In the North Pacific, northern fulmar (Fulmarus glacialis) forms extensive colonies in few locales, which may lead to limited gene flow and locale-specific population threats. In the Atlantic, there are thousands of colonies of varying sizes and in Europe the species is considered threatened. Prior screens and classical microsatellite development in fulmar failed to provide a suite of markers adequate for population genetics studies.
The objective of this study was to isolate a suite of polymorphic microsatellite loci with sufficient variability to quantify levels of gene flow, population affinity, and identify familial relationships in fulmar. We also performed a cross-species screening of these markers in eight other species.
We used shotgun sequencing to isolate 26 novel microsatellite markers in fulmar to screen for variability using individuals from two distinct regions: the Pacific (Chagulak Island, Alaska) and the Atlantic (Hafnarey Island, Iceland).
Polymorphism was present in 24 loci in Chagulak and 23 in Hafnarey, while one locus failed to amplify in either colony. Polymorphic loci exhibited moderate levels of genetic diversity and this suite of loci uncovered genetic structuring between the regions. Among the other species screened, polymorphism was present in one to seven loci.
The loci yielded sufficient variability for use in population studies and estimation of familial relationships; as few as five loci provide resolution to determine individual identity. These markers will allow further insight into the global population dynamics and phylogeography of fulmars. We also demonstrated some markers are transferable to other species.
We estimate earthquake ground-motion variability in the central and eastern U.S. (CEUS) by varying the model parameters of a deterministic physics-based and a stochastic site-based simulation method. Utilizing a moderate-magnitude database of recordings, we simulate ground motions for larger-magnitude scenarios M6.0, 6.5, 7.0, 7.5, and 8.0. For the physics-based method, we vary the faulting mechanism, slip, stress drop, rupture velocity, source depth, and 1D velocity structure. For the stochastic method, we simulate realizations using a set of six model parameters, each of which has a pre-assigned probability distribution. The median spectral accelerations over all synthetic realizations are compared with the NGA-East models. The synthetic standard deviation for deterministic simulations ranges from approximately 0.4 to 0.85 for various magnitudes and distances, whereas that for stochastic simulations is between 0.48 and 1.04. Based on the simulation results and comparisons with NGA-East variability models, a range for ground motion variability in the CEUS is discussed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"ICASP 13 Proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13)","conferenceDate":"May 26-30, 2019","conferenceLocation":"Seoul, South Korea","language":"English","publisher":"Seoul National University","doi":"10.22725/ICASP13.075","usgsCitation":"Sun, X., Rezaeian, S., Clayton, B., and Hartzell, S.H., 2019, Estimation of ground motion variability in the CEUS using simulations, in ICASP 13 Proceedings, Seoul, South Korea, May 26-30, 2019, 75; 8 p., https://doi.org/10.22725/ICASP13.075.","productDescription":"75; 8 p.","ipdsId":"IP-105826","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":365065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":758280,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Sun, Xiaodan","contributorId":139583,"corporation":false,"usgs":false,"family":"Sun","given":"Xiaodan","email":"","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. Current address: TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":false,"id":758278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":758277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clayton, Brandon 0000-0003-0502-7184 bclayton@usgs.gov","orcid":"https://orcid.org/0000-0003-0502-7184","contributorId":197196,"corporation":false,"usgs":true,"family":"Clayton","given":"Brandon","email":"bclayton@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":758279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":765123,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208130,"text":"70208130 - 2019 - Probabilistic seismic hazard analysis using stochastic simulated ground motions","interactions":[],"lastModifiedDate":"2020-01-31T11:16:05","indexId":"70208130","displayToPublicDate":"2019-05-26T11:10:52","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Probabilistic seismic hazard analysis using stochastic simulated ground motions","docAbstract":": In recent years, ground motion models used in probabilistic seismic hazard analyses (PSHA) have evolved from the traditional approach of using ground motion prediction equations (GMPEs) to using ground motion time series models. The purpose of this paper is to develop an approach to perform a probabilistic seismic hazard analysis using stochastic site-based simulation techniques. These techniques consist of empirical stochastic models that simulate both near-fault and far-field ground motion time series. The near-fault models consider directivity pulses, which can impose large seismic demands. The proposed approach was applied to a site located in Los Angeles Downtown and the corresponding hazard curves were developed. The results were compared to hazard curves derived for the same site from CyberShake, which uses a physics-based simulation approach, and from a traditional GMPE approach. The comparison indicated that the proposed methodology accurately describes the seismic hazard at the site at high hazard levels. The proposed approach is computationally efficient compared to the use of physics-based simulations like CyberShake.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of ICASP13","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"13th International Conference on Applications of Statistics and Probability in Civil Engineering","conferenceDate":"May 26-30, 2019","conferenceLocation":"Seoul, South Korea","language":"English","publisher":"Seoul National University","doi":"10.22725/ICASP13.235","usgsCitation":"Azar, S., Dabaghi, M., and Rezaeian, S., 2019, Probabilistic seismic hazard analysis using stochastic simulated ground motions, in Proceedings of ICASP13, Seoul, South Korea, May 26-30, 2019, 8 p., https://doi.org/10.22725/ICASP13.235.","productDescription":"8 p.","ipdsId":"IP-105873","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":371808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Azar, Sarah","contributorId":221887,"corporation":false,"usgs":false,"family":"Azar","given":"Sarah","email":"","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":780639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dabaghi, Mayssa","contributorId":221888,"corporation":false,"usgs":false,"family":"Dabaghi","given":"Mayssa","email":"","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":780640,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":780638,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205083,"text":"70205083 - 2019 - Mismatches between breeding phenology and resource abundance of resident alpine ptarmigan negatively affect chick survival","interactions":[],"lastModifiedDate":"2019-09-04T14:52:15","indexId":"70205083","displayToPublicDate":"2019-05-26T07:30:24","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Mismatches between breeding phenology and resource abundance of resident alpine ptarmigan negatively affect chick survival","docAbstract":"1. Phenological mismatches – defined here as the difference in reproductive timing of an individual relative to the availability of its food resources – occur in many avian species. Mistiming breeding activities in environments with constrained breeding windows may have severe fitness costs due to reduced opportunities for repeated breeding attempts. Therefore, species occurring in alpine environments may be particularly vulnerable. 2. We studied fitness consequences of timing of breeding in an alpine-endemic species, the white-tailed ptarmigan (Lagopus leucura), to investigate its influence on chick survival. We estimated phenological mismatch by measuring plant and arthropods used by ptarmigan in relation to their timing of breeding. 3. We monitored 120 nests and 67 broods over a three-year period (2013–2015) at three alpine study sites in the Rocky Mountains of Colorado. During this same period we actively monitored food resource abundance in brood-use areas to develop year and site specific resource phenology curves. We developed several mismatch indices from these curves that were then fit as covariates in mark-recapture chick survival models. 4. A correlation analysis between seasonal changes in arthropod and food plant abundance indicated that a normalized difference vegetation index (NDVI) was likely the best predictor for food available to hens and chicks. A survival model that included an interaction between NDVI mismatch and chick age received strong support and indicated young chicks were more susceptible to mismatch than older chicks. 5. We provide evidence that individual females of a resident alpine species can be negatively affected by phenological mismatch. Our study focused on individual females and did not examine if phenological mismatch was present at the population level. Future work in animal populations occurring in mountain systems focusing on a combination of both individual- and population- level metrics of mismatch will be beneficial.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5290","usgsCitation":"Wann, G.T., Aldridge, C.L., Seglund, A.E., Oyler-McCance, S.J., Kondratieff, B.C., and Braun, C.E., 2019, Mismatches between breeding phenology and resource abundance of resident alpine ptarmigan negatively affect chick survival: Ecology and Evolution, v. 9, no. 12, p. 7200-7212, https://doi.org/10.1002/ece3.5290.","productDescription":"13 p.","startPage":"7200","endPage":"7212","ipdsId":"IP-108042","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467594,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5290","text":"Publisher Index Page"},{"id":367106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Wann, Gregory T 0000-0001-9076-7819","orcid":"https://orcid.org/0000-0001-9076-7819","contributorId":218685,"corporation":false,"usgs":false,"family":"Wann","given":"Gregory","email":"","middleInitial":"T","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":769909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":769908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seglund, Amy E.","contributorId":218686,"corporation":false,"usgs":false,"family":"Seglund","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":769910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":769913,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kondratieff, Boris C.","contributorId":24868,"corporation":false,"usgs":false,"family":"Kondratieff","given":"Boris","email":"","middleInitial":"C.","affiliations":[{"id":17860,"text":"Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":769911,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Braun, Clait E.","contributorId":200013,"corporation":false,"usgs":false,"family":"Braun","given":"Clait","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":769912,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208423,"text":"70208423 - 2019 - Implications of seismic design values for economic losses","interactions":[],"lastModifiedDate":"2020-02-10T06:51:24","indexId":"70208423","displayToPublicDate":"2019-05-26T06:50:15","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Implications of seismic design values for economic losses","docAbstract":"In the U.S., seismic design values are determined mostly through a risk-targeting process, which combines information about the expected collapse fragility of code-designed structures with seismic hazard at a site. However, this target only applies where the risk-targeted ground motions govern the design. In other areas, primarily close to active faults, seismic design values are reduced to values calculated from deterministic seismic hazard analysis, increasing seismic risk for near-fault sites by an unknown quantity. This study investigates the implications of designing buildings using deterministic and probabilistic design values in terms of earthquake-induced economic consequences. This investigation is carried out using a performance-based seismic risk assessment of modern code-designed buildings with various structural systems, following the FEMA P-58 framework. Specifically, structural responses and losses associated with code-designed systems (i.e., reinforced concrete, steel, wood light frame, and precast tilt-up buildings) considering different design values (i.e., risk-targeted, deterministic, and uniform-hazard) are assessed. This study finds that, while risk-targeted design maps specify a uniform collapse risk, they do not provide uniform risk of economic losses to modern buildings across the U.S. and are instead dependent on building type and site properties. Also, for the sites in this study governed by deterministic capping, design values in the current code may be up to 30% lower than design values derived from risk-targeted design maps, resulting in up to 40% higher expected seismic losses.","conferenceTitle":"13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13)","conferenceDate":"May 26-30, 2019","conferenceLocation":"Seoul, South Korea","language":"English","publisher":"SNU","doi":"10.22725/ICASP13.206","usgsCitation":"Cook, D., Liel, A.B., Luco, N., Almeter, E., and Haselton, C.B., 2019, Implications of seismic design values for economic losses, 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13), Seoul, South Korea, May 26-30, 2019, 8 p., https://doi.org/10.22725/ICASP13.206.","productDescription":"8 p.","ipdsId":"IP-105831","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":372179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Dustin","contributorId":222296,"corporation":false,"usgs":false,"family":"Cook","given":"Dustin","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":781820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liel, Abbie B.","contributorId":184158,"corporation":false,"usgs":false,"family":"Liel","given":"Abbie","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":781821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":781819,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Almeter, Edward","contributorId":222297,"corporation":false,"usgs":false,"family":"Almeter","given":"Edward","email":"","affiliations":[{"id":40513,"text":"Haselton Baker Risk Group","active":true,"usgs":false}],"preferred":false,"id":781822,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haselton, Curt B.","contributorId":202457,"corporation":false,"usgs":false,"family":"Haselton","given":"Curt","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":781823,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205123,"text":"70205123 - 2019 - Characterizing groundwater/surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA","interactions":[],"lastModifiedDate":"2019-09-04T15:44:24","indexId":"70205123","displayToPublicDate":"2019-05-25T15:37:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing groundwater/surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA","docAbstract":"The Mississippi Delta, located in northwest Mississippi, is an area dense with industrial-level agriculture sustained by groundwater-dependent irrigation supplied by the Mississippi River Valley Alluvial aquifer (alluvial aquifer). The Delta provides agricultural commodities across the United States and around the world. Observed declines in groundwater altitudes and streamflow contemporaneous with increases in irrigation have raised concerns about future groundwater availability and the effects of groundwater withdrawals on streamflow. To quantify the impacts of groundwater withdrawals on streamflow and increase understanding of groundwater and surface-water interaction, hydrograph-separation techniques were used to estimate baseflow and identify statistical streamflow trends. The analysis was conducted using the U.S. Geological Survey Groundwater Toolbox open-source software and daily hydrologic data provided by a spatially-distributed network of paired groundwater wells and streamgaging sites. This study found that effects of groundwater withdrawals on streamflow were observed as statistically significant reductions in baseflow in areas with substantial groundwater-altitude declines. Hydrograph-separation and trend analyses may be applicable to assess the impacts of groundwater withdrawals in altered environments and streamflow may be used as a proxy for changes in groundwater availability. Characterizing and defining hydrologic relations between groundwater and surface water will help scientists and water-resource managers refine a regional groundwater-flow model that includes the Mississippi Delta that will be used to aid water-resource managers in future decisions concerning the alluvial aquifer.","language":"English","publisher":"Springer","doi":"10.1007/s10040-019-01981-6","usgsCitation":"Killian, C.D., Asquith, W.H., Barlow, J.R., Bent, G., Kress, W., Barlow, P.M., and Schmitz, D.W., 2019, Characterizing groundwater/surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA: Hydrogeology Journal, v. 27, no. 6, p. 2167-2179, https://doi.org/10.1007/s10040-019-01981-6.","productDescription":"13 p.","startPage":"2167","endPage":"2179","ipdsId":"IP-092609","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":467595,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-019-01981-6","text":"Publisher Index Page"},{"id":367194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River Valley Alluvial Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.30810546875,\n 37.23032838760387\n ],\n [\n -88.846435546875,\n 37.67512527892127\n ],\n [\n -89.71435546875,\n 37.71859032558816\n ],\n [\n -90.439453125,\n 37.020098201368114\n ],\n [\n -92.054443359375,\n 35.02999636902566\n ],\n [\n -91.5380859375,\n 33.4039312002347\n ],\n [\n -91.95556640625,\n 32.4263401615464\n ],\n [\n -92.098388671875,\n 32.03602003973755\n ],\n [\n -91.505126953125,\n 31.475524020001806\n ],\n [\n -90.8349609375,\n 31.42866311735861\n ],\n [\n -89.791259765625,\n 33.14675022877648\n ],\n [\n -90.186767578125,\n 34.66032236481892\n ],\n [\n -88.9892578125,\n 36.76529191711624\n ],\n [\n -88.30810546875,\n 37.23032838760387\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"6","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Killian, Courtney D. 0000-0002-2137-2722","orcid":"https://orcid.org/0000-0002-2137-2722","contributorId":213990,"corporation":false,"usgs":true,"family":"Killian","given":"Courtney","email":"","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barlow, Jeannie R. B. 0000-0002-0799-4656 jbarlow@usgs.gov","orcid":"https://orcid.org/0000-0002-0799-4656","contributorId":3701,"corporation":false,"usgs":true,"family":"Barlow","given":"Jeannie","email":"jbarlow@usgs.gov","middleInitial":"R. B.","affiliations":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bent, Gardner C. 0000-0002-5085-3146","orcid":"https://orcid.org/0000-0002-5085-3146","contributorId":205226,"corporation":false,"usgs":true,"family":"Bent","given":"Gardner C.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770121,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barlow, Paul M. 0000-0003-4247-6456 pbarlow@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6456","contributorId":1200,"corporation":false,"usgs":true,"family":"Barlow","given":"Paul","email":"pbarlow@usgs.gov","middleInitial":"M.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":770119,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmitz, Darrel W. 0000-0002-6154-8040","orcid":"https://orcid.org/0000-0002-6154-8040","contributorId":218742,"corporation":false,"usgs":false,"family":"Schmitz","given":"Darrel","email":"","middleInitial":"W.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":770122,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215293,"text":"70215293 - 2019 - Predicting hydrologic disturbance of streams using species occurrence data","interactions":[],"lastModifiedDate":"2020-10-14T15:39:43.431592","indexId":"70215293","displayToPublicDate":"2019-05-25T10:32:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Predicting hydrologic disturbance of streams using species occurrence data","docAbstract":"Aquatic organisms have adapted over evolutionary time-scales to hydrologic variability represented by the natural flow regime of rivers and streams in their unimpaired state. Rapid landscape change coupled with growing human demand for water have altered natural flow regimes of many rivers and streams on a global scale. Climate non-stationarity is expected to further intensify hydrologic variability, placing increased pressure on aquatic communities. Using a machine learning approach and georeferenced species occurrence data, we modeled and mapped spatial patterns of hydrologic disturbance for streams in Arkansas, Missouri, and eastern Oklahoma. Random forest (RF) models trained on fish community data, hydrologic, and landscape metrics for gaged streams in the National Hydrography (NHDPlusV2) database were used to predict a hydrologic disturbance index (HDI) for ungaged streams. The HDI is part of the USGS Geospatial Attributes of Gages for Evaluating Streamflow (GAGESII) database and is a composite index of watershed-scale disturbance from anthropogenic stressors. Fish presence/absence data had similar overall model prediction accuracy (77%; 95% CI: 0.74, 0.80) as flow variables (76%; CI: 0.73, 0.80). Including topographic variables increased the RF prediction accuracy of both the fish (90%; CI: 0.88, 0.92) and flow models (86%; CI: 0.84, 0.89). Spatial patterns of hydrologic disturbance suggest distinct ecohydrological regions exist where conservation actions may be focused. Streams with low HDI were predominately located in the Ozark Highlands, Boston Mountains, and Ouachita Mountains. Correlation analysis of HDI by flow regime showed groundwater stable streams had the lowest disturbance frequency, with over 50% of stream reaches with low HDI located in forested land cover. HDI was highest for big rivers, intermittent runoff streams and streams in areas of agricultural land use. Our results show long-term georeferenced biological data can provide a valuable resource for predictive modeling of hydrologic disturbance for ungaged rivers and streams.
We evaluated relationships among hydrogeomorphology, vegetation structure and composition, and avian communities among three subreaches of the San Acacia Reach of the Middle Rio Grande (MRG) River of New Mexico. The subreaches varied in degradation, with Subreach 1 being severely entrenched and hydrologically disconnected, Subreach 2 being the least impacted, and Subreach 3 being intermediately disturbed. Avian point count and habitat surveys were conducted to determine avian community structure and abundance, geomorphic feature, surface flooding, and vegetation structure and composition. Ground-nesting birds and low shrub-nesting birds were insensitive to hydrogeomorphic changes as they do not rely on native understory but can use exotic understory or woody debris. In contrast, canopy-nesting birds required native overstory; therefore, they were sensitive to hydrogeomorphic changes as native overstory species require surface floods to germinate and establish. Additionally, native overstory did not vary as expected as the moderately impacted subreach, Subreach 3, had more native overstory (
Avian avulavirus (commonly known as avian paramyxovirus-1 or APMV-1) can cause disease of varying severity in both domestic and wild birds. Understanding how viruses move among hosts and geography would be useful for informing prevention and control efforts. A Bayesian statistical framework was employed to estimate the evolutionary history of 1602 complete fusion gene APMV-1 sequences collected from 1970 to 2016 in order to infer viral transmission between avian host orders and diffusion among geographic regions. Ancestral states were estimated with a non-reversible continuous-time Markov chain model, allowing transition rates between discrete states to be calculated. The evolutionary analyses were stratified by APMV-1 classes I (n = 198) and II (n = 1404), and only those sequences collected between 2006 and 2016 were allowed to contribute host and location information to the viral migration networks.
While the current data was unable to assess impact of host domestication status on APMV-1 diffusion, these analyses supported the sharing of APMV-1 among divergent host taxa. The highest supported transition rate for both classes existed from domestic chickens to Anseriformes (class I:6.18 transitions/year, 95% highest posterior density (HPD) 0.31–20.02, Bayes factor (BF) = 367.2; class II:2.88 transitions/year, 95%HPD 1.9–4.06, BF = 34,582.9). Further, among class II viruses, domestic chickens also acted as a source for Columbiformes (BF = 34,582.9), other Galliformes (BF = 34,582.9), and Psittaciformes (BF = 34,582.9). Columbiformes was also a highly supported source to Anseriformes (BF = 322.0) and domestic chickens (BF = 402.6). Additionally, our results provide support for the diffusion of viruses among continents and regions, but no interhemispheric viral exchange between 2006 and 2016. Among class II viruses, the highest transition rates were estimated from South Asia to the Middle East (1.21 transitions/year; 95%HPD 0.36–2.45; BF = 67,107.8), from Europe to East Asia (1.17 transitions/year; 95%HPD 0.12–2.61; BF = 436.2) and from Europe to Africa (1.06 transitions/year, 95%HPD 0.07–2.51; BF = 169.3).
While migration appears to occur infrequently, geographic movement may be important in determining viral diversification and population structure. In contrast, inter-order transmission of APMV-1 may occur readily, but most events are transient with few lineages persisting in novel hosts.
Groundwater inundation (GWI) is a particularly challenging consequence of sea-level rise (SLR), as it progressively inundates infrastructure located above and below the ground surface. Paths of flooding by GWI differ from other types of SLR flooding (i.e., wave overwash, storm-drain backflow) such that it is more difficult to mitigate, and thus requires a separate set of highly innovative adaptation strategies to manage. To spur consideration of GWI in planning, data-intensive numerical modeling methods have been developed that produce locally specific visualizations of GWI, though the accessibility of such methods is limited by extensive data requirements. Conversely, the hydrostatic (or 'bathtub') modeling approach is widely used in adaptation planning owing to easily accessed visualizations (i.e., NOAA SLR Viewer), yet its capacity to simulate GWI has never been tested. Given the separate actions necessary to mitigate GWI relative to marine overwash, this is a significant gap. Here we compare a simple hydrostatic modeling method with a more deterministic, dynamic and robust 3D numerical modeling approach to explore the effectiveness of the hydrostatic method in simulating equilibrium aquifer effects of multi-decadal sea-level rise, and in turn GWI for Honolulu, Hawai'i. We find hydrostatic modeling in the Honolulu area and likely other settings may yield similar results to numerical modeling when referencing the local mean higher-high water tide datum (generally typical of flood studies). These findings have the potential to spur preliminary understanding of GWI impacts in municipalities that lack the required data to conduct rigorous groundwater-modeling investigations. We note that the methods explored here for Honolulu do not simulate dynamic coastal processes (i.e., coastal erosion, sediment accretion or changes in land cover) and thus are most appropriately applied to regions that host heavily armored shorelines behind which GWI can develop.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/2515-7620/ab21fe","usgsCitation":"Habel, S., Fletcher, C., Rotzoll, K., El-Kadi, A.I., and Oki, D., 2019, Comparison of a simple hydrostatic and a data-intensive 3D numerical modeling method of simulating sea-level rise induced groundwater inundation for Honolulu, Hawai'i, USA: Environmental Research Letters, v. 1, no. 4, 041005, 12 p., https://doi.org/10.1088/2515-7620/ab21fe.","productDescription":"041005, 12 p.","ipdsId":"IP-102017","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":467598,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2515-7620/ab21fe","text":"Publisher Index Page"},{"id":385147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.04931640625,\n 21.289374355860424\n ],\n [\n -157.9010009765625,\n 21.289374355860424\n ],\n [\n -157.9010009765625,\n 21.4121622297254\n ],\n [\n -158.04931640625,\n 21.4121622297254\n ],\n [\n -158.04931640625,\n 21.289374355860424\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Habel, Shellie 0000-0001-9295-0596","orcid":"https://orcid.org/0000-0001-9295-0596","contributorId":257499,"corporation":false,"usgs":false,"family":"Habel","given":"Shellie","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":814397,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fletcher, Charles H.","contributorId":257500,"corporation":false,"usgs":false,"family":"Fletcher","given":"Charles H.","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":814398,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rotzoll, Kolja 0000-0002-5910-888X","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":201087,"corporation":false,"usgs":false,"family":"Rotzoll","given":"Kolja","affiliations":[],"preferred":false,"id":814399,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"El-Kadi, Aly I. 0000-0002-9623-5458","orcid":"https://orcid.org/0000-0002-9623-5458","contributorId":257501,"corporation":false,"usgs":false,"family":"El-Kadi","given":"Aly","email":"","middleInitial":"I.","affiliations":[{"id":35886,"text":"University of Hawaii at Manoa, Water Resources Research Center","active":true,"usgs":false}],"preferred":false,"id":814400,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":207735,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814401,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249342,"text":"70249342 - 2019 - Prototype downscaling algorithm for MODIS Satellite 1 km daytime active fire detections","interactions":[],"lastModifiedDate":"2023-10-04T12:12:32.70824","indexId":"70249342","displayToPublicDate":"2019-05-23T07:09:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Prototype downscaling algorithm for MODIS Satellite 1 km daytime active fire detections","docAbstract":"Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\\or small farms with mixed signatures from different crop types and\\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R2 = 0.95 for South Korea and R2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281.
Modern microbialites are often located within groundwater discharge zones, yet the role of groundwater in microbialite accretion has yet to be resolved. To understand relationships between groundwater, microbialites, and associated microbial communities, we quantified and characterized groundwater flow and chemistry in active thrombolitic microbialites in Lake Clifton, Western Australia, and compared these observations to inactive thrombolites and lakebed sediments. Groundwater flows upward through an interconnected network of pores within the microstructure of active thrombolites, discharging directly from thrombolite heads into the lake. This upwelling groundwater is fresher than lake water and is hypothesized to support microbial mat growth by reducing salinity and providing limiting nutrients in an osmotically stressful and oligotrophic habitat. This is in contrast to inactive thrombolites that show no evidence of microbial mat colonization and are infiltrated by hypersaline lake water. Groundwater discharge through active thrombolites contrasts with the surrounding lakebed, where hypersaline lake water flows downward through sandy sediments at very low rates. Based on an appreciation for the role of microorganisms in thrombolite accretion, our findings suggest conditions favorable to thrombolite formation still exist in certain locations of Lake Clifton despite increasing lake water salinity. This study is the first to characterize groundwater flow rates, paths, and chemistry within a microbialite‐forming environment and provides new insight into how groundwater can support microbial mats believed to contribute to microbialite formation in modern and ancient environments.
Whereas research in volcano geodesy seeks to push the boundaries of our knowledge of the physics of volcanoes, monitoring looks at changes in volcano behavior to predict when a volcanic crisis might develop. To be effective, geodetic monitoring must be done before, during, and after eruptions and must be integrated with other monitoring techniques. It requires the type of long-term commitment of time and resources that academic and industry scientists generally cannot make. A few, well-placed geodetic monitoring stations can make a huge difference to a country's ability to alert its people to an imminent volcanic eruption.
Monitoring strategies vary greatly depending on several factors such as the activity of the individual volcano, access, and available personnel and funding. Rapid advances in technology allow for more precise geodetic monitoring today than was imaginable when many of the existing volcano observatories were established. Today, deformation measurements at active volcanoes are usually made with continuous Global Positioning System (CGPS) stations, supplemented by Interferometric Synthetic Aperture Radar (InSAR) images. Neither method requires a continuous presence of personnel in the field, except for the installation and maintenance of the GPS stations; however subsequent data analysis can be highly complex.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.10902-9","usgsCitation":"Battaglia, M., Alpala, J., Alpala, R., Angarita, M., Arcos, D., Eullides, L., Euillades, P., Mueller, C., and Narvaez, L., 2019, Monitoring volcanic deformation, chap. of Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.10902-9.","ipdsId":"IP-103581","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":364027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":762901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alpala, Jorge","contributorId":139634,"corporation":false,"usgs":false,"family":"Alpala","given":"Jorge","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alpala, Rosa","contributorId":215654,"corporation":false,"usgs":false,"family":"Alpala","given":"Rosa","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Angarita, Mario","contributorId":215655,"corporation":false,"usgs":false,"family":"Angarita","given":"Mario","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":762903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arcos, Dario","contributorId":139636,"corporation":false,"usgs":false,"family":"Arcos","given":"Dario","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eullides, Leonardo","contributorId":215656,"corporation":false,"usgs":false,"family":"Eullides","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":762905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euillades, Pablo","contributorId":215657,"corporation":false,"usgs":false,"family":"Euillades","given":"Pablo","email":"","affiliations":[],"preferred":false,"id":762906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Cyrill","contributorId":215658,"corporation":false,"usgs":false,"family":"Mueller","given":"Cyrill","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":762907,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Narvaez, Lourdes","contributorId":215659,"corporation":false,"usgs":false,"family":"Narvaez","given":"Lourdes","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762908,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215202,"text":"70215202 - 2019 - From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows","interactions":[],"lastModifiedDate":"2020-10-13T22:52:05.481431","indexId":"70215202","displayToPublicDate":"2019-05-21T08:49:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows","docAbstract":"Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.
Simulation models are often used to identify hydrologic regimes suitable for different riparian or floodplain tree species. However, most existing models pay little attention to forest successional processes or other disturbances that may interact with the hydrologic regime of river systems to alter forest dynamics in space and time. In this study, we introduce a flood disturbance module to the LANDIS-II forest succession modelling framework to enable investigations into how inundation interacts with other disturbances and successional processes to alter floodplain forest cover and community dynamics. We illustrate the functionality of the model using a case study with multiple scenarios in the Upper Mississippi River floodplain, USA. We found that model predictions of total forest cover and the abundance of specific forest community types were generally related to uncertainty in the susceptibility of different species and age classes to inundation. By simulation year 100, increases or decreases in total forest cover and forest type distributions were roughly proportional to the initial differences in the susceptibility of species and age classes to inundation. The largest decrease in total forest cover was associated with a scenario that included disturbance by the emerald ash borer (Agrilus planipennis) and when using susceptibility parameters corresponding to the weakest flood tolerance. In contrast, changes in the composition of aboveground biomass were not sensitive to differences in susceptibility, and generally showed shifts toward later successional species with higher shade tolerance and longer lifespans for all scenarios. Our findings suggest that flood inundation interacts with other disturbances (e.g., insect outbreaks) and forest successional processes to alter forest abundance, distribution, and species composition in this system. Our modelling framework should allow for future studies that examine such interactions in other systems, and in the context of alternative hydrologic scenarios and other disturbance regimes.
Understanding threats to species persistence requires knowledge of where species currently occur. We explore methods for estimating two important facets of species distributions, namely where the range limit occurs and how species interactions structure distributions. Accurate understanding of range limits is crucial for predicting range dynamics and shifts in response to interspecific interactions and climate change. Additionally, species interactions are increasingly recognized as an important but not well‐understood predictor of range shifts. Our objective was to predict range limits and contact zones for two plethodontid salamanders, the highly range‐restricted Shenandoah salamander (Plethodon shenandoah) and the wide‐ranging red‐backed salamander (Plethodon cinereus). Using detection/non‐detection data, we assess four methodological decisions when estimating species’ distributions: (1) accounting for imperfect detection, (2) covariates to predict species occurrences, (3) accounting for species interactions, and (4) the inclusion of spatial autocorrelation. We found that Shenandoah salamander and red‐backed salamander co‐occurrence would have been underestimated and the range edge misidentified had we not accounted for incomplete detection. Covariates related to habitat were not sufficient to explain species’ range boundaries. Models that included spatial autocorrelation (i.e., a conditional autoregressive random effect) performed better than models that included just species interactions (i.e., detection and occurrence were conditional on the other species being present) and models that included both spatial autocorrelation and species interactions. Further, we found that the breadth of primary contact zones was typically 60–170 m, which is greater on average than previous estimates. In addition, we frequently observed secondary, disjunct contact zones along the range boundary. Understanding the extent to which species co‐occur and how the range boundaries are shaped is crucial to conservation efforts. Our work indicates that accounting for detection is crucial for accurately characterizing range edges and that spatial models may be especially effective in modeling distributions at the boundary.
In 2015, the U.S. Geological Survey (USGS) entered into a cooperative agreement with the North Carolina Department of Transportation (NCDOT) to develop a North Carolina-enhanced variation of the national Stochastic Empirical Loading and Dilution Model (SELDM) with available North Carolina-specific streamflow and water-quality data and to demonstrate use of the model by documenting selected simulation scenarios. The USGS developed the national SELDM in cooperation with the Federal Highway Administration (FHWA) to provide the tools and techniques necessary for performing stormwater-quality simulations. SELDM uses a stochastic mass-balance approach to estimate combinations of flows, concentrations, and loads of stormwater constituents from the site of interest (often a highway catchment; nonhighway areas, such as a large impervious area at a shopping center complex, also can be used) and the basin upstream from the stormwater outfall to assess the risk for adverse effects of runoff. SELDM also can be used to simulate the effectiveness of volume reduction, hydrograph extension, and water-quality concentration reductions by stormwater best management practices (BMPs), which are designed to help mitigate the effects of runoff on receiving water bodies.
Some of the statistical inputs needed for the North Carolina-enhanced SELDM were either calculated or augmented using local or regional data from North Carolina. Streamflow statistics used by SELDM were determined for 266 streamgages across North Carolina on the basis of data available through the 2015 water year. Recession ratio statistics used for triangular hydrographs were also developed for 30 streamgages across the State. The NCDOT identified previous research reports on highway-runoff and BMP studies in North Carolina for review of potential data addition to the national FHWA Highway-Runoff Database (HRDB). Following USGS review of these data, a total of 25,087 event mean concentration values and 1,140 storm events for 39 highway-runoff sites and 195 analytes were uploaded to the national HRDB from six North Carolina highway-runoff research reports and a recent USGS bridge deck runoff study. Using data for 27 streamgages in North Carolina, a total of 57 water-quality transport curves were developed for seven constituents for use in simulating water-quality conditions in the upstream basin. Performance data for three BMPs (bioretention, grass strip or swale, and wetland channel) from NCDOT research data were incorporated into the North Carolina-enhanced SELDM for volume-reduction statistics, including the effectiveness of treating four water-quality constituents (total suspended solids, total nitrogen, total phosphorus, nitrate plus nitrite) and turbidity.
Simulations using the North Carolina-enhanced SELDM are presented for two hypothetical upstream basins in the Piedmont ecoregion and one hypothetical highway site to demonstrate how simulations can be used to provide risk-based information about potential effects of stormwater runoff on downstream water quality and the potential for mitigating those risks by using BMPs. The first group of simulations explores the stochastic variability in dilution factors (the ratio of the highway runoff to the total downstream stormflow) for a hypothetical Piedmont rural creek having drainage areas ranging from 1 to 100 square miles. The second group of simulations examines dilution factors based on variations in precipitation, streamflow, and recession ratios for two hypothetical Piedmont upstream basins (rural and urban) where the drainage area was held constant at 25 square miles. These simulations indicate the sensitivity of results to variations in each of the three variables. The third group of simulations examines the effects of varied concentrations in the upstream basin on water-quality conditions downstream from the highway crossing. Variations in upstream water-quality conditions for three constituents (suspended sediment concentration, total nitrogen, and total phosphorus) are based on water-quality transport curves selected from among the 57 curves developed as part of this study to represent low-, medium-, and high-concentration statistics. Simulations completed for this third group also examine the potential effects of grass swale and bioretention BMP treatment on total nitrogen and total phosphorus concentrations in highway runoff. The BMP performance data from the NCDOT research reports were applied in this group of simulations.
The stochastic mass-balance approach used in SELDM analyses and simulations provides a strong tool for engineers and water-resource managers to use in exploring a wide range of possible hydrologic and water-quality inputs and their effects on downstream water quality. The results of this study can not only aid engineers and managers in planning for potential adverse effects of runoff at site-specific locations, they can also help the USGS and other Federal and State agencies with oversight responsibilities in stormwater-quality issues to continue gathering data on potential water-quality effects in receiving streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195031","collaboration":"Prepared in cooperation with the North Carolina Department of Transportation, Division of Highways, Hydraulics Unit and the U.S. Department of Transportation, Federal Highway Administration, Office of Project Development and Environmental Review","usgsCitation":"Weaver, J.C., Granato, G.E., and Fitzgerald, S.A., 2019, Assessing water quality from highway runoff at selected sites in North Carolina with the Stochastic Empirical Loading and Dilution Model (SELDM) (ver 1.1, July 2, 2019): U.S. Geological Survey Scientific Investigations Report 2019–5031, 99 p., https://doi.org/10.3133/sir20195031.","productDescription":"Report: x, 99 p.; Data Releases","numberOfPages":"113","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095625","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":364015,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YG44VQ","text":"USGS data release","description":"USGS data release","linkHelpText":"Highway-Runoff Database (HRDB) Version 1.0.0b"},{"id":364014,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69HV8","text":"USGS data release","description":"USGS data release","linkHelpText":"Assessing Water Quality from Highway Runoff at Selected Sites in North Carolina with the Stochastic Empirical Loading and Dilution Model (SELDM)"},{"id":365269,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5031/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":364016,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5031/sir20195031_ncseldm-application.zip","size":"3.16 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- North Carolina SELDM Application"},{"id":364012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5031/coverthb2.jpg"},{"id":366638,"rank":7,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/sir/2019/5031/errata.txt","text":"Scientific Investigations Report 2019-5031","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":364013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5031/sir20195031.pdf","text":"Report","size":"6.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5031"}],"country":"United States","state":"North Carolina","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-75.753765,35.199612],[-75.718015,35.209377],[-75.684006,35.232913],[-75.664512,35.227514],[-75.630358,35.238487],[-75.599005,35.256253],[-75.596915,35.269491],[-75.581935,35.263917],[-75.535741,35.272856],[-75.529393,35.288272],[-75.487678,35.485056],[-75.487528,35.525889],[-75.47861,35.553069],[-75.48133,35.622896],[-75.487678,35.648287],[-75.507385,35.680564],[-75.515397,35.73038],[-75.533512,35.773577],[-75.522232,35.774178],[-75.496086,35.728515],[-75.458659,35.596597],[-75.471355,35.479615],[-75.486771,35.391652],[-75.52592,35.233839],[-75.533627,35.225825],[-75.560225,35.232048],[-75.610101,35.227514],[-75.769705,35.180359],[-75.944725,35.105091],[-76.013145,35.061855],[-76.013561,35.068832],[-75.99188,35.092395],[-75.989175,35.115165],[-75.98395,35.120042],[-75.9547,35.1196],[-75.893942,35.150433],[-75.801444,35.183079],[-75.785729,35.194244],[-75.753765,35.199612]]],[[[-75.675245,35.929024],[-75.65954,35.919564],[-75.662019,35.906522],[-75.64512,35.905788],[-75.62767,35.883149],[-75.616833,35.856331],[-75.619772,35.847606],[-75.614361,35.815659],[-75.620454,35.809253],[-75.63898,35.818639],[-75.667891,35.82354],[-75.675054,35.830204],[-75.660086,35.83861],[-75.663356,35.869835],[-75.67283,35.882423],[-75.681415,35.88398],[-75.697672,35.901639],[-75.696871,35.909556],[-75.702165,35.915428],[-75.723782,35.925569],[-75.727251,35.93362],[-75.718266,35.939714],[-75.705323,35.939403],[-75.675245,35.929024]]],[[[-76.12236,36.550621],[-75.867044,36.550754],[-75.818735,36.357579],[-75.773329,36.231529],[-75.71831,36.113674],[-75.658537,36.02043],[-75.569794,35.863301],[-75.533012,35.787377],[-75.536428,35.780118],[-75.543259,35.779691],[-75.573083,35.828867],[-75.588878,35.844926],[-75.619151,35.889415],[-75.620114,35.925288],[-75.648899,35.965758],[-75.668379,35.978394],[-75.678909,35.993925],[-75.723662,36.003139],[-75.727084,36.01051],[-75.722609,36.037362],[-75.737088,36.040784],[-75.74051,36.046839],[-75.73972,36.07527],[-75.75572,36.153922],[-75.783676,36.215949],[-75.811588,36.244014],[-75.808165,36.259545],[-75.814483,36.285344],[-75.822907,36.291662],[-75.837913,36.294558],[-75.845284,36.305614],[-75.841335,36.328517],[-75.831858,36.339047],[-75.831595,36.346418],[-75.836201,36.363135],[-75.85147,36.379456],[-75.85147,36.415785],[-75.864106,36.430527],[-75.888325,36.441583],[-75.899908,36.482124],[-75.907279,36.485809],[-75.924127,36.482124],[-75.935473,36.490601],[-75.972545,36.494671],[-76.003708,36.506235],[-76.023627,36.500778],[-76.031949,36.482496],[-76.012337,36.447462],[-75.98005,36.435464],[-75.962285,36.41724],[-75.940676,36.41885],[-75.928369,36.428588],[-75.923601,36.425788],[-75.916409,36.38901],[-75.923331,36.361863],[-75.895285,36.319615],[-75.882154,36.284674],[-75.864933,36.284674],[-75.86052,36.280607],[-75.867356,36.252483],[-75.864154,36.235522],[-75.858703,36.222628],[-75.848838,36.21657],[-75.838367,36.200129],[-75.839924,36.17711],[-75.823915,36.158332],[-75.822531,36.145957],[-75.800378,36.112728],[-75.791637,36.082267],[-75.793974,36.07171],[-75.836084,36.092616],[-75.867792,36.127262],[-75.863914,36.159226],[-75.882987,36.186807],[-75.910658,36.212157],[-75.922344,36.244122],[-75.94984,36.25787],[-75.96462,36.254433],[-75.957058,36.247903],[-75.945372,36.222468],[-75.956027,36.198065],[-75.936436,36.18088],[-75.904999,36.164188],[-75.939047,36.165518],[-76.016984,36.186367],[-76.029086,36.202036],[-76.043838,36.210126],[-76.054308,36.229162],[-76.08148,36.237935],[-76.132005,36.287773],[-76.184702,36.298166],[-76.188717,36.281242],[-76.171378,36.265806],[-76.149486,36.263902],[-76.115851,36.214219],[-76.080106,36.19944],[-76.05992,36.15514],[-76.064224,36.143775],[-76.092555,36.135794],[-76.178946,36.123424],[-76.206873,36.137521],[-76.254064,36.18419],[-76.273316,36.189062],[-76.27699,36.184952],[-76.247401,36.161823],[-76.228527,36.130647],[-76.191715,36.107197],[-76.216599,36.095409],[-76.265037,36.104886],[-76.329921,36.133396],[-76.373571,36.138208],[-76.3935,36.163251],[-76.447812,36.192514],[-76.454414,36.189901],[-76.456061,36.183577],[-76.375892,36.12042],[-76.346418,36.121023],[-76.334965,36.110903],[-76.298733,36.1012],[-76.303998,36.092776],[-76.323478,36.084879],[-76.355069,36.086458],[-76.410878,36.078034],[-76.420881,36.06066],[-76.451418,36.039073],[-76.459316,36.024331],[-76.491959,36.018013],[-76.514335,36.00564],[-76.547505,36.009852],[-76.580674,36.00722],[-76.60384,36.033018],[-76.615423,36.037757],[-76.653332,36.035124],[-76.676484,36.043612],[-76.721445,36.147838],[-76.719401,36.199441],[-76.675462,36.266882],[-76.693253,36.278357],[-76.744436,36.212725],[-76.7521,36.147328],[-76.722996,36.066585],[-76.679657,35.991951],[-76.70019,35.964573],[-76.692376,35.945342],[-76.667547,35.933509],[-76.528551,35.944039],[-76.473795,35.960888],[-76.460632,35.970365],[-76.398242,35.984317],[-76.38192,35.971681],[-76.381394,35.96273],[-76.362966,35.942197],[-76.340327,35.94325],[-76.317687,35.946935],[-76.272408,35.972734],[-76.213966,35.988002],[-76.176585,35.993267],[-76.093697,35.993001],[-76.083131,35.989845],[-76.062071,35.993004],[-76.024162,35.970891],[-76.014159,35.957202],[-76.01995,35.934036],[-76.014353,35.920746],[-76.063203,35.853433],[-76.050485,35.806689],[-76.046813,35.717935],[-76.036393,35.690344],[-76.046361,35.659067],[-76.04015,35.65131],[-76.029863,35.649443],[-76.013808,35.669103],[-75.9869,35.768194],[-75.987148,35.836967],[-75.97783,35.897181],[-75.962562,35.901393],[-75.94782,35.920347],[-75.927286,35.93193],[-75.92676,35.940354],[-75.943608,35.952464],[-75.947293,35.959835],[-75.899382,35.977209],[-75.84989,35.976156],[-75.80935,35.959308],[-75.800926,35.944566],[-75.782498,35.935615],[-75.778813,35.918241],[-75.751961,35.878227],[-75.748276,35.852428],[-75.734587,35.839266],[-75.727216,35.822703],[-75.726689,35.811361],[-75.739357,35.770994],[-75.724743,35.742892],[-75.71294,35.69849],[-75.713502,35.693993],[-75.741605,35.672073],[-75.742167,35.655212],[-75.729802,35.625985],[-75.747225,35.610248],[-75.778138,35.592262],[-75.775328,35.579335],[-75.837154,35.570904],[-75.859636,35.586641],[-75.895045,35.573152],[-75.916403,35.538305],[-75.950126,35.530998],[-75.964178,35.511326],[-75.963053,35.493903],[-75.987222,35.484348],[-75.995652,35.475355],[-75.997901,35.453435],[-76.009704,35.442194],[-76.01139,35.423084],[-76.020945,35.410719],[-76.025441,35.408471],[-76.050171,35.415778],[-76.059726,35.410157],[-76.063661,35.405099],[-76.059726,35.383741],[-76.069281,35.370813],[-76.132793,35.349455],[-76.14291,35.338776],[-76.14291,35.32866],[-76.149655,35.326411],[-76.182254,35.336528],[-76.20586,35.336528],[-76.235087,35.350017],[-76.253072,35.350017],[-76.257569,35.344397],[-76.265437,35.343273],[-76.282299,35.345521],[-76.304781,35.355638],[-76.327263,35.356762],[-76.335132,35.355638],[-76.340752,35.346645],[-76.349745,35.345521],[-76.382344,35.356762],[-76.399206,35.348893],[-76.408199,35.350017],[-76.431805,35.362383],[-76.436301,35.37812],[-76.448666,35.383741],[-76.462156,35.380368],[-76.472273,35.371375],[-76.485762,35.371375],[-76.540292,35.410657],[-76.586349,35.508957],[-76.476706,35.511707],[-76.456427,35.550546],[-76.471207,35.55742],[-76.48358,35.538172],[-76.55679,35.528892],[-76.600441,35.538516],[-76.634468,35.510332],[-76.601472,35.460838],[-76.580187,35.387113],[-76.606041,35.387113],[-76.710083,35.427155],[-76.759234,35.418906],[-76.830897,35.447949],[-76.942022,35.473529],[-77.023912,35.514802],[-77.026638,35.490569],[-76.967214,35.438296],[-76.891938,35.433649],[-76.664027,35.345696],[-76.500375,35.321915],[-76.482389,35.314046],[-76.467776,35.276951],[-76.467776,35.261213],[-76.477893,35.243228],[-76.490258,35.233111],[-76.494755,35.212877],[-76.521733,35.192643],[-76.536346,35.174657],[-76.539719,35.166788],[-76.536346,35.142058],[-76.546463,35.122948],[-76.557704,35.116204],[-76.568945,35.097094],[-76.60042,35.067867],[-76.631895,35.056626],[-76.801426,34.964369],[-76.982904,35.060607],[-76.989778,35.045484],[-76.977404,35.004926],[-76.89354,34.957495],[-76.762931,34.920374],[-76.635072,34.989116],[-76.588055,34.991428],[-76.566697,34.998173],[-76.502623,35.007166],[-76.491382,35.017283],[-76.490258,35.034144],[-76.474521,35.070116],[-76.463468,35.076411],[-76.435762,35.057941],[-76.425461,35.001464],[-76.395625,34.975179],[-76.332044,34.970917],[-76.326361,34.976245],[-76.329557,34.986901],[-76.364367,35.034853],[-76.318546,35.020645],[-76.288354,35.005726],[-76.296524,34.976245],[-76.275567,34.960971],[-76.277698,34.940014],[-76.347673,34.872171],[-76.368274,34.872881],[-76.379641,34.86258],[-76.400242,34.855476],[-76.463016,34.785076],[-76.524712,34.681964],[-76.586236,34.698805],[-76.582421,34.767757],[-76.604796,34.787482],[-76.620606,34.784389],[-76.616567,34.714059],[-76.673619,34.71491],[-76.673537,34.70757],[-76.523303,34.652271],[-76.383827,34.807906],[-76.322808,34.86116],[-76.233672,34.925926],[-76.093349,35.048705],[-76.069906,35.075701],[-76.043621,35.070017],[-76.035933,35.058987],[-76.137269,34.987858],[-76.233088,34.905477],[-76.31021,34.852309],[-76.386804,34.784579],[-76.494068,34.66197],[-76.524199,34.615416],[-76.535946,34.588577],[-76.555196,34.615993],[-76.549343,34.645585],[-76.579467,34.660174],[-76.642939,34.677618],[-76.676312,34.693151],[-76.770044,34.696899],[-76.817453,34.693722],[-76.990262,34.669623],[-77.136843,34.632926],[-77.209161,34.605032],[-77.322524,34.535574],[-77.462922,34.471354],[-77.556943,34.417218],[-77.661673,34.341868],[-77.740136,34.272546],[-77.829209,34.162618],[-77.878161,34.067963],[-77.915536,33.971723],[-77.946568,33.912261],[-77.960172,33.853315],[-77.970606,33.844517],[-78.009973,33.861406],[-78.018689,33.888289],[-78.095429,33.906031],[-78.17772,33.914272],[-78.276147,33.912364],[-78.383964,33.901946],[-78.509042,33.865515],[-78.541087,33.851112],[-79.358317,34.545358],[-79.675299,34.804744],[-80.797543,34.819786],[-80.782042,34.935782],[-80.93495,35.107409],[-81.041489,35.044703],[-81.057648,35.062433],[-81.058029,35.07319],[-81.052078,35.096276],[-81.032806,35.108049],[-81.038968,35.126299],[-81.05042,35.131048],[-81.044391,35.147918],[-81.239358,35.159974],[-82.27492,35.200071],[-82.314863,35.191089],[-82.32335,35.184789],[-82.344554,35.193115],[-82.361469,35.190831],[-82.36899,35.181747],[-82.379712,35.186884],[-82.378744,35.198053],[-82.390439,35.215395],[-82.403348,35.204473],[-82.417597,35.200131],[-82.439595,35.165863],[-82.448969,35.165037],[-82.455609,35.177425],[-82.460092,35.178143],[-82.483937,35.173798],[-82.495506,35.164312],[-82.516044,35.163442],[-82.529973,35.155617],[-82.550508,35.159498],[-82.556168,35.151736],[-82.563767,35.151575],[-82.578316,35.142104],[-82.609706,35.139039],[-82.629031,35.126155],[-82.642237,35.129215],[-82.662381,35.118123],[-82.683625,35.125833],[-82.694898,35.098456],[-82.72701,35.094142],[-82.738379,35.079453],[-82.749491,35.078487],[-82.757704,35.068019],[-82.777376,35.064143],[-82.781973,35.066817],[-82.776357,35.081349],[-82.787867,35.085024],[-83.108535,35.000771],[-83.620185,34.992091],[-83.619985,34.986592],[-84.321869,34.988408],[-84.29024,35.225572],[-84.28322,35.226577],[-84.223718,35.269078],[-84.211818,35.266078],[-84.202879,35.255772],[-84.200117,35.244679],[-84.188417,35.239979],[-84.170416,35.245779],[-84.12889,35.243679],[-84.12115,35.250644],[-84.097508,35.247382],[-84.081117,35.261146],[-84.052612,35.269982],[-84.02141,35.301383],[-84.02651,35.309283],[-84.03501,35.311983],[-84.029377,35.333197],[-84.038081,35.348363],[-84.024756,35.353896],[-84.007586,35.371661],[-84.008207,35.389683],[-84.021782,35.407418],[-84.00225,35.422548],[-83.992568,35.438065],[-83.973057,35.448921],[-83.971439,35.455145],[-83.966656,35.454941],[-83.961054,35.462838],[-83.949389,35.461164],[-83.937015,35.471511],[-83.911773,35.476028],[-83.905612,35.48906],[-83.880074,35.518745],[-83.859261,35.521851],[-83.848502,35.519259],[-83.827428,35.524653],[-83.802434,35.541588],[-83.780129,35.550387],[-83.771736,35.562118],[-83.749894,35.561146],[-83.735669,35.565455],[-83.723459,35.561874],[-83.707199,35.568533],[-83.676268,35.570289],[-83.640498,35.566075],[-83.608889,35.579451],[-83.582,35.562684],[-83.56609,35.565993],[-83.498335,35.562981],[-83.485527,35.568204],[-83.479317,35.582764],[-83.455722,35.598045],[-83.445802,35.611803],[-83.421576,35.611186],[-83.396626,35.62272],[-83.388602,35.632352],[-83.366941,35.638728],[-83.35156,35.659858],[-83.334965,35.665471],[-83.321101,35.662815],[-83.312757,35.654809],[-83.297154,35.65775],[-83.290682,35.672638],[-83.258117,35.691924],[-83.255489,35.714974],[-83.251247,35.719916],[-83.240669,35.72676],[-83.214501,35.724434],[-83.18837,35.729798],[-83.159208,35.764892],[-83.120183,35.766234],[-83.07403,35.790016],[-83.036209,35.787405],[-83.001473,35.773752],[-82.992053,35.773948],[-82.964088,35.78998],[-82.961724,35.800491],[-82.945515,35.824662],[-82.920171,35.841664],[-82.918312,35.863977],[-82.901301,35.872593],[-82.901843,35.890274],[-82.911936,35.921618],[-82.901577,35.931446],[-82.898506,35.9451],[-82.874159,35.952698],[-82.860724,35.94743],[-82.852554,35.949089],[-82.826045,35.929721],[-82.82257,35.922531],[-82.804997,35.927168],[-82.805771,35.935316],[-82.800431,35.944155],[-82.787465,35.952163],[-82.785356,35.96253],[-82.774905,35.971978],[-82.785558,35.977795],[-82.785267,35.987927],[-82.776001,36.000103],[-82.750065,36.006004],[-82.688865,36.038604],[-82.684765,36.045004],[-82.637165,36.065805],[-82.618664,36.056105],[-82.618164,36.047005],[-82.609663,36.044906],[-82.596177,36.03188],[-82.595525,36.026012],[-82.614362,36.003506],[-82.613028,35.994],[-82.604239,35.987319],[-82.610889,35.967409],[-82.581003,35.965557],[-82.576678,35.959255],[-82.557874,35.953901],[-82.549682,35.964275],[-82.507068,35.977475],[-82.483498,35.996284],[-82.460658,36.007809],[-82.409458,36.083409],[-82.355157,36.115609],[-82.336756,36.114909],[-82.321448,36.119551],[-82.289455,36.13571],[-82.270954,36.12761],[-82.260353,36.13371],[-82.247521,36.130865],[-82.213852,36.159112],[-82.182549,36.143714],[-82.147948,36.149516],[-82.136547,36.128817],[-82.137974,36.119576],[-82.127146,36.104417],[-82.105444,36.108119],[-82.080303,36.105728],[-82.061342,36.113121],[-82.054142,36.126821],[-82.033141,36.120422],[-81.908137,36.302013],[-81.879382,36.313767],[-81.857333,36.334787],[-81.841268,36.343321],[-81.800812,36.358073],[-81.766102,36.338517],[-81.730976,36.341187],[-81.707438,36.335171],[-81.707785,36.346007],[-81.721334,36.353101],[-81.732865,36.376502],[-81.729813,36.388033],[-81.737952,36.39719],[-81.739648,36.406686],[-81.720734,36.422537],[-81.715229,36.436532],[-81.71489,36.45722],[-81.695311,36.467912],[-81.697829,36.507544],[-81.707573,36.526101],[-81.707963,36.536209],[-81.699962,36.536829],[-81.69003,36.552154],[-81.690236,36.568718],[-81.677036,36.570718],[-81.677535,36.588117],[-81.003802,36.563629],[-80.837954,36.559131],[-80.704831,36.562319],[-80.295243,36.543973],[-80.122183,36.542646],[-78.529722,36.540981],[-77.16966,36.547315],[-77.152691,36.544078],[-76.916048,36.543815],[-76.916989,36.550742],[-76.12236,36.550621]]]]},\"properties\":{\"name\":\"North Carolina\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: May 2019; Version 1.1: July 2019","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Inversion of airborne electromagnetic (AEM) data is an under-determined inverse problem, in that infinitely many resistivity models exist that will be able to explain the observed data, within measurement errors. Therefore, additional information or constraints must be taken into account to solve the inverse problem. In deterministic approaches, the goal is to locate one optimal model that can be obtained by using some form of smoothness constraints implied through a number of regularization choices. This model, however, will not necessarily represent realistic geological features. Probabilistic methods offer an alternative in which the solution is not one model, but a collection of models, whose variability represents the uncertainty. The probabilistic approach can also rely on implicit model assumptions, representing prior information (a type of regularization information) that may or may not be consistent with the actual available information. Here, we present an approach for AEM inversion in which the prior model is explicitly chosen by a user, preferably selected based on actual prior information available and then integrated with AEM data using a general Monte Carlo based sampling approach. This approach leads to a new workflow to AEM inversion in which geological prior information is independently and explicitly chosen before inversion is carried out. The main benefit of this approach is that each model obtained will, by construction, be consistent with prior (geological) information as well as geophysical data. Through examples based on synthetic and real AEM data, we will demonstrate the methodology, not least that the choice of prior information cannot be avoided: Either it is done explicitly, or it will be chosen implicitly by the choice of method used to invert the AEM data.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggz230","usgsCitation":"Hansen, T.M., and Minsley, B.J., 2019, Inversion of airborne EM data with an explicit choice of prior model: Geophysical Journal International, v. 218, no. 2, p. 1348-1366, https://doi.org/10.1093/gji/ggz230.","productDescription":"17 p.","startPage":"1348","endPage":"1366","ipdsId":"IP-106050","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":467607,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/731bd10f-dcc4-4142-a377-2f42a561b2c9","text":"External Repository"},{"id":382276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"218","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Thomas Mejer","contributorId":199735,"corporation":false,"usgs":false,"family":"Hansen","given":"Thomas","email":"","middleInitial":"Mejer","affiliations":[{"id":27198,"text":"Niels Bohr Institute, University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":808408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":808409,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205612,"text":"70205612 - 2019 - Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests","interactions":[],"lastModifiedDate":"2019-09-27T10:33:51","indexId":"70205612","displayToPublicDate":"2019-05-20T10:27:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests","docAbstract":"Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upwards of $300 million US dollars annually. Salinity source models have generally included coarse spatial data to represent non‐agriculture sources. We developed new predictive soil property and cover maps at 30 m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices were also created based on soil properties, remotely sensed bare ground exposure, and topographic factors to examine potential surface soil erosion drivers. These new maps and data from previous SPARROW models were related to recently updated records of salinity at 309 stream gauges in the UCRB using random forest regressions. Resulting salinity yield predictions indicate more diffuse salinity sources, with slightly higher yields in more arid portions of the UCRB, and less overall load coming from irrigated agricultural sources. Model simulations still indicate irrigation to be the major human source of salinity (661,000 Mg, or 12%), but also suggest that 75,000 Mg (1.4%) of annual salinity in the UCRB is coming from areas with excessive exposed bare ground in high elevation mountain areas. Model inputs allow for field scale screening of locations that could be targeted for salinity control projects. Results confirm recent studies indicating limited surface erosional influence on salinity loading in UCRB surface waters, but impacts of monsoonal runoff events are still not fully understood, particularly in drylands. The study highlights the utility of new predictive soil maps and machine learning for environmental modeling.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR024054","usgsCitation":"Nauman, T., Ely, C., Miller, M., and Duniway, M.C., 2019, Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests: Water Resources Research, v. 55, no. 6, p. 4954-4973, https://doi.org/10.1029/2018WR024054.","productDescription":"20 p.","startPage":"4954","endPage":"4973","ipdsId":"IP-099154","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":499841,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/678588abb83c4b249680e0982160eaf7","text":"External Repository"},{"id":437460,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QSFDJN","text":"USGS data release","linkHelpText":"Salinity yield modeling spatial data for the Upper Colorado River Basin, USA"},{"id":367766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.89697265625,\n 41.062786068733026\n ],\n [\n -108.687744140625,\n 42.17154633452751\n ],\n [\n -110.225830078125,\n 43.42100882994726\n ],\n [\n -110.687255859375,\n 43.0287452513488\n ],\n [\n -110.863037109375,\n 41.09591205639546\n ],\n [\n -111.456298828125,\n 39.985538414809746\n ],\n [\n -112.203369140625,\n 36.76529191711624\n ],\n [\n -111.09374999999999,\n 36.146746777814364\n ],\n [\n -108.643798828125,\n 35.43381992014202\n ],\n [\n -107.64404296875,\n 36.33282808737917\n ],\n [\n -106.754150390625,\n 37.142803443716836\n ],\n [\n -106.6552734375,\n 37.84015683604136\n ],\n [\n -106.80908203125,\n 38.324420427006544\n ],\n [\n -106.424560546875,\n 39.32579941789298\n ],\n [\n -106.424560546875,\n 40.25437660372649\n ],\n [\n -106.89697265625,\n 41.062786068733026\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":219281,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":771865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ely, Christopher P. 0000-0001-5276-5046","orcid":"https://orcid.org/0000-0001-5276-5046","contributorId":219282,"corporation":false,"usgs":true,"family":"Ely","given":"Christopher P.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":219283,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":771868,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204004,"text":"70204004 - 2019 - Effects of climate change on habitat and connectivity for populations of a vulnerable, endemic salamander in Iran","interactions":[],"lastModifiedDate":"2019-06-26T15:50:26","indexId":"70204004","displayToPublicDate":"2019-05-17T15:37:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate change on habitat and connectivity for populations of a vulnerable, endemic salamander in Iran","docAbstract":"Habitat loss and fragmentation are among the biggest threats to amphibian populations and anthropogenic climate change may exacerbate these. The response of Iran's amphibians to climate change is uncertain and yet making an accurate prediction of how the species will respond is critical for conservation. We assessed how expected future climate scenarios before the years 2050 and 2070 might influence the geographic distribution and habitat connectivity of the Lorestan Mountain Newt (Neurergus kaiseri). We examined presence data (2010–2018) of the species according to environmental and anthropogenic factors, and created an ensemble model of habitat suitability based on eight species distribution models (SDMs). Then, we used the concept of circuit theory to estimate potential linkages between the habitat patches. We applied the ensemble calibrated models and quantified spatial connectivity to assess the influence of climate change on the species range for the years 2050 and 2070 under four representative concentration pathways (RCPs) of three general circulation models (GCMs). Models using current climate predicted that 6.8% of the 267,609 km2 study area has suitable conditions for the species, but only about 7% of these climatically suitable landscapes are covered by conservation areas. Temperature and precipitation-related climatic variables made the largest contribution to the distribution model. Under projected climate conditions, we found a decline of 56–98% of the suitable habitat and predicted a potential for distributional shifts towards higher elevations by 2050 and 2070. Although there is relatively good connectivity between many habitat patches today, models predict that suitable areas available to the newt will become increasingly fragmented under projected climate change scenarios. Our findings support the hypothesis that projected climatic shifts will negatively influence suitable habitats of amphibians and likely cause upward shifts in elevation in range of some species. Identifying potentially suitable habitats and important linkages between habitat patches under different climate scenarios are crucial steps in conservation planning for the Lorestan Mountain Newt.","language":"English","doi":"10.1016/j.gecco.2019.e00637","collaboration":"Shahrekord University","usgsCitation":"Ashrafzadeh, M.R., Naghipour, A.A., Haidarian, M., Kusza, S., and Pilliod, D.S., 2019, Effects of climate change on habitat and connectivity for populations of a vulnerable, endemic salamander in Iran: Global Ecology and Conservation, v. 19, e00637; 13 p., https://doi.org/10.1016/j.gecco.2019.e00637.","productDescription":"e00637; 13 p.","ipdsId":"IP-103213","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":467611,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2019.e00637","text":"Publisher Index Page"},{"id":365095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 47.30712890625,\n 33.32134852669881\n ],\n [\n 49.81201171875,\n 30.845647420182598\n ],\n [\n 50.83374023437499,\n 31.672083485607402\n ],\n [\n 48.328857421875,\n 34.298068350990825\n ],\n [\n 47.30712890625,\n 33.32134852669881\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ashrafzadeh, Mohammad Reza","contributorId":216617,"corporation":false,"usgs":false,"family":"Ashrafzadeh","given":"Mohammad","email":"","middleInitial":"Reza","affiliations":[],"preferred":false,"id":765173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Naghipour, Ali Asghar","contributorId":216618,"corporation":false,"usgs":false,"family":"Naghipour","given":"Ali","email":"","middleInitial":"Asghar","affiliations":[],"preferred":false,"id":765174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haidarian, Maryam","contributorId":216619,"corporation":false,"usgs":false,"family":"Haidarian","given":"Maryam","email":"","affiliations":[],"preferred":false,"id":765175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kusza, Szilvia","contributorId":216620,"corporation":false,"usgs":false,"family":"Kusza","given":"Szilvia","email":"","affiliations":[],"preferred":false,"id":765176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":765167,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228355,"text":"70228355 - 2019 - Using an individual-based model to assess common biases in lek-based count data to estimate population trajectories of lesser prairie-chickens","interactions":[],"lastModifiedDate":"2022-02-09T19:58:18.2157","indexId":"70228355","displayToPublicDate":"2019-05-17T13:52:38","publicationYear":"2019","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":"Using an individual-based model to assess common biases in lek-based count data to estimate population trajectories of lesser prairie-chickens","docAbstract":"Researchers and managers are often interested in monitoring the underlying state of a population (e.g., abundance), yet error in the observation process might mask underlying changes due to imperfect detection, availability for sampling, and heterogeneity in abundance. Additional heterogeneity can be introduced into a monitoring program when male-based surveys are used as an index for the total population. Often, male-based surveys are used for lekking species, as males are conspicuous and more easily monitored when lekking than females. To determine if lek surveys capture changes or trends in population abundance based on female survival and reproduction, we developed a virtual ecologist approach using the lesser prairie-chicken (Tympanuchus pallidicinctus) as an example. Our approach used an individual-based model to simulate lek counts based on female vital rate data from lesser prairie-chickens, included models where detection probability and lek attendance were <1, and analyzed using unadjusted counts and an N-mixture model to compare estimates of population abundance and growth rates. When lek attendance rates were <1, the estimate of abundance was biased low, even when using N-mixture models to account for detection probability. Additionally, using lek counts to estimate population growth rates without accounting for detection probability consistently overestimated population growth rates, indicating a stable population when the population was decreasing. Our results therefore suggest that lek-based surveys used without accounting for lek attendance and detection probability may miss important trends in population changes. Rather than population-level inference, lek-based surveys not accounting for lek attendance and detection probability may instead be better for inferring broad-scale range shifts of lesser prairie-chicken populations in a presence/absence framework.","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0217172","usgsCitation":"Ross, B., Sullins, D.S., and Haukos, D.A., 2019, Using an individual-based model to assess common biases in lek-based count data to estimate population trajectories of lesser prairie-chickens: PLoS ONE, 0217172, 17 p., https://doi.org/10.1371/journal.pone.0217172.","productDescription":"0217172, 17 p.","ipdsId":"IP-098683","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0217172","text":"Publisher Index Page"},{"id":395719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullins, Daniel S.","contributorId":275280,"corporation":false,"usgs":false,"family":"Sullins","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":833921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833922,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}