Detecting temporal changes in fish abundance is an essential component of fisheries management. Because of the need to understand short‐term and nonlinear changes in fish abundance, traditional linear models may not provide adequate information for management decisions. This study highlights the utility of Bayesian dynamic linear models (DLMs) as a tool for quantifying temporal dynamics in fish abundance. To achieve this goal, we quantified temporal trends of Smallmouth Bass Micropterus dolomieu catch per effort (CPE) from rivers in the mid‐Atlantic states, and we calculated annual probabilities of decline from the posterior distributions of annual rates of change in CPE. We were interested in annual declines because of recent concerns about fish health in portions of the study area. In general, periods of decline were greatest within the Susquehanna River basin, Pennsylvania. The declines in CPE began in the late 1990s—prior to observations of fish health problems—and began to stabilize toward the end of the time series (2011). In contrast, many of the other rivers investigated did not have the same magnitude or duration of decline in CPE. Bayesian DLMs provide information about annual changes in abundance that can inform management and are easily communicated with managers and stakeholders.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10051","usgsCitation":"Schall, M.K., Blazer, V., Lorantas, R.M., Smith, G., Mullican, J.E., Keplinger, B.J., and Wagner, T., 2018, Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations: North American Journal of Fisheries Management, v. 38, no. 2, p. 493-501, https://doi.org/10.1002/nafm.10051.","productDescription":"9 p.","startPage":"493","endPage":"501","ipdsId":"IP-085001","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":353909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, West Virginia","otherGeospatial":"Allegheny River, Delaware River, Juniata River, Potomac River. Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.09033203125,\n 37.90953361677018\n ],\n [\n -74.696044921875,\n 37.90953361677018\n ],\n [\n -74.696044921875,\n 42.15525946577863\n ],\n [\n -80.09033203125,\n 42.15525946577863\n ],\n [\n -80.09033203125,\n 37.90953361677018\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5afee6c5e4b0da30c1bfbe04","contributors":{"authors":[{"text":"Schall, Megan K.","contributorId":115964,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":17758,"text":"Pennsylvania State Univ.","active":true,"usgs":false}],"preferred":false,"id":734549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":734532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorantas, Robert M.","contributorId":204631,"corporation":false,"usgs":false,"family":"Lorantas","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":734550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Geoffrey","contributorId":115958,"corporation":false,"usgs":true,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":734551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mullican, John E.","contributorId":203245,"corporation":false,"usgs":false,"family":"Mullican","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":734552,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keplinger, Brandon J.","contributorId":204644,"corporation":false,"usgs":false,"family":"Keplinger","given":"Brandon","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":734553,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":734531,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197428,"text":"70197428 - 2018 - Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information","interactions":[],"lastModifiedDate":"2018-06-04T10:36:56","indexId":"70197428","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information","docAbstract":"Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass (Cenchrus ciliaris), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2279","usgsCitation":"Jarnevich, C.S., Young, N.E., Talbert, M., and Talbert, C., 2018, Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information: Ecosphere, v. 9, no. 5, p. 1-12, https://doi.org/10.1002/ecs2.2279.","productDescription":"e02279; 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-097154","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468799,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2279","text":"Publisher Index Page"},{"id":437929,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y99UFF","text":"USGS data release","linkHelpText":"Data for forecasting buffelgrass distribution with global distribution data, local data, and physiological information"},{"id":354686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Saguaro National Park","volume":"9","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-29","publicationStatus":"PW","scienceBaseUri":"5b155d84e4b092d9651e1b61","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":737118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":737119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbert, Marian 0000-0003-0588-0265 mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":196740,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":737120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":737121,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196821,"text":"70196821 - 2018 - Reduced arctic tundra productivity linked with landform and climate change interactions","interactions":[],"lastModifiedDate":"2018-05-03T13:48:20","indexId":"70196821","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Reduced arctic tundra productivity linked with landform and climate change interactions","docAbstract":"Arctic tundra ecosystems have experienced unprecedented change associated with climate warming over recent decades. Across the Pan-Arctic, vegetation productivity and surface greenness have trended positively over the period of satellite observation. However, since 2011 these trends have slowed considerably, showing signs of browning in many regions. It is unclear what factors are driving this change and which regions/landforms will be most sensitive to future browning. Here we provide evidence linking decadal patterns in arctic greening and browning with regional climate change and local permafrost-driven landscape heterogeneity. We analyzed the spatial variability of decadal-scale trends in surface greenness across the Arctic Coastal Plain of northern Alaska (~60,000 km²) using the Landsat archive (1999–2014), in combination with novel 30 m classifications of polygonal tundra and regional watersheds, finding landscape heterogeneity and regional climate change to be the most important factors controlling historical greenness trends. Browning was linked to increased temperature and precipitation, with the exception of young landforms (developed following lake drainage), which will likely continue to green. Spatiotemporal model forecasting suggests carbon uptake potential to be reduced in response to warmer and/or wetter climatic conditions, potentially increasing the net loss of carbon to the atmosphere, at a greater degree than previously expected.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-018-20692-8","usgsCitation":"Lara, M.J., Nitze, I., Grosse, G., Martin, P., and McGuire, A.D., 2018, Reduced arctic tundra productivity linked with landform and climate change interactions: Scientific Reports, v. 8, Article 2345; 10 p., https://doi.org/10.1038/s41598-018-20692-8.","productDescription":"Article 2345; 10 p.","ipdsId":"IP-085871","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468793,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-20692-8","text":"Publisher Index Page"},{"id":353942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-05","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbe02","contributors":{"authors":[{"text":"Lara, Mark J.","contributorId":194640,"corporation":false,"usgs":false,"family":"Lara","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":734605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nitze, Ingmar","contributorId":191057,"corporation":false,"usgs":false,"family":"Nitze","given":"Ingmar","affiliations":[],"preferred":false,"id":734606,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":734607,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Philip","contributorId":204661,"corporation":false,"usgs":false,"family":"Martin","given":"Philip","affiliations":[{"id":27594,"text":"Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":734608,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":734604,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196771,"text":"70196771 - 2018 - Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches","interactions":[],"lastModifiedDate":"2018-05-01T11:40:01","indexId":"70196771","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches","docAbstract":"Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision making. Therefore, we also discuss alternative approaches to yield unbiased estimates of population state variables using similar data types, and we stress that there is no substitute for an effective sample design that is grounded upon well-defined management objectives.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.02.007","usgsCitation":"Duarte, A., Adams, M.J., and Peterson, J., 2018, Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches: Ecological Modelling, v. 374, p. 51-59, https://doi.org/10.1016/j.ecolmodel.2018.02.007.","productDescription":"9 p.","startPage":"51","endPage":"59","ipdsId":"IP-090875","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468791,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2018.02.007","text":"Publisher Index Page"},{"id":353873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6cce4b0da30c1bfbe16","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":79822,"corporation":false,"usgs":true,"family":"Duarte","given":"Adam","email":"","affiliations":[],"preferred":false,"id":734395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, M. J. 0000-0001-8844-042X mjadams@usgs.gov","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":3133,"corporation":false,"usgs":false,"family":"Adams","given":"M.","email":"mjadams@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734311,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196892,"text":"70196892 - 2018 - Landscape assessment of side channel plugs and associated cumulative side channel attrition across a large river floodplain","interactions":[],"lastModifiedDate":"2018-05-17T15:41:05","indexId":"70196892","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Landscape assessment of side channel plugs and associated cumulative side channel attrition across a large river floodplain","docAbstract":"Determining the influences of anthropogenic perturbations on side channel dynamics in large rivers is important from both assessment and monitoring perspectives because side channels provide critical habitat to numerous aquatic species. Side channel extents are decreasing in large rivers worldwide. Although riprap and other linear structures have been shown to reduce side channel extents in large rivers, we hypothesized that small “anthropogenic plugs” (flow obstructions such as dikes or berms) across side channels modify whole-river geomorphology via accelerating side channel senescence. To test this hypothesis, we conducted a geospatial assessment, comparing digitized side channel areas from aerial photographs taken during the 1950s and 2001 along 512 km of the Yellowstone River floodplain. We identified longitudinal patterns of side channel recruitment (created/enlarged side channels) and side channel attrition (destroyed/senesced side channels) across n = 17 river sections within which channels were actively migrating. We related areal measures of recruitment and attrition to the density of anthropogenic side channel plugs across river sections. Consistent with our hypothesis, a positive spatial relationship existed between the density of anthropogenic plugs and side channel attrition, but no relationship existed between plug density and side channel recruitment. Our work highlights important linkages among side channel plugs and the persistence and restoration of side channels across floodplain landscapes. Specifically, management of small plugs represents a low-cost, high-benefit restoration opportunity to facilitate scouring flows in side channels to enable the persistence of these habitats over time.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-018-6673-8","usgsCitation":"Reinhold, A.M., Poole, G., Bramblett, R.G., Zale, A.V., and Roberts, D.W., 2018, Landscape assessment of side channel plugs and associated cumulative side channel attrition across a large river floodplain: Environmental Monitoring and Assessment, v. 190, p. 1-15, https://doi.org/10.1007/s10661-018-6673-8.","productDescription":"Article 305; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-064957","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":354286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.7646484375,\n 45.65244828675087\n ],\n [\n -104.04602050781249,\n 45.65244828675087\n ],\n [\n -104.04602050781249,\n 47.82790816919329\n ],\n [\n -108.7646484375,\n 47.82790816919329\n ],\n [\n -108.7646484375,\n 45.65244828675087\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"190","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-24","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbdfe","contributors":{"authors":[{"text":"Reinhold, Ann Marie","contributorId":200043,"corporation":false,"usgs":false,"family":"Reinhold","given":"Ann","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":734921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poole, Geoffrey C.","contributorId":25540,"corporation":false,"usgs":true,"family":"Poole","given":"Geoffrey C.","affiliations":[],"preferred":false,"id":734922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bramblett, Robert G.","contributorId":169857,"corporation":false,"usgs":false,"family":"Bramblett","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":5098,"text":"Department of Ecology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":734923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zale, Alexander V. 0000-0003-1703-885X zale@usgs.gov","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":3010,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"zale@usgs.gov","middleInitial":"V.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734920,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, David W.","contributorId":56235,"corporation":false,"usgs":true,"family":"Roberts","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":734924,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196898,"text":"70196898 - 2018 - Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA","interactions":[],"lastModifiedDate":"2018-05-17T15:35:17","indexId":"70196898","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA","docAbstract":"Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2017.1382065","usgsCitation":"Boyte, S.P., Wylie, B.K., Rigge, M.B., and Dahal, D., 2018, Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA: GIScience and Remote Sensing, v. 55, no. 3, p. 376-399, https://doi.org/10.1080/15481603.2017.1382065.","productDescription":"24 p.","startPage":"376","endPage":"399","ipdsId":"IP-087872","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499993,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/d0da5ee1cd9c49fab95dfe363f4d48a7","text":"External Repository"},{"id":437930,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R20ZVX","text":"USGS data release","linkHelpText":"Downscaled 30 m weekly MODIS NDVI for the Central Great Basin"},{"id":354284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin rangelands","volume":"55","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-28","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbdfc","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":734937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":734938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":734939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":734940,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197033,"text":"70197033 - 2018 - Density of American black bears in New Mexico","interactions":[],"lastModifiedDate":"2018-05-15T16:00:38","indexId":"70197033","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Density of American black bears in New Mexico","docAbstract":"Considering advances in noninvasive genetic sampling and spatially explicit capture–recapture (SECR) models, the New Mexico Department of Game and Fish sought to update their density estimates for American black bear (Ursus americanus) populations in New Mexico, USA, to aide in setting sustainable harvest limits. We estimated black bear density in the Sangre de Cristo, Sandia, and Sacramento Mountains, New Mexico, 2012–2014. We collected hair samples from black bears using hair traps and bear rubs and used a sex marker and a suite of microsatellite loci to individually genotype hair samples. We then estimated density in a SECR framework using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We sampled the populations using 554 hair traps and 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 male, 358 female) individuals. Our density estimates varied from 16.5 bears/100 km2 (95% CI = 11.6–23.5) in the southern Sacramento Mountains to 25.7 bears/100 km2 (95% CI = 13.2–50.1) in the Sandia Mountains. Overall, detection probability at the activity center (g0) was low across all study areas and ranged from 0.00001 to 0.02. The low values of g0 were primarily a result of half of all hair samples for which genotypes were attempted failing to produce a complete genotype. We speculate that the low success we had genotyping hair samples was due to exceedingly high levels of ultraviolet (UV) radiation that degraded the DNA in the hair. Despite sampling difficulties, we were able to produce density estimates with levels of precision comparable to those estimated for black bears elsewhere in the United States.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21432","usgsCitation":"Gould, M.J., Cain, J.W., Roemer, G.W., Gould, W., and Liley, S., 2018, Density of American black bears in New Mexico: Journal of Wildlife Management, v. 82, no. 4, p. 775-788, https://doi.org/10.1002/jwmg.21432.","productDescription":"14 p.","startPage":"775","endPage":"788","ipdsId":"IP-092872","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":354191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-105.998003,32.002328],[-106.099756,32.002492],[-106.125534,32.002533],[-106.18184,32.00205],[-106.200699,32.001785],[-106.205915,32.001762],[-106.313307,32.001512],[-106.376861,32.001172],[-106.377165,32.001177],[-106.394298,32.001484],[-106.411075,32.001334],[-106.565142,32.000736],[-106.566056,32.000759],[-106.587972,32.000749],[-106.595333,32.000778],[-106.598639,32.000754],[-106.599096,32.000731],[-106.618486,32.000495],[-106.619448,31.994733],[-106.623568,31.990999],[-106.631182,31.989809],[-106.636492,31.985719],[-106.639529,31.980348],[-106.638186,31.97682],[-106.630114,31.971258],[-106.626466,31.97069],[-106.623216,31.97291],[-106.621873,31.972933],[-106.619569,31.971578],[-106.618745,31.966955],[-106.619371,31.964777],[-106.620454,31.963403],[-106.624299,31.961054],[-106.625535,31.957476],[-106.625123,31.954531],[-106.622819,31.952891],[-106.617708,31.956008],[-106.614702,31.956],[-106.616136,31.948439],[-106.623659,31.94551],[-106.622377,31.940863],[-106.622117,31.936621],[-106.622529,31.934863],[-106.625322,31.930053],[-106.629747,31.92657],[-106.628663,31.923614],[-106.623933,31.925335],[-106.611846,31.920003],[-106.614346,31.918003],[-106.623445,31.914034],[-106.625947,31.912227],[-106.633668,31.90979],[-106.64084,31.904598],[-106.645479,31.89867],[-106.645646,31.895649],[-106.645296,31.894859],[-106.6429,31.892933],[-106.638154,31.891663],[-106.633927,31.889184],[-106.630692,31.886411],[-106.629197,31.883717],[-106.630799,31.879697],[-106.634873,31.874478],[-106.63588,31.871514],[-106.635926,31.866235],[-106.627808,31.860593],[-106.625763,31.856276],[-106.621857,31.852854],[-106.614637,31.84649],[-106.605845,31.846305],[-106.605245,31.845905],[-106.602045,31.844405],[-106.601945,31.839605],[-106.605267,31.827912],[-106.602727,31.825024],[-106.593826,31.824901],[-106.589045,31.822706],[-106.588045,31.822106],[-106.582144,31.815506],[-106.581344,31.813906],[-106.577244,31.810406],[-106.570944,31.810206],[-106.566844,31.813306],[-106.563444,31.812606],[-106.562945,31.811104],[-106.558444,31.810406],[-106.547144,31.807305],[-106.545344,31.805007],[-106.544714,31.804287],[-106.542144,31.802107],[-106.542097,31.802146],[-106.535843,31.798607],[-106.535343,31.797507],[-106.535154,31.797089],[-106.534743,31.796107],[-106.533043,31.791907],[-106.533,31.791829],[-106.53248,31.791914],[-106.530515,31.792103],[-106.527943,31.790507],[-106.527738,31.789761],[-106.527623,31.789119],[-106.527997,31.786945],[-106.528543,31.784407],[-106.528543,31.783907],[-106.750547,31.783706],[-106.750547,31.783898],[-106.993544,31.783689],[-106.998235,31.783671],[-107.00056,31.783679],[-107.00056,31.783513],[-107.296824,31.783762],[-107.422246,31.783599],[-107.422495,31.783599],[-108.208394,31.783599],[-108.208087,31.613489],[-108.208521,31.499798],[-108.208572,31.499742],[-108.208573,31.333395],[-108.707657,31.333191],[-108.788711,31.332365],[-108.851105,31.332301],[-108.861028,31.332315],[-109.050044,31.332502],[-109.050173,31.480004],[-109.049843,31.499515],[-109.049813,31.499528],[-109.049112,31.636598],[-109.049195,31.796551],[-109.048763,31.810776],[-109.049106,31.843715],[-109.048769,31.861383],[-109.04859,31.870791],[-109.048599,32.013651],[-109.048731,32.028174],[-109.048296,32.084093],[-109.048286,32.089114],[-109.047612,32.426377],[-109.047653,32.681379],[-109.047653,32.686327],[-109.047645,32.689988],[-109.047638,32.693439],[-109.047117,32.777569],[-109.047117,32.77757],[-109.04748,33.06842],[-109.047453,33.069427],[-109.046905,33.091931],[-109.047013,33.092917],[-109.047117,33.137559],[-109.047116,33.137995],[-109.047237,33.208965],[-109.04747,33.250063],[-109.046827,33.365272],[-109.046909,33.36557],[-109.047045,33.36928],[-109.04687,33.372654],[-109.046564,33.37506],[-109.047298,33.409783],[-109.046662,33.625055],[-109.047145,33.74001],[-109.046941,33.778233],[-109.046426,33.875052],[-109.047006,34.00005],[-109.046182,34.522393],[-109.046182,34.522553],[-109.046156,34.579291],[-109.046086,34.771016],[-109.045363,34.785406],[-109.046104,34.799981],[-109.045624,34.814226],[-109.046072,34.828566],[-109.045851,34.959718],[-109.046024,35.175499],[-109.046084,35.250025],[-109.046796,35.363606],[-109.046481,35.546326],[-109.046509,35.54644],[-109.046296,35.614251],[-109.046295,35.616517],[-109.046024,35.8798],[-109.046055,35.888721],[-109.046054,35.92586],[-109.046011,35.925896],[-109.045973,36.002338],[-109.045729,36.117028],[-109.046183,36.181751],[-109.045431,36.500001],[-109.045433,36.874589],[-109.045407,36.874998],[-109.045272,36.968871],[-109.045244,36.969489],[-109.045223,36.999084],[-108.958868,36.998913],[-108.954404,36.998906],[-108.620309,36.999287],[-108.619689,36.999249],[-108.379203,36.999459],[-108.320721,36.99951],[-108.320464,36.999499],[-108.2884,36.99952],[-108.288086,36.999555],[-108.250635,36.999561],[-108.249358,36.999015],[-108.000623,37.0000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Mexico\",\"nation\":\"USA \"}}]}","volume":"82","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-20","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbdf4","contributors":{"authors":[{"text":"Gould, Matthew J.","contributorId":201504,"corporation":false,"usgs":false,"family":"Gould","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":735420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":735321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roemer, Gary W.","contributorId":95355,"corporation":false,"usgs":true,"family":"Roemer","given":"Gary","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":735421,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gould, William R.","contributorId":63780,"corporation":false,"usgs":true,"family":"Gould","given":"William R.","affiliations":[],"preferred":false,"id":735422,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liley, Stewart","contributorId":171908,"corporation":false,"usgs":false,"family":"Liley","given":"Stewart","affiliations":[],"preferred":false,"id":735423,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196206,"text":"sir20185044 - 2018 - Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick","interactions":[],"lastModifiedDate":"2018-05-01T16:07:09","indexId":"sir20185044","displayToPublicDate":"2018-04-30T11:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5044","title":"Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick","docAbstract":"The U.S. Geological Survey, in cooperation with the International Joint Commission, compiled historical data on regulated streamflows and lake levels and estimated unregulated streamflows and lake levels on Forest City Stream at Forest City, Maine, and East Grand Lake on the United States-Canada border between Maine and New Brunswick to study the effects on streamflows and lake levels if two or all three dam gates are left open. Historical regulated monthly mean streamflows in Forest City Stream at the outlet of East Grand Lake (referred to as Grand Lake by Environment Canada) fluctuated between 114 cubic feet per second (ft3 /s) (3.23 cubic meters per second [m3 /s]) in November and 318 ft3 /s (9.01 m3 /s) in September from 1975 to 2015 according to Environment Canada streamgaging data. Unregulated monthly mean streamflows at this location estimated from regression equations for unregulated sites range from 59.2 ft3 /s (1.68 m3 /s) in September to 653 ft3 /s (18.5 m3 /s) in April. Historical lake levels in East Grand Lake fluctuated between 431.3 feet (ft) (131.5 meters [m]) in October and 434.0 ft (132.3 m) in May from 1969 to 2016 according to Environment Canada lake level data for East Grand Lake. Average monthly lake levels modeled by using the estimated hydrology for unregulated flows, and an outflow rating built from a hydraulic model with all gates at the dam open, range from 427.7 ft (130.4 m) in September to 431.1 ft (131.4 m) in April. Average monthly lake levels would likely be from 1.8 to 5.4 ft (0.55 to 1.6 m) lower with the gates at the dam opened than they have been historically. The greatest lake level changes would be from June through September.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185044","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Lombard, P.J., 2018, Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick: U.S. Geological Survey Scientific Investigations Report 2018–5044, 8 p., https://doi.org/10.3133/sir20185044.","productDescription":"Report: iv, 8 p.; Data release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092951","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":353763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PN94VN","text":"USGS data release","description":"USGS data release","linkHelpText":"Bathymetric data for St. Croix River at outlet to East Grand Lake and Forest City Dam Survey, United States-Canadian border between Maine and New Brunswick"},{"id":353745,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5044/sir20185044.pdf","text":"Report","size":"873 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5044"},{"id":353744,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5044/coverthb.jpg"}],"country":"Canada, United States","state":"Maine, New Brunswick","otherGeospatial":"East Grand Lake, Forest City Stream","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.884521484375,\n 45.60587170876381\n ],\n [\n -67.68951416015625,\n 45.60587170876381\n ],\n [\n -67.68951416015625,\n 45.82066487514085\n ],\n [\n -67.884521484375,\n 45.82066487514085\n ],\n [\n -67.884521484375,\n 45.60587170876381\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
196 Whitten Road
Augusta, ME 04330
How reproductive output changes with age or size is a key life-history trait that can affect which demographic rates most influence population growth. Although many studies have investigated the reproductive ecology of gartersnakes, we know little about reproduction in the threatened Giant Gartersnake, Thamnophis gigas. We used X-radiography to determine reproductive status and estimated fecundity for 73 female T. gigas collected from several regions within the range of this species in the Sacramento Valley of California, USA, and synthesize these data with data from litters born in captivity to improve our understanding of reproduction in this species. Average total litter size determined from X-rays (15.9) and captive-born litters (15.5) are within the ranges reported from other gartersnakes, but captive-born litters had high rates of stillbirth. Only 154 of 202 neonates from captive snakes were born alive, and seven of 13 litters contained at least one stillborn neonate. We found that fecundity was positively related to maternal snout-vent length, and some evidence that larger litters contained smaller neonates. The proportion of X-rayed females that were gravid was 0.50 in 2014, 0.47 in and 2015, and 0.64 in 2016. Central California experienced an exceptional drought from 2012–2015, which may have affected the reproductive output and frequency of T. gigas. Our estimates of reproductive frequency and size-dependent fecundity in T. gigas provide valuable information that can be used in demographic models of this threatened species. Our results demonstrate that X-radiography is a useful, minimally invasive means to study fecundity in wild populations of snakes.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Rose, J.P., Ersan, J., Wylie, G., Casazza, M.L., and Halstead, B., 2018, Reproductive frequency and size-dependence of fecundity in the Giant Gartersnake (Thamnophis gigas): Herpetological Conservation and Biology, v. 13, no. 1, p. 80-90.","productDescription":"11 p.","startPage":"80","endPage":"90","ipdsId":"IP-087900","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":367252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367242,"type":{"id":15,"text":"Index Page"},"url":"https://herpconbio.org/contents_vol13_issue1.html"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.354736328125,\n 38.37611542403604\n ],\n [\n -120.69580078125001,\n 38.37611542403604\n ],\n [\n -120.69580078125001,\n 39.85072092501597\n ],\n [\n -122.354736328125,\n 39.85072092501597\n ],\n [\n -122.354736328125,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":770354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ersan, Julia 0000-0002-1549-7561","orcid":"https://orcid.org/0000-0002-1549-7561","contributorId":218034,"corporation":false,"usgs":true,"family":"Ersan","given":"Julia","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":770355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Glenn D. 0000-0002-7061-6658","orcid":"https://orcid.org/0000-0002-7061-6658","contributorId":207594,"corporation":false,"usgs":false,"family":"Wylie","given":"Glenn D.","affiliations":[],"preferred":false,"id":770357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":770356,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":770353,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196760,"text":"70196760 - 2018 - Associations between urban sprawl and life expectancy in the United States","interactions":[],"lastModifiedDate":"2018-04-30T13:10:32","indexId":"70196760","displayToPublicDate":"2018-04-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2041,"text":"International Journal of Environmental Research and Public Health","active":true,"publicationSubtype":{"id":10}},"title":"Associations between urban sprawl and life expectancy in the United States","docAbstract":"In recent years, the United States has had a relatively poor performance with respect to life expectancy compared to the other developed nations. Urban sprawl is one of the potential causes of the high rate of mortality in the United States. This study investigated cross-sectional associations between sprawl and life expectancy for metropolitan counties in the United States in 2010. In this study, the measure of life expectancy in 2010 came from a recently released dataset of life expectancies by county. This study modeled average life expectancy with a structural equation model that included five mediators: annual vehicle miles traveled (VMT) per household, average body mass index, crime rate, and air quality index as mediators of sprawl, as well as percentage of smokers as a mediator of socioeconomic status. After controlling for sociodemographic characteristics, this study found that life expectancy was significantly higher in compact counties than in sprawling counties. Compactness affects mortality directly, but the causal mechanism is unclear. For example, it may be that sprawling areas have higher traffic speeds and longer emergency response times, lower quality and less accessible health care facilities, or less availability of healthy foods. Compactness affects mortality indirectly through vehicle miles traveled, which is a contributor to traffic fatalities, and through body mass index, which is a contributor to many chronic diseases. This study identified significant direct and indirect associations between urban sprawl and life expectancy. These findings support further research and practice aimed at identifying and implementing changes to urban planning designed to support health and healthy behaviors.
","language":"English","publisher":"MDPI","doi":"10.3390/ijerph15050861","usgsCitation":"Hamidi, S., Ewing, R., Tatalovich, Z., Grace, J.B., and Berrigan, D., 2018, Associations between urban sprawl and life expectancy in the United States: International Journal of Environmental Research and Public Health, v. 15, no. 5, p. 1-11, https://doi.org/10.3390/ijerph15050861.","productDescription":"Article 861; 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-056461","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468802,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijerph15050861","text":"Publisher Index Page"},{"id":353857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-26","publicationStatus":"PW","scienceBaseUri":"5afee6cde4b0da30c1bfbe24","contributors":{"authors":[{"text":"Hamidi, Shima","contributorId":204538,"corporation":false,"usgs":false,"family":"Hamidi","given":"Shima","email":"","affiliations":[{"id":12734,"text":"University of Texas at Arlington","active":true,"usgs":false}],"preferred":false,"id":734278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ewing, Reid","contributorId":204537,"corporation":false,"usgs":false,"family":"Ewing","given":"Reid","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":734277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tatalovich, Zaria","contributorId":204539,"corporation":false,"usgs":false,"family":"Tatalovich","given":"Zaria","email":"","affiliations":[{"id":36952,"text":"National Cancer Institute, NIH","active":true,"usgs":false}],"preferred":false,"id":734279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":734276,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berrigan, David","contributorId":204540,"corporation":false,"usgs":false,"family":"Berrigan","given":"David","email":"","affiliations":[{"id":36952,"text":"National Cancer Institute, NIH","active":true,"usgs":false}],"preferred":false,"id":734280,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195291,"text":"ofr20181010 - 2018 - Laboratory observations of artificial sand and oil agglomerates","interactions":[],"lastModifiedDate":"2018-04-30T10:54:20","indexId":"ofr20181010","displayToPublicDate":"2018-04-27T15:45:00","publicationYear":"2018","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":"2018-1010","title":"Laboratory observations of artificial sand and oil agglomerates","docAbstract":"Sand and oil agglomerates (SOAs) form when weathered oil reaches the surf zone and combines with suspended sediments. The presence of large SOAs in the form of thick mats (up to 10 centimeters [cm] in height and up to 10 square meters [m2] in area) and smaller SOAs, sometimes referred to as surface residual balls (SRBs), may lead to the re-oiling of beaches previously affected by an oil spill. A limited number of numerical modeling and field studies exist on the transport and dynamics of centimeter-scale SOAs and their interaction with the sea floor. Numerical models used to study SOAs have relied on shear-stress formulations to predict incipient motion. However, uncertainty exists as to the accuracy of applying these formulations, originally developed for sand grains in a uniformly sorted sediment bed, to larger, nonspherical SOAs. In the current effort, artificial sand and oil agglomerates (aSOAs) created with the size, density, and shape characteristics of SOAs were studied in a small-oscillatory flow tunnel. These experiments expanded the available data on SOA motion and interaction with the sea floor and were used to examine the applicability of shear-stress formulations to predict SOA mobility. Data collected during these two sets of experiments, including photographs, video, and flow velocity, are presented in this report, along with an analysis of shear-stress-based formulations for incipient motion. The results showed that shear-stress thresholds for typical quartz sand predicted the incipient motion of aSOAs with 0.5–1.0-cm diameters, but were inaccurate for aSOAs with larger diameters (>2.5 cm). This finding implies that modified parameterizations of incipient motion may be necessary under certain combinations of aSOA characteristics and environmental conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181010","usgsCitation":"Jenkins, R.L., Dalyander, P.S., Penko, Allison, and Long, J.W., 2018, Laboratory observations of artificial sand and oil agglomerates: U.S. Geological Survey Open-File Report 2018–1010, https://doi.org/10.3133/ofr20181010.","productDescription":"HTML","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079703","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":353721,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1010","text":"Report HTML"},{"id":353720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1010/coverthb2.jpg"}],"contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
A whole lake eutrophication (WLE) model approach for phosphorus and cyanobacterial biomass in Upper Klamath Lake, south-central Oregon, is presented here. The model is a successor to a previous model developed to inform a Total Maximum Daily Load (TMDL) for phosphorus in the lake, but is based on net primary production (NPP), which can be calculated from dissolved oxygen, rather than scaling up a small-scale description of cyanobacterial growth and respiration rates. This phase 3 WLE model is a refinement of the proof-of-concept developed in phase 2, which was the first attempt to use NPP to simulate cyanobacteria in the TMDL model. The calibration of the calculated NPP WLE model was successful, with performance metrics indicating a good fit to calibration data, and the calculated NPP WLE model was able to simulate mid-season bloom decreases, a feature that previous models could not reproduce.
In order to use the model to simulate future scenarios based on phosphorus load reduction, a multivariate regression model was created to simulate NPP as a function of the model state variables (phosphorus and chlorophyll a) and measured meteorological and temperature model inputs. The NPP time series was split into a low- and high-frequency component using wavelet analysis, and regression models were fit to the components separately, with moderate success.
The regression models for NPP were incorporated in the WLE model, referred to as the “scenario” WLE (SWLE), and the fit statistics for phosphorus during the calibration period were mostly unchanged. The fit statistics for chlorophyll a, however, were degraded. These statistics are still an improvement over prior models, and indicate that the SWLE is appropriate for long-term predictions even though it misses some of the seasonal variations in chlorophyll a.
The complete whole lake SWLE model, with multivariate regression to predict NPP, was used to make long-term simulations of the response to 10-, 20-, and 40-percent reductions in tributary nutrient loads. The long-term mean water column concentration of total phosphorus was reduced by 9, 18, and 36 percent, respectively, in response to these load reductions. The long-term water column chlorophyll a concentration was reduced by 4, 13, and 44 percent, respectively. The adjustment to a new equilibrium between the water column and sediments occurred over about 30 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185042","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Wherry, S.A., and Wood, T.M., 2018, A metabolism-based whole lake eutrophication model to estimate the magnitude and time scales of the effects of restoration in Upper Klamath Lake, south-central Oregon: U.S. Geological Survey Scientific Investigations Report 2018–5042, 43 p., https:/doi.org/10.3133/sir20185042.","productDescription":"vii, 43 p.","onlineOnly":"Y","ipdsId":"IP-081297","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":353789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5042/coverthb.jpg"},{"id":353790,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5042/sir20185042.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5042"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10273742675781,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.22750046697999\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
The objective of this chapter is to create a methodology to model the emergent behavior during a disruption in the transportation system and that calculates economic losses due to such a disruption, and to understand how an extreme event affects the road transportation network. The chapter discusses a system dynamics approach which is used to model the transportation road infrastructure system to evaluate the different factors that render road segments inoperable and calculate economic consequences of such inoperability. System dynamics models have been integrated with business process simulation model to evaluate, design, and optimize the business process. The chapter also explains how different factors affect the road capacity. After identifying the various factors affecting the available road capacity, a causal loop diagram (CLD) is created to visually represent the causes leading to a change in the available road capacity and the effects on travel costs when the available road capacity changes.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Emergent behavior in complex systems engineering: A modeling and simulation approach","language":"English","publisher":"Wiley","doi":"10.1002/9781119378952.ch15","usgsCitation":"Ojha, A., Corns, S., Shoberg, T.G., Qin, R., and Long, S.K., 2018, Modeling and simulation of emergent behavior in transportation infrastructure restoration, chap. of Emergent behavior in complex systems engineering: A modeling and simulation approach, p. 249-368, https://doi.org/10.1002/9781119378952.ch15.","productDescription":"120 p.","startPage":"249","endPage":"368","ipdsId":"IP-087912","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":353780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-16","publicationStatus":"PW","scienceBaseUri":"5afee6cde4b0da30c1bfbe2a","contributors":{"authors":[{"text":"Ojha, Akhilesh","contributorId":204482,"corporation":false,"usgs":false,"family":"Ojha","given":"Akhilesh","email":"","affiliations":[{"id":36947,"text":"Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO, 65401","active":true,"usgs":false}],"preferred":false,"id":734135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corns, Steven","contributorId":146271,"corporation":false,"usgs":false,"family":"Corns","given":"Steven","affiliations":[{"id":16655,"text":"Dept. of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":734136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoberg, Thomas G. 0000-0003-0173-1246 tshoberg@usgs.gov","orcid":"https://orcid.org/0000-0003-0173-1246","contributorId":3764,"corporation":false,"usgs":true,"family":"Shoberg","given":"Thomas","email":"tshoberg@usgs.gov","middleInitial":"G.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qin, Ruwen","contributorId":204483,"corporation":false,"usgs":false,"family":"Qin","given":"Ruwen","email":"","affiliations":[{"id":36947,"text":"Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO, 65401","active":true,"usgs":false}],"preferred":false,"id":734137,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Long, Suzanna K.","contributorId":146270,"corporation":false,"usgs":false,"family":"Long","given":"Suzanna","email":"","middleInitial":"K.","affiliations":[{"id":16655,"text":"Dept. of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":734138,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203196,"text":"70203196 - 2018 - Using regional scale flow–ecology modeling to identify catchments where fish assemblages are most vulnerable to changes in water availability","interactions":[],"lastModifiedDate":"2019-04-26T16:44:27","indexId":"70203196","displayToPublicDate":"2018-04-26T16:33:13","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Using regional scale flow–ecology modeling to identify catchments where fish assemblages are most vulnerable to changes in water availability","docAbstract":"Wave‐driven water level variability (and runup at the shoreline) is a significant cause of coastal flooding induced by storms. Wave runup is challenging to predict, particularly along tropical coral reef‐fringed coastlines due to the steep bathymetric profiles and large bottom roughness generated by reef organisms, which can violate assumptions in conventional models applied to open sandy coastlines. To investigate the mechanisms of wave‐driven water level variability on a reef‐fringed coastline, we performed a set of laboratory flume experiments on an along‐shore uniform bathymetric profile with and without bottom roughness. Wave setup and waves at frequencies lower than the incident sea‐swell forcing (infragravity waves) were found to be the dominant components of runup. These infragravity waves were positively correlated with offshore wave groups, signifying they were generated in the surf zone by the oscillation of the breakpoint. On the reef flat and at the shoreline, the low‐frequency waves formed a standing wave pattern with energy concentrated at the natural frequencies of the reef flat, indicating resonant amplification. Roughness elements used in the flume to mimic large reef bottom roughness reduced low frequency motions on the reef flat and reduced wave run up by 30% on average, compared to the runs over a smooth bed. These results provide insight into sea‐swell and infragravity wave transformation and wave setup dynamics on steep‐sloped coastlines, and the effect that future losses of reef bottom roughness may have on coastal flooding along reef‐fringed coasts.
","language":"English","publisher":"Wiley","doi":"10.1029/2018JC013933","usgsCitation":"Buckley, M.L., Lowe, R.J., Hansen, J.E., van Dongeren, A.R., and Storlazzi, C.D., 2018, Mechanisms of wave‐driven water level variability on reef‐fringed coastlines: Journal of Geophysical Research C: Oceans, v. 123, no. 5, p. 3811-3831, https://doi.org/10.1029/2018JC013933.","productDescription":"21 p.","startPage":"3811","endPage":"3831","ipdsId":"IP-092740","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468807,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://admin.research-repository.uwa.edu.au/en/publications/db5339a2-0de9-4c15-9798-f5eaddf31687","text":"Publisher Index Page"},{"id":437931,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71V5D7J","text":"USGS data release","linkHelpText":"Water level and velocity measurements from the 2012 University of Western Australia Fringing Reef Experiment (UWAFRE)"},{"id":353758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-26","publicationStatus":"PW","scienceBaseUri":"5afee6cee4b0da30c1bfbe3e","contributors":{"authors":[{"text":"Buckley, Mark L. 0000-0002-1909-4831","orcid":"https://orcid.org/0000-0002-1909-4831","contributorId":203481,"corporation":false,"usgs":true,"family":"Buckley","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":734068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Ryan J.","contributorId":152265,"corporation":false,"usgs":false,"family":"Lowe","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":734069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Jeff E.","contributorId":204340,"corporation":false,"usgs":false,"family":"Hansen","given":"Jeff","email":"","middleInitial":"E.","affiliations":[{"id":24588,"text":"The University of Western Australia","active":true,"usgs":false}],"preferred":true,"id":734070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Dongeren, Ap R.","contributorId":203482,"corporation":false,"usgs":false,"family":"van Dongeren","given":"Ap","email":"","middleInitial":"R.","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":734071,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":734072,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196702,"text":"70196702 - 2018 - Reproductive success and contaminant associations in tree swallows (Tachycineta bicolor) used to assess a Beneficial Use Impairment in U.S. and Binational Great Lakes’ Areas of Concern","interactions":[],"lastModifiedDate":"2018-04-26T11:21:22","indexId":"70196702","displayToPublicDate":"2018-04-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reproductive success and contaminant associations in tree swallows (Tachycineta bicolor) used to assess a Beneficial Use Impairment in U.S. and Binational Great Lakes’ Areas of Concern","title":"Reproductive success and contaminant associations in tree swallows (Tachycineta bicolor) used to assess a Beneficial Use Impairment in U.S. and Binational Great Lakes’ Areas of Concern","docAbstract":"During 2010-2014, tree swallow (Tachycineta bicolor) reproductive success was monitored at 68 sites across all 5 Great Lakes, including 58 sites located within Great Lakes Areas of Concern (AOCs) and 10 non-AOCs. Sample eggs were collected from tree swallow clutches and analyzed for contaminants including polychlorinated biphenyls (PCBs), dioxins and furans, polybrominated diphenyl ethers, and 34 other organic compounds. Contaminant data were available for 360 of the clutches monitored. Markov chain multistate modeling was used to assess the importance of 5 ecological variables and 11 of the dominant contaminants in explaining the pattern of egg and nestling failure rates. Four of 5 ecological variables (Female Age, Date within season, Year, and Site) were important explanatory variables. Of the 11 contaminants, only total dioxin and furan toxic equivalents (TEQs) explained a significant amount of the egg failure probabilities. Neither total PCBs nor PCB TEQs explained the variation in egg failure rates. In a separate analysis, polycyclic aromatic hydrocarbon exposure in nestling diet, used as a proxy for female diet during egg laying, was significantly correlated with the daily probability of egg failure. The 8 sites within AOCs which had poorer reproduction when compared to 10 non-AOC sites, the measure of impaired reproduction as defined by the Great Lakes Restoration Initiative, were associated with exposure to dioxins and furan TEQs, PAHs, or depredation. Only 2 sites had poorer reproduction than the poorest performing non-AOC. Using a classic (non-modeling) approach to estimating reproductive success, 82% of nests hatched at least 1 egg, and 75% of eggs laid, excluding those collected for contaminant analyses, hatched.","language":"English","publisher":"Springer","doi":"10.1007/s10646-018-1913-9","usgsCitation":"Custer, C.M., Custer, T.W., Etterson, M.A., Dummer, P.M., Goldberg, D., and Franson, J.C., 2018, Reproductive success and contaminant associations in tree swallows (Tachycineta bicolor) used to assess a Beneficial Use Impairment in U.S. and Binational Great Lakes’ Areas of Concern: Ecotoxicology, v. 27, no. 4, p. 457-476, https://doi.org/10.1007/s10646-018-1913-9.","productDescription":"20 p.","startPage":"457","endPage":"476","ipdsId":"IP-086919","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":353728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes","volume":"27","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-09","publicationStatus":"PW","scienceBaseUri":"5afee6cfe4b0da30c1bfbe46","contributors":{"authors":[{"text":"Custer, Christine M. 0000-0003-0500-1582 ccuster@usgs.gov","orcid":"https://orcid.org/0000-0003-0500-1582","contributorId":1143,"corporation":false,"usgs":true,"family":"Custer","given":"Christine","email":"ccuster@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Custer, Thomas W. 0000-0003-3170-6519 tcuster@usgs.gov","orcid":"https://orcid.org/0000-0003-3170-6519","contributorId":2835,"corporation":false,"usgs":true,"family":"Custer","given":"Thomas","email":"tcuster@usgs.gov","middleInitial":"W.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Etterson, Matthew A.","contributorId":108012,"corporation":false,"usgs":false,"family":"Etterson","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":734045,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dummer, Paul M. 0000-0002-2055-9480 pdummer@usgs.gov","orcid":"https://orcid.org/0000-0002-2055-9480","contributorId":3015,"corporation":false,"usgs":true,"family":"Dummer","given":"Paul","email":"pdummer@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734046,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldberg, Diana R. 0000-0001-8540-8512","orcid":"https://orcid.org/0000-0001-8540-8512","contributorId":82252,"corporation":false,"usgs":true,"family":"Goldberg","given":"Diana R.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":734047,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Franson, J. Christian 0000-0002-0251-4238 jfranson@usgs.gov","orcid":"https://orcid.org/0000-0002-0251-4238","contributorId":177499,"corporation":false,"usgs":true,"family":"Franson","given":"J.","email":"jfranson@usgs.gov","middleInitial":"Christian","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":734048,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196701,"text":"70196701 - 2018 - Slow and steady wins the race? Future climate and land use change leaves the imperiled Blanding's turtle (Emydoidea blandingii) behind","interactions":[],"lastModifiedDate":"2018-04-26T11:15:01","indexId":"70196701","displayToPublicDate":"2018-04-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Slow and steady wins the race? Future climate and land use change leaves the imperiled Blanding's turtle (Emydoidea blandingii) behind","title":"Slow and steady wins the race? Future climate and land use change leaves the imperiled Blanding's turtle (Emydoidea blandingii) behind","docAbstract":"Climate change is accompanied by shifts in species distributions, as portions of current ranges become less suitable. Maintaining or improving landscape connectivity to facilitate species movements is a primary approach to mitigate the effects of climate change on biodiversity. However, it is not clear how ongoing changes in land use and climate may affect the existing connectivity of landscapes. We evaluated shifts in habitat suitability and connectivity for the imperiled Blanding's turtle (Emydoidea blandingii) in Wisconsin using species distribution modeling in combination with different future scenarios of both land use change and climate change for the 2050s. We found that climate change had significant effects on both habitat suitability and connectivity, however, there was little difference in the magnitude of effects among different economic scenarios. Under both our low- and high-CO2 emissions scenarios, suitable habitat for the Blanding's turtle shifted northward. In the high-emissions scenario, almost no suitable habitat remained for Blanding's turtle in Wisconsin by the 2050s and there was up to a 100,000-fold increase in landscape resistance to turtle movement, suggesting the landscape essentially becomes impassable. Habitat loss and landscape resistance were exponentially greater in southern versus northern Wisconsin, indicating a strong trailing edge effect. Thus, populations at the southern edge of the range are likely to “fall behind” shifts in suitable habitat faster than northern populations. Given its limited dispersal capability, loss of suitable habitat may occur at a rate far faster than the Blanding's turtle can adjust to changing conditions via shifts in range.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2018.03.026","usgsCitation":"Hamilton, C.M., Bateman, B.L., Gorzo, J.M., Reid, B., Thogmartin, W.E., Peery, M.Z., Heglund, P.J., Radeloff, V.C., and Pidgeon, A.M., 2018, Slow and steady wins the race? 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Center","active":true,"usgs":true}],"preferred":true,"id":734034,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peery, M. Zachariah","contributorId":171534,"corporation":false,"usgs":false,"family":"Peery","given":"M.","email":"","middleInitial":"Zachariah","affiliations":[],"preferred":false,"id":734039,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heglund, Patricia J.","contributorId":149499,"corporation":false,"usgs":false,"family":"Heglund","given":"Patricia","email":"","middleInitial":"J.","affiliations":[{"id":17755,"text":"U.S. Fish and Wildlife Service, Upper Midwest Environmental Sciences Center","active":true,"usgs":false}],"preferred":false,"id":734040,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Radeloff, Volker C.","contributorId":141124,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker","email":"","middleInitial":"C.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":734041,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pidgeon, Anna M.","contributorId":141123,"corporation":false,"usgs":false,"family":"Pidgeon","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":734042,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70196712,"text":"70196712 - 2018 - Introduction to “Global tsunami science: Past and future, Volume III”","interactions":[],"lastModifiedDate":"2018-04-26T16:50:23","indexId":"70196712","displayToPublicDate":"2018-04-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Introduction to “Global tsunami science: Past and future, Volume III”","docAbstract":"Twenty papers on the study of tsunamis are included in Volume III of the PAGEOPH topical issue “Global Tsunami Science: Past and Future”. Volume I of this topical issue was published as PAGEOPH, vol. 173, No. 12, 2016 and Volume II as PAGEOPH, vol. 174, No. 8, 2017. Two papers in Volume III focus on specific details of the 2009 Samoa and the 1923 northern Kamchatka tsunamis; they are followed by three papers related to tsunami hazard assessment for three different regions of the world oceans: South Africa, Pacific coast of Mexico and the northwestern part of the Indian Ocean. The next six papers are on various aspects of tsunami hydrodynamics and numerical modelling, including tsunami edge waves, resonant behaviour of compressible water layer during tsunamigenic earthquakes, dispersive properties of seismic and volcanically generated tsunami waves, tsunami runup on a vertical wall and influence of earthquake rupture velocity on maximum tsunami runup. Four papers discuss problems of tsunami warning and real-time forecasting for Central America, the Mediterranean coast of France, the coast of Peru, and some general problems regarding the optimum use of the DART buoy network for effective real-time tsunami warning in the Pacific Ocean. Two papers describe historical and paleotsunami studies in the Russian Far East. The final set of three papers importantly investigates tsunamis generated by non-seismic sources: asteroid airburst and meteorological disturbances. Collectively, this volume highlights contemporary trends in global tsunami research, both fundamental and applied toward hazard assessment and mitigation.
","language":"English","publisher":"Springer","doi":"10.1007/s00024-018-1851-8","usgsCitation":"Rabinovich, A.B., Fritz, H.M., Tanioka, Y., and Geist, E.L., 2018, Introduction to “Global tsunami science: Past and future, Volume III”: Pure and Applied Geophysics, v. 175, no. 4, p. 1231-1237, https://doi.org/10.1007/s00024-018-1851-8.","productDescription":"7 p.","startPage":"1231","endPage":"1237","ipdsId":"IP-096461","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":353757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"175","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-19","publicationStatus":"PW","scienceBaseUri":"5afee6cee4b0da30c1bfbe3c","contributors":{"authors":[{"text":"Rabinovich, Alexander B.","contributorId":177506,"corporation":false,"usgs":false,"family":"Rabinovich","given":"Alexander","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":734130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fritz, Hermann M.","contributorId":194830,"corporation":false,"usgs":false,"family":"Fritz","given":"Hermann","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":734131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tanioka, Yuichiro","contributorId":177507,"corporation":false,"usgs":false,"family":"Tanioka","given":"Yuichiro","email":"","affiliations":[],"preferred":false,"id":734132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":734133,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196714,"text":"70196714 - 2018 - Temperature-influenced energetics model for migrating waterfowl","interactions":[],"lastModifiedDate":"2018-04-26T16:18:45","indexId":"70196714","displayToPublicDate":"2018-04-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Temperature-influenced energetics model for migrating waterfowl","docAbstract":"Climate and weather affect avian migration by influencing when and where birds fly, the energy costs and risks of flight, and the ability to sense cues necessary for proper navigation. We review the literature of the physiology of avian migration and the influence of climate, specifically temperature, on avian migration dynamics. We use waterfowl as a model guild because of the ready availability of empirical physiological data and their enormous economic value, but our discussion and expectations are broadly generalizable to migratory birds in general. We detail potential consequences of an increasingly warm climate on avian migration, including the possibility of the cessation of migration by some populations and species. Our intent is to lay the groundwork for including temperature effects on energetic gains and losses of migratory birds with the expected consequences of increasing temperatures into a predictive modeling framework. To this end, we provide a simulation of migration progression exclusively focused on the influence of temperature on the physiological determinants of migration. This simulation produced comparable results to empirically derived and observed values for different migratory factors (e.g., body fat content, flight range, departure date). By merging knowledge from the arenas of avian physiology and migratory theory we have identified a clear need for research and have developed hypotheses for a path forward.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.04.001","usgsCitation":"Aagaard, K., Thogmartin, W.E., and Lonsdorg, E.V., 2018, Temperature-influenced energetics model for migrating waterfowl: Ecological Modelling, v. 378, p. 46-58, https://doi.org/10.1016/j.ecolmodel.2018.04.001.","productDescription":"13 p.","startPage":"46","endPage":"58","ipdsId":"IP-084841","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468806,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2018.04.001","text":"Publisher Index Page"},{"id":353754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"378","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6cee4b0da30c1bfbe38","contributors":{"authors":[{"text":"Aagaard, Kevin 0000-0003-0756-2172 kaagaard@usgs.gov","orcid":"https://orcid.org/0000-0003-0756-2172","contributorId":147393,"corporation":false,"usgs":true,"family":"Aagaard","given":"Kevin","email":"kaagaard@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lonsdorg, Eric V.","contributorId":204474,"corporation":false,"usgs":false,"family":"Lonsdorg","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":734083,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196713,"text":"70196713 - 2018 - Effect of dynamical phase on the resonant interaction among tsunami edge wave modes","interactions":[],"lastModifiedDate":"2018-04-26T16:21:37","indexId":"70196713","displayToPublicDate":"2018-04-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Effect of dynamical phase on the resonant interaction among tsunami edge wave modes","docAbstract":"Different modes of tsunami edge waves can interact through nonlinear resonance. During this process, edge waves that have very small initial amplitude can grow to be as large or larger than the initially dominant edge wave modes. In this study, the effects of dynamical phase are established for a single triad of edge waves that participate in resonant interactions. In previous studies, Jacobi elliptic functions were used to describe the slow variation in amplitude associated with the interaction. This analytical approach assumes that one of the edge waves in the triad has zero initial amplitude and that the combined phase of the three waves φ = θ1 + θ2 − θ3 is constant at the value for maximum energy exchange (φ = 0). To obtain a more general solution, dynamical phase effects and non-zero initial amplitudes for all three waves are incorporated using numerical methods for the governing differential equations. Results were obtained using initial conditions calculated from a subduction zone, inter-plate thrust fault geometry and a stochastic earthquake slip model. The effect of dynamical phase is most apparent when the initial amplitudes and frequencies of the three waves are within an order of magnitude. In this case, non-zero initial phase results in a marked decrease in energy exchange and a slight decrease in the period of the interaction. When there are large differences in frequency and/or initial amplitude, dynamical phase has less of an effect and typically one wave of the triad has very little energy exchange with the other two waves. Results from this study help elucidate under what conditions edge waves might be implicated in late, large-amplitude arrivals.
","language":"English","publisher":"Springer","doi":"10.1007/s00024-018-1796-y","usgsCitation":"Geist, E.L., 2018, Effect of dynamical phase on the resonant interaction among tsunami edge wave modes: Pure and Applied Geophysics, v. 175, no. 4, p. 1341-1354, https://doi.org/10.1007/s00024-018-1796-y.","productDescription":"14 p.","startPage":"1341","endPage":"1354","ipdsId":"IP-093470","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":353755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"175","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-12","publicationStatus":"PW","scienceBaseUri":"5afee6cee4b0da30c1bfbe3a","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":734080,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70196715,"text":"70196715 - 2018 - Pedestrian evacuation modeling to reduce vehicle use for distant tsunami evacuations in Hawaiʻi","interactions":[],"lastModifiedDate":"2018-04-26T16:17:10","indexId":"70196715","displayToPublicDate":"2018-04-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"Pedestrian evacuation modeling to reduce vehicle use for distant tsunami evacuations in Hawaiʻi","docAbstract":"Tsunami waves that arrive hours after generation elsewhere pose logistical challenges to emergency managers due to the perceived abundance of time and inclination of evacuees to use vehicles. We use coastal communities on the island of Oʻahu (Hawaiʻi, USA) to demonstrate regional evacuation modeling that can identify where successful pedestrian-based evacuations are plausible and where vehicle use could be discouraged. The island of Oʻahu has two tsunami-evacuation zones (standard and extreme), which provides the opportunity to examine if recommended travel modes vary based on zone. Geospatial path distance models are applied to estimate population exposure as a function of pedestrian travel time and speed out of evacuation zones. The use of the extreme zone triples the number of residents, employees, and facilities serving at-risk populations that would be encouraged to evacuate and slightly reduces the percentage of residents (98–76%) that could evacuate in less than 15 min at a plausible speed (with similar percentages for employees). Areas with lengthy evacuations are concentrated in the North Shore region for the standard zone but found all around the Oʻahu coastline for the extreme zone. The use of the extreme zone results in a 26% increase in the number of hotel visitors that would be encouraged to evacuate, and a 76% increase in the number of them that may require more than 15 min. Modeling can identify where pedestrian evacuations are plausible; however, there are logistical and behavioral issues that warrant attention before localized evacuation procedures may be realistic.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2018.03.009","usgsCitation":"Wood, N.J., Jones, J., Peters, J., and Richards, K., 2018, Pedestrian evacuation modeling to reduce vehicle use for distant tsunami evacuations in Hawaiʻi: International Journal of Disaster Risk Reduction, v. 28, p. 271-283, https://doi.org/10.1016/j.ijdrr.2018.03.009.","productDescription":"13 p.","startPage":"271","endPage":"283","ipdsId":"IP-092793","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468805,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2018.03.009","text":"Publisher Index Page"},{"id":437932,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7862FNT","text":"USGS data release","linkHelpText":"Pedestrian tsunami evacuation results for two tsunami-evacuation zones (standard and extreme) and three travel speeds (impaired, slow, and fast walk) for O'ahu, HI"},{"id":353753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.31573486328125,\n 21.235622362422877\n ],\n [\n -157.6153564453125,\n 21.235622362422877\n ],\n [\n -157.6153564453125,\n 21.73717777095988\n ],\n [\n -158.31573486328125,\n 21.73717777095988\n ],\n [\n -158.31573486328125,\n 21.235622362422877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6cee4b0da30c1bfbe36","contributors":{"authors":[{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":734084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Jamie 0000-0002-9967-3314","orcid":"https://orcid.org/0000-0002-9967-3314","contributorId":204480,"corporation":false,"usgs":true,"family":"Jones","given":"Jamie","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":734085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peters, Jeff 0000-0003-4312-0590 jpeters@usgs.gov","orcid":"https://orcid.org/0000-0003-4312-0590","contributorId":4711,"corporation":false,"usgs":true,"family":"Peters","given":"Jeff","email":"jpeters@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":734086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richards, Kevin krichard@usgs.gov","contributorId":204475,"corporation":false,"usgs":false,"family":"Richards","given":"Kevin","email":"krichard@usgs.gov","affiliations":[{"id":36945,"text":"Hawaii Emergency Management Agency","active":true,"usgs":false}],"preferred":false,"id":734087,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227679,"text":"70227679 - 2018 - Adaptive management of animal populations with significant unknowns and uncertainties: A case study","interactions":[],"lastModifiedDate":"2022-01-26T16:37:24.221638","indexId":"70227679","displayToPublicDate":"2018-04-25T10:35:58","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Adaptive management of animal populations with significant unknowns and uncertainties: A case study","docAbstract":"Conservation and management decision making in natural resources is challenging due to numerous uncertainties and unknowns, especially relating to understanding system dynamics. Adaptive resource management (ARM) is a formal process to making logical and transparent recurrent decisions when there are uncertainties about system dynamics. Despite wide recognition and calls for implementing adaptive natural resource management, applications remain limited. More common is a reactive approach to decision making, which ignores future system dynamics. This contrasts with ARM, which anticipates future dynamics of ecological process and management actions using a model-based framework. Practitioners may be reluctant to adopt ARM because of the dearth of comparative evaluations between ARM and more common approaches to making decisions. We compared the probability of meeting management objectives when managing a population under both types of decision frameworks, specifically in relation to typical uncertainties and unknowns. We use a population of Sandhill Cranes as our case study. We evaluate each decision process under varying levels of monitoring and ecological uncertainty, where the true underlying population dynamics followed a stochastic age-structured population model with environmentally driven vital rate density dependence. We found that the ARM framework outperformed the currently employed reactive decision framework to manage Sandhill Cranes in meeting the population objective across an array of scenarios. This was even the case when the candidate set of population models contained only naïve representations of the true population process. Under the reactive decision framework, we found little improvement in meeting the population objective even if monitoring uncertainty was eliminated. In contrast, if the population was monitored without error within the ARM framework, the population objective was always maintained, regardless of the population models considered. Contrary to expectation, we found that age-specific optimal harvest decisions are not always necessary to meet a population objective when population dynamics are age structured. Population managers can decrease risks and gain transparency and flexibility in management by adopting an ARM framework. If population monitoring data has high sampling variation and/or limited empirical knowledge is available for constructing mechanistic population models, ARM model sets should consider a range of mechanistic, descriptive, and predictive model types.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1734","usgsCitation":"Gerber, B.D., and Kendall, W.L., 2018, Adaptive management of animal populations with significant unknowns and uncertainties: A case study: Ecological Applications, v. 28, no. 5, p. 1325-1341, https://doi.org/10.1002/eap.1734.","productDescription":"17 p.","startPage":"1325","endPage":"1341","ipdsId":"IP-073120","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489030,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/84","text":"External Repository"},{"id":394876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gerber, Brian D.","contributorId":187620,"corporation":false,"usgs":false,"family":"Gerber","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":831785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831704,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196696,"text":"70196696 - 2018 - A numerical model investigation of the impacts of Hurricane Sandy on water level variability in Great South Bay, New York","interactions":[],"lastModifiedDate":"2018-04-25T15:58:46","indexId":"70196696","displayToPublicDate":"2018-04-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"A numerical model investigation of the impacts of Hurricane Sandy on water level variability in Great South Bay, New York","docAbstract":"Hurricane Sandy was a large and intense storm with high winds that caused total water levels from combined tides and storm surge to reach 4.0 m in the Atlantic Ocean and 2.5 m in Great South Bay (GSB), a back-barrier bay between Fire Island and Long Island, New York. In this study the impact of the hurricane winds and waves are examined in order to understand the flow of ocean water into the back-barrier bay and water level variations within the bay. To accomplish this goal, a high resolution hurricane wind field is used to drive the coupled Delft3D-SWAN hydrodynamic and wave models over a series of grids with the finest resolution in GSB. The processes that control water levels in the back-barrier bay are investigated by comparing the results of four cases that include: (i) tides only; (ii) tides, winds and waves with no overwash over Fire Island allowed; (iii) tides, winds, waves and limited overwash at the east end of the island; (iv) tides, winds, waves and extensive overwash along the island. The results indicate that strong local wind-driven storm surge along the bay axis had the largest influence on the total water level fluctuations during the hurricane. However, the simulations allowing for overwash have higher correlation with water level observations in GSB and suggest that island overwash provided a significant contribution of ocean water to eastern GSB during the storm. The computations indicate that overwash of 7500–10,000 m3s−1 was approximately the same as the inflow from the ocean through the major existing inlet. Overall, the model results indicate the complex variability in total water levels driven by tides, ocean storm surge, surge from local winds, and overwash that had a significant impact on the circulation in Great South Bay during Hurricane Sandy.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2018.04.003","usgsCitation":"Bennett, V.C., Mulligan, R.P., and Hapke, C.J., 2018, A numerical model investigation of the impacts of Hurricane Sandy on water level variability in Great South Bay, New York: Continental Shelf Research, v. 161, p. 1-11, https://doi.org/10.1016/j.csr.2018.04.003.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-082328","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468811,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.csr.2018.04.003","text":"Publisher Index Page"},{"id":353706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Great South Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.7,\n 40.55\n ],\n [\n -72.7,\n 40.55\n ],\n [\n -72.7,\n 40.8\n ],\n [\n -73.7,\n 40.8\n ],\n [\n -73.7,\n 40.55\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"161","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6d0e4b0da30c1bfbe52","contributors":{"authors":[{"text":"Bennett, Vanessa C. C.","contributorId":204457,"corporation":false,"usgs":false,"family":"Bennett","given":"Vanessa","email":"","middleInitial":"C. C.","affiliations":[{"id":36943,"text":"Queens University","active":true,"usgs":false}],"preferred":false,"id":734015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mulligan, Ryan P.","contributorId":194423,"corporation":false,"usgs":false,"family":"Mulligan","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":35723,"text":"Queen's University - Kingston, Ontario","active":true,"usgs":false}],"preferred":false,"id":734016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hapke, Cheryl J. 0000-0002-2753-4075 chapke@usgs.gov","orcid":"https://orcid.org/0000-0002-2753-4075","contributorId":2981,"corporation":false,"usgs":true,"family":"Hapke","given":"Cheryl","email":"chapke@usgs.gov","middleInitial":"J.","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":true,"id":734014,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196620,"text":"70196620 - 2018 - Assessing roadway contributions to stormwater flows, concentrations, and loads with the StreamStats application","interactions":[],"lastModifiedDate":"2019-03-06T12:06:36","indexId":"70196620","displayToPublicDate":"2018-04-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3647,"text":"Transportation Research Record","active":true,"publicationSubtype":{"id":10}},"title":"Assessing roadway contributions to stormwater flows, concentrations, and loads with the StreamStats application","docAbstract":"The Oregon Department of Transportation (ODOT) and other state departments of transportation need quantitative information about the percentages of different land cover categories above any given stream crossing in the state to assess and address roadway contributions to water-quality impairments and resulting total maximum daily loads. The U.S. Geological Survey, in cooperation with ODOT and the FHWA, added roadway and land cover information to the online StreamStats application to facilitate analysis of stormwater runoff contributions from different land covers. Analysis of 25 delineated basins with drainage areas of about 100 mi2 indicates the diversity of land covers in the Willamette Valley, Oregon. On average, agricultural, developed, and undeveloped land covers comprise 15%, 2.3%, and 82% of these basin areas. On average, these basins contained about 10 mi of state highways and 222 mi of non-state roads. The Stochastic Empirical Loading and Dilution Model was used with available water-quality data to simulate long-term yields of total phosphorus from highways, non-highway roadways, and agricultural, developed, and undeveloped areas. These yields were applied to land cover areas obtained from StreamStats for the Willamette River above Wilsonville, Oregon. This analysis indicated that highway yields were larger than yields from other land covers because highway runoff concentrations were higher than other land covers and the highway is fully impervious. However, the total highway area was a fraction of the other land covers. Accordingly, highway runoff mitigation measures can be effective for managing water quality locally, they may have limited effect on achieving basin-wide stormwater reduction goals.