Mobile organisms in marine environments are expected to modify their behavior in response to external stressors. Among environmental drivers of animal movement are long-term climatic indices influencing organism distribution and short-term meteorological events anticipated to alter acute movement behavior. However, few studies exist documenting the response of vagile species to meteorological anomalies in coastal and marine systems.
Here we examined the movements of Eastern brown pelicans (Pelecanus occidentalis carolinensis) in the South Atlantic Bight in response to the passage of three separate hurricane events in 2 years. Pelicans (n = 32) were tracked with GPS satellite transmitters from four colonies in coastal South Carolina, USA, for the entirety of at least one storm event. An Expectation Maximization binary Clustering algorithm was used to discretize pelican behavioral states, which were pooled into ‘active’ versus ‘inactive’ states. Multinomial logistic regression was used to assess behavioral state probabilities in relation to changes in barometric pressure and wind velocity.
Individual pelicans were more likely to remain inactive during tropical cyclone passage compared to baseline conditions generally, although responses varied by hurricane. When inactive, pelicans tended to seek shelter using local geomorphological features along the coastline such as barrier islands and estuarine systems.
Our telemetry data showed that large subtropical seabirds such as pelicans may mitigate risk associated with spatially-extensive meteorological events by decreasing daily movements. Sheltering may be related to changes in barometric pressure and wind velocity, and represents a strategy common to several other classes of marine vertebrate predators for increasing survival probabilities.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-019-0178-0","usgsCitation":"Wilkinson, B.P., Satge, Y.G., Lamb, J.S., and Jodice, P.G., 2019, Tropical cyclones alter short-term activity patterns of a coastal seabird: Movement Ecology, v. 7, 30, 11 p., https://doi.org/10.1186/s40462-019-0178-0.","productDescription":"30, 11 p.","ipdsId":"IP-108429","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459342,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-019-0178-0","text":"Publisher Index Page"},{"id":437290,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D5IP0G","text":"USGS data release","linkHelpText":"Movement ecology of Brown Pelican in the South Atlantic Bight, 2017-2019"},{"id":394817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.5947265625,\n 25.839449402063185\n ],\n [\n -75.6298828125,\n 25.839449402063185\n ],\n [\n -75.6298828125,\n 35.88905007936091\n ],\n [\n -84.5947265625,\n 35.88905007936091\n ],\n [\n -84.5947265625,\n 25.839449402063185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2019-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkinson, B. P.","contributorId":272128,"corporation":false,"usgs":false,"family":"Wilkinson","given":"B.","email":"","middleInitial":"P.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":831568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Satge, Y. G.","contributorId":272129,"corporation":false,"usgs":false,"family":"Satge","given":"Y.","email":"","middleInitial":"G.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":831569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamb, J. S.","contributorId":272130,"corporation":false,"usgs":false,"family":"Lamb","given":"J.","email":"","middleInitial":"S.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":831570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":831571,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206444,"text":"70206444 - 2019 - Sources, fate, and flux of geothermal solutes in the Yellowstone and Gardner Rivers, Yellowstone National Park, WY","interactions":[],"lastModifiedDate":"2019-11-05T06:35:50","indexId":"70206444","displayToPublicDate":"2019-10-25T13:03:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Sources, fate, and flux of geothermal solutes in the Yellowstone and Gardner Rivers, Yellowstone National Park, WY","docAbstract":"The total discharge and thermal output from the numerous hydrothermal features in Yellowstone National Park (YNP) can be estimated from the chloride (Cl) flux in the Madison, Yellowstone, Falls, and Snake Rivers. Monitoring the Cl flux in these four major rivers provides a holistic view of the hydrothermal output from YNP and changes in the Cl flux may indicate changes in geothermal or magmatic activity. In this study, the source, fate, and flux of geothermal solutes in the Yellowstone River and Gardner Rivers were determined. Beginning in 2012, the fluxes of geothermal solutes, including Cl, were determined at monitoring sites in the Yellowstone and Gardner Rivers downstream of geothermal inputs within YNP. A method was developed using specific conductance as a surrogate measure for solute concentrations at these monitoring sites. Combining continuous (15-min) specific conductance and discharge data, Cl and other geothermal solute fluxes were determined at both sites and approximately 32% of the Cl flux exiting YNP is from the Yellowstone River watershed. Synoptic sampling of river water and discharge measurements were performed during low-flow conditions of September 2014 allowed for the determinations of geothermal solute sources and their downstream fate. Thus, the contribution of geothermal solutes from the various geothermal areas at the downstream monitoring sites was quantified. The thermal features draining into Yellowstone Lake account for 34% of the Cl flux at the Yellowstone River monitoring site which is located approximately 5 km north of YNP. The Gardner River, which captures geothermal water from Mammoth Hot Springs, is responsible for 22% of the Cl at the Yellowstone River monitoring site. Because the Yellowstone River watershed is large and contains numerous thermal areas, knowing the source and fate of geothermal solutes is import baseline information that can be used to identify future changes in thermal activity.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2019.104458","usgsCitation":"McCleskey, R., Roth, D.A., Mahony, D., Nordstrom, D.K., and Kinsey, S., 2019, Sources, fate, and flux of geothermal solutes in the Yellowstone and Gardner Rivers, Yellowstone National Park, WY: Applied Geochemistry, v. 111, p. 1-14, https://doi.org/10.1016/j.apgeochem.2019.104458.","productDescription":"104458, 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-108919","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":459351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2019.104458","text":"Publisher Index Page"},{"id":368928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.86578369140625,\n 44.53959000445632\n ],\n [\n -110.1214599609375,\n 44.53959000445632\n ],\n [\n -110.1214599609375,\n 44.99782485158904\n ],\n [\n -110.86578369140625,\n 44.99782485158904\n ],\n [\n -110.86578369140625,\n 44.53959000445632\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"111","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":503,"text":"Office of Water Quality","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":774565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roth, David A. 0000-0002-7515-3533 daroth@usgs.gov","orcid":"https://orcid.org/0000-0002-7515-3533","contributorId":2340,"corporation":false,"usgs":true,"family":"Roth","given":"David","email":"daroth@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":774566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mahony, D.","contributorId":220237,"corporation":false,"usgs":false,"family":"Mahony","given":"D.","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":774567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","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":false,"id":774568,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kinsey, Stacy 0000-0001-7629-2634 skinsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7629-2634","contributorId":220238,"corporation":false,"usgs":true,"family":"Kinsey","given":"Stacy","email":"skinsey@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774569,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204747,"text":"sim3437 - 2019 - Three-dimensional geologic map of the southern Carson Sink, Nevada, including the Fallon FORGE area","interactions":[],"lastModifiedDate":"2019-10-25T15:53:06","indexId":"sim3437","displayToPublicDate":"2019-10-25T10:17:17","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3437","displayTitle":"Three-Dimensional Geologic Map of the Southern Carson Sink, Nevada, Including the Fallon FORGE area","title":"Three-dimensional geologic map of the southern Carson Sink, Nevada, including the Fallon FORGE area","docAbstract":"The three-dimensional (3–D) geologic map characterizes the subsurface in the southern Carson Sink region. We created the 3–D map by integrating the results from seismic-reflection, potential-field-geophysical, and lithologic well-logging investigations completed in and around the Fallon FORGE site as part of the U.S. Department of Energy Frontier Observatory for Research in Geothermal Energy (FORGE) initiative from 2015–2018. The FORGE initiative was part of an effort to develop the technologies, techniques, and knowledge needed to make enhanced geothermal systems a commercially viable electricity-generation option for the United States. Geologic units and structures mapped during the Fallon FORGE study, which particularly focused on the Mesozoic basement, were extrapolated to create the 3–D map of the southern Carson Sink area. The 3–D map area is 10 km wide along the east-west and north-south axes and extends 2.5 km below sea level, ~3.7 km below the land surface. Views of the map include horizontal and vertical sections and oblique perspective views from several angles. We describe the geologic units and structures and discuss the methods used to integrate the geologic and geophysical information in a 3–D geologic interpretation. We provide digital data for elements of the map, such as individual 3–D fault and stratigraphic surfaces and surface-fault traces. Input data are available from various data repositories through cited web links. A brief movie displaying the 3–D map is available at https://doi.org/10.3133/sim3437.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3437","usgsCitation":"Siler, D.L., Faulds, J.E., Glen, J.M.G., Hinz, N.H., Witter, J.B., Blake, K., Queen, J., and Fortuna, M., 2019, Three-dimensional geologic map of the southern Carson Sink, Nevada, including the Fallon FORGE area: U.S. Geological Survey Scientific Investigations Map 3437, pamphlet 22 p., https://doi.org/10.3133/sim3437.","productDescription":"Pamphlet: iv, 22 p.; Map, 46.35 x 39 inches; Video; Database; Metadata; Readme","numberOfPages":"22","additionalOnlineFiles":"Y","ipdsId":"IP-100960","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":368577,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3437/coverthb.jpg"},{"id":368582,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3437/sim3437_metadata.zip","size":"20 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3437"},{"id":368583,"rank":7,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3437/sim3437_readme.docx","size":"20 KB docx","description":"SIM 3437"},{"id":368578,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3437/sim3437_pamphlet.pdf","text":"Pamphlet","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3437"},{"id":368579,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3437/sim3437_map.pdf","text":"Map","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3437"},{"id":368580,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sim/3437/sim3437_3dmap_1080.mp4","text":"3-Dimensional Map Video","size":"30 MB mp4","description":"SIM 3437"},{"id":368581,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3437/sim3437_database.zip","size":"600 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3437"}],"country":"United States","state":"Nevada","otherGeospatial":"Southern Carson Sink, Fallon FORGE area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.20989990234374,\n 39.12153746241925\n ],\n [\n -118.08380126953125,\n 39.12153746241925\n ],\n [\n -118.08380126953125,\n 40.264856517201856\n ],\n [\n -119.20989990234374,\n 40.264856517201856\n ],\n [\n -119.20989990234374,\n 39.12153746241925\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
Oyster reefs provide habitat for numerous fish and decapod crustacean species that mediate ecosystem functioning and support vibrant fisheries. Recent focus on the restoration of eastern oyster (Crassostrea virginica) reefs stems from this role as a critical ecosystem engineer. Within the shallow estuaries of the northern Gulf of Mexico (nGoM), the eastern oyster is the dominant reef building organism. This study synthesizes data on fish and decapod crustacean occupancy of oyster reefs across nGoM with the goal of providing management and restoration benchmarks, something that is currently lacking for the region. Relevant data from 23 studies were identified, representing data from all five U.S. nGoM states over the last 28 years. Cumulatively, these studies documented over 120,000 individuals from 115 fish and 41 decapod crustacean species. Densities as high as 2,800 ind m−2 were reported, with individual reef assemblages composed of as many as 52 species. Small, cryptic organisms that occupy interstitial spaces within the reefs, and sampled using trays, were found at an average density of 647 and 20 ind m−2 for decapod crustaceans and fishes, respectively. Both groups of organisms were comprised, on average, of 8 species. Larger-bodied fishes captured adjacent to the reef using gill nets were found at an average density of 6 ind m−2, which came from 23 species. Decapod crustaceans sampled with gill nets had a much lower average density, <1 ind m−2, and only contained 2 species. On average, seines captured the greatest number of fish species (n = 33), which were made up of both facultative residents and transients. These data provide general gear-specific benchmarks, based on values currently found in the region, to assist managers in assessing nekton occupancy of oyster reefs, and assessing trends or changes in status of oyster reef associated nekton support. More explicit reef descriptions (e.g., rugosity, height, area, adjacent habitat) would allow for more precise benchmarks as these factors are important in determining nekton assemblages, and sampling efficiency.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2019.00666","usgsCitation":"LaPeyre, M.K., Marshall, D.A., Miller, L., and Humphries, A.T., 2019, Oyster reefs in northern Gulf of Mexico estuaries harbor diverse fish and decapod crustacean assemblages: A meta-synthesis: Frontiers in Marine Science, v. 6, 666, 13 p., https://doi.org/10.3389/fmars.2019.00666.","productDescription":"666, 13 p.","ipdsId":"IP-080466","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2019.00666","text":"Publisher Index Page"},{"id":485132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.77507531536082,\n 26.434595440924554\n ],\n [\n -82.36851770898288,\n 29.179744856981316\n ],\n [\n -84.05763771804244,\n 30.18102965026621\n ],\n [\n -86.45906479945596,\n 30.564740988936478\n ],\n [\n -88.96978854642646,\n 30.601009360135762\n ],\n [\n -90.98398001483329,\n 29.849499692049946\n ],\n [\n -93.94726229901573,\n 30.024980309521595\n ],\n [\n -95.54944591403085,\n 29.35885238728197\n ],\n [\n -97.86986826415085,\n 27.75470647893117\n ],\n [\n -97.64835626287109,\n 26.38999825884062\n ],\n [\n -81.77507531536082,\n 26.434595440924554\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2019-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Danielle Aguilar","contributorId":341509,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle","email":"","middleInitial":"Aguilar","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":934845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Lindsay","contributorId":353924,"corporation":false,"usgs":false,"family":"Miller","given":"Lindsay","affiliations":[],"preferred":false,"id":934766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphries, Austin T.","contributorId":15943,"corporation":false,"usgs":true,"family":"Humphries","given":"Austin","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":934767,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215203,"text":"70215203 - 2019 - Genetic structure of Mycoplasma ovipneumoniae informs pathogen spillover dynamics between domestic and wild Caprinae in the western United States","interactions":[],"lastModifiedDate":"2020-10-13T22:45:14.307253","indexId":"70215203","displayToPublicDate":"2019-10-25T09:22:52","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Genetic structure of Mycoplasma ovipneumoniae informs pathogen spillover dynamics between domestic and wild Caprinae in the western United States","title":"Genetic structure of Mycoplasma ovipneumoniae informs pathogen spillover dynamics between domestic and wild Caprinae in the western United States","docAbstract":"Spillover diseases have significant consequences for human and animal health, as well as wildlife conservation. We examined spillover and transmission of the pneumonia-associated bacterium Mycoplasma ovipneumoniae in domestic sheep, domestic goats, bighorn sheep, and mountain goats across the western United States using 594 isolates, collected from 1984 to 2017. Our results indicate high genetic diversity of M. ovipneumoniae strains within domestic sheep, whereas only one or a few strains tend to circulate in most populations of bighorn sheep or mountain goats. These data suggest domestic sheep are a reservoir, while the few spillovers to bighorn sheep and mountain goats can persist for extended periods. Domestic goat strains form a distinct clade from those in domestic sheep, and strains from both clades are found in bighorn sheep. The genetic structure of domestic sheep strains could not be explained by geography, whereas some strains are spatially clustered and shared among proximate bighorn sheep populations, supporting pathogen establishment and spread following spillover. These data suggest that the ability to predict M. ovipneumoniae spillover into wildlife populations may remain a challenge given the high strain diversity in domestic sheep and need for more comprehensive pathogen surveillance.
The metapopulation concept has far-reaching implications in ecology and conservation biology. Hanski’s criteria operationally define metapopulations, yet testing them is hindered by logistical and financial constraints inherent to the collection of long-term demographic data. Hence, ecologists and conservationists often assume metapopulation existence for dispersal-limited species that occupy patchy habitats. To advance understanding of metapopulation theory and improve conservation of metapopulations, we used population and landscape genetic tools to develop a methodological framework for evaluating Hanski’s criteria. We used genotypic data (11 microsatellite loci) from a purported metapopulation of Boreal Chorus Frogs (Pseudacris maculata (Agassiz, 1850)) in Colorado, U.S.A., to test Hanski’s four criteria. We found support for each criterion: (1) significant genetic differentiation between wetlands, suggesting distinct breeding populations; (2) wetlands had small effective population sizes and recent bottlenecks, suggesting populations do not experience long-term persistence; (3) population graphs provided evidence of gene flow between patches, indicating potential for recolonization; and (4) multiscale bottleneck analyses suggest asynchrony, indicating that simultaneous extinction of all populations was unlikely. Our methodological framework provides a logistically and financially feasible alternative to long-term demographic data for identifying amphibian metapopulations.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjz-2018-0275","usgsCitation":"Billerman, S.M., Jesmer, B., Watts, A.G., Schlichting, P., Fortin, M., Funk, W., Hapeman, P., Muths, E., and Murphy, M., 2019, Testing theoretical metapopulation conditions with genotypic data from Boreal Chorus Frogs (Pseudacris maculata): Canadian Journal of Zoology, v. 97, no. 11, p. 1042-1053, https://doi.org/10.1139/cjz-2018-0275.","productDescription":"12 p.","startPage":"1042","endPage":"1053","ipdsId":"IP-083940","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":380188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"97","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Billerman, S. 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G.","contributorId":244508,"corporation":false,"usgs":false,"family":"Watts","given":"A.","email":"","middleInitial":"G.","affiliations":[{"id":48924,"text":"3Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Canada","active":true,"usgs":false}],"preferred":false,"id":804079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schlichting, P.","contributorId":244509,"corporation":false,"usgs":false,"family":"Schlichting","given":"P.","email":"","affiliations":[{"id":48925,"text":"Department of Natural Resources Management, Texas Tech University, Lubbock, TX","active":true,"usgs":false}],"preferred":false,"id":804080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fortin, M.","contributorId":244510,"corporation":false,"usgs":false,"family":"Fortin","given":"M.","email":"","affiliations":[{"id":48924,"text":"3Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Canada","active":true,"usgs":false}],"preferred":false,"id":804081,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Funk, W. C.","contributorId":167546,"corporation":false,"usgs":false,"family":"Funk","given":"W. 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The method selected to construct the hydrostratigraphic framework can affect the extent of geologic heterogeneity that can be included in the model. The detail generated from a hydrostratigraphic framework can affect groundwater simulation results. The effects of detail on model accuracy, groundwater-flow simulations, and particle-tracking simulations are described in this study. This report compares differences in hydrostratigraphic frameworks and results of groundwater models using (1) a method that incorporates more hydrologic judgment at the expense of using limited lithologic data and (2) a method that is more automated and uses all available lithologic data. The study additionally evaluates the effect of model discretization and inclusion of more (or less) geologic detail on simulation results.
Two methods were used to create hydrostratigraphic frameworks of glacial deposits in the St. Joseph River Basin. One method, referred to as the subjective method, manually identifies stratigraphic boundaries using a sample of well logs from State databases and uses two-dimensional kriging to create three model layers of the study area. Indicator kriging is used to define aquifer extent in each layer. The second method, referred to as the objective method, uses three-dimensional kriging to automatically create a detailed heterogeneous model of the study area using all wells logs from the State database. The objective method increases detail in the vertical by greatly increasing the number of computer groundwater model layers from 3 to 30. In Elkhart County, Indiana, a previously published model represents the product of the subjective method, and a newly calibrated model of the same area represents the product of the objective method.
An automated calibration procedure was used with the objective model (derived from the objective method) for Elkhart County. The two most-sensitive parameters for the Elkhart County objective model are horizontal hydraulic conductivity of the sand and the combined sand and gravel/gravel deposits. Vertical hydraulic conductivity of the fine-grained and intermediate-sized deposits could not be estimated, possibly indicating major flow paths are along a continuously connected series of sand and gravel deposits and not through a confining layer.
The statistics measuring model calibration accuracy for the objective model were slightly better than statistics for the subjective model (model derived from the subjective method) of Elkhart County, but the hydraulic conductivities and flow rates for the two models were different. The mean absolute errors between simulated and measured groundwater levels are 2.04 and 2.16 feet for the objective and subjective models, respectively. Simulated seepage losses from and groundwater discharges to measured stream reaches in the objective model were evenly balanced in terms of over and under simulations of measured values; the subjective model tended to overpredict measured groundwater discharge to streams. The overprediction may be related to the 58 percent greater total inflow and outflow through the subjective model. The greater flow rate through the subjective model results from higher horizontal hydraulic conductivities in the subjective model than in the objective model. Horizontal hydraulic conductivity ranged from 23.9 to 111 feet per day in the objective model and generally ranged from 170 to 370 feet per day in the subjective model. The improvement in calibration statistics for the objective model relative to the subjective model may be from increased detail in how the objective model represents the distribution of fine- and coarse-grained deposits. The improvement also could be associated with the difference in methods used to represent the continuity of the confining unit.
The effect of differences in horizontal hydraulic conductivity distributions between the two models for Elkhart County is evident in the groundwater-flow paths simulated by the objective and subjective models. At a withdrawal well location, the flow lines produced by the objective model indicate a wider contributing area than that for the subjective model. The discontinuous confining unit represented in the objective model provided the opportunity for groundwater flow to split into an upper and lower path. The split in flow simulated by the objective model at one location was independently supported by bromide concentrations in groundwater; the subjective model did not duplicate the split in flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195088","collaboration":"U.S. Geological Survey Groundwater Resources Program","usgsCitation":"Arihood, L.D., Lampe, D.C., Bayless, E.R., and Brown, S.E., 2019, Comparison of groundwater-model construction methods, representations of glacial geology, model designs, and groundwater-model flow simulations within Elkhart County, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5088, 44 p., https://doi.org/10.3133/sir20195088.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-065522 ","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368474,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5088/sir20195088.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5088"},{"id":368475,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QN65RW","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"MODFLOW-2000 model used to illustrate the differences in flow paths and travel times when three-dimensional kriging is used to estimate the hydraulic conductivity distribution as compared to manual determinations of hydraulic conductivity distribution"},{"id":368473,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5088/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Elkhart County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-85.7874,41.7615],[-85.7591,41.7613],[-85.6606,41.7608],[-85.6589,41.699],[-85.6575,41.6122],[-85.6554,41.5251],[-85.6542,41.4733],[-85.6552,41.4384],[-85.7704,41.4377],[-85.8874,41.4379],[-86.0008,41.4375],[-86.059,41.4367],[-86.0594,41.4644],[-86.0593,41.474],[-86.0593,41.479],[-86.0592,41.4935],[-86.0598,41.4999],[-86.0624,41.7619],[-85.932,41.7623],[-85.7874,41.7615]]]},\"properties\":{\"name\":\"Elkhart\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278-1996
Quantifying heterogeneity in animal distributions through space and time is a precursor to addressing many important research and management issues. Obtaining these distributional data is especially difficult for mobile organisms that use broader geographic extents. Here, we asked if the merger between 2 research directions—(1) quantifying spatial linkages between fish and geomorphic features (e.g. confluences) and (2) analyzing larger-scale, multi-metric organismal patterns—can provide a broader geographic context for ecological issues that depend on understanding dynamic fish distribution. To address these objectives, we collected data from 59 tagged striped bass Morone saxatilis that were detected by a 26 acoustic receiver array deployed within Plum Island Estuary, MA, USA. We examined these telemetry data using generalized linear mixed models and chi-squared, cluster, and network analyses. Geomorphic site types informed the estuary-wide distribution of striped bass in that tagged fish spent the most time at confluence junctions; however, they did not spend the same amount of time at all junctions. Relative to integrating multiple metrics, number of tagged fish, residence time, and number of movements were not the same across all receivers. When all 3 metrics were considered together, 4 distinct clusters of distributional patterns emerged. Network analyses connected geomorphology and multi-metric seascape patterns. Confluence junctions in the Rowley and Middle regions were the most connected (high centrality) and most used sites (high residence time). Although confluence junctions function as ecological hotspots, researchers and managers will benefit from interpreting geomorphology within a larger geographic context.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13088","usgsCitation":"Taylor, R., Mather, M.E., Smith, J., and Gerber, K., 2019, Confluences function as ecological hotspots: Geomorphic and regional drivers can help identify patterns of fish distribution within a seascape: Marine Ecology Progress Series, v. 629, p. 133-148, https://doi.org/10.3354/meps13088.","productDescription":"16 p.","startPage":"133","endPage":"148","ipdsId":"IP-095357","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467315,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/65379","text":"External Repository"},{"id":395278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.87520599365234,\n 42.69934284303157\n ],\n [\n -70.77220916748047,\n 42.69934284303157\n ],\n [\n -70.77220916748047,\n 42.8\n ],\n [\n -70.87520599365234,\n 42.8\n ],\n [\n -70.87520599365234,\n 42.69934284303157\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"629","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Ryland","contributorId":273166,"corporation":false,"usgs":false,"family":"Taylor","given":"Ryland","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph","contributorId":273167,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":832650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerber, Kayla","contributorId":273168,"corporation":false,"usgs":false,"family":"Gerber","given":"Kayla","affiliations":[{"id":56437,"text":"KY wr","active":true,"usgs":false}],"preferred":false,"id":832651,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204737,"text":"sir20195073 - 2019 - Sediment classification and the characterization, identification, and mapping of geologic substrates for the glaciated Gulf of Maine seabed and other terrains, providing a physical framework for ecological research and seabed management","interactions":[],"lastModifiedDate":"2019-10-24T11:23:12","indexId":"sir20195073","displayToPublicDate":"2019-10-24T10:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5073","displayTitle":"Sediment Classification and the Characterization, Identification, and Mapping of Geologic Substrates for the Glaciated Gulf of Maine Seabed and Other Terrains, Providing a Physical Framework for Ecological Research and Seabed Management","title":"Sediment classification and the characterization, identification, and mapping of geologic substrates for the glaciated Gulf of Maine seabed and other terrains, providing a physical framework for ecological research and seabed management","docAbstract":"A geologic substrate is a surface (or volume) of sediment or rock where physical, chemical, and biological processes occur, such as the movement and deposition of sediment, the formation of bedforms, and the attachment, burrowing, feeding, reproduction, and sheltering of organisms. Seabed mapping surveys in the Stellwagen Bank region off Boston, Massachusetts, from 1993 to 2004 have led to the development of a methodology for characterizing, identifying, and mapping geologic substrates. The resulting high-resolution interpretive maps (1:25,000) show the distribution of substrates in a glaciated terrain of banks and basins in water depths of 30 to 185 meters. Data sources used to characterize substrates are multibeam sonar bathymetric and backscatter imagery to document seabed topography and patterns of sediment and rock distribution, grain-size analyses of sediment samples to determine substrate composition, and video and photographic imagery of the seabed to aid in the interpretation of multibeam sonar imagery and to provide information on substrate layering and mobility, seabed structures, and sediments and nonsediment materials that cannot be physically sampled.
Sediment composition is a major property of many seabed substrates. Sediment grains belong to a continuum of grain-diameter sizes previously classified into grades (for example, fine sand, medium sand) and into aggregates (mud, sand, gravel). The definition of grade and aggregate boundaries in a classification is arbitrary, and a useful classification is limited to as few classes as are needed to effectively organize and apply information. For the purpose of mapping substrates, sediment grades and aggregates were simplified and re-classified into eight composite grades based on grain-size content, mode of transport, and ecological role. Five composite grades are identified using grain-size analysis and three are identified using video and photographic imagery of the seabed.
Naturally occurring sediments contain various amounts of the aggregates mud, sand, and gravel. The separation of naturally occurring sediments into sediment classes, based on grain-size analysis, requires that limits be set on the amount of mud, sand, and gravel each class contains. Fifteen previously identified basic sediment classes provided interpretive information on sediment transport by emphasizing gravel content (a low 0.01-weight-percent threshold) and on winnowing processes based on the sand-to-mud ratio. The present study recognizes 20 basic sediment classes that are combinations of aggregates in which the lower limits for recognition of mud and sand are 10 weight percent and of gravel, 25 weight percent. These sediment classes can be made more specific by listing their content of the composite grades fine-grained sand (3 and 4 phi), which is transported in suspension, and coarse-grained sand (0, 1, and 2 phi), which is transported as bedload. Additional sediment classes and nonsediment classes that cannot be sampled are recognized on the basis of visual analysis of seabed video and photographic imagery and include pebble, cobble, and boulder gravel, rock outcrops, and shell beds, among others.
Substrates are not classified because their properties are too varied for a classification to be concise and useful. Rather, substrates are characterized and identified by sediment grain-size composition (the sediment class); the distribution, in millimeters, of grain diameters in the sediment; the presence of nonsediments (for example, rock outcrops); substrate mobility based on the presence of sediment ripples; substrate layering (for example, a partial veneer of sand on gravel); and seabed structures. These properties have interpretive value by providing information about sedimentary processes acting on a substrate and about its ecological function. A geologic substrate, when it is associated with one or more species, is an important element of a habitat.
This methodology was developed to map a glaciated terrain characterized by geologic substrates that typify a wide range of erosional and depositional sedimentary environments, and it likely will be useful for mapping substrates in other terrains. Substrate maps provide the physical framework required for identifying sediment transport processes, validating sediment transport models, studying the ecology of species and communities, and managing marine resources and seabed usage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195073","usgsCitation":"Valentine, P.C., 2019, Sediment classification and the characterization, identification, and mapping of geologic substrates for the glaciated Gulf of Maine seabed and other terrains, providing a physical framework for ecological research and seabed management: U.S. Geological Survey Scientific Investigations Report 2019–5073, 37 p., https://doi.org/10.3133/sir20195073.","productDescription":"vii, 37 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102650","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":368354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5073/coverthb.jpg"},{"id":368358,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5073/sir20195073.pdf","text":"Report","size":"2.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5073"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Atlantic Ocean, Stellwagen Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.2301025390625,\n 42.809506838324204\n ],\n [\n -70.5157470703125,\n 42.65214190481525\n ],\n [\n -70.61737060546875,\n 42.56117285531808\n ],\n [\n -70.4718017578125,\n 42.114523952464246\n ],\n [\n -70.015869140625,\n 42.05133213230167\n ],\n [\n -70.2301025390625,\n 42.809506838324204\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
(508) 548–8700 or (508) 457–2200
Policy makers, individuals from government agencies, and natural resource managers face increasing demands to manage coastal areas in a way that meets economic, social, and ecological needs as sea levels rise. Scientific knowledge of how coastal processes drive beach and barrier island changes and how those changes affect habitat use can support decision makers as they balance sometimes conflicting human and ecological needs. However, uncertainties in the knowledge of the cumulative results of coastal processes make it challenging to forecast specific changes for a particular location and time. The U.S. Geological Survey is developing tools for identifying and forecasting barrier island characteristics as well as suitable coastal habitats for species of concern (such as piping plovers, Charadrius melodus) given ongoing sea-level rise. As part of this effort, we use three Bayesian networks to calculate probabilities of shoreline change rates, changes in barrier island biogeomorphic characteristics, and piping plover habitat availability, which together forecast the effects of different sea-level-rise rates and storm regimes. This report details the methodology used to derive geospatial biogeomorphic datasets that are used as inputs for two of these Bayesian networks, which forecast barrier island geomorphology and piping plover habitat availability at sites along the U.S. Atlantic coast (Maine to North Carolina). Further information about the project, including specific study sites, can be found at https://woodshole.er.usgs.gov/project-pages/beach-dependent-shorebirds/.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191071","usgsCitation":"Zeigler, S.L., Sturdivant, E.J., and Gutierrez, B.T., 2019, Evaluating barrier island characteristics and piping plover (Charadrius melodus) habitat availability along the U.S. Atlantic coast—Geospatial approaches and methodology (ver. 1.1, October 2019): U.S. Geological Survey Open-File Report 2019–1071, 34 p., https://doi.org/10.3133/ofr20191071.","productDescription":"Report: vii, 34 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095609","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437293,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V7F6UX","text":"USGS data release","linkHelpText":"Barrier island geomorphology and shorebird habitat metrics: 16 sites on the U.S. Atlantic Coast, 2013-2014"},{"id":365872,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1071/ofr20191071.pdf","text":"Report","size":"4.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1071"},{"id":365871,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1071/coverthb2.jpg"},{"id":368509,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1071/versionhist.txt","text":"Version History","size":"5.73 KB","linkFileType":{"id":2,"text":"txt"}},{"id":365873,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P944FPA4","text":"USGS data release","description":"USGS data release","linkHelpText":"Barrier Island Geomorphology and Shorebird Habitat Metrics—Four Sites in New York, New Jersey, and Virginia, 2010–2014"}],"country":"United States","state":"Connecticut, Maine, Maryland, Massachusetts, North Carolina, New Jersey, Rhode Island, Virginia","otherGeospatial":"Mid-Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.4228515625,\n 44.84029065139799\n ],\n [\n -70.9716796875,\n 43.61221676817573\n ],\n [\n -71.3232421875,\n 42.52069952914966\n ],\n [\n -71.015625,\n 41.902277040963696\n ],\n [\n -72.333984375,\n 41.705728515237524\n ],\n [\n -74.267578125,\n 41.0130657870063\n ],\n [\n -74.70703125,\n 40.212440718286466\n ],\n [\n -75.849609375,\n 38.37611542403604\n ],\n [\n -76.37695312499999,\n 36.4566360115962\n ],\n [\n -76.1572265625,\n 35.60371874069731\n ],\n [\n -76.025390625,\n 34.84987503195418\n ],\n [\n -75.1025390625,\n 35.10193405724606\n ],\n [\n -75.498046875,\n 37.16031654673677\n ],\n [\n -74.7509765625,\n 38.58252615935333\n ],\n [\n -74.091796875,\n 39.50404070558415\n ],\n [\n -72.4658203125,\n 40.613952441166596\n ],\n [\n -69.697265625,\n 41.04621681452063\n ],\n [\n -69.78515625,\n 41.80407814427234\n ],\n [\n -70.57617187499999,\n 43.004647127794435\n ],\n [\n -69.4775390625,\n 43.644025847699496\n ],\n [\n -68.115234375,\n 44.11914151643737\n ],\n [\n -68.4228515625,\n 44.84029065139799\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: October 23, 2019; Version 1.0: July 25, 2019","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543
Debris flows represent one of the most dangerous types of mass movements, because of their high velocities, large impact forces and long runout distances. This review describes the available debris-flow monitoring techniques and proposes recommendations to inform the design of future monitoring and warning/alarm systems. The selection and application of these techniques is highly dependent on site and hazard characterization, which is illustrated through detailed descriptions of nine monitoring sites: five in Europe, three in Asia and one in the USA. Most of these monitored catchments cover less than ∼10 km2 and are topographically rugged with Melton Indices greater than 0.5. Hourly rainfall intensities between 5 and 15 mm/h are sufficient to trigger debris flows at many of the sites, and observed debris-flow volumes range from a few hundred up to almost one million cubic meters. The sensors found in these monitoring systems can be separated into two classes: a class measuring the initiation mechanisms, and another class measuring the flow dynamics. The first class principally includes rain gauges, but also contains of soil moisture and pore-water pressure sensors. The second class involves a large variety of sensors focusing on flow stage or ground vibrations and commonly includes video cameras to validate and aid in the data interpretation. Given the sporadic nature of debris flows, an essential characteristic of the monitoring systems is the differentiation between a continuous mode that samples at low frequency (“non-event mode”) and another mode that records the measurements at high frequency (“event mode”). The event detection algorithm, used to switch into the “event mode” depends on a threshold that is typically based on rainfall or ground vibration. Identifying the correct definition of these thresholds is a fundamental task not only for monitoring purposes, but also for the implementation of warning and alarm systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2019.102981","usgsCitation":"Hurlimann, M., Coviello, V., Bel, C., Guo, X., Berti, M., Graf, C., Hubl, J., Miyata, S., Smith, J.B., and Yin, H., 2019, Debris-flow monitoring and warning: Review and examples: Earth-Science Reviews, v. 199, 102981, 26 p., https://doi.org/10.1016/j.earscirev.2019.102981.","productDescription":"102981, 26 p.","ipdsId":"IP-112575","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":459437,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2117/177770","text":"External Repository"},{"id":378905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"199","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hurlimann, Marcel","contributorId":241626,"corporation":false,"usgs":false,"family":"Hurlimann","given":"Marcel","email":"","affiliations":[{"id":48365,"text":"Department Division of Geotechnical Engineering and Geosciences, Department of Civil and Environmental Engineering UPC BarcelonaTECH, Barcelona, Spain","active":true,"usgs":false}],"preferred":false,"id":799791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coviello, Velio","contributorId":241627,"corporation":false,"usgs":false,"family":"Coviello","given":"Velio","email":"","affiliations":[{"id":48366,"text":"Faculty of Science and Technology, Free University of Bozen-Bolzano, Italy","active":true,"usgs":false}],"preferred":false,"id":799792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bel, Coraline","contributorId":241628,"corporation":false,"usgs":false,"family":"Bel","given":"Coraline","email":"","affiliations":[{"id":48367,"text":"Université Grenoble Alpes, Irstea, UR ETNA, St-Martin-d’Hères, France","active":true,"usgs":false}],"preferred":false,"id":799793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guo, Xiaojun","contributorId":241629,"corporation":false,"usgs":false,"family":"Guo","given":"Xiaojun","email":"","affiliations":[{"id":48368,"text":"Key Laboratory of Mountain Surface Process and Hazards/Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China","active":true,"usgs":false}],"preferred":false,"id":799794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berti, Matteo","contributorId":241630,"corporation":false,"usgs":false,"family":"Berti","given":"Matteo","affiliations":[{"id":48369,"text":"Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Università di Bologna, Bologna, Italy","active":true,"usgs":false}],"preferred":false,"id":799795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graf, Christoph","contributorId":241631,"corporation":false,"usgs":false,"family":"Graf","given":"Christoph","email":"","affiliations":[{"id":34058,"text":"Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":799796,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hubl, Johannes","contributorId":241632,"corporation":false,"usgs":false,"family":"Hubl","given":"Johannes","email":"","affiliations":[{"id":48370,"text":"Institute of Mountain Risk engineering, Department of Natural Hazards and Civil Engineering, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria","active":true,"usgs":false}],"preferred":false,"id":799797,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miyata, Shusuke","contributorId":241633,"corporation":false,"usgs":false,"family":"Miyata","given":"Shusuke","email":"","affiliations":[{"id":48371,"text":"Disaster Prevention Research Institute, Kyoto University, Takayama, Japan","active":true,"usgs":false}],"preferred":false,"id":799798,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799799,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yin, Hsiao-Yuan","contributorId":241634,"corporation":false,"usgs":false,"family":"Yin","given":"Hsiao-Yuan","email":"","affiliations":[{"id":48373,"text":"Soil and Water Conservation Bureau, Council of Agriculture, Nantou, Taiwan","active":true,"usgs":false}],"preferred":false,"id":799800,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70215430,"text":"70215430 - 2019 - Total grain size distribution of an intense Hawaiian fountaining event: Case study of the1959 Kīlauea Iki eruption","interactions":[],"lastModifiedDate":"2020-10-20T13:12:18.157354","indexId":"70215430","displayToPublicDate":"2019-10-19T15:19:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Total grain size distribution of an intense Hawaiian fountaining event: Case study of the1959 Kīlauea Iki eruption","docAbstract":"The 1959 eruption of Kīlauea Iki on the Island of Hawai’i is a principal example of powerful Hawaiian fountaining. Over 36 days (including repose periods), 16 fountaining episodes created a small cone, a downwind tephra blanket of approximately 0.003 km3 and a lava lake of about 0.04 km3 volume. During the explosive activity, the maximum fountain heights reached 600 m. Based on a dataset of more than 450 tephra grain size samples, we present both a total grain size distribution (TGSD) of the entire downwind tephra deposit, and also TGSDs for two eruptive subunits (the opening and the closing stages). The opening stage was characterized by persistent fountaining over a period of 8 days with fountain heights averaging ∼ 100 m; in contrast, the closing stage was characterized by two short (hours-long) but powerful fountaining episodes (up to 600 m). The significantly different fountaining intensities are reflected in the characteristics of the TGSDs. For the closing stages, we link bimodality of TGSDs to periods of simultaneous deposition of ballistics and fallout from the convective cloud, both of which are a function of the maximum fountain height. The 1959 Kīlauea Iki case study presents a well-constrained set of TGSD data linked with Hawaiian-style fountaining of two contrasting intensities and can be used as a valuable reference point for eruption source parameters in future modeling of pyroclast dispersal during Hawaiian fountaining eruptions.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-019-1304-y","usgsCitation":"Mueller, S.B., Houghton, B.F., Swanson, D., Poret, M., and Fagents, S.A., 2019, Total grain size distribution of an intense Hawaiian fountaining event: Case study of the1959 Kīlauea Iki eruption: Bulletin of Volcanology, v. 81, 43, 13 p., https://doi.org/10.1007/s00445-019-1304-y.","productDescription":"43, 13 p.","ipdsId":"IP-102777","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":379533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.6103515625,\n 19.129599439736836\n ],\n [\n -154.9896240234375,\n 19.129599439736836\n ],\n [\n -154.9896240234375,\n 19.65810729872147\n ],\n [\n -155.6103515625,\n 19.65810729872147\n ],\n [\n -155.6103515625,\n 19.129599439736836\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","noUsgsAuthors":false,"publicationDate":"2019-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Mueller, Sebastian B","contributorId":243387,"corporation":false,"usgs":false,"family":"Mueller","given":"Sebastian","email":"","middleInitial":"B","affiliations":[{"id":48709,"text":"University of Hawai`i","active":true,"usgs":false}],"preferred":false,"id":802178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houghton, Bruce F. 0000-0002-7532-9770","orcid":"https://orcid.org/0000-0002-7532-9770","contributorId":140077,"corporation":false,"usgs":false,"family":"Houghton","given":"Bruce","email":"","middleInitial":"F.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":802179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swanson, Donald A. 0000-0002-1680-3591","orcid":"https://orcid.org/0000-0002-1680-3591","contributorId":229682,"corporation":false,"usgs":true,"family":"Swanson","given":"Donald A.","affiliations":[],"preferred":true,"id":802180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poret, Matthieu","contributorId":243388,"corporation":false,"usgs":false,"family":"Poret","given":"Matthieu","email":"","affiliations":[{"id":33971,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy","active":true,"usgs":false}],"preferred":false,"id":802181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fagents, Sarah A.","contributorId":243389,"corporation":false,"usgs":false,"family":"Fagents","given":"Sarah","email":"","middleInitial":"A.","affiliations":[{"id":48709,"text":"University of Hawai`i","active":true,"usgs":false}],"preferred":false,"id":802182,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215417,"text":"70215417 - 2019 - Historical land use and land cover for assessing the northern Colorado Front Range urban landscape","interactions":[],"lastModifiedDate":"2020-10-20T13:15:57.557465","indexId":"70215417","displayToPublicDate":"2019-10-19T14:46:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2375,"text":"Journal of Maps","active":true,"publicationSubtype":{"id":10}},"title":"Historical land use and land cover for assessing the northern Colorado Front Range urban landscape","docAbstract":"We describe historical land-use and land-cover (LULC) maps for the northern Colorado urban Front Range. The Front Range urban landscape is diverse and interspersed with highly productive agriculture as well as natural land cover types including evergreen forest in the Rocky Mountain foothills and Great Plains grassland. To understand the dynamics of urban growth, raster maps were created at a 1 meter resolution for each of four time steps, nominally 1937, 1957, 1977, and 1997. In total, 38 detailed LULC classes were identified using manual interpretation techniques, aerial photographs, historical maps, and other available information. The maps provide high resolution spatial data for understanding the historical progression of urbanization and will allow further analysis of the effects of urban growth on social and ecological systems.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/17445647.2018.1548383","usgsCitation":"Drummond, M.A., Stier, M.P., and Diffendorfer, J., 2019, Historical land use and land cover for assessing the northern Colorado Front Range urban landscape: Journal of Maps, v. 15, no. 2, p. 89-93, https://doi.org/10.1080/17445647.2018.1548383.","productDescription":"5 p.","startPage":"89","endPage":"93","ipdsId":"IP-093283","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459446,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/17445647.2018.1548383","text":"Publisher Index Page"},{"id":437298,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P995RGC9","text":"USGS data release","linkHelpText":"Data release for the Historical land use and land cover for assessing the northern Colorado Front Range urban landscape"},{"id":379530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Northern Colorado Front Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.49072265625,\n 39.317300373271024\n ],\n [\n -104.501953125,\n 39.317300373271024\n ],\n [\n -104.501953125,\n 40.9964840143779\n ],\n [\n -105.49072265625,\n 40.9964840143779\n ],\n [\n -105.49072265625,\n 39.317300373271024\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Drummond, Mark A. 0000-0001-7420-3503 madrummond@usgs.gov","orcid":"https://orcid.org/0000-0001-7420-3503","contributorId":3053,"corporation":false,"usgs":true,"family":"Drummond","given":"Mark","email":"madrummond@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":802109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stier, Michael P. 0000-0002-8518-9855 mpstier@usgs.gov","orcid":"https://orcid.org/0000-0002-8518-9855","contributorId":3121,"corporation":false,"usgs":true,"family":"Stier","given":"Michael","email":"mpstier@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":802110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":802111,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215416,"text":"70215416 - 2019 - Comparing and improving methods for reconstructing peatland water-table depth from testate amoebae","interactions":[],"lastModifiedDate":"2020-10-19T19:40:15.940198","indexId":"70215416","displayToPublicDate":"2019-10-19T14:19:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1905,"text":"Holocene","active":true,"publicationSubtype":{"id":10}},"title":"Comparing and improving methods for reconstructing peatland water-table depth from testate amoebae","docAbstract":"Proxies that use changes in the composition of ecological communities to reconstruct temporal changes in an environmental covariate are commonly used in paleoclimatology and paleolimnology. Existing methods, such as weighted averaging and modern analog technique,\nrelate compositional data to the covariate in very simple ways, and different methods are seldom compared systematically. We present a new Bayesian model that better represents the underlying data and the complexity in the relationships between species’ abundances and a paleoenvironmental covariate. Using testate amoeba-based reconstructions of water-table depth as a test case, we systematically compare new and existing models in a cross-validation experiment on a large training dataset from North America. We then apply the different\nmodels to a new 7500-year record of testate amoeba assemblages from Caribou Bog in Maine and compare the resulting water-table depth reconstructions. We find that Bayesian models represent an improvement over existing methods in three key ways: more complete use of the underlying compositional data, full and meaningful treatment of uncertainty, and clear paths toward methodological improvements. Furthermore, we highlight how developing and systematically comparing methods leads to an improved understanding of the proxy system.\nThis paper focuses on testate amoebae and water-table depth, but the framework and ideas are widely applicable to other proxies based on compositional data.","language":"English","publisher":"SAGE Publications","doi":"10.1177/0959683619846969","usgsCitation":"Nolan, C., Tipton, J., Booth, R., Hooten, M., and Jackson, S., 2019, Comparing and improving methods for reconstructing peatland water-table depth from testate amoebae: Holocene, v. 29, no. 8, p. 1350-1361, https://doi.org/10.1177/0959683619846969.","productDescription":"12 p.","startPage":"1350","endPage":"1361","ipdsId":"IP-098724","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459448,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/0959683619846969","text":"Publisher Index 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35.67514743608467\n ],\n [\n -108.017578125,\n 40.91351257612758\n ],\n [\n -110.478515625,\n 40.91351257612758\n ],\n [\n -110.478515625,\n 35.67514743608467\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.13671875,\n 44.08758502824516\n ],\n [\n -111.796875,\n 44.08758502824516\n ],\n [\n -111.796875,\n 46.437856895024204\n ],\n [\n -115.13671875,\n 46.437856895024204\n ],\n [\n -115.13671875,\n 44.08758502824516\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"8","noUsgsAuthors":false,"publicationDate":"2019-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nolan, Connor","contributorId":197051,"corporation":false,"usgs":false,"family":"Nolan","given":"Connor","affiliations":[],"preferred":false,"id":802104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tipton, John","contributorId":166999,"corporation":false,"usgs":false,"family":"Tipton","given":"John","affiliations":[],"preferred":false,"id":802105,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Booth, Robert K.","contributorId":243345,"corporation":false,"usgs":false,"family":"Booth","given":"Robert K.","affiliations":[{"id":16160,"text":"Lehigh University","active":true,"usgs":false}],"preferred":false,"id":802106,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":802107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":802108,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215415,"text":"70215415 - 2019 - Differential effects of temperature and salinity on growth and mortality of oysters (Crassostrea virginica) in Barataria Bay and Breton Sound, Louisiana","interactions":[],"lastModifiedDate":"2020-10-19T19:17:19.843075","indexId":"70215415","displayToPublicDate":"2019-10-19T14:12:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2455,"text":"Journal of Shellfish Research","active":true,"publicationSubtype":{"id":10}},"title":"Differential effects of temperature and salinity on growth and mortality of oysters (Crassostrea virginica) in Barataria Bay and Breton Sound, Louisiana","docAbstract":"Temperature and salinity and their interaction exert a major control on the life cycle of the eastern oyster (Crassostrea virginica), affecting reproduction, development, growth, and mortality. Quantifying specific temperature and salinity relationships on oyster growth and mortality has however proven difficult, with data suggesting potentially region-specific responses. Legacy and recent data from field tray studies from public oyster grounds in Barataria Bay and Breton Sound were used to estimate growth and mortality rates as a function of temperature and salinity. Previous studies conducted in Barataria Bay and Breton Sound reported differences in growth and mortality between the basins. In the present study, environmental conditions were synchronized to compare growth and mortality between basins at similar combinations of temperature and salinity. Results indicate that when temperature and salinity are the same (synchronized), seasonal oyster growth and mortality rates still differ between Barataria Bay and Breton Sound. Given the same salinity and temperature conditions, differences in growth and mortality rates between estuaries may persist due to differences in other environmental conditions (i.e., food quality and composition, hydrology, site history, salinity variation) or localized genetic adaptations to environmental conditions.","language":"English","publisher":"BioOne","doi":"10.2983/035.038.0212","usgsCitation":"Sehlinger, T., Lowe, M., LaPeyre, M.K., and Soniat, T., 2019, Differential effects of temperature and salinity on growth and mortality of oysters (Crassostrea virginica) in Barataria Bay and Breton Sound, Louisiana: Journal of Shellfish Research, v. 38, no. 2, p. 317-326, https://doi.org/10.2983/035.038.0212.","productDescription":"10 p.","startPage":"317","endPage":"326","ipdsId":"IP-105718","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":379528,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Lousianna","otherGeospatial":"Barataria Bay and Brenton Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.8349609375,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 28.714678586705976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sehlinger, T.","contributorId":243342,"corporation":false,"usgs":false,"family":"Sehlinger","given":"T.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":802099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, M.R.","contributorId":243343,"corporation":false,"usgs":false,"family":"Lowe","given":"M.R.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":802100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":802101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soniat, T.M.","contributorId":243344,"corporation":false,"usgs":false,"family":"Soniat","given":"T.M.","email":"","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":802102,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215413,"text":"70215413 - 2019 - A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study","interactions":[],"lastModifiedDate":"2020-10-20T13:24:52.488251","indexId":"70215413","displayToPublicDate":"2019-10-19T14:01:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study","docAbstract":"Nutrient-reduction efforts have been undertaken in recent decades to mitigate the impacts of eutrophication in coastal and estuarine systems worldwide. To track progress in response to one of these efforts we use Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay, an estuary on the east coast of the United States. Model development included selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporating hydrologic variability via either river flow or salinity, and using interventions to model method or laboratory changes suspected to impact data. This approach, transferable to other systems, allows for evaluation of water quality data in a statistically rigorous way, while being suitable for application to many sites and variables. This enables consistent generation of annual updates, while providing a tool for developing insights to a range of management- and research-focused questions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2019.03.027","usgsCitation":"Murphy, R., Perry, E., Harcum, J., and Keisman, J.L., 2019, A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study: Environmental Modelling & Software, v. 118, 13 p., https://doi.org/10.1016/j.envsoft.2019.03.027.","productDescription":"13 p.","ipdsId":"IP-105288","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":379527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.40966796875,\n 36.756490329505176\n ],\n [\n -75.5419921875,\n 36.756490329505176\n ],\n [\n -75.5419921875,\n 39.57182223734374\n ],\n [\n -77.40966796875,\n 39.57182223734374\n ],\n [\n -77.40966796875,\n 36.756490329505176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"118","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Rebecca 0000-0003-3391-1823","orcid":"https://orcid.org/0000-0003-3391-1823","contributorId":199777,"corporation":false,"usgs":false,"family":"Murphy","given":"Rebecca","email":"","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":true,"id":802095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Elgin","contributorId":243340,"corporation":false,"usgs":false,"family":"Perry","given":"Elgin","affiliations":[{"id":48694,"text":"Statistics Consultant","active":true,"usgs":false}],"preferred":false,"id":802096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harcum, Jon","contributorId":243341,"corporation":false,"usgs":false,"family":"Harcum","given":"Jon","email":"","affiliations":[{"id":48695,"text":"Tetra Tech, Inc.","active":true,"usgs":false}],"preferred":false,"id":802097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keisman, Jennifer L. 0000-0001-6808-9193 jkeisman@usgs.gov","orcid":"https://orcid.org/0000-0001-6808-9193","contributorId":198107,"corporation":false,"usgs":true,"family":"Keisman","given":"Jennifer","email":"jkeisman@usgs.gov","middleInitial":"L.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802098,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215410,"text":"70215410 - 2019 - Geese mediate vegetation state changes with parallel effects on N cycling that leave nutritional legacies for offspring","interactions":[],"lastModifiedDate":"2020-10-20T13:48:08.938633","indexId":"70215410","displayToPublicDate":"2019-10-19T13:03:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Geese mediate vegetation state changes with parallel effects on N cycling that leave nutritional legacies for offspring","docAbstract":"Along the coastal fringe of the Yukon–Kuskokwim River Delta in southwestern Alaska, geese maintain grazing lawns dominated by a rhizomatous sedge that, when ungrazed, transitions to a taller, less palatable growth form that is taxonomically described as a different species. Nutrients recycled in goose feces, in conjunction with grazing, are critical to the rapid, nutritious growth of grazing lawns, and selective foraging on lawns has positive life‐history consequences for goslings. To examine whether bidirectional vegetation shifts were accompanied by parallel changes in N cycling, we studied how 15N‐urea and 13C15N‐glycine were processed through soils and plants of native and recently reverted vegetation states. Biomass and plant 15N uptake from plots reverted to the tall growth form using exclosures and from those shifted to grazing lawns by experimental clipping and then goose grazing were identical to their native counterparts. Total recovery of 15N within the tall vegetation types was significantly greater than within grazing lawns, although when expressed on a per‐gram biomass basis, percentage of 15N recovery was significantly higher in grazing lawns compared with the tall vegetation state. Patterns of 13C enrichment in CO2 soil efflux showed rapid use of 13C‐glycine as a respiratory substrate within the first hour following injection, with both the timing and magnitude of efflux occurring at similar time points for all four vegetation types. However, higher soil respiration rates and a shorter half‐life for 13C‐glycine in soils from tall meadows resulted in a greater proportional loss of 13CO2 compared with grazing lawns. Despite daily‐to‐weekly tidal inundation, all of 15N from labeled substrates could be accounted for within 1 m of the injection grid from soils of both states after 30 d, with significant levels of 15N in soils and vegetation after one year. Geese have remarkably high fidelity to brood‐rearing areas, returning as adults to the same grazing lawns where they were raised as goslings. Our data suggest that the role fecal‐derived nutrients play in the positive feedback loop between geese and their food resources can provide a long‐term legacy that spans generations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2850","usgsCitation":"Ruess, R.W., McFarland, J., Person, B.T., and Sedinger, J.S., 2019, Geese mediate vegetation state changes with parallel effects on N cycling that leave nutritional legacies for offspring: Ecosphere, v. 10, no. 8, e02850, 16 p., https://doi.org/10.1002/ecs2.2850.","productDescription":"e02850, 16 p.","ipdsId":"IP-107059","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":459459,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2850","text":"Publisher Index Page"},{"id":379523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon–Kuskokwim River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.31927490234372,\n 60.7672084234438\n ],\n [\n -163.85284423828125,\n 60.7672084234438\n ],\n [\n -163.85284423828125,\n 61.55280114177263\n ],\n [\n -166.31927490234372,\n 61.55280114177263\n ],\n [\n -166.31927490234372,\n 60.7672084234438\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"8","noUsgsAuthors":false,"publicationDate":"2019-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruess, Roger W.","contributorId":45483,"corporation":false,"usgs":false,"family":"Ruess","given":"Roger","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":802085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McFarland, Jack 0000-0001-9672-8597","orcid":"https://orcid.org/0000-0001-9672-8597","contributorId":214819,"corporation":false,"usgs":true,"family":"McFarland","given":"Jack","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":802086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Person, Brian T.","contributorId":107457,"corporation":false,"usgs":false,"family":"Person","given":"Brian","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":802088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sedinger, James S.","contributorId":213694,"corporation":false,"usgs":false,"family":"Sedinger","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":802087,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}