The size of animal home ranges often varies inversely with population density among populations of a species. This fact has implications for population monitoring using spatially explicit capture–recapture (SECR) models, in which both the scale of home-range movements σ and population density D usually appear as parameters, and both may vary among populations. It will often be appropriate to model a structural relationship between population-specific values of these parameters, rather than to assume independence. We suggest re-parameterizing the SECR model using kp = σp √Dp, where kp relates to the degree of overlap between home ranges and the subscript p distinguishes populations. We observe that kp is often nearly constant for populations spanning a range of densities. This justifies fitting a model in which the separate kp are replaced by the single parameter k and σp is a density-dependent derived parameter. Continuous density-dependent spatial variation in σ may also be modelled, using a scaled non-Euclidean distance between detectors and the locations of animals. We illustrate these methods with data from automatic photography of tigers (Panthera tigris) across India, in which the variation is among populations, from mist-netting of ovenbirds (Seiurus aurocapilla) in Maryland, USA, in which the variation is within a single population over time, and from live-trapping of brushtail possums (Trichosurus vulpecula) in New Zealand, modelling spatial variation within one population. Possible applications and limitations of the methods are discussed. A model in which kp is constant, while density varies, provides a parsimonious null model for SECR. The parameter k of the null model is a concise summary of the empirical relationship between home-range size and density that is useful in comparative studies. We expect deviations from this model, particularly the dependence of kp on covariates, to be biologically interesting.
","language":"English","publisher":"Blackwell Publishers","publisherLocation":"Oxford","doi":"10.1111/ecog.01511","usgsCitation":"Efford, M., Dawson, D.K., Jhala, Y., and Qureshi, Q., 2016, Density-dependent home-range size revealed by spatially explicit capture–recapture: Ecography, v. 39, no. 7, p. 676-688, https://doi.org/10.1111/ecog.01511.","productDescription":"13 p.","startPage":"676","endPage":"688","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065283","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":324803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-07","publicationStatus":"PW","scienceBaseUri":"577f6f1ae4b0ef4d2f45d42c","contributors":{"authors":[{"text":"Efford, M.G.","contributorId":13352,"corporation":false,"usgs":true,"family":"Efford","given":"M.G.","affiliations":[],"preferred":false,"id":641693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Deanna K. ddawson@usgs.gov","contributorId":1257,"corporation":false,"usgs":true,"family":"Dawson","given":"Deanna","email":"ddawson@usgs.gov","middleInitial":"K.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":641690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jhala, Y.V.","contributorId":96889,"corporation":false,"usgs":true,"family":"Jhala","given":"Y.V.","email":"","affiliations":[],"preferred":false,"id":641694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qureshi, Q.","contributorId":172713,"corporation":false,"usgs":false,"family":"Qureshi","given":"Q.","email":"","affiliations":[],"preferred":false,"id":641695,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170884,"text":"fs20163029 - 2016 - Hyperspectral surveying for mineral resources in Alaska","interactions":[],"lastModifiedDate":"2018-11-05T09:28:12","indexId":"fs20163029","displayToPublicDate":"2016-07-07T11:40:00","publicationYear":"2016","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":"2016-3029","title":"Hyperspectral surveying for mineral resources in Alaska","docAbstract":"Alaska is a major producer of base and precious metals and has a high potential for additional undiscovered mineral resources. However, discovery is hindered by Alaska’s vast size, remoteness, and rugged terrain. New methods are needed to overcome these obstacles in order to fully evaluate Alaska’s geology and mineral resource potential. Hyperspectral surveying is one method that can be used to rapidly acquire data about the distributions of surficial materials, including different types of bedrock and ground cover. In 2014, the U.S. Geological Survey began the Alaska Hyperspectral Project to assess the applicability of this method in Alaska. The primary study area is a remote part of the eastern Alaska Range where porphyry deposits are exposed. In collaboration with the Alaska Division of Geological and Geophysical Surveys, the University of Alaska Fairbanks, and the National Park Service, the U.S. Geological Survey is collecting and analyzing hyperspectral data with the goals of enhancing geologic mapping and developing methods to identify and characterize mineral deposits elsewhere in Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163029","collaboration":"Prepared in collaboration with Alaska Department of Natural Resources Division of Geological and Geophysical Surveys, University of Alaska Fairbanks, and National Park Service","usgsCitation":"Kokaly, R.F., Graham, G.E., Hoefen, T.M., Kelley, K.D., Johnson, M.R., and Hubbard, B.E., Hyperspectral surveying for mineral resources in Alaska: U.S. Geological Survey Fact Sheet 2016-3029, 2 p., https://dx.doi.org/10.3133/fs20163029.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074232","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science 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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"U.S. Geological Survey
Director, Crustal Geophysics and Geochemistry Science Center
Box 25046, MS-973
Denver Federal Center
Denver, CO 80225-0046
http://crustal.usgs.gov
This map summarizes the geology of the Murray quadrangle in the Ozark Plateaus region of northern Arkansas. Geologically, the area is on the southern flank of the Ozark dome, an uplift that has the oldest rocks exposed at its center, in Missouri. Physiographically, the Murray quadrangle is within the Boston Mountains, a high plateau region underlain by Pennsylvanian sandstones and shales. Valleys of the Buffalo River and Little Buffalo River and their tributaries expose an approximately 1,600-ft-thick (488-meter-thick) sequence of Ordovician, Mississippian, and Pennsylvanian carbonate and clastic sedimentary rocks that have been mildly deformed by a series of faults and folds. The Buffalo National River, a park that encompasses the Buffalo River and adjacent land that is administered by the National Park Service is present at the northwestern edge of the quadrangle.
Mapping for this study was carried out by field inspection of numerous sites and was compiled as a 1:24,000 geographic information system (GIS) database. Locations and elevation of sites were determined with the aid of a global positioning satellite receiver and a hand-held barometric altimeter that was frequently recalibrated at points of known elevation. Hill-shade relief and slope maps derived from a U.S. Geological Survey 10-meter digital elevation model as well as orthophotographs were used to help trace ledge-forming units between field traverses within the Upper Mississippian and Pennsylvanian part of the stratigraphic sequence. Strike and dip of beds were typically measured along stream drainages or at well-exposed ledges. Structure contours, constructed on the top of the Boone Formation and the base of a prominent sandstone unit within the Bloyd Formation, were drawn based on the elevations of field sites on these contacts well as other limiting information for their minimum elevations above hilltops or their maximum elevations below valley bottoms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3360","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hudson, M.R., and Turner, K.J., 2016, Geologic map of the Murray quadrangle, Newton County, Arkansas: U.S. Geological Survey Scientific Investigations Map 3360, 1 sheet, scale 1:24,000, https://dx.doi.org/10.3133/sim3360.","productDescription":"Sheet: 51.07 x 36.00 inches; Metadata; Read Me; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062555","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":324754,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_georeferenced.pdf","text":"Georeferenced geologic map","size":"127.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3360 Geologic Georeferenced Map"},{"id":324755,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_GIS.gdb.zip","text":"Geodatabase","size":"9.16 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Geodatabase"},{"id":324756,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_shapefiles.zip","text":"Shapefiles","size":"1.25 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Shapefiles"},{"id":324751,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3360/coverthb.jpg"},{"id":324758,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_metadata.zip","text":"Metadata","size":"16.0 kB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Metadata"},{"id":324752,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_readme_version2.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3360 ReadMe"},{"id":324757,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3360/sim3360_basemaps.zip","text":"Base maps","size":"17.0 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3360 Base maps"},{"id":324753,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3360/sim3360.pdf","text":"Geologic map","size":"34.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3360 Geologic Map"}],"country":"United States","state":"Arkansas","county":"Newton County","otherGeospatial":"Murray Quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.375,\n 35.875\n ],\n [\n -93.375,\n 36\n ],\n [\n -93.25,\n 36\n ],\n [\n -93.25,\n 35.875\n ],\n [\n -93.375,\n 35.875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"U.S. Geological Survey
Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, MS-980
Denver Federal Center
Denver, CO 80225-0046
http://gec.cr.usgs.gov
Current and future breeding ranges of 15 bird and 16 reptile species were modeled in the Southwestern United States. Rather than taking a broad-scale, vulnerability-assessment approach, we created a species distribution model (SDM) for each focal species incorporating climatic, landscape, and plant variables. Baseline climate (1940–2009) was characterized with Parameter-elevation Regressions on Independent Slopes Model (PRISM) data and future climate with global-circulation-model data under an A1B emission scenario. Climatic variables included monthly and seasonal temperature and precipitation; landscape variables included terrain ruggedness, soil type, and insolation; and plant variables included trees and shrubs commonly associated with a focal species. Not all species-distribution models contained a plant, but if they did, we included a built-in annual migration rate for more accurate plant-range projections in 2039 or 2099. We conducted a group meta-analysis to (1) determine how influential each variable class was when averaged across all species distribution models (birds or reptiles), and (2) identify the correlation among contemporary (2009) habitat fragmentation and biological attributes and future range projections (2039 or 2099). Projected changes in bird and reptile ranges varied widely among species, with one-third of the ranges predicted to expand and two-thirds predicted to contract. A group meta-analysis indicated that climatic variables were the most influential variable class when averaged across all models for both groups, followed by landscape and plant variables (birds), or plant and landscape variables (reptiles), respectively. The second part of the meta-analysis indicated that numerous contemporary habitat-fragmentation (for example, patch isolation) and biological-attribute (for example, clutch size, longevity) variables were significantly correlated with the magnitude of projected range changes for birds and reptiles. Patch isolation was a significant trans-specific driver of projected bird and reptile ranges, suggesting that strategic actions should focus on restoration and enhancement of habitat at local and regional scales to promote landscape connectivity and conservation of core areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161085","usgsCitation":"Hatten, J.R., Giermakowski, J.T., Holmes, J.A., Nowak, E.M., Johnson, M.J., Ironside, K.E., van Riper, Charles, III, Peters, Michael, Truettner, Charles, and Cole, K.L., 2016, Identifying bird and reptile vulnerabilities to climate change in the Southwestern United States: U.S. Geological Survey Open-File Report 2016-1085, 76 p., https://dx.doi.org/10.3133/ofr20161085.","productDescription":"vi, 76 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070152","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":324760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1085/coverthb.jpg"},{"id":324761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1085/ofr20161085.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1085 Report PDF"}],"country":"United States","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.053249,41.001406],[-102.124972,41.002338],[-102.051292,40.749591],[-102.04192,37.035083],[-102.979613,36.998549],[-103.002247,36.911587],[-103.064423,32.000518],[-106.565142,32.000736],[-106.577244,31.810406],[-106.750547,31.783706],[-108.208394,31.783599],[-108.208573,31.333395],[-111.000643,31.332177],[-114.813613,32.494277],[-114.722746,32.713071],[-117.118868,32.534706],[-117.50565,33.334063],[-118.088896,33.729817],[-118.428407,33.774715],[-118.519514,34.027509],[-119.159554,34.119653],[-119.616862,34.420995],[-120.441975,34.451512],[-120.608355,34.556656],[-120.644311,35.139616],[-120.873046,35.225688],[-120.884757,35.430196],[-121.851967,36.277831],[-121.932508,36.559935],[-121.788278,36.803994],[-121.880167,36.950151],[-122.140578,36.97495],[-122.419113,37.24147],[-122.511983,37.77113],[-122.425942,37.810979],[-122.168449,37.504143],[-122.144396,37.581866],[-122.385908,37.908136],[-122.301804,38.105142],[-122.484411,38.11496],[-122.492474,37.82484],[-122.972378,38.020247],[-123.103706,38.415541],[-123.725367,38.917438],[-123.851714,39.832041],[-124.373599,40.392923],[-124.063076,41.439579],[-124.536073,42.814175],[-124.150267,43.91085],[-123.962887,45.280218],[-123.996766,46.20399],[-123.548194,46.248245],[-124.029924,46.308312],[-124.06842,46.601397],[-123.97083,46.47537],[-123.84621,46.716795],[-124.022413,46.708973],[-124.108078,46.836388],[-123.86018,46.948556],[-124.138035,46.970959],[-124.425195,47.738434],[-124.672427,47.964414],[-124.727022,48.371101],[-123.981032,48.164761],[-122.748911,48.117026],[-122.637425,47.889945],[-123.15598,47.355745],[-122.527593,47.905882],[-122.578211,47.254804],[-122.725738,47.33047],[-122.691771,47.141958],[-122.796646,47.341654],[-122.863732,47.270221],[-122.67813,47.103866],[-122.364168,47.335953],[-122.429841,47.658919],[-122.230046,47.970917],[-122.425572,48.232887],[-122.358375,48.056133],[-122.512031,48.133931],[-122.424102,48.334346],[-122.689121,48.476849],[-122.425271,48.599522],[-122.796887,48.975026],[-104.048736,48.999877],[-104.053249,41.001406]]],[[[-119.789798,34.05726],[-119.5667,34.053452],[-119.795938,33.962929],[-119.916216,34.058351],[-119.789798,34.05726]]],[[[-118.524531,32.895488],[-118.573522,32.969183],[-118.369984,32.839273],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.32446,33.348782],[-118.593969,33.467198],[-118.500212,33.449592]]],[[[-122.519535,48.288314],[-122.66921,48.240614],[-122.400628,48.036563],[-122.419274,47.912125],[-122.744612,48.20965],[-122.664928,48.374823],[-122.519535,48.288314]]],[[[-122.800217,48.60169],[-122.883759,48.418793],[-123.173061,48.579086],[-122.949116,48.693398],[-122.743049,48.661991],[-122.800217,48.60169]]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA \"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
The U.S. Geological Survey Branch of Quality Systems operated five distinct programs to provide external quality assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network and Mercury Deposition Network during 2013–14. The National Trends Network programs include (1) a field audit program to evaluate sample contamination and stability, (2) an interlaboratory comparison program to evaluate analytical laboratory performance, and (3) a colocated sampler program to evaluate bias from precipitation sampler upgrades. The Mercury Deposition Network programs include the (4) system blank program and (5) an interlaboratory comparison program. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends for chemical constituents in wet deposition.
\nThe field audit program results indicate that sample contamination levels for calcium, nitrate, and sulfate continued to increase during the study period while sodium and chloride contamination decreased and magnesium, potassium, ammonium, and hydrogen-ion contamination have remained relatively constant. Analyte losses due to potential sample instability were negligible. The NADP Central Analytical Laboratory produced interlaboratory comparison results with low bias and variability compared to other domestic and international laboratories that support atmospheric deposition monitoring.
\nColocated sampler program results from dissimilar colocated collectors suggest that the retrofit of the National Trends Network with N-CON Systems precipitation collectors could cause shifts in NADP annual deposition (concentration multiplied by depth) values from +6.2 to +51 percent for ammonium, from +8.1 to +61 percent for nitrate, from 3.8 to 71 percent for sulfate, from –24 to +15 percent for hydrogenion deposition, and larger shifts (from –14 to +102 percent) for calcium, magnesium, sodium, potassium, and chloride. The N-CON Systems collector typically catches more precipitation than the NADP-approved Aerochem Metrics Model 301 collector, but it typically caught slightly less precipitation than the Aerochem Metrics collector at a wind-swept, high-altitude site during water year 2013.
\nPaired, identical OTT Pluvio-2 and ETI Noah IV rain gages were operated at the same sites. Results of the colocated rain gages indicate from 0 to 3.7 percent median absolute percent difference for weekly precipitation-depth measurements and from 0.05 to 5.6 absolute percent difference for annual total precipitation depth.
\nThe Mercury Deposition Network programs include the system blank program and an interlaboratory comparison program. System blank results indicated that maximum total mercury contamination concentrations in samples were less than the third percentile of all Mercury Deposition Network sample concentrations. The Mercury Analytical Laboratory produced chemical concentration results with low bias and variability compared with other domestic and international laboratories that support atmospheric-deposition monitoring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165069","usgsCitation":"Wetherbee, G.A., and Martin, RoseAnn, 2016, External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2013–14: U.S. Geological Survey Scientific Investigations Report 2016–5069, 22 p., https://dx.doi.org/10.3133/sir20165069.","productDescription":"vi, 22 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070529","costCenters":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"links":[{"id":324394,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5069/coverthb.jpg"},{"id":324395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5069/sir20165069.pdf","text":"Report","size":"4.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5069"}],"contact":"Chief, USGS Branch of Quality Systems
Box 25046, Mail Stop 401
Denver, CO 80225
Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.2292","usgsCitation":"Wright, W., Irvine, K.M., and Rodhouse, T., 2016, A goodness-of-fit test for occupancy models with correlated within-season revisits: Ecology and Evolution, v. 6, no. 15, p. 5404-5415, https://doi.org/10.1002/ece3.2292.","productDescription":"12 p.","startPage":"5404","endPage":"5415","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072344","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":470769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2292","text":"Publisher Index Page"},{"id":324896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324890,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/ece3.2292/full"}],"volume":"6","issue":"15","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-05","publicationStatus":"PW","scienceBaseUri":"5780ceaee4b0811616822292","chorus":{"doi":"10.1002/ece3.2292","url":"http://dx.doi.org/10.1002/ece3.2292","publisher":"Wiley-Blackwell","authors":"Wright Wilson J., Irvine Kathryn M., Rodhouse Thomas J.","journalName":"Ecology and Evolution","publicationDate":"7/5/2016"},"contributors":{"authors":[{"text":"Wright, Wilson","contributorId":172748,"corporation":false,"usgs":false,"family":"Wright","given":"Wilson","affiliations":[{"id":5120,"text":"Montana State University, Department of Mathematical Sciences, Bozeman, MT 59717","active":true,"usgs":false}],"preferred":false,"id":641895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":641894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodhouse, Thomas J.","contributorId":127378,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas J.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":641896,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175248,"text":"70175248 - 2016 - Preserving reptiles for research","interactions":[],"lastModifiedDate":"2016-08-16T12:11:23","indexId":"70175248","displayToPublicDate":"2016-07-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Preserving reptiles for research","docAbstract":"What are voucher specimens and why do we collect them? Voucher specimens are animals and/or their parts that are deposited in a research museum to document the occurrence of a taxon at a specific location in space and time (Pleijel et al., 2008; Reynolds and McDiarmid, 2012). For field biologists, vouchers are the repeatable element of a field study as they allow other biologists, now and in the future, to confirm the identity of species that were studied. The scientific importance of a voucher specimen or series of specimens is that other people are afforded the opportunity to examine the entire animal and confirm or correct identifications. A photographic record is somewhat useful for recording the occurrence of a species, but such records can be insufficient for reliable confirmation of specific identity. Even if a photo shows diagnostic characters of currently recognized taxa, it may not show characters that separate taxa that may be described in the future. Substantial cryptic biodiversity is being found in even relatively well-known herpetofaunas (Crawford et al., 2010), and specimens allow researchers to retroactively evaluate the true diversity in a study as understanding of taxonomy evolves. They enable biologists to study the systematic relationships of populations by quantifying variation in different traits. Specimens are also a source of biological data such as behaviour, ecology, epidemiology, and reproduction through examination of their anatomy, reproductive and digestive tracts, and parasites (Suarez and Tsutsui, 2004). Preserving reptiles as vouchers is not difficult, although doing it properly requires care, effort, and time. Poorly preserved vouchers can invalidate the results and conclusions of your study because of the inability to confirm the identity of your study animals. Good science requires repeatability of observations, and the absence of vouchers or poorly preserved ones prevents such confirmation. Due to space restrictions, we are unable to go into as much detail as we would like in this chapter. A number of publications give more details on some topics discussed in this chapter, such as Pisani (1973), Pisani and Villa (1974), Etheridge (1996), Karns (1986), McDiarmid (1994), Cortez et al. (2006), Foster (2012) (and subchapters therein), Reynolds and McDiarmid (2012), and Simmons (2015). Although some of these works focus on amphibians, they also apply to reptiles in many aspects.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reptile ecology and conservation","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Oxford University Press","isbn":"9780198726142","usgsCitation":"Gotte, S.W., Jacobs, J.F., and Zug, G.R., 2016, Preserving reptiles for research, chap. of Reptile ecology and conservation, p. 73-86.","productDescription":"14 p.","startPage":"73","endPage":"86","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066992","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":326084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":326018,"type":{"id":15,"text":"Index Page"},"url":"https://global.oup.com/academic/product/reptile-ecology-and-conservation-9780198726142"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a315cee4b006cb45558b71","contributors":{"editors":[{"text":"Dodd, C. Kenneth","contributorId":55550,"corporation":false,"usgs":true,"family":"Dodd","given":"C. Kenneth","affiliations":[],"preferred":false,"id":645593,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Gotte, Steve W. 0000-0001-5509-4495 sgotte@usgs.gov","orcid":"https://orcid.org/0000-0001-5509-4495","contributorId":4481,"corporation":false,"usgs":true,"family":"Gotte","given":"Steve","email":"sgotte@usgs.gov","middleInitial":"W.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":644530,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobs, Jeremy F.","contributorId":41130,"corporation":false,"usgs":true,"family":"Jacobs","given":"Jeremy","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":644531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zug, George R.","contributorId":76874,"corporation":false,"usgs":true,"family":"Zug","given":"George","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":644532,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199855,"text":"70199855 - 2016 - A framework for effective use of hydroclimate models in climate-change adaptation planning for managed habitats with limited hydrologic response data","interactions":[],"lastModifiedDate":"2018-10-01T15:34:04","indexId":"70199855","displayToPublicDate":"2016-07-01T15:33:56","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"A framework for effective use of hydroclimate models in climate-change adaptation planning for managed habitats with limited hydrologic response data","docAbstract":"Climate-change adaptation planning for managed wetlands is challenging under uncertain futures when the impact of historic climate variability on wetland response is unquantified. We assessed vulnerability of Modoc National Wildlife Refuge (MNWR) through use of the Basin Characterization Model (BCM) landscape hydrology model, and six global climate models, representing projected wetter and drier conditions. We further developed a conceptual model that provides greater value for water managers by incorporating the BCM outputs into a conceptual framework that links modeled parameters to refuge management outcomes. This framework was used to identify landscape hydrology parameters that reflect refuge sensitivity to changes in (1) climatic water deficit (CWD) and recharge, and (2) the magnitude, timing, and frequency of water inputs. BCM outputs were developed for 1981–2100 to assess changes and forecast the probability of experiencing wet and dry water year types that have historically resulted in challenging conditions for refuge habitat management. We used a Yule’s Q skill score to estimate the probability of modeled discharge that best represents historic water year types. CWD increased in all models across 72.3–100 % of the water supply basin by 2100. Earlier timing in discharge, greater cool season discharge, and lesser irrigation season water supply were predicted by most models. Under the worst-case scenario, moderately dry years increased from 10–20 to 40–60 % by 2100. MNWR could adapt by storing additional water during the cool season for later use and prioritizing irrigation of habitats during dry years.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-015-0569-y","usgsCitation":"Esralew, R.A., Flint, L.E., Thorne, J.H., Boynton, R., and Flint, A.L., 2016, A framework for effective use of hydroclimate models in climate-change adaptation planning for managed habitats with limited hydrologic response data: Environmental Management, v. 58, no. 1, p. 60-75, https://doi.org/10.1007/s00267-015-0569-y.","productDescription":"16 p.","startPage":"60","endPage":"75","ipdsId":"IP-077879","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":470772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00267-015-0569-y","text":"Publisher Index Page"},{"id":357987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Alturas","otherGeospatial":"Modoc National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.64498901367186,\n 41.376808565702355\n ],\n [\n -120.4314422607422,\n 41.376808565702355\n ],\n [\n -120.4314422607422,\n 41.534796133205184\n ],\n [\n -120.64498901367186,\n 41.534796133205184\n ],\n [\n -120.64498901367186,\n 41.376808565702355\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-04","publicationStatus":"PW","scienceBaseUri":"5bc03300e4b0fc368eb53a76","contributors":{"authors":[{"text":"Esralew, Rachel A.","contributorId":104862,"corporation":false,"usgs":true,"family":"Esralew","given":"Rachel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":746916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, James H.","contributorId":139144,"corporation":false,"usgs":false,"family":"Thorne","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":12659,"text":"U C Davis","active":true,"usgs":false}],"preferred":false,"id":746917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boynton, Ryan","contributorId":36403,"corporation":false,"usgs":true,"family":"Boynton","given":"Ryan","affiliations":[],"preferred":false,"id":746918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746919,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70171185,"text":"ofr20161082 - 2016 - Assessing landslide potential on coastal bluffs near Mukilteo, Washington—Geologic site characterization for hydrologic monitoring","interactions":[],"lastModifiedDate":"2016-07-01T11:11:23","indexId":"ofr20161082","displayToPublicDate":"2016-07-01T11:00:00","publicationYear":"2016","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":"2016-1082","title":"Assessing landslide potential on coastal bluffs near Mukilteo, Washington—Geologic site characterization for hydrologic monitoring","docAbstract":"During the summer 2015, the U.S. Geological Survey collected geologic and geotechnical data for two sites on coastal bluffs along the eastern shore of Puget Sound, Washington. The U.S. Geological Survey also installed hydrologic instrumentation at the sites and collected specimens for laboratory testing. The two sites are located on City of Mukilteo open-space land and are about 0.6 kilometers apart. The bluffs at each site are approximately 42 meters high, and rise steeply from the shoreline with 32–35° slopes. The more northerly of the two sites occupies an active landslide and is mostly unvegetated. The other site is forested, and although stable during the preparation of this report, shows evidence of historical and potential landslide activity. The slopes of the bluffs at both sites are mantled by a thin, nonuniform colluvium underlain by clay-rich glacial deposits and tills of the Whidbey Formation or Double Bluff Drift. Till consisting of sand, gravel, and cobbles caps the bluffs and rests on finer grained glacial deposits of sand, silt, and clay. These types of different glacial deposits are dense, vertically fractured, and generally have low permeability, but field observations indicate that locally the deposits are sufficiently permeable to allow lateral flow of water along fractures and subhorizontal boundaries between deposits of different texture. Laboratory tests indicate that many of the deposits are highly plastic, with low hydraulic conductivity, and moderate shear strength. Steep slopes combined with the strength and hydraulic characteristics of the deposits leave the bluffs prone to slope instability, particularly during the wet season when infiltrating rainfall changes moisture content, pore-water pressure, and effective stress within the hillslope. The instrumentation was designed to primarily observe rainfall variability and hydrologic changes in the subsurface that can affect stability of the bluffs, and also to compare the hydrologic response between areas where previous landslides have disturbed vegetation and areas where the bluff is apparently more stable and well vegetated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20161082","collaboration":"Prepared as part of a Technical Assistance Agreement with Sound Transit","usgsCitation":"Mirus, B.B., Smith, J.B., Stark, Benjamin, Lewis, York, Michel, Abigail, and Baum, R.L., 2016, Assessing landslide potential on coastal bluffs near Mukilteo, Washington—Geologic site characterization for hydrologic monitoring: U.S. Geological Survey Open-File Report 2016–1082, 28 p., https://dx.doi.org/10.3133/ofr20161082.","productDescription":"Report: vi, 34 p. HTML Document: Data Release","startPage":"1","endPage":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075317","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438597,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H13033","text":"USGS data release","linkHelpText":"Lab tests for specimens from Mukilteo, WA, 2016"},{"id":324697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1082/ofr20161082.pdf","text":"Report","size":"28.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1082"},{"id":324696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1082/coverthb.jpg"},{"id":324698,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7H13033","text":"Laboratory Testing Results: Material strength and hydraulic properties for specimens collected from coastal bluffs near Mukilteo, Washington","description":"OFR 2016-1082 Data"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.66098022460939,\n 48.448333001219005\n ],\n [\n -122.68157958984375,\n 48.448333001219005\n ],\n [\n -122.68844604492186,\n 48.43193420325806\n ],\n [\n -122.67608642578126,\n 48.4164415885222\n ],\n [\n -122.67333984374999,\n 48.37632112598019\n ],\n [\n -122.77084350585938,\n 48.26034139584532\n ],\n [\n -122.80517578125,\n 48.17158081783164\n ],\n [\n -122.91778564453125,\n 48.09275716032736\n ],\n [\n -122.90679931640624,\n 47.7476346002088\n ],\n [\n -122.90267944335938,\n 47.6441113460123\n ],\n [\n -122.80517578125,\n 47.646886969413\n ],\n [\n -122.75024414062499,\n 47.666312203609145\n ],\n [\n -122.67745971679688,\n 47.585789182379905\n ],\n [\n -122.56759643554689,\n 47.304378285521565\n ],\n [\n -122.46734619140625,\n 47.280160670764744\n ],\n [\n -122.39730834960938,\n 47.26711582310954\n ],\n [\n -122.29980468749999,\n 47.352780247239586\n ],\n [\n -122.3272705078125,\n 47.525547335516556\n ],\n [\n -122.30804443359375,\n 47.78363463526376\n ],\n [\n -122.20642089843749,\n 47.989002568678686\n ],\n [\n -122.1844482421875,\n 48.060643120324514\n ],\n [\n -122.38632202148438,\n 48.31882083063846\n ],\n [\n -122.66098022460939,\n 48.448333001219005\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225-0046
This summary of international mineral exploration activities for the year 2015 draws upon information from industry sources, published literature, the SNL Metals & Mining (SNL) (Charlottesville, VA) data base, and specialists in the U.S. Geological Survey (USGS) National Minerals Information Center. The summary provides data on exploration budgets by region and mineral commodity, identifies significant mineral discoveries and areas of mineral exploration, discusses government programs affecting the mineral exploration industry, and presents analyses of exploration activities performed by the mineral industry.
","language":"English","publisher":"Society for Mining, Metallurgy & Exploration","usgsCitation":"Wilburn, D., and Karl, N., 2016, Exploration review: Mining Engineering, no. May, p. 30-51.","productDescription":"22 p.","startPage":"30","endPage":"51","ipdsId":"IP-074890","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":381831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"May","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wilburn, David R.","contributorId":246002,"corporation":false,"usgs":true,"family":"Wilburn","given":"David R.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":807474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Nick 0000-0003-2858-2498 nkarl@usgs.gov","orcid":"https://orcid.org/0000-0003-2858-2498","contributorId":178317,"corporation":false,"usgs":true,"family":"Karl","given":"Nick","email":"nkarl@usgs.gov","affiliations":[],"preferred":true,"id":807475,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192729,"text":"70192729 - 2016 - Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009","interactions":[],"lastModifiedDate":"2017-11-08T13:33:49","indexId":"70192729","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009","docAbstract":"A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.
","language":"English","publisher":"AGU","doi":"10.1002/2016GB005405","usgsCitation":"McGuire, A.D., Koven, C., Lawrence, D.M., Clein, J.S., Xia, J., Beer, C., Burke, E.J., Chen, G., Chen, X., Delire, C., Jafarov, E., MacDougall, A.H., Marchenko, S., Nicolsky, D.J., Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, P., Decharme, B., Ekici, A., Gouttevin, I., Hajima, T., Hayes, D.J., Ji, D., Krinner, G., Lettenmaier, D.P., Luo, Y., Miller, P.A., Moore, J., Romanovsky, V., Schädel, C., Schaefer, K., Schuur, E.A., Smith, B., Sueyoshi, T., and Zhuang, Q., 2016, Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009: Global Biogeochemical Cycles, v. 30, no. 7, p. 1015-1037, https://doi.org/10.1002/2016GB005405.","productDescription":"23 p.","startPage":"1015","endPage":"1037","ipdsId":"IP-073905","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470798,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/2016gb005405","text":"External Repository"},{"id":348460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-08","publicationStatus":"PW","scienceBaseUri":"5a0425bfe4b0dc0b45b453f5","contributors":{"authors":[{"text":"McGuire, A. 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Qianlai","contributorId":101975,"corporation":false,"usgs":true,"family":"Zhuang","given":"Qianlai","affiliations":[],"preferred":false,"id":721264,"contributorType":{"id":1,"text":"Authors"},"rank":39}]}} ,{"id":70182781,"text":"70182781 - 2016 - The statistical power to detect cross-scale interactions at macroscales","interactions":[],"lastModifiedDate":"2017-03-01T12:46:23","indexId":"70182781","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"The statistical power to detect cross-scale interactions at macroscales","docAbstract":"Macroscale studies of ecological phenomena are increasingly common because stressors such as climate and land-use change operate at large spatial and temporal scales. Cross-scale interactions (CSIs), where ecological processes operating at one spatial or temporal scale interact with processes operating at another scale, have been documented in a variety of ecosystems and contribute to complex system dynamics. However, studies investigating CSIs are often dependent on compiling multiple data sets from different sources to create multithematic, multiscaled data sets, which results in structurally complex, and sometimes incomplete data sets. The statistical power to detect CSIs needs to be evaluated because of their importance and the challenge of quantifying CSIs using data sets with complex structures and missing observations. We studied this problem using a spatially hierarchical model that measures CSIs between regional agriculture and its effects on the relationship between lake nutrients and lake productivity. We used an existing large multithematic, multiscaled database, LAke multiscaled GeOSpatial, and temporal database (LAGOS), to parameterize the power analysis simulations. We found that the power to detect CSIs was more strongly related to the number of regions in the study rather than the number of lakes nested within each region. CSI power analyses will not only help ecologists design large-scale studies aimed at detecting CSIs, but will also focus attention on CSI effect sizes and the degree to which they are ecologically relevant and detectable with large data sets.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1417","usgsCitation":"Wagner, T., Fergus, C.E., Stow, C., Cheruvelil, K.S., and Soranno, P.A., 2016, The statistical power to detect cross-scale interactions at macroscales: Ecosphere, v. 7, no. 7, HTML document , https://doi.org/10.1002/ecs2.1417.","productDescription":"HTML document ","ipdsId":"IP-071692","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470783,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1417","text":"Publisher Index Page"},{"id":336753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-28","publicationStatus":"PW","scienceBaseUri":"58b7eba8e4b01ccd5500bb1b","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":673735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fergus, C. Emi","contributorId":150608,"corporation":false,"usgs":false,"family":"Fergus","given":"C.","email":"","middleInitial":"Emi","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":680427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stow, Craig A.","contributorId":49733,"corporation":false,"usgs":true,"family":"Stow","given":"Craig A.","affiliations":[],"preferred":false,"id":680428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheruvelil, Kendra S.","contributorId":172029,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"Kendra","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":680429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":680430,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176820,"text":"70176820 - 2016 - Fault zone characteristics and basin complexity in the southern Salton Trough, California","interactions":[],"lastModifiedDate":"2016-10-11T13:03:55","indexId":"70176820","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Fault zone characteristics and basin complexity in the southern Salton Trough, California","docAbstract":"Ongoing oblique slip at the Pacific–North America plate boundary in the Salton Trough produced the Imperial Valley (California, USA), a seismically active area with deformation distributed across a complex network of exposed and buried faults. To better understand the shallow crustal structure in this region and the connectivity of faults and seismicity lineaments, we used data primarily from the Salton Seismic Imaging Project to construct a three-dimensional P-wave velocity model down to 8 km depth and a velocity profile to 15 km depth, both at 1 km grid spacing. A VP = 5.65–5.85 km/s layer of possibly metamorphosed sediments within, and crystalline basement outside, the valley is locally as thick as 5 km, but is thickest and deepest in fault zones and near seismicity lineaments, suggesting a causative relationship between the low velocities and faulting. Both seismicity lineaments and surface faults control the structural architecture of the western part of the larger wedge-shaped basin, where two deep subbasins are located. We estimate basement depths, and show that high velocities at shallow depths and possible basement highs characterize the geothermal areas.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G38033.1","usgsCitation":"Persaud, P., Ma, Y., Stock, J.M., Hole, J.A., Fuis, G.S., and Han, L., 2016, Fault zone characteristics and basin complexity in the southern Salton Trough, California: Geology, v. 44, no. 9, p. 747-750, https://doi.org/10.1130/G38033.1.","productDescription":"4 p.","startPage":"747","endPage":"750","ipdsId":"IP-078827","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":329437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salton Trough","volume":"44","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-01","publicationStatus":"PW","scienceBaseUri":"57fe679ee4b0824b2d143711","contributors":{"authors":[{"text":"Persaud, Patricia","contributorId":175210,"corporation":false,"usgs":false,"family":"Persaud","given":"Patricia","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":650423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Yiran","contributorId":175211,"corporation":false,"usgs":false,"family":"Ma","given":"Yiran","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":650424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stock, Joann M.","contributorId":21057,"corporation":false,"usgs":true,"family":"Stock","given":"Joann","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":650425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hole, John A.","contributorId":104801,"corporation":false,"usgs":true,"family":"Hole","given":"John","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":650426,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuis, Gary S. 0000-0002-3078-1544 fuis@usgs.gov","orcid":"https://orcid.org/0000-0002-3078-1544","contributorId":2639,"corporation":false,"usgs":true,"family":"Fuis","given":"Gary","email":"fuis@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":650422,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Han, Liang","contributorId":49690,"corporation":false,"usgs":true,"family":"Han","given":"Liang","email":"","affiliations":[],"preferred":false,"id":650427,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184348,"text":"70184348 - 2016 - Structure of high latitude currents in global magnetospheric-ionospheric models","interactions":[],"lastModifiedDate":"2017-04-04T09:25:52","indexId":"70184348","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3454,"text":"Space Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Structure of high latitude currents in global magnetospheric-ionospheric models","docAbstract":"Using three resolutions of the Lyon-Fedder-Mobarry global magnetosphere-ionosphere model (LFM) and the Weimer 2005 empirical model we examine the structure of the high latitude field-aligned current patterns. Each resolution was run for the entire Whole Heliosphere Interval which contained two high speed solar wind streams and modest interplanetary magnetic field strengths. Average states of the field-aligned current (FAC) patterns for 8 interplanetary magnetic field clock angle directions are computed using data from these runs. Generally speaking the patterns obtained agree well with results obtained from the Weimer 2005 computing using the solar wind and IMF conditions that correspond to each bin. As the simulation resolution increases the currents become more intense and narrow. A machine learning analysis of the FAC patterns shows that the ratio of Region 1 (R1) to Region 2 (R2) currents decreases as the simulation resolution increases. This brings the simulation results into better agreement with observational predictions and the Weimer 2005 model results. The increase in R2 current strengths also results in the cross polar cap potential (CPCP) pattern being concentrated in higher latitudes. Current-voltage relationships between the R1 and CPCP are quite similar at the higher resolution indicating the simulation is converging on a common solution. We conclude that LFM simulations are capable of reproducing the statistical features of FAC patterns.
","language":"English","publisher":"Springer","doi":"10.1007/s11214-016-0271-2","usgsCitation":"Wiltberger, M., Rigler, E.J., Merkin, V., and Lyon, J.G., 2016, Structure of high latitude currents in global magnetospheric-ionospheric models: Space Science Reviews, v. 206, no. 1, p. 575-598, https://doi.org/10.1007/s11214-016-0271-2.","productDescription":"24 p.","startPage":"575","endPage":"598","ipdsId":"IP-077646","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":336986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"206","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-12","publicationStatus":"PW","scienceBaseUri":"58bfd4f5e4b014cc3a3ba4c4","contributors":{"authors":[{"text":"Wiltberger, M","contributorId":187628,"corporation":false,"usgs":false,"family":"Wiltberger","given":"M","affiliations":[],"preferred":false,"id":681102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigler, E. J.","contributorId":187639,"corporation":false,"usgs":false,"family":"Rigler","given":"E.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":681103,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merkin, V","contributorId":187629,"corporation":false,"usgs":false,"family":"Merkin","given":"V","email":"","affiliations":[],"preferred":false,"id":681104,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyon, J. G","contributorId":187630,"corporation":false,"usgs":false,"family":"Lyon","given":"J.","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":681105,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193671,"text":"70193671 - 2016 - A long-term study of ecological impacts of river channelization on the population of an endangered fish: Lessons learned for assessment and restoration","interactions":[],"lastModifiedDate":"2017-11-13T14:13:43","indexId":"70193671","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"A long-term study of ecological impacts of river channelization on the population of an endangered fish: Lessons learned for assessment and restoration","docAbstract":"Projects to assess environmental impact or restoration success in rivers focus on project-specific questions but can also provide valuable insights for future projects. Both restoration actions and impact assessments can become “adaptive” by using the knowledge gained from long-term monitoring and analysis to revise the actions, monitoring, conceptual model, or interpretation of findings so that subsequent actions or assessments are better informed. Assessments of impact or restoration success are especially challenging when the indicators of interest are imperiled species and/or the impacts being addressed are complex. From 1997 to 2015, we worked closely with two federal agencies to monitor habitat availability for and population density of Roanoke logperch (Percina rex), an endangered fish, in a 24-km-long segment of the upper Roanoke River, VA. We primarily used a Before-After-Control-Impact analytical framework to assess potential impacts of a river channelization project on the P. rex population. In this paper, we summarize how our extensive monitoring facilitated the evolution of our (a) conceptual understanding of the ecosystem and fish population dynamics; (b) choices of ecological indicators and analytical tools; and (c) conclusions regarding the magnitude, mechanisms, and significance of observed impacts. Our experience with this case study taught us important lessons about how to adaptively develop and conduct a monitoring program, which we believe are broadly applicable to assessments of environmental impact and restoration success in other rivers. In particular, we learned that (a) pre-treatment planning can enhance monitoring effectiveness, help avoid unforeseen pitfalls, and lead to more robust conclusions; (b) developing adaptable conceptual and analytical models early was crucial to organizing our knowledge, guiding our study design, and analyzing our data; (c) catchment-wide processes that we did not monitor, or initially consider, had profound implications for interpreting our findings; and (d) using multiple analytical frameworks, with varying assumptions, led to clearer interpretation of findings than the use of a single framework alone. Broader integration of these guiding principles into monitoring studies, though potentially challenging, could lead to more scientifically defensible assessments of project effects.
","language":"English","publisher":"MDPI","doi":"10.3390/w8060240","usgsCitation":"Roberts, J.H., Anderson, G.B., and Angermeier, P.L., 2016, A long-term study of ecological impacts of river channelization on the population of an endangered fish: Lessons learned for assessment and restoration: Water, v. 8, no. 6, p. 1-38, https://doi.org/10.3390/w8060240.","productDescription":"Article 240; 38 p.","startPage":"1","endPage":"38","ipdsId":"IP-073154","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470796,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w8060240","text":"Publisher Index Page"},{"id":348710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-03","publicationStatus":"PW","scienceBaseUri":"5a60fd1fe4b06e28e9c24779","contributors":{"authors":[{"text":"Roberts, James H.","contributorId":83811,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":721841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Gregory B.","contributorId":65988,"corporation":false,"usgs":true,"family":"Anderson","given":"Gregory","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":721842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angermeier, Paul L. 0000-0003-2864-170X biota@usgs.gov","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":166679,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul","email":"biota@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719847,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193661,"text":"70193661 - 2016 - Slab melting and magma formation beneath the southern Cascade arc","interactions":[],"lastModifiedDate":"2017-11-02T15:21:45","indexId":"70193661","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Slab melting and magma formation beneath the southern Cascade arc","docAbstract":"The processes that drive magma formation beneath the Cascade arc and other warm-slab subduction zones have been debated because young oceanic crust is predicted to largely dehydrate beneath the forearc during subduction. In addition, geochemical variability along strike in the Cascades has led to contrasting interpretations about the role of volatiles in magma generation. Here, we focus on the Lassen segment of the Cascade arc, where previous work has demonstrated across-arc geochemical variations related to subduction enrichment, and H-isotope data suggest that H2O in basaltic magmas is derived from the final breakdown of chlorite in the mantle portion of the slab. We use naturally glassy, olivine-hosted melt inclusions (MI) from the tephra deposits of eight primitive (MgO>7 wt%) basaltic cinder cones to quantify the pre-eruptive volatile contents of mantle-derived melts in this region. The melt inclusions have B concentrations and isotope ratios that are similar to mid-ocean ridge basalt (MORB), suggesting extensive dehydration of the downgoing plate prior to reaching sub-arc depths and little input of slab-derived B into the mantle wedge. However, correlations of volatile and trace element ratios (H2O/Ce, Cl/Nb, Sr/Nd) in the melt inclusions demonstrate that geochemical variability is the result of variable addition of a hydrous subduction component to the mantle wedge. Furthermore, correlations between subduction component tracers and radiogenic isotope ratios show that the subduction component has less radiogenic Sr and Pb than the Lassen sub-arc mantle, which can be explained by melting of subducted Gorda MORB beneath the arc. Agreement between pMELTS melting models and melt inclusion volatile, major, and trace element data suggests that hydrous slab melt addition to the mantle wedge can produce the range in primitive compositions erupted in the Lassen region. Our results provide further evidence that chlorite-derived fluids from the mantle portion of the slab (∼7–9 km below the slab top) cause flux melting of the subducted oceanic crust, producing hydrous slab melts that migrate into the overlying mantle, where they react with peridotite to induce further melting.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2016.03.044","usgsCitation":"Walowski, K.J., Wallace, P.J., Clynne, M.A., Rasmussen, D., and Weis, D., 2016, Slab melting and magma formation beneath the southern Cascade arc: Earth and Planetary Science Letters, v. 446, p. 100-112, https://doi.org/10.1016/j.epsl.2016.03.044.","productDescription":"12 p.","startPage":"100","endPage":"112","ipdsId":"IP-066861","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470787,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.research.ed.ac.uk/en/publications/ac26caa7-78c7-4d82-b689-f1ab62b89bd3","text":"Publisher Index Page"},{"id":348125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Cascades","volume":"446","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2ea6e4b0531197b27f8b","contributors":{"authors":[{"text":"Walowski, Kristina J.","contributorId":199699,"corporation":false,"usgs":false,"family":"Walowski","given":"Kristina","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":719800,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wallace, Paul J.","contributorId":199700,"corporation":false,"usgs":false,"family":"Wallace","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":719801,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":719799,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rasmussen, D.J.","contributorId":199701,"corporation":false,"usgs":false,"family":"Rasmussen","given":"D.J.","email":"","affiliations":[],"preferred":false,"id":719802,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weis, D.","contributorId":199702,"corporation":false,"usgs":false,"family":"Weis","given":"D.","email":"","affiliations":[],"preferred":false,"id":719803,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178837,"text":"70178837 - 2016 - National Park Service Vegetation Mapping Inventory Program: Natchez Trace Parkway vegetation mapping project report","interactions":[],"lastModifiedDate":"2017-04-17T15:22:29","indexId":"70178837","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/GULN/NRR—2016/1255","title":"National Park Service Vegetation Mapping Inventory Program: Natchez Trace Parkway vegetation mapping project report","docAbstract":"The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VMI Program is managed by the NPS I&M Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, NPS Gulf Coast Network, and NPS Natchez Trace Parkway (NATR; also referred to as Parkway) have completed vegetation classification and mapping of NATR for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types within the U.S. National Vegetation Classification (USNVC) of NATR and to determine how best to map them by using aerial imagery. Analyses of data from 589 vegetation plots had been used to describe an initial 99 USNVC associations in the Parkway; this classification work was completed prior to beginning this NATR vegetation mapping project. Data were collected during this project from another eight quick plots to support new vegetation types not previously identified at the Parkway. Data from 120 verification sites were collected to test the field key to vegetation associations and the application of vegetation associations to a sample set of map polygons. Furthermore, data from 900 accuracy assessment (AA) sites were collected (of which 894 were used to test accuracy of the vegetation map layer). The collective of all these datasets resulted in affirming 122 USNVC associations at NATR.
To map the vegetation and open water of NATR, 63 map classes were developed. including the following: 54 map classes represent natural (including ruderal) vegetation types in the USNVC, 5 map classes represent cultural (agricultural and developed) vegetation types in the USNVC, 3 map classes represent nonvegetation open-water bodies (non-USNVC), and 1 map class represents landscapes that had received tornado damage a few months prior to the time of aerial imagery collection. Features were interpreted from viewing 4-band digital aerial imagery by means of digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems. (The aerial imagery was collected during mid-October 2011 for the northern reach of the Parkway and mid-November 2011 for the southern reach of the Parkway to capture peak leaf-phenology of trees.) The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in geographic information systems. Polygon units were mapped to either a 0.5 hectare (ha) or 0.25 ha minimum mapping unit, depending on vegetation type or scenario.
A geodatabase containing various feature-class layers and tables present the locations of USNVC vegetation types (vegetation map), vegetation plot samples, verification sites, AA sites, project boundary extent, and aerial image centers. The feature-class layer and related tables for the vegetation map provide 13,529 polygons of detailed attribute data covering 21,655.5 ha, with an average polygon size of 1.6 ha; the vegetation map coincides closely with the administrative boundary for NATR.
Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 12,648 polygons (93.5% of polygons) and cover 18,542.7 ha (85.6%) of the map extent for NATR. The map layer indicates the Parkway to be 70.5% forest and woodland (15,258.7 ha), 0.3% shrubland (63.0 ha), and 14.9% herbaceous cover (3,221.0 ha). Map classes representing USNVC cultural types apply to 678 polygons (5.0% of polygons) and cover 2,413.9 ha (11.1%) of the map extent.
The green wave hypothesis (GWH) states that migrating animals should track or ‘surf’ high-quality forage at the leading edge of spring green-up. To index such high-quality forage, recent work proposed the instantaneous rate of green-up (IRG), i.e. rate of change in the normalized difference vegetation index over time. Despite this important advancement, no study has tested the assumption that herbivores select habitat patches at peak IRG. We evaluated this assumption using step selection functions parametrized with movement data during the green-up period from two populations each of bighorn sheep, mule deer, elk, moose and bison, totalling 463 individuals monitored 1–3 years from 2004 to 2014. Accounting for variables that typically influence habitat selection for each species, we found seven of 10 populations selected patches exhibiting high IRG—supporting the GWH. Nonetheless, large herbivores selected for the leading edge, trailing edge and crest of the IRG wave, indicating that other mechanisms (e.g. ruminant physiology) or measurement error inherent with satellite data affect selection for IRG. Our evaluation indicates that IRG is a useful tool for linking herbivore movement with plant phenology, paving the way for significant advancements in understanding how animals track resource quality that varies both spatially and temporally.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2016.0456","usgsCitation":"Merkle, J., Monteith, K.L., Aikens, E.O., Hayes, M.M., Hersey, K., Middleton, A., Oates, B., Sawyer, H., Scurlock, B., and Kauffman, M., 2016, Large herbivores surf waves of green-up during spring: Proceedings of the Royal Society B: Biological Sciences, v. 283, no. 1833, p. 1-8, https://doi.org/10.1098/rspb.2016.0456.","productDescription":"Article 20160456; 8 p.","startPage":"1","endPage":"8","ipdsId":"IP-073825","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2016.0456","text":"Publisher Index Page"},{"id":348554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"283","issue":"1833","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-29","publicationStatus":"PW","scienceBaseUri":"5a06c8d2e4b09af898c86154","contributors":{"authors":[{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":721528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monteith, Kevin L.","contributorId":198656,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":721529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aikens, Ellen O.","contributorId":198653,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":721530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Matthew M.","contributorId":172344,"corporation":false,"usgs":false,"family":"Hayes","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hersey, Kent","contributorId":99873,"corporation":false,"usgs":false,"family":"Hersey","given":"Kent","affiliations":[{"id":6763,"text":"Utah Division of Wildlife Resources, Salt Lake City, Utah","active":true,"usgs":false}],"preferred":false,"id":721532,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Middleton, Arthur D.","contributorId":99440,"corporation":false,"usgs":true,"family":"Middleton","given":"Arthur D.","affiliations":[],"preferred":false,"id":721533,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Oates, Brendan","contributorId":200235,"corporation":false,"usgs":false,"family":"Oates","given":"Brendan","affiliations":[],"preferred":false,"id":721534,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sawyer, Hall","contributorId":39930,"corporation":false,"usgs":false,"family":"Sawyer","given":"Hall","affiliations":[],"preferred":false,"id":721535,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scurlock, Brandon","contributorId":145744,"corporation":false,"usgs":false,"family":"Scurlock","given":"Brandon","email":"","affiliations":[{"id":16219,"text":"Wyoming Game and Fish Department, PO Box 850, Pinedale, Wyoming","active":true,"usgs":false}],"preferred":false,"id":721536,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900 mkauffman@usgs.gov","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":189179,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew J.","email":"mkauffman@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":false,"id":716617,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70184978,"text":"70184978 - 2016 - Contemporary deformation in the Yakima fold and thrust belt estimated with GPS","interactions":[],"lastModifiedDate":"2017-03-14T16:01:22","indexId":"70184978","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Contemporary deformation in the Yakima fold and thrust belt estimated with GPS","docAbstract":"Geodetic, geologic and palaeomagnetic data reveal that Oregon (western USA) rotates clockwise at 0.3 to 1.0° Ma−1 (relative to North America) about an axis near the Idaho–Oregon–Washington border, while northeast Washington is relatively fixed. This rotation has been going on for at least 15 Ma. The Yakima fold and thrust belt (YFTB) forms the boundary between northern Oregon and central Washington where convergence of the clockwise-rotating Oregon block is apparently accommodated. North–south shortening across the YFTB has been thought to occur in a fan-like manner, increasing in rate to the west. We obtained high-accuracy, high-density geodetic GPS measurements in 2012–2014 that are used with earlier GPS measurements from the 1990s to characterize YFTB kinematics. The new results show that the deformation associated with the YFTB starts at the Blue Mountains Anticline in northern Oregon and extends north beyond the Frenchman Hills in Washington, past the epicentre of the 1872 Mw 7.0 Entiat earthquake to 49°N. The north–south strain rate across the region is 2 to 3 × 10−9 yr−1 between the volcanic arc and the eastern edge of the YFTB (241.0°E); east of there it drops to about 10−9 yr−1. At the eastern boundary of the YFTB, faults and earthquake activity are truncated by a north-trending, narrow zone of deformation that runs along the Pasco Basin and Moses Lake regions near 240.9°E. This zone, abutting the Department of Energy Hanford Nuclear Reservation, accommodates about 0.5 mm yr−1 of east to northeast shortening. A similar zone of N-trending transpression is seen along 239.9°E where there is a change in the strike of the Yakima folds. The modern deformation of the YFTB is about 600 km wide from south to north and internally may be controlled by pre-existing crustal structure.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/gji/ggw252","usgsCitation":"McCaffrey, R., King, R.W., Wells, R.E., Lancaster, M., and Miller, M.M., 2016, Contemporary deformation in the Yakima fold and thrust belt estimated with GPS: Geophysical Journal International, v. 207, no. 1, p. 1-11, https://doi.org/10.1093/gji/ggw252.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-073652","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":470784,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggw252","text":"Publisher Index Page"},{"id":337545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"207","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-11","publicationStatus":"PW","scienceBaseUri":"58c90127e4b0849ce97abced","contributors":{"authors":[{"text":"McCaffrey, Robert","contributorId":189078,"corporation":false,"usgs":false,"family":"McCaffrey","given":"Robert","email":"","affiliations":[],"preferred":false,"id":683802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Robert W.","contributorId":189079,"corporation":false,"usgs":false,"family":"King","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":683803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wells, Ray E. 0000-0002-7796-0160 rwells@usgs.gov","orcid":"https://orcid.org/0000-0002-7796-0160","contributorId":149772,"corporation":false,"usgs":true,"family":"Wells","given":"Ray","email":"rwells@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":683801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lancaster, Matthew","contributorId":189080,"corporation":false,"usgs":false,"family":"Lancaster","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":683804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, M. Meghan","contributorId":189081,"corporation":false,"usgs":false,"family":"Miller","given":"M.","email":"","middleInitial":"Meghan","affiliations":[],"preferred":false,"id":683805,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70185026,"text":"70185026 - 2016 - Discovery of alunite in Cross crater, Terra Sirenum, Mars: Evidence for acidic, sulfurous waters","interactions":[],"lastModifiedDate":"2018-11-14T08:22:27","indexId":"70185026","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":738,"text":"American Mineralogist","active":true,"publicationSubtype":{"id":10}},"title":" Discovery of alunite in Cross crater, Terra Sirenum, Mars: Evidence for acidic, sulfurous waters","docAbstract":"Cross crater is a 65 km impact crater, located in the Noachian highlands of the Terra Sirenum region of Mars (30°S, 158°W), which hosts aluminum phyllosilicate deposits first detected by the Observatoire pour la Minéralogie, L’Eau, les Glaces et l’Activitié (OMEGA) imaging spectrometer on Mars Express. Using high-resolution data from the Mars Reconnaissance Orbiter, we examine Cross crater’s basin-filling sedimentary deposits. Visible/shortwave infrared (VSWIR) spectra from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) show absorptions diagnostic of alunite. Combining spectral data with high-resolution images, we map a large (10 km × 5 km) alunite-bearing deposit in southwest Cross crater, widespread kaolin-bearing sediments with variable amounts of alunite that are layered in <10 m scale beds, and silica- and/or montmorillonite-bearing deposits that occupy topographically lower, heavily fractured units. The secondary minerals are found at elevations ranging from 700 to 1550 m, forming a discontinuous ring along the crater wall beneath darker capping materials. The mineralogy inside Cross crater is different from that of the surrounding terrains and other martian basins, where Fe/Mg-phyllosilicates and Ca/Mg-sulfates are commonly found. Alunite in Cross crater indicates acidic, sulfurous waters at the time of its formation. Waters in Cross crater were likely supplied by regionally upwelling groundwaters as well as through an inlet valley from a small adjacent depression to the east, perhaps occasionally forming a lake or series of shallow playa lakes in the closed basin. Like nearby Columbus crater, Cross crater exhibits evidence for acid sulfate alteration, but the alteration in Cross is more extensive/complete. The large but localized occurrence of alunite suggests a localized, high-volume source of acidic waters or vapors, possibly supplied by sulfurous (H2S- and/or SO2-bearing) waters in contact with a magmatic source, upwelling steam or fluids through fracture zones. The unique, highly aluminous nature of the Cross crater deposits relative to other martian acid sulfate deposits indicates acid waters, high water throughput during alteration, atypically glassy and/or felsic materials, or a combination of these conditions.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2138/am-2016-5574","usgsCitation":"Ehlmann, B.L., Swayze, G.A., Milliken, R.E., Mustard, J.F., Clark, R.N., Murchie, S.L., Breit, G., Wray, J.J., Gondet, B., Poulet, F., Carter, J., Calvin, W.M., Benzel, W., and Seelos, K.D., 2016, Discovery of alunite in Cross crater, Terra Sirenum, Mars: Evidence for acidic, sulfurous waters: American Mineralogist, v. 101, no. 7, p. 1527-1542, https://doi.org/10.2138/am-2016-5574.","productDescription":"16 p.","startPage":"1527","endPage":"1542","ipdsId":"IP-069689","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488402,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2138/am-2016-5574","text":"Publisher Index 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F.","contributorId":189152,"corporation":false,"usgs":false,"family":"Mustard","given":"John","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":683994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Roger N. 0000-0002-7021-1220","orcid":"https://orcid.org/0000-0002-7021-1220","contributorId":189154,"corporation":false,"usgs":true,"family":"Clark","given":"Roger","email":"","middleInitial":"N.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":683996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murchie, Scott L. 0000-0002-1616-8751","orcid":"https://orcid.org/0000-0002-1616-8751","contributorId":189161,"corporation":false,"usgs":false,"family":"Murchie","given":"Scott","email":"","middleInitial":"L.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":684004,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Breit, George N.","contributorId":189153,"corporation":false,"usgs":false,"family":"Breit","given":"George N.","affiliations":[],"preferred":false,"id":683995,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wray, James J.","contributorId":81736,"corporation":false,"usgs":false,"family":"Wray","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":7032,"text":"School of Earth and Atmospheric Sciences, Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":683993,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gondet, Brigitte","contributorId":189155,"corporation":false,"usgs":false,"family":"Gondet","given":"Brigitte","email":"","affiliations":[],"preferred":false,"id":683997,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Poulet, Francois","contributorId":189156,"corporation":false,"usgs":false,"family":"Poulet","given":"Francois","email":"","affiliations":[],"preferred":false,"id":683998,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carter, John","contributorId":189157,"corporation":false,"usgs":false,"family":"Carter","given":"John","email":"","affiliations":[],"preferred":false,"id":683999,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Calvin, Wendy M. 0000-0002-6097-9586","orcid":"https://orcid.org/0000-0002-6097-9586","contributorId":189159,"corporation":false,"usgs":false,"family":"Calvin","given":"Wendy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":684001,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Benzel, William 0000-0002-4085-1876 wbenzel@usgs.gov","orcid":"https://orcid.org/0000-0002-4085-1876","contributorId":3594,"corporation":false,"usgs":true,"family":"Benzel","given":"William","email":"wbenzel@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":684002,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Seelos, Kimberly D.","contributorId":189160,"corporation":false,"usgs":false,"family":"Seelos","given":"Kimberly","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":684003,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70184330,"text":"70184330 - 2016 - Predicting arsenic in drinking water wells of the Central Valley, California","interactions":[],"lastModifiedDate":"2018-09-12T16:43:45","indexId":"70184330","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Predicting arsenic in drinking water wells of the Central Valley, California","docAbstract":"Probabilities of arsenic in groundwater at depths used for domestic and public supply in the Central Valley of California are predicted using weak-learner ensemble models (boosted regression trees, BRT) and more traditional linear models (logistic regression, LR). Both methods captured major processes that affect arsenic concentrations, such as the chemical evolution of groundwater, redox differences, and the influence of aquifer geochemistry. Inferred flow-path length was the most important variable but near-surface-aquifer geochemical data also were significant. A unique feature of this study was that previously predicted nitrate concentrations in three dimensions were themselves predictive of arsenic and indicated an important redox effect at >10 μg/L, indicating low arsenic where nitrate was high. Additionally, a variable representing three-dimensional aquifer texture from the Central Valley Hydrologic Model was an important predictor, indicating high arsenic associated with fine-grained aquifer sediment. BRT outperformed LR at the 5 μg/L threshold in all five predictive performance measures and at 10 μg/L in four out of five measures. BRT yielded higher prediction sensitivity (39%) than LR (18%) at the 10 μg/L threshold–a useful outcome because a major objective of the modeling was to improve our ability to predict high arsenic areas.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.6b01914","usgsCitation":"Ayotte, J.D., Nolan, B.T., and Gronberg, J.M., 2016, Predicting arsenic in drinking water wells of the Central Valley, California: Environmental Science & Technology, v. 50, no. 14, p. 7555-7563, https://doi.org/10.1021/acs.est.6b01914.","productDescription":"9 p.","startPage":"7555","endPage":"7563","ipdsId":"IP-074943","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":336970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"50","issue":"14","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-11","publicationStatus":"PW","scienceBaseUri":"58bfd4f6e4b014cc3a3ba4c8","contributors":{"authors":[{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":681022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681023,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191850,"text":"70191850 - 2016 - Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem","interactions":[],"lastModifiedDate":"2017-10-18T14:21:25","indexId":"70191850","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5519,"text":"Journal of King Saud University - Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem","title":"Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem","docAbstract":"Accurately estimating the size of wildlife populations is critical to wildlife management and conservation of species. Raw counts or “minimum counts” are still used as a basis for wildlife management decisions. Uncorrected raw counts are not only negatively biased due to failure to account for undetected animals, but also provide no estimate of precision on which to judge the utility of counts. We applied a hybrid population estimation technique that combined sightability modeling, radio collar-based mark-resight, and simultaneous double count (double-observer) modeling to estimate the population size of elk in a high elevation desert ecosystem. Combining several models maximizes the strengths of each individual model while minimizing their singular weaknesses. We collected data with aerial helicopter surveys of the elk population in the San Luis Valley and adjacent mountains in Colorado State, USA in 2005 and 2007. We present estimates from 7 alternative analyses: 3 based on different methods for obtaining a raw count and 4 based on different statistical models to correct for sighting probability bias. The most reliable of these approaches is a hybrid double-observer sightability model (model MH), which uses detection patterns of 2 independent observers in a helicopter plus telemetry-based detections of radio collared elk groups. Data were fit to customized mark-resight models with individual sighting covariates. Error estimates were obtained by a bootstrapping procedure. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to double-observer modeling. The resulting population estimate corrected for multiple sources of undercount bias that, if left uncorrected, would have underestimated the true population size by as much as 22.9%. Our comparison of these alternative methods demonstrates how various components of our method contribute to improving the final estimate and demonstrates why each is necessary.
","language":"English","publisher":"King Saud University","doi":"10.1016/j.jksus.2015.09.004","usgsCitation":"Schoenecker, K.A., and Lubow, B., 2016, Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem: Journal of King Saud University - Science, v. 28, no. 3, p. 205-215, https://doi.org/10.1016/j.jksus.2015.09.004.","productDescription":"11 p.","startPage":"205","endPage":"215","ipdsId":"IP-061911","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470786,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jksus.2015.09.004","text":"Publisher Index Page"},{"id":346881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.270751953125,\n 37.49011473195046\n ],\n [\n -105.556640625,\n 37.49011473195046\n ],\n [\n -105.556640625,\n 38.58896696823242\n ],\n [\n -106.270751953125,\n 38.58896696823242\n ],\n [\n -106.270751953125,\n 37.49011473195046\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e86839e4b05fe04cd4d21c","contributors":{"authors":[{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X schoeneckerk@usgs.gov","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":2001,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn","email":"schoeneckerk@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubow, Bruce C.","contributorId":59520,"corporation":false,"usgs":true,"family":"Lubow","given":"Bruce C.","affiliations":[],"preferred":false,"id":713547,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70182762,"text":"70182762 - 2016 - Automated mapping of persistent ice and snow cover across the western U.S. with Landsat","interactions":[],"lastModifiedDate":"2017-02-28T11:15:56","indexId":"70182762","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated mapping of persistent ice and snow cover across the western U.S. with Landsat","docAbstract":"We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.
We present high-quality focal mechanisms based on a refined earthquake location catalog for the Island of Hawai'i, focusing on Mauna Loa and Kīlauea volcanoes. The relocation catalog is based on first-arrival times and waveform data of both compressional and shear waves for about 180,000 events on and near the Island of Hawai'i between 1986 and 2009 recorded by the seismic stations at the Hawaiian Volcano Observatory. We relocate all the earthquakes by applying ray tracing through an existing three-dimensional velocity model, similar event cluster analysis, and a differential-time relocation method. The resulting location catalog represents an expansion of previous relocation studies, covering a longer time period and consisting of more events with well-constrained absolute locations. The focal mechanisms are obtained based on the compressional-wave first-motion polarities and compressional-to-shear wave amplitude ratios by applying the HASH program to the waveform cross correlation relocated earthquakes. Overall, the good-quality (defined by the HASH parameters) focal solutions are dominated by normal faulting in our study area, especially in the active Ka'ōiki and Hīlea seismic zones. Kīlauea caldera is characterized by a mixture of approximately equal numbers of normal, strike-slip, and reverse faults, whereas its south flank has slightly fewer strike-slip events. Our relocation and focal mechanism results will be useful for mapping the seismic stress and strain fields and for understanding the seismic-volcanic-tectonic relationships within the magmatic systems.
","language":"English","publisher":"AGU","doi":"10.1002/2016JB013042","usgsCitation":"Lin, G., and Okubo, P.G., 2016, A large refined catalog of earthquake relocations and focal mechanisms for the Island of Hawai'i and its seismotectonic implications: Journal of Geophysical Research, v. 121, no. 7, p. 5031-5048, https://doi.org/10.1002/2016JB013042.","productDescription":"18 p.","startPage":"5031","endPage":"5048","ipdsId":"IP-076229","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470801,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jb013042","text":"Publisher Index Page"},{"id":348180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Island of Hawai'i","volume":"121","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-09","publicationStatus":"PW","scienceBaseUri":"59fd802ae4b0531197b50148","contributors":{"authors":[{"text":"Lin, Guoqing","contributorId":168856,"corporation":false,"usgs":false,"family":"Lin","given":"Guoqing","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":720042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okubo, Paul G. 0000-0002-0381-6051 pokubo@usgs.gov","orcid":"https://orcid.org/0000-0002-0381-6051","contributorId":2730,"corporation":false,"usgs":true,"family":"Okubo","given":"Paul","email":"pokubo@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":720041,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179078,"text":"70179078 - 2016 - Contrasts between channels and backwaters in a large, floodplain river: Testing our understanding of nutrient cycling, phytoplankton abundance, and suspended solids dynamics","interactions":[],"lastModifiedDate":"2016-12-15T15:24:40","indexId":"70179078","displayToPublicDate":"2016-07-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Contrasts between channels and backwaters in a large, floodplain river: Testing our understanding of nutrient cycling, phytoplankton abundance, and suspended solids dynamics","docAbstract":"In floodplain rivers, variability in hydraulic connectivity interacts with biogeochemistry to determine the distribution of suspended and dissolved substances. Nutrient, chlorophyll a, and suspended solids data spanning longitudinal (5 study reaches across 1300 river km), lateral (main channel and backwaters), and temporal (1994–2011) gradients in the Upper Mississippi River (UMR) were used to examine the extent to which observed differences between the main channel and backwaters were consistent with expectations based on current understanding of biogeochemical processes in large rivers. For N and P, the results largely conformed to expectations. N concentrations were greater in the main channel than in the backwaters in 82 to 96% of the observations across river reaches. Maximum TP concentrations generally occurred in backwaters during summer, when backwater TP often exceeded that of the main channel. Flux of P from sediments may be a substantial source of water-column P in UMR backwaters in summer. The data for suspended solids and chlorophyll a suggest that some refinements are needed of our understanding of ecosystem processes in large rivers. During low-discharge conditions, concentrations of inorganic suspended solids often were greater in backwaters than in the main channel, suggesting the importance of sediment resuspension. Chlorophyll a concentrations were usually greater in backwaters than in the main channel, but exceptions indicate that phytoplankton abundance in the main channel of the UMR can sometimes be greater than is typically expected for large rivers.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/686171","usgsCitation":"Houser, J.N., 2016, Contrasts between channels and backwaters in a large, floodplain river: Testing our understanding of nutrient cycling, phytoplankton abundance, and suspended solids dynamics: Freshwater Science, v. 35, no. 2, p. 457-473, https://doi.org/10.1086/686171.","productDescription":"17 p.","startPage":"457","endPage":"473","ipdsId":"IP-066890","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":332189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5853ba41e4b0e2663625f2be","contributors":{"authors":[{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":655949,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}