The introduction of West Nile virus (WNV) into North America in 1999 is a classical example of viral emergence in a new environment, with its subsequent dispersion across the continent having a major impact on local bird populations. Despite the importance of this epizootic, the pattern, dynamics and determinants of WNV spread in its natural hosts remain uncertain. In particular, it is unclear whether the virus encountered major barriers to transmission, or spread in an unconstrained manner, and if specific viral lineages were favored over others indicative of intrinsic differences in fitness. To address these key questions in WNV evolution and ecology we sequenced the complete genomes of approximately 300 avian isolates sampled across the USA between 2001-2012. Phylogenetic analysis revealed a relatively ‘star-like' tree structure, indicative of explosive viral spread in US, although with some replacement of viral genotypes through time. These data are striking in that viral sequences exhibit relatively limited clustering according to geographic region, particularly for those viruses sampled from birds, and no strong phylogenetic association with well sampled avian species. The genome sequence data analysed here also contain relatively little evidence for adaptive evolution, particularly on structural proteins, suggesting that most viral lineages are of similar fitness, and that WNV is well adapted to the ecology of mosquito vectors and diverse avian hosts in the USA. In sum, the molecular evolution of WNV in North America depicts a largely unfettered expansion within a permissive host and geographic population with little evidence of major adaptive barriers.
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The goal of this study was to develop a fully automated water quality management system for multiple beaches using predictive empirical models (EM) and state-of-the-art technology. Many recent EMs rely on samples or data collected manually, which adds to analysis time and increases the burden to the beach manager. In this study, data from water quality buoys and weather stations were transmitted through cellular telemetry to a web hosting service. An executable program simultaneously retrieved and aggregated data for regression equations and calculated EM results each morning at 9:30 AM; results were transferred through RSS feed to a website, mapped to each beach, and received by the lifeguards to be posted at the beach. Models were initially developed for five beaches, but by the third year, 21 beaches were managed using refined and validated modeling systems. The adjusted R2 of the regressions relating Escherichia coli to hydrometeorological variables for the EMs were greater than those for the PMs, and ranged from 0.220 to 0.390 (2011) and 0.103 to 0.381 (2012). Validation results in 2013 revealed reduced predictive capabilities; however, three of the originally modeled beaches showed improvement in 2013 compared to 2012. The EMs generally showed higher accuracy and specificity than those of the PMs, and sensitivity was low for both approaches. In 2012 EM accuracy was 70–97%; specificity, 71–100%; and sensitivity, 0–64% and in 2013 accuracy was 68–97%; specificity, 73–100%; and sensitivity 0–36%. Factors that may have affected model capabilities include instrument malfunction, non-point source inputs, and sparse calibration data. The modeling system developed is the most extensive, fully-automated system for recreational water quality developed to date. Key insights for refining and improving large-scale empirical models for beach management have been developed through this multi-year effort.
","language":"English","publisher":"Elsevier","publisherLocation":"Oxford, UK","doi":"10.1016/j.jenvman.2015.10.011","usgsCitation":"Shively, D., Nevers, M., Breitenbach, C., Phanikumar, M., Przybyla-Kelly, K., Spoljaric, A., and Whitman, R.L., 2016, Prototypic automated continuous recreational water quality monitoring of nine Chicago beaches: Journal of Environmental Management, v. 166, p. 285-293, https://doi.org/10.1016/j.jenvman.2015.10.011.","productDescription":"9 p.","startPage":"285","endPage":"293","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061853","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":311747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","city":"Chicago","otherGeospatial":"Great Lakes, Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.65647888183594,\n 42.03552434403621\n ],\n [\n -87.62626647949217,\n 41.99011884096809\n ],\n [\n -87.615966796875,\n 41.91454130182335\n ],\n [\n -87.59193420410156,\n 41.89767733570708\n ],\n [\n -87.56309509277344,\n 41.80766096027984\n ],\n [\n -87.53700256347656,\n 41.7672146942102\n ],\n [\n -87.52601623535156,\n 41.75338543627463\n ],\n [\n -87.52670288085938,\n 41.73084248230534\n ],\n [\n -87.52395629882812,\n 41.70931682567252\n ],\n [\n -87.53837585449219,\n 41.699063978799174\n ],\n [\n -87.63107299804688,\n 41.840408844786396\n ],\n [\n -87.681884765625,\n 41.99522219923445\n ],\n [\n -87.69218444824219,\n 42.02838405374952\n ],\n [\n -87.68119812011717,\n 42.037054301883806\n ],\n [\n -87.659912109375,\n 42.03603433407788\n ],\n [\n -87.65647888183594,\n 42.03552434403621\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"166","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"565d8140e4b071e7ea543480","contributors":{"authors":[{"text":"Shively, Dawn","contributorId":150065,"corporation":false,"usgs":false,"family":"Shively","given":"Dawn","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":580618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nevers, Meredith 0000-0001-6963-6734 mnevers@usgs.gov","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":2013,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith","email":"mnevers@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":580617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breitenbach, Cathy","contributorId":150066,"corporation":false,"usgs":false,"family":"Breitenbach","given":"Cathy","email":"","affiliations":[{"id":17899,"text":"Chicago Park District","active":true,"usgs":false}],"preferred":false,"id":580619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phanikumar, Mantha S.","contributorId":17888,"corporation":false,"usgs":true,"family":"Phanikumar","given":"Mantha S.","affiliations":[],"preferred":false,"id":580623,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Przybyla-Kelly, Kasia","contributorId":150067,"corporation":false,"usgs":false,"family":"Przybyla-Kelly","given":"Kasia","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":580620,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spoljaric, Ashley M.","contributorId":150068,"corporation":false,"usgs":false,"family":"Spoljaric","given":"Ashley M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":580621,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whitman, Richard L.","contributorId":150069,"corporation":false,"usgs":false,"family":"Whitman","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":12443,"text":"U.S. Geological Survey (retired)","active":true,"usgs":false}],"preferred":false,"id":580622,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70159410,"text":"70159410 - 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Constructing such models requires robust underpinning science that integrates formerly disparate data types to achieve effective species management.
\nGreater sage-grouse Centrocercus urophasianus, hereafter “sage-grouse” populations are declining throughout sagebrush-steppe ecosystems in North America, particularly within the Great Basin, which heightens the need for novel management tools that maximize use of available information.
\nHerein, we improve upon existing species distribution models by combining information about sage-grouse habitat quality, distribution, and abundance from multiple data sources. To measure habitat, we created spatially explicit maps depicting habitat selection indices (HSI) informed by > 35 500 independent telemetry locations from > 1600 sage-grouse collected over 15 years across much of the Great Basin. These indices were derived from models that accounted for selection at different spatial scales and seasons. A region-wide HSI was calculated using the HSI surfaces modelled for 12 independent subregions and then demarcated into distinct habitat quality classes.
\nWe also employed a novel index to describe landscape patterns of sage-grouse abundance and space use (AUI). The AUI is a probabilistic composite of: (i) breeding density patterns based on the spatial configuration of breeding leks and associated trends in male attendance; and (ii) year-round patterns of space use indexed by the decreasing probability of use with increasing distance to leks. The continuous AUI surface was then reclassified into two classes representing high and low/no use and abundance.
\nSynthesis and applications. Using the example of sage-grouse, we demonstrate how the joint application of indices of habitat selection, abundance, and space use derived from multiple data sources yields a composite map that can guide effective allocation of management intensity across multiple spatial scales. As applied to sage-grouse, the composite map identifies spatially explicit management categories within sagebrush steppe that are most critical to sustaining sage-grouse populations as well as those areas where changes in land use would likely have minimal impact. Importantly, collaborative efforts among stakeholders guide which intersections of habitat selection indices and abundance and space use classes are used to define management categories. Because sage-grouse are an umbrella species, our joint-index modelling approach can help target effective conservation for other sagebrush obligate species, and can be readily applied to species in other ecosystems with similar life histories, such as central-placed breeding.
\nDemand for woody biomass fuels is increasing amidst concerns about global energy security and climate change, but there may be negative implications of increased harvesting for forest ecosystem functions and their benefits to society (ecosystem services). Using new methods for assessing ecosystem services based on long-term experimental research, post-harvest changes in ten potential benefits were assessed for ten first-order northern hardwood forest watersheds at three long-term experimental research sites in northeastern North America. As expected, we observed near-term tradeoffs between biomass provision and greenhouse gas regulation, as well as tradeoffs between intensive harvest and the capacity of the forest to remediate nutrient pollution. In both cases, service provision began to recover along with the regeneration of forest vegetation; in the case of pollution remediation, the service recovered to pre-harvest levels within 10 years. By contrast to these two services, biomass harvesting had relatively nominal and transient impacts on other ecosystem services. Our results are sensitive to empirical definitions of societal demand, including methods for scaling societal demand to ecosystem units, which are often poorly resolved. Reducing uncertainty around these parameters can improve confidence in our results and increase their relevance for decision-making. Our synthesis of long-term experimental studies provides insights on the social-ecological resilience of managed forest ecosystems to multiple drivers of change.
","language":"English","publisher":"Springer-Verlag","publisherLocation":"New York, NY","doi":"10.1007/s10021-015-9928-z","collaboration":"New York State Energy Research and Development Authority; USGS","usgsCitation":"Caputo, J., Beier, C.D., Groffman, P., Burns, D.A., Beall, F.D., Hazlett, P.W., and Yorks, T.E., 2016, Effects of harvesting forest biomass on water and climate regulation services: A synthesis of long-term ecosystem experiments in eastern North America: Ecosystems, v. 19, no. 2, p. 271-283, https://doi.org/10.1007/s10021-015-9928-z.","productDescription":"13 p.","startPage":"271","endPage":"283","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065229","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":320495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":320494,"type":{"id":15,"text":"Index 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]\n}","volume":"19","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-27","publicationStatus":"PW","scienceBaseUri":"571f3fb7e4b071321fe56a21","contributors":{"authors":[{"text":"Caputo, Jesse","contributorId":168871,"corporation":false,"usgs":false,"family":"Caputo","given":"Jesse","affiliations":[{"id":25371,"text":"Dept of Forest & Natural Resources Mgmt, SUNY College of ESF","active":true,"usgs":false}],"preferred":false,"id":627576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beier, Colin D","contributorId":168872,"corporation":false,"usgs":false,"family":"Beier","given":"Colin","email":"","middleInitial":"D","affiliations":[{"id":25371,"text":"Dept of Forest & Natural Resources Mgmt, SUNY College of ESF","active":true,"usgs":false}],"preferred":false,"id":627577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Groffman, Peter M","contributorId":168873,"corporation":false,"usgs":false,"family":"Groffman","given":"Peter M","affiliations":[{"id":25372,"text":"Senior Research Scientist, Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":627578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":627579,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beall, Frederick D","contributorId":168874,"corporation":false,"usgs":false,"family":"Beall","given":"Frederick","email":"","middleInitial":"D","affiliations":[{"id":25373,"text":"Research Forester, Natual Resources Canada - Canada Forest Service","active":true,"usgs":false}],"preferred":false,"id":627580,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hazlett, Paul W.","contributorId":101177,"corporation":false,"usgs":true,"family":"Hazlett","given":"Paul","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":627581,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yorks, Thad E","contributorId":168875,"corporation":false,"usgs":false,"family":"Yorks","given":"Thad","email":"","middleInitial":"E","affiliations":[{"id":25374,"text":"Environmental Biology Program, Cazenovia College NY","active":true,"usgs":false}],"preferred":false,"id":627582,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70156695,"text":"70156695 - 2016 - Engagement with indigenous peoples and honoring traditional knowledge systems","interactions":[],"lastModifiedDate":"2016-12-14T12:22:53","indexId":"70156695","displayToPublicDate":"2015-10-26T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Engagement with indigenous peoples and honoring traditional knowledge systems","docAbstract":"The organizers of the 2014 US National Climate Assessment (NCA) made a concerted effort to reach out to and collaborate with Indigenous peoples, resulting in the most comprehensive information to date on climate change impacts to Indigenous peoples in a US national assessment. Yet, there is still much room for improvement in assessment processes to ensure adequate recognition of Indigenous perspectives and Indigenous knowledge systems. This article discusses the process used in creating the Indigenous Peoples, Land, and Resources NCA chapter by a team comprised of tribal members, agencies, academics, and non-governmental organizations, who worked together to solicit, collect, and synthesize traditional knowledges and data from a diverse array of Indigenous communities across the US. It also discusses the synergy and discord between traditional knowledge systems and science and the emergence of cross-cutting issues and vulnerabilities for Indigenous peoples. The challenges of coalescing information about climate change and its impacts on Indigenous communities are outlined along with recommendations on the types of information to include in future assessment outputs. We recommend that future assessments – not only NCA, but other relevant local, regional, national, and international efforts aimed at the translation of climate information and assessments into meaningful actions – should support integration of Indigenous perspectives in a sustained way that builds respectful relationships and effectively engages Indigenous communities. Given the large number of tribes in the US and the current challenges and unique vulnerabilities of Indigenous communities, a special report focusing solely on climate change and Indigenous peoples is warranted.
This article is part of a special issue on “The National Climate Assessment: Innovations in Science and Engagement” edited by Katharine Jacobs, Susanne Moser, and James Buizer.
The lone star tick, Amblyomma americanum, is a disease vector of significance for human and animal health throughout much of the eastern United States. To model the potential effects of climate change on this tick, a better understanding is needed of the relative roles of temperature-dependent and temperature-independent (day-length-dependent behavioral or morphogenetic diapause) processes acting on the tick lifecycle. In this study, we explored the roles of these processes by simulating seasonal activity patterns using models with site-specific temperature and day-length-dependent processes. We first modeled the transitions from engorged larvae to feeding nymphs, engorged nymphs to feeding adults, and engorged adult females to feeding larvae. The simulated seasonal patterns were compared against field observations at three locations in United States. Simulations suggested that 1) during the larva-to-nymph transition, some larvae undergo no diapause while others undergo morphogenetic diapause of engorged larvae; 2) molted adults undergo behavioral diapause during the transition from nymph-to-adult; and 3) there is no diapause during the adult-to-larva transition. A model constructed to simulate the full lifecycle of A. americanum successfully predicted observed tick activity at the three U.S. study locations. Some differences between observed and simulated seasonality patterns were observed, however, identifying the need for research to refine some model parameters. In simulations run using temperature data for Montreal, deterministic die-out of A. americanum populations did not occur, suggesting the possibility that current climate in parts of southern Canada is suitable for survival and reproduction of this tick.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jme/tjv150","usgsCitation":"Ludwig, A., Ginsberg, H., Hickling, G., and Ogden, N.H., 2016, A dynamic population model to investigate effects of climate and climate-independent factors on the lifecycle of the tick Amblyomma americanum (Acari: Ixodidae): Journal of Medical Entomology, v. 53, no. 1, p. 99-115, https://doi.org/10.1093/jme/tjv150.","productDescription":"17 p.","startPage":"99","endPage":"115","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067271","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":486932,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/pls_facpubs/144","text":"External Repository"},{"id":312845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-26","publicationStatus":"PW","scienceBaseUri":"567bd3bae4b0a04ef491a1ee","contributors":{"authors":[{"text":"Ludwig, Antoinette","contributorId":147666,"corporation":false,"usgs":false,"family":"Ludwig","given":"Antoinette","email":"","affiliations":[{"id":16890,"text":"Public Health Agency of Canada","active":true,"usgs":false}],"preferred":false,"id":572282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":572281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hickling, Graham J.","contributorId":88639,"corporation":false,"usgs":true,"family":"Hickling","given":"Graham J.","affiliations":[],"preferred":false,"id":572283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ogden, Nicholas H.","contributorId":147667,"corporation":false,"usgs":false,"family":"Ogden","given":"Nicholas","email":"","middleInitial":"H.","affiliations":[{"id":16890,"text":"Public Health Agency of Canada","active":true,"usgs":false}],"preferred":false,"id":572284,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177904,"text":"70177904 - 2016 - Using occupancy modeling and logistic regression to assess the distribution of shrimp species in lowland streams, Costa Rica: Does regional groundwater create favorable habitat?","interactions":[],"lastModifiedDate":"2016-12-06T13:03:19","indexId":"70177904","displayToPublicDate":"2015-10-26T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Using occupancy modeling and logistic regression to assess the distribution of shrimp species in lowland streams, Costa Rica: Does regional groundwater create favorable habitat?","docAbstract":"Freshwater shrimps are an important biotic component of tropical ecosystems. However, they can have a low probability of detection when abundances are low. We sampled 3 of the most common freshwater shrimp species, Macrobrachium olfersii, Macrobrachium carcinus, and Macrobrachium heterochirus, and used occupancy modeling and logistic regression models to improve our limited knowledge of distribution of these cryptic species by investigating both local- and landscape-scale effects at La Selva Biological Station in Costa Rica. Local-scale factors included substrate type and stream size, and landscape-scale factors included presence or absence of regional groundwater inputs. Capture rates for 2 of the sampled species (M. olfersii and M. carcinus) were sufficient to compare the fit of occupancy models. Occupancy models did not converge for M. heterochirus, but M. heterochirus had high enough occupancy rates that logistic regression could be used to model the relationship between occupancy rates and predictors. The best-supported models for M. olfersii and M. carcinus included conductivity, discharge, and substrate parameters. Stream size was positively correlated with occupancy rates of all 3 species. High stream conductivity, which reflects the quantity of regional groundwater input into the stream, was positively correlated with M. olfersii occupancy rates. Boulder substrates increased occupancy rate of M. carcinus and decreased the detection probability of M. olfersii. Our models suggest that shrimp distribution is driven by factors that function at local (substrate and discharge) and landscape (conductivity) scales.
","language":"English","publisher":"Society for Freshwater Science","doi":"10.1086/684486","usgsCitation":"Snyder, M., Freeman, M., Purucker, S.T., and Pringle, C.M., 2016, Using occupancy modeling and logistic regression to assess the distribution of shrimp species in lowland streams, Costa Rica: Does regional groundwater create favorable habitat?: Freshwater Science, v. 35, no. 1, p. 80-90, https://doi.org/10.1086/684486.","productDescription":"11 p.","startPage":"80","endPage":"90","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062926","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":330405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"La Selva Biological Station, Saltito River, Salto River, Sura River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.09004211425781,\n 10.3270789193731\n ],\n [\n -84.09004211425781,\n 10.530007387221294\n ],\n [\n -83.92833709716797,\n 10.530007387221294\n ],\n [\n -83.92833709716797,\n 10.3270789193731\n ],\n [\n -84.09004211425781,\n 10.3270789193731\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f4e4b0f497e79a5a8d","contributors":{"authors":[{"text":"Snyder, Marcia","contributorId":176290,"corporation":false,"usgs":false,"family":"Snyder","given":"Marcia","affiliations":[],"preferred":false,"id":652095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":652094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Purucker, S. Thomas","contributorId":176291,"corporation":false,"usgs":false,"family":"Purucker","given":"S.","email":"","middleInitial":"Thomas","affiliations":[],"preferred":false,"id":652096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pringle, Catherine M.","contributorId":176292,"corporation":false,"usgs":false,"family":"Pringle","given":"Catherine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652097,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169122,"text":"70169122 - 2016 - Novel and lost forests in the Upper Midwestern United States, from new estimates of settlement-era composition, stem density, and biomass","interactions":[],"lastModifiedDate":"2017-01-10T13:17:12","indexId":"70169122","displayToPublicDate":"2015-10-24T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Novel and lost forests in the Upper Midwestern United States, from new estimates of settlement-era composition, stem density, and biomass","docAbstract":"EuroAmerican land-use and its legacies have transformed forest structure and composition across the United States (US). More accurate reconstructions of historical states are critical to understanding the processes governing past, current, and future forest dynamics. Here we present new gridded (8x8km) reconstructions of pre-settlement (1800s) forest composition and structure from the upper Midwestern US (Minnesota, Wisconsin, and most of Michigan), using 19th Century Public Land Survey System (PLSS), with estimates of relative composition, above-ground biomass, stem density, and basal area for 28 tree types. This mapping is more robust than past efforts, using spatially varying correction factors to accommodate sampling design, azimuthal censoring, and biases in tree selection.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0151935","usgsCitation":"Goring, S., Mladenoff, D.J., Cogbill, C., Record, S., Paciorek, C.J., Dietze, M.C., Dawson, A., Matthes, J., McLachlan, J.S., and Williams, J.W., 2016, Novel and lost forests in the Upper Midwestern United States, from new estimates of settlement-era composition, stem density, and biomass: PLoS ONE, v. 11, no. 12, e0151935; 34 p., https://doi.org/10.1371/journal.pone.0151935.","productDescription":"e0151935; 34 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066196","costCenters":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":471443,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0151935","text":"Publisher Index Page"},{"id":319089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Jaclyn","contributorId":167494,"corporation":false,"usgs":false,"family":"Matthes","given":"Jaclyn","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":623106,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McLachlan, Jason S.","contributorId":167179,"corporation":false,"usgs":false,"family":"McLachlan","given":"Jason","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":623107,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Williams, John W.","contributorId":16761,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":623098,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70168405,"text":"70168405 - 2016 - Metabolism correlates with variation in post-natal growth rate among songbirds at three latitudes","interactions":[],"lastModifiedDate":"2016-12-16T10:50:10","indexId":"70168405","displayToPublicDate":"2015-10-20T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Metabolism correlates with variation in post-natal growth rate among songbirds at three latitudes","docAbstract":"1. Variation in post-natal growth rates is substantial among organisms and especially strong among latitudes because tropical and south temperate species typically have slower growth than north temperate relatives. Metabolic rate is thought to be a critical mechanism underlying growth rates after accounting for allometric effects of body mass. However, comparative tests on a large spatial scale are lacking, and the importance of metabolism for growth rates remains unclear both within and particularly across latitudes.
\n2. Songbirds exhibit strong interspecific variation in growth rates across geographic space, although within latitudes an association between metabolic rate and growth rate has not always been observed. Moreover, the hypothesis that differences in growth rates across latitudes reflect underlying differences in metabolism is untested. Here, we investigate these possibilities across north temperate, south temperate and tropical study sites.
\n3. Phylogenetic analyses showed that, for a given body mass, metabolic rates of north temperate nestlings were higher than tropical and south temperate species. Metabolic rates controlled for body mass correlated with post-natal growth rates both within and among latitudes. Offspring body mass explained substantial residual variation in growth rates as expected under classic allometric theory.
\n4. Our results suggest that variation in metabolic rates has an important influence on broad patterns of avian growth rates at a global scale. We suggest further studies that address the ecological and physiological costs and consequences of variation in metabolism and growth rates.
","language":"English","publisher":"Wiley","doi":"10.1111/1365-2435.12548","usgsCitation":"Ton, R., and Martin, T.E., 2016, Metabolism correlates with variation in post-natal growth rate among songbirds at three latitudes: Functional Ecology, v. 30, no. 5, p. 743-748, https://doi.org/10.1111/1365-2435.12548.","productDescription":"6 p.","startPage":"743","endPage":"748","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056706","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471444,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2435.12548","text":"Publisher Index Page"},{"id":317982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-20","publicationStatus":"PW","scienceBaseUri":"56bf1057e4b06458514b6918","contributors":{"authors":[{"text":"Ton, Riccardo","contributorId":138795,"corporation":false,"usgs":false,"family":"Ton","given":"Riccardo","email":"","affiliations":[],"preferred":false,"id":620043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":619967,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159200,"text":"70159200 - 2016 - Population trends, bend use relative to available habitat and within-river-bend habitat use of eight indicator species of Missouri and Lower Kansas River benthic fishes: 15 years after baseline assessment","interactions":[],"lastModifiedDate":"2016-01-11T10:52:23","indexId":"70159200","displayToPublicDate":"2015-10-20T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Population trends, bend use relative to available habitat and within-river-bend habitat use of eight indicator species of Missouri and Lower Kansas River benthic fishes: 15 years after baseline assessment","docAbstract":"A baseline assessment of the Missouri River fish community and species-specific habitat use patterns conducted from 1996 to 1998 provided the first comprehensive analysis of Missouri River benthic fish population trends and habitat use in the Missouri and Lower Yellowstone rivers, exclusive of reservoirs, and provided the foundation for the present Pallid Sturgeon Population Assessment Program (PSPAP). Data used in such studies are frequently zero inflated. To address this issue, the zero-inflated Poisson (ZIP) model was applied. This follow-up study is based on PSPAP data collected up to 15 years later along with new understanding of how habitat characteristics among and within bends affect habitat use of fish species targeted by PSPAP, including pallid sturgeon. This work demonstrated that a large-scale, large-river, PSPAP-type monitoring program can be an effective tool for assessing population trends and habitat usage of large-river fish species. Using multiple gears, PSPAP was effective in monitoring shovelnose and pallid sturgeons, sicklefin, shoal and sturgeon chubs, sand shiner, blue sucker and sauger. For all species, the relationship between environmental variables and relative abundance differed, somewhat, among river segments suggesting the importance of the overall conditions of Upper and Middle Missouri River and Lower Missouri and Kansas rivers on the habitat usage patterns exhibited. Shoal and sicklefin chubs exhibited many similar habitat usage patterns; blue sucker and shovelnose sturgeon also shared similar responses. For pallid sturgeon, the primary focus of PSPAP, relative abundance tended to increase in Upper and Middle Missouri River paralleling stocking efforts, whereas no evidence of an increasing relative abundance was found in the Lower Missouri River despite stocking.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.2846","usgsCitation":"Wildhaber, M.L., Yang, W., and Arab, A., 2016, Population trends, bend use relative to available habitat and within-river-bend habitat use of eight indicator species of Missouri and Lower Kansas River benthic fishes: 15 years after baseline assessment: River Research and Applications, v. 32, p. 36-65, https://doi.org/10.1002/rra.2846.","productDescription":"30 p.","startPage":"36","endPage":"65","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055609","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":310114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Kansas River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.99414062499999,\n 45.72152152227954\n ],\n [\n -113.719482421875,\n 49.49667452747045\n ],\n [\n -99.7119140625,\n 49.01625665778159\n ],\n [\n -96.591796875,\n 45.90529985724796\n ],\n [\n -94.932861328125,\n 43.52465500687185\n ],\n [\n -93.09814453125,\n 40.60561205826018\n ],\n [\n -89.97802734375,\n 38.865374851611634\n ],\n [\n -89.835205078125,\n 36.50963615733049\n ],\n [\n -94.537353515625,\n 36.500805317604794\n ],\n [\n -102.095947265625,\n 39.99395569397331\n ],\n [\n -114.08203125,\n 43.96119063892024\n ],\n [\n -113.99414062499999,\n 45.72152152227954\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-11-04","publicationStatus":"PW","scienceBaseUri":"562757a8e4b0d158f5926505","contributors":{"authors":[{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":577837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Wen-Hsi","contributorId":45228,"corporation":false,"usgs":true,"family":"Yang","given":"Wen-Hsi","email":"","affiliations":[],"preferred":false,"id":577838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arab, Ali","contributorId":75002,"corporation":false,"usgs":true,"family":"Arab","given":"Ali","email":"","affiliations":[],"preferred":false,"id":577839,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159637,"text":"70159637 - 2016 - Mycobacterial infection in Northern snakehead (Channa argus) from the Potomac River catchment","interactions":[],"lastModifiedDate":"2018-08-08T10:38:02","indexId":"70159637","displayToPublicDate":"2015-10-16T03:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2286,"text":"Journal of Fish Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Mycobacterial infection in Northern snakehead (Channa argus) from the Potomac River catchment","docAbstract":"The Northern snakehead, Channa argus (Cantor), is a non-native predatory fish that has become established regionally in some temperate freshwater habitats within the United States. Over the past decade, Northern snakehead populations have developed within aquatic ecosystems throughout the eastern USA, including the Potomac River system within Virginia, Maryland and Washington, D.C. Since this species was initially observed in this region in 2002, the population has expanded considerably (Odenkirk & Owens 2007). In the Chesapeake Bay watershed, populations of Northern snakehead exist in the lower Potomac River and Rappahannock Rivers on the Western shore of the Bay, and these fish have also been found in middle or upper reaches of river systems on the Eastern shore of the Bay, including the Nanticoke and Wicomico Rivers among others. Over the past several years, many aspects of Northern snakehead life history in the Potomac River have been described, including range and dispersal patterns, microhabitat selection and diet (Lapointe, Thorson & Angermeier 2010; Saylor, Lapointe & Angermeier 2012; Lapointe, Odenkirk & Angermeier 2013). However, comparatively little is known about their health status including susceptibility to parasitism and disease and their capacity to serve as reservoirs of disease for native wildlife. Although considered hardy by fisheries biologists, snakehead fish have demonstrated susceptibility to a number of described piscine diseases within their native range and habitat in Asia. Reported pathogens of significance in snakehead species in Asia include snakehead rhabdovirus (Lio-Po et al. 2000), aeromonad bacteria (Zheng, Cao & Yang 2012), Nocardia (Wang et al. 2007) andMycobacterium spp. (Chinabut, Limsuwan & Chantatchakool 1990; ). Mycobacterial isolates recovered from another snakehead species (Channa striata) in the previous studies have included M. marinum and M. fortuitum, as identified through molecular-based diagnostics (Puttinaowarat et al. 2002). We have conducted health screenings of Northern snakehead from the Potomac River system over the past several years and have detected few associated pathogens. Typical observations have largely consisted of incidental identification of parasitism with protozoal, monogenean or trematode organisms (unpublished data). We have also identified largemouth bass virus (LMBV) in clinically normal Northern snakehead collected from the Potomac River (Iwanowicz et al. 2013). Continued research concerning these and other pathogens of this introduced species is important to fully understand the potential impacts of these fish on indigenous wildlife and aquatic ecosystems.
","language":"English","publisher":"Blackwell Science","publisherLocation":"Oxford, England","doi":"10.1111/jfd.12412","usgsCitation":"Densmore, C.L., Iwanowicz, L., Henderson, A., Iwanowicz, D.D., and Odenkirk, J., 2016, Mycobacterial infection in Northern snakehead (Channa argus) from the Potomac River catchment: Journal of Fish Diseases, v. 39, no. 6, p. 771-775, https://doi.org/10.1111/jfd.12412.","productDescription":"5 p.","startPage":"771","endPage":"775","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065068","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":311384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Potomac River, Pohick Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.12471008300781,\n 38.6895295540336\n ],\n [\n -77.13912963867188,\n 38.6999799615129\n ],\n [\n -77.16041564941406,\n 38.70667813760075\n ],\n [\n -77.19543457031249,\n 38.6999799615129\n ],\n [\n -77.20333099365233,\n 38.674253135311496\n ],\n [\n -77.19509124755858,\n 38.662726661586646\n ],\n [\n -77.17655181884766,\n 38.65387950468725\n ],\n [\n -77.15835571289062,\n 38.64369051578083\n ],\n [\n -77.13912963867188,\n 38.628940728833264\n ],\n [\n -77.10479736328125,\n 38.622503507032874\n ],\n [\n -77.07664489746094,\n 38.626258623311166\n ],\n [\n -77.05432891845703,\n 38.64288606020925\n ],\n [\n -77.05982208251953,\n 38.67452117076055\n ],\n [\n -77.08076477050781,\n 38.691673351832996\n ],\n [\n -77.12471008300781,\n 38.6895295540336\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-16","publicationStatus":"PW","scienceBaseUri":"564b0c55e4b0ebfbef0d3172","contributors":{"authors":[{"text":"Densmore, Christine L. 0000-0001-6440-0781 cdensmore@usgs.gov","orcid":"https://orcid.org/0000-0001-6440-0781","contributorId":4560,"corporation":false,"usgs":true,"family":"Densmore","given":"Christine","email":"cdensmore@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":579837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178 liwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":150383,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R. ","email":"liwanowicz@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":579896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Anne ahenderson@usgs.gov","contributorId":4373,"corporation":false,"usgs":true,"family":"Henderson","given":"Anne","email":"ahenderson@usgs.gov","affiliations":[],"preferred":true,"id":579897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":579898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Odenkirk, J.S.","contributorId":149880,"corporation":false,"usgs":false,"family":"Odenkirk","given":"J.S.","affiliations":[],"preferred":false,"id":579899,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70158932,"text":"70158932 - 2016 - Relating mesocarnivore relative abundance to anthropogenic land-use with a hierarchical spatial count model","interactions":[],"lastModifiedDate":"2016-06-02T10:32:38","indexId":"70158932","displayToPublicDate":"2015-10-13T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Relating mesocarnivore relative abundance to anthropogenic land-use with a hierarchical spatial count model","docAbstract":"There is growing need to develop models of spatial patterns in animal abundance, yet comparatively few examples of such models exist. This is especially true in situations where the abundance of one species may inhibit that of another, such as the intensively-farmed landscape of the Prairie Pothole Region (PPR) of the central United States, where waterfowl production is largely constrained by mesocarnivore nest predation. We used a hierarchical Bayesian approach to relate the distribution of various land-cover types to the relative abundances of four mesocarnivores in the PPR: coyote Canis latrans, raccoon Procyon lotor, red fox Vulpes vulpes, and striped skunk Mephitis mephitis. We developed models for each species at multiple spatial resolutions (41.4 km2, 10.4 km2, and 2.6 km2) to address different ecological and management-related questions. Model results for each species were similar irrespective of resolution. We found that the amount of row-crop agriculture was nearly ubiquitous in our best models, exhibiting a positive relationship with relative abundance for each species. The amount of native grassland land-cover was positively associated with coyote and raccoon relative abundance, but generally absent from models for red fox and skunk. Red fox and skunk were positively associated with each other, suggesting potential niche overlap. We found no evidence that coyote abundance limited that of other mesocarnivore species, as might be expected under a hypothesis of mesopredator release. The relationships between relative abundance and land-cover types were similar across spatial resolutions. Our results indicated that mesocarnivores in the PPR are most likely to occur in portions of the landscape with large amounts of agricultural land-cover. Further, our results indicated that track-survey data can be used in a hierarchical framework to gain inferences regarding spatial patterns in animal relative abundance.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.01179","collaboration":"Prepared in collaboration with U.S. Fish and Wildlife Service","usgsCitation":"Crimmins, S.M., Walleser, L.R., Hertel, D.R., McKann, P., Rohweder, J.J., and Thogmartin, W.E., 2016, Relating mesocarnivore relative abundance to anthropogenic land-use with a hierarchical spatial count model: Ecography, v. 39, no. 6, p. 524-532, https://doi.org/10.1111/ecog.01179.","productDescription":"9 p.","startPage":"524","endPage":"532","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057940","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":309843,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.8115234375,\n 42.71473218539458\n ],\n [\n -96.8115234375,\n 47.754097979680026\n ],\n [\n -93.71337890625,\n 47.754097979680026\n ],\n [\n -93.71337890625,\n 42.71473218539458\n ],\n [\n -96.8115234375,\n 42.71473218539458\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-06-30","publicationStatus":"PW","scienceBaseUri":"561e1d28e4b0cdb063e59ca5","contributors":{"authors":[{"text":"Crimmins, Shawn M. 0000-0001-6229-5543 scrimmins@usgs.gov","orcid":"https://orcid.org/0000-0001-6229-5543","contributorId":5498,"corporation":false,"usgs":true,"family":"Crimmins","given":"Shawn","email":"scrimmins@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walleser, Liza R. lwalleser@usgs.gov","contributorId":4329,"corporation":false,"usgs":true,"family":"Walleser","given":"Liza","email":"lwalleser@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hertel, Dan R.","contributorId":149113,"corporation":false,"usgs":false,"family":"Hertel","given":"Dan","email":"","middleInitial":"R.","affiliations":[{"id":17647,"text":"United States Fish and Wildlife Service, Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":576940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKann, Patrick C.","contributorId":14940,"corporation":false,"usgs":true,"family":"McKann","given":"Patrick C.","affiliations":[],"preferred":false,"id":576941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohweder, Jason J. jrohweder@usgs.gov","contributorId":460,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":576942,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":576937,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70158943,"text":"70158943 - 2016 - Measuring spatial variation in secondary production and food quality using a common consumer approach in Lake Erie","interactions":[],"lastModifiedDate":"2016-06-02T10:31:07","indexId":"70158943","displayToPublicDate":"2015-10-08T09:45:00","publicationYear":"2016","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":"Measuring spatial variation in secondary production and food quality using a common consumer approach in Lake Erie","docAbstract":"Lake Erie is a large lake straddling the border of the U.S. and Canada that has become increasingly eutrophic in recent years. Eutrophication is particularly focused in the shallow western basin. The western basin of Lake Erie is hydrodynamically similar to a large estuary, with riverine inputs from the Detroit and Maumee Rivers mixing together and creating gradients in chemical and physical conditions. This study was driven by two questions: How does secondary production and food quality for consumers vary across this large mixing zone? and Are there correlations between cyanobacterial abundance and secondary production or food quality for consumers? Measuring spatial and temporal variation in secondary production and food quality is difficult for a variety of logistical reasons, so here a common consumer approach was used. In a common consumer approach, individuals of a single species are raised under similar conditions until placed in the field across environmental gradients of interest. After some period of exposure, the response of that common consumer is measured to provide an index of spatial variation in conditions. Here, a freshwater mussel (Lampsilis siliquoidea) was deployed at 32 locations that spanned habitat types and a gradient in cyanobacterial abundance in the western basin of Lake Erie to measure spatial variation in growth (an index of secondary production) and fatty acid (FA) content (an index of food quality). We found secondary production was highest within the Maumee rivermouth and lowest in the open waters of the lake. Mussel tissues in the Maumee rivermouth also included more eicosapentaenoic and docosapentaenoic fatty acids (EPA and DPA, respectively), but fewer bacterial FAs, suggesting more algae at the base of the food web in the Maumee rivermouth compared to open lake sites. The satellite-derived estimate of cyanobacterial abundance was not correlated to secondary production, but was positively related to EPA and DPA content in the mussels, suggesting more of these important FAs in locations with more cyanobacteria. These results suggest that growth of secondary consumers and the availability of important fatty acids in the western basin are centered on the Maumee rivermouth.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0440","collaboration":"Prepared in collaboration with National Oceanic and Atmospheric Administration","usgsCitation":"Larson, J.H., Richardson, W.B., Evans, M.A., Schaeffer, J., Wynne, T., Bartsch, M., Bartsch, L., Nelson, J., and Vallazza, J., 2016, Measuring spatial variation in secondary production and food quality using a common consumer approach in Lake Erie: Ecological Applications, v. 26, no. 3, p. 873-885, https://doi.org/10.1890/15-0440.","productDescription":"13 p.","startPage":"873","endPage":"885","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063448","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":471445,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1890/15-0440","text":"External Repository"},{"id":438649,"rank":0,"type":{"id":30,"text":"Data 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Rocky reefs in 45 many temperate areas worldwide serve as habitat for canopy forming macroalgae and these 46 structure forming species of kelps (order Laminariales) often serve as important habitat for a great 47 diversity of species. Macrocystis pyrifera is the most common canopy forming kelp species found 48 along the coast of California but the distribution and abundance of M. pyrifera varies in space and 49 time. The purpose of this study is to determine what environmental parameters are correlated with 50 the spatial and temporal persistence of M. pyrifera along the central coast of California and how 51 well those environmental parameters can be used to predict areas where M. pyrifera is more likely 52 to persist. Nine environmental variables considered in this study included depth of the seafloor, 53 structure of the rocky reef, proportion of rocky reef, size of kelp patch, biomass of kelp within a 54 patch, distance from the edge of a kelp patch, sea surface temperature, wave orbital velocities, and 55 population connectivity of individual kelp patches. Using a generalized linear mixed effects model 56 (GLMM), the persistence of M. pyrifera was significantly associated with seven of the nine 57 variables considered: depth, complexity of the rocky reef, proportion of rock, patch biomass, 58 distance from the edge of a patch, population connectivity, and wave-orbital velocities. These 59 seven environmental variables were then used to predict the persistence of kelp across the central 60 coast and these predictions were compared to a reserved dataset of M. pyrifera persistence, which 61 was not used in the creation of the GLMM. The environmental variables were shown to accurately 62 predict the persistence of M. pyrifera within the central coast of California (r = 0.71, P<0.001). 63 Because persistence of giant kelp is important to the community structure of kelp forests, 64 understanding those factors that support persistent populations of M. pyrifera will enable more 65 effective management of these ecosystems.
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To help guide ongoing and future conservation efforts focused on Q. mitchelli we set out to resolve several uncertainties regarding its evolutionary history, specifically its unknown generic position and untested species boundaries. We designed a molecular matrix consisting of two loci (cytochrome c oxidase subunit I and internal transcribed spacer I) and 57 terminal taxa to test the generic position of Q. mitchelli using Bayesian inference and maximum likelihood phylogenetic reconstruction. We also employed two Bayesian species validation methods to test five a priori species models (i.e. hypotheses of species delimitation). Our study is the first to test the generic position of Q.mitchelli and we found robust support for its inclusion in the genusFusconaia. Accordingly, we introduce the binomial, Fusconaia mitchelli comb. nov., to accurately represent the systematic position of the species. We resolved F. mitchelli individuals in two well supported and divergent clades that were generally distinguished as distinct species using Bayesian species validation methods, although alternative hypotheses of species delineation were also supported. Despite strong evidence of genetic isolation within F. mitchelli, we do not advocate for species-level status of the two clades as they are allopatrically distributed and no morphological, behavioral, or ecological characters are known to distinguish them. These results are discussed in the context of the systematics, distribution, and conservation ofF. mitchelli.
","language":"English","publisher":"Kluwer Academic Publishers","publisherLocation":"Dordrecht","doi":"10.1007/s10592-015-0780-7","usgsCitation":"Pfeiffer, J.M., Johnson, N.A., Randklev, C.R., Howells, R.G., and Williams, J.D., 2016, Generic reclassification and species boundaries in the rediscovered freshwater mussel ‘Quadrula’ mitchelli (Simpson in Dall, 1896): Conservation Genetics, v. 17, no. 2, p. 279-292, https://doi.org/10.1007/s10592-015-0780-7.","productDescription":"14 p.","startPage":"279","endPage":"292","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059406","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":309395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-26","publicationStatus":"PW","scienceBaseUri":"563486b7e4b048076347fb22","contributors":{"authors":[{"text":"Pfeiffer, John M. 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This is due, in part, to ecorestoration specialists making the false assumption that some types of data (e.g. discrete variables such as species identification and abundance classes) are not subject to evaluations of data quality. Moreover, emergent behavior manifested by complex, adapting, and nonlinear organizations responsible for monitoring the success of ecorestoration projects tend to unconsciously minimize disorder, QA/QC being an activity perceived as creating disorder. We discuss similarities and differences in assessing precision and accuracy for field and laboratory data. Although the concepts for assessing precision and accuracy of ecorestoration field data are conceptually the same as laboratory data, the manner in which these data quality attributes are assessed is different. From a sample analysis perspective, a field crew is comparable to a laboratory instrument that requires regular “recalibration,” with results obtained by experts at the same plot treated as laboratory calibration standards. Unlike laboratory standards and reference materials, the “true” value for many field variables is commonly unknown. In the laboratory, specific QA/QC samples assess error for each aspect of the measurement process, whereas field revisits assess precision and accuracy of the entire data collection process following initial calibration. Rigorous QA/QC data in an ecorestoration project are essential for evaluating the success of a project, and they provide the only objective “legacy” of the dataset for potential legal challenges and future uses.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.12284","usgsCitation":"Stapanian, M.A., Lewis, T.E., Palmer, C.J., and Middlebrook Amos, M., 2016, Assessing accuracy and precision for field and laboratory data: a perspective in ecosystem restoration: Restoration Ecology, v. 24, no. 1, p. 18-26, https://doi.org/10.1111/rec.12284.","productDescription":"9 p.","startPage":"18","endPage":"26","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054244","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":309393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-23","publicationStatus":"PW","scienceBaseUri":"563486aae4b048076347fafb","contributors":{"authors":[{"text":"Stapanian, Martin A. 0000-0001-8173-4273 mstapanian@usgs.gov","orcid":"https://orcid.org/0000-0001-8173-4273","contributorId":3425,"corporation":false,"usgs":true,"family":"Stapanian","given":"Martin","email":"mstapanian@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":572040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewis, Timothy E","contributorId":147563,"corporation":false,"usgs":false,"family":"Lewis","given":"Timothy","email":"","middleInitial":"E","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":572041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmer, Craig J.","contributorId":36028,"corporation":false,"usgs":true,"family":"Palmer","given":"Craig","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":572042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middlebrook Amos, Molly","contributorId":147564,"corporation":false,"usgs":false,"family":"Middlebrook Amos","given":"Molly","email":"","affiliations":[{"id":6937,"text":"CSC – IT Centre for Science, P.O. Box 405, 02101, Espoo, Finland","active":true,"usgs":false}],"preferred":false,"id":572043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191236,"text":"70191236 - 2016 - An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States","interactions":[],"lastModifiedDate":"2017-10-02T16:19:59","indexId":"70191236","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1191,"text":"Cartography and Geographic Information Science","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States","docAbstract":"Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230406.2015.1067829","usgsCitation":"Wendel, J., Buttenfield, B., and Stanislawski, L.V., 2016, An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States: Cartography and Geographic Information Science, v. 43, no. 3, p. 233-249, https://doi.org/10.1080/15230406.2015.1067829.","productDescription":"17 p.","startPage":"233","endPage":"249","ipdsId":"IP-062949","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":346332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"43","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-15","publicationStatus":"PW","scienceBaseUri":"59d35029e4b05fe04cc34d65","contributors":{"authors":[{"text":"Wendel, Jochen","contributorId":196803,"corporation":false,"usgs":false,"family":"Wendel","given":"Jochen","affiliations":[],"preferred":false,"id":711651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P.","contributorId":145538,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":711652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":711650,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173663,"text":"70173663 - 2016 - Animal movement constraints improve resource selection inference in the presence of telemetry error","interactions":[],"lastModifiedDate":"2016-06-07T15:20:38","indexId":"70173663","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Animal movement constraints improve resource selection inference in the presence of telemetry error","docAbstract":"Multiple factors complicate the analysis of animal telemetry location data. Recent advancements address issues such as temporal autocorrelation and telemetry measurement error, but additional challenges remain. Difficulties introduced by complicated error structures or barriers to animal movement can weaken inference. We propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. We specify a model for telemetry data observed with error conditional on unobserved true locations that reflects prior knowledge about constraints in the animal movement process. The observed telemetry data are modeled using a flexible distribution that accommodates extreme errors and complicated error structures. Although constraints to movement are often viewed as a nuisance, we use constraints to simultaneously estimate and account for telemetry error. We apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with measurement error and movement constraints. We then apply our framework to an Argos satellite telemetry data set on harbor seals (Phoca vitulina) in the Gulf of Alaska, a species that is constrained to move within the marine environment and adjacent coastlines.
","language":"English","publisher":"Ecological Society of America, Wiley","doi":"10.1890/15-0472.1","usgsCitation":"Brost, B.M., Hooten, M., Hanks, E., and Small, R.J., 2016, Animal movement constraints improve resource selection inference in the presence of telemetry error: Ecology, v. 96, no. 10, p. 2590-2597, https://doi.org/10.1890/15-0472.1.","productDescription":"8 p.","startPage":"2590","endPage":"2597","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060441","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471447,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/15-0472.1","text":"Publisher Index Page"},{"id":323199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5757f02ee4b04f417c24da1b","contributors":{"authors":[{"text":"Brost, Brian M.","contributorId":171484,"corporation":false,"usgs":false,"family":"Brost","given":"Brian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":637471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Ephraim M.","contributorId":104630,"corporation":false,"usgs":true,"family":"Hanks","given":"Ephraim M.","affiliations":[],"preferred":false,"id":637596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Small, Robert J.","contributorId":171486,"corporation":false,"usgs":false,"family":"Small","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":637597,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168681,"text":"70168681 - 2016 - Evaluating abundance and trends in a Hawaiian avian community using state-space analysis","interactions":[],"lastModifiedDate":"2018-01-04T12:37:05","indexId":"70168681","displayToPublicDate":"2015-09-30T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1048,"text":"Bird Conservation International","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating abundance and trends in a Hawaiian avian community using state-space analysis","docAbstract":"Estimating population abundances and patterns of change over time are important in both ecology and conservation. Trend assessment typically entails fitting a regression to a time series of abundances to estimate population trajectory. However, changes in abundance estimates from year-to-year across time are due to both true variation in population size (process variation) and variation due to imperfect sampling and model fit. State-space models are a relatively new method that can be used to partition the error components and quantify trends based only on process variation. We compare a state-space modelling approach with a more traditional linear regression approach to assess trends in uncorrected raw counts and detection-corrected abundance estimates of forest birds at Hakalau Forest National Wildlife Refuge, Hawai‘i. Most species demonstrated similar trends using either method. In general, evidence for trends using state-space models was less strong than for linear regression, as measured by estimates of precision. However, while the state-space models may sacrifice precision, the expectation is that these estimates provide a better representation of the real world biological processes of interest because they are partitioning process variation (environmental and demographic variation) and observation variation (sampling and model variation). The state-space approach also provides annual estimates of abundance which can be used by managers to set conservation strategies, and can be linked to factors that vary by year, such as climate, to better understand processes that drive population trends.
","language":"English","publisher":"Cambridge University Press","publisherLocation":"Cambridge","doi":"10.1017/S0959270915000088","usgsCitation":"Camp, R., Brinck, K., Gorresen, P.M., and Paxton, E., 2016, Evaluating abundance and trends in a Hawaiian avian community using state-space analysis: Bird Conservation International, v. 26, no. 2, p. 225-242, https://doi.org/10.1017/S0959270915000088.","productDescription":"18 p.","startPage":"225","endPage":"242","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064721","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":318360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Hakalau Forest National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.467529296875,\n 19.427743935948932\n ],\n [\n -155.467529296875,\n 19.89330573274471\n ],\n [\n -155.15167236328125,\n 19.89330573274471\n ],\n [\n -155.15167236328125,\n 19.427743935948932\n ],\n [\n -155.467529296875,\n 19.427743935948932\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-30","publicationStatus":"PW","scienceBaseUri":"56cee261e4b015c306ec5ebf","contributors":{"authors":[{"text":"Camp, Richard J.","contributorId":27392,"corporation":false,"usgs":true,"family":"Camp","given":"Richard J.","affiliations":[],"preferred":false,"id":621250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W.","contributorId":78215,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","affiliations":[],"preferred":false,"id":621251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorresen, P. M. mgorresen@usgs.gov","contributorId":18552,"corporation":false,"usgs":true,"family":"Gorresen","given":"P.","email":"mgorresen@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":false,"id":621252,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":621249,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170002,"text":"70170002 - 2016 - A new method for discovering behavior patterns among animal movements","interactions":[],"lastModifiedDate":"2017-08-26T16:45:50","indexId":"70170002","displayToPublicDate":"2015-09-29T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2046,"text":"International Journal of Geographical Information Science","active":true,"publicationSubtype":{"id":10}},"title":"A new method for discovering behavior patterns among animal movements","docAbstract":"Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.
","language":"English","publisher":"Royal Institute of International Affairs","publisherLocation":"London","doi":"10.1080/13658816.2015.1091462","usgsCitation":"Wang, Y., Luo, Z., Takekawa, J.Y., Prosser, D.J., Xiong, Y., Newman, S., Xiao, X., Batbayar, N., Spragens, K.A., Balachandran, S., and Yan, B., 2016, A new method for discovering behavior patterns among animal movements: International Journal of Geographical Information Science, v. 30, no. 5, p. 929-947, https://doi.org/10.1080/13658816.2015.1091462.","productDescription":"19 p.","startPage":"929","endPage":"947","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066274","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471449,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4874732","text":"External Repository"},{"id":319713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-29","publicationStatus":"PW","scienceBaseUri":"56ff9bb7e4b0328dcb7eaa38","contributors":{"authors":[{"text":"Wang, Y.","contributorId":64213,"corporation":false,"usgs":true,"family":"Wang","given":"Y.","affiliations":[],"preferred":false,"id":625839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luo, Ze","contributorId":41307,"corporation":false,"usgs":true,"family":"Luo","given":"Ze","affiliations":[],"preferred":false,"id":625841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":625840,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625838,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiong, Y.","contributorId":168415,"corporation":false,"usgs":false,"family":"Xiong","given":"Y.","email":"","affiliations":[{"id":25285,"text":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":625842,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newman, S.","contributorId":7678,"corporation":false,"usgs":true,"family":"Newman","given":"S.","affiliations":[],"preferred":false,"id":625843,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xiao, X.","contributorId":82869,"corporation":false,"usgs":true,"family":"Xiao","given":"X.","email":"","affiliations":[],"preferred":false,"id":625844,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Batbayar, N.","contributorId":47074,"corporation":false,"usgs":true,"family":"Batbayar","given":"N.","email":"","affiliations":[],"preferred":false,"id":625845,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spragens, Kyle A. kspragens@usgs.gov","contributorId":5775,"corporation":false,"usgs":true,"family":"Spragens","given":"Kyle","email":"kspragens@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":625846,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Balachandran, S.","contributorId":26891,"corporation":false,"usgs":true,"family":"Balachandran","given":"S.","affiliations":[],"preferred":false,"id":625847,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yan, B.","contributorId":11739,"corporation":false,"usgs":true,"family":"Yan","given":"B.","email":"","affiliations":[],"preferred":false,"id":625848,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70157593,"text":"70157593 - 2016 - Human activities cause distinct dissolved organic matter composition across freshwater ecosystems","interactions":[],"lastModifiedDate":"2016-02-01T13:16:17","indexId":"70157593","displayToPublicDate":"2015-09-29T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Human activities cause distinct dissolved organic matter composition across freshwater ecosystems","docAbstract":"Dissolved organic matter (DOM) composition in freshwater ecosystems is influenced by interactions between physical, chemical, and biological processes that are controlled, at one level, by watershed landscape, hydrology, and their connections. Against this environmental template, humans may strongly influence DOM composition. Yet, we lack a comprehensive understanding of DOM composition variation across freshwater ecosystems differentially affected by human activity. Using optical properties, we described DOM variation across five ecosystem groups of the Laurentian Great Lakes Region: large lakes, Kawartha Lakes, Experimental Lakes Area, urban stormwater ponds, and rivers (n = 184 sites). We determined how between ecosystem variation in DOM composition related to watershed size, land use and cover, water quality measures (conductivity, dissolved organic carbon (DOC), nutrient concentration, chlorophyll a), and human population density. The five freshwater ecosystem groups had distinctive DOM composition from each other. These significant differences were not explained completely through differences in watershed size nor spatial autocorrelation. Instead, multivariate partial least squares regression showed that DOM composition was related to differences in human impact across freshwater ecosystems. In particular, urban/developed watersheds with higher human population densities had a unique DOM composition with a clear anthropogenic influence that was distinct from DOM composition in natural land cover and/or agricultural watersheds. This nonagricultural, human developed impact on aquatic DOM was most evident through increased levels of a microbial, humic-like parallel factor analysis component (C6). Lotic and lentic ecosystems with low human population densities had DOM compositions more typical of clear water to humic-rich freshwater ecosystems but C6 was only present at trace to background levels. Consequently, humans are strongly altering the quality of DOM in waters nearby or flowing through highly populated areas, which may alter carbon cycles in anthropogenically disturbed ecosystems at broad scales.
","language":"English","publisher":"John Wiley & Sons Ltd.","doi":"10.1111/gcb.13094","usgsCitation":"Williams, C.J., Frost, P.C., Morales-Williams, A.M., Larson, J.H., Richardson, W.B., Chiandet, A.S., and Xenopoulos, M.A., 2016, Human activities cause distinct dissolved organic matter composition across freshwater ecosystems: Global Change Biology, v. 22, no. 2, p. 613-626, https://doi.org/10.1111/gcb.13094.","productDescription":"14 p.","startPage":"613","endPage":"626","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064700","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":308689,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-11","publicationStatus":"PW","scienceBaseUri":"560ba83de4b058f706e53a7f","contributors":{"authors":[{"text":"Williams, Clayton J.","contributorId":138631,"corporation":false,"usgs":false,"family":"Williams","given":"Clayton","email":"","middleInitial":"J.","affiliations":[{"id":12468,"text":"Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA","active":true,"usgs":false}],"preferred":false,"id":573702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frost, Paul C.","contributorId":138628,"corporation":false,"usgs":false,"family":"Frost","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":573703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morales-Williams, Ana M.","contributorId":148057,"corporation":false,"usgs":false,"family":"Morales-Williams","given":"Ana","email":"","middleInitial":"M.","affiliations":[{"id":16985,"text":"Trent University & Iowa State University","active":true,"usgs":false}],"preferred":false,"id":573704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":573701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":573705,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chiandet, Aisha S.","contributorId":148058,"corporation":false,"usgs":false,"family":"Chiandet","given":"Aisha","email":"","middleInitial":"S.","affiliations":[{"id":16986,"text":"Severn Sound Environmental Association","active":true,"usgs":false}],"preferred":false,"id":573706,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xenopoulos, Marguerite A.","contributorId":138629,"corporation":false,"usgs":false,"family":"Xenopoulos","given":"Marguerite","email":"","middleInitial":"A.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":573707,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70176942,"text":"70176942 - 2016 - Precipitation regime classification for the Mojave Desert: Implications for fire occurrence","interactions":[],"lastModifiedDate":"2017-04-07T13:55:04","indexId":"70176942","displayToPublicDate":"2015-09-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Precipitation regime classification for the Mojave Desert: Implications for fire occurrence","docAbstract":"Long periods of drought or above-average precipitation affect Mojave Desert vegetation condition, biomass and susceptibility to fire. Changes in the seasonality of precipitation alter the likelihood of lightning, a key ignition source for fires. The objectives of this study were to characterize the relationship between recent, historic, and future precipitation patterns and fire. Classifying monthly precipitation data from 1971 to 2010 reveals four precipitation regimes: low winter/low summer, moderate winter/moderate summer, high winter/low summer and high winter/high summer. Two regimes with summer monsoonal precipitation covered only 40% of the Mojave Desert ecoregion but contain 88% of the area burned and 95% of the repeat burn area. Classifying historic precipitation for early-century (wet) and mid-century (drought) periods reveals distinct shifts in regime boundaries. Early-century results are similar to current, while the mid-century results show a sizeable reduction in area of regimes with a strong monsoonal component. Such a shift would suggest that fires during the mid-century period would be minimal and anecdotal records confirm this. Predicted precipitation patterns from downscaled global climate models indicate numerous epochs of high winter precipitation, inferring higher fire potential for many multi-decade periods during the next century.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2015.09.002","usgsCitation":"Tagestad, J., Brooks, M.L., Cullinan, V., Downs, J., and McKinley, R., 2016, Precipitation regime classification for the Mojave Desert: Implications for fire occurrence: Journal of Arid Environments, v. 124, p. 388-397, https://doi.org/10.1016/j.jaridenv.2015.09.002.","productDescription":"10 p.","startPage":"388","endPage":"397","numberOfPages":"10","ipdsId":"IP-063012","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":329530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.817138671875,\n 33.47727218776036\n ],\n [\n -118.817138671875,\n 37.96152331396614\n ],\n [\n -112.664794921875,\n 37.96152331396614\n ],\n [\n -112.664794921875,\n 33.47727218776036\n ],\n [\n -118.817138671875,\n 33.47727218776036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57ffdefee4b0824b2d179cf6","contributors":{"authors":[{"text":"Tagestad, Jerry","contributorId":175339,"corporation":false,"usgs":false,"family":"Tagestad","given":"Jerry","email":"","affiliations":[{"id":27560,"text":"PNNL","active":true,"usgs":false}],"preferred":false,"id":650820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":650819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cullinan, Valerie","contributorId":175340,"corporation":false,"usgs":false,"family":"Cullinan","given":"Valerie","affiliations":[{"id":27560,"text":"PNNL","active":true,"usgs":false}],"preferred":false,"id":650821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downs, Janelle","contributorId":175341,"corporation":false,"usgs":false,"family":"Downs","given":"Janelle","email":"","affiliations":[{"id":27560,"text":"PNNL","active":true,"usgs":false}],"preferred":false,"id":650822,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKinley, Randy 0000-0001-7644-6365 rmckinley@usgs.gov","orcid":"https://orcid.org/0000-0001-7644-6365","contributorId":1354,"corporation":false,"usgs":true,"family":"McKinley","given":"Randy","email":"rmckinley@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":650823,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70157236,"text":"70157236 - 2016 - Blind identification of the Millikan Library from earthquake data considering soil–structure interaction","interactions":[],"lastModifiedDate":"2016-06-17T09:37:01","indexId":"70157236","displayToPublicDate":"2015-09-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5007,"text":"Structural Control and Health Monitoring","active":true,"publicationSubtype":{"id":10}},"title":"Blind identification of the Millikan Library from earthquake data considering soil–structure interaction","docAbstract":"The Robert A. Millikan Library is a reinforced concrete building with a basement level and nine stories above the ground. Located on the campus of California Institute of Technology (Caltech) in Pasadena California, it is among the most densely instrumented buildings in the U.S. From the early dates of its construction, it has been the subject of many investigations, especially regarding soil–structure interaction effects. It is well accepted that the structure is significantly interacting with the surrounding soil, which implies that the true foundation input motions cannot be directly recorded during earthquakes because of inertial effects. Based on this limitation, input–output modal identification methods are not applicable to this soil–structure system. On the other hand, conventional output-only methods are typically based on the unknown input signals to be stationary whitenoise, which is not the case for earthquake excitations. Through the use of recently developed blind identification (i.e. output-only) methods, it has become possible to extract such information from only the response signals because of earthquake excitations. In the present study, we employ such a blind identification method to extract the modal properties of the Millikan Library. We present some modes that have not been identified from force vibration tests in several studies to date. Then, to quantify the contribution of soil–structure interaction effects, we first create a detailed Finite Element (FE) model using available information about the superstructure; and subsequently update the soil–foundation system's dynamic stiffnesses at each mode such that the modal properties of the entire soil–structure system agree well with those obtained via output-only modal identification.
","language":"English","publisher":"International Association for Structural Control and Monitoring","publisherLocation":"Chichester, UK","doi":"10.1002/stc.1803","usgsCitation":"Ghahari, S.F., Abazarsa, F., Avci, O., Çelebi, M., and Taciroglu, E., 2016, Blind identification of the Millikan Library from earthquake data considering soil–structure interaction: Structural Control and Health Monitoring, v. 23, no. 4, p. 684-706, https://doi.org/10.1002/stc.1803.","productDescription":"23 p.","startPage":"684","endPage":"706","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068999","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471450,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/stc.1803","text":"Publisher Index Page"},{"id":318538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Pasadena","otherGeospatial":"Robert A. Millikan Library, California Insttitute of Technology","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.12946319580077,\n 34.13136240467381\n ],\n [\n -118.12946319580077,\n 34.14203648796777\n ],\n [\n -118.12130928039551,\n 34.14203648796777\n ],\n [\n -118.12130928039551,\n 34.13136240467381\n ],\n [\n -118.12946319580077,\n 34.13136240467381\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-29","publicationStatus":"PW","scienceBaseUri":"56d96e3ce4b015c306f7644c","contributors":{"authors":[{"text":"Ghahari, S. F.","contributorId":147707,"corporation":false,"usgs":false,"family":"Ghahari","given":"S.","email":"","middleInitial":"F.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":572365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abazarsa, F.","contributorId":147708,"corporation":false,"usgs":false,"family":"Abazarsa","given":"F.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":572366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avci, O.","contributorId":147709,"corporation":false,"usgs":false,"family":"Avci","given":"O.","email":"","affiliations":[{"id":16914,"text":"University of Qatar","active":true,"usgs":false}],"preferred":false,"id":572367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Çelebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":3205,"corporation":false,"usgs":true,"family":"Çelebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":572364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taciroglu, E.","contributorId":147710,"corporation":false,"usgs":false,"family":"Taciroglu","given":"E.","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":572368,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211939,"text":"70211939 - 2016 - Seasonal temperature and precipitation regulate brook trout young-of-the-year abundance and population dynamics","interactions":[],"lastModifiedDate":"2021-04-27T18:50:22.024529","indexId":"70211939","displayToPublicDate":"2015-09-28T11:43:50","publicationYear":"2016","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":"Seasonal temperature and precipitation regulate brook trout young-of-the-year abundance and population dynamics","docAbstract":"