Monitoring of the Colorado River near the Moab, Utah, wastewater treatment plant (WWTP) outflow has detected pharmaceuticals, hormones, and estrogen-receptor (ER)-, glucocorticoid receptor (GR)-, and peroxisome proliferator-activated receptor-gamma (PPARγ)-mediated biological activities. The aim of the present multi-year study was to assess effects of a WWTP replacement on bioactive chemical (BC) concentrations. Water samples were collected bimonthly, pre- and post-replacement, at 11 sites along the Colorado River upstream and downstream of the WWTP and analyzed for in vitro bioactivities (e.g., agonism of ER, GR, and PPARγ) and BC concentrations; fathead minnows were cage deployed pre- and post-replacement at sites with varying proximities to the WWTP. Before the WWTP replacement, in vitro ER (24 ng 17β-estradiol equivalents/L)-, GR (60 ng dexamethasone equivalents/L)-, and PPARγ-mediated activities were detected at the WWTP outflow but diminished downstream. In March 2018, the WWTP effluent was acutely toxic to the fish, likely due to elevated ammonia concentrations. Following the WWTP replacement, ER, GR, and PPARγ bioactivities were reduced by approximately 60–79%, no toxicity was observed in caged fish, and there were marked decreases in concentrations of many BCs. Results suggest that replacement of the Moab WWTP achieved a significant reduction in BC concentrations to the Colorado River.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c05269","usgsCitation":"Cavallin, J., Battaglin, W., Beihoffer, J., Blackwell, B.D., Bradley, P., Cole, A., Ekman, D.R., Hofer, R., Kinsey, J., Keteles, K., Weissinger, R., Winkelman, D.L., and Villeneuve, D.L., 2021, Effects-based monitoring of bioactive chemicals discharged to the Colorado River before and after a municipal wastewater treatment plant replacement: Environmental Science and Technology, v. 55, no. 2, p. 974-984, https://doi.org/10.1021/acs.est.0c05269.","productDescription":"11 p.","startPage":"974","endPage":"984","ipdsId":"IP-119670","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":454003,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8135223","text":"External Repository"},{"id":381797,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","city":"Moab","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.7149658203125,\n 38.453588708941375\n ],\n [\n -109.3798828125,\n 38.453588708941375\n ],\n [\n -109.3798828125,\n 38.64261790634527\n ],\n [\n -109.7149658203125,\n 38.64261790634527\n ],\n [\n -109.7149658203125,\n 38.453588708941375\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-12-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cavallin, J.E. 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0000-0003-1296-4539","orcid":"https://orcid.org/0000-0003-1296-4539","contributorId":198381,"corporation":false,"usgs":false,"family":"Blackwell","given":"Bradley","email":"","middleInitial":"D.","affiliations":[{"id":18090,"text":"U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL","active":true,"usgs":false}],"preferred":false,"id":807432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807433,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cole, AR","contributorId":245980,"corporation":false,"usgs":false,"family":"Cole","given":"AR","email":"","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":807434,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ekman, Drew R.","contributorId":217483,"corporation":false,"usgs":false,"family":"Ekman","given":"Drew","email":"","middleInitial":"R.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":807435,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hofer, R","contributorId":245981,"corporation":false,"usgs":false,"family":"Hofer","given":"R","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":807436,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kinsey, J","contributorId":245982,"corporation":false,"usgs":false,"family":"Kinsey","given":"J","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":807437,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Keteles, Kristen","contributorId":200072,"corporation":false,"usgs":false,"family":"Keteles","given":"Kristen","email":"","affiliations":[],"preferred":false,"id":807438,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Weissinger, R","contributorId":172623,"corporation":false,"usgs":false,"family":"Weissinger","given":"R","email":"","affiliations":[{"id":20307,"text":"US National Park Service","active":true,"usgs":false}],"preferred":false,"id":807439,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":807440,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Villeneuve, Daniel L. 0000-0003-2801-0203","orcid":"https://orcid.org/0000-0003-2801-0203","contributorId":197436,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":807470,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70218019,"text":"70218019 - 2021 - Lithium in groundwater used for drinking-water supply in the United States","interactions":[],"lastModifiedDate":"2021-02-12T13:36:33.223193","indexId":"70218019","displayToPublicDate":"2020-12-26T07:31:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Lithium in groundwater used for drinking-water supply in the United States","docAbstract":"Lithium concentrations in untreated groundwater from 1464 public-supply wells and 1676 domestic-supply wells distributed across 33 principal aquifers in the United States were evaluated for spatial variations and possible explanatory factors. Concentrations nationwide ranged from <1 to 396 μg/L (median of 8.1) for public supply wells and <1 to 1700 μg/L (median of 6 μg/L) for domestic supply wells. For context, lithium concentrations were compared to a Health Based Screening Level (HBSL, 10 μg/L) and a drinking-water only threshold (60 μg/L). These thresholds were exceeded in 45% and 9% of samples from public-supply wells and in 37% and 6% from domestic-supply wells, respectively. However, exceedances and median concentrations ranged broadly across geographic regions and principal aquifers. Concentrations were highest in arid regions and older groundwater, particularly in unconsolidated clastic aquifers and sandstones, and lowest in carbonate-rock aquifers, consistent with differences in lithium abundance among major lithologies and rock weathering extent. The median concentration for public-supply wells in the unconsolidated clastic High Plains aquifer (central United States) was 24.6 μg/L; 24% of the wells exceeded the drinking-water only threshold and 86% exceeded the HBSL. Other unconsolidated clastic aquifers in the arid West had exceedance rates comparable to the High Plains aquifer, whereas no public supply wells in the Biscayne aquifer (southern Florida) exceeded either threshold, and the highest concentration in that aquifer was 2.6 μg/L. Multiple lines of evidence indicate natural sources for the lithium concentrations; however, anthropogenic sources may be important in the future because of the rapid increase of lithium battery use and subsequent disposal. Geochemical models demonstrate that extensive evaporation, mineral dissolution, cation exchange, and mixing with geothermal waters or brines may account for the observed lithium and associated constituent concentrations, with the latter two processes as major contributing factors.
Observations of crustal stress orientation from the regional inversion of earthquake focal mechanisms often conflict with those from borehole breakouts, possibly indicating local stress heterogeneity, either laterally or with depth. To investigate this heterogeneity, we compiled SHmax estimates from previous studies for 57 near‐vertical boreholes with measured breakout azimuths across the Los Angeles region. We identified subsets of earthquake focal mechanisms from established earthquake catalogs centered around each borehole with various criteria for maximum depth and maximum lateral distance from the borehole. Each subset was independently inverted for 3‐D stress orientation and corresponding SHmax probability distributions, then compared with the corresponding borehole breakout‐derived estimate. We find good agreement when both methods sample the basement stress (breakouts are close to the sediment‐basement interface), or when both methods sample the mid‐basin stress (sufficient earthquakes are present within a sedimentary basin). Along sedimentary basin margins, in contrast, we find acceptable agreement only when focal mechanisms are limited to shallow and close earthquakes, implying short‐length‐scale heterogeneity of <20 km. While the region as a whole shows evidence of both lateral and vertical stress orientation heterogeneity, we find a more homogeneous stress state within basement rock, over length scales of 1–35 km. These results reconcile the apparently conflicting observations of short‐length‐scale heterogeneity observed in boreholes, which sample primarily the basins, with the relative homogeneity of stress inferred from focal mechanisms, which sample primarily the basement, and imply distinct regimes for the appropriate use of each type of stress indicator.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB020817","usgsCitation":"Luttrell, K., and Hardebeck, J.L., 2021, A unified model of crustal stress heterogeneity from borehole breakouts and earthquake focal mechanisms: JGR Solid Earth, v. 126, no. 2, e2020JB020817, 13 p., https://doi.org/10.1029/2020JB020817.","productDescription":"e2020JB020817, 13 p.","ipdsId":"IP-121662","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":454007,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb020817","text":"Publisher Index Page"},{"id":384262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Luttrell, Karen 0000-0003-1405-1207","orcid":"https://orcid.org/0000-0003-1405-1207","contributorId":254967,"corporation":false,"usgs":false,"family":"Luttrell","given":"Karen","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":811558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780 jhardebeck@usgs.gov","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":841,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"jhardebeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":811559,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219488,"text":"70219488 - 2021 - Shared functional traits explain synchronous changes in long‐term count trends of migratory raptors","interactions":[],"lastModifiedDate":"2021-10-26T16:09:06.790716","indexId":"70219488","displayToPublicDate":"2020-12-26T06:49:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Shared functional traits explain synchronous changes in long‐term count trends of migratory raptors","docAbstract":"Assessing long‐term shifts in faunal assemblages is important to understand the consequences of ongoing global environmental change. One approach to assess drivers of assemblage changes is to identify the traits associated with synchronous shifts in count trends among species. Our research identified traits influencing trends in 73 years of count data on migrating raptors recorded in the north‐eastern USA.
Pennsylvania, USA.
1946–2018.
Birds of prey/raptors.
Migrating raptors were counted during autumn, following a standardized protocol. We used a hierarchical breakpoint model to identify when count trends shifted and to assess the role of traits in driving these trends before and after the breakpoint. Specifically, we quantified the probability of the direction (PD) of an effect of body mass, habitat or dietary specialization, migratory behaviour and susceptibility to dichlorodiphenyltrichloroethane (DDT) on count trends.
We documented an assemblage‐wide mean shift in count trends of migrating raptors in 1974. In general, species that exhibited negative count trends before the breakpoint exhibited positive count trends afterwards. We found that traits associated with resource use (diet and habitat specialization) had high probabilities of affecting count trends, pre‐ and post‐breakpoint (> 90%). Moreover, the direction of their effects differed during both periods. Unexpectedly, other traits we evaluated, including DDT susceptibility, had relatively weaker associations with count trends.
Trait‐based frameworks have promise for testing generalized assumptions about drivers of population trajectories. Historically, DDT was considered a key driver of changes in raptor population trends. However, our analysis suggests that other factors were also relevant. Moreover, the positive association between count trends and generalist behaviour depended on the temporal context. This result has implications for other settings where demographic trends can be linked to traits and help to identify drivers of biodiversity change.
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Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"A watershed moment: Analysis of sub-basins refocuses the geography of turtle conservation across the globe","docAbstract":"Conservation planners use a variety of decision-making tools, many of which require identifying and prioritizing spatial units based on their biodiversity and levels of imperilment. Turtles are highly imperiled, but present schemes for determining global priority areas are focused mostly on broad regional scales. We conduct the first global evaluation of turtle biodiversity and imperilment at a sub-basin level to identify geographically smaller areas of high conservation value, and compare with these existing prioritizations. We employed two spatial analyses—bivariate maps and local indicator of spatial association (LISA)—to identify and prioritize sub-basin clusters based on multiple biodiversity and conservation metrics in addition to species richness. Most high-priority sub-basin clusters were located along tropical and subtropical coastlines. A new area of global significance for turtle conservation was identified in southwest India. Many sub-basins of the Indomalayan Realm were clustered as high or intermediate priority, with large clusters of high-priority sub-basins also in tropical Australasia. Other high and intermediate priority sub-basin clusters were found in the Afrotropical, Neotropical, and Nearctic realms, often in previously recognized turtle biodiversity hotspots. Many conservation-priority sub-basins with high turtle-species richness and endemism are in lowland and coastal areas where endemics (some from ancient lineages) are imperiled in association with a high human footprint. Our findings reiterate the global significance of Asia as a key area of chelonian conservation need, while identifying focal areas across the globe where the need for targeted turtle conservation is especially great.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108925","usgsCitation":"Ennen, J., Agha, M., Sweat, S.C., Matamoros, W.A., Lovich, J.E., Iverson, J.B., Rhodin, A.G., Thomson, R., Shaffer, H.B., and Hoagstrom, C.W., 2021, A watershed moment: Analysis of sub-basins refocuses the geography of turtle conservation across the globe: Biological Conservation, v. 253, 108925, 9 p.,, https://doi.org/10.1016/j.biocon.2020.108925.","productDescription":"108925, 9 p.,","ipdsId":"IP-122122","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"253","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ennen, Joshua R.","contributorId":60368,"corporation":false,"usgs":false,"family":"Ennen","given":"Joshua R.","affiliations":[{"id":13216,"text":"Tennessee Aquarium Conservation Institute","active":true,"usgs":false}],"preferred":false,"id":807405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Agha, Mickey","contributorId":22235,"corporation":false,"usgs":false,"family":"Agha","given":"Mickey","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false},{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":807406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweat, Sarah C.","contributorId":195519,"corporation":false,"usgs":false,"family":"Sweat","given":"Sarah","email":"","middleInitial":"C.","affiliations":[{"id":13216,"text":"Tennessee Aquarium Conservation Institute","active":true,"usgs":false}],"preferred":false,"id":807407,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matamoros, Wildredo A.","contributorId":245972,"corporation":false,"usgs":false,"family":"Matamoros","given":"Wildredo","email":"","middleInitial":"A.","affiliations":[{"id":49391,"text":"Facultad de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas, Museo de Zoología, Tuxtla Gutiérrez, Chiapas, México Apartado Postal 29000, México","active":true,"usgs":false}],"preferred":false,"id":807408,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":807409,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Iverson, John B.","contributorId":147488,"corporation":false,"usgs":false,"family":"Iverson","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":807410,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rhodin, Anders G.J.","contributorId":212691,"corporation":false,"usgs":false,"family":"Rhodin","given":"Anders","email":"","middleInitial":"G.J.","affiliations":[{"id":38677,"text":"(1) Chelonian Research Foundation, Lunenburg, Massachusetts, USA (rhodincrf@aol.com); (2) University of Southern California, Los Angeles, California, USA (stanford@usc.edu)","active":true,"usgs":false}],"preferred":false,"id":807411,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thomson, Robert C.","contributorId":245973,"corporation":false,"usgs":false,"family":"Thomson","given":"Robert C.","affiliations":[{"id":49393,"text":"School of Life Sciences, University of Hawaiʻi, 2500 Campus Road, Honolulu, HI 96822, USA","active":true,"usgs":false}],"preferred":false,"id":807412,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shaffer, H. Bradley","contributorId":202769,"corporation":false,"usgs":false,"family":"Shaffer","given":"H.","email":"","middleInitial":"Bradley","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":807413,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hoagstrom, Christopher W.","contributorId":195520,"corporation":false,"usgs":false,"family":"Hoagstrom","given":"Christopher","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":807414,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70217071,"text":"70217071 - 2021 - The occurrence and distribution of strontium in U.S. groundwater","interactions":[],"lastModifiedDate":"2021-01-19T16:01:50.785334","indexId":"70217071","displayToPublicDate":"2020-12-24T07:19:16","publicationYear":"2021","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":"The occurrence and distribution of strontium in U.S. groundwater","docAbstract":"Groundwater samples from 32 principal aquifers across the United States (U.S.) provide a broad spatial scope of the occurrence and distribution of strontium (Sr) and are used to assess environments and factors that influence Sr concentration. Strontium is a common trace element in soils, rocks, and water and is ubiquitous in groundwater with detectable concentrations in 99.8% of samples (n=4,824; median = 225 μg/L). Concentrations in 2.3% of samples exceeded the 4,000 μg/L health-based screening level. The relative importance of controlling factors on Sr concentration are spatially variable and partly dependent on the type of groundwater well. Three case settings illustrate controls on Sr concentration. For drinking-water supply wells, most high concentrations (>4,000 μg/L) were measured in samples from carbonate aquifers that resulted from water-rock interaction with Sr-bearing rocks and minerals. High Sr concentrations from monitoring wells were more common in unconsolidated sand and gravel aquifers in arid or semi-arid setting where shallow groundwater is affected by irrigation and evaporative concentration of dissolved constituents in combination with lithologic or applied Sr sources. Upwelling saline groundwater is also a source of Sr source in some locations. Total dissolved solids concentration is an indicator of high Sr in all settings. An estimated 2.2 million people in the conterminous U.S. are potentially supplied water from public-supply wells with high Sr concentration, ∼86% of whom use carbonate aquifers (with > half supplied by the Floridan aquifer system). An additional 120,000 people are potentially supplied high-Sr-concentration water from domestic wells, >half of whom (∼58%) are in Texas. This study markedly expands the coverage of previous surveys of Sr in groundwater and is of interest given potential adverse human-health effects related to elevated concentrations of Sr and consideration of Sr for drinking-water regulation. Case settings with elevated Sr described for U.S. groundwater are likely indicative of settings and processes affecting Sr concentration in groundwater globally.
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turbulence to identify preferential areas for grass carp (Ctenopharyngodon idella) larvae in streams: A laboratory study","interactions":[],"lastModifiedDate":"2021-04-02T12:10:34.552968","indexId":"70219269","displayToPublicDate":"2020-12-24T07:07:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Using turbulence to identify preferential areas for grass carp (Ctenopharyngodon idella) larvae in streams: A laboratory study","docAbstract":"In this experimental series, we studied the swimming capabilities and response of grass carp (Ctenopharyngodon idella) larvae to flow turbulence in a laboratory flume. We compared three different experimental configurations, representing in‐stream obstructions commonly found in natural streams (e.g., a gravel bump, a single vertical cylinder, and patches of submerged rigid vegetation). Grass carp larvae (postgas bladder emergence) were introduced to each experimental configuration and subjected to a variety of hydrodynamic forces of different magnitudes and scales. We varied the flow velocities and water depths and found ranges of turbulent kinetic energy and Reynolds stresses that triggered a response in larval trajectories, identified by measured horizontal and vertical swimming speeds for each flow condition. Larvae apparently actively avoided areas with increased levels of turbulence by swimming away, moving faster in short bursts, and expending more energy. In addition to the magnitude of turbulent kinetic energy, the length scale and time scale of turbulent eddies also influenced the larvae response. These findings support the development of new strategies for controlling the spread of grass carp larvae in rivers, as well as the development of numerical tools incorporating active swimming capabilities to predict larval transport in streams.
Understanding transmission dynamics that link wild and domestic animals is a key element of predicting the emergence of infectious disease, an event that has highest likelihood of occurring wherever human livelihoods depend on agriculture and animal trade. Contact between poultry and wild birds is a key driver of the emergence of highly pathogenic avian influenza (HPAI), a process that allows for host-switching and accelerated reassortment, diversification and spread of virus between otherwise unconnected regions. This study addresses questions relevant to the spillover of HPAI at a transmission hotspot: what is the nature of the wild bird-poultry interface in Egypt and adjacent Black Sea-Mediterranean countries and how has this contributed to outbreaks occurring worldwide? Using a spatio-temporal model of infection risk informed by satellite tracking of waterfowl and viral phylogenetics, this study identified ecological conditions that contribute to spillover in this understudied region. Results indicated that multiple ducks (Northern Shoveler and Northern Pintail) hosted segments that shared ancestry with HPAI H5 from both clade 2.2.1 and clade 2.3.4 supporting the role of Anseriformes in linking viral populations in East Asia and Africa over large-distances. Quantifying the interface between wild ducks and H5N1-infected poultry revealed an increasing interface in late winter peaking in early spring when ducks expanded their range before migration, with key differences in the timing of poultry contact risk between local and long-distance migrants.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ve/veaa093","usgsCitation":"Hill, N.J., Smith, L.M., Muzaffar, S.B., Nagel, J.L., Prosser, D., Sullivan, J., Spragens, K.A., DeMattos, C.A., Demattos, C.C., El Sayed, L., Erciyas-Yavuz, K., Davis, C.T., Jones, J., Kis, Z., Donis, R.O., Newman, S., and Takekawa, J.Y., 2021, Crossroads of highly pathogenic H5N1: overlap between wild and domestic birds in the Black Sea-Mediterranean impacts global transmission: Virus Evolution, v. 7, no. 1, veaa093, 12 p., https://doi.org/10.1093/ve/veaa093.","productDescription":"veaa093, 12 p.","ipdsId":"IP-099380","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":454017,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ve/veaa093","text":"Publisher Index 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Studies of long-term trends in Lake Michigan have shown 2005–2016 densities to be 50–80% lower than 1990s densities, but these observations have been based on annual monitoring that is either spatially or seasonally limited. We combined Lake Michigan Mysis data from three annual programs and the 2015 Cooperative Science and Monitoring Initiative to achieve broad spatial coverage during spring, summer, and fall of 2015 and broad depth coverage during spring 2016. Lake-wide, annual density and biomass were 82 (SE: 10) Mysis/m2 and 200 (SE: 36) mg dry mass/m2. Density and biomass estimates were highest offshore, generally higher in the north basin, and seasonally highest in summer. Annual lake-wide averages for depths >30 m were better captured by seasonally-extensive annual programs than spatially-extensive annual programs, although spring sampling may bias annual values low. Mysis cohorts grew 0.026 mm/d (age-0) and 0.007 to 0.027 mm/d (age-1). Annual mortality was 81–98%. Reproduction was fall-spring and seasonal lake-wide estimates ranged from 0.6 to 19.1% females brooding, 13–20 embryos/brood, and 3–46 embryos/m2. Annual production (423 mg dry mass/m2/yr, SE: 31) was lower than all but one previous estimate from lakes Michigan, Huron, and Ontario. While Mysis tend to persist, low Mysis production may be a concern for prey fishes that feed on Mysis.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.11.012","usgsCitation":"Holda, T., Rudstam, L.G., Pothoven, S.A., Warner, D., Krystenko, D.S., and Watkins, J.M., 2021, Lake-wide annual status of Mysis diluviana population in Lake Michigan in 2015: Journal of Great Lakes Research, v. 47, no. 1, p. 190-203, https://doi.org/10.1016/j.jglr.2020.11.012.","productDescription":"14 p.","startPage":"190","endPage":"203","ipdsId":"IP-118982","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454020,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2020.11.012","text":"Publisher Index Page"},{"id":382441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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G.","contributorId":56609,"corporation":false,"usgs":false,"family":"Rudstam","given":"Lars","email":"","middleInitial":"G.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":808683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pothoven, Steven A.","contributorId":92998,"corporation":false,"usgs":false,"family":"Pothoven","given":"Steven","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, David 0000-0003-4939-5368","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":216543,"corporation":false,"usgs":true,"family":"Warner","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":808595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krystenko, Dmytro S.","contributorId":248259,"corporation":false,"usgs":false,"family":"Krystenko","given":"Dmytro","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":808685,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watkins, James M.","contributorId":189286,"corporation":false,"usgs":false,"family":"Watkins","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":808686,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263411,"text":"70263411 - 2021 - Rupture process of the M6.5 Stanley, Idaho, earthquake inferred from seismic waveform and geodetic data","interactions":[],"lastModifiedDate":"2025-02-10T16:40:39.947665","indexId":"70263411","displayToPublicDate":"2020-12-23T10:36:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Rupture process of the M6.5 Stanley, Idaho, earthquake inferred from seismic waveform and geodetic data","docAbstract":"The 2020 M 6.5 Stanley, Idaho, earthquake produced rupture in the north of the active Sawtooth fault in the northern basin and range at depth, without any observable surface rupture. Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data yield several millimeters of static offsets out to ∼100 km from the rupture and up to ∼0.1 m of near‐field crustal deformation. We combine the GPS and InSAR data with long‐period regional seismic waveforms to derive models of kinematic slip and afterslip. We find that the coseismic rupture is complex, likely involving up to 2 m combined left‐lateral strike slip and normal slip on a previously unidentified ∼south‐southeast‐striking fault. This slip is predominantly left‐lateral strike slip, different from the dominant east‐northeast–west‐northwest normal faulting of the region. At least one ∼northeast‐trending fault, likely associated with the Trans‐Challis fault system, is inferred to have accommodated a few decimeters of right‐lateral afterslip, consistent with vigorous aftershock activity at depth along northeast‐trending lineations.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200315","usgsCitation":"Pollitz, F., Hammond, W.C., and Wicks, C., 2021, Rupture process of the M6.5 Stanley, Idaho, earthquake inferred from seismic waveform and geodetic data: Seismological Research Letters, v. 92, no. 2A, p. 699-709, https://doi.org/10.1785/0220200315.","productDescription":"11 p.","startPage":"699","endPage":"709","ipdsId":"IP-124061","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481877,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","city":"Stanley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.45,\n 44.5\n ],\n [\n -115.45,\n 43.99\n ],\n [\n -114.5,\n 43.99\n ],\n [\n -114.5,\n 44.5\n ],\n [\n -115.45,\n 44.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"92","issue":"2A","noUsgsAuthors":false,"publicationDate":"2020-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hammond, William C.","contributorId":73735,"corporation":false,"usgs":true,"family":"Hammond","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":926888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wicks, Charles 0000-0002-0809-1328","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":9023,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926889,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217371,"text":"70217371 - 2021 - The impacts of the 2015/2016 El Niño on California's sandy beaches","interactions":[],"lastModifiedDate":"2021-01-20T14:17:42.716823","indexId":"70217371","displayToPublicDate":"2020-12-23T08:15:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"The impacts of the 2015/2016 El Niño on California's sandy beaches","docAbstract":"The El Niño Southern Oscillation is the most dominant mode of interannual climate variability in the Pacific. The 2015/2016 El Niño event was one of the strongest of the last 145 years, resulting in anomalously high wave energy across the U.S. West Coast, and record coastal erosion for many California beaches. To better manage coastal resources, it is critical to understand the impacts of both short-term climate variability and long-term climate impacts across the varied coastal settings of California. This study is the first to quantify the coastal response for one of the strongest El Niño events in the historical record across the coast of California through the analysis of nearshore wave conditions and seasonal beach changes for 8000 shore-normal transects. Through the analysis of pre- and post- El Niño LiDAR, we find that that central and northern California experienced the most sandy beach shoreline retreat/erosion during the El Niño winter, with a mean of 45.7 m of erosion (96% of beaches) in central California, a mean of 25.5 m of erosion (89% of beaches) in northern California, and a mean of 9.7 m of erosion (79% of beaches) in southern California. These patterns are compared to LiDAR and satellite-derived long-term shoreline change rates, in which southern California and central California beaches are moderately accreting, while northern California is eroding at an average of 79 cm per year. A significant correlation is found between cumulative wave energy flux and shoreline change during the El Niño winter across the state of California. Although local beach response during the El Niño winter was highly variable, heightened erosion was observed at river mouths and on the southern side of structures impeding littoral drift, with accretion observed on the northern side of these structures. These erosional patterns, driven by a northerly wave direction anomaly, contrast those of classic El Niño events such as the 1982–.83 and 1997–98 events, where more southerly storm tracks and southerly wave directions were key factors controlling shoreline behavior, and may indicate a shift in El Niño storm patterns driven by climate change.
Obstacle marks are instream bedforms, typically composed of an upstream frontal scour hole and a downstream sediment accumulation in the vicinity of an obstacle. Local scouring at infrastructure (e.g. bridge piers) is a well‐studied phenomenon in hydraulic engineering, while less attention is given to the time‐dependent evolution of frontal scour holes at instream boulders and their geometric relations (depth to width, and length ratio). Furthermore, a comparison between laboratory studies and field observations is rare. Therefore, the morphodynamic importance of such scour features to fluvial sediment transport and morphological change is largely unknown. In this study, obstacle marks at boulder‐like obstructions were physically modelled in 30 unscaled process‐focused flume experiments (runtime per experiment ≥ 5760 min) at a range of flows (subcritical, clear‐water conditions, emergent and submerged water levels) and boundary conditions designed to represent the field setting (i.e. obstacle tilting, and limited thickness of the alluvial layer). Additionally, geometries of scour holes at 90 in‐situ boulders (diameter ≥ 1 m) located in a 50‐km segment of the Colorado River in Marble Canyon (AZ) were measured from a 1 m‐resolution digital elevation model. Flume experiments reveal similar evolution of local scouring, irrespective of hydraulic conditions, controlled by the scour incision, whereas the thickness of the alluvial layer and obstacle tilting into the evolving frontal scour hole limit incision. Three temporal evolution phases—(1) rapid incision, (2) decreasing incision, and (3) scour widening—are identified based on statistical analysis of spatiotemporal bed elevation time series. A quantitative model is presented that mechanistically predicts enlargement in local scour length and width based on (1) scour depth, (2) the inclination of scour slopes, and (3) the planform area of the frontal scour hole bottom. The comparison of field observations and laboratory results demonstrates scale invariance of geometry, which implies similitude of processes and form rather than equifinality.
One of the challenges in assessing temporal and spatial aspects of landslide hazard using process-based models is estimating model input parameters, especially in areas where limited measurements of soil and rock properties are available. In an effort to simplify and streamline parameter estimation, development of a simple, rapid approach to sensitivity analysis relies on field measurements of landslide characteristics, especially slope and depth. This method is demonstrated for a case study in Puerto Rico where widespread destruction resulted from tens of thousands of debris flows induced by Hurricanes Irma and María in Puerto Rico in 2017. The approach can be applied to estimation of shear strength as well as hydrologic parameters that control infiltration and flow of water in the subsurface and ultimately the timing of landslides resulting from heavy rainfall. Results narrow the possible range of cohesion and friction parameters as well as hydraulic conductivity and other soil water parameters by counting the fraction of field observations that can be explained by each combination of parameters. For cases studied in Puerto Rico, the method identified combinations of cohesion and friction values that explain more than 80–90% of observed landslide source areas.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"WLF 2020: Understanding and reducing landslide disaster risk","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-60227-7_37","usgsCitation":"Baum, R.L., 2021, Rapid sensitivity analysis for reducing uncertainty in landslide hazard assessments, in WLF 2020: Understanding and reducing landslide disaster risk, p. 329-335, https://doi.org/10.1007/978-3-030-60227-7_37.","productDescription":"7 p.","startPage":"329","endPage":"335","ipdsId":"IP-117901","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":381876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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Rico\",\"nation\":\"USA \"}}]}","noUsgsAuthors":false,"publicationDate":"2020-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807564,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70226971,"text":"70226971 - 2021 - Intrinsic and extrinsic drivers of life-history variability for a south-western cutthroat trout","interactions":[],"lastModifiedDate":"2021-12-23T13:49:00.421423","indexId":"70226971","displayToPublicDate":"2020-12-23T07:44:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Intrinsic and extrinsic drivers of life-history variability for a south-western cutthroat trout","docAbstract":"The impacts of climate change on cold-water fishes will likely negatively manifest in populations at the trailing edge of their distributions. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid south-western U.S. streams at the southern-most edge of all cutthroat trout distributions, making RGCT particularly vulnerable to the anticipated warming and drying in this region. We hypothesised that RGCT possess a portfolio of life-history traits that aid in their persistence within streams of varying temperature and stream drying conditions. We used otolith and multistate capture–mark–recapture data to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity) and demography in RGCT populations from northern New Mexico, United States. We found evidence that RGCT reached maturity fastest at sites with warm stream temperatures and low densities. We did not find a strong relationship between discharge and any demographic rate, although apparent survival of mature RGCT decreased as stream temperature increased. Our study suggests plasticity in trait expression may be a life-history characteristic which can assist trailing edge populations like RGCT persist in a changing climate.
Ongoing ocean warming can release methane (CH4) currently stored in ocean sediments as free gas and gas hydrates. Once dissolved in ocean waters, this CH4 can be oxidized to carbon dioxide (CO2). While it has been hypothesized that the CO2 produced from aerobic CH4 oxidation could enhance ocean acidification, a previous study conducted in Hudson Canyon shows that CH4 oxidation has a small short‐term influence on ocean pH and dissolved inorganic radiocarbon. Here we expand upon that investigation to assess the impact of widespread CH4 seepage on CO2 chemistry and possible accumulation of this carbon injection along 234 km of the U.S. Mid‐Atlantic Bight. Consistent with the estimates from Hudson Canyon, we demonstrate that a small fraction of ancient CH4‐derived carbon is being assimilated into the dissolved inorganic radiocarbon (mean fraction of 0.5 ± 0.4 %). The areas with the highest fractions of ancient carbon coincide with elevated CH4 concentration and active gas seepage. This suggests that aerobic CH4 oxidation has a greater influence on the dissolved inorganic pool in areas where CH4 concentrations are locally elevated, instead of displaying a cumulative effect downcurrent from widespread groupings of CH4 seeps. An upper limit approximation of the input rate of ancient‐derived DIC into the waters overlying the northern U. S Mid‐Atlantic Bight further suggests that oxidation of ancient CH4‐derived carbon is not negligible on the global scale and could contribute to deep‐water acidification over longer time scales.
Because of their inaccessibility, submarine landslides are typically studied individually and at great effort and expense to provide knowledge of the specific site conditions where these landslides occur. Statistical analysis of submarine landslide scars can offer generalized perspectives on the processes that initiate submarine landslides and can help toward hazard assessment in areas that have not been studied in detail. The following review discusses more than a decade of development of statistical approaches to studying submarine landslides. Landslides were previously viewed together with other natural hazards, such as earthquakes and fires, as a phenomenon whose size distribution obeys an inverse power law. Inverse power law distributions are the result of self-organized avalanche processes, in which the final hazard size cannot be predicted at the onset of the disturbance. We find that volume and area distributions of submarine landslides along the U.S. Atlantic continental slope and along nine other margins worldwide do not follow an inverse power law. Rigorous statistical tests of several different probability distribution models indicate that the lognormal model is most appropriate for these siliciclastic environments. Lognormal distributions can be simulated by assuming that the area of slope failure depends on earthquake magnitude, in other words, failure occurs simultaneously over the area affected by horizontal ground shaking and does not cascade from nucleating sources. Therefore, the maximum landslide size can be predicted from the earthquake magnitude and the distance from the rupturing fault. Moreover, earthquakes <~M4.5 cannot generate significant submarine landslides. We further demonstrate that empirical, offshore landslide hazard curves can be developed from these lognormal landslide size distributions, if the duration of mapped landslide activity is known. In addition to hazard estimation, scaling relationships can yield insights on the physical processes associated with landslide failure. For example, the log-log relationship between volume and area of landslide scars in siliciclastic margins is observed to be almost linear implying that most landslides are translational. Carbonate margins, in contrast, show a power-law distribution of scar volumes and their volume to area relationship is ~1.3. These results suggest that landslides in carbonate margins are governed by the random distributions of existing fissures, and they act like rock falls on land. Although earthquakes are the principal trigger of submarine landslides, the effects of earthquake frequency on slope stability can be counterintuitive. The average size of landslide scars decreases non-linearly with increasing frequency of earthquakes and increases with increasing sedimentation rate. The effect is interpreted as evidence for densification and shear strength increase of margin sediment, induced by repeated seismic shaking.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Understanding and reducing landslide disaster risk","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer Link","doi":"10.1007/978-3-030-60196-6_23","usgsCitation":"ten Brink, U., and Geist, E.L., 2021, On the use of statistical analysis to understand submarine landslide processes and assess their hazard, chap. of Understanding and reducing landslide disaster risk, p. 329-341, https://doi.org/10.1007/978-3-030-60196-6_23.","productDescription":"13 p.","startPage":"329","endPage":"341","ipdsId":"IP-118112","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":387818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":false,"id":820876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geist, Eric L. 0000-0003-0611-1150","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":15543,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":820877,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218227,"text":"70218227 - 2021 - Progress and lessons learned from responses to landslide disasters","interactions":[],"lastModifiedDate":"2021-04-19T15:07:17.984422","indexId":"70218227","displayToPublicDate":"2020-12-22T09:43:44","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Progress and lessons learned from responses to landslide disasters","docAbstract":"Landslides have the incredible power to transform landscapes and also, tragically, to cause disastrous societal impacts. Whereas the mechanics and effects of many landslide disasters have been analyzed in detail, the means by which landslide experts respond to these events has garnered much less attention. Herein, we evaluate nine landslide response case histories conducted by the U.S. Geological Survey over the past two decades and summarize the event history, the response conducted, and the lessons learned from each event. We group the responses into three categories—providing event context from past events, addressing ongoing hazards, and acquiring data for the future—and present the nine case studies accordingly. We also summarize the progress in landslide response that has been made over the past two decades, including insights and advancements on the preparation for such events, the use of new technologies, and the importance of clear communication between all parties during disasters. We believe that exchanging and sharing experiences such as these will promote more clear and successful approaches for responses to landslide disasters in the future.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Understanding and reducing landslide disaster risk","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-60196-6_4","collaboration":"National Park Service","usgsCitation":"Collins, B.D., Reid, M.E., Coe, J.A., Kean, J.W., Baum, R.L., Jibson, R.W., Godt, J.W., Slaughter, S., and Stock, G.M., 2021, Progress and lessons learned from responses to landslide disasters, chap. of Understanding and reducing landslide disaster risk, p. 85-111, https://doi.org/10.1007/978-3-030-60196-6_4.","productDescription":"17 p.","startPage":"85","endPage":"111","ipdsId":"IP-117641","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":385192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":810500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":810501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":810502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":810503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":810504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jibson, Randall W. 0000-0003-3399-0875 jibson@usgs.gov","orcid":"https://orcid.org/0000-0003-3399-0875","contributorId":2985,"corporation":false,"usgs":true,"family":"Jibson","given":"Randall","email":"jibson@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":810505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":810506,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Slaughter, Stephen","contributorId":216500,"corporation":false,"usgs":false,"family":"Slaughter","given":"Stephen","affiliations":[{"id":13477,"text":"Washington Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":810507,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stock, Greg M.","contributorId":202873,"corporation":false,"usgs":false,"family":"Stock","given":"Greg","email":"","middleInitial":"M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":810508,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70224290,"text":"70224290 - 2021 - Identifying information gaps in predicting winter foraging habitat for juvenile Gulf Sturgeon","interactions":[],"lastModifiedDate":"2023-07-07T13:40:08.78752","indexId":"70224290","displayToPublicDate":"2020-12-22T07:38:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Identifying information gaps in predicting winter foraging habitat for juvenile Gulf Sturgeon","docAbstract":"The Gulf Sturgeon Acipenser oxyrinchus desotoi is an anadromous species that inhabits Gulf of Mexico coastal waters from Louisiana to Florida and is listed as threatened under the U.S. Endangered Species Act. Seasonal cues (e.g., freshwater discharge) determine the timing of spawning and migration and may influence the availability of critical habitat during winter months in six estuaries. Large information gaps, especially related to critical estuarine habitat for juveniles, hinder recovery efforts to protect these habitats and assess risks from emerging threats. Using Apalachicola Bay, Florida, as a model system, we developed and analyzed a preliminary Bayesian network model so that we could identify knowledge gaps (i.e., where expert knowledge was lacking) and data gaps (i.e., where data were unavailable) that limit the ability to assess the quantity of critical estuarine habitat for juvenile Gulf Sturgeon. The model hypothesized habitat availability per winter month in estuarine habitat under alternative scenarios of river discharge and length of the winter foraging season. A search for geospatial data sets revealed that the largest gap involved salinity, temperature, and oxygen (i.e., water condition) monitoring data, with data available only for Apalachicola Bay. For the Apalachicola Bay model, data gaps prevented the development of 53% of water condition geospatial data sets and a sensitivity analysis showed that water condition data most limited the ability to predict habitat availability. Expert knowledge was low, and conditional certainty scores showed that the relationships with the lowest certainty were abiotic suitability and habitat availability. Reducing information gaps could aid the development of a model that is appropriate for informing management. Future efforts could prioritize the expansion of water monitoring within critical habitat estuaries and predicting abiotic suitability and habitat availability. Bayesian network models can easily incorporate prior and new information for complex systems. Thus, our model could be updated as future research and monitoring efforts close these information gaps.
An assessment of a drainage basin and its stream corridor will provide the data and information needed to understand current biophysical conditions and trends. Developing an understanding of the drivers of change is the next essential step for restoration success (Osterkamp and Toy, 1997; Corenbilt et al., 2007; Briggs and Osterkamp, 2003), Shields et al. 2003; Osterkamp et al., 2011). Establishing such a robust scientific foundation will allow stream practitioners to develop realistic restoration objectives and the tactics that will be effective to achieve them. Accomplishing this requires collecting data at watershed and reach scales and by drawing on scientific data from areas where conditions may be similar to, or applicable to, your site. Although decisions should be backed by conclusive data, to make progress we need to rely on the best available information, even if scientific uncertainty remains. Additional information will become available, so it is necessary to plan a project in a manner that incorporates available knowledge and permits goals, objectives, and tactics to be adjusted accordingly.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Renewing our rivers—Stream corridor restoration in dryland regions","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of Arizona Press","usgsCitation":"Osterkamp, W., Briggs, M.K., Dean, D.J., and Rodriquez, A., 2021, Assessing the hydrologic and physical conditions of a drainage basin, chap. 3 of Renewing our rivers—Stream corridor restoration in dryland regions, p. 43-103.","productDescription":"61 p.","startPage":"43","endPage":"103","ipdsId":"IP-060923","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381603,"type":{"id":15,"text":"Index Page"},"url":"https://uapress.arizona.edu/book/renewing-our-rivers"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osterkamp, Waite","contributorId":245868,"corporation":false,"usgs":false,"family":"Osterkamp","given":"Waite","affiliations":[{"id":49352,"text":"(Emeritus) National Research Program, Western Branch; University of Arizona Press, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":807215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Mark K.","contributorId":177076,"corporation":false,"usgs":false,"family":"Briggs","given":"Mark","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":807216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":131047,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodriquez, Alfredo","contributorId":245869,"corporation":false,"usgs":false,"family":"Rodriquez","given":"Alfredo","email":"","affiliations":[{"id":37767,"text":"World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":807218,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217084,"text":"70217084 - 2021 - Effect of organic matter concentration and characteristics on mercury mobilization and methylmercury production at an abandoned mine site","interactions":[],"lastModifiedDate":"2021-01-05T13:28:54.365941","indexId":"70217084","displayToPublicDate":"2020-12-22T07:23:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Effect of organic matter concentration and characteristics on mercury mobilization and methylmercury production at an abandoned mine site","docAbstract":"Thousands of abandoned mines throughout the western region of North America contain elevated total-mercury (THg) concentrations. Mercury is mobilized from these sites primarily due to erosion of particulate-bound Hg (THg-P). Organic matter-based soil amendments can promote vegetation growth on mine tailings, reducing erosion and subsequent loading of THg-P into downstream waterbodies. However, the introduction of a labile carbon source may stimulate microbial activity that can produce methylmercury (MeHg)—the more toxic and bioaccumulative form of Hg. Our objectives were to investigate how additions of different organic matter substrates impact Hg mobilization and methylation using a combination of field observations and controlled experiments. Field measurements of water, sediment, and porewater were collected downstream of the site and multi-year monitoring (and load calculations) were conducted at a downstream gaging station. MeHg production was assessed using stable isotope methylation assays and mesocosm experiments that were conducted using different types of organic carbon soil amendments mixed with materials from the mine site. The results showed that >80% of the THg mobilized from the mine was bound to particles and that >90% of the annual Hg loading occurred during the period of elevated discharge during spring snowmelt. Methylation rates varied between different types of soil amendments and were correlated with the components of excitation emission matrices (EEMs) associated with humic acid fractions of organic matter. The mesocosm experiments showed that under anoxic conditions carbon amendments to tailings could significantly increase porewater MeHg concentrations (up to 13 ± 3 ng/L). In addition, the carbon amendments significantly increased THg partitioning into porewater. Overall, these results indicate that soil amendment applications to reduce surface erosion at abandoned mine sites could be effective at reducing particulate Hg mobilization to downstream waterbodies; however, some types of carbon amendments can significantly increase Hg methylation as well as increase the mobilization of dissolved THg from the site.
Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn has intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually-resolved snowpack reconstructions from regional to high (<12 km) spatial scales in western North America, and by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/abd5de","usgsCitation":"Coulthard, B.L., Anchukaitis, K.J., Pederson, G.T., Cook, E.R., Littell, J., and Smith, D.J., 2021, Snowpack signals in North American tree rings: Environmental Research Letters, v. 16, no. 3, 034037, 13 p., https://doi.org/10.1088/1748-9326/abd5de.","productDescription":"034037, 13 p.","ipdsId":"IP-122302","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":454037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abd5de","text":"Publisher Index Page"},{"id":383252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Coulthard, Bethany L.","contributorId":250711,"corporation":false,"usgs":false,"family":"Coulthard","given":"Bethany","email":"","middleInitial":"L.","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":810246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anchukaitis, Kevin J.","contributorId":195005,"corporation":false,"usgs":false,"family":"Anchukaitis","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":810247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Edward R","contributorId":218752,"corporation":false,"usgs":false,"family":"Cook","given":"Edward","email":"","middleInitial":"R","affiliations":[{"id":17701,"text":"Lamont-Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":810249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":810250,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Dan J.","contributorId":250712,"corporation":false,"usgs":false,"family":"Smith","given":"Dan","email":"","middleInitial":"J.","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":810251,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218733,"text":"70218733 - 2021 - Spatial clustering of aftershocks impacts the performance of physics‐based earthquake forecasting models","interactions":[],"lastModifiedDate":"2021-03-10T13:13:17.025736","indexId":"70218733","displayToPublicDate":"2020-12-22T07:10:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Spatial clustering of aftershocks impacts the performance of physics‐based earthquake forecasting models","docAbstract":"I explore why physics‐based models of earthquake triggering rarely outperform statistical models in prospective testing, outside of limited spatial‐temporal windows. Pseudo‐prospective tests on suites of synthetic aftershock sequences show that a major factor is the level of unmodeled spatial clustering of the direct aftershocks triggered by the mainshock. The synthetic sequences are generated from generalized “physical” triggering models, optionally superimposed on background heterogeneity that controls the level of clustering. The statistical Epidemic Type Aftershock Sequence (ETAS) model performs relatively better the more clustered the direct aftershocks, while the true generalized “physical” model performs relatively worse. Real aftershocks appear to be sufficiently clustered to allow ETAS to perform as well as or better than physical models such as Coulomb stress triggering. A likely cause of the spatial clustering of direct aftershocks is heterogeneity of the background physical conditions, which typically is not modeled in physics‐based forecasts. This implies that the forecast performance of physical models could be substantially improved through a better understanding of the interaction between earthquake stress changes and variable background physical conditions such as stress state, fault strength, and fluid pressure.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB020824","usgsCitation":"Hardebeck, J.L., 2021, Spatial clustering of aftershocks impacts the performance of physics‐based earthquake forecasting models: JGR Solid Earth, v. 126, no. 2, e2020JB020824, 16 p., https://doi.org/10.1029/2020JB020824.","productDescription":"e2020JB020824, 16 p.","ipdsId":"IP-117739","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":384260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":811557,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218214,"text":"70218214 - 2021 - Sentinel coyote pathogen survey to assess declining black-footed ferret (Mustela nigripes) population in South Dakota, USA","interactions":[],"lastModifiedDate":"2022-01-24T16:00:47.907583","indexId":"70218214","displayToPublicDate":"2020-12-21T14:21:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Sentinel coyote pathogen survey to assess declining black-footed ferret (Mustela nigripes) population in South Dakota, USA","title":"Sentinel coyote pathogen survey to assess declining black-footed ferret (Mustela nigripes) population in South Dakota, USA","docAbstract":"As part of the national recovery effort, endangered black-footed ferrets (Mustela nigripes) were reintroduced to the Cheyenne River Sioux Reservation in South Dakota, US in 2000. Despite an encouraging start, numbers of ferrets at the site have declined. In an effort to determine possible causes of the population decline, we undertook a pathogen survey in 2012 to detect exposure to West Nile virus (WNV), canine distemper virus (CDV), plague (Yersinia pestis), tularemia (Francisella tularensis), and heartworm (Dirofilaria immitis) using coyotes (Canis latrans) as a sentinel animal. The highest seroprevalence was for WNV with 71% (20/28) of coyotes testing antibody-positive. Seroprevalence of CDV and plague were lower, 27% and 13%, respectively. No evidence of active infection with tularemia or heartworm was seen in the coyotes sampled. As this study did not sample black-footed ferrets themselves, the definitive cause for the decline of this population cannot be determined. However, the presence of coyotes seropositive for two diseases, plague and CDV, lethal to black-footed ferrets, indicated the potential for exposure and infection. The high seroprevalence of WNV in the coyotes indicated a wide exposure to the virus; therefore, exposure of black-footed ferrets to the virus is also likely. Due to the ability of WNV to cause fatal disease in other species, studies may be useful to elucidate the impact that WNV could have on the success of reintroduced black-footed ferrets as well as factors influencing the spread and incidence of the disease in a prairie ecosystem.
","language":"English","publisher":"Allen Press","doi":"10.7589/JWD-D-20-00015","usgsCitation":"Schuler, K.L., Claymore, M., Schnitzler, H., Dubovi, E., Rocke, T.E., Perry, M.J., Bowman, D., and Abbott, R., 2021, Sentinel coyote pathogen survey to assess declining black-footed ferret (Mustela nigripes) population in South Dakota, USA: Journal of Wildlife Diseases, v. 57, no. 2, p. 264-272, https://doi.org/10.7589/JWD-D-20-00015.","productDescription":"9 p.","startPage":"264","endPage":"272","ipdsId":"IP-117390","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":383396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Cheyenne River Sioux Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.0684814453125,\n 44.46123053905879\n ],\n [\n -100.2227783203125,\n 44.46123053905879\n ],\n [\n -100.2227783203125,\n 45.4947963896697\n ],\n [\n -102.0684814453125,\n 45.4947963896697\n ],\n [\n -102.0684814453125,\n 44.46123053905879\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schuler, Krysten L.","contributorId":176255,"corporation":false,"usgs":false,"family":"Schuler","given":"Krysten","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":810438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Claymore, Michael","contributorId":251727,"corporation":false,"usgs":false,"family":"Claymore","given":"Michael","email":"","affiliations":[{"id":50381,"text":"Cheyenne River Sioux Tribe, Prairie Management Program, P.O. Box 590, Eagle Butte, SD USA","active":true,"usgs":false}],"preferred":false,"id":810439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schnitzler, Hannah","contributorId":251728,"corporation":false,"usgs":false,"family":"Schnitzler","given":"Hannah","email":"","affiliations":[{"id":50384,"text":"Cornell Wildlife Health Lab, Cornell University College of Veterinary Medicine, 240","active":true,"usgs":false}],"preferred":false,"id":810440,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dubovi, Edward","contributorId":251729,"corporation":false,"usgs":false,"family":"Dubovi","given":"Edward","email":"","affiliations":[{"id":50385,"text":"Animal Health Diagnostic Center, Cornell University, 240 Farrier Rd., Ithaca, NY","active":true,"usgs":false}],"preferred":false,"id":810441,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":810442,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Michael J.","contributorId":251730,"corporation":false,"usgs":false,"family":"Perry","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":50386,"text":"New York State Department of Health, Wadsworth Center Biodefense Laboratory, Albany NY","active":true,"usgs":false}],"preferred":false,"id":810443,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bowman, Dwight","contributorId":251731,"corporation":false,"usgs":false,"family":"Bowman","given":"Dwight","email":"","affiliations":[{"id":50387,"text":"Department of Microbiology and Immunology, Cornell University College ofVeterinary Medicine, Ithaca, NY","active":true,"usgs":false}],"preferred":false,"id":810444,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Abbott, Rachel","contributorId":240909,"corporation":false,"usgs":false,"family":"Abbott","given":"Rachel","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":810445,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70222340,"text":"70222340 - 2021 - A long-term geothermal observatory across subseafloor gas hydrates, IODP Hole U1364A, Cascadia accretionary prism","interactions":[],"lastModifiedDate":"2021-07-22T14:58:08.11708","indexId":"70222340","displayToPublicDate":"2020-12-21T09:52:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9110,"text":"Frontiers in Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A long-term geothermal observatory across subseafloor gas hydrates, IODP Hole U1364A, Cascadia accretionary prism","docAbstract":"We report 4 years of temperature profiles collected from May 2014 to May 2018 in Integrated Ocean Drilling Program Hole U1364A in the frontal accretionary prism of the Cascadia subduction zone. The temperature data extend to depths of nearly 300 m below seafloor (mbsf), spanning the gas hydrate stability zone at the location and a clear bottom-simulating reflector (BSR) at ∼230 mbsf. When the hole was drilled in 2010, a pressure-monitoring Advanced CORK (ACORK) observatory was installed, sealed at the bottom by a bridge plug and cement below 302 mbsf. In May 2014, a temperature profile was collected by lowering a probe down the hole from the ROV ROPOS. From July 2016 through May 2018, temperature data were collected during a nearly two-year deployment of a 24-thermistor cable installed to 268 m below seafloor (mbsf). The cable and a seismic-tilt instrument package also deployed in 2016 were connected to the Ocean Networks Canada (ONC) NEPTUNE cabled observatory in June of 2017, after which the thermistor temperatures were logged by Ocean Networks Canada at one-minute intervals until failure of the main ethernet switch in the integrated seafloor control unit in May 2018. The thermistor array had been designed with concentrated vertical spacing around the bottom-simulating reflector and two pressure-monitoring screens at 203 and 244 mbsf, with wider thermistor spacing elsewhere to document the geothermal state up to seafloor. The 4 years of data show a generally linear temperature gradient of 0.055°C/m consistent with a heat flux of 61–64 mW/m2. The data show no indications of thermal transients. A slight departure from a linear gradient provides an approximate limit of ∼10−10 m/s for any possible slow upward advection of pore fluids. In-situ temperatures are ∼15.8°C at the BSR position, consistent with methane hydrate stability at that depth and pressure.
","language":"English","publisher":"Frontiers Media SA","doi":"10.3389/feart.2020.568566","usgsCitation":"Becker, K.E., Davis, E.E., Hessemann, M., Collins, J.A., and McGuire, J., 2021, A long-term geothermal observatory across subseafloor gas hydrates, IODP Hole U1364A, Cascadia accretionary prism: Frontiers in Earth Sciences, v. 8, 568566, 10 p., https://doi.org/10.3389/feart.2020.568566.","productDescription":"568566, 10 p.","ipdsId":"IP-119212","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":454042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.568566","text":"Publisher Index Page"},{"id":387385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Cascadia subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.3759765625,\n 49.10983779052439\n ],\n [\n -133.22021484375,\n 48.472921272487824\n ],\n [\n -135.46142578124997,\n 47.32393057095941\n ],\n [\n -136.6259765625,\n 46.14939437647686\n ],\n [\n -135.46142578124997,\n 45.413876460821086\n ],\n [\n -132.802734375,\n 45.49094569262732\n ],\n [\n -130.36376953125,\n 45.38301927899065\n ],\n [\n -126.38671874999999,\n 47.57652571374621\n ],\n [\n -125.3759765625,\n 49.10983779052439\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-12-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Becker, K. Elizabeth","contributorId":196545,"corporation":false,"usgs":false,"family":"Becker","given":"K.","email":"","middleInitial":"Elizabeth","affiliations":[],"preferred":false,"id":819674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, E. E.","contributorId":261294,"corporation":false,"usgs":false,"family":"Davis","given":"E.","email":"","middleInitial":"E.","affiliations":[{"id":52792,"text":"Geol. Surv. of Canada","active":true,"usgs":false}],"preferred":false,"id":819675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hessemann, M.","contributorId":261295,"corporation":false,"usgs":false,"family":"Hessemann","given":"M.","email":"","affiliations":[{"id":52794,"text":"Ocean Networks Canada","active":true,"usgs":false}],"preferred":false,"id":819676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, J. A.","contributorId":213074,"corporation":false,"usgs":false,"family":"Collins","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":819677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":819678,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229076,"text":"70229076 - 2021 - Variation in black bass angler characteristics by stream size and accessibility in Oklahoma’s Ozark Highland streams","interactions":[],"lastModifiedDate":"2022-02-28T15:31:45.800061","indexId":"70229076","displayToPublicDate":"2020-12-21T09:27:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Variation in black bass angler characteristics by stream size and accessibility in Oklahoma’s Ozark Highland streams","docAbstract":"Fishing in streams and rivers is a popular outdoor recreation activity in eastern Oklahoma, where most anglers target black bass (Micropterus) species. Since the early 1990s, when the last assessment of black bass fishing in the region was conducted, broadscale factors such as harvesting behavior, state fishery regulations, and bass population dynamics have changed. In 2018, we conducted creel and fish tagging surveys in three tributaries of Lake Tenkiller (Caney Creek, Baron Fork, and Illinois River) that differed in size and accessibility to provide current estimates of catch, harvest, and effort directed toward black bass. We then related these estimates to angler socioeconomic characteristics. The amount of angler effort was concomitant with stream size and accessibility, being greatest in the largest stream with the most access (Illinois River). However, catch rates were highest in the medium-sized stream (Baron Fork). Harvest rates and exploitation were near zero in all systems. Anglers fishing Caney Creek, the smallest and least accessible stream, were nearly all local, coming from zip codes ~42 km away, with low median household incomes compared to anglers at the other streams who came from a broader array of more distant zip codes and had higher median household incomes. Anglers fishing the smallest stream were also more interested in harvesting fish and having higher creel limits than anglers at the other two systems. In the Oklahoma Ozark Highlands, stream size and accessibility appear to be a significant factor in angler demographics, potentially necessitating different management strategies.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10565","usgsCitation":"Chapagain, B., Long, J.M., Taylor, A.T., and Joshi, O., 2021, Variation in black bass angler characteristics by stream size and accessibility in Oklahoma’s Ozark Highland streams: North American Journal of Fisheries Management, v. 41, no. 3, p. 585-599, https://doi.org/10.1002/nafm.10565.","productDescription":"15 p.","startPage":"585","endPage":"599","ipdsId":"IP-121658","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454045,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11244/334600","text":"External Repository"},{"id":396553,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Ozark Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.22125244140625,\n 35.572448615622804\n ],\n [\n -93.94958496093749,\n 35.572448615622804\n ],\n [\n -93.94958496093749,\n 36.910372213522535\n ],\n [\n -95.22125244140625,\n 36.910372213522535\n ],\n [\n -95.22125244140625,\n 35.572448615622804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-12-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Chapagain, B.","contributorId":280237,"corporation":false,"usgs":false,"family":"Chapagain","given":"B.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":836417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Andrew T.","contributorId":177197,"corporation":false,"usgs":false,"family":"Taylor","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":836419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joshi, O.","contributorId":280236,"corporation":false,"usgs":false,"family":"Joshi","given":"O.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":836420,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}