Streamflow data and statistics are vitally important for proper protection and management of the water quality and water quantity of Alabama streams. Such data and statistics are generally available at U.S. Geological Survey streamflow-gaging stations, also referred to as streamgages or stations, but are often needed at ungaged stream locations. To address this need, the U.S. Geological Survey, in cooperation with numerous Alabama State agencies and organizations, developed regional regression equations for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama that are not substantially affected by tides, regulation, diversions, or other anthropogenic influences. A small percentage of the streamgages included in this study experience zero flows during certain periods; thus, the final low-flow frequency regression equations were developed by using weighted left-censored regression analyses to analyze the flow data in an unbiased manner, with weights based on number of years of record.
The equations developed include the annual minimum 1- and 7-day average streamflows with a 10-year recurrence interval (referred to as the 1Q10 and 7Q10 flows), the annual minimum 7-day average streamflow with a 2-year recurrence interval (referred to as the 7Q2 flow), and the mean annual flow using data from 174 streamgages from Alabama and surrounding States. For the 1Q10, 7Q2, and 7Q10 low-flow frequency statistics, the regional regression equations are functions of drainage area, streamflow-variability index, mean annual precipitation, and percentage of the drainage basin located in the Piedmont and Southeastern Plains ecoregions. The mean annual flow regression equation is a function of drainage area, mean annual precipitation, and percentage of the drainage basin located in the Southeastern Plains ecoregion. For the mean annual flow regression equation, the average standard error of estimate was 11.2 percent. For the selected low-flow frequency equations, the average standard errors of estimate ranged from 18.1 to 38.8 percent.
The regional regression equations developed from this investigation have been incorporated into the U.S. Geological Survey StreamStats application for Alabama. StreamStats (https://streamstats.usgs.gov/ss/) is a web-based geographic information system application that delineates drainage basins at selected stream locations and then generates the needed basin characteristics for available regional regression equations. Along with the low-flow frequency equations developed in this investigation, the StreamStats application also has regional regression equations for estimating flood-frequency statistics at locations on rural and urban streams in Alabama.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205099","collaboration":"Prepared in cooperation with Alabama Power; Alabama Farmers Federation; Alabama Association of Conservation Districts; Alabama Association of Resource Conservation and Development Councils; Alabama Department of Agriculture and Industries; Alabama Department of Conservation and Natural Resources— Wildlife and Freshwater Fisheries Division; Alabama Department of Economic and Community Affairs—Office of Water Resources; Alabama Department of Environmental Management; Alabama Soil and Water Conservation Committee; Choctawhatchee, Pea and Yellow Rivers Watershed Management Authority; Geological Survey of Alabama; and The University of Alabama—Alabama Water Institute","usgsCitation":"Feaster, T.D., Kolb, K.R., Painter, J.A., and Clark, J.M., 2020, Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama (ver. 1.1, November 20, 2020): U.S. Geological Survey Scientific Investigations Report 2020–5099, 21 p., https://doi.org/10.3133/sir20205099.","productDescription":"Report: vii, 21 p.; Appendixes: 4; Data Release; Version History","numberOfPages":"34","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-114774","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":378594,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P994UFS7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supporting data for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in Alabama"},{"id":380616,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5099/versionHist.txt","text":"Version History","size":"1.47 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 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\"}}]}","edition":"Version 1.0: September 21, 2020; Version 1.1: September 29, 2020; Version 1.2: November 20, 2020","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, Tennessee 37211
The Clean Water Act (CWA) requires States to identify waters that are impaired for designated uses. These waters are published through a State’s §303(d) list. The CWA also requires that a total maximum daily load (TMDL) be completed for each water body to calculate the maximum amount of contaminants that can be present in that water body and still meet water-quality standards. The Mississippi Department of Environmental Quality (MDEQ) uses a statewide monitoring and assessment strategy to collect benthic macroinvertebrate community data to assess the health of streams and rivers and to identify impaired waters. Waters that are found to be impaired based on the macroinvertebrate community data are listed on the Mississippi §303(d) list, and the cause of impairment is listed as “biological impairment.” Although the CWA requires TMDLs to be developed for applicable contaminants identified in the §303(d) list, TMDLs cannot be computed for stream reaches in Mississippi listed for biological impairment because the actual stressors causing the impairment have not yet been determined. The MDEQ and other water-resource managers in Mississippi require a framework for stressor identification in biologically impaired streams and rivers. This report is organized to (1) provide a general overview of biological impairment and stressor identification in stream ecosystems and (2) provide a detailed framework for stressor identification of Mississippi streams that are biologically impaired. The intent is for the framework to reduce subjectivity, provide consistency, and allow for adaptation as the science evolves. The stressor identification framework for Mississippi involves six key steps:
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
There is an active debate regarding the influence that climate has on the risk of armed conflict, which stems from challenges in assembling unbiased datasets, competing hypotheses on the mechanisms of climate influence, and the difficulty of disentangling direct and indirect climate effects. We use gridded historical non-state conflict records, satellite data, and land surface models in a structural equation modeling approach to uncover the direct and indirect effects of climate on violent conflicts in Africa and the Middle East (ME). We show that climate–conflict linkages in these regions are more complex than previously suggested, with multiple mechanisms at work. Warm temperatures and low rainfall direct effects on conflict risk were stronger than indirect effects through food and water supplies. Warming increases the risk of violence in Africa but unexpectedly decreases this risk in the ME. Furthermore, at the country level, warming decreases the risk of violence in most West African countries. Overall, we find a non-linear response of conflict to warming across countries that depends on the local temperature conditions. We further show that magnitude and sign of the effects largely depend on the scale of analysis and geographical context. These results imply that extreme caution should be exerted when attempting to explain or project local climate–conflict relationships based on a single, generalized theory.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/aba97d","usgsCitation":"Helman, D., Zaitchik, B., and Funk, C., 2020, Climate has contrasting direct and indirect effects on armed conflicts: Environmental Research Letters, v. 15, 104017, 12 p., https://doi.org/10.1088/1748-9326/aba97d.","productDescription":"104017, 12 p.","ipdsId":"IP-118530","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455254,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aba97d","text":"Publisher Index Page"},{"id":421737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa, Middle East","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 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]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2020-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Helman, David","contributorId":330683,"corporation":false,"usgs":false,"family":"Helman","given":"David","affiliations":[{"id":37540,"text":"John Hopkins University","active":true,"usgs":false}],"preferred":false,"id":885582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zaitchik, Benjamin F.","contributorId":330684,"corporation":false,"usgs":false,"family":"Zaitchik","given":"Benjamin F.","affiliations":[{"id":37540,"text":"John Hopkins University","active":true,"usgs":false}],"preferred":false,"id":885583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885584,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227074,"text":"70227074 - 2020 - Ultrasonic Acoustic Surveys of State Endangered Northern Flying Squirrels in the Pocono Mountains, Pennsylvania","interactions":[],"lastModifiedDate":"2021-12-29T14:46:51.794308","indexId":"70227074","displayToPublicDate":"2020-09-21T08:36:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Ultrasonic Acoustic Surveys of State Endangered Northern Flying Squirrels in the Pocono Mountains, Pennsylvania","docAbstract":"Surveying for flying squirrels by using traditional techniques produces extremely low detection rates compared with ultrasonic acoustics. Within Pennsylvania, the northern flying squirrel subspecies Glaucomys sabrinus macrotis is state listed as endangered due to habitat loss and parasite-mediated competition by and hybridization with the southern flying squirrel Glaucomys volans. This subspecies is isolated from adjacent populations in West Virginia and New York and has experienced drastic population declines. The discovery and characterization of ultrasonic vocalizations of G. s. macrotis and G. volans, as well as successful field surveys with ultrasonic acoustic detectors in the southern Appalachian Mountains, highlight the potential use of this technique for determining the presence of G. s. macrotis. To confirm the feasibility of using this technique on declining populations of G. s. macrotis sympatric with G. volans, we conducted 108 nights of passive ultrasonic acoustic surveys for G. s. macrotis at six survey sites by using two detectors per survey site (N = 12 detectors) in June 2017. We considered sites high quality (“high”) or low quality (“low”) based on the number of physical capture records during the past 2 decades and the dominance of boreo-montane conifer tree species in the overstory. We detected G. s. macrotis at four study sites and G. volans at all six study sites. We found higher average probability of detection for G. s. macrotis in high vs. low sites (0.28 ± 0.06 [mean ± SE] and 0.09 ± 0.07, respectively), whereas probability of detection was similar for G. volans between high and low sites (0.13 ± 0.05 and 0.17 ± 0.05, respectively). We also found G. s. macrotis had lower latency of detection at high vs. low sites (2.7 ± 0.8 and 7.83 ± 1.5 nights, respectively) but G. volans did not vary in latency of detection between sites (5 ± 1.6 and 3.8 ± 1.5 nights, respectively). Our study shows acoustics can be successfully used to efficiently survey G. s. macrotis in Pennsylvania, where populations are small and monitoring these populations more effectively is critical to determining changes in persistence due to climate- and disease-induced factors.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-020","usgsCitation":"Diggins, C., Gilley, L., Turner, G., and Ford, W., 2020, Ultrasonic Acoustic Surveys of State Endangered Northern Flying Squirrels in the Pocono Mountains, Pennsylvania: Journal of Fish and Wildlife Management, v. 11, no. 2, p. 644-653, https://doi.org/10.3996/JFWM-20-020.","productDescription":"10 p.","startPage":"644","endPage":"653","ipdsId":"IP-117892","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-020","text":"Publisher Index Page"},{"id":393578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Pocono Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.65597534179688,\n 40.96123389519331\n ],\n [\n -74.91714477539062,\n 40.96123389519331\n ],\n [\n -74.91714477539062,\n 41.36444153054222\n ],\n [\n -75.65597534179688,\n 41.36444153054222\n ],\n [\n -75.65597534179688,\n 40.96123389519331\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Diggins, Corinne A.","contributorId":270521,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":829525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilley, L. Michelle","contributorId":270523,"corporation":false,"usgs":false,"family":"Gilley","given":"L. Michelle","affiliations":[{"id":55452,"text":"Mars Hill University","active":true,"usgs":false}],"preferred":false,"id":829526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turner, Gregory G.","contributorId":270525,"corporation":false,"usgs":false,"family":"Turner","given":"Gregory G.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":829527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":829524,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223247,"text":"70223247 - 2020 - Temporal and spatial changes in Myotis lucifugus acoustic activity before and after white-nose syndrome on Fort Drum Army Installation, New York, USA","interactions":[],"lastModifiedDate":"2021-08-19T16:47:04.818752","indexId":"70223247","displayToPublicDate":"2020-09-20T11:42:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":629,"text":"Acta Chiropterologica","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Temporal and spatial changes in Myotis lucifugus acoustic activity before and after white-nose syndrome on Fort Drum Army Installation, New York, USA","title":"Temporal and spatial changes in Myotis lucifugus acoustic activity before and after white-nose syndrome on Fort Drum Army Installation, New York, USA","docAbstract":"Changes to bat distribution and habitat associations at the local to sub-landscape scale in the post white-nose syndrome (WNS) environment have received little attention to date despite being critical information for managers. To better understand the spatial nature of bat population declines, we modelled both activity patterns and occupancy from acoustic surveys for the Myotis lucifugus (little brown bat) on Fort Drum Military Installation in New York, USA over 15 summers (2003–2017) that span the pre-WNS, WNS-advent (2008) and post-WNS periods, using a set of generalized linear mixed models and geospatial analysis. Our best supported model indicated significant differences between years with significant declines in activity post-WNS. M. lucifugus activity was most closely associated with woody wetland habitats over the study period, however, the spatial patterns of high activity areas were variable over years, with the areal extent of these high activity areas decreasing post-WNS. Our best supported occupancy model varied by year. However, the null occupancy model [Ψ(.)] was either competing (within 2 ΔAIC units) or was the best supported model. Meaning that none of our environmental variables seemed to impact occupancy, and when they did, these differences were not significant. There was high disagreement between our relative activity models and predictions compared to our occupancy models, suggesting that geographic spatial scale and the resolution of the data impacts model outcome. Our results indicate that continued acoustic monitoring of bat species in the Northeast to assess ongoing temporal and spatial changes in habitat associations and to provide direction for future mist-netting studies should rely more on relative activity as the metric of choice rather than site occupancy.
","language":"English","publisher":"Museum and Institute of Zoology PAS","doi":"10.3161/15081109ACC2020.22.1.011","usgsCitation":"Ford, W., Nocera, T., Silvis, A., and Dobony, C.A., 2020, Temporal and spatial changes in Myotis lucifugus acoustic activity before and after white-nose syndrome on Fort Drum Army Installation, New York, USA: Acta Chiropterologica, v. 22, no. 1, p. 121-134, https://doi.org/10.3161/15081109ACC2020.22.1.011.","productDescription":"14 p.","startPage":"121","endPage":"134","ipdsId":"IP-101094","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455261,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/102442","text":"External Repository"},{"id":388164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fort Drum Army Installation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.574951171875,\n 44.006644643819655\n ],\n [\n -75.36895751953125,\n 44.188112606916484\n ],\n [\n -75.56121826171875,\n 44.268804788566165\n ],\n [\n -75.8660888671875,\n 44.05403780323783\n ],\n [\n -75.75897216796875,\n 43.98688630934305\n ],\n [\n -75.574951171875,\n 44.006644643819655\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":821520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nocera, Tomás","contributorId":264425,"corporation":false,"usgs":false,"family":"Nocera","given":"Tomás","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":821521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":264426,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","affiliations":[{"id":54472,"text":"RES Inc.","active":true,"usgs":false}],"preferred":false,"id":821522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobony, Christopher A.","contributorId":264428,"corporation":false,"usgs":false,"family":"Dobony","given":"Christopher","email":"","middleInitial":"A.","affiliations":[{"id":54473,"text":"Fort Drum Military Installation","active":true,"usgs":false}],"preferred":false,"id":821523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228508,"text":"70228508 - 2020 - A demographic projection model to support conservation decision making for an endangered snake with limited monitoring data","interactions":[],"lastModifiedDate":"2022-02-11T15:35:55.593402","indexId":"70228508","displayToPublicDate":"2020-09-20T09:28:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"A demographic projection model to support conservation decision making for an endangered snake with limited monitoring data","docAbstract":"Conservation planning for rare and threatened species is often made more difficult by a lack of research and monitoring data. In such cases, managers may rely on qualitative assessments of species risk that lack explicit acknowledgement of uncertainty. Snakes are a group of conservation concern that are also notoriously difficult to monitor. Here, we demonstrate a quantitative population projection for a data-deficient species, the Puerto Rican boa (Chilabothrus inornatus) using expert knowledge and published information about species life history and threats to persistence. Using this model, we simulated population dynamics over 30 years under four scenarios of future urbanization and found that there was an increased probability of population decline as urbanization rates increased. We conduct a sensitivity analysis to evaluate the sensitivity of outcomes to model inputs, a practice that may also be useful in recovery planning. The sensitivity analyses also provide insight into how the future trajectories would change if the elicited demographic rates are incorrect. Even when data are sparse, quantitative methods can often be used to produce rigorous and reproducible estimates of future status with quantifiable uncertainty.
","language":"English","publisher":"Wiley","doi":"10.1111/acv.12641","usgsCitation":"Tucker, A.M., McGowan, C.P., Mulero Oliveras, E., Angeli, N., and Zegarra, J., 2020, A demographic projection model to support conservation decision making for an endangered snake with limited monitoring data: Animal Conservation, v. 24, no. 2, p. 291-301, https://doi.org/10.1111/acv.12641.","productDescription":"11 p.","startPage":"291","endPage":"301","ipdsId":"IP-117213","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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M.","contributorId":276002,"corporation":false,"usgs":false,"family":"Tucker","given":"A.","email":"","middleInitial":"M.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":834464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mulero Oliveras, E.","contributorId":276003,"corporation":false,"usgs":false,"family":"Mulero Oliveras","given":"E.","email":"","affiliations":[{"id":38462,"text":"University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":834465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Angeli, N.F.","contributorId":276004,"corporation":false,"usgs":false,"family":"Angeli","given":"N.F.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zegarra, J.P.","contributorId":242909,"corporation":false,"usgs":false,"family":"Zegarra","given":"J.P.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834467,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226676,"text":"70226676 - 2020 - Neonicotinoid insecticide concentrations in agricultural wetlands and associations with aquatic invertebrate communities","interactions":[],"lastModifiedDate":"2021-12-03T13:03:38.319596","indexId":"70226676","displayToPublicDate":"2020-09-20T07:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":682,"text":"Agriculture, Ecosystems and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Neonicotinoid insecticide concentrations in agricultural wetlands and associations with aquatic invertebrate communities","docAbstract":"Neonicotinoids are considered a superior insecticide for agricultural pest management, although their impacts on non-target insects is a rising concern. Aside from laboratory and mesocosm studies, limited research has been directed towards the role neonicotinoids may have in structuring aquatic invertebrate communities in field settings. Therefore, we simultaneously collected aquatic invertebrate and surface water samples from 26 wetlands within a highly modified agricultural landscape of Nebraska’s Rainwater Basin during spring 2015. Water samples were tested for six different neonicotinoids, nutrients, and physical properties. Trace levels of clothianidin and imidacloprid were the only neonicotinoids detected, occurring in 85% and 15%, respectively, of wetlands sampled. All measurements for clothianidin and imidacloprid were below chronic toxicity benchmarks set by the United States Environmental Protection Agency. Neonicotinoid concentrations were significantly lower (W26, 0.05 = 42.5) at wetlands with vegetative buffer strips >50 m wide compared to wetlands with vegetative buffers strips <50 m. Although neonicotinoids were below benchmark concentrations proposed by government regulations, a significant negative association between neonicotinoid concentrations and aquatic invertebrate biomass was observed across all wetlands studied (Parameter Estimate = -0.031; SE = 0.014).
Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.
Hydrological and bioclimatic processes that lead to drought may stress plants and wildlife, restructure plant community type and architecture, increase monotypic stands and bare soils, facilitate the invasion of non‐native plant species and accelerate soil erosion. Our study focuses on the impact of a paucity of Colorado River surface flows from the United States (U.S.) to Mexico. We measured change in riparian plant greenness and water use over the past two decades using remotely sensed measurements of vegetation index (VI), evapotranspiration (ET), and a new annualized Phenology Assessment Metric (PAM) for ET. We measure these long‐term (2000‐2019) metrics and their short‐term (2014‐2019) response to an environmental, pulse flow in 2014, as prescribed under Minute 319 of the 1944 Water Treaty between the two nations. In subsequent years, small directed flows were provided to restoration areas under Minute 323. We use 250 m MODIS and 30 m Landsat imagery to evaluate three vegetation indices (NDVI, EVI, EVI2). We select EVI2 to parameterize an optical‐based ET algorithm and test the relationship between ET from Landsat and MODIS by regression approaches. Our analyses show significant decreases in VIs and ET for both the 20‐year and post‐pulse 5‐year periods. Over the last 20 years, EVI Landsat declined 34% (30% by EVIMODIS) and ETLandsat‐EVI declined 38% (27% by ETMODIS‐EVI), overall ca. 1.61 mmd‐1 or 476 mmyr‐1 drop in ET. Over the 5 years since the 2014 pulse flow, EVI Landsat declined 20% (13% by EVIMODIS) and ETLandsat‐EVI declined 23% (4% by ETMODIS‐EVI) with a 0.77 mmd‐1 or a 209 mmyr‐1 5‐year drop in ET. Data and change maps show the pulse flow contributed enough water to slow the rate of loss, but only for the very short‐term (1‐2 years). These findings are critically important as they suggest further deterioration of biodiversity, wildlife habitat and key ecosystem services due to anthropogenic diversions of water in the U.S. and Mexico and from land clearing, fires, and plant‐related drought which affect hydrological processes.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13911","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Chavoshi Borujeni, S., Jarchow, C., Gómez‐Sapiens, M., Nouri, H., Herrmann, S.M., and Didan, K., 2020, Ecohydrological responses to surface flow across borders: Two decades of changes in vegetation greenness and water use in the riparian corridor of the Colorado River Delta: Hydrological Processes, v. 34, no. 25, p. 4851-4883, https://doi.org/10.1002/hyp.13911.","productDescription":"33 p.; Data Release","startPage":"4851","endPage":"4883","ipdsId":"IP-117414","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":378804,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":436784,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PGDJ1","text":"USGS data release","linkHelpText":"Colorado River Delta Project: A compilation of vegetation indices, phenology assessment metrics, estimates of evapotranspiration and change maps for seven reaches of the delta's 150 km region, for nearly the last two decades"}],"country":"Mexico, United States","otherGeospatial":"Colorado River delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.15869140624999,\n 31.606609719226917\n ],\n [\n -114.521484375,\n 31.606609719226917\n ],\n [\n -114.521484375,\n 32.76880048488168\n ],\n [\n -115.15869140624999,\n 32.76880048488168\n ],\n [\n -115.15869140624999,\n 31.606609719226917\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"25","noUsgsAuthors":false,"publicationDate":"2020-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":799708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":799709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chavoshi Borujeni, Sattar","contributorId":241612,"corporation":false,"usgs":false,"family":"Chavoshi Borujeni","given":"Sattar","email":"","affiliations":[{"id":48363,"text":"Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Centre, AREEO, Isfahan, Iran","active":true,"usgs":false}],"preferred":false,"id":799710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":799711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gómez‐Sapiens, Marth M.","contributorId":241615,"corporation":false,"usgs":false,"family":"Gómez‐Sapiens","given":"Marth M.","affiliations":[],"preferred":false,"id":799732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nouri, Hamideh","contributorId":178847,"corporation":false,"usgs":false,"family":"Nouri","given":"Hamideh","affiliations":[],"preferred":false,"id":799733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herrmann, Stefanie M. 0000-0002-4069-2019","orcid":"https://orcid.org/0000-0002-4069-2019","contributorId":20234,"corporation":false,"usgs":true,"family":"Herrmann","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":799735,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218272,"text":"70218272 - 2020 - Application of a new species-richness based flow ecology framework for assessing flow reduction effects on aquatic communities","interactions":[],"lastModifiedDate":"2021-02-23T13:33:50.365096","indexId":"70218272","displayToPublicDate":"2020-09-19T07:30:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Application of a new species-richness based flow ecology framework for assessing flow reduction effects on aquatic communities","docAbstract":"Water‐resources managers are challenged with maintaining a balance among beneficial uses throughout river networks and need robust means of assessing potential risks to aquatic life resulting from flow alterations. This study generated ecological limit functions from species‐streamflow relations to quantify potential fish richness response to flow alteration and compared results to currently accepted streamflow management guidelines. Modeled responses of absolute richness change were watershed specific and varied among sample sets derived from hydrologic unit classifications of different sizes (large HUC 6 basins to regional scale HUC 8). With a 20% flow reduction, 10% of HUC 8 predicted a richness decrease in one or more taxa. While absolute richness change was consistent across streams within a HUC, percent richness change was stream size dependent. Comparisons with Instream Flow Incremental Methodology habitat models predicted habitat loss greater than percent richness change; however, predictions for habitat and richness decreased similarly as stream size decreased. Watershed‐specific responses from flow reductions could allow water‐resources management decisions to be made locally based on the predicted richness change for certain sized streams. Quantitative results highlight the utility of a richness‐based framework for generating watershed‐specific risk assessments that validate and inform currently employed water‐resources management practices.
In terrestrial and marine ecosystems, remote sensing has been used to estimate gross primary productivity (GPP) for decades, but few applications exist for shallow freshwater ecosystems.Here we show field-based GPP correlates with satellite and airborne lake color across a range of optically and limnologically diverse lakes in interior Alaska. A strong relationship between in situ GPP derived from stable oxygen isotopes (δ18O) and space-based lake color from satellites (e.g. Landsat-8, Sentinel-2 and CubeSats) and airborne imagery (AVIRIS-NG) demonstrates the potential power of this technique for improving spatial and temporal monitoring of lake GPP when coupled with additional field validation measurements across different systems. In shallow waters clear enough for sunlight to reach lake bottoms, both submerged vegetation (macrophytes and algae) and phytoplankton likely contribute to GPP. The stable isotopes and remotely sensed shallow lake color used here integrate both components. These results demonstrate the utility of lake color as a feasible means for mapping lake GPP from remote sensing. This novel methodology estimates GPP from remote sensing in shallow lakes by combining field measurements of oxygen isotopes with airborne, satellite and CubeSat imagery. This use of lake color for providing insight into ecological processes of shallow lakes is recommended, especially for remote arctic and boreal landscapes.
","language":"English","publisher":"ioP Science","doi":"10.1088/1748-9326/aba46f","usgsCitation":"Kuhn, C.D., Bogard, M.J., Johnston, S.E., John, A., Vermote, E., Spencer, R., Dornblaser, M.M., Wickland, K.P., Striegl, R.G., and Butman, D., 2020, Satellite and airborne remote sensing of gross primary productivity in boreal Alaskan lakes: Environmental Research Letters, v. 10, no. 15, 105001, 13 p., https://doi.org/10.1088/1748-9326/aba46f.","productDescription":"105001, 13 p.","ipdsId":"IP-113821","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aba46f","text":"Publisher Index Page"},{"id":378668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.68347167968747,\n 65.80727906299441\n ],\n [\n -144.0802001953125,\n 65.80727906299441\n ],\n [\n -144.0802001953125,\n 66.59631225137328\n ],\n [\n -148.68347167968747,\n 66.59631225137328\n ],\n [\n -148.68347167968747,\n 65.80727906299441\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuhn, Catherine D. 0000-0002-9220-630X","orcid":"https://orcid.org/0000-0002-9220-630X","contributorId":213255,"corporation":false,"usgs":false,"family":"Kuhn","given":"Catherine","email":"","middleInitial":"D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":799398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bogard, Matthew J. 0000-0001-9491-0328","orcid":"https://orcid.org/0000-0001-9491-0328","contributorId":213254,"corporation":false,"usgs":false,"family":"Bogard","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":799399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnston, Sarah Ellen","contributorId":213256,"corporation":false,"usgs":false,"family":"Johnston","given":"Sarah","email":"","middleInitial":"Ellen","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":799400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"John, Aji","contributorId":241042,"corporation":false,"usgs":false,"family":"John","given":"Aji","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":799401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vermote, Eric","contributorId":198856,"corporation":false,"usgs":false,"family":"Vermote","given":"Eric","email":"","affiliations":[],"preferred":false,"id":799402,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Rob 0000-0003-0860-4717","orcid":"https://orcid.org/0000-0003-0860-4717","contributorId":241050,"corporation":false,"usgs":false,"family":"Spencer","given":"Rob","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":799403,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dornblaser, Mark M. 0000-0002-6298-3757 mmdornbl@usgs.gov","orcid":"https://orcid.org/0000-0002-6298-3757","contributorId":1636,"corporation":false,"usgs":true,"family":"Dornblaser","given":"Mark","email":"mmdornbl@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":799404,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":799405,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":false,"id":799406,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Butman, David","contributorId":224754,"corporation":false,"usgs":false,"family":"Butman","given":"David","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":799407,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70214033,"text":"70214033 - 2020 - Reliability of external characteristics to age Barrow’s goldeneye","interactions":[],"lastModifiedDate":"2021-01-19T15:41:05.4695","indexId":"70214033","displayToPublicDate":"2020-09-18T10:14:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Reliability of external characteristics to age Barrow’s goldeneye","docAbstract":"Accurate assignment of age class is critical for understanding most demographic processes. For waterfowl, most techniques for determining age class require birds in hand, reducing utility for quickly and efficiently sampling a large portion of the population. As an alternative, we sought to establish an observation‐based methodology, achievable in the field with standard optics, for determining age class of Barrow's goldeneyes (Bucephala islandica). We photographed heads, wings, and bellies of 232 Barrow's goldeneyes captured during late winter (February–April) of 2007–2015 along the north Pacific Coast. From these photographs, we focused on 5 external characteristics for both males and females, with binary states that putatively corresponded to 2 age classes—first‐year birds (<1 yr) and adults (>1 yr). For males, all 5 external traits (belly color, head color, eye color, facial crescent, median secondary coverts color) had binary states that were reliably distinguishable by observers. Moreover, all 5 external traits were highly predictive of age class (≥96% concordance between external vs. bursal‐derived ages), and novice observers, after receiving training, were able to accurately age 96% of first‐year and 99% of adult males. In contrast, patterns were weaker for females; putative external characteristics of female age class (belly color, bill radiance, bill blackness, eye color, median secondary coverts color) had 77–91% concordance with bursal‐derived age, compared with 96–100% for males, and observers misidentified age classes of 15% of females, compared with only 2% of males. Overall, age classes of male Barrow's goldeneyes were accurately and reliably distinguishable during winter based on several external characteristics, whereas those of females were not. Our technique may be used to estimate age composition of male Barrow's goldeneyes during winter, providing a useful metric for monitoring annual changes in adult‐to‐juvenile ratios and other important demographic parameters.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1123","usgsCitation":"Lewis, T.L., Esler, D., Hogan, D.H., Boyd, W., Bowman, T.D., and Thompson, J., 2020, Reliability of external characteristics to age Barrow’s goldeneye: Wildlife Society Bulletin, v. 44, no. 4, p. 654-661, https://doi.org/10.1002/wsb.1123.","productDescription":"8 p.","startPage":"654","endPage":"661","ipdsId":"IP-106102","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":436786,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92GRT3C","text":"USGS data release","linkHelpText":"Data for Determining Age of Barrow's Goldeneye (Bucephala islandica) from External Characteristics, Alaska and Canada, 2007-2015"},{"id":378606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Indian Arm, Prince William Sound, Stephens Passage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -139.5703125,\n 59.66774058164963\n ],\n [\n -132.890625,\n 53.225768435790194\n ],\n [\n -126.298828125,\n 48.86471476180277\n ],\n [\n -123.04687499999999,\n 49.724479188712984\n ],\n [\n -129.814453125,\n 55.47885346331034\n ],\n [\n -135.35156249999997,\n 59.93300042374631\n ],\n [\n -139.5703125,\n 59.66774058164963\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lewis, Tyler L. 0000-0002-7399-8137 tyler.lewis@alaska.gov","orcid":"https://orcid.org/0000-0002-7399-8137","contributorId":241007,"corporation":false,"usgs":true,"family":"Lewis","given":"Tyler","email":"tyler.lewis@alaska.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":799288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":799289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hogan, Danica H.","contributorId":241001,"corporation":false,"usgs":false,"family":"Hogan","given":"Danica","email":"","middleInitial":"H.","affiliations":[{"id":48188,"text":"Environment Canada","active":true,"usgs":false}],"preferred":false,"id":799290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyd, W. Sean","contributorId":241002,"corporation":false,"usgs":false,"family":"Boyd","given":"W. Sean","affiliations":[{"id":48188,"text":"Environment Canada","active":true,"usgs":false}],"preferred":false,"id":799291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowman, Timothy D.","contributorId":80779,"corporation":false,"usgs":false,"family":"Bowman","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":799292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Jonathan","contributorId":222570,"corporation":false,"usgs":false,"family":"Thompson","given":"Jonathan","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":799293,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70214109,"text":"70214109 - 2020 - Endocrine and physiological responses of hatchling American kestrels (Falco sparverius) following embryonic exposure to technical short-chain chlorinated paraffins (C10-13)","interactions":[],"lastModifiedDate":"2020-09-23T15:09:40.762233","indexId":"70214109","displayToPublicDate":"2020-09-18T10:06:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Endocrine and physiological responses of hatchling American kestrels (Falco sparverius) following embryonic exposure to technical short-chain chlorinated paraffins (C10-13)","title":"Endocrine and physiological responses of hatchling American kestrels (Falco sparverius) following embryonic exposure to technical short-chain chlorinated paraffins (C10-13)","docAbstract":"Short chain chlorinated paraffins (SCCPs) are complex mixtures of polychlorinated n-alkanes, shown to bioaccumulate but with unknown effects in wild birds. The present study examined development-related effects of SCCPs on captive American kestrels (Falco sparverius) treated in ovo on embryonic day (ED) 5 by injection with technical Chloroparaffin® (C10-13, 55.5% Cl) at environmentally relevant nominal (measured) concentrations of 10 (10), 50 (29) or 100 (97) ng ΣSCCP/g egg ww, and artificially incubated until hatching (ED27–ED29). The SCCP concentrations measured in the yolk sacs of the hatchling kestrels bracketed concentrations reported in the eggs of wild birds. Uptake and deposition of these SCCPs differed between male and female hatchlings, with only males showing differences in SCCP concentrations, being highest in the high-dose males than each of the other male groups. Embryonic exposure to SCCPs suppressed glandular total thyroxine (TT4) (20–33%) and reduced circulating triiodothyronine (TT3) (37–40%) in male hatchlings only when compared to control males, but had no effect on glandular TT3 or circulating TT4 in male or female kestrels. Histological assessments of thyroid glands showed that both sexes experienced significant structural changes indicative of gland activation. These thyroid glandular changes and the variations in SCCP concentrations were related to circulating TT3 in female hatchlings. Hepatic deiodinase enzyme (D1, D2) activities were stable and no SCCP-related changes were observed in hatching success, hatchling size, or immune organ size. However, several of the thyroid function indicators were correlated with hatchling size and smaller bursas and spleens, possibly indirectly through SCCP-induced changes in thyroid function. Because changes in thyroid function were evident at concentrations measured in wild bird eggs, similar changes may occur in wild nestlings. The potential impact of these changes on thyroid-mediated growth and survival in wild birds requires further investigation.
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These costly ecological impacts cascade to human communities, and understanding this changing drought landscape is one of today’s grand challenges. By using a modified horizon-scanning approach that integrated scientists, managers, and decision-makers, we identified the emerging issues in ecological drought that represent key challenges to timely and effective responses. Here we review the themes that most urgently need attention, including novel drought conditions, the potential for transformational drought impacts, and the need for anticipatory drought management. This horizon scan and review provides a roadmap to facilitate the research and management innovations that will support forward-looking, co-developed approaches to reduce the risk of drought to our socio-ecological systems during the 21st century.
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A unifying feature among these national parks, monuments, and historic sites is mixed-grass prairie, which not only provides background scenery but is the very foundation of many park missions. As recognition of the prairie’s importance to park fundamental resources and values has grown, so too has the realization that invasive plants threaten these values by reducing native species diversity, altering food webs, and marring the visitor experience. Parks manage invasive species despite uncertainties in treatment effectiveness because management cannot wait for research to provide definitive answers. Under these circumstances, adaptive management (AM) is an appropriate approach. In the NGP, we formed a collaborative adaptive vegetation management team to apply AM towards reducing invasive species (with a focus on exotic annual grasses) and improving native vegetation conditions. In our AM framework, the team uses a Bayesian model built from NPS Inventory & Monitoring and Fire Effects monitoring data and experimental results to predict the effects of management actions on park management units, according to those units’ vegetation condition and management history. These predictions inform management decisions, which are then applied.
","language":"English","publisher":"University of California at Berkeley","doi":"10.5070/P536349865","usgsCitation":"Ashton, I.W., Symstad, A., Baldwin, H., Post van der Burg, M., Bekedam, S., Borgman, E., Haar, M., Hogan, T., Rockwood, S., Swanson, D., Thomson, C., and Wienk, C., 2020, A new decision support tool for collaborative adaptive vegetation management in northern Great Plains national parks: Parks Stewardship Forum, v. 3, no. 36, 11 p., https://doi.org/10.5070/P536349865.","productDescription":"11 p.","ipdsId":"IP-115660","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455290,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5070/p536349865","text":"Publisher Index 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SD","active":true,"usgs":false}],"preferred":false,"id":799144,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hogan, Terri","contributorId":240929,"corporation":false,"usgs":false,"family":"Hogan","given":"Terri","email":"","affiliations":[{"id":48162,"text":"National Park Service, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":799145,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rockwood, Stephanie","contributorId":240930,"corporation":false,"usgs":false,"family":"Rockwood","given":"Stephanie","email":"","affiliations":[{"id":38111,"text":"National Park Service, Rapid City, SD","active":true,"usgs":false}],"preferred":false,"id":799146,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Swanson, Daniel J","contributorId":240932,"corporation":false,"usgs":false,"family":"Swanson","given":"Daniel J","affiliations":[{"id":48165,"text":"National Park Service, Hot Springs, SD","active":true,"usgs":false}],"preferred":false,"id":799147,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thomson, Carmen","contributorId":240933,"corporation":false,"usgs":false,"family":"Thomson","given":"Carmen","email":"","affiliations":[{"id":48167,"text":"National Park Service, Omaha, NE","active":true,"usgs":false}],"preferred":false,"id":799148,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wienk, Cody","contributorId":240934,"corporation":false,"usgs":false,"family":"Wienk","given":"Cody","affiliations":[{"id":48167,"text":"National Park Service, Omaha, NE","active":true,"usgs":false}],"preferred":false,"id":799149,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70213247,"text":"ofr20201093 - 2020 - Use of time domain electromagnetic soundings and borehole electromagnetic induction logs to delineate the freshwater/saltwater interface on southwestern Long Island, New York, 2015–17","interactions":[],"lastModifiedDate":"2020-09-17T19:29:41.433193","indexId":"ofr20201093","displayToPublicDate":"2020-09-17T14:25:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1093","displayTitle":"Use of Time Domain Electromagnetic Soundings and Borehole Electromagnetic Induction Logs to Delineate the Freshwater/Saltwater Interface on Southwestern Long Island, New York, 2015–17","title":"Use of time domain electromagnetic soundings and borehole electromagnetic induction logs to delineate the freshwater/saltwater interface on southwestern Long Island, New York, 2015–17","docAbstract":"The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, used surface and borehole geophysical methods to delineate the freshwater/saltwater interface in coastal plain aquifers along the southwestern part of Long Island, New York. Over pumping of groundwater in the early 20th century combined with freshwater/saltwater interfaces at the coastline created saltwater intrusion in the upper glacial, Jameco, Magothy, and Lloyd aquifers. This study documents, for the first time, extensive saltwater intrusion of the Lloyd aquifer along the southwestern coast of Long Island, N.Y. Several public-supply wells in the southern parts of Nassau, Queens, and Kings Counties have been adversely affected by saltwater intrusion causing supply wells to be shutdown and abandoned. Due to the ongoing groundwater pumping in southern Nassau County, the freshwater/saltwater interface requires delineation and monitoring for any inland movement.
In 2015–17, the U.S. Geological Survey collected time domain electromagnetic soundings at 12 locations and borehole electromagnetic induction conductivity logs at 9 wells within the study area to delineate several saltwater intrusion wedges. The upper glacial, Jameco, and Magothy aquifers were grouped into one aquifer complex within the study area to simplify interpretations. The coastal plain sediments increase in thickness from west to east and north to south because of their regional dip toward the southeast. Three separate wedges, shallow, intermediate, and deep, of saltwater intrusion were delineated in the upper glacial, Jameco, and Magothy aquifer complex. In addition, analysis of geophysical logs collected in an open borehole of a test well in southern Queens County in 1989 revealed the Lloyd aquifer was nearly completely intruded by saltwater with an estimated chloride concentration of 15,000 milligrams per liter. The geophysical logs from this well provides, for the first time, definitive proof of saltwater intrusion of the Lloyd aquifer on Long Island’s south shore, suggesting the freshwater/saltwater interface was at the coastline and not miles offshore as theorized by previous studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201093","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stumm, F., Como, M.D., and Zuck, M.A., 2020, Use of time domain electromagnetic soundings and borehole electromagnetic induction logs to delineate the freshwater/saltwater interface on southwestern Long Island, New York, 2015–17: U.S. Geological Survey Open-File Report 2020–1093, 27 p., https://doi.org/10.3133/ofr20201093.","productDescription":"Report: vi, 27 p.; Data Release; Database","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118303","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378439,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7X63KT0","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS GeoLog Locator"},{"id":378438,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens County, New York, October–November 2017"},{"id":378437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1093/ofr20201093.pdf","text":"Report","size":"3.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1093"},{"id":378436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1093/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.03961181640625,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.54406959045767\n ],\n [\n -73.32687377929688,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.77638178482896\n ],\n [\n -74.03961181640625,\n 40.54406959045767\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey (USGS) changed the management and delivery of Landsat products to the public in its archive through the implementation of Collections. The Collections process ensures consistent data quality through time and across all the Landsat sensors with a few modifications to the metadata. The consistent data products from Collections are more conducive for applications such as time-series analysis, because the data can be used without a need for sensor-specific geometric or radiometric adjustments. The Collections process also allows for calibration improvements from updated reference sources, model trending, and enhanced algorithms that are grouped together and applied at one time, thus limiting the operational impacts to users. The first collection, Collection-1 was released in 2016, and Collection-2 is expected to be released in 2020. This paper addresses the geometric improvements to the Collection -2 dataset. Geometric improvements in Collection-2 include improvements to the geometric accuracy and interoperability of all Landsat products by implementing the Sentinel 2 Global Reference Image (GRI), improved elevation dataset using NASADEM and other sources of Digital Elevation Model (DEM) for terrain correction, improvements to the precision correction process for Enhanced Thematic Mapper Plus (ETM+) and Thematic Mapper (TM) sensors, changes to the Thermal Infrared Sensor (TIRS) to Operational Land Imager (OLI) alignment estimates, thermal band detector alignments and focal plane adjustments for ETM+, and improvements to the ETM+ sensor to attitude control system (ACS).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: Earth observing systems XXV","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Earth Observing Systems XXV","conferenceDate":"Aug 24-Sep 4, 2020","language":"English","publisher":"SPIE","doi":"10.1117/12.2570429","usgsCitation":"Rengarajan, R., Choate, M.J., Storey, J.C., Franks, S., and Micijevic, E., 2020, Landsat Collection 2 geometric calibration updates, in Proceedings: Earth observing systems XXV, v. 11501, Aug 24-Sep 4, 2020, 115010N, 11 p., https://doi.org/10.1117/12.2570429.","productDescription":"115010N, 11 p.","ipdsId":"IP-122667","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":378968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11501","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rengarajan, R. 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":56036,"corporation":false,"usgs":true,"family":"Rengarajan","given":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":800336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":800337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":800436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Franks, Shannon 0000-0003-1335-5401","orcid":"https://orcid.org/0000-0003-1335-5401","contributorId":93362,"corporation":false,"usgs":true,"family":"Franks","given":"Shannon","affiliations":[],"preferred":false,"id":800437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":800438,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70213102,"text":"cir1470 - 2020 - U.S. Geological Survey sagebrush ecosystem research annual report for 2020","interactions":[],"lastModifiedDate":"2020-09-17T11:59:40.032585","indexId":"cir1470","displayToPublicDate":"2020-09-17T08:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1470","displayTitle":"U.S. Geological Survey Sagebrush Ecosystem Research Annual Report for 2020","title":"U.S. Geological Survey sagebrush ecosystem research annual report for 2020","docAbstract":"The sagebrush (Artemisia spp.) ecosystem extends across 251,473 square miles over portions of 13 western States. Affected by multiple stressors, including interactions among fire, invasive plants, and human land uses, this ecosystem has experienced significant loss, fragmentation, and degradation of landscapes once dominated by sagebrush. In turn, wildlife populations have declined following these deleterious conditions. Federal, State, local, and Tribal agencies, nongovernmental organizations, and industry have been galvanized by declining wildlife populations to implement management actions to confront the impacts of these stressors and ensure the long-term availability of the sagebrush ecosystem for the broad range of uses critical to stakeholders in the Western United States.
The sagebrush ecosystem provides habitat for more than 350 species of plants and animals that are dependent on sagebrush for all or part of their annual life history. The greater sage-grouse (Centrocercus urophasianus) stands out as an iconic species of this ecosystem. Sage-grouse populations occur in 11 States, and 2 Canadian Provinces and require relatively large expanses of sagebrush-dominated habitat to meet all their seasonal habitat needs. Recent management actions to conserve and maintain the sagebrush ecosystem have focused on the protection and restoration of sage-grouse habitat; however, each of the 350 species has a unique life history and differing area requirements (for example, large areas for mule deer [Odocoileus hemionus] and small areas for pygmy rabbit [Brachylagus idahoensis]), and some species, such as migratory birds, rely on various parts of the sagebrush ecosystem but only for part of the year (for example, Brewer’s sparrow [Spizella breweri]).
The U.S. Geological Survey (USGS) has a broad research program focused on the sagebrush ecosystem, wildlife species within the ecosystem, and the species’ response to stressors and management actions. The program provides a foundation of scientific information for use in major land and resource management decisions in the sagebrush ecosystem. By providing the science to inform these decisions, the USGS is assisting land and resource managers at the Federal, State, Tribal, and local levels working towards the goal of sustainable wildlife populations and restored landscapes. This information can inform planning and management conducted by nongovernmental organizations as well.
USGS research is tailored specifically to inform adaptive management, improve strategies for maintaining existing areas of intact sagebrush, and restoring degraded landscapes. Examples of research support for partners include providing information for actions such as the preclusion of the need to list the greater sage-grouse under the Endangered Species Act and recent revisions to Bureau of Land Management and U.S. Department of Agriculture Forest Service resource management plans and land use. The USGS continues to provide foundational science to inform science-based decisions within the U.S. Department of the Interior and other Federal, State, and local agencies and their continued conservation, management, and restoration of the sagebrush ecosystem to help support local economies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1470","usgsCitation":"Hanser, S.E., and Wiechman, L.A., eds., 2020, U.S. Geological Survey sagebrush ecosystem research annual report for 2020: U.S. Geological Survey Circular 1470, 94 p., https://doi.org/10.3133/cir1470.","productDescription":"vi, 94 p.","numberOfPages":"94","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119993","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":378250,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1470/coverthb.gif"},{"id":378249,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1470/cir1470.pdf","text":"Report","size":"11.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1470"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 48.69096039092549\n ],\n [\n -121.904296875,\n 45.583289756006316\n ],\n [\n -121.37695312499999,\n 41.376808565702355\n ],\n [\n -120.32226562500001,\n 38.41055825094609\n ],\n [\n -117.7734375,\n 35.460669951495305\n ],\n [\n -116.54296874999999,\n 33.65120829920497\n ],\n [\n -114.9609375,\n 34.379712580462204\n ],\n [\n -115.31249999999999,\n 36.1733569352216\n ],\n [\n -114.60937499999999,\n 36.66841891894786\n ],\n [\n -113.818359375,\n 35.53222622770337\n ],\n [\n -113.115234375,\n 34.08906131584994\n ],\n [\n -111.181640625,\n 33.43144133557529\n ],\n [\n -108.369140625,\n 33.43144133557529\n ],\n [\n -106.61132812499999,\n 33.94335994657882\n ],\n [\n -104.765625,\n 38.685509760012\n ],\n [\n -104.765625,\n 41.96765920367816\n ],\n [\n -104.853515625,\n 43.068887774169625\n ],\n [\n -103.71093749999999,\n 43.45291889355465\n ],\n [\n -103.18359375,\n 44.213709909702054\n ],\n [\n -103.0078125,\n 45.02695045318546\n ],\n [\n -102.568359375,\n 46.255846818480315\n ],\n [\n -102.3046875,\n 47.517200697839414\n ],\n [\n -102.568359375,\n 48.574789910928864\n ],\n [\n -105.46875,\n 48.86471476180277\n ],\n [\n -108.28125,\n 48.574789910928864\n ],\n [\n -111.4453125,\n 48.574789910928864\n ],\n [\n -113.203125,\n 47.21956811231547\n ],\n [\n -115.31249999999999,\n 46.619261036171515\n ],\n [\n -117.333984375,\n 46.800059446787316\n ],\n [\n -117.7734375,\n 47.87214396888731\n ],\n [\n -118.47656249999999,\n 48.86471476180277\n ],\n [\n -119.970703125,\n 49.15296965617042\n ],\n [\n -120.234375,\n 48.69096039092549\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Sage-Grouse and Sagebrush Ecosystem Program
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2150 Centre Ave, Building B
Fort Collins, Colorado
Endocrine disrupting chemicals (EDCs), such as bisphenol A (BPA) and 17α-ethinylestradiol (EE2), can have far reaching health effects, including transgenerational abnormalities in offspring that never directly contacted either chemical. We previously reported reduced fertilization rates and embryo survival at F2 and F3 generations caused by 7-day embryonic exposure (F0) to 100 μg/L BPA or 0.05 μg/L EE2 in medaka. Crossbreeding of fish in F2 generation indicated subfertility in males. To further understand the mechanisms underlying BPA or EE2-induced adult onset and transgenerational reproductive defects in males, the present study examined the expression of genes regulating the brain–pituitary–testis (BPT) axis in the same F0 and F2 generation male medaka. Embryonic exposure to BPA or EE2 led to hyperactivation of brain and pituitary genes, which are actively involved in reproduction in adulthood of the F0 generation male fish, and some of these F0 effects continued to the F2 generation (transgenerational effects). Particularly, the F2 generation inherited the hyperactivated state of expression for kisspeptin (kiss1 and kiss2) and their receptors (kiss1r and kiss2r), and gnrh and gnrh receptors. At F2 generation, expression of DNA methyltransferase 1 (dnmt1) decreased in brain of the BPA treatment lineage, while EE2 treatment lineage showed increased dnmt3bb expression. Global hypomethylation pattern was observed in the testis of both F0 and F2 generation fish. Taken together, these results demonstrated that BPA or EE2-induced transgenerational reproductive impairment in the F2 generation was associated with alterations of reproductive gene expression in brain and testis and global DNA methylation in testis.
The Norwegian Geochemical Standard North Sea Oil-1 was analyzed by gas chromatography triple quadrupole mass spectrometry (GC-QQQ-MS) on two instruments using independently developed analytical methods. Biomarker ratios determined by GC-QQQ-MS were compared to each other and to previously reported values determined by gas chromatography single quadrupole mass spectrometry (GC-Q-MS) or flame ionization detection (GC-FID). Hopane, sterane, and tricyclic ratio values determined by GC-QQQ-MS in multiple reaction monitoring (MRM) mode are comparable to each other, but their comparability to reported values measured by GC-Q-MS in selected ion monitoring (SIM) mode depends in part on whether the compounds in the ratio have similar or dissimilar mass spectral responses. For example, sterane and hopane stereoisomer thermal maturity ratios measured by GC-QQQ-MS in MRM mode agree with previously reported GC-Q-MS SIM values, but an offset is observed for ratios of rearranged hopanes or steranes to their non-rearranged counterparts. Triaromatic steroid, monoaromatic steroid, phenanthrene, and methylphenanthrene ratios measured by GC-QQQ-MS are comparable to each other and to reported GC-Q-MS SIM values. The carbon preference index is comparable across GC-QQQ-MS measurements and reported GC-FID values, while comparability is more variable for other ratios based on pristane, phytane, and/or n-alkanes. Comparability of variably acquired biomarker data could be enhanced in the future by developing instrument- and ratio-specific correction factors or by modifying GC-QQQ-MS parameters and MRM transitions to more closely reproduce previously reported values.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2020.104124","usgsCitation":"French, K.L., Leider, A., and Hallmann, C., 2020, Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS: Organic Geochemistry, v. 150, 104124, 4 p., https://doi.org/10.1016/j.orggeochem.2020.104124.","productDescription":"104124, 4 p.","ipdsId":"IP-119234","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":455295,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2020.104124","text":"Publisher Index Page"},{"id":436788,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99ZMFJ5","text":"USGS data release","linkHelpText":"Data Release for "Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS""},{"id":379343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"150","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":false,"id":801281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leider, Arne","contributorId":242996,"corporation":false,"usgs":false,"family":"Leider","given":"Arne","email":"","affiliations":[{"id":48601,"text":"Max-Planck-Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":801282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hallmann, Christian","contributorId":242997,"corporation":false,"usgs":false,"family":"Hallmann","given":"Christian","email":"","affiliations":[{"id":48601,"text":"Max-Planck-Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":801283,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231640,"text":"70231640 - 2020 - Palaeotsunamis in the Sino-Pacific region","interactions":[],"lastModifiedDate":"2022-05-17T11:43:24.846924","indexId":"70231640","displayToPublicDate":"2020-09-17T06:41:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Palaeotsunamis in the Sino-Pacific region","docAbstract":"Palaeotsunami research in the Sino-Pacific region has increased markedly following the 2011 Tōhoku-oki tsunami. Recent studies encompass a variety of potential sources and cover a full range of research activities from detailed studies at individual sites through to region-wide data collation for the purposes of database development. We synthesise palaeotsunami data from around the region drawing on key examples to highlight the progress made since 2011. We focus on a wide range of spatial and temporal scales, from region-wide to local events, from multi-millennial site records to estimates of magnitude and frequency along national coastlines. The review is based on sub-regions but in reviewing the combined records highlights common events and anomalies. In doing so we identify future research opportunities and notable findings arising from our review.
Water-quality conditions (including temperature) in the Willamette River and many of its adjacent off-channel features, such as alcoves and side channels, were monitored between river miles 67 (near Salem, Oregon) and 168 (near Eugene, Oregon) during the summers of 2015 and 2016. One or more parameters (water temperature, dissolved oxygen, pH, specific conductance, and [or] water depth) were continuously measured at sites in the main channel (9 sites in 2015; 5 sites in 2016) and select off-channel features (20 features in 2015; 22 features in 2016). This study was initiated in reaction to the unusually warm, dry weather and resulting low streamflows that occurred in the Pacific Northwest in 2015 and the need for flow managers to understand the effects of streamflow on water-quality conditions in off-channel features of the Willamette River. Field monitoring was focused on documenting water-quality conditions during low summer streamflows and during fluctuations in streamflow, including when side channels became alcoves and reconnected to become side channels again.
Water in the main channel of the Willamette River upstream from river mile 50 near Newberg typically is well mixed during summer, with warm water temperatures (greater than 18 degrees Celsius) and high dissolved-oxygen concentrations (often greater than 7.7 milligrams per liter). During low summer flows, a diverse suite of off-channel features exists adjacent to the main channel of the Willamette River. Despite temporal and spatial variability within individual features, comparison of continuous water-temperature data between the main channel and off-channel features indicated that some off-channel features were consistently cooler than the main channel, some were consistently warmer than the main channel, and others frequently fluctuated between warmer or cooler than the main channel. Site-specific characteristics including upstream connection, depth, and presence or absence of aquatic or riparian vegetation were factors that seemed to affect the water quality of a feature.
Results from this study showed a relation between the geomorphology, hydrology, ecology, and water quality of an off-channel feature. Data confirmed that many features that can be classified as cold-water refuges based on water-temperature standards also contained low concentrations of dissolved oxygen that may not be suitable for sensitive fish species. A simplified site classification scheme is proposed that links water-quality conditions in measured off-channel features with site-specific characteristics and summer streamflows. The site classification scheme was extended to create a theoretical process matrix that relates measured water-quality conditions to a list of the processes and site-specific characteristics that could create those conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205068","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Smith, C.D., Mangano, J.F., and Rounds, S.A., 2020, Temperature and water-quality diversity and the effects of surface-water connection in off-channel features of the Willamette River, Oregon, 2015–16: U.S. Geological Survey Scientific Investigations Report 2020–5068, 70 p., https://doi.org/10.3133/sir20205068.","productDescription":"Report: viii, 70 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-102289","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":378475,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73T9FPK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Continuous temperature measurements to assess upstream connection of off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"},{"id":378473,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ315D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Point measurements of temperature and water quality in main-channel and off-channel features of the Willamette River, 2015 -16"},{"id":378472,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5068/sir20205068.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5068"},{"id":378471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5068/coverthb.jpg"},{"id":378474,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77M06DV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water surface elevations recorded by submerged water level loggers in off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46435546875,\n 44.133333\n ],\n [\n -122.43713378906249,\n 44.133333\n ],\n [\n -122.43713378906249,\n 45.216667\n ],\n [\n -123.46435546875,\n 45.216667\n ],\n [\n -123.46435546875,\n 44.133333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Throughout the 2018 eruption of Kīlauea volcano (Hawai‘i), episodic collapses of a portion of the volcano’s summit caldera produced repeated