This chapter provides an overview of the National Park Service (NPS) methods for estimating visitor spending and calculating economic contributions of visitor spending in terms of jobs supported, wage and labor income, and total economic activity. The Visitor Spending Effects model combines visitor spending patterns and trip characteristic data with visitor use data to estimate total visitor spending. Economic contributions measure the total economic activity within a regional economy stemming from visitor spending, and include the effects of spending by both local visitors who live within gateway regions and non-local visitors who travel to NPS sites from outside of gateway regions. The Social Science Program collaborates with individual parks to develop visitor counting instructions that contain the procedures for measuring, compiling, and recording required visitor use data. Visitor surveys are used to collect the essential visitor spending and trip characteristic data necessary for developing spending profiles to represent distinct visitor spending patterns for each park.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Valuing U.S. National Parks and Programs","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Routledge","doi":"10.4324/9781351055789","collaboration":"National Park Service","usgsCitation":"Koontz, L., and Cullinane Thomas, C., 2020, Estimating visitor use and economic contributions of National Park visitor spending, chap. of Valuing U.S. National Parks and Programs, 13 p., https://doi.org/10.4324/9781351055789.","productDescription":"13 p.","ipdsId":"IP-099361","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":375349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Koontz, Lynne koontzl@usgs.gov","contributorId":2174,"corporation":false,"usgs":false,"family":"Koontz","given":"Lynne","email":"koontzl@usgs.gov","affiliations":[{"id":7016,"text":"Environmental Quality Division, National Park Service, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":789508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cullinane Thomas, Catherine 0000-0001-8168-1271 ccullinanethomas@usgs.gov","orcid":"https://orcid.org/0000-0001-8168-1271","contributorId":141097,"corporation":false,"usgs":true,"family":"Cullinane Thomas","given":"Catherine","email":"ccullinanethomas@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":789509,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221772,"text":"70221772 - 2020 - Hydraulic tomography: 3D hydraulic conductivity, fracture network, and connectivity in mudstone","interactions":[],"lastModifiedDate":"2021-07-02T12:19:27.708888","indexId":"70221772","displayToPublicDate":"2019-06-12T07:14:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Hydraulic tomography: 3D hydraulic conductivity, fracture network, and connectivity in mudstone","docAbstract":"We present the first demonstration of hydraulic tomography (HT) to estimate the three-dimensional (3D) hydraulic conductivity (K) distribution of a fractured aquifer at high-resolution field scale (HRFS), including the fracture network and connectivity through it. We invert drawdown data collected from packer-isolated borehole intervals during 42 pumping tests in a wellfield at the former Naval Air Warfare Center, West Trenton, New Jersey, in the Newark Basin. Five additional tests were reserved for a quality check of HT results. We used an equivalent porous medium forward model and geostatistical inversion to estimate 3D K at high resolution (K blocks <1 m3), using no strict assumptions about K variability or fracture statistics. The resulting 3D K estimate ranges from approximately 0.1 (highest-K fractures) to approximately 10−13 m/s (unfractured mudstone). Important estimated features include: (1) a highly fractured zone (HFZ) consisting of a sequence of high-K bedding-plane fractures; (2) a low-K zone that disrupts the HFZ; (3) several secondary fractures of limited extent; and (4) regions of very low-K rock matrix. The 3D K estimate explains complex drawdown behavior observed in the field. Drawdown tracing and particle tracking simulations reveal a 3D fracture network within the estimated K distribution, and connectivity routes through the network. Model fit is best in the shallower part of the wellfield, with high density of observations and tests. The capabilities of HT demonstrated for 3D fractured aquifer characterization at HRFS may support improved in situ remediation for contaminant source zones, and applications in mining, repository assessment, or geotechnical engineering.
In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; the primary forecasts of interest were heads in pumping wells after a river was modified. The model was calibrated using manual trial‐and‐error approaches where a model's forecast quality was not related to how well it was calibrated. Here, we investigate whether tools widely available today obviate the shortcomings identified 30 years ago. A reconstructed version of the 1988 true model was tested using increasing parameter estimation sophistication. The parameter estimation demonstrated the inverse problem was non‐unique because only head data were available for calibration. When a flux observation was included, current parameter estimation approaches were able to overcome all calibration and forecast issues noted in 1988. The best forecasts were obtained from a highly parameterized model that used pilot points for hydraulic conductivity and was constrained with soft knowledge. Like the 1988 results, however, the best calibrated model did not produce the best forecasts due to parameter overfitting. Finally, a computationally frugal linear uncertainty analysis demonstrated that the single‐zone model was oversimplified, with only half of the forecasts falling within the calculated uncertainty bounds. Uncertainties from the highly parameterized models had all six forecasts within the calculated uncertainty. The current results outperformed those of the 1988 effort, demonstrating the value of quantitative parameter estimation and uncertainty analysis methods.
Species–environment relationships for highly mobile species outside of the breeding season are often highly dynamic in response to the collective effects of ever‐changing climatic conditions, food resources, and anthropogenic disturbance. Capturing dynamic space‐use patterns in a model‐based framework is critical as model inference often drives place‐based conservation planning. We applied dynamic occupancy models to broad‐scale golden eagle Aquila chrysaetos survey data collected annually from 2006 to 2012 during the late summer post‐fledging period in the western US. We defined survey sites as 10 km transect segments with a 1 km buffer on either transect side (n = 3540). Derived estimates of occupancy were low (4.4–7.9%) and turnover rates – the probability that occupied sites were newly occupied – were high (88–94%), demonstrating that annual transiency in occupancy dominates late summer behavior for golden eagles. Despite low philopatry during late summer, variation in golden eagle occupancy could be explained by a suite of land cover and annual‐varying covariates including gross primary productivity, drought severity, and human disturbance. Our summary of 13 years of predicted occupancy by golden eagles across the western United States identified areas that are consistently used and that may contribute significantly to golden eagle conservation. Restricting development and targeting mitigation efforts in these areas offers practitioners a framework for conservation prioritization.
","language":"English","publisher":"Zoological Society of London","doi":"10.1111/acv.12511","usgsCitation":"Tack, J.D., Noon, B., Bowen, Z.H., and Fedy, B., 2020, Ecosystem processes, landcover, climate, and human settlement shape dynamic distributions for golden eagle across the western US: Animal Conservation, v. 23, no. 1, p. 72-82, https://doi.org/10.1111/acv.12511.","productDescription":"11 p.","startPage":"72","endPage":"82","ipdsId":"IP-087715","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":458768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/acv.12511","text":"Publisher Index Page"},{"id":364168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, New Mexico, Nebraska, Nevada, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-96.443408,42.489495],[-96.079915,41.757895],[-96.089714,41.531778],[-95.871489,41.295797],[-95.885349,40.721093],[-95.336242,40.019104],[-102.051744,40.003078],[-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],[-97.229039,49.000687],[-97.116185,48.709348],[-97.145243,48.174046],[-96.854812,47.606328],[-96.774763,46.607461],[-96.557952,46.102442],[-96.612512,45.794442],[-96.82616,45.654164],[-96.452315,45.208986],[-96.453049,43.500415],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.443408,42.489495]]],[[[-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 \"}}]}","volume":"23","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Tack, J. D.","contributorId":222253,"corporation":false,"usgs":false,"family":"Tack","given":"J.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":781683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noon, B.R.","contributorId":24311,"corporation":false,"usgs":true,"family":"Noon","given":"B.R.","email":"","affiliations":[],"preferred":false,"id":781684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bowen, Zachary H. 0000-0002-8656-1831 bowenz@usgs.gov","orcid":"https://orcid.org/0000-0002-8656-1831","contributorId":821,"corporation":false,"usgs":true,"family":"Bowen","given":"Zachary","email":"bowenz@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":763301,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fedy, B.C.","contributorId":35427,"corporation":false,"usgs":true,"family":"Fedy","given":"B.C.","email":"","affiliations":[],"preferred":false,"id":781685,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209712,"text":"70209712 - 2020 - Assessment experimental semivariogram uncertainty in the presence of a polynomial drift","interactions":[],"lastModifiedDate":"2020-04-27T12:23:13.834768","indexId":"70209712","displayToPublicDate":"2019-05-14T09:54:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Assessment experimental semivariogram uncertainty in the presence of a polynomial drift","docAbstract":"The semivariogram, which measures the spatial variability between experimental data, is generally used as a structural input in all two-point geostatistical procedures. However, in most geoscience applications, experimental semivariograms are usually computed from a limited number of sparsely spaced measurements, which results in uncertainty associated with the semivariance values estimated for a specified number of lags. More importantly, considering a spatial variable modelled by a nonstationary random field, uncertainty is not only in the experimental semivariogram of the residuals, but also in the coefficients of the drift model estimated from the available experimental data. Therefore, when assessing the reliability of an experimental semivariogram (or estimated semivariances) in the nonstationary case, both aforementioned uncertainties should be taken into account. The aim of this paper is to extend the “Generalised Bootstrap” procedure to the nonstationary model by propagating the uncertainty associated with the estimated drift coefficients into the uncertainty in the experimental semivariogram of the residuals. The proposed methodology is demonstrated in a case study using abundant geophysical measurements characterised by a nonstationary random function. Two scenarios are evaluated in the case study: (1) it is assumed that the drift coefficients can be estimated without any uncertainty, and (2) uncertainty of the drift coefficients is taken into account. We have explained the methodology that allows to assess the uncertainty of the semivariogram lag estimates in the presence of the drift in the mean. Considering the second scenario, uncertainty is obviously larger than the case where uncertainty of the drift in the mean is ignored. This evaluation should be considered in applications where the data is often rather limited, such as subsurface hydrology (i.e. porosity, transmissivity), soil science (i.e. heavy metal content, soil moisture) and mining (i.e. scoping or pre-feasibility stage of the project). In fact, modern geostatistics should provide not only the semivariogram estimates but also estimation of its uncertainty.","language":"English","publisher":"Springer","doi":"10.1007/s11053-019-09496-3","collaboration":"","usgsCitation":"Oktay, E., Pardo-Iguzquiza, E., and Olea, R.A., 2020, Assessment experimental semivariogram uncertainty in the presence of a polynomial drift: Natural Resources Research, v. 29, no. 2, p. 1087-1099, https://doi.org/10.1007/s11053-019-09496-3.","productDescription":"13 p.","startPage":"1087","endPage":"1099","ipdsId":"IP-087621","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":374190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Oktay, Erten","contributorId":224283,"corporation":false,"usgs":false,"family":"Oktay","given":"Erten","email":"","affiliations":[{"id":40846,"text":"Curtin U. Australia","active":true,"usgs":false}],"preferred":false,"id":787635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pardo-Iguzquiza, Eulogio","contributorId":208073,"corporation":false,"usgs":false,"family":"Pardo-Iguzquiza","given":"Eulogio","email":"","affiliations":[{"id":40847,"text":"Instituto Geologico y Minero de Espana","active":true,"usgs":false}],"preferred":false,"id":787636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olea, Ricardo A. 0000-0003-4308-0808 rolea@usgs.gov","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":208109,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo","email":"rolea@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":787637,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227891,"text":"70227891 - 2020 - Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring","interactions":[],"lastModifiedDate":"2022-02-01T16:36:33.563216","indexId":"70227891","displayToPublicDate":"2019-05-06T10:26:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5084,"text":"Bioacoustics: The International Journal of Animal Sound and its Recording","active":true,"publicationSubtype":{"id":10}},"title":"Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring","docAbstract":"Audio sampling of the environment can provide long-term, landscape-scale presence-absence data to model populations of sound-producing wildlife. Automated detection systems allow researchers to avoid manually searching through large volumes of recordings, but often produce unacceptable false positive rates. We developed methods that allow researchers to improve template-based automated detection using a suite of statistical learning algorithms when false positive rates are problematic. To test our method, we acquired 668 hours of recordings in the Sonoran Desert, California USA between March 2016 and May 2017, and created spectrogram cross-correlation templates for three target avian species. We trained and tested five classification algorithms and four performance-weighted ensemble classifier methods on target signals and false alarms from March 2016, and then selected high-performing ensemble classifiers from the train/test phase to predict the class of new detections thereafter. For three target species, our ensemble classifiers were able to identify 98%, 81%, and 100% of false alarms compared with the baseline template detection system, and comparative positive predictive values improved from 6% to 69%, 87% to 95%, and 2% to 77%. We show that statistical learning approaches can be implemented to mitigate false detections acquired via template-based automated detection in automated acoustic wildlife monitoring.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/09524622.2019.1605309","usgsCitation":"Balantic, C.M., and Donovan, T.M., 2020, Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring: Bioacoustics: The International Journal of Animal Sound and its Recording, v. 29, no. 3, p. 296-321, https://doi.org/10.1080/09524622.2019.1605309.","productDescription":"27 p.","startPage":"296","endPage":"321","ipdsId":"IP-093445","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":458772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/09524622.2019.1605309","text":"Publisher Index Page"},{"id":395210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sonoran Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.88928222656249,\n 32.62087018318113\n ],\n [\n -114.62585449218749,\n 32.699488680852674\n ],\n [\n -114.46105957031249,\n 32.8334428466495\n ],\n [\n -114.46105957031249,\n 32.93953889877841\n ],\n [\n -114.49951171875,\n 33.054716488042736\n ],\n [\n -114.6478271484375,\n 33.054716488042736\n ],\n [\n -114.6807861328125,\n 33.09614359735857\n ],\n [\n -114.65881347656249,\n 33.25706340236547\n ],\n [\n -114.69177246093749,\n 33.30298618122413\n ],\n [\n -114.6697998046875,\n 33.394759218577995\n ],\n [\n -114.510498046875,\n 33.55512901742288\n ],\n [\n -114.49951171875,\n 33.696922692957685\n ],\n [\n -114.510498046875,\n 33.93424531117312\n ],\n [\n -114.4390869140625,\n 34.00258128543371\n ],\n [\n -114.4061279296875,\n 34.093610452768715\n ],\n [\n -114.23583984374999,\n 34.18454183141725\n ],\n [\n -114.1314697265625,\n 34.30714385628804\n ],\n [\n -114.3511962890625,\n 34.474863669009004\n ],\n [\n -114.46105957031249,\n 34.710009159224946\n ],\n [\n -114.6478271484375,\n 34.92647493584645\n ],\n [\n -114.6368408203125,\n 35.02099970111467\n ],\n [\n -115.84533691406249,\n 35.17380831799959\n ],\n [\n -115.84533691406249,\n 34.56085936708384\n ],\n [\n -115.9771728515625,\n 34.08451193447477\n ],\n [\n -115.960693359375,\n 33.52307880890422\n ],\n [\n -115.8233642578125,\n 33.39934533042092\n ],\n [\n -115.6640625,\n 33.32593850874471\n ],\n [\n -115.59265136718749,\n 33.25706340236547\n ],\n [\n -115.67504882812501,\n 33.073130945006625\n ],\n [\n -116.1199951171875,\n 33.169743600216165\n ],\n [\n -116.103515625,\n 32.79651010951669\n ],\n [\n -115.88928222656249,\n 32.62087018318113\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Balantic, Cathleen M.","contributorId":273038,"corporation":false,"usgs":false,"family":"Balantic","given":"Cathleen","email":"","middleInitial":"M.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":832481,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832480,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270778,"text":"70270778 - 2020 - Using environmental DNA (eDNA) to assess the presence of cavefish and cave crayfish populations in caves of the Ozark Highlands","interactions":[],"lastModifiedDate":"2025-08-27T14:58:10.15187","indexId":"70270778","displayToPublicDate":"2019-03-03T09:54:08","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-135-2020","title":"Using environmental DNA (eDNA) to assess the presence of cavefish and cave crayfish populations in caves of the Ozark Highlands","docAbstract":"Many cavefishes and cave crayfishes are considered of conservation concern; however, sampling these species is inherently difficult given their occupied environments. The goal of our project was to verify the presence of select karst organisms while developing the foundation for sampling approaches that might be useful to conservation and management agencies. Our project objectives were to develop assays to amplify deoxyribonucleic acid (DNA) from several species of Ozark cavefishes and cave crayfishes and complete an initial surveillance of locations across the Ozark Highlands using environmental DNA (eDNA). Using DNA either provided by agency cooperators or that we extracted from tissue samples, we PCR amplified and then sequenced the Cytochrome Oxidase 1 (CO1) gene for cave crayfishes and the NADH Dehydrogenase Subunit 2 (ND2) gene for cavefishes. We developed species-specific primers and probes for five cave crayfishes and two cavefishes. From February 2017 to May 2017, we sampled 1–5 sampling units from 42 caves, wells, and springs (i.e., sites) using eDNA and traditional visual surveys. We measured physicochemical parameters at each sampling unit to estimate detection probability associated with both techniques. We also calculated two occupancy covariates for each site using geospatial data. We successfully amplified Troglichthys rosae DNA from the environment and detected DNA representing this species at 24 of 40 sites. At 16 of the sites where we detected T. rosae DNA, we did not visually observe the species. Although our assay for Typlichthys eigenmanni successfully amplified the target DNA from the environment, it also resulted in false absences where the species was visually confirmed. Using eDNA to detect cave crayfishes was much more difficult. The assay for Cambarus subterraneus did not work for eDNA samples and we were unable to pick up DNA from the environment, even at locations where it was visually confirmed. Alternatively, the eDNA surveys worked well for C. tartarus and we were able to amplify DNA at every site where it was visually observed. Our assay for C. aculabrum was based on a single sample obtained from GenBank, and did not amplify eDNA from field samples. Lastly, our eDNA results from samples in the known range of Orconectes stygocaneyi suggested the species may be found at an additional cave. Detection using eDNA based on our O. stygocaneyi assay was likely low because it was designed from a pseudogene; however, positive eDNA samples were sequenced to confirm species-specific DNA. Detection probability of both cavefishes and cave crayfishes varied by survey technique and was influenced by water volume, water clarity, water velocity, and substrate. Detection of cavefishes and cave crayfishes via visual surveys decreased when water volume increased, whereas detection using eDNA increased with greater water volume. Detection between taxa using either sample method was highest in habitats classified by fine substrates, except for eDNA detection of crayfishes which was greatest in coarse substrates. Detection of cavefishes increased with water clarity, but detection of cave crayfishes increased with turbidity. Detection probability of both cavefishes and crayfishes using eDNA increased slightly with water velocity, but decreased with visual surveys as water velocity increased. Occupancy by both taxa was positively related to particular geologic series. Crayfish occupancy was negatively related to fine-scale anthropogenic disturbance (i.e., 500-m buffer around the site), whereas crayfish showed no relationship with disturbance. Our results suggest possible range extensions, provide insights to factors driving detection using both sample techniques, and suggest areas where recharge zones may be shared among caves. Future efforts focused on a comprehensive evaluation of genetic diversity among cave crayfishes to improve assay design could improve detection and the applicability of eDNA as a supplemental and non-invasive sampling approach.
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There is a need for a thorough assessment of the potential tag effects on small birds, as previous meta‐analyses did not evaluate unpublished data and impact of multiple life‐history traits, focused mainly on large species and the number of published studies tagging small birds has increased substantially.
We quantitatively reviewed 549 records extracted from 74 published and 48 unpublished studies on over 7,800 tagged and 17,800 control individuals to examine the effects of geolocator tagging on small bird species (body mass <100 g). We calculated the effect of tagging on apparent survival, condition, phenology and breeding performance and identified the most important predictors of the magnitude of effect sizes.
Even though the effects were not statistically significant in phylogenetically controlled models, we found a weak negative impact of geolocators on apparent survival. The negative effect on apparent survival was stronger with increasing relative load of the device and with geolocators attached using elastic harnesses. Moreover, tagging effects were stronger in smaller species.
In conclusion, we found a weak effect on apparent survival of tagged birds and managed to pinpoint key aspects and drivers of tagging effects. We provide recommendations for establishing matched control group for proper effect size assessment in future studies and outline various aspects of tagging that need further investigation. Finally, our results encourage further use of geolocators on small bird species but the ethical aspects and scientific benefits should always be considered.
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To help address this challenge, we developed and tested five multi-species environmental DNA (eDNA) primer sets for the simultaneous detection of up to 37 target fish species in a single sample. Using these primer sets can reduce the cost and time needed to perform future studies of fish communities. Our results comparing multiple samples from multiple lakes and streams using multiple next-generation sequencing runs show the efficacy and reproducibility of these primers.
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HRSC has returned images, including stereo and color coverage of most of Mars at decameter scales. The instrument team has developed an extremely powerful processing pipeline and delivered a large number of high-level data products, but our independent software is nevertheless of interest because it provides a check on the standard products, sheds light on the capabilities of software elements we use for multiple missions besides HRSC, and is publicly available, giving users the opportunity to make products that may not (yet) be released by the team and custom products such as local mosaics. We have tested our software on images of three areas: Candor Chasma and Nanedi Valles (both the subject of past DTM comparisons reported by Heipke et al., 2007) and Gale crater, which was extensively mapped at pixel scales 50 times finer than HRSC before its selection as the landing site of the Curiosity rover. We find the vertical precision and mean deviation from the altimetry data used as a control reference for our DTMs to be comparable to the nadir image pixel size. The horizontal resolution of the DTMs appears to be an order of magnitude coarser than the lower limit of 3–5 image pixels that is commonly stated.
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With respect to the calibration of these sensors, they are broadly classified into two types, namely government systems and commercial systems. Because of the differences in the design and mission of these sensor types, calibration approaches are often substantially different. Thus, an opportunity exists to foster discussion between calibration teams for these sensors with the goal of improving overall sensor calibration and data interoperability. The approach used to accomplish this task was a one-day workshop where team members from both government and commercial sensors could share best practices, discuss methods for collaboration and improvement, and make recommendations for continuing activities. Five major recommendations were developed from the event that focused on coordinated activities using pseudo invariant calibration sites (PICS), broader and more consistent communication, collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products. Workshop participants concluded that regular interactions between these teams could foster a better calibration of all sensor systems and accelerate the improved interoperability of surface products.
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Previous research shows Modern Portfolio Theory (MPT) can help optimize environmental investment targeting to reduce outcome risk with minimal loss of expected level of environmental benefits, but no work has yet identified the types of cases for which MPT is most useful. This paper assembles data on 26 different conservation cases in three distinct ecological settings and develops new metrics to evaluate how well MPT can reduce uncertainty in future outcomes of a set of environmental investments. We find MPT is broadly but not universally useful and works best when multiple investments have negatively correlated outcomes across climate scenarios, a second-best investment has expected value almost as good as the value in the best investment; or multiple investments have little uncertainty in ecological outcomes.","language":"English","publisher":"Wiley","doi":"10.1111/conl.12596","usgsCitation":"Ando, A.W., Fraterrigo, J.M., Guntenspergen, G.R., Howlader, A., Mallory, M.L., Olker, J.H., and Stickley, S., 2020, When portfolio theory can help environmental investment planning to reduce climate risk to future environmental outcomes - and when it cannot: Conservation Letters, v. 11, no. 6, e12596, 10 p., https://doi.org/10.1111/conl.12596.","productDescription":"e12596, 10 p.","ipdsId":"IP-098951","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458799,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/conl.12596","text":"Publisher Index Page"},{"id":372628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ando, Amy W.","contributorId":189611,"corporation":false,"usgs":false,"family":"Ando","given":"Amy","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":783117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fraterrigo, Jennifer M.","contributorId":150046,"corporation":false,"usgs":false,"family":"Fraterrigo","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":783118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":783119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howlader, Aparna","contributorId":222772,"corporation":false,"usgs":false,"family":"Howlader","given":"Aparna","email":"","affiliations":[],"preferred":false,"id":783120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mallory, Mindy L.","contributorId":189610,"corporation":false,"usgs":false,"family":"Mallory","given":"Mindy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":783121,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olker, Jennifer H.","contributorId":208040,"corporation":false,"usgs":false,"family":"Olker","given":"Jennifer","email":"","middleInitial":"H.","affiliations":[{"id":6915,"text":"University of Minnesota - Duluth","active":true,"usgs":false}],"preferred":false,"id":783122,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stickley, Samuel","contributorId":222773,"corporation":false,"usgs":false,"family":"Stickley","given":"Samuel","email":"","affiliations":[],"preferred":false,"id":783123,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202572,"text":"ofr20191023A - 2019 - Focus areas for data acquisition for potential domestic sources of critical minerals—Rare earth elements","interactions":[],"lastModifiedDate":"2026-03-25T16:52:23.790127","indexId":"ofr20191023A","displayToPublicDate":"2022-07-14T10:30:00","publicationYear":"2019","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":"2019-1023","chapter":"A","displayTitle":"Focus Areas for Data Acquisition for Potential Domestic Sources of Critical Minerals—Rare Earth Elements","title":"Focus areas for data acquisition for potential domestic sources of critical minerals—Rare earth elements","docAbstract":"Rare earth elements (REEs) are critical mineral commodities for the United States. 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States\"}}]}","edition":"Version 1.0: March 2019; Version 1.1: July 2022","contact":"Mineral Resources Program
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: Minerals@usgs.gov
We provide a detailed breeding biology account for the Grey-breasted Wood-Wren (Henicorhina leucophrys) and a comparison of the reproductive life history of tropical and temperate wrens using literature data. We conducted this study at Yacambú National Park in Venezuela from 2002 to 2008. Clutch size was 1.99 (SE 0.01) and fresh egg mass was 2.35 g (0.02). Females incubated the eggs for 19.74 d (0.37), and nestlings left nests at 17.37 d (0.18). Nest attentiveness (percent time spent on the nest) increased across the incubation period while brooding attentiveness decreased as nestlings aged. Brooding effort began with similar attentiveness as at the end of incubation. Food provisioning rate and feeding rate per nestling increased as nestlings aged. Growth rates (K) based on mass, tarsus, and wing chord were relatively slow at 0.375, 0.246, and 0.257, respectively. The nesting season extended from mid-March to late June for 7 years and the average nesting season length was 64.5 d (3.68) with a median of May 4. Nest success was 22%. Nest predation was the cause of 77% of nest failures with a total daily predation rate of 0.030 (0.002). Results obtained from the literature demonstrated that tropical wrens averaged smaller clutch sizes and longer incubation periods than relatives in the temperate region.
","language":"English","publisher":"Allen Press","doi":"10.1676/18-12","usgsCitation":"Arslan, N.S., and Martin, T.E., 2019, Reproductive biology of Grey-breasted Wood-Wren (Henicorhina leucophrys): A comparative study of tropical and temperate wrens: Wilson Journal of Ornithology, v. 131, no. 1, p. 1-11, https://doi.org/10.1676/18-12.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-091643","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":501024,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11491/1636","text":"External Repository"},{"id":395046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Venezuela","otherGeospatial":"Yacambú National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.88677978515625,\n 9.446352499964977\n ],\n [\n -69.46929931640624,\n 9.446352499964977\n ],\n [\n -69.46929931640624,\n 9.82138870534266\n ],\n [\n -69.88677978515625,\n 9.82138870534266\n ],\n [\n -69.88677978515625,\n 9.446352499964977\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arslan, Necmiye Sahin","contributorId":272527,"corporation":false,"usgs":false,"family":"Arslan","given":"Necmiye","email":"","middleInitial":"Sahin","affiliations":[{"id":50219,"text":"um","active":true,"usgs":false}],"preferred":false,"id":832048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832049,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240329,"text":"70240329 - 2019 - Bighorn sheep habitat and model extrapolation across remote landscapes","interactions":[],"lastModifiedDate":"2023-02-06T15:05:41.116143","indexId":"70240329","displayToPublicDate":"2021-12-31T09:05:05","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Bighorn sheep habitat and model extrapolation across remote landscapes","docAbstract":"Determining a species’ habitat use is an essential first step in any wildlife conservation action. We described habitat use, animal movements and probable lambing areas in a remote, restricted-access region of the Mojave Desert. Differences in habitat use between sexes was apparent, supporting the often-reported concept of risk-aversion by females. Animals exhibited low variability in distances travelled, although males travelled further and with more variability than females. All females demonstrated what we interpret as lambing behavior during the same 2 ½ month periods over the two years, strongly supporting our inference of lambing sites. Water appeared critical to animal long-range movements, with no animal moving beyond 8.5 km from known sources. Modeling habitat use across the landscape of concern is another necessary step for conservation of species, allowing managers to plan for and predict the outcomes of management actions. In ecology, models are often created within relatively small areas, then extrapolated across larger regions of concern. The ability to extrapolate ecological models may be especially useful across remote areas, where consistent access by wildlife managers may be highly restricted. These restrictions on access require that most data be collected remotely, necessitating the need for extrapolating models developed in other areas. We used data from GPS-collared desert bighorn sheep to describe and model habitat use across the Pintwater Range, located on the Nevada Test and Training Range of southern Nevada, a highly restricted military training ground. We tested the efficacy of habitat model extrapolation by comparing the performance of two models derived from adjacent but independent desert bighorn sheep populations. The predictive power of seasonal habitat models derived from adjacent mountain ranges was lower than those derived from the local population. However, the performance of the extrapolated model suggests it could still be a feasible alternative for estimating general habitat use.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Desert Bighorn Council Transactions 2019: A compilation of papers presented at the 55th meeting","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Desert Bighorn Council Transactions 2019","conferenceDate":"April 17-19, 2019","conferenceLocation":"Mesquite, NV","language":"English","publisher":"Desert Bighorn Council","usgsCitation":"Lowrey, C., Schuster, S., Longshore, K., Cummings, P., Sprunger, A., Johnson, A., and Wilson-Henjum, G.E., 2019, Bighorn sheep habitat and model extrapolation across remote landscapes, in Desert Bighorn Council Transactions 2019: A compilation of papers presented at the 55th meeting, Mesquite, NV, April 17-19, 2019, p. 1-20.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-116209","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412735,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertbighorncouncil.com/transactions/download-past-dbc-transactions/"}],"country":"United States","state":"Nevada","county":"Clark County, Lincoln County, Nye County","otherGeospatial":"Nevada Test and Training Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.96047652820107,\n 37.46829180279936\n ],\n [\n -115.96047652820107,\n 36.56268736637935\n ],\n [\n -115.28108193619912,\n 36.56268736637935\n ],\n [\n -115.28108193619912,\n 37.46829180279936\n ],\n [\n -115.96047652820107,\n 37.46829180279936\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lowrey, Chris 0000-0001-5084-7275","orcid":"https://orcid.org/0000-0001-5084-7275","contributorId":216375,"corporation":false,"usgs":true,"family":"Lowrey","given":"Chris","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schuster, Sara","contributorId":302080,"corporation":false,"usgs":false,"family":"Schuster","given":"Sara","email":"","affiliations":[{"id":65407,"text":"Center for Environmental Management of Military Lands","active":true,"usgs":false}],"preferred":false,"id":863427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Longshore, Kathleen 0000-0001-6621-1271","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":216374,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cummings, Patrick","contributorId":174650,"corporation":false,"usgs":false,"family":"Cummings","given":"Patrick","email":"","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":863429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sprunger, Amy","contributorId":302081,"corporation":false,"usgs":false,"family":"Sprunger","given":"Amy","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":863430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Anna","contributorId":287611,"corporation":false,"usgs":false,"family":"Johnson","given":"Anna","email":"","affiliations":[{"id":52650,"text":"Pennsylvania Natural Heritage Program","active":true,"usgs":false}],"preferred":false,"id":863431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilson-Henjum, Grete Elyse 0000-0002-2284-8745","orcid":"https://orcid.org/0000-0002-2284-8745","contributorId":302082,"corporation":false,"usgs":true,"family":"Wilson-Henjum","given":"Grete","email":"","middleInitial":"Elyse","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863432,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70226901,"text":"70226901 - 2019 - Integration of microfacies analysis, inorganic geochemical data, and hyperspectral imaging to unravel mudstone depositional and diagenetic processes in two cores from the Triassic Shublik Formation, Northern Alaska","interactions":[],"lastModifiedDate":"2022-01-20T17:39:02.379393","indexId":"70226901","displayToPublicDate":"2021-10-16T11:32:17","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Integration of microfacies analysis, inorganic geochemical data, and hyperspectral imaging to unravel mudstone depositional and diagenetic processes in two cores from the Triassic Shublik Formation, Northern Alaska","docAbstract":"The Middle – Upper Triassic Shublik Formation is an organic-rich heterogeneous carbonate-siliciclastic-phosphatic unit that generated much of the oil in the Prudhoe Bay field and other hydrocarbon accumulations in northern Alaska. A large dataset, including total organic carbon (TOC), X-ray diffraction (XRD), X-ray fluorescence (XRF) and inductively coupled plasma – mass spectrometry (ICP-MS) measurements, has been built from core and outcrop samples of the Shublik, with a focus on the organic-rich intervals. In addition, two core intervals from the Shublik were analyzed using a hyperspectral imaging system in the visible, near-infrared and shortwave-infrared range. Integration of the hyperspectral results with core descriptions, microfacies interpretations, and analytical data is being used to decipher mudstone depositional and diagenetic processes.
Petrographic analysis of Upper Triassic organic-rich intervals within the Shublik suggests that the main microfacies is a laminated bioclastic wackestone/packstone that was episodically disrupted by energetic events of variable intensity. These energetic events produced transitional and sparry calcite bioclastic packstone to grainstone intervals, depending on the depth of sediment column disturbance. By using hyperspectral imaging data from the Ikpikpuk core, individual distribution maps for minerals of interest have been generated and corroborate the microfacies interpretations. These maps also illustrate small-scale vertical changes in mineralogy. The laminated bioclastic wackestone/packstone intervals contain less calcite than the adjacent sparry bioclastic packstone to grainstone intervals. The calcite in these laminated intervals is more iron rich. This interpretation suggests that lower iron concentrations should be expected in the disrupted intervals than in nearby laminated intervals. Textural features are also enhanced in the hyperspectral images relative to visual description of the cores by combining the extraction of the average reflectance in the visible part of the electromagnetic spectrum and the depth of the main carbonate-related feature belonging to calcite. Examples noted in the enhanced imagery include low-angle features, calcite grain-size, and the size, shape and orientation of phosphatic nodules. This enhancement is being used to differentiate laminated from sparry bioclastic packstone to grainstone-rich intervals and provides a more comprehensive assessment of the microfacies than is practical by thin-section analysis.
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Black Scoters have a broad wintering distribution and are the only open water species of sea duck that is abundant along the southeastern coast of the United States. Our study identified variables that affected Black Scoter (Melanitta americana) distribution and abundance in the Atlantic Ocean along the southeastern coast of the United States. We used aerial survey data from 2009 to 2012 provided by the United States Fish and Wildlife Service to identify variables that influenced Black Scoter distribution. We used indicator variable selection to evaluate relationships between Black Scoter habitat use and a variety of broad- and fine-scale oceanographic and weather variables. Average time between waves, ocean floor slope, and the interaction of bathymetry and distance to shore had the strongest association with southeastern Black Scoter distribution.
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Wildland Fire Science Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Wildland fire characteristics, such as area burned, number of large fires, burn intensity, and fire season duration, have increased steadily over the past 30 years, resulting in substantial increases in the costs of suppressing fires and managing damages from wildland fire events (National Academies of Sciences, Engineering, and Medicine, 2017). Wildland fire management could benefit from sound decision making based on reliable scientific information. Fire scientists produce data, tools, and information to support fire and land management decision making. With ever-changing land use scenarios, environmental conditions, and emerging technological capabilities, new assessments and studies are continually needed. Established by Congress in 1879, the U.S. Geological Survey (USGS) is the primary science branch of the Department of the Interior (DOI), which manages more than 400 million acres of public lands in the United States. The USGS has more than 100 scientists across seven Mission Areas that help address the wildland fire science needs of DOI bureaus and their stakeholders. The diverse expertise of these scientists allows them to address complex interdisciplinary challenges. In this report, we identify and characterize scientific literature produced by USGS scientists during 2006–17 that addresses topics associated with wildland fire science. Our goals were to (1) make the most complete list possible of product citations readily available in an organized format, and (2) use bibliometric analysis approaches to highlight the productivity of USGS scientists and the impact of contributions that the Bureau has provided to the scientific, land management, and fire management communities.
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Ecosystems Mission Area
U.S. Geological Survey
Mail Stop 300
12201 Sunrise Valley Drive
Reston, VA 20192
Director, Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
MS 415
12201 Sunrise Valley Drive
Reston, VA 20192
Regression equations, which may be used to estimate flood flows at select annual exceedance probabilities, were developed for ungaged streams in Pennsylvania. The equations were developed using annual peak flow data through water year 2015 and basin characteristics for 285 streamflow gaging stations across Pennsylvania and surrounding states. The streamgages included active and discontinued continuous-record stations, as well as crest-stage partial-record stations, and required a minimum of 10 years of annual peak streamflow data for inclusion in the study. Explanatory variables significant at the 95-percent confidence level for one or more regression equations included the following basin characteristics: drainage area, maximum basin elevation, mean basin slope, percent storage, and the percentage of carbonate bedrock within a basin. The State was divided into five regions, and regional regression equations were developed to estimate flood flows associated with the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (which correspond to the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively). Although the regression equations can be used to estimate the magnitude of flood flows for most streams in the State, they are not valid for streams with drainage areas generally greater than 1,500 square miles or with substantial regulation, diversion, or mining activity within the basin. The regional regression equations will be incorporated into the U.S. Geological Survey StreamStats application (https://water.usgs.gov/osw/streamstats/).
Additionally, annual peak flow data for 356 streamgages initially considered for inclusion in the analysis for development of updated flood-flow regression equations were analyzed for the existence of trends; estimates of flood-flow magnitude and frequency were also computed for these streamgages. Estimates of flood-flow magnitude and frequency for streamgages substantially affected by upstream regulation are also presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195094","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the Pennsylvania Department of Transportation","usgsCitation":"Roland, M.A., and Stuckey, M.H., 2020, Development of regression equations for the estimation of flood flows at ungaged streams in Pennsylvania (ver. 1.1, December 2020): U.S. Geological Survey Scientific Investigations Report 2019–5094, 36 p., https://doi.org/10.3133/sir20195094. [Supersedes USGS Scientific Investigations Report 2008–5102]","productDescription":"Report: vi, 36 p.; Appendices 1-3; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104380","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":437232,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YHIU6G","text":"USGS data release","linkHelpText":"Data in support of Development of Regression Equations for the Estimation of Flood Flows at Ungaged Streams in Pennsylvania"},{"id":381091,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5094/versionHist.txt","size":"1.09 KB","linkFileType":{"id":2,"text":"txt"}},{"id":368468,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5094/sir20195094.pdf","text":"Report","size":"15.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5094"},{"id":368467,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5094/sir20195094_appendix3.xlsx","text":"Appendix 3","size":"40.1 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019-5094","linkHelpText":"- Magnitude, variance, and confidence intervals of annual exceedance probability floods for select streamgages in Pennsylvania substantially affected by upstream regulation"},{"id":368466,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5094/sir20195094_appendix2.xlsx","text":"Appendix 2","size":"389 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019-5094","linkHelpText":"- Magnitude, variance, and confidence intervals of annual exceedance probability floods for select unregulated streamgages in Pennsylvania and surrounding states"},{"id":368462,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5c1aa7a4e4b0708288c5b35c","text":"USGS data 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\"}}]}","edition":"Version 1.0: October 2019; Version 1.1: December 2020","publicComments":"Scientific Investigations Report 2019-5094 supersedes Scientific Investigations Report 2008–5102.","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Volcanic plumes are complex environments composed of gases and ash particles, where chemical and physical processes occur at different temperature and compositional regimes. Commonly, soluble sulphate- and chloride-bearing salts are formed on ash as gases interact with ash surfaces. Exposure to respirable volcanic ash following an eruption is potentially a significant health concern. The impact of such gas-ash interactions on ash toxicity is wholly un-investigated. Here, we study, for the first time, whether the interaction of volcanic particles with sulphur dioxide (SO2) gas, and the resulting presence of sulphate salt deposits on particle surfaces, influences toxicity to the respiratory system, using an advanced in vitro approach.
To emplace surface sulphate salts on particles, via replication of the physicochemical reactions that occur between pristine ash surfaces and volcanic gas, analogue substrates (powdered synthetic volcanic glass and natural pumice) were exposed to SO2 at 500 °C, in a novel Advanced Gas-Ash Reactor, resulting in salt-laden particles. The solubility of surface salt deposits was then assessed by leaching in water and geochemical modelling. A human multicellular lung model was exposed to aerosolised salt-laden and pristine (salt-free) particles, and incubated for 24 h. Cell cultures were subsequently assessed for biological endpoints, including cytotoxicity (lactate dehydrogenase release), oxidative stress (oxidative stress-related gene expression; heme oxygenase 1 and NAD(P)H dehydrogenase [quinone] 1) and its (pro-)inflammatory response (tumour necrosis factor α, interleukin 8 and interleukin 1β at gene and protein levels).
In the lung cell model no significant effects were observed between the pristine and SO2-exposed particles, indicating that the surface salt deposits, and the underlying alterations to the substrate, do not cause acute adverse effects in vitro. Based on the leachate data, the majority of the sulphate salts from the ash surfaces are likely to dissolve in the lungs prior to cellular uptake.
The findings of this study indicate that interaction of volcanic ash with SO2 during ash generation and transport does not significantly affect the respiratory toxicity of volcanic ash in vitro. Therefore, sulphate salts are unlikely a dominant factor controlling variability in in vitro toxicity assessments observed during previous eruption response efforts.
Sustainability has been at the forefront of the environmental research agenda of the integrated anthroposphere, hydrosphere, and biosphere since the last century and will continue to be critically important for future environmental science. However, linking humans and the environment through effective policy remains a major challenge for sustainability research and practice. Here we address this gap using an agent-based model (ABM) for a coupled natural and human systems in the Smoky Hill River Watershed (SHRW), Kansas, USA. For this freshwater-dependent agricultural watershed with a highly variable flow regime influenced by human-induced land-use and climate change, we tested the support for an environmental policy designed to conserve and protect fish biodiversity in the SHRW. We develop a proof of concept interdisciplinary ABM that integrates field data on hydrology, ecology (fish richness), social-psychology (value-belief-norm) and economics, to simulate human agents' decisions to support environmental policy. The mechanism to link human behaviors to environmental changes is the social-psychological sequence identified by the value-belief-norm framework and is informed by hydrological and fish ecology models. Our results indicate that (1) cultural factors influence the decision to support the policy; (2) a mechanism modifying social-psychological factors can influence the decision-making process; (3) there is resistance to environmental policy in the SHRW, even under potentially extreme climate conditions; and (4) the best opportunities for policy acceptance were found immediately after extreme environmental events. The modeling approach presented herein explicitly links biophysical and social science has broad generality for sustainability problems.