This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge‐coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation (https://github.com/USGS-Astrogeology/usgscsm). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.
","language":"English","publisher":"Wiley","doi":"10.1029/2019EA000713","usgsCitation":"Laura, J., Mapel, J., and Hare, T.M., 2020, Planetary sensor models interoperability using the community sensor model specification: Earth and Space Science, v. 7, no. 6, e2019EA000713, 17 p., https://doi.org/10.1029/2019EA000713.","productDescription":"e2019EA000713, 17 p.","ipdsId":"IP-108414","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":457556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019ea000713","text":"Publisher Index Page"},{"id":379404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mapel, Jesse 0000-0001-5756-0373","orcid":"https://orcid.org/0000-0001-5756-0373","contributorId":206344,"corporation":false,"usgs":true,"family":"Mapel","given":"Jesse","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227142,"text":"70227142 - 2020 - Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","interactions":[],"lastModifiedDate":"2022-01-03T16:02:02.227914","indexId":"70227142","displayToPublicDate":"2020-03-01T08:28:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","docAbstract":"Eurasian zebra and quagga mussels were likely introduced to the Laurentian Great Lakes via ballast water release in the 1980s, and their range has since expanded across the US, including some of their southernmost occurrences in Texas. Their spread into the state has resulted in a need to revise previous delimitations of suitable dreissenid habitat. We therefore assessed invasion risk in Texas by 1) predicting distribution of suitable habitat of zebra and quagga mussels using Maxent species distribution models based upon global occurrence and climate data; and 2) refining lake-specific predictions via collection and analysis of physicochemical data. Maxent models predicted a lack of suitable habitat for quagga mussels within Texas. However, models did predict the presence of suitable zebra mussel habitat, with hotspots of suitable habitat occurring along the Red and Sabine Rivers of north and east Texas, as well as patches of suitable habitat in central Texas between the Colorado and Brazos Rivers and extending inland along the Gulf Coast. Although predicted suitable habitat extended further west than in previous models, most of the Texas panhandle, west Texas extending toward El Paso, and the Rio Grande valley were predicted to provide poor zebra mussel habitat suitability. Collection of physicochemical data (i.e., dissolved oxygen, pH, specific conductance, and temperature on-site as well as laboratory analysis for Ca, N, and P) from zebra mussel-invaded lakes and a subset of uninvaded but high-risk lakes of North and Central Texas, did not refine model predictions because there was no apparent distinction between invaded and uninvaded lakes. Overall, we demonstrated that while quagga mussels do not appear to represent an invasive threat in Texas, abundant suitable habitat for continuing zebra mussel invasion exists within the state. The threat of continued expansion of this poster-child for negative invasive species impacts warrants further prevention efforts, management, and research.
","language":"English","publisher":"REABIC","usgsCitation":"Barnes, M., and Patino, R., 2020, Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics: Management of Biological Invasions, v. 11, no. 1, p. 63-79.","productDescription":"17 p.","startPage":"63","endPage":"79","ipdsId":"IP-107295","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":393733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393746,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.reabic.net/journals/mbi/2020/Issue1.aspx"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.30541992187499,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 29.334298230315675\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnes, M. A.","contributorId":270689,"corporation":false,"usgs":false,"family":"Barnes","given":"M. A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":829770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":829769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255818,"text":"70255818 - 2020 - How do we stop fungal disease from devastating North American salamanders","interactions":[],"lastModifiedDate":"2024-07-08T13:30:18.415061","indexId":"70255818","displayToPublicDate":"2020-03-01T08:25:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17992,"text":"Wildlife Professional","active":true,"publicationSubtype":{"id":10}},"title":"How do we stop fungal disease from devastating North American salamanders","docAbstract":"No abstract available.
","language":"English","publisher":"The Wildlife Society","usgsCitation":"Periera, K., Gray, M.L., Kerby, J., Campbell Grant, E.H., and Voyles, J., 2020, How do we stop fungal disease from devastating North American salamanders: Wildlife Professional, v. 14, no. 2, p. 41-46.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-112740","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":430798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Periera, K","contributorId":339937,"corporation":false,"usgs":false,"family":"Periera","given":"K","email":"","affiliations":[{"id":30221,"text":"Duquesne University","active":true,"usgs":false}],"preferred":false,"id":905669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Margaret L 0000-0002-4810-8876","orcid":"https://orcid.org/0000-0002-4810-8876","contributorId":221166,"corporation":false,"usgs":false,"family":"Gray","given":"Margaret","email":"","middleInitial":"L","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":905670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kerby, Jacob","contributorId":244593,"corporation":false,"usgs":false,"family":"Kerby","given":"Jacob","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":905671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":905672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Voyles, Jamie","contributorId":127709,"corporation":false,"usgs":false,"family":"Voyles","given":"Jamie","email":"","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":905673,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209032,"text":"70209032 - 2020 - The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes","interactions":[],"lastModifiedDate":"2020-06-04T16:59:39.703625","indexId":"70209032","displayToPublicDate":"2020-03-01T07:42:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes","docAbstract":"(Munson) The challenges of restoration in dryland ecosystems are growing due to a rise in anthropogenic disturbance and increasing aridity. Plant functional traits are often used to predict plant performance and can offer a window into the potential outcomes of restoration efforts across environmental gradients. We tracked 15 years of seeding outcomes across 150 sites on the Colorado Plateau, a cold desert ecoregion in the western United States, and analyzed the independent and interactive effects of functional traits (seed mass, height, and specific leaf area) and local biologically relevant climate variables on seeding success. We predicted that the best models would include an interaction between plant traits and climate, indicating a need to match the right trait value to the right climate conditions in order to maximize seeding success. Indeed, we found that both plant height and seed size significantly interacted with temperature seasonality, with larger seeds and taller plants performing better in more seasonal environments. We also determined that these trait-environment patterns are not driven by the use of native vs. non-native species. Our results lend insight to using plant traits to inform the selection of seed mixes for restoring areas with specific climatic conditions, while also demonstrating the strong influence of temperature seasonality on seeding success in the Colorado Plateau region.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2110","usgsCitation":"Balazs, K.R., Kramer, A.T., Munson, S.M., Talkington, N., Still, S., and Butterfield, B.J., 2020, The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes: Ecological Applications, v. 30, no. 4, e02110, 7 p., https://doi.org/10.1002/eap.2110.","productDescription":"e02110, 7 p.","ipdsId":"IP-104892","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":457560,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2110","text":"Publisher Index Page"},{"id":373164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.42138671875,\n 39.57182223734374\n ],\n [\n -118.23486328125,\n 36.65079252503471\n ],\n [\n -111.7529296875,\n 33.76088200086917\n ],\n [\n -107.1826171875,\n 33.137551192346145\n ],\n [\n -104.0185546875,\n 33.284619968887675\n ],\n [\n -104.7216796875,\n 39.027718840211605\n ],\n [\n -107.70996093749999,\n 40.111688665595956\n ],\n [\n -111.4013671875,\n 41.77131167976407\n ],\n [\n -114.5654296875,\n 42.52069952914966\n ],\n [\n -117.2900390625,\n 42.06560675405716\n ],\n [\n -118.87207031250001,\n 40.84706035607122\n ],\n [\n -119.42138671875,\n 39.57182223734374\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Balazs, Kathleen R.","contributorId":223214,"corporation":false,"usgs":false,"family":"Balazs","given":"Kathleen","email":"","middleInitial":"R.","affiliations":[{"id":24810,"text":"Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":784588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Andrea T.","contributorId":207328,"corporation":false,"usgs":false,"family":"Kramer","given":"Andrea","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":784589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":784590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Talkington, Nora","contributorId":223215,"corporation":false,"usgs":false,"family":"Talkington","given":"Nora","email":"","affiliations":[{"id":40685,"text":"Botanic Gardens Conservation International US, Chicago Botanic Garden, 1000 Lake Cook Road, Glencoe, IL 60022, USA","active":true,"usgs":false}],"preferred":false,"id":784591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Still, Shannon","contributorId":149504,"corporation":false,"usgs":false,"family":"Still","given":"Shannon","email":"","affiliations":[{"id":17752,"text":"Chicago Botanic Garden","active":true,"usgs":false}],"preferred":false,"id":784592,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":784593,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208943,"text":"70208943 - 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To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).
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techniques to the Nevada geothermal play fairway analysis","interactions":[],"lastModifiedDate":"2020-08-12T15:03:49.11224","indexId":"70211874","displayToPublicDate":"2020-02-29T10:53:30","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis","docAbstract":"We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis. Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Faulds, J., Brown, S.C., Coolbaugh, M.F., Queen, J.H., Treitel, S., Fehler, M., Mlawsky, E., Glen, J.M., Lindsey, C., Burns, E., Smith, C.M., Gu, C., and Ayling, B.F., 2020, Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis, in Proceedings: 45th workshop on geothermal reservoir engineering, 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Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","interactions":[],"lastModifiedDate":"2020-08-12T15:04:28.21349","indexId":"70211876","displayToPublicDate":"2020-02-29T10:39:46","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","docAbstract":"The Snake River volcanic province (SRP) has long been considered a target for geothermal development. It overlies a thermal anomaly that extends deep into the mantle and represents one of the highest heat flow provinces in North America, but systematic exploration been hindered by lack of a conceptual model. Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that integrates data at the regional or basin scale to define favorable plays for exploration in a systematic fashion. The success of play fairway analysis in geothermal exploration depends critically on defining a systematic methodology that is grounded in theory and adapted to the geologic and hydrologic framework of real geothermal systems. \nThis study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the Snake River Plain: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for Heat were heat flow, the distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Permeability was derived from stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetic and gravity data. Data for Seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment (CRS) maps for Heat, Permeability and Seal, which were combined to create a Composite Common Risk Segment (CCRS) map for all of southern Idaho that reflects the risk associated with geothermal resource exploration and helps to identify favorable resource tracks. \nOur data suggests that important undiscovered geothermal resources may be located in several areas of the SRP, including the western SRP (associated with buried lineaments capped by lacustrine sediment), at lineament intersections in the central SRP, and along the margins of the eastern SRP. These blind resources are associated with temperatures sufficient to support electricity production, and may be exploitable with existing deep drilling technology. We are testing our methodology by drilling a geothermal test well in Camas Prairie, ID, confirm our predictions of permeability and reservoir temperature.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Shervais, J., Glen, J.M., Siler, D.L., Liberty, L., Nielson, D., Garg, S., Dobson, P., Gasperikova, E., Sonnenthal, E., Newell, D., Evans, J.E., DeAngelo, J., Peacock, J., Earney, T.E., Schermerhorn, W.D., and Neupane, G., 2020, Play fairway analysis in geothermal exploration: The Snake River plain volcanic province, in Proceedings: 45th workshop on geothermal reservoir engineering, Stanford, CA, February 10-12, 2020, p. 186-194.","productDescription":"9 p.","startPage":"186","endPage":"194","ipdsId":"IP-115891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377334,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.proceedings.com/53283.html"}],"country":"United States","state":"Idaho","otherGeospatial":"Snake River Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5936279296875,\n 43.36512572875844\n ],\n [\n -111.544189453125,\n 44.15462243076731\n ],\n [\n -112.587890625,\n 44.33956524809713\n ],\n [\n -113.192138671875,\n 43.47285413777968\n ],\n [\n -114.60937499999999,\n 43.32517767999296\n ],\n [\n -115.7464599609375,\n 43.32517767999296\n ],\n [\n -116.72973632812499,\n 44.06390660801779\n ],\n [\n -116.92199707031249,\n 43.34914966389313\n ],\n [\n -116.1474609375,\n 42.59757641618889\n ],\n [\n -114.42260742187499,\n 42.293564192170095\n ],\n [\n -112.994384765625,\n 42.36666166373274\n ],\n [\n -111.9342041015625,\n 42.94033923363181\n ],\n [\n -111.5936279296875,\n 43.36512572875844\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shervais, John W.","contributorId":237914,"corporation":false,"usgs":false,"family":"Shervais","given":"John W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liberty, Lee","contributorId":189113,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","affiliations":[],"preferred":false,"id":795550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielson, Dennis","contributorId":237918,"corporation":false,"usgs":false,"family":"Nielson","given":"Dennis","affiliations":[{"id":47642,"text":"DOSECC Exploration Services","active":true,"usgs":false}],"preferred":false,"id":795551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garg, Sabodh","contributorId":193564,"corporation":false,"usgs":false,"family":"Garg","given":"Sabodh","email":"","affiliations":[],"preferred":false,"id":795552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dobson, Patrick","contributorId":193558,"corporation":false,"usgs":false,"family":"Dobson","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":795553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gasperikova, Erika","contributorId":193561,"corporation":false,"usgs":false,"family":"Gasperikova","given":"Erika","affiliations":[],"preferred":false,"id":795554,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":795555,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Newell, Dennis","contributorId":237921,"corporation":false,"usgs":false,"family":"Newell","given":"Dennis","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795556,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Evans, James E.","contributorId":194435,"corporation":false,"usgs":false,"family":"Evans","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":795557,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795558,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795559,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795560,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795561,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Neupane, Ghanashyam","contributorId":237924,"corporation":false,"usgs":false,"family":"Neupane","given":"Ghanashyam","email":"","affiliations":[{"id":27243,"text":"Idaho National Laboratory","active":true,"usgs":false}],"preferred":false,"id":795562,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70209967,"text":"70209967 - 2020 - Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures","interactions":[],"lastModifiedDate":"2020-05-07T12:38:54.176536","indexId":"70209967","displayToPublicDate":"2020-02-29T07:32:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures","docAbstract":"Elevated nitrate (NO3) and sulfate (SO4) in surface water are of global concern, and studies are needed to generate toxicity data to develop environmental guideline values for NO3 and SO4. The present study was designed to fill existing gaps in toxicity databases by determining the acute and/or chronic toxicity of NO3 (tested as NaNO3) to a unionid mussel (Lampsilis siliquoidea), a midge (Chironomus dilutus), a fish (rainbow trout, Oncorhynchus mykiss), and 2 amphibians (Hyla versicolor and Lithobates sylvaticus), and to determine the acute and/or chronic toxicity of SO4 (tested as Na2SO4) to 2 unionid mussels (L. siliquoidea and Villosa iris), an amphipod (Hyalella azteca), and 2 fish species (fathead minnow, Pimephales promelas and O. mykiss). Among the different test species, acute NO3 median effect concentrations (EC50s) ranged from 189 to >883 mg NO3‐N/L, and chronic NO3 20% effect concentrations (EC20s) based on the most sensitive endpoint ranged from 9.6 to 47 mg NO3‐N/L. The midge was the most sensitive species, and the trout was the least sensitive species in both acute and chronic NO3 exposures. Acute SO4 EC50s for the 2 mussel species (2071 and 2064 mg SO4/L) were similar to the EC50 for the amphipod (2689 mg SO4/L), whereas chronic EC20s for the 2 mussels (438 and 384 mg SO4/L) were >2‐fold lower than the EC20 of the amphipod (1111 mg SO4/L), indicating the high sensitivity of mussels in chronic SO4 exposures. However, the fathead minnow, with an EC20 of 374 mg SO4/L, was the most sensitive species in chronic SO4 exposures whereas the rainbow trout was the least sensitive species (EC20 > 3240 mg SO4/L). The high sensitivity of fathead minnow was consistent with the finding in a previous chronic Na2SO4 study. However, the EC20 values from the present study conducted in test water containing a higher potassium concentration (3 mg K/L) were >2‐fold greater than those in the previous study at a lower potassium concentration (1 mg K/L), which confirmed the influence of potassium on chronic Na2SO4 toxicity to the minnow.","language":"English","publisher":"SETAC","doi":"10.1002/etc.4701","collaboration":"","usgsCitation":"Wang, N., Dorman, R.A., Ivey, C.D., Soucek, D.J., Dickinson, A., Kunz, B.K., Steevens, J.A., Hammer, E.J., and Bauer, C.R., 2020, Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures: Environmental Toxicology and Chemistry, v. 39, no. 5, p. 1071-1085, https://doi.org/10.1002/etc.4701.","productDescription":"15 p.","startPage":"1071","endPage":"1085","ipdsId":"IP-114888","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":437081,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V2O84R","text":"USGS data release","linkHelpText":"Chemical and biological data from acute and chronic exposure to sodium nitrate and sodium sulfate for several freshwater organisms in water-only bioassays"},{"id":374531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-02-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorman, Rebecca A. 0000-0002-5748-7046","orcid":"https://orcid.org/0000-0002-5748-7046","contributorId":28522,"corporation":false,"usgs":true,"family":"Dorman","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soucek, David J. 0000-0002-7741-0193","orcid":"https://orcid.org/0000-0002-7741-0193","contributorId":224591,"corporation":false,"usgs":false,"family":"Soucek","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":788626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickinson, Amy","contributorId":224592,"corporation":false,"usgs":false,"family":"Dickinson","given":"Amy","email":"","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":788627,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kunz, Bethany K. 0000-0002-7193-9336 bkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-7193-9336","contributorId":3798,"corporation":false,"usgs":true,"family":"Kunz","given":"Bethany","email":"bkunz@usgs.gov","middleInitial":"K.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788629,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":788630,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bauer, Candice R.","contributorId":150724,"corporation":false,"usgs":false,"family":"Bauer","given":"Candice","email":"","middleInitial":"R.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":788631,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209184,"text":"70209184 - 2020 - A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states","interactions":[],"lastModifiedDate":"2020-07-09T14:43:19.756855","indexId":"70209184","displayToPublicDate":"2020-02-29T07:07:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states","docAbstract":"Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population models and is much faster. Our manuscript has two goals: 1) to introduce readers unfamiliar with marginalization to the concept and provide worked examples, and 2) to address topics associated with marginalization that have not been previously synthesized and are relevant to both Bayesian and maximum likelihood models. We begin by explaining marginalization using a Cormack-Jolly-Seber model. Next, we apply marginalization to multistate capture-recapture, community occupancy, and integrated population models and briefly discuss random effects, priors, and pseudo-R2. Then, we focus on recovery of discrete latent states, defining different types of conditional probabilities and showing how quantities such as population abundance or species richness can be estimated in marginalized code. Lastly, we show that occupancy and site abundance models with auto-covariates can be fit with marginalized code with minimal impact on parameter estimates.\n\nMarginalized code was anywhere from five to >1000 times faster than discrete code. Differences in inferences were minimal using marginalized code. Discrete latent states and fully conditional approaches provide the best estimates of conditional probabilities for a given site or individual. However, estimates for parameters and derived quantities such as species richness and abundance were minimally affected by marginalization and use of imperfect estimates of conditional probabilities. The results applied even when auto-covariates based on imperfect estimates of conditional probabilities were used. Understanding how marginalization works shrinks the divide between Bayesian and maximum likelihood approaches to population models. Some models that have only been presented in a Bayesian framework can easily be fit in maximum likelihood. On the other hand, factors such as informative priors, random effects, or pseudo-R2 values may motivate a Bayesian approach in some applications. An understanding of marginalization allows users to minimize the speed that is sacrificed when switching from a maximum likelihood approach. Widespread application of marginalization in Bayesian population models will facilitate more thorough simulation studies, comparisons of alternative model structures, and faster learning.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2112","usgsCitation":"Yackulic, C.B., Dodrill, M.J., Dzul, M.C., Sanderlin, J.S., and Reid, J.A., 2020, A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states: Ecological Applications, v. 30, no. 5, e02112, 19 p., https://doi.org/10.1002/eap.2112.","productDescription":"e02112, 19 p.","ipdsId":"IP-108648","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437083,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JN5C0L","text":"USGS data release","linkHelpText":"Marginalizing Bayesian population models - data for examples in the Grand Canyon region, southeastern Arizona, western Oregon USA - 1990-2015"},{"id":373429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dodrill, Michael J. 0000-0002-7038-7170 mdodrill@usgs.gov","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":5468,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","email":"mdodrill@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanderlin, Jamie S.","contributorId":223514,"corporation":false,"usgs":false,"family":"Sanderlin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":40727,"text":"USDA Forest Service, Rocky Mountain Research Station, Flagstaff, AZ 86001 USA","active":true,"usgs":false}],"preferred":false,"id":785283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reid, Janice A.","contributorId":223515,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","email":"","middleInitial":"A.","affiliations":[{"id":40726,"text":"USDA Forest Service, Pacific Northwest Research Station, Roseburg Field Station, Roseburg, OR USA","active":true,"usgs":false}],"preferred":false,"id":785284,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205260,"text":"fs20193051 - 2020 - The 3D Elevation Program and energy for the Nation","interactions":[],"lastModifiedDate":"2020-03-02T06:19:52","indexId":"fs20193051","displayToPublicDate":"2020-02-28T16:25:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3051","displayTitle":"The 3D Elevation Program and Energy for the Nation","title":"The 3D Elevation Program and energy for the Nation","docAbstract":"High-resolution light detection and ranging (lidar) data are used in energy infrastructure siting, design, permitting, construction, and monitoring to promote public safety through the reduction of risks. For example, lidar data are used to identify safe locations for energy infrastructure by analyzing terrain parameters and identifying and evaluating geologic hazards (for example, landslide and fault locations) and their potential public safety effects on the location or design of infrastructure. Increasingly, engineering companies and regulatory agencies are using lidar and other remote sensing techniques as an efficient method to collect accurate, comprehensive data while reducing risks to field personnel.
The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) is collecting lidar data nationwide (interferometric synthetic aperture radar [IfSAR] data in Alaska) to support a wide range of applications, including projects related to energy infrastructure construction and safety. Renewable energy resources, resource mining, and oil and gas resources were identified by the National Enhanced Elevation Assessment as business uses requiring three-dimensional (3D) elevation data.
Elevation data are critical in assessing potential sites for energy infrastructure, such as pipelines, refineries and other facilities, to mitigate risks from natural hazards. For example, the Federal Energy Regulatory Commission (FERC), an independent agency that regulates the interstate transmission of electricity, natural gas, and oil, uses enhanced elevation data to conduct National Environmental Policy Act (NEPA) compliance assessments. The acquisition of high-resolution lidar data by the USGS 3DEP initiative helps the FERC and NEPA permit applicants by providing accurate and consistent data for hazards analysis. The use of these data accelerates the application and review process and avoids the much higher costs of acquiring elevation data along proposed energy facility locations and pipeline corridors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193051","usgsCitation":"Thatcher, C.A., Lukas, Vicki, and Stoker, J.M., 2020, The 3D Elevation Program and energy for the Nation: U.S. Geological Survey Fact Sheet 2019–3051, 2 p., https://doi.org/10.3133/fs20193051.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-107266","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":372745,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3051/coverthb.jpg"},{"id":372746,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3051/fs20193051.pdf","text":"Report","size":"566 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3051"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Dauphin Island, Alabama, located in the Northern Gulf of Mexico just outside of Mobile Bay, is Alabama’s only barrier island and provides an array of historical, natural, and economic resources. The dynamic island shoreline of Dauphin Island evolved across time scales while constantly acted upon by waves and currents during both storms and calm periods. Reductions in the vulnerability and enhancements to the resiliency of Dauphin Island—through offshore sand placement, breach closure, berm construction, and other means—have been used to protect the island and its vital resources. Planning for a resilient Dauphin Island requires predicting the long-term evolution of the barrier island system and the dominant, temporally varying processes that influence it, including littoral alongshore sediment transport under typical wave conditions, beach and dune erosion, the island overwash and breaching that occur rapidly during storm events, and the recovery of primary sand dunes through Aeolian transport over decadal time scales. Littoral sediment transport within the Dauphin Island decadal-scale framework was simulated using the Delft-3D modeling software suite. The influences of wind, waves, water levels, and sediment transport are incorporated into the model. Model skill in the prediction of waves, water levels, currents, volumetric flow rates through inlets, and shoreline position was assessed by using a set of deterministic and statistical hindcast simulations. The Delft-3D modeling application described here can be coupled with validated models of storm-response and dune recovery to predict the evolution of Dauphin Island on decadal time scales.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201011","usgsCitation":"Jenkins, R.L., III, Long, J.W., Dalyander, P.S., Thompson, D.M., and Mickey, R.C., 2020, Development of a process-based littoral sediment transport model for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1011, 43 p., https://doi.org/10.3133/ofr20201011.","productDescription":"vii, 43 p.","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109477","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399455,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109731.htm"},{"id":372743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1011/ofr20201011.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1011"},{"id":372742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1011/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.36715698242186,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.210421455819937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
A study was conducted by the U.S. Geological Survey (USGS) in cooperation with the Washington, D.C., Department of Energy & Environment to estimate the loads of suspended-sediment-bound chemical compounds in five gaged tributaries and four ungaged tributaries of the Anacostia River (known locally as “Lower Anacostia River”) in Washington, D.C. Tributaries whose discharge is measured by the USGS are the Northeast and Northwest Branches of the Anacostia River, referred to in this report as “Northeast Branch” (NEB) and “Northwest Branch” (NWB), respectively; Watts Branch (WB); and Hickey Run (HR). A USGS streamflow-gaging station was established in 2016 on Beaverdam Creek (known locally as “Lower Beaverdam Creek” [LBDC]) to support this study. The ungaged streams studied include Nash Run; Pope Branch; an unnamed stream at Fort DuPont, referred to in this report as “Fort DuPont Creek”; and an unnamed stream at Fort Stanton, referred to in this report as “Fort Stanton Creek.” The gaged streams were sampled during four to five storms and two low-flow events during January, March, May, and July 2017. The ungaged streams were sampled during one storm and one low-flow event during July 2017. Storm sampling involved collecting large-volume (60- to 70-liter) composite samples, then removing sediment by filtration in the laboratory. Low-flow samples were obtained by filtering streamwater directly in the field. Continuously recording data sondes were deployed throughout the study to measure turbidity and other water-quality characteristics. During sampling, multiple discrete samples of streamwater were collected to determine suspended-sediment concentration (SSC) and particulate organic carbon (POC) concentration. Shortly after each storm, bed sediment was collected for chemical analysis.
Sediment samples were analyzed for 209 polychlorinated biphenyl (PCB) congeners; 35 polyaromatic hydrocarbon (PAH) compounds, including 20 nonalkylated and 15 alkylated species; and 20 organochlorine pesticide (OP) compounds. Sediment from one storm was analyzed for 23 metals.
Relations were developed among turbidity, discharge, and measured SSC by using multiple linear regression of log-transformed data. These relations were used to estimate SSC from continuous records of discharge and turbidity and were subsequently used to estimate sediment loads for the 2017 calendar year. USGS continuous records of turbidity in NEB, NWB, Watts Branch, and Hickey Run were available for 2013–17, which allowed sediment loads to be calculated for these years. Sediment loads for the ungaged streams were estimated by using loads measured in Watts Branch adjusted on the basis of stream-basin areas.
Sediment loads for 2017 total 3.10×107 kilograms (kg), with 1.02×107 kg (33 percent of total) from the NEB, 1.55×107 kg (50 percent) from the NWB, 4.45×106 kg (14 percent) from LBDC, 5.62×105 kg (2 percent) from Watts Branch, and 2.82×105 kg (1 percent) from Hickey Run. Sediment yields were highest from NWB and LBDC (3.13×105 kilograms per year per square mile [kg/yr/mi2] and 3.01 kg/yr/mi2, respectively). As a result of gaps in turbidity and discharge data, the load for LBDC reported here was calculated from measurements representing only 88 percent of the year (2017), and thus underestimates the actual load. All other gaged tributaries had datasets covering 100 percent of the year and are considered to fully represent actual loads. Estimated sediment loads for the ungaged streams during 2017 total 3.5×105 kg, with 1.2×105 kg from Nash Run, 6.2×104 kg from Pope Branch, 1.1×105 kg from Fort DuPont Creek, and 5.6×104 kg from Fort Stanton Creek.
Concentrations of PCBs, PAHs, and chlorinated pesticides in streamwater are presented for stormflow and low-flow conditions. Average concentrations (in stormflow and low-flow samples) of total PCBs (sum of all congeners, including coelutions) are 5.9 micrograms per kilogram (µg/kg) for NEB, 6.6 µg/kg for NWB, 130 µg/kg for LBDC, 34 µg/kg for Watts Branch, and 69 µg/kg for Hickey Run. Average concentrations of total PAHs (tPAH) (total of nonalkylated and alkylated species) are 2,000 µg/kg for NEB, 3,300 µg/kg for NWB, 2,200 µg/kg for LBDC, 2,400 µg/kg for Watts Branch, and 18,000 µg/kg for Hickey Run. tPAH concentrations among the ungaged streams were highest in Nash Run (5,500 µg/kg); concentrations in the other ungaged streams were less than (<) 700 µg/kg.
The general magnitude of tPCB and tPAH concentrations in streamwater samples was low-flow samples greater than (>) stormflow samples greater than or equal to (≥) bed-sediment samples. PCB congener profiles in the three types of samples were nearly identical in each stream and were similar in all streams except for LBDC, where the dominant PCBs shifted to the lighter di- through tetra- homologs. LBDC showed higher tPCB concentrations and a distinct congener profile from the other streams. The similarity in congener makeup supported that averaging PCB concentrations in stormflow and low-flow samples was appropriate for calculating chemical loads.
Loads of tPCB, tPAH (total of alkylated and nonalkylated forms), and pesticides were estimated for each stream by multiplying average contaminant concentrations by the respective sediment loads. Total PCB loads for 2017 were estimated to be 820 grams (g) with 8 percent (60 g) from NEB, 12 percent (95 g) from NWB, 75 percent (590 g) from LBDC, 3 percent (25 g) from Watts Branch, and 2.5 percent (19 g) from Hickey Run. PCB toxicity totaled 3.8×10−3 µg/kg, with the largest contribution (47 percent) derived from LBDC. Total PAH loads (sum of alkylated and nonalkylated forms) for 2017 were estimated to be 89,000 g, with 23 percent (20,000 g) from NEB, 59 percent (52,000 g) from NWB, 11 percent (9,800 g) from LBDC, 2 percent (1,400 g) from Watts Branch, and 6 percent (5,200 g) from Hickey Run. These results indicate that the largest contributor (75 percent) of PCBs to the Anacostia River is LBDC, although it contributes only 15 percent of the sediment and its basin area represents only 10 percent of the area of the Anacostia River watershed. The majority of the PAH load originates from NWB (59 percent of total) and NEB (22 percent). The ungaged tributaries contribute extremely small loads of PCBs and PAHs, totaling 8.1 g and 765 kg, respectively. More than 94 percent of the total load from the ungaged tributaries is derived from the Nash Run Basin.
Various organochlorine pesticides were present in suspended and bed sediment from all gaged and ungaged tributaries; however, elevated detection levels associated with the analytical methods resulted in numerous unquantifiable concentrations in the suspended-sediment samples. Only the pesticide chlordane was found in measurable concentrations in all gaged tributaries. As a result, in this report, a combination of analytical data from suspended-sediment and bed-sediment samples was used to estimate the maximum pesticide loading for each tributary. Chlordane was the principal compound present in the gaged tributaries; the highest average concentration (average of stormflow and low-flow samples from each stream) was 62 µg/kg in sediment from Watts Branch. Chlordane loads for 2017 totaled 1,100 g, of which 7 percent (430 g) was from NEB, 28 percent (320 g) was from NWB, 28 percent (310 g) was from LBDC, 5 percent (56 g) was from Watts Branch, and 1 percent (11 g) was from Hickey Run. Chlordane was not present in suspended or bed sediment from any of the ungaged tributaries. Loads of the other pesticides were estimated by using the highest concentration measured in the combined suspended-sediment and bed-sediment data for each stream. Notable loads include dieldrin (860 g from NWB), methoxychlor (205 g from LBDC), endrin aldehyde (150 g from NWB), and 4,4-DDT (79 g from Watts Branch). Compared with pesticide loads from the gaged streams, those from the ungaged streams were minimal, with only the Pope Branch contribution exceeding 1 gram per year for 4,4-DDE (1.05 g) and 4,4’-DDT (1.3 g).
The results of this study show that the dominant source of PCBs and chlordane is LBDC, despite its relatively small basin area. PAHs are ubiquitous throughout the study area, with the largest sources being NEB and NWB; this finding is a result of the large sediment load originating from these basins. The small, ungaged streams supply only minimal PCB and PAH loads, with Nash Run being the largest contributor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195092","collaboration":"Prepared in cooperation with the Washington, D.C., Department of Energy & Environment","usgsCitation":"Wilson, T.P., 2019, Sediment and chemical contaminant loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5092, 146 p., https://doi.org/10.3133/sir20195092.","productDescription":"Report: x, 146 p.; Data Release","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-099743","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":399540,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109730.htm"},{"id":372690,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RUZSMV","text":"USGS data release","linkHelpText":"Discharge and sediment data for selected tributaries to the Anacostia River, Washington, District of Columbia, 2003–18"},{"id":372692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5092/sir20195092.pdf","text":"Report","size":"5.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5092"},{"id":372691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5092/coverthb.jpg"}],"country":"United States","state":"District of Columbia","county":"Washington","otherGeospatial":"Anacostia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.0797,\n 38.8447\n ],\n [\n -76.7689,\n 38.8447\n ],\n [\n -76.7689,\n 39.1611\n ],\n [\n -77.0797,\n 39.1611\n ],\n [\n -77.0797,\n 38.8447\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
A fully worked example of decision-support-scale uncertainty quantification (UQ) and parameter estimation (PE) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional PE is used to history-match simulated outputs to corresponding state observations of spring flow and groundwater level. Then a hindcast of a historical drought is made. Using available state observations recorded during drought conditions, the combined UQ and PE analyses are shown to yield an ensemble of model results that bracket the observed hydrologic responses. All files and scripts used for the analyses are placed in the public domain to serve as a template for other practitioners who are interested in undertaking these types of analyses.
","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00050","usgsCitation":"White, J.T., Foster, L.K., Fienen, M., Knowling, M.J., Hemmings, B., and Winterle, J.R., 2020, Towards reproducible environmental modeling for decision support: A worked example: Frontiers in Earth Science, Hydrosphere, v. 28, 50, 11 p., https://doi.org/10.3389/feart.2020.00050.","productDescription":"50, 11 p.","ipdsId":"IP-115342","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":457567,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00050","text":"Publisher Index Page"},{"id":437084,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AUZMI7","text":"USGS data release","linkHelpText":"Towards reproducible environmental modeling for decision support: a worked example"},{"id":386114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"southern-central Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.96435546875,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 28.76765910569123\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowling, Matthew J.","contributorId":251909,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hemmings, Brioch","contributorId":259187,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winterle, James R.","contributorId":259189,"corporation":false,"usgs":false,"family":"Winterle","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":52328,"text":"Edwards Aquifer Authority","active":true,"usgs":false}],"preferred":false,"id":816777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208796,"text":"70208796 - 2020 - Spatial epidemiological patterns suggest mechanisms of land-sea transmission for Sarcocystis neurona in a coastal marine mammal","interactions":[],"lastModifiedDate":"2020-03-02T06:48:31","indexId":"70208796","displayToPublicDate":"2020-02-28T06:45:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Spatial epidemiological patterns suggest mechanisms of land-sea transmission for Sarcocystis neurona in a coastal marine mammal","docAbstract":"Sarcocystis neurona was recognised as an important cause of mortality in southern sea otters (Enhydra lutris nereis) after an outbreak in April 2004 and has since been detected in many marine mammal species in the Northeast Pacific Ocean. Risk of S. neurona exposure in sea otters is associated with consumption of clams and soft-sediment prey and is temporally associated with runoff events. We examined the spatial distribution of S. neurona exposure risk based on serum antibody testing and assessed risk factors for exposure in animals from California, Washington, British Columbia and Alaska. Significant spatial clustering of seropositive animals was observed in California and Washington, compared with British Columbia and Alaska. Adult males were at greatest risk for exposure to S. neurona, and there were strong associations with terrestrial features (wetlands, cropland, high human housing-unit density). In California, habitats containing soft sediment exhibited greater risk than hard substrate or kelp beds. Consuming a diet rich in clams was also associated with increased exposure risk. These findings suggest a transmission pathway analogous to that described for Toxoplasma gondii, with infectious stages traveling in freshwater runoff and being concentrated in particular locations by marine habitat features, ocean physical processes, and invertebrate bioconcentration.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-020-60254-5","usgsCitation":"Burgess, T., Tinker, M., Miller, M.A., Smith, W.A., Bodkin, J.L., Murray, M.J., Nichol, L.M., Saarinen, J.A., Larson, S.E., Tomoleoni, J.A., Conrad, P.A., and Johnson, C., 2020, Spatial epidemiological patterns suggest mechanisms of land-sea transmission for Sarcocystis neurona in a coastal marine mammal: Scientific Reports, v. 10, 3683, 9 p., https://doi.org/10.1038/s41598-020-60254-5.","productDescription":"3683, 9 p.","ipdsId":"IP-114629","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457569,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-60254-5","text":"Publisher Index Page"},{"id":372757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","state":"California, Washington, British Columbia, Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.71874999999999,\n 33.578014746143985\n ],\n [\n -121.9921875,\n 39.639537564366684\n ],\n [\n -122.6953125,\n 47.15984001304432\n ],\n [\n -122.6953125,\n 48.45835188280866\n ],\n [\n -127.265625,\n 54.57206165565852\n ],\n [\n -135.703125,\n 60.58696734225869\n ],\n [\n -143.7890625,\n 62.67414334669093\n ],\n [\n -152.2265625,\n 62.59334083012024\n ],\n [\n -156.97265625,\n 58.99531118795094\n ],\n [\n -160.48828125,\n 55.57834467218206\n ],\n [\n -158.37890625,\n 54.87660665410869\n ],\n [\n -145.01953124999997,\n 59.17592824927136\n ],\n [\n -142.20703125,\n 55.87531083569679\n ],\n [\n -138.33984375,\n 48.10743118848039\n ],\n [\n -128.671875,\n 38.54816542304656\n ],\n [\n -117.94921874999999,\n 31.952162238024975\n ],\n [\n -116.71874999999999,\n 33.578014746143985\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Burgess, Tristan","contributorId":214303,"corporation":false,"usgs":false,"family":"Burgess","given":"Tristan","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":783410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M. 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David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":936775,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70208828,"text":"70208828 - 2020 - Egg counts of Southern Leopard Frog, Lithobates sphenocephalus, egg masses from southern Louisiana, USA","interactions":[],"lastModifiedDate":"2020-03-03T09:00:25","indexId":"70208828","displayToPublicDate":"2020-02-27T08:58:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1899,"text":"Herpetology Notes","active":true,"publicationSubtype":{"id":10}},"title":"Egg counts of Southern Leopard Frog, Lithobates sphenocephalus, egg masses from southern Louisiana, USA","docAbstract":"Southern Leopard Frogs, Lithobates sphenocephalus (Cope, 1889), lay eggs year-round in their southern range, including Louisiana, but their peak breeding season is the cooler months from late fall through early spring (Mount, 1975; Caldwell, 1986; Dundee and Rossman, 1989). Double-enveloped eggs in globular masses are typically deposited in shallow water, but deeper waters are used when temperatures are warmer (Dodd, 2013). Egg masses are often attached to vegetation when present but may also lie free on the substrate (Dundee and Rossman, 1989; Dodd, 2013). Egg masses may be deposited singly, but are often found communally, with hundreds of egg masses in a small area (Dundee and Rossman, 1989; Trauth, 1989). Communal laying in Southern Leopard Frogs may be an adaptative response to cold temperatures (Caldwell, 1986), as has been noted in congeners (Waldman and Ryan, 1983).","language":"English","publisher":"Societas Europaea Herpetologica","usgsCitation":"Glorioso, B.M., Muse, L.J., and Waddle, J.H., 2020, Egg counts of Southern Leopard Frog, Lithobates sphenocephalus, egg masses from southern Louisiana, USA: Herpetology Notes, v. 13, p. 187-189.","productDescription":"3 p.","startPage":"187","endPage":"189","ipdsId":"IP-111582","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":372793,"type":{"id":15,"text":"Index Page"},"url":"https://www.biotaxa.org/hn/article/view/57036/60009"},{"id":372837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.71337890625,\n 29.621221113784504\n ],\n [\n -92.6806640625,\n 29.420460341013133\n ],\n [\n -90.615234375,\n 28.786918085420226\n ],\n [\n -88.846435546875,\n 28.748396571187406\n ],\n [\n -88.53881835937499,\n 29.5830116903775\n ],\n [\n -89.09912109375,\n 30.088107753367257\n ],\n [\n -89.571533203125,\n 30.211608223816906\n ],\n [\n -89.84619140625,\n 30.65681556429287\n ],\n [\n -89.725341796875,\n 31.005862904624205\n ],\n [\n -91.64794921875,\n 30.996445897426373\n ],\n [\n -93.58154296875,\n 30.817346256492073\n ],\n [\n -93.80126953124999,\n 30.315987718557867\n ],\n [\n -93.944091796875,\n 29.81205076752506\n ],\n [\n -93.71337890625,\n 29.621221113784504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glorioso, Brad M. 0000-0002-5400-7414 gloriosob@usgs.gov","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":4241,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","email":"gloriosob@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":783512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muse, Lindy J.","contributorId":172438,"corporation":false,"usgs":false,"family":"Muse","given":"Lindy","email":"","middleInitial":"J.","affiliations":[{"id":27041,"text":"Cherokee at USGS-WARC Lafayette","active":true,"usgs":false}],"preferred":false,"id":783513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waddle, J. 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Estimated ages for samples that contain a mixture of young and old groundwater will be particularly sensitive to model parametrization as relatively small additions of modern 14C from recent recharge can mask the presence and amount of old groundwater. A novel multi-model approach based on inverse geochemical modeling and lumped parameter modeling of age tracers (3H, 3Hetrit, and SF6) was used to better constrain 14C dilution caused by dissolution of carbonates in the unsaturated zone or shallow parts of the Glacial aquifer, which extends over 2000 miles across the northern contiguous United States. Calibration of 14C inverse geochemical models to LPM computed 14C concentrations in modern water indicated that 14C of soil zone and shallow aquifer carbonates were not 14C-dead (0 pmC), as is typically assumed for 14C correction models. 14C of such carbonates was on average about 53 pmC (ranged 0-110 pmC, n = 72). This information was used to correct 14C concentrations for water recharged entirely before 1950 and water that is a mixture of pre- and post-1950 water. The multi-model approach developed here was compared to an analytical 14C-adjustment model (Revised Fontes and Garnier) that assumed solid carbonates were 14C-dead. 14C corrections using the analytical adjustment model tended to over-correct final 14C concentrations by 21 pmC and underestimates mean ages by 40% for groundwater mixtures. In fact, 14C corrections based on analytical model yielded negative ages (14C > 120 pmC) in nearly 36% of mixed samples. This work presents a new approach to constraining 14C corrections and age estimates of mixtures of young and old groundwater. The new method is applied to three well networks distributed across the spatially expansive Glacial aquifer.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.124766","usgsCitation":"Solder, J.E., and Jurgens, B., 2020, Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources: Journal of Hydrology, v. 588, 124766, 14 p., https://doi.org/10.1016/j.jhydrol.2020.124766.","productDescription":"124766, 14 p.","ipdsId":"IP-098920","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":376259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Glacier aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.3544921875,\n 49.15296965617039\n ],\n [\n -123.79394531249999,\n 47.517200697839414\n ],\n [\n -122.16796875,\n 46.800059446787316\n ],\n [\n -120.234375,\n 48.019324184801185\n ],\n [\n -114.3896484375,\n 47.249406957888446\n ],\n [\n -108.984375,\n 47.931066347509756\n ],\n [\n -107.75390625,\n 48.268569112964336\n ],\n [\n -105.64453125,\n 48.28319289548347\n ],\n [\n -102.1728515625,\n 46.830133640447386\n ],\n [\n -101.3818359375,\n 43.739352079154706\n ],\n [\n -99.66796875,\n 42.87596410238256\n ],\n [\n -98.5693359375,\n 40.83043687764923\n ],\n [\n -96.9873046875,\n 38.99357205820944\n ],\n [\n -94.76806640625,\n 38.27268853598095\n ],\n [\n -92.2412109375,\n 38.5825261593533\n ],\n [\n -90,\n 38.41055825094609\n ],\n [\n -86.39648437499999,\n 38.65119833229951\n ],\n [\n -82.705078125,\n 39.36827914916011\n ],\n [\n -82.001953125,\n 40.54720023441049\n ],\n [\n -81.07910156249999,\n 41.11246878918086\n ],\n [\n -80.2001953125,\n 41.40977583200954\n ],\n [\n -77.87109375,\n 41.343824581185686\n ],\n [\n -74.7509765625,\n 40.68063802521456\n ],\n [\n -73.5205078125,\n 41.07935114946896\n ],\n [\n -71.1474609375,\n 41.54147766679026\n ],\n [\n -69.9169921875,\n 41.83682786072712\n ],\n [\n -70.9716796875,\n 42.35854391749705\n ],\n [\n -70.8837890625,\n 43.67581809328341\n ],\n [\n -67.03857421875,\n 44.88701247981298\n ],\n [\n -67.82958984375,\n 45.75219336063106\n ],\n [\n -68.4228515625,\n 47.398349200359235\n ],\n [\n -69.4775390625,\n 47.487513008956554\n ],\n [\n -70.9716796875,\n 45.38301927899063\n ],\n [\n -75.322265625,\n 45.10454630976873\n ],\n [\n -76.4208984375,\n 44.18220395771563\n ],\n [\n -76.376953125,\n 43.64402584769947\n ],\n [\n -79.453125,\n 43.389081939117496\n ],\n [\n -79.0576171875,\n 42.84375132629018\n ],\n [\n -83.4521484375,\n 42.00032514831621\n ],\n [\n -82.265625,\n 44.3709869629717\n ],\n [\n -84.6826171875,\n 46.649436163350245\n ],\n [\n -88.1982421875,\n 47.487513008956554\n ],\n [\n -91.9775390625,\n 46.89023157359399\n ],\n [\n -91.318359375,\n 48.166085419012504\n ],\n [\n -94.6142578125,\n 48.777912755501845\n ],\n [\n -95.3173828125,\n 49.468124067331615\n ],\n [\n -95.44921875,\n 49.03786794532641\n ],\n [\n -123.3544921875,\n 49.15296965617039\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"588","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Solder, John E. 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":201953,"corporation":false,"usgs":true,"family":"Solder","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792357,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211043,"text":"70211043 - 2020 - Food and temperature stressors have opposing effects in determining flexible migration decisions in brown trout (Salmo trutta )","interactions":[],"lastModifiedDate":"2020-07-13T13:54:48.113896","indexId":"70211043","displayToPublicDate":"2020-02-27T08:48:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Food and temperature stressors have opposing effects in determining flexible migration decisions in brown trout (Salmo trutta)","title":"Food and temperature stressors have opposing effects in determining flexible migration decisions in brown trout (Salmo trutta )","docAbstract":"With rapid global change, organisms in natural systems are exposed to a multitude of stressors that likely co‐occur, with uncertain impacts. We explored individual and cumulative effects of co‐occurring environmental stressors on the striking, yet poorly understood, phenomenon of facultative migration. We reared offspring of a brown trout population that naturally demonstrates facultative anadromy (sea migration), under different environmental stressor treatments and measured life history responses in terms of migratory tactics and freshwater maturation rates. Juvenile fish were exposed to reduced food availability, temperatures elevated to 1.8°C above natural conditions or both treatments in combination over 18 months of experimental tank rearing. When considered in isolation, reduced food had negative effects on the size, mass and condition of fish across the experiment. We detected variable effects of warm temperatures (negative effects on size and mass, but positive effect on lipids). When combined with food restriction, temperature effects on these traits were less pronounced, implying antagonistic stressor effects on morphological traits. Stressors combined additively, but had opposing effects on life history tactics: migration increased and maturation rates decreased under low food conditions, whereas the opposite occurred in the warm temperature treatment. Not all fish had expressed maturation or migration tactics by the end of the study, and the frequency of these ‘unassigned’ fish was higher in food deprivation treatments, but lower in warm treatments. Fish showing migration tactics were smaller and in poorer condition than fish showing maturation tactics, but were similar in size to unassigned fish. We further detected effects of food restriction on hypo‐osmoregulatory function of migrants that may influence the fitness benefits of the migratory tactic at sea. We also highlight that responses to multiple stressors may vary depending on the response considered. Collectively, our results indicate contrasting effects of environmental stressors on life history trajectories in a facultatively migratory species.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14990","usgsCitation":"Archer, L., Hutton, S.A., Harman, L., McCormick, S.D., O’Grady, M.N., Kerry, J.P., Poole, W., Gargan, P., McGinnity, P., and Reed, T.E., 2020, Food and temperature stressors have opposing effects in determining flexible migration decisions in brown trout (Salmo trutta ): Global Change Biology, v. 26, no. 5, p. 2878-2896, https://doi.org/10.1111/gcb.14990.","productDescription":"19 p.","startPage":"2878","endPage":"2896","ipdsId":"IP-109996","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457575,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.14990","text":"External Repository"},{"id":376303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Archer, Louise C","contributorId":228950,"corporation":false,"usgs":false,"family":"Archer","given":"Louise C","affiliations":[{"id":41530,"text":"University College Cork","active":true,"usgs":false}],"preferred":false,"id":792577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hutton, Stephen A.","contributorId":228963,"corporation":false,"usgs":false,"family":"Hutton","given":"Stephen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":792578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harman, Luke","contributorId":228952,"corporation":false,"usgs":false,"family":"Harman","given":"Luke","email":"","affiliations":[{"id":41530,"text":"University College Cork","active":true,"usgs":false}],"preferred":false,"id":792579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Grady, Michael N","contributorId":228953,"corporation":false,"usgs":false,"family":"O’Grady","given":"Michael","email":"","middleInitial":"N","affiliations":[{"id":41530,"text":"University College Cork","active":true,"usgs":false}],"preferred":false,"id":792581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kerry, Joseph P.","contributorId":228964,"corporation":false,"usgs":false,"family":"Kerry","given":"Joseph","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":792619,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poole, W Russel","contributorId":228954,"corporation":false,"usgs":false,"family":"Poole","given":"W Russel","affiliations":[{"id":41530,"text":"University College Cork","active":true,"usgs":false}],"preferred":false,"id":792582,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gargan, Patrick","contributorId":228955,"corporation":false,"usgs":false,"family":"Gargan","given":"Patrick","email":"","affiliations":[{"id":41531,"text":"Marine Inst","active":true,"usgs":false}],"preferred":false,"id":792583,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McGinnity, Philip","contributorId":224809,"corporation":false,"usgs":false,"family":"McGinnity","given":"Philip","email":"","affiliations":[],"preferred":false,"id":792584,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reed, Thomas E","contributorId":228956,"corporation":false,"usgs":false,"family":"Reed","given":"Thomas","email":"","middleInitial":"E","affiliations":[{"id":41530,"text":"University College Cork","active":true,"usgs":false}],"preferred":false,"id":792585,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70208763,"text":"70208763 - 2020 - Increased prespawning mortality threatens an integrated natural- and hatchery-origin sockeye salmon population in the Lake Washington Basin","interactions":[],"lastModifiedDate":"2020-03-02T06:23:12","indexId":"70208763","displayToPublicDate":"2020-02-27T06:44:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Increased prespawning mortality threatens an integrated natural- and hatchery-origin sockeye salmon population in the Lake Washington Basin","docAbstract":"The life cycle of diadromous fishes such as salmonids involves natural mortality in a series of distinct life history stages, occurring sequentially in different habitats. Decades of research have emphasized mortality at the embryo, juvenile, and sub-adult stages but it is increasingly clear that some adults that survive and return to freshwater habitats die during the final homeward migration or after they reach the spawning grounds, prior to breeding. These are termed “en route” and “prespawning” mortality, respectively, and can threaten populations depleted by mortality at previous stages. In this study, we present evidence that the sockeye salmon, Oncorhynchus nerka, population that returns to the Lake Washington Basin, in Washington State, USA, is experiencing both forms of adult mortality. Counts of the salmon entering the basin on their return migration in June and July were compared to counts in the major spawning grounds in September through November for 1995–2018. The disparity has increased markedly in recent years. The counts on the spawning grounds have decreased as a proportion of the number entering the system with an average 49 % of sockeye unaccounted for, consistent with increased en route mortality. In addition, prespawning mortality rates have increased in salmon that reach the Cedar River, the main spawning tributary, both at a hatchery holding adult fish in 1995–2018, and in the naturally spawning populations when monitored in the last five years. Hatchery records indicated <10 % prespawning mortality for 1995–2010, increasing to an average 43 % for 2014 – 2018. Recent carcass surveys in the Cedar River documented that 33.6% (2014), 22.3% (2015), 30.3% (2016) and 50.0% (2018) of female sockeye died before completing spawning. These recent increases in prespawning mortality have been associated with warm water during entry to freshwater, but comparably warm water in past decades had no such effect. Steady warming of river temperatures around the median run completion date from < 8.0 °C to > 13.0 °C was correlated with increased prespawning mortality rates at the hatchery from 1995–2018. We conclude that warming conditions during migration and spawning, in concert with other factors such as infections with pathogens, are responsible for the increased prespawning mortality of adult sockeye salmon that are high enough to threaten the population’s viability.","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2020.105527","usgsCitation":"Barnett, H.K., Quinn, T.P., Bhuthimethee, M., and Winton, J., 2020, Increased prespawning mortality threatens an integrated natural- and hatchery-origin sockeye salmon population in the Lake Washington Basin: Fisheries Research, v. 227, 105527, 10 p., https://doi.org/10.1016/j.fishres.2020.105527.","productDescription":"105527, 10 p.","ipdsId":"IP-115029","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":372723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Washington Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.29980468749999,\n 47.49772004565105\n ],\n [\n -122.18238830566406,\n 47.49772004565105\n ],\n [\n -122.18238830566406,\n 47.758714187846294\n ],\n [\n -122.29980468749999,\n 47.758714187846294\n ],\n [\n -122.29980468749999,\n 47.49772004565105\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"227","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnett, Heidy K","contributorId":222835,"corporation":false,"usgs":false,"family":"Barnett","given":"Heidy","email":"","middleInitial":"K","affiliations":[{"id":40608,"text":"West Fork Environmental, Tumwater, WA","active":true,"usgs":false}],"preferred":false,"id":783313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinn, Thomas P.","contributorId":167272,"corporation":false,"usgs":false,"family":"Quinn","given":"Thomas","email":"","middleInitial":"P.","affiliations":[{"id":24671,"text":"School of Aquatic and Fsiery Sciences, UW, Box 355020, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":783314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bhuthimethee, Mary","contributorId":222836,"corporation":false,"usgs":false,"family":"Bhuthimethee","given":"Mary","email":"","affiliations":[{"id":40609,"text":"Seattle Public Utilities, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":783315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winton, James 0000-0002-3505-5509 jwinton@usgs.gov","orcid":"https://orcid.org/0000-0002-3505-5509","contributorId":179330,"corporation":false,"usgs":true,"family":"Winton","given":"James","email":"jwinton@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":783316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208725,"text":"70208725 - 2020 - Evidence for a growing population of eastern migratory monarch butterflies is currently insufficient","interactions":[],"lastModifiedDate":"2020-06-19T16:25:57.924581","indexId":"70208725","displayToPublicDate":"2020-02-26T15:31:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for a growing population of eastern migratory monarch butterflies is currently insufficient","docAbstract":"The eastern migratory population of monarch butterflies has experienced a multi-decadal decline, but a recent increase in abundance (to 6.05 ha in winter 2018) has led some observers to question whether the population has reversed its long-standing decline and embarked on a trajectory of increasing abundance. We examined this possibility through changepoint analyses, first assessing whether a change in trajectory existed and whether that change was sufficient to alter our estimated risk for the population. We found evidence of a change in trajectory in 2014, but insufficient statistical support for a significantly increasing population since that time (β = 0.285, 95% CI = -0.127, 0.697). If the population estimate for winter 2019 is ≥4.0 ha, we will then be able to credibly assert the population has been increasing since 2014. However, given estimated levels of time series variability, presumed habitat capacity and no recent change in status or trend, there was a 13.5% probability of observing a population estimate as large or larger than was reported for winter 2018. Despite insufficient evidence for an increasing population, near-term risk of quasi-extinction by 2023 has declined (mean risk declining from 43% to 20%) because of higher abundance estimates since 2014. Our analyses highlight the incredible difficulty in drawing robust conclusions from annual changes in abundance over a short time series, especially for an insect that commonly exhibits considerable year-to-year variation. Thus, we urge caution when drawing conclusions regarding species status and trends for any species for which limited data are available.","language":"English","publisher":"Frontiers Media SA","doi":"10.3389/fevo.2020.00043","usgsCitation":"Thogmartin, W.E., Szymanski, J.A., and Weiser, E.L., 2020, Evidence for a growing population of eastern migratory monarch butterflies is currently insufficient: Frontiers in Ecology and Evolution, v. 8, 43, 5 p., https://doi.org/10.3389/fevo.2020.00043.","productDescription":"43, 5 p.","ipdsId":"IP-106927","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":457579,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.00043","text":"Publisher Index Page"},{"id":437085,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94OWFSM","text":"USGS data release","linkHelpText":"R code Evidence for a growing population of eastern migratory monarch butterflies is currently insufficient"},{"id":372656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":783179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Szymanski, Jennifer A","contributorId":222787,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","email":"","middleInitial":"A","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":783180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":783181,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208985,"text":"70208985 - 2020 - Non-freezing cold event stresses can cause significant damage to mangrove seedlings: Assessing the role of warming and nitrogen enrichment in a mesocosm study","interactions":[],"lastModifiedDate":"2020-06-22T11:43:36.060529","indexId":"70208985","displayToPublicDate":"2020-02-26T14:25:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1561,"text":"Environmental Research","active":true,"publicationSubtype":{"id":10}},"title":"Non-freezing cold event stresses can cause significant damage to mangrove seedlings: Assessing the role of warming and nitrogen enrichment in a mesocosm study","docAbstract":"Mangroves are expanding poleward along coastlines globally as a response to rising temperatures and reduced incidence of freezing under climate change. Yet, knowledge of mangrove responses to infrequent cold events in the context of future global and regional environmental changes is limited. We initiated a mesocosm experiment in which the seedlings of two mangrove species were grown either at ambient temperature or under warming with and without nitrogen (N) loading. During a short winter period, an unusually severe cold event occurred with the lowest temperature of 2°C. We assessed the possible response of these two mangrove species to the cold stress. We found that the cold event caused various degrees of damage to the seedlings of both mangrove species, with the warming treatment seemingly protecting leaves and branches from the cold damage. However, warming did not buffer mangroves to mortality from those low temperatures in either species. The cold event resulted in a significant decrease in seedling growth rates and net ecosystem CO2 uptake in the post-cold period relative to the pre-cold period, though the cold event did not alter the effects of warming on these parameters of both mangrove species. The cold event differentially altered physiological responses of the two species growing under N loading, with A. marina growing in higher N concentrations having a reduced growth response after the cold event, whereas B. gymnorrhiza displayed no change in post-cold period versus pre-cold period growth. Our findings suggest the pivotal role of cold events, versus freeze events, in regulating mangrove survival and growth even under future warming scenarios. Two mangrove species exhibited differential survival and growth responses to the cold event at different N concentrations, which has implications for how we can restore and conserve mangroves among the world's eutrophied sub-tropical estuaries and with future warming.","language":"English","publisher":"IOPScience","doi":"10.1088/2515-7620/ab7a77","usgsCitation":"Song, W., Feng, J., Krauss, K.W., Zhao, Y., Wang, Z., Luo, Y., and Lin, G., 2020, Non-freezing cold event stresses can cause significant damage to mangrove seedlings: Assessing the role of warming and nitrogen enrichment in a mesocosm study: Environmental Research, v. 2, 031003, 13 p., https://doi.org/10.1088/2515-7620/ab7a77.","productDescription":"031003, 13 p.","ipdsId":"IP-107849","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":457581,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2515-7620/ab7a77","text":"Publisher Index Page"},{"id":373075,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Weimin","contributorId":223168,"corporation":false,"usgs":false,"family":"Song","given":"Weimin","email":"","affiliations":[{"id":40681,"text":"Department of Earth System Science, Tsinghua University, Beijing","active":true,"usgs":false}],"preferred":false,"id":784416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feng, Jianxiang","contributorId":223169,"corporation":false,"usgs":false,"family":"Feng","given":"Jianxiang","email":"","affiliations":[{"id":40682,"text":"Graduate School at Shenzhen, Tsinghua University, Shenzhen","active":true,"usgs":false}],"preferred":false,"id":784417,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":784415,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhao, Yan","contributorId":220290,"corporation":false,"usgs":false,"family":"Zhao","given":"Yan","email":"","affiliations":[],"preferred":false,"id":784418,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Zhonglei","contributorId":223170,"corporation":false,"usgs":false,"family":"Wang","given":"Zhonglei","email":"","affiliations":[{"id":40682,"text":"Graduate School at Shenzhen, Tsinghua University, Shenzhen","active":true,"usgs":false}],"preferred":false,"id":784419,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luo, Yiqi","contributorId":177420,"corporation":false,"usgs":false,"family":"Luo","given":"Yiqi","email":"","affiliations":[],"preferred":false,"id":784420,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lin, Guanghui","contributorId":177296,"corporation":false,"usgs":false,"family":"Lin","given":"Guanghui","email":"","affiliations":[{"id":25577,"text":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":784421,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}