounds and pesticides) and special-interest (perchlorate) constituent RCs were classified as “high” (RC greater than 1.0), “moderate” (RC less than or equal to 1.0 and greater than 0.1), or “low” (RC less than or equal to 0.1). For inorganic (radioactive, trace element, major ion, and nutrient) constituents, the boundary between low and moderate RCs was set at 0.5.
Aquifer-scale proportion was used as the primary metric in the status assessment for evaluating groundwater quality at the study-unit scale or for its component areas. High aquifer-scale proportion was defined as the percentage of the area of the primary aquifer system with a RC greater than 1.0 for a particular constituent or class of constituents; the percentage is based on area rather than volume. Moderate and low aquifer-scale proportions were defined as the percentage of the primary aquifer system with moderate and low RCs, respectively. A spatially weighted statistical approach was used to evaluate aquifer-scale proportions for individual constituents and classes of constituents.
The status assessment for the Bear Valley study area found that inorganic constituents with health-based benchmarks were detected at high RCs in 9.0 percent of the primary aquifer system and at moderate RCs in 13 percent. The high RCs of inorganic constituents primarily reflected high aquifer-scale proportions of fluoride (in 5.4 percent of the primary aquifer system) and arsenic (3.6 percent). The RCs of organic constituents with health-based benchmarks were high in 1.0 percent of the primary aquifer system, moderate in 8.1 percent, and low in 70 percent. Organic constituents were detected in 79 percent of the primary aquifer system. Two groups of organic constituents and two individual organic constituents were detected at frequencies greater than 10 percent of samples from the USGS grid sites: trihalomethanes (THMs), solvents, methyl tert-butyl ether (MTBE), and simazine. The special-interest constituent perchlorate was detected in 93 percent of the primary aquifer system; it was detected at moderate RCs in 7.1 percent and at low RCs in 86 percent.
The status assessment in the Lake Arrowhead Watershed study area showed that inorganic constituents with human-health benchmarks were detected at high RCs in 25 percent of the primary aquifer system and at moderate RCs in 41 percent. The high aquifer-scale proportion of inorganic constituents primarily reflected high aquifer-scale proportions of radon‑222 (in 62 percent of the primary aquifer system) and uranium (26 percent). RCs of organic constituents with health-based benchmarks were moderate in 7.7 percent of the primary aquifer system and low in 46 percent. Organic constituents were detected in 54 percent of the primary aquifer system. The only organic constituents that were detected at frequencies greater than 10 percent of samples from the USGS grid sites were THMs. Perchlorate was detected in 62 percent of the primary aquifer system at uniformly low RCs.
The second component of this study, the understanding assessment, identified the natural and human factors that could have affected the groundwater quality in the BEAR study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were land use (including density of septic tanks and leaking or formerly leaking underground fuel tanks), site type, aquifer lithology, well construction (well depth and depth to the top-of-perforated interval), elevation, aridity index, groundwater-age distribution, and oxidation-reduction condition (including pH and dissolved oxygen concentration). Results of the statistical evaluations were used to explain the distribution of constituents in groundwater of the BEAR study unit.
In the Bear Valley study area, high and moderate RCs of fluoride were found in sites known to be influenced by hydrothermic conditions or that had high concentrations of fluoride historically. The high RC of arsenic can likely be attributed to desorption of arsenic from aquifer sediments saturated in old groundwater with high pH under reducing conditions. The THMs were detected more frequently at USGS grid sites that were wells, part of a large urban water system, and surrounded by urban land use. Solvents, MTBE, and simazine were all detected more frequently at USGS grid sites that were wells with a greater urban percentage of surrounding land use and that accessed older groundwater than other USGS grid sites. Comparison between the observed and predicted detection frequencies of perchlorate at USGS grid sites indicated that anthropogenic sources could have contributed to low levels of perchlorate in the groundwater of the Bear Valley study area.
In the Lake Arrowhead Watershed study area, high and moderate RCs of radon-222 and uranium can be attributed to older groundwater from the granitic fractured-rock primary aquifer system. Low RCs of THMs were detected at USGS grid sites that were wells and part of small water systems. The similarities between the observed and predicted detection frequencies of perchlorate in samples from USGS grid sites indicated that the source and distribution of perchlorate were most likely attributable to precipitation (rain and snow), with minimal, if any, contribution from anthropogenic sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175043","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Mathany, T.M., and Burton, C.A., 2017, Status and understanding of groundwater quality in the Bear Valley and Lake Arrowhead Watershed Study Unit, 2010: California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2017–5043, 71 p., https://doi.org/10.3133/sir20175043.","productDescription":"xii, 71 p.","onlineOnly":"Y","ipdsId":"IP-051454","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":342654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5043/coverthb2.jpg"},{"id":342655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5043/sir20175043.pdf","text":"Report","size":"10 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The U.S. Geological Survey (USGS) serves the Nation by providing reliable scientific information and tools to build resilience in communities exposed to subduction zone earthquakes, tsunamis, landslides, and volcanic eruptions. Improving the application of USGS science to successfully reduce risk from these events relies on whole community efforts, with continuing partnerships among scientists and stakeholders, including researchers from universities, other government labs and private industry, land-use planners, engineers, policy-makers, emergency managers and responders, business owners, insurance providers, the media, and the general public.
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(650) 329-4668
We studied how drought and an associated stressor, wildfire, influenced stream flow permanence and thermal regimes in a Great Basin stream network. We quantified these responses by collecting information with a spatially extensive network of data loggers. To understand the effects of wildfire specifically, we used data from 4 additional sites that were installed prior to a 2012 fire that burned nearly the entire watershed. Within the sampled network 73 reaches were classified as perennial, yet only 51 contained surface water during logger installation in 2014. Among the sites with pre-fire temperature data, we observed 2–4 °C increases in maximum daily stream temperature relative to an unburned control in the month following the fire; effects (elevated up to 6.6 °C) appeared to persist for at least one year. When observed August mean temperatures in 2015 (the peak of regionally severe drought) were compared to those predicted by a regional stream temperature model, we observed deviations of −2.1°-3.5°. The model under-predicted and over-predicted August mean by > 1 °C in 54% and 10% of sites, respectively, and deviance from predicted was negatively associated with elevation. Combined drought and post-fire conditions appeared to greatly restrict thermally-suitable habitat for Lahontan cutthroat trout (Oncorhynchus clarkii henshawi).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2017.05.008","usgsCitation":"Schultz, L., Heck, M., Hockman-Wert, D., Allai, T., Wenger, S., Cook, and Dunham, J.B., 2017, Spatial and temporal variability in the effects of wildfire and drought on thermal habitat for a desert trout: Journal of Arid Environments, v. 145, p. 60-68, https://doi.org/10.1016/j.jaridenv.2017.05.008.","productDescription":"9 p.","startPage":"60","endPage":"68","ipdsId":"IP-083354","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":438296,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QFV","text":"USGS data release","linkHelpText":"Stream temperature data from Willow-Whitehorse and Little Blitzen watersheds, southeast Oregon, 2011-2015"},{"id":342751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willow-Whitehorse Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.62213134765626,\n 42.04521345501039\n ],\n [\n -117.14447021484375,\n 42.04521345501039\n ],\n [\n -117.14447021484375,\n 43.113014204188914\n ],\n [\n -118.62213134765626,\n 43.113014204188914\n ],\n [\n -118.62213134765626,\n 42.04521345501039\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"594cd73fe4b062508e3951d8","contributors":{"authors":[{"text":"Schultz, Luke 0000-0002-6751-4626 lschultz@usgs.gov","orcid":"https://orcid.org/0000-0002-6751-4626","contributorId":193171,"corporation":false,"usgs":true,"family":"Schultz","given":"Luke","email":"lschultz@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":698993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heck, Michael 0000-0001-8858-7325 mheck@usgs.gov","orcid":"https://orcid.org/0000-0001-8858-7325","contributorId":4796,"corporation":false,"usgs":true,"family":"Heck","given":"Michael","email":"mheck@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hockman-Wert, David 0000-0003-2436-6237 dhockman-wert@usgs.gov","orcid":"https://orcid.org/0000-0003-2436-6237","contributorId":3891,"corporation":false,"usgs":true,"family":"Hockman-Wert","given":"David","email":"dhockman-wert@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allai, T","contributorId":193172,"corporation":false,"usgs":false,"family":"Allai","given":"T","affiliations":[],"preferred":false,"id":698996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":698997,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cook","contributorId":193173,"corporation":false,"usgs":false,"family":"Cook","email":"","affiliations":[],"preferred":false,"id":698998,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698992,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192350,"text":"70192350 - 2017 - Earthquake source properties from instrumented laboratory stick-slip","interactions":[],"lastModifiedDate":"2017-10-25T11:49:49","indexId":"70192350","displayToPublicDate":"2017-06-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","title":"Earthquake source properties from instrumented laboratory stick-slip","docAbstract":"Stick-slip experiments were performed to determine the influence of the testing apparatus on source properties, develop methods to relate stick-slip to natural earthquakes and examine the hypothesis of McGarr [2012] that the product of stiffness, k, and slip duration, Δt, is scale-independent and the same order as for earthquakes. The experiments use the double-direct shear geometry, Sierra White granite at 2 MPa normal stress and a remote slip rate of 0.2 µm/sec. To determine apparatus effects, disc springs were added to the loading column to vary k. Duration, slip, slip rate, and stress drop decrease with increasing k, consistent with a spring-block slider model. However, neither for the data nor model is kΔt constant; this results from varying stiffness at fixed scale.
In contrast, additional analysis of laboratory stick-slip studies from a range of standard testing apparatuses is consistent with McGarr's hypothesis. kΔt is scale-independent, similar to that of earthquakes, equivalent to the ratio of static stress drop to average slip velocity, and similar to the ratio of shear modulus to wavespeed of rock. These properties result from conducting experiments over a range of sample sizes, using rock samples with the same elastic properties as the Earth, and scale-independent design practices.
Here, I present a database of >160 finite fault models for all earthquakes of M 7.5 and above since 1990, created using a consistent modeling approach. The use of a common approach facilitates easier comparisons between models, and reduces uncertainties that arise when comparing models generated by different authors, data sets and modeling techniques.
I use this database to verify published scaling relationships, and for the first time show a clear and intriguing relationship between maximum potency (the product of slip and area) and average potency for a given earthquake. This relationship implies that earthquakes do not reach the potential size given by the tectonic load of a fault (sometimes called “moment deficit,” calculated via a plate rate over time since the last earthquake, multiplied by geodetic fault coupling). Instead, average potency (or slip) scales with but is less than maximum potency (dictated by tectonic loading). Importantly, this relationship facilitates a more accurate assessment of maximum earthquake size for a given fault segment, and thus has implications for long-term hazard assessments. The relationship also suggests earthquake cycles may not completely reset after a large earthquake, and thus repeat rates of such events may appear shorter than is expected from tectonic loading. This in turn may help explain the phenomenon of “earthquake super-cycles” observed in some global subduction zones.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2017.04.003","usgsCitation":"Hayes, G.P., 2017, The finite, kinematic rupture properties of great-sized earthquakes since 1990: Earth and Planetary Science Letters, v. 468, p. 94-100, https://doi.org/10.1016/j.epsl.2017.04.003.","productDescription":"7 p.","startPage":"94","endPage":"100","ipdsId":"IP-085877","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"468","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5944ee10e4b062508e3335d6","contributors":{"authors":[{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698456,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70189224,"text":"70189224 - 2017 - Large carnivore science: non-experimental studies are useful, but experiments are better","interactions":[],"lastModifiedDate":"2017-12-11T13:54:53","indexId":"70189224","displayToPublicDate":"2017-06-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5453,"text":"Food Webs","active":true,"publicationSubtype":{"id":10}},"title":"Large carnivore science: non-experimental studies are useful, but experiments are better","docAbstract":"We recently described the following six interrelated issues that justify\nquestioning some of the discourse about the reliability of the literature on the\necological roles of large carnivores (Allen et al. In press):\n1. The overall paucity of available data,\n2. The reliability of carnivore population sampling techniques,\n3. The general disregard for alternative hypotheses to top-down forcing,\n4. The lack of applied science studies,\n5. The frequent use of logical fallacies,\n6. The generalisation of results from relatively pristine systems to those\nsubstantially altered by humans.","largerWorkTitle":"Food Webs","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2017.06.002","usgsCitation":"Allen, B.L., Allen, L.R., Andren, H., Ballard, G., Boitani, L., Engeman, R.M., Fleming, P., Ford, A.T., Haswell, P.M., Kowalczyk, R., Linnell, J., Mech, L.D., and Parker, D.M., 2017, Large carnivore science: non-experimental studies are useful, but experiments are better: Food Webs, v. 13, p. 49-50, https://doi.org/10.1016/j.fooweb.2017.06.002.","productDescription":"2 p.","startPage":"49","endPage":"50","ipdsId":"IP-087848","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469745,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.bangor.ac.uk/portal/en/researchoutputs/large-carnivore-science-nonexperimental-studies-are-useful-but-experiments-are-better(ff9f346e-3810-4039-8bf8-80e0775fab81).html","text":"External Repository"},{"id":343387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"595f4c3be4b0d1f9f057e32d","contributors":{"authors":[{"text":"Allen, Benjamin L.","contributorId":193210,"corporation":false,"usgs":false,"family":"Allen","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":703583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Lee R.","contributorId":193219,"corporation":false,"usgs":false,"family":"Allen","given":"Lee","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":703584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andren, Henrik","contributorId":193220,"corporation":false,"usgs":false,"family":"Andren","given":"Henrik","email":"","affiliations":[],"preferred":false,"id":703585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ballard, Guy","contributorId":193221,"corporation":false,"usgs":false,"family":"Ballard","given":"Guy","email":"","affiliations":[],"preferred":false,"id":703586,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boitani, Luigi","contributorId":193211,"corporation":false,"usgs":false,"family":"Boitani","given":"Luigi","email":"","affiliations":[],"preferred":false,"id":703587,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engeman, Richard M.","contributorId":194249,"corporation":false,"usgs":false,"family":"Engeman","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":703588,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fleming, Peter J. S.","contributorId":194250,"corporation":false,"usgs":false,"family":"Fleming","given":"Peter J. S.","affiliations":[],"preferred":false,"id":703589,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ford, Adam T.","contributorId":193223,"corporation":false,"usgs":false,"family":"Ford","given":"Adam","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":703590,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haswell, Peter M.","contributorId":193224,"corporation":false,"usgs":false,"family":"Haswell","given":"Peter","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":703591,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kowalczyk, Rafal","contributorId":193212,"corporation":false,"usgs":false,"family":"Kowalczyk","given":"Rafal","email":"","affiliations":[],"preferred":false,"id":703592,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Linnell, John D. C.","contributorId":194251,"corporation":false,"usgs":false,"family":"Linnell","given":"John D. C.","affiliations":[],"preferred":false,"id":703593,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":703582,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Parker, Daniel M.","contributorId":193226,"corporation":false,"usgs":false,"family":"Parker","given":"Daniel","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":703594,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70187653,"text":"ofr20171036 - 2017 - Elevation Difference and Bouguer Anomaly Analysis Tool (EDBAAT) User's Guide","interactions":[],"lastModifiedDate":"2017-06-16T11:20:58","indexId":"ofr20171036","displayToPublicDate":"2017-06-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1036","title":"Elevation Difference and Bouguer Anomaly Analysis Tool (EDBAAT) User's Guide","docAbstract":"This report describes a software tool that imports gravity anomaly point data from the Gravity Database of the United States (GDUS) of the National Geospatial-Intelligence Agency and University of Texas at El Paso along with elevation data from The National Map (TNM) of the U.S. Geological Survey that lie within a user-specified geographic area of interest. Further, the tool integrates these two sets of data spatially and analyzes the consistency of the elevation of each gravity station from the GDUS with TNM elevation data; it also evaluates the consistency of gravity anomaly data within the GDUS data repository. The tool bins the GDUS data based on user-defined criteria of elevation misfit between the GDUS and TNM elevation data. It also provides users with a list of points from the GDUS data, which have Bouguer anomaly values that are considered outliers (two standard deviations or greater) with respect to other nearby GDUS anomaly data. “Nearby” can be defined by the user at time of execution. These outputs should allow users to quickly and efficiently choose which points from the GDUS would be most useful in reconnaissance studies or in augmenting and extending the range of individual gravity studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171036","usgsCitation":"Smittle, A.M., and Shoberg, T.G., 2017, Elevation Difference and Bouguer Anomaly Analysis Tool (EDBAAT) user’s guide: U.S. Geological Survey Open File Report 2017–1036, 9 p., https://doi.org/10.3133/ofr20171036.","productDescription":"Report: iv, 9 p.; Software","startPage":"1","endPage":"9","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-080225","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":342263,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1036/coverthb.jpg"},{"id":342264,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1036/ofr20171036.pdf","text":"Report","size":"354 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1036"},{"id":342265,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://cegis.usgs.gov/data_integration.html","text":"Software","linkHelpText":"Elevation Difference and Bouguer Anomaly Analysis Tool (EBDAAT)"}],"contact":"Director, National Geospatial Technical Operations Center (NGTOC)
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
One of the largest rechargeable groundwater systems by total available volume in the Rio Grande/Río Bravo Basin (hereinafter referred to as the “Rio Grande”) region of the United States and Mexico, the Mesilla Basin/Conejos-Médanos aquifer system, supplies water for irrigation as well as for cities of El Paso, Texas; Las Cruces, New Mexico; and Ciudad Juárez, Chihuahua, Mexico. The U.S. Geological Survey in cooperation with the Bureau of Reclamation assessed the groundwater resources in the Mesilla Basin and surrounding areas in Doña Ana County, N. Mex., and El Paso County, Tex., by using a combination of geophysical and geochemical methods. The study area consists of approximately 1,400 square miles in Doña Ana County, N. Mex., and 100 square miles in El Paso County, Tex. The Mesilla Basin composes most of the study area and can be divided into three parts: the Mesilla Valley, the West Mesa, and the East Bench. The Mesilla Valley is the part of the Mesilla Basin that was incised by the Rio Grande between Selden Canyon to the north and by a narrow valley (about 4 miles wide) to the southeast near El Paso, Tex., named the Paso del Norte, which is sometimes referred to in the literature as the “El Paso Narrows.”
Previously published geophysical data for the study area were compiled and these data were augmented by collecting additional geophysical and geochemical data. Geophysical resistivity measurements from previously published helicopter frequency domain electromagnetic data, previously published direct-current resistivity soundings, and newly collected (2012) time-domain electromagnetic soundings were used in the study to detect spatial changes in the electrical properties of the subsurface, which reflect changes that occur within the hydrogeology. The geochemistry of the groundwater system was evaluated by analyzing groundwater samples collected in November 2010 for physicochemical properties, major ions, trace elements, nutrients, pesticides (reported but not used in the assessment), and environmental tracers. The data obtained from these samples (with the exception of the pesticide data) were used to gain insights into processes controlling the groundwater movement through the groundwater system in the study area. Results from the geophysical and geochemical assessments facilitated the interpretation of the geochemical characteristics of the groundwater sources and geochemical groups within the groundwater system.
The groundwater-flow system in the study area consists primarily of the Mesilla Basin aquifer system, which can be divided into four hydrogeologic units by using an informal classification scheme based on basin-fill stratigraphy and sedimentology with an emphasis on aquifer characteristics. The four hydrogeologic units are (1) the Rio Grande alluvium, which is the shallow aquifer of the Mesilla Basin within the confines of the Mesilla Valley, and the three hydrogeologic units that compose the Santa Fe Group: (2) the lower part of the Santa Fe Group, which is the least productive zone, (3) the middle part of the Santa Fe Group, which is the primary water-bearing hydrogeologic unit in the basin and is generally saturated, and (4) the upper part of the Santa Fe Group, which is the most productive water-bearing unit within the Santa Fe Group but is only partially saturated in the north and largely unsaturated in the south and western parts of the Mesilla Basin.
The helicopter frequency domain electromagnetic survey results indicated that approximately half of the resistivity values were less than 10 ohm-meters at depths of 50 and 100 feet with a transition where the resistivity values changed from relatively high values (greater than 20 ohm-meters) to relatively low resistivity values (less than 10 ohm-meters) near Vado, New Mexico. Slightly more than 25 percent of the gridded resistivity values from the three-dimensional grid of the combined inverse modeling results of the direct-current resistivity and time-domain electromagnetic soundings were equal to or less than 10 ohm-meters with large regions of low resistivity becoming apparent in the southernmost part of the study area near the Paso Del Norte where these low resistivity features are spatially the widest at or below the top of the bedrock. These low resistivity values might represent clayey deposits, sediments composed largely of sand and gravel saturated with saline water, or both. Historical dissolved-solids-concentration data within the surface geophysical subset area of the study area were compiled and compared to the inverse modeling results of the combined direct-current resistivity and time-domain soundings; this comparison was done to strengthen the interpretation made from the combined inverse modeling results that the low resistivity features were representative of sand and gravel deposits saturated with saline water and not clayey deposits.
Water-level altitudes within the Rio Grande alluvium generally decreased from north to south, with a west to east decrease in water-level altitudes near Las Cruces, New Mexico, as a result of groundwater pumping. Groundwater flow within the Santa Fe Group is more complex than the groundwater flow within the Rio Grande alluvium because of the larger lateral and vertical extent of the Santa Fe Group compared to the Rio Grande alluvium. Groundwater from the Organ Mountains flows directly south towards the Paso del Norte. Groundwater from the Robledo Mountains, the Rough and Ready Hills, and the Sleeping Lady Hills generally flows to the southeast. Groundwater flowing near the north end of the midbasin uplift generally continues east towards the Rio Grande and then flows south on the east side of the midbasin uplift. Groundwater flowing near the west side of the midbasin uplift generally continues south parallel to the faults that make up the midbasin uplift and then flows east towards the Paso del Norte when it reaches the south end of the midbasin uplift. Groundwater from the Aden Hills and the East and West Potrillo Mountains flows to the south end of the midbasin uplift and then continues east towards the Paso del Norte. Throughout most of the Mesilla Valley, the vertical hydraulic gradient was downward because the water-level altitude in the Rio Grande alluvium was higher than it was in the Santa Fe Group, but in some areas (typically in the middle and southern parts of the Mesilla Valley), the vertical hydraulic gradient was substantially reduced or even reversed to an upward hydraulic gradient.
The geochemistry data indicate that there was a complex system of multiple geochemical endmembers and mixing between these endmembers with recharge to the Rio Grande alluvium and Santa Fe Group composed mostly of seepage from the Rio Grande, inflows from deeper or neighboring water systems, and mountain-front recharge. Five distinct geochemical groups were identified in the Mesilla Basin study area: (1) ancestral Rio Grande (pre-Pleistocene) geochemical group, (2) modern Rio Grande (Pleistocene to present) geochemical group, (3) mountain-front geochemical group, (4) deep groundwater upwelling geochemical group, and (5) unknown freshwater geochemical group. The ancestral Rio Grande groundwater was water that recharged into the system as seepage losses from the ancestral Rio Grande; this groundwater generally flows from north to south-southeast towards the Paso del Norte. Groundwater on the west side of the midbasin uplift generally flows south until it reaches the southern part of the study area; from the southern part of the study area, the groundwater flows east towards the Paso del Norte. Groundwater on the east side of the midbasin uplift flows south-southeast towards the Paso del Norte where it mixes with groundwater from the modern Rio Grande, uplifted areas in the west, and the deep saline source. The water type of the modern Rio Grande geochemical group ranged from calcium-sulfate water type in the northern part of the study area to sodium-chloride-sulfate water type in the southern part of the study area; from north to south there was a substantial increase in specific conductance, strontium-87/strontium-86 ratio, potassium, and the trace metals of iron and lithium, changing the water chemistry such that it became similar to the water chemistry of the deep groundwater upwelling geochemical group. From age-dating results, water in the modern Rio Grande geochemical group was recharged to the Rio Grande alluvium within the past 10 years. The mountain-front geochemical group was generally old water (apparent age was greater than 10,000 carbon-14 years before present) that was somewhat mineralized and has relatively high concentrations of fluoride and silica, which might indicate longer exposure to volcanic and siliciclastic rocks or aluminosilicate minerals. There were five different locations of recharge determined from the groundwater geochemistry within the mountain-front geochemical group, all having a slightly different geochemical signature: (1) the Rough and Ready Hills, Robledo Mountains, and the Sleeping Lady Hills, (2) the Doña Ana Mountains, (3) the Aden Hills and West Potrillo Mountains, (4) the East Potrillo Mountains, and (5) the Sierra Juárez in Mexico. The groundwater from the Rough and Ready Hills, Robledo Mountains, the Sleeping Lady Hills, and the Doña Ana Mountains generally flows toward the Rio Grande and eventually mixes together and with the modern Rio Grande groundwater. The groundwater originating from the Aden Hills and East and West Potrillo Mountains generally flows east to southeast at a slow rate and eventually mixes and continues east, where it mixes with groundwater from the ancestral Rio Grande geochemical group and with the groundwater from the Sierra Juárez. The groundwater from the Sierra Juárez flows north and then east towards the Paso del Norte where it mixes with groundwater from the uplifted areas in the west, ancestral and modern Rio Grande groundwater, and the upwelling groundwater from a deep saline source. The deep groundwater upwelling geochemical group had the highest concentrations of bicarbonate, potassium, silica, aluminum, iron, and lithium within the study area, indicating that it had been in contact with carbonate and siliciclastic rocks for a much longer period of time and at higher temperatures compared to the other geochemical groups, and was most likely ancient marine groundwater originating from the Paleozoic and Cretaceous carbonate rocks which was upwelling into the Mesilla Basin aquifer system in the southeastern part of the study area through the extensive fault systems. Direct-current resistivity and time-domain electromagnetic soundings support the interpretation of ancient marine groundwater upwelling into the Mesilla Basin aquifer system, as do the analytical results from wells, and the helicopter frequency domain electromagnetic data collected along the Rio Grande. The hydrogen-2/hydrogen-1 ratio and oxygen-18/oxygen-16 ratio isotopic results for samples in the unknown freshwater geochemical group did not plot on the Rio Grande evaporation line, indicating this group did not have a Rio Grande signature (that is, there was no isotopic evidence of a component of Rio Grande water) and it also had the lowest mineralized content of any geochemical group in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175028","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Teeple, A.P., 2017, Geophysics- and geochemistry-based assessment of the geochemical characteristics and groundwater-flow system of the U.S. part of the Mesilla Basin/Conejos-Médanos aquifer system in Doña Ana County, New Mexico, and El Paso County, Texas, 2010–12: U.S. Geological Survey Scientific Investigations Report 2017–5028, 183 p., https://doi.org/10.3133/sir20175028.","productDescription":"Report: x, 183 p.; 2 Figures; Project Sites, Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070474","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":438297,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PV6HJ3","text":"USGS data release","linkHelpText":"Time-Domain Electromagnetic Data Used in the Assessment of the U.S. Part of the Mesilla Basin/Conejos-Mdanos Aquifer System in Doa Ana County, New Mexico, and El Paso County, Texas"},{"id":342582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028.pdf","text":"Report","size":"16.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028"},{"id":342585,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028_readme_figures14_17.pdf","size":"890 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028 Read Me"},{"id":342586,"rank":6,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/ogw/","text":"Office of Groundwater"},{"id":342587,"rank":7,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/science/mission-areas/water/national-water-quality-program?qt-programs_l2_landing_page=0#qt-programs_l2_landing_page","text":"National Water-Quality Assessment Program"},{"id":342581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5028/coverthb.jpg"},{"id":342584,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028_figure17.pdf","text":"Figure 17","size":"23.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028 Figure 17"},{"id":342583,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028_figure14.pdf","text":"Figure 14","size":"22.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028 Figure 14"}],"country":"United States","state":"New Mexico, Texas","county":"Doña Ana County, El Paso County","otherGeospatial":"Mesilla Basin/Conejos-Médanos Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.25,\n 31.65\n ],\n [\n -106.375,\n 31.65\n ],\n [\n -106.375,\n 32.625\n ],\n [\n -107.25,\n 32.625\n ],\n [\n -107.25,\n 31.65\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl (Strix varia) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1831","usgsCitation":"Zipkin, E.F., Rossman, S., Yackulic, C.B., Wiens, D., Thorson, J.T., Davis, R.J., and Grant, E., 2017, Integrating count and detection–nondetection data to model population dynamics: Ecology, v. 98, no. 6, p. 1640-1650, https://doi.org/10.1002/ecy.1831.","productDescription":"11 p.","startPage":"1640","endPage":"1650","ipdsId":"IP-075390","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":438298,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7348J9M","text":"USGS data release","linkHelpText":"Count and detection-nondetection survey data of barred owls (Strix varia) in historical breeding territories of Northern Spotted Owls (Strix occidentalis caurina) in the Oregon Coast Range, 1995-2016"},{"id":342540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-11","publicationStatus":"PW","scienceBaseUri":"59439c92e4b062508e31a993","contributors":{"authors":[{"text":"Zipkin, Elise F. 0000-0003-4155-6139","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":192755,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":698309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rossman, Sam","contributorId":8759,"corporation":false,"usgs":false,"family":"Rossman","given":"Sam","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":698310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiens, David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":167538,"corporation":false,"usgs":true,"family":"Wiens","given":"David","email":"jwiens@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":698312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorson, James T.","contributorId":146580,"corporation":false,"usgs":false,"family":"Thorson","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":698313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Raymond J.","contributorId":150574,"corporation":false,"usgs":false,"family":"Davis","given":"Raymond","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":698314,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grant, Evan H. Campbell 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":167017,"corporation":false,"usgs":true,"family":"Grant","given":"Evan H. Campbell","email":"ehgrant@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":698308,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188566,"text":"70188566 - 2017 - The role of paleoecology in restoration and resource management—The past as a guide to future decision-making: Review and example from the Greater Everglades Ecosystem, U.S.A","interactions":[],"lastModifiedDate":"2017-06-15T13:12:17","indexId":"70188566","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"The role of paleoecology in restoration and resource management—The past as a guide to future decision-making: Review and example from the Greater Everglades Ecosystem, U.S.A","docAbstract":"Resource managers around the world are challenged to develop feasible plans for sustainable conservation and/or restoration of the lands, waters, and wildlife they administer—a challenge made greater by anticipated climate change and associated effects over the next century. Increasingly, paleoecologic and geologic archives are being used to extend the period of record of observed data and provide information on centennial to millennial scale responses to long-term drivers of ecosystem change. The development of paleoecology from an emerging field investigating past environments to a highly relevant applied science is reviewed and general examples of the application of paleoecologic research to resource management questions in diverse habitats and regions are provided. Specific examples of the application of paleoecologic research to the restoration of the Greater Everglades Ecosystem of south Florida (U.S.A) are presented. Conducting valuable scientific research that would benefit resource management decisions, however, is not enough. Scientists and resource managers need to be engaged in collaborative discussions from the beginning of the research process to ensure that management questions are being addressed and that the science reaches the people who will benefit from the information. Paleoecology and related disciplines provide an understanding of how ecosystems and individual species function and change over time in response to both natural and anthropogenic drivers. Information on pre-anthropogenic baseline conditions is provided by paleoecologic research, but it is the detection of long-term trends and cycles that allow resource managers to set realistic goals and targets by moving away from the fixed-point baseline concept to one of dynamic landscapes that anticipates and incorporates an expectation of change into decision-making.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2017.00011","usgsCitation":"Wingard, G.L., Bernhardt, C.E., and Wachnicka, A., 2017, The role of paleoecology in restoration and resource management—The past as a guide to future decision-making: Review and example from the Greater Everglades Ecosystem, U.S.A: Frontiers in Ecology and Evolution, v. 5, Article 11: 24 p., https://doi.org/10.3389/fevo.2017.00011.","productDescription":"Article 11: 24 p.","ipdsId":"IP-079801","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":469750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2017.00011","text":"Publisher Index Page"},{"id":342551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.5347900390625,\n 24.42214378185897\n ],\n [\n -79.9200439453125,\n 24.42214378185897\n ],\n [\n -79.9200439453125,\n 27.196014383173306\n ],\n [\n -82.5347900390625,\n 27.196014383173306\n ],\n [\n -82.5347900390625,\n 24.42214378185897\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-06","publicationStatus":"PW","scienceBaseUri":"59439c92e4b062508e31a986","contributors":{"authors":[{"text":"Wingard, G. Lynn 0000-0002-3833-5207 lwingard@usgs.gov","orcid":"https://orcid.org/0000-0002-3833-5207","contributorId":605,"corporation":false,"usgs":true,"family":"Wingard","given":"G.","email":"lwingard@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":698366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernhardt, Christopher E. 0000-0003-0082-4731 cbernhardt@usgs.gov","orcid":"https://orcid.org/0000-0003-0082-4731","contributorId":2131,"corporation":false,"usgs":true,"family":"Bernhardt","given":"Christopher","email":"cbernhardt@usgs.gov","middleInitial":"E.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":698367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wachnicka, Anna","contributorId":15500,"corporation":false,"usgs":true,"family":"Wachnicka","given":"Anna","email":"","affiliations":[],"preferred":false,"id":698368,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}