{"pageNumber":"571","pageRowStart":"14250","pageSize":"25","recordCount":165309,"records":[{"id":70211866,"text":"ofr20201098 - 2020 - Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada","interactions":[],"lastModifiedDate":"2020-08-12T14:23:02.871456","indexId":"ofr20201098","displayToPublicDate":"2020-08-11T13:57:34","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1098","displayTitle":"Understanding and Documenting the Scientific Basis of Selenium Ecological Protection in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada","title":"Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada","docAbstract":"

Modeling of ecosystems is a part of the U.S. Environmental Protection Agency’s protocol for developing site-specific selenium guidelines for protection of aquatic life. Selenium as an environmental contaminant is known to bioaccumulate and cause reproductive effects in fish and wildlife. Here we apply a modeling methodology—ecosystem-scale selenium modeling—to understand and document the scientific basis for predicting and validating ecological protection for Lake Koocanusa, a transboundary reservoir between Montana and British Columbia. A comprehensive set of site-specific data compiled from public databases (Federal, State, and Provincial) and reports by Teck Coal Ltd., is available in a companion U.S. Geological Survey data release. The tissue guideline used within modeling here to assess protection is the U.S. Environmental Protection Agency’s national selenium guideline for whole-body fish (dry weight); however, other numeric values for a whole-body guideline or other tissue types may be assumed if applicable tissue-to-tissue conversion factors are available. 

We consider the report assembled here as a working document that presents a model that can effectively address and structure the needs of (1) scientific understanding in representing the lake’s ecosystem and selenium biodynamics and (2) policy and management development during a decision-making process, but it is open to modification and updating as more ecologically detailed data become available. The approach brings together the main concerns involved in selenium toxicity: likelihood of high exposure, inherent species sensitivity, and close connectivity of ecosystem characteristics and behavioral ecology of predators. Detailed site-specific modeling equations are provided to document the linked factors that determine the responses of ecosystems to selenium. A series of scenarios quantifies the implications of choices of site-specific variables including food-web species, bioavailability of particulate material, and partitioning between the dissolved and particulate phases at the base of food webs. A gradient mapping tool applied to Lake Koocanusa provides a precedent for ecosystem-scale modeling of lakes by recognizing the importance of lake strata and hydrodynamics as components of modeling. 

Data requirements for ecosystem modeling, including ecological and hydrological process information fundamental to the dietary biodynamics of selenium in site-specific food webs, were assessed as a precursor to model validation for Lake Koocanusa. Understanding these relationships is necessary to connect modeling outcomes to reproductive effects and establish boundaries, in the case of Lake Koocanusa, for the influences of dam operation, fish-community viability, and its Clean Water Act impaired 303(d)-listing status on ecosystem function. 

We find that an assemblage of conditions affects the representation of Lake Koocanusa’s ecosystem within modeling scenarios but that the constructed gradient maps, mechanistic model, and associated bioaccumulation potentials portray and quantify the variables that are determinative to protection of predator species. Ecological and hydrological sorting of compiled individual data points on a site- and species-specific basis helps identify and address model uncertainties. Sources of uncertainty include (1) the scarcity of data for some environmental media compartments across time and locations, (2) the complexity of hydrodynamic conditions that can lead to seasonal ecological disconnects such as in selenium partitioning from water into particulates, and (3) the functional status of Lake Koocanusa’s ecosystem because of cumulative effects of various environmental stresses (for example, fish-community changes, flow regime changes, parasites, gonadal dysfunction, and increasing mining input-selenium concentrations since 1984). To this last point, it is important to determine where Lake Koocanusa is in an impairment-restoration cycle so as not to base protection on survivor bias, the maintenance of a currently degraded ecosystem, or normalized toxicity. In a broader context, one of the overall consequences of revised selenium regulations is that their derivation is now dependent on being able to define and understand the status of the ecosystem on which protection is based.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201098","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Presser, T.S., and Naftz, D.L., 2020, Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada: U.S. Geological Survey Open-File Report 2020–1098, 40 p., https://doi.org/10.3133/ofr20201098.","productDescription":"Report: viii, 40 p.; 3 Tables; Data Releases","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436823,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99LM27E","text":"USGS data release","linkHelpText":"Results of Ecosystem Scale Selenium Modeling in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada, 2020"},{"id":377297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HB5S5F","text":"USGS data release","description":"USGS Data Release","linkHelpText":"USGS measurements of dissolved and suspended particulate material selenium in Lake Koocanusa in the vicinity of Libby Dam (MT), 2015–2017 (update)"},{"id":377296,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VXYSNZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selenium concentrations in food webs of Lake Koocanusa in the vicinity of Libby Dam (MT) and the Elk River (BC) as the basis for applying ecosystem-scale modeling, 2008–2018"},{"id":377295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1098"},{"id":377294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1098/coverthb.jpg"},{"id":377363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098_tables_1_and_3_to_10.xlsx","text":"Tables 1 and 3–10","size":"91.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1098 Tables"}],"country":"United States, Canada","state":"Montana, British Columbia","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.72998046875,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 48.33251726168281\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Water Mission Area
U.S. Geological Survey
345 Middlefield Rd.
Menlo Park, CA 94025

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-08-11","noUsgsAuthors":false,"publicationDate":"2020-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Presser, Theresa S. 0000-0001-5643-0147 tpresser@usgs.gov","orcid":"https://orcid.org/0000-0001-5643-0147","contributorId":2467,"corporation":false,"usgs":true,"family":"Presser","given":"Theresa","email":"tpresser@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":795464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795465,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211865,"text":"70211865 - 2020 - A stress-similarity triggering model for aftershocks of the MW6.4 and MW7.1 Ridgecrest earthquakes","interactions":[],"lastModifiedDate":"2020-08-14T13:19:14.908276","indexId":"70211865","displayToPublicDate":"2020-08-11T12:04:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"A stress-similarity triggering model for aftershocks of the MW6.4 and MW7.1 Ridgecrest earthquakes","docAbstract":"

The July 2019 Mw\">Mw 6.4 and 7.1 Ridgecrest earthquakes triggered numerous aftershocks, including clusters of off‐fault aftershocks in an extensional stepover of the Garlock fault, near the town of Olancha, and near Panamint Valley. The locations of the off‐fault aftershocks are consistent with the stress‐similarity model of triggering, which hypothesizes that aftershocks preferentially occur in areas where the mainshock static stress change tensor is similar in orientation to the background stress tensor. The background stress field is determined from the inversion of earthquake focal mechanisms, with the spatial resolution adapted to the local density of earthquakes. The mainshock static stress change is computed using finite‐source models for the Mw\">Mw 6.4 foreshock and Mw\">Mw 7.1 mainshock. I quantify the similarity between these two stress fields using the tensor dot product of the normalized deviatoric stress tensors. The off‐fault aftershocks in the Garlock stepover and the Olancha area fall within lobes of positive stress similarity, whereas the aftershocks near Panamint Valley are partially within a lobe. The cluster in the Garlock fault stepover and the smaller of two clusters near Olancha occur in regions of locally anomalous background stress that results in higher stress similarity. I compute the spatial density of M≥2.0\">M≥2.0 aftershocks and find that the aftershock density increases as a function of stress similarity, with a factor of ∼15\">∼15 difference between high stress‐similarity and low stress‐similarity areas. This result is robust with respect to the choice of mainshock model and the uncertainty of the background stress field. The aftershock density varies substantially inside the high stress‐similarity lobes, however, indicating that other variable background conditions, such as material properties, temperature, and fluid pressure, may also be playing a role. Specifically, temperature and fluid pressure conditions might help explain the low rate of aftershocks in the Coso geothermal field.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200015","usgsCitation":"Hardebeck, J.L., 2020, A stress-similarity triggering model for aftershocks of the MW6.4 and MW7.1 Ridgecrest earthquakes: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1716-1727, https://doi.org/10.1785/0120200015.","productDescription":"12 p.","startPage":"1716","endPage":"1727","ipdsId":"IP-113938","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":377345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert, Panamint Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.72900390625001,\n 38.87392853923629\n ],\n [\n -120.84960937499999,\n 38.30718056188316\n ],\n [\n -119.0478515625,\n 35.71083783530009\n ],\n [\n -117.39990234375,\n 34.72355492704221\n ],\n [\n -116.08154296875001,\n 34.27083595165\n ],\n [\n -114.89501953124999,\n 35.31736632923788\n ],\n [\n -119.72900390625001,\n 38.87392853923629\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-02","publicationStatus":"PW","contributors":{"authors":[{"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":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":795463,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211854,"text":"70211854 - 2020 - Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products","interactions":[],"lastModifiedDate":"2020-08-12T14:30:08.809626","indexId":"70211854","displayToPublicDate":"2020-08-11T11:59:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products","docAbstract":"The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. Interventions to mitigate food insecurity impacts require early warning of droughts —preferably as early as possible before the harvest season (typically, starting in April) and lean season (typically, starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available rootzone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of RZSM products from a recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). Results show that the NHyFAS products would have identified the regional severe drought event—which peaked during December-February of 2015/2016—at least as early as November 1, 2015. Next, it is shown that during 1982-2016, February RZSM forecasts [monitoring product] available in early November [early March] have a correlation of 0.49 [0.79] with the detrended regional crop yield. It is also found that when the February RZSM forecast [monitoring product] available in early November [early March] is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds of the time [always], at least over the sample years considered. Additionally, it is shown that February RZSM forecast [monitoring product] can provide “out-of-sample” crop yield forecasts with comparable [substantially better with 40% reduction in mean error] skill to December-February ENSO. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region. Finally, since a framework similar to NHyFAS can be used to provide RZSM monitoring and forecasting products over other regions of the globe, this case study also demonstrates potential for supporting food insecurity early warning globally.","language":"English","publisher":"Copernicus","doi":"10.5194/nhess-20-1187-2020","usgsCitation":"Shukla, S., Arsenault, K., Hazra, A., Peters-Lidard, C., Davenport, F., Magadzire, T., and Funk, C., 2020, Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products: Hydrology and Earth System Sciences, v. 20, p. 1187-1201, https://doi.org/10.5194/nhess-20-1187-2020.","productDescription":"15 p.","startPage":"1187","endPage":"1201","ipdsId":"IP-111564","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-20-1187-2020","text":"Publisher Index Page"},{"id":377344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Southern Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 40.166015625,\n -10.746969318459989\n ],\n [\n 26.455078125,\n -14.604847155053898\n ],\n [\n 12.83203125,\n -17.14079039331664\n ],\n [\n 11.513671874999998,\n -17.72775860985227\n ],\n [\n 18.896484375,\n -36.17335693522159\n ],\n [\n 30.322265625000004,\n -34.161818161230386\n ],\n [\n 40.78125,\n -17.811456088564473\n ],\n [\n 40.166015625,\n -10.746969318459989\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationDate":"2020-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":224784,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":795403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arsenault, Kristi","contributorId":198836,"corporation":false,"usgs":false,"family":"Arsenault","given":"Kristi","affiliations":[],"preferred":false,"id":795404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazra, Abda","contributorId":237825,"corporation":false,"usgs":false,"family":"Hazra","given":"Abda","email":"","affiliations":[],"preferred":false,"id":795405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peters-Lidard, Christa","contributorId":198839,"corporation":false,"usgs":false,"family":"Peters-Lidard","given":"Christa","email":"","affiliations":[],"preferred":false,"id":795406,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":795407,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Magadzire, Tamuka","contributorId":145822,"corporation":false,"usgs":false,"family":"Magadzire","given":"Tamuka","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":795408,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":795409,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211875,"text":"70211875 - 2020 - Batrachochytrium salamandrivorans (Bsal) not detected in an intensive survey of wild North American amphibians","interactions":[],"lastModifiedDate":"2020-08-12T14:32:00.537988","indexId":"70211875","displayToPublicDate":"2020-08-11T11:20:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Batrachochytrium salamandrivorans (Bsal) not detected in an intensive survey of wild North American amphibians","docAbstract":"The salamander chytrid fungus (Batrachochytrium salamandrivorans [Bsal]) is causing massive mortality of salamanders in Europe. The potential for spread via international trade into North America and the high diversity of salamanders has catalyzed concern about Bsal in the U.S. Surveillance programs for invading pathogens must initially meet challenges that include low rates of occurrence on the landscape, low prevalence at a site, and imperfect detection of the diagnostic tests. We implemented a large-scale survey to determine if Bsal was present in North America designed to target taxa and localities where Bsal was determined highest risk to be present based on species susceptibility and geography. Our analysis included a Bayesian model to estimate the probability of occurrence of Bsal given our prior knowledge of the occurrence and prevalence of the pathogen. We failed to detect Bsal in any of 11,189 samples from 594 sites in 223 counties within 35 U.S. states and one site in Mexico. Our modeling indicates that Bsal is highly unlikely to occur within wild amphibians in the U.S. and suggests that the best proactive response is to continue mitigation efforts against the introduction and establishment of the disease and to develop plans to reduce impacts should Bsal establish.","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-69486-x","usgsCitation":"Waddle, J., Grear, D.A., Mosher, B., Campbell Grant, E.H., Adams, M.J., Backlin, A.R., Barichivich, W., Brand, A.B., Bucciarelli, G.M., Calhoun, D.L., Chestnut, T., Davenport, J., Dietrich, A.E., Fisher, R.N., Glorioso, B., Halstead, B., Hayes, M.P., Honeycutt, R.K., Hossack, B., Kleeman, P.M., Lemos-Espinal, J.A., Lorch, J.M., Atkinson, R.W., Muths, E.L., Pearl, C., Richgels, K., Robinson, C.W., Roth, M.F., Rowe, J., Sadinski, W., Sigafus, B.H., Stasiak, I., Sweet, S., Walls, S., Watkins-Colwell, G.J., White, C.L., Williams, L.A., and Winzeler, M.E., 2020, Batrachochytrium salamandrivorans (Bsal) not detected in an intensive survey of wild North American amphibians: Scientific Reports, v. 10, 13012, 7 p., https://doi.org/10.1038/s41598-020-69486-x.","productDescription":"13012, 7 p.","ipdsId":"IP-107657","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455658,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-69486-x","text":"Publisher Index Page"},{"id":436825,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BGQA1T","text":"USGS data release","linkHelpText":"Data from a national survey for the amphibian chytrid fungus Batrachochytrium salamandrivorans"},{"id":377339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Continental United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.185546875,\n 48.86471476180277\n ],\n [\n -122.958984375,\n 48.980216985374994\n ],\n [\n -126.91406249999999,\n 48.574789910928864\n ],\n [\n -124.892578125,\n 34.23451236236987\n ],\n [\n -118.65234374999999,\n 32.84267363195431\n ],\n [\n -117.68554687499999,\n 33.94335994657882\n ],\n [\n -110.478515625,\n 31.50362930577303\n ],\n [\n -103.798828125,\n 29.075375179558346\n ],\n [\n -97.03125,\n 25.48295117535531\n ],\n [\n -79.62890625,\n 24.84656534821976\n ],\n [\n -75.498046875,\n 28.459033019728043\n ],\n [\n -68.291015625,\n 35.53222622770337\n ],\n [\n -65.7421875,\n 40.91351257612758\n ],\n [\n -66.4453125,\n 44.213709909702054\n ],\n [\n -67.763671875,\n 47.21956811231547\n ],\n [\n -69.78515625,\n 47.69497434186282\n ],\n [\n -75.234375,\n 45.02695045318546\n ],\n [\n -84.55078125,\n 46.86019101567027\n ],\n [\n -88.154296875,\n 49.03786794532644\n ],\n [\n -95.185546875,\n 48.86471476180277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Waddle, J. 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LeAnn 0000-0002-5004-5165 clwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-5004-5165","contributorId":4315,"corporation":false,"usgs":true,"family":"White","given":"C.","email":"clwhite@usgs.gov","middleInitial":"LeAnn","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":795544,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Williams, Lori A","contributorId":237909,"corporation":false,"usgs":false,"family":"Williams","given":"Lori","email":"","middleInitial":"A","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":795545,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Winzeler, Megan E.","contributorId":237910,"corporation":false,"usgs":false,"family":"Winzeler","given":"Megan","email":"","middleInitial":"E.","affiliations":[{"id":36513,"text":"University of Georgia Savannah River Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":795546,"contributorType":{"id":1,"text":"Authors"},"rank":38}]}} ,{"id":70211879,"text":"70211879 - 2020 - Novel molecular resources to facilitate future genetics research on freshwater mussels (Bivalvia: Unionidae)","interactions":[],"lastModifiedDate":"2020-08-12T14:33:25.106487","indexId":"70211879","displayToPublicDate":"2020-08-11T11:16:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5226,"text":"Data","active":true,"publicationSubtype":{"id":10}},"title":"Novel molecular resources to facilitate future genetics research on freshwater mussels (Bivalvia: Unionidae)","docAbstract":"Molecular data have been an integral tool in the resolution of the evolutionary relationships and systematics of freshwater mussels, despite the limited number of nuclear markers available for Sanger sequencing. To facilitate future studies, we evaluated the phylogenetic informativeness of loci from the recently published anchored hybrid enrichment (AHE) probe set Unioverse and developed novel Sanger primer sets to amplify two protein-coding nuclear loci with high net phylogenetic informativeness scores: fem-1 homolog C (FEM1) and UbiA prenyltransferase domain-containing protein 1 (UbiA). We report the methods used for marker development, along with the primer sequences and optimized PCR and thermal cycling conditions. To demonstrate the utility of these markers, we provide haplotype networks, DNA alignments, and summary statistics regarding the sequence variation for the two protein-coding nuclear loci (FEM1 and UbiA). Additionally, we compare the DNA sequence variation of FEM1 and UbiA to three loci commonly used in freshwater mussel genetic studies: the mitochondrial genes cytochrome c oxidase subunit 1 (CO1) and NADH dehydrogenase subunit 1 (ND1), and the nuclear internal transcribed spacer 1 (ITS1). All five loci distinguish among the three focal species (Potamilus fragilis, Potamilus inflatus, and Potamilus purpuratus), and the sequence variation was highest for ND1, followed by CO1, ITS1, UbiA, and FEM1, respectively. The newly developed Sanger PCR primers and methodologies for extracting additional loci from AHE probe sets have great potential to facilitate molecular investigations targeting supraspecific relationships in freshwater mussels, but may be of limited utility at shallow taxonomic scales.","language":"English","publisher":"MDPI","doi":"10.3390/data5030065","usgsCitation":"Johnson, N., and Smith, C.H., 2020, Novel molecular resources to facilitate future genetics research on freshwater mussels (Bivalvia: Unionidae): Data, v. 5, no. 3, 65, 12 p., https://doi.org/10.3390/data5030065.","productDescription":"65, 12 p.","ipdsId":"IP-120413","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455661,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/data5030065","text":"Publisher Index Page"},{"id":377338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Chase H. 0000-0002-1499-0311","orcid":"https://orcid.org/0000-0002-1499-0311","contributorId":225140,"corporation":false,"usgs":false,"family":"Smith","given":"Chase","email":"","middleInitial":"H.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":795568,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211863,"text":"70211863 - 2020 - Are the stress drops of small earthquakes good predictors of the stress drops of moderate-to-large earthquakes?","interactions":[],"lastModifiedDate":"2023-03-27T17:16:03.685468","indexId":"70211863","displayToPublicDate":"2020-08-11T09:51:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5999,"text":"Journal of Geophysical Research- Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Are the stress drops of small earthquakes good predictors of the stress drops of moderate-to-large earthquakes?","docAbstract":"The stress drops of small earthquakes often exhibit spatial patterns of variability. If moderate and large earthquakes follow the same spatial patterns, the stress drops of possible future damaging earthquakes could be better predicted by considering the stress drops of nearby small events. Better stress drop predictability could reduce ground-motion uncertainty in Probabilistic Seismic Hazard Assessment (PSHA) and Earthquake Early Warning (EEW). I find that for an internally consistent stress drop catalog of M1.8-3.1 events in southern California, the stress drops of the bigger earthquakes are predictable from the nearby smaller events. However, this catalog only weakly spatially correlates with another catalog of M3.0-5.8 earthquakes, and is spatially uncorrelated with five other stress drop catalogs of M≥3.4 earthquakes. For southern California events M5.5-7.5, stress drops compiled from the literature are weakly spatially correlated with the stress drops of the M1.8-3.1 events, although the correlations are not statistically significant. The lack of strong spatial correlation may be due to actual differences in the controlling factors of stress drop, for example dynamic weakening in moderate-to-large earthquakes. Alternatively, a stronger spatial correlation may exist that is obscured by methodological heterogeneity and large errors in the stress drop estimates. Either way, the stress drops of small earthquakes do not appear to be good predictors of the stress drops of nearby moderate-to-large earthquakes, at least for current techniques of stress drop estimation. If these results are representative, small-earthquake stress drops are not currently useful for substantially reducing uncertainty in PSHA and EEW.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB018831","usgsCitation":"Hardebeck, J.L., 2020, Are the stress drops of small earthquakes good predictors of the stress drops of moderate-to-large earthquakes?: Journal of Geophysical Research- Solid Earth, v. 125, no. 3, e2019JB018831, 23 p., https://doi.org/10.1029/2019JB018831.","productDescription":"e2019JB018831, 23 p.","ipdsId":"IP-108095","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455664,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jb018831","text":"Publisher Index Page"},{"id":377342,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"southern California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.52001953124999,\n 39.58875727696545\n ],\n [\n -123.04687499999999,\n 38.238180119798635\n ],\n [\n -120.56396484375,\n 34.397844946449865\n ],\n [\n -119.46533203125,\n 33.8339199536547\n ],\n [\n -118.125,\n 33.797408767572485\n ],\n [\n -117.09228515624999,\n 32.7503226078097\n ],\n [\n -114.6533203125,\n 32.65787573695528\n ],\n [\n -114.49951171875,\n 33.88865750124075\n ],\n [\n -114.27978515625,\n 34.288991865037524\n ],\n [\n -114.63134765625001,\n 35.02999636902566\n ],\n [\n -120.52001953124999,\n 39.58875727696545\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-23","publicationStatus":"PW","contributors":{"authors":[{"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":795457,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211887,"text":"70211887 - 2020 - Using density surface models to estimate spatio-temporal changes in population densities and trend","interactions":[],"lastModifiedDate":"2020-08-12T14:40:17.389474","indexId":"70211887","displayToPublicDate":"2020-08-11T09:27:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Using density surface models to estimate spatio-temporal changes in population densities and trend","docAbstract":"Precise measures of population abundance and trend are needed for species conservation; these are most difficult to obtain for rare and rapidly changing populations. We compare uncertainty in densities estimated from spatio–temporal models with that from standard design‐based methods. Spatio–temporal models allow us to target priority areas where, and at times when, a population may most benefit. Generalised additive models were fitted to a 31‐year time series of point‐transect surveys of an endangered Hawaiian forest bird, the Hawai‘i ‘ākepa Loxops coccineus . This allowed us to estimate bird densities over space and time. We used two methods to quantify uncertainty in density estimates from the spatio–temporal model: the delta method (which assumes independence between detection and distribution parameters) and a variance propagation method. With the delta method we observed a 52% decrease in the width of the design‐based 95% confidence interval (CI), while we observed a 37% decrease in CI width when propagating the variance. We mapped bird densities as they changed across space and time, allowing managers to evaluate management actions. Integrating detection function modelling with spatio–temporal modelling exploits survey data more efficiently by producing finer‐grained abundance estimates than are possible with design‐based methods as well as producing more precise abundance estimates. Model‐based approaches require switching from making assumptions about the survey design to assumptions about bird distribution. Such a switch warrants consideration. In this case the model‐based approach benefits conservation planning through improved management efficiency and reduced costs by taking into account both spatial shifts and temporal changes in population abundance and distribution.","language":"English","publisher":"Wiley","doi":"10.1111/ecog.04859","usgsCitation":"Camp, R.J., Miller, D.L., Thomas, L., Buckland, S.T., and Kendall, S.J., 2020, Using density surface models to estimate spatio-temporal changes in population densities and trend: Ecography, v. 43, no. 7, p. 1079-1089, https://doi.org/10.1111/ecog.04859.","productDescription":"11 p.","startPage":"1079","endPage":"1089","ipdsId":"IP-111902","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":455666,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.04859","text":"Publisher Index 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\"}}]}","volume":"43","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":795665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, David L 0000-0002-9640-6755","orcid":"https://orcid.org/0000-0002-9640-6755","contributorId":237961,"corporation":false,"usgs":false,"family":"Miller","given":"David","email":"","middleInitial":"L","affiliations":[{"id":47659,"text":"University of St Andrews, CREEM","active":true,"usgs":false}],"preferred":false,"id":795666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Len 0000-0002-7436-067X","orcid":"https://orcid.org/0000-0002-7436-067X","contributorId":194663,"corporation":false,"usgs":false,"family":"Thomas","given":"Len","email":"","affiliations":[],"preferred":false,"id":795667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buckland, Steve T. 0000-0002-9939-709X","orcid":"https://orcid.org/0000-0002-9939-709X","contributorId":194665,"corporation":false,"usgs":false,"family":"Buckland","given":"Steve","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":795668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kendall, Steve J. 0000-0002-9290-5629","orcid":"https://orcid.org/0000-0002-9290-5629","contributorId":169663,"corporation":false,"usgs":false,"family":"Kendall","given":"Steve","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":795669,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211885,"text":"70211885 - 2020 - The effects of tissue fixation on sequencing and transcript abundance of nucleic acids from microdissected liver samples of smallmouth bass (Micropterus dolomieu)","interactions":[],"lastModifiedDate":"2020-08-11T14:26:46.724597","indexId":"70211885","displayToPublicDate":"2020-08-11T09:16:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"The effects of tissue fixation on sequencing and transcript abundance of nucleic acids from microdissected liver samples of smallmouth bass (Micropterus dolomieu)","docAbstract":"There is an increasing emphasis on effects-based monitoring to document responses associated with exposure to complex mixtures of chemicals, climate change, pathogens, parasites and other environmental stressors in fish populations. For decades aquatic monitoring programs have included the collection of tissues preserved for microscopic pathology. Consequently, formalin-fixed, paraffin-embedded (FFPE) tissue can be an important reservoir of nucleic acids as technologies emerge that utilize molecular endpoints. Despite the cross-linking effects of formalin, its impact on nucleic acid quality and concentration, amplification, and sequencing are not well described. While fresh-frozen tissue is optimal for working with nucleic acids, FFPE samples have been shown to be conducive for molecular studies. Laser capture microdissection (LCM) is one technology which allows for collection of specific regions or cell populations from fresh or preserved specimens with pathological alterations, pathogens, or parasites. In this study, smallmouth bass (Micropterus dolomieu) liver was preserved in three different fixatives, including 10% neutral buffered formalin (NBF), Z-Fix® (ZF), and PAXgene® (PG) for four time periods (24 hr, 48 hr, seven days, and 14 days). Controls consisted of pieces of liver preserved in RNALater® or 95% ethanol. Smallmouth bass were chosen as they are an economically important sportfish and have been utilized as indicators of exposure to endocrine disruptors and other environmental stressors. Small liver sections were cut out with laser microdissection and DNA and RNA were purified and analyzed for nucleic acid concentration and quality. Sanger sequencing and the NanoString nCounter® technology were used to assess the suitability of these samples in downstream molecular techniques. The results revealed that of the formalin fixatives, NBF samples fixed for 24 and 48 hr were superior to ZF samples for both Sanger sequencing and the Nanostring nCounter®. The non-formalin PAXgene® samples were equally successful and they showed greater stability in nucleic acid quality and concentration over longer fixation times. This study demonstrated that small quantities of preserved tissue from smallmouth bass can be utilized in downstream molecular techniques; however, future studies will need to optimize the methods presented here for different tissue types, fish species, and pathological conditions.","language":"English","doi":"10.1371/journal.pone.0236104","collaboration":"None","usgsCitation":"Walsh, H.L., Sperry, A., and Blazer, V., 2020, The effects of tissue fixation on sequencing and transcript abundance of nucleic acids from microdissected liver samples of smallmouth bass (Micropterus dolomieu): PLoS ONE, e0236104, 19p., https://doi.org/10.1371/journal.pone.0236104.","productDescription":"e0236104, 19p.","onlineOnly":"Y","ipdsId":"IP-117789","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":455671,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0236104","text":"Publisher Index Page"},{"id":436826,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A12EL5","text":"USGS data release","linkHelpText":"Abundance of 50 transcripts from microdissected liver samples of smallmouth bass"},{"id":377326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Walsh, Heather L. 0000-0001-6392-4604 hwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":4696,"corporation":false,"usgs":true,"family":"Walsh","given":"Heather","email":"hwalsh@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sperry, Adam 0000-0002-4815-3730","orcid":"https://orcid.org/0000-0002-4815-3730","contributorId":203243,"corporation":false,"usgs":true,"family":"Sperry","given":"Adam","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795719,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795657,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212615,"text":"70212615 - 2020 - Management of remnant tallgrass prairie by grazing or fire: Effects on plant communities and soil properties","interactions":[],"lastModifiedDate":"2020-08-25T13:27:57.188782","indexId":"70212615","displayToPublicDate":"2020-08-11T09:15:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Management of remnant tallgrass prairie by grazing or fire: Effects on plant communities and soil properties","docAbstract":"

Tallgrass prairie is a disturbance‐dependent ecosystem that has suffered steep declines in the midwestern United States. The necessity of disturbance, typically fire or grazing, presents challenges to managers who must apply them on increasingly small and fragmented parcels. The goal of this study was to compare effects of management using cattle grazing or fire on vegetation and soil characteristics to aid managers in making decisions regarding the kind of disturbance to apply. We selected 73 sites, of which 27 were managed solely by cattle grazing and 46 solely by fire, for at least 11 yr leading up to the study. We stratified the sites by prairie type (dry, mesic, and wet) and sampled frequency of plant species on randomly placed transects, supplemented with botanist‐directed walks, and collected and composited five soil cores on a randomly selected transect within each prairie type at each site. We calculated rarefied richness and Shannon evenness from the transect data and mean coefficient of conservatism (CofC) from the total list of species. Soil samples were analyzed for texture, bulk density, total N and C, and potential net N nitrification and mineralization. A nonmetric multidimensional scaling analysis of the plant community data revealed differences in species associated with mesic and wet prairies, but no separation by management type. Similarly, none of the vegetation variables we calculated varied by management type, as determined by mixed‐effects models, but soil bulk density was 17.5% higher and total N was 22% higher on grazed sites than burned sites. Sites burned more recently had higher species richness and mean CofC, but fire was not associated with any soil variables. Sites grazed more recently had higher bulk density, total N and C, and faster N cycling rates. Overall, 28% of plant species were found exclusively in one management type or the other, but these species did not vary in mean CofC. We conclude that, at the levels of burning and grazing intensity we studied, both management approaches produce similar C storage and vegetation responses. To maintain maximum diversity across the landscape, however, both approaches are necessary.

","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3213","usgsCitation":"Larson, D., Hernández, D., Larson, J.L., Leone, J.B., and Pennarola, N.P., 2020, Management of remnant tallgrass prairie by grazing or fire: Effects on plant communities and soil properties: Ecosphere, v. 11, no. 8, e03213, 17 p., https://doi.org/10.1002/ecs2.3213.","productDescription":"e03213, 17 p.","ipdsId":"IP-111800","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488712,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3213","text":"Publisher Index Page"},{"id":436827,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N8X0ZY","text":"USGS data release","linkHelpText":"Management of remnant tallgrass prairie by grazing or fire in western Minnesota, 2016-2017"},{"id":377790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Prairie Parkland Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.119140625,\n 49.03786794532644\n ],\n [\n -97.3388671875,\n 48.16608541901253\n ],\n [\n -96.8115234375,\n 47.517200697839414\n ],\n [\n -96.85546875,\n 46.58906908309182\n ],\n [\n -96.6357421875,\n 45.9511496866914\n ],\n [\n -96.767578125,\n 45.644768217751924\n ],\n [\n -96.45996093749999,\n 45.30580259943578\n ],\n [\n -96.328125,\n 43.644025847699496\n ],\n [\n -93.33984375,\n 43.48481212891603\n ],\n [\n -93.2958984375,\n 44.18220395771566\n ],\n [\n -93.9990234375,\n 44.84029065139799\n ],\n [\n -94.833984375,\n 45.73685954736049\n ],\n [\n -95.537109375,\n 46.07323062540835\n ],\n [\n -95.7568359375,\n 46.73986059969267\n ],\n [\n -96.064453125,\n 47.635783590864854\n ],\n [\n -96.416015625,\n 48.28319289548349\n ],\n [\n -96.6357421875,\n 49.095452162534826\n ],\n [\n -97.119140625,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, Diane L. 0000-0001-5202-0634","orcid":"https://orcid.org/0000-0001-5202-0634","contributorId":239526,"corporation":false,"usgs":true,"family":"Larson","given":"Diane L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":797099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hernández, Daniel L.","contributorId":239527,"corporation":false,"usgs":false,"family":"Hernández","given":"Daniel L.","affiliations":[{"id":33615,"text":"Carleton College","active":true,"usgs":false}],"preferred":false,"id":797100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larson, Jennifer L.","contributorId":178444,"corporation":false,"usgs":false,"family":"Larson","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":797101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leone, Julia B.","contributorId":216121,"corporation":false,"usgs":false,"family":"Leone","given":"Julia","email":"","middleInitial":"B.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":797102,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pennarola, Nora P.","contributorId":239528,"corporation":false,"usgs":false,"family":"Pennarola","given":"Nora","email":"","middleInitial":"P.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":797103,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212555,"text":"70212555 - 2020 - Landslides after wildfire: Initiation, magnitude, and mobility","interactions":[],"lastModifiedDate":"2020-10-28T15:48:54.141039","indexId":"70212555","displayToPublicDate":"2020-08-11T08:50:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Landslides after wildfire: Initiation, magnitude, and mobility","docAbstract":"

In the semiarid Southwestern USA, wildfires are commonly followed by runoff-generated debris flows because wildfires remove vegetation and ground cover, which reduces soil infiltration capacity and increases soil erodibility. At a study site in Southern California, we initially observed runoff-generated debris flows in the first year following fire. However, at the same site three years after the fire, the mass-wasting response to a long-duration rainstorm with high rainfall intensity peaks was shallow landsliding rather than runoff-generated debris flows. Moreover, the same storm caused landslides on unburned hillslopes as well as on slopes burned 5 years prior to the storm and areas burned by successive wildfires, 10 years and 3 years before the rainstorm. The landslide density was the highest on the hillslopes that had burned 3 years beforehand, and the hillslopes burned 5 years prior to the storm had low landslide densities, similar to unburned areas. We also found that reburning (i.e., two wildfires within the past 10 years) had little influence on landslide density. Our results indicate that landscape susceptibility to shallow landslides might return to that of unburned conditions after as little as 5 years of vegetation recovery. Moreover, most of the landslide activity was on steep, equatorial-facing slopes that receive higher solar radiation and had slower rates of vegetation regrowth, which further implicates vegetation as a controlling factor on post-fire landslide susceptibility. Finally, the total volume of sediment mobilized by the year 3 landslides was much smaller than the year 1 runoff-generated debris flows, and the landslides were orders of magnitude less mobile than the runoff-generated debris flows.

","language":"English","publisher":"Springer","doi":"10.1007/s10346-020-01506-3","usgsCitation":"Rengers, F.K., McGuire, L., Oakley, N.S., Kean, J.W., Staley, D.M., and Tang, H., 2020, Landslides after wildfire: Initiation, magnitude, and mobility: Landslides, v. 17, p. 2631-2641, https://doi.org/10.1007/s10346-020-01506-3.","productDescription":"11 p.","startPage":"2631","endPage":"2641","onlineOnly":"Y","ipdsId":"IP-119170","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455675,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10346-020-01506-3","text":"Publisher Index Page"},{"id":436829,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97GU3UV","text":"USGS data release","linkHelpText":"Inventory of landslides triggered by rainfall on 16-17 January 2019, Los Angeles County, 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Juneau Icefield","docAbstract":"

The past decade includes some of the most extensive boreal forest fires in the historical record. Warming temperatures, changing precipitation patterns, the desiccation of thick organic soil layers, and increased ignition from lightning all contribute to a combustive combination. Smoke aerosols travel thousands of kilometers, before blanketing the surfaces on which they fall, such as the Juneau Icefield. However, many aerosols found in smoke plumes are also produced by other processes and therefore can be ambiguous indicators of fire activity. Here, we use the monosaccharide anhydrides levoglucosan, mannosan, and galactosan as specific indicators of biomass burning to unambiguously demonstrate that fire aerosols reach the Juneau Icefield and are integrated into the snowpack. Back trajectories and satellite observations demonstrate that smoke plumes originating in central Alaska and eastern Siberia affect the Juneau Icefield. These regional sources of fire differ from other combustion aerosols deposited on the Juneau Icefield, such as black carbon, that originate from local fossil fuel burning. Ratios of levoglucosan/mannosan (L/M) and levoglucosan/(mannosan + galactosan) (L/(M + G)) demonstrate that while the majority of fire aerosols reaching the Juneau Icefield originate from softwood burning, grasslands and hardwood forests are also sources. The presence of these hardwoods suggests that fire aerosols may reach the Juneau Icefield from locations as far away as East Asia.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab8fd2","usgsCitation":"Kehrwald, N., Jasmann, J.R., Dunham, M.E., Ferris, D.G., Osterburg, E.C., Kennedy, J., Havens, J.C., Fortner, S.K., and Barber, L., 2020, Boreal blazes: Biomass burning and vegetation types archived in the Juneau Icefield: Environmental Research Letters, v. 15, no. 8, 085005, 15 p., https://doi.org/10.1088/1748-9326/ab8fd2.","productDescription":"085005, 15 p.","ipdsId":"IP-111615","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab8fd2","text":"Publisher Index Page"},{"id":377596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Juneau icefield","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -136.51611328125,\n 58.00809779306888\n ],\n [\n -133.6376953125,\n 58.00809779306888\n ],\n [\n -133.6376953125,\n 59.7563950493563\n ],\n [\n -136.51611328125,\n 59.7563950493563\n ],\n [\n -136.51611328125,\n 58.00809779306888\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kehrwald, Natalie 0000-0002-9160-2239","orcid":"https://orcid.org/0000-0002-9160-2239","contributorId":220636,"corporation":false,"usgs":true,"family":"Kehrwald","given":"Natalie","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796402,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jasmann, Jeramy Roland 0000-0002-5251-6987","orcid":"https://orcid.org/0000-0002-5251-6987","contributorId":238713,"corporation":false,"usgs":true,"family":"Jasmann","given":"Jeramy","email":"","middleInitial":"Roland","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":796403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunham, Melissa E.","contributorId":238714,"corporation":false,"usgs":false,"family":"Dunham","given":"Melissa","email":"","middleInitial":"E.","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":796404,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferris, David G.","contributorId":238715,"corporation":false,"usgs":false,"family":"Ferris","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":796405,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Osterburg, Erich C.","contributorId":238716,"corporation":false,"usgs":false,"family":"Osterburg","given":"Erich","email":"","middleInitial":"C.","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":796406,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kennedy, Joshua","contributorId":238717,"corporation":false,"usgs":false,"family":"Kennedy","given":"Joshua","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":796407,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Havens, Jeremy C. 0000-0002-8685-2823","orcid":"https://orcid.org/0000-0002-8685-2823","contributorId":238719,"corporation":false,"usgs":false,"family":"Havens","given":"Jeremy","email":"","middleInitial":"C.","affiliations":[{"id":37768,"text":"USGS Contractor","active":true,"usgs":false}],"preferred":false,"id":796628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":796408,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fortner, Sarah K.","contributorId":238718,"corporation":false,"usgs":false,"family":"Fortner","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":47748,"text":"Wittenberg University","active":true,"usgs":false}],"preferred":false,"id":796409,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70223104,"text":"70223104 - 2020 - Metamorphic amphiboles in the Ironwood Iron-Formation, Gogebic Iron Range, Wisconsin: Implications for potential resource development","interactions":[],"lastModifiedDate":"2021-08-11T13:26:06.348883","indexId":"70223104","displayToPublicDate":"2020-08-11T08:24:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":738,"text":"American Mineralogist","active":true,"publicationSubtype":{"id":10}},"title":"Metamorphic amphiboles in the Ironwood Iron-Formation, Gogebic Iron Range, Wisconsin: Implications for potential resource development","docAbstract":"

No abstract available. 

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\"}}]}","volume":"105","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Green, Carlin J. 0000-0002-6557-6268 cjgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6557-6268","contributorId":193013,"corporation":false,"usgs":true,"family":"Green","given":"Carlin","email":"cjgreen@usgs.gov","middleInitial":"J.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":820965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":820966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piatak, Nadine M. 0000-0002-1973-8537 npiatak@usgs.gov","orcid":"https://orcid.org/0000-0002-1973-8537","contributorId":193010,"corporation":false,"usgs":true,"family":"Piatak","given":"Nadine","email":"npiatak@usgs.gov","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":820967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cannon, William F. 0000-0002-2699-8118","orcid":"https://orcid.org/0000-0002-2699-8118","contributorId":201972,"corporation":false,"usgs":true,"family":"Cannon","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":820968,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":820969,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nord, Julia","contributorId":264146,"corporation":false,"usgs":false,"family":"Nord","given":"Julia","email":"","affiliations":[],"preferred":false,"id":820970,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211867,"text":"ofr20201091 - 2020 - Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, fifth annual report","interactions":[],"lastModifiedDate":"2020-08-12T14:18:03.408351","indexId":"ofr20201091","displayToPublicDate":"2020-08-11T07:44:16","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1091","displayTitle":"Kelp Forest Monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, Fifth Annual Report","title":"Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, fifth annual report","docAbstract":"

Introduction

Kelp forests and rocky reefs are among the most recognized marine ecosystems and provide the primary habitat for several species of fishes, invertebrates, and algal assemblages (Stephens and others, 2006). In addition, kelp forests have been shown to be important carbon dioxide sinks (Wilmers and others, 2012) and are an important source of nearshore marine primary production (Duggins and others, 1989). These highly dynamic ecosystems are extremely variable, and both top-down and bottom-up ecological controls drive this rich trophic environment. Giant kelp (Macrocystis pyrifera) forests and the species that inhabit these ecosystems are influenced by several environmental conditions, such as wave exposure, water temperature, water clarity, bottom depth and composition, species composition, and the density of kelp and other algal assemblages (Schiel and Foster, 2015). However, in addition to “normal” variability, kelp forests can undergo extreme regime shifts from kelp canopy forested areas to barrens characterized by high densities of urchins and encrusting coralline algae (Harrold and Reed, 1985). 

San Nicolas Island (SNI), outermost of the California Channel Islands, is home to a diverse group of terrestrial and marine organisms and includes kelp bed and rocky reef habitats (fig. 1). The SNI kelp forests not only provide food and shelter for fishes and invertebrates within the habitat, but also they support higher trophic level consumers such as marine birds and several marine mammal species including the southern sea otter (Enhydra lutris nereis), a major predator on sea urchins and other marine invertebrates. 

Owing to concern about the vulnerability of the California population, the U.S. Fish and Wildlife Service (USFWS) translocated 140 southern sea otters from the central California coast to SNI between 1987 and 1990. Although only approximately 14 translocated otters are thought to have remained at SNI (U.S. Fish and Wildlife Service, 2012), their population at the island has increased and is currently greater than 120 individuals (Hatfield and others, 2019). Sea otters are a natural part of the kelp forest ecosystem, but their presence has implications for community dynamics as they repopulate a region from which they were extirpated in the 19th century. At SNI, sea otters have been concentrated mostly around the west end of the island, with some use of the south side and very little, but expanding, use of the northeast side. An ecosystem shift from urchin dominated to kelp dominated, that occurred at a site at the west end of the island in the early 2000s, though initiated by sea urchin disease, was likely facilitated to some degree by sea otter foraging (Kenner and Tinker, 2018). 

These ecosystems also are the target of many fisheries, including urchin and lobster. Urchin fisheries, which target the larger red sea urchin, may release the smaller but more mobile purple sea urchin from competitive control (Dayton and others, 1998). Lobster fisheries may release purple sea urchins from predatory control (Lafferty, 2004). Owing to the distance from the mainland, however, SNI kelp forests and reefs have been somewhat protected from the degree of harvest and other anthropogenic impacts experienced by the southern California mainland. Invasive species are another issue, and there are a few invasive subtidal macroalgae of concern in southern California waters. Although the brown alga Sargassum muticum has been established at the island for decades, S. horneri has only recently been seen at SNI and, so far, the invasive kelp Undaria pinnatifida and the green alga Caulerpa taxifolia have not been observed there. Sargassum horneri, in particular, has demonstrated a capability to outcompete native kelps at some of the other Channel Islands but it is unclear what indirect effects it may have on community structure (Marks and others, 2015). 

Because the surrounding kelp forests fall within the management boundary of the SNI Integrated Natural Resources Management Plan (INRMP; U.S. Navy, 2015), USGS works with the Navy to provide surveys of this ecologically important ecosystem that inform natural resource managers of trends in the population abundance of particular species. In addition, long-term surveys allow for an understanding of potential changes in species diversity and community composition as a result of trophic or other interactions. 

The U.S. Geological Survey (USGS) implemented a kelp forest monitoring program for the U.S. Navy at San Nicolas Island in 2014, building on sites and methods established by USFWS scientists in 1980 (appendix 1). This report focuses on data collected during sampling expeditions to these sites in fall 2018 (October 2–5) and spring 2019 (April 3–6). Together they will be herein referred to as year 5 because, although the trips were made in different calendar years, they were approximately 6 months apart and were conducted under the fifth year of this contract. The previous sampling year (fall 2017 and spring 2018) is referred to as year 4. The year 5 data are compared with data collected during eight trips from fall 2014 through spring 2018. Differences in counts between these expeditions can result from seasonal factors, stochastic variation, or sampling error, but temporal comparison can reveal population trends. Where appropriate, long-term data collected during the 33 years prior to the implementation of these slightly revised protocols will be presented in order to lend some context to the observations reported here. 

Genus and species names used in this report are those currently recognized as valid in the Integrated Taxonomic Information System (ITIS.gov). Upon first use, the name recognized as valid by the World Register of Marine Species (WoRMS; marinespecies.org) is shown in brackets if different. The exception is Sargassum horneri which does not show up in any discernable form in ITIS.gov.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201091","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J.A., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, fifth annual report: U.S. Geological Survey Open-File Report 2020–1091, 93 p., https://doi.org/10.3133/ofr20201091.","productDescription":"ix, 93 p.","onlineOnly":"Y","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":377300,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1091/coverthb.jpg"},{"id":377301,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1091/ofr20201091.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1091"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"Naval Facility San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.60197448730467,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.19675310661128\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishedDate":"2020-08-11","noUsgsAuthors":false,"publicationDate":"2020-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Kenner, Michael C. 0000-0003-4659-461X","orcid":"https://orcid.org/0000-0003-4659-461X","contributorId":208151,"corporation":false,"usgs":true,"family":"Kenner","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":795466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":208133,"corporation":false,"usgs":false,"family":"Tomoleoni","given":"Joseph A.","email":"jtomoleoni@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":795467,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211670,"text":"ofr20201090 - 2020 - Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River","interactions":[],"lastModifiedDate":"2020-08-11T12:30:03.982099","indexId":"ofr20201090","displayToPublicDate":"2020-08-10T15:26:46","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1090","displayTitle":"Characterization of Peak Streamflow and Stages at Selected Streamgages in Eastern and Northeastern Oklahoma from the May to June 2019 Flood Event—With an Emphasis on Flood Peaks Downstream from Dams and on Tributaries to the Arkansas River","title":"Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River","docAbstract":"

As much as 22 inches of rain fell in Oklahoma in May 2019, resulting in historic flooding along the Arkansas River and its tributaries in eastern and northeastern Oklahoma. The flooding along the Arkansas River and its tributaries that began in May continued into June 2019. Peaks of record were measured at nine U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) streamgages on various streams in eastern and northeastern Oklahoma. This report documents the peak streamflows and stages for 38 selected streamgages in eastern and northeastern Oklahoma and is a followup to a previous report by the USGS that documented flood peaks associated with the May 2019 flood event. Most of the flood peaks occurred from May 26 to June 4, 2019. This report includes data from streamgages on tributaries to the Arkansas River and uses modeling methods to extend the period of record for Arkansas River streamgages. The historic flooding caused homes to fall into the river as a result of bank erosion, forced some towns to be evacuated, and resulted in the highest flood depths in Tulsa, Oklahoma, since 1986. Several USGS and USACE streamgages along the Arkansas River and its tributaries recorded new peaks of record.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Lewis, J.M., Williams, D.J., Harris, S.J., and Trevisan, A.R., 2020, Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River: U.S. Geological Survey Open-File Report 2020–1090, 18 p., https://doi.org/10.3133/ofr20201090.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-118379","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":377112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3Q6MB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"RiverWare model outputs for flood calculations along the Arkansas River for a flood event in eastern and northeastern Oklahoma during May–June 2019"},{"id":377111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1090/ofr20201090.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1090"},{"id":377110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1090/coverthb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 34.59704151614417\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-08-10","noUsgsAuthors":false,"publicationDate":"2020-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Lewis, Jason M. 0000-0001-5337-1890 jmlewis@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1890","contributorId":3854,"corporation":false,"usgs":true,"family":"Lewis","given":"Jason","email":"jmlewis@usgs.gov","middleInitial":"M.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, David J.","contributorId":150357,"corporation":false,"usgs":true,"family":"Williams","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":794970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, Sarah J.","contributorId":237011,"corporation":false,"usgs":false,"family":"Harris","given":"Sarah","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":794971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trevisan, A.R. 0000-0002-7295-145X","orcid":"https://orcid.org/0000-0002-7295-145X","contributorId":220399,"corporation":false,"usgs":true,"family":"Trevisan","given":"A.R.","email":"","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794972,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211947,"text":"70211947 - 2020 - Modelling marsh-forest boundary transgression in response to storms and sea-level rise","interactions":[],"lastModifiedDate":"2020-09-10T20:30:47.100551","indexId":"70211947","displayToPublicDate":"2020-08-10T14:51:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Modelling marsh-forest boundary transgression in response to storms and sea-level rise","docAbstract":"

The lateral extent and vertical stability of salt marshes experiencing rising sea levels depend on interacting drivers and feedbacks with potential for non‐linear behaviors. A two‐dimensional transect model was developed to examine changes in marsh and upland forest lateral extent and to explore controls on marsh inland transgression. Model behavior demonstrates limited and abrupt forest retreat with long‐term upland boundary migration rates controlled by slope, sea level rise (SLR), high water events and biotic‐abiotic interactions. For low to moderate upland slopes the landward marsh edge is controlled by the interaction of these inundation events and forest recovery resulting in punctuated transgressive events. As SLR rates increase, the importance of the timing and frequency of water level deviations diminishes, and migration rates revert back to a slope‐SLR dominated process.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088998","usgsCitation":"Carr, J., Guntenspergen, G.R., and Kirwan, M.L., 2020, Modelling marsh-forest boundary transgression in response to storms and sea-level rise: Geophysical Research Letters, v. 47, no. 17, e2020GL088998, 10 p., https://doi.org/10.1029/2020GL088998.","productDescription":"e2020GL088998, 10 p.","ipdsId":"IP-112010","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455681,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088998","text":"Publisher Index Page"},{"id":436830,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQ27F5","text":"USGS data release","linkHelpText":"Water levels (November 11 2016 through November 11 2017) for four wells and Light intensity data (October 1 2015 through September 2019): from marsh to upland forest, for Moneystump Marsh, Blackwater National Wildlife Refuge, Maryland"},{"id":377420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirwan, Matt L.","contributorId":189205,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matt","middleInitial":"L.","affiliations":[],"preferred":false,"id":795926,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196827,"text":"sim3406 - 2020 - Geomorphic map of western Whatcom County, Washington","interactions":[],"lastModifiedDate":"2021-11-29T11:25:56.313865","indexId":"sim3406","displayToPublicDate":"2020-08-10T14:26:38","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3406","displayTitle":"Geomorphic Map of Western Whatcom County, Washington","title":"Geomorphic map of western Whatcom County, Washington","docAbstract":"

Western Whatcom County has a rich history of glaciation, sea-level change, fluvial erosion and deposition, landsliding, nearby volcanic activity, and human landscape modification. This lidar-derived geomorphic map interprets this history from the form and position of the Earth’s surface.

The geomorphic record is broken into nine phases, beginning with the peak of the Vashon stade of the Fraser glaciation of Armstrong and others (1965) (phase 1), shortly after 16,000 years ago. The Cordilleran ice sheet was ≥1.6 km thick in the Bellingham area. Glacial lineations on high ground demonstrate that ice flow was from north to south. Storage of water in ice sheets at this time resulted in global sea level ~120 m lower than at present. The weight of the ice sheet depressed the land so that local relative sea level was at least 150 m higher than at present. As the ice sheet melted and thinned, it floated, broke up, and was replaced by salt water. The margin of the ice sheet—or at least its grounding line—retreated to the northeast of the map area during or before phase 2. Marine deposition, currents, and waves smoothed earlier-formed surfaces in the western part of the map area. Global sea level rose (because of melting of continental ice sheets), but the Fraser Lowland rose even faster (due to glacio-isostatic rebound following the loss of ice-sheet load), and thus local relative sea level fell.

The Cordilleran ice sheet readvanced during the Sumas stade of Armstrong and others (1965). Oldest Sumas moraines formed when relative sea level at Bellingham was ~55 m (phase 3). Younger moraines formed when relative sea level at Bellingham was ~25 m (phase 4). The amount of Sumas ice retreat and readvance between these times is unknown. Younger Sumas events are marked by local moraines, progressive isostatic rebound and lowering of relative sea level, and changes in the flow of ice-marginal water. During phase 5, the southeast margin of the ice sheet advanced, perhaps because capture of ice-marginal drainage by the Samish River (east and south of the map area) meant the ice sheet was no longer trimmed by high-discharge flow along Squalicum channel. Farther west and north, the ice margin retreated between phases 4 and 5. Phases 6 through 9 may mark stillstands during further ice retreat. There were glacial outburst floods (jökulhlaups) during phases 7 and 8, and perhaps during phase 5. 

When Sumas ice left the area, perhaps about 11,500 years ago, the Nooksack River appears to have discharged northeast through Sumas Valley to the Fraser River. Details of the switch to its modern course are speculative, but archaeological and sediment-supply arguments suggest that the modern Nooksack River delta south of Ferndale formed within the past 5,000 years.

The foothills of the North Cascades are decorated with abundant post-glacial deep-seated landslides. Anomalously high late Holocene beaches are found at Birch Bay, Neptune Beach, perhaps at Maple Beach on the east side of Point Roberts, and perhaps at the northwest corner of the Lummi Peninsula. These beaches may have been uplifted by earthquakes that did not rupture the surface.

The low-relief landscape shaped by the Cordilleran ice sheet, along with fluvial infilling of low areas, resulted in abundant wetland, at least 70 percent of which has been diked and (or) drained to control flooding and facilitate farming.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3406","collaboration":"Prepared in cooperation with Whatcom County and the Washington State Department of Natural Resources","usgsCitation":"Kovanen, D.J., Haugerud, R.A., and Easterbrook, D.J., 2020, Geomorphic map of western Whatcom County, Washington (ver. 1.1, November 2021): U.S. Geological Survey Scientific Investigations Map 3406, pamphlet 42 p., scale 1:50,000, https://doi.org/10.3133/sim3406.","productDescription":"Pamphlet: vi, 42 p.; Plate: 65.10 x 39.00 inches; Metadata; Read Me; 4 Databases; Version History","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086454","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":392128,"rank":11,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3406/versionHist.txt"},{"id":377137,"rank":10,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/XML_metadata.zip","text":"XML_metadata","size":"109 KB","linkFileType":{"id":6,"text":"zip"},"description":"XML_metadata.zip"},{"id":377136,"rank":9,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-simple.zip","text":"SIM3406-simple","size":"13.9 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-simple.zip"},{"id":377135,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-open.zip","text":"SIM3406-open","size":"13.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-open.zip"},{"id":377134,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-gdb.zip","text":"SIM3406-gdb","size":"62.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-gdb.zip"},{"id":377133,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3406/00Readme.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3406 Read Me"},{"id":377132,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_metadata.xml","size":"18 KB xml","description":"SIM 3406 Metadata xml"},{"id":377131,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_metadata.txt","size":"17 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3406 Metadata text"},{"id":377130,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3406/sim3406.pdf","text":"Map","size":"16.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3406"},{"id":377129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_pamphlet_v1.1.pdf","text":"Pamphlet","size":"11.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3406 Pamphlet"},{"id":377128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3406/coverthb.jpg"}],"country":"United States","state":"Washington","county":"Whatcom County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1185,\n 48.6169\n ],\n [\n -122.2390,\n 48.6169\n ],\n [\n -122.2390,\n 49.0156\n ],\n [\n -123.1185,\n 49.0156\n ],\n [\n -123.1185,\n 48.6169\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2020; Version 1.1: November 2021","contact":"

Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-08-10","revisedDate":"2021-11-26","noUsgsAuthors":false,"publicationDate":"2020-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kovanen, Dori J.","contributorId":204670,"corporation":false,"usgs":false,"family":"Kovanen","given":"Dori","email":"","middleInitial":"J.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":734632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haugerud, Ralph A. 0000-0001-7302-4351 rhaugerud@usgs.gov","orcid":"https://orcid.org/0000-0001-7302-4351","contributorId":2691,"corporation":false,"usgs":true,"family":"Haugerud","given":"Ralph","email":"rhaugerud@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":734631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Easterbrook, Don J.","contributorId":204671,"corporation":false,"usgs":false,"family":"Easterbrook","given":"Don","email":"","middleInitial":"J.","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":734633,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209319,"text":"ofr20201010 - 2020 - Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida","interactions":[],"lastModifiedDate":"2020-08-11T12:26:13.109316","indexId":"ofr20201010","displayToPublicDate":"2020-08-10T13:45:24","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1010","displayTitle":"Repurposing a Hindcast Simulation of the 1926 Great Miami Hurricane, South Florida","title":"Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida","docAbstract":"

Hydrodynamic model hindcasts of the surface water and groundwater of the Everglades and the greater Miami, Florida, area were used to simulate hydrology using estimated storm surge height, wind field, and rainfall for the Great Miami Hurricane (GMH), which struck on September 18, 1926. Ranked estimates of losses from hurricanes in inflation-adjusted dollars indicate that the GMH was one of the most damaging tropical cyclones to make landfall in the United States, but little hydrologic data were collected because many types of field stations did not exist at the time. Several techniques were used to estimate previously unknown critical storm variables for model input, demonstrating the value of reanalyzing historical storm events using modern hydrodynamic modeling. This representation of the 1926 GMH was then used to develop a hypothetical simulation of the hydrologic effects of a similar hurricane occurring in contemporary (1996) times. Results indicate that the 18-centimeter sea-level rise between 1926 and 1996 had a greater effect on salinity intrusion than climatic differences or the development of modern canal-based infrastructure. Moreover, the post-1926 canal infrastructure does not seem to substantially mitigate the deleterious effects of sea-level rise.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201010","usgsCitation":"Krohn, M.D., Swain, E.D., Langtimm, C.A., and Obeysekera, J., 2020, Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida: U.S. Geological Survey Open-File Report 2020–1010, 9 p., https://doi.org/10.3133/ofr20201010.","productDescription":"Report: iv, 9 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-073595","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375607,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C681IV","text":"USGS data release","linkHelpText":"FTLOADDS (combined SWIFT2D surface-water model and SEAWAT groundwater model) simulator used to repurpose a hindcast simulation of the 1926 Great Miami Hurricane using the south Florida peninsula for the Biscayne and Southern Everglades Coastal Transport (BISECT) model"},{"id":375605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1010/coverthb.jpg"},{"id":375606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1010/ofr20201010.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1010"}],"country":"United States","state":"Florida","city":"Miami","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.55999755859375,\n 25.209911213827688\n ],\n [\n -80.28533935546875,\n 25.199970890386023\n ],\n [\n -80.04638671875,\n 25.403584973186703\n ],\n [\n -80.04638671875,\n 26.23430203240673\n ],\n [\n -80.52978515625,\n 26.23430203240673\n ],\n [\n -80.55999755859375,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Caribbean-Florida Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, Florida 33559

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-08-10","noUsgsAuthors":false,"publicationDate":"2020-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Krohn, M. Dennis","contributorId":223706,"corporation":false,"usgs":false,"family":"Krohn","given":"M.","email":"","middleInitial":"Dennis","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":false,"id":786039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swain, Eric D. 0000-0001-7168-708X edswain@usgs.gov","orcid":"https://orcid.org/0000-0001-7168-708X","contributorId":1538,"corporation":false,"usgs":true,"family":"Swain","given":"Eric","email":"edswain@usgs.gov","middleInitial":"D.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langtimm, Catherine A. 0000-0001-8499-5743","orcid":"https://orcid.org/0000-0001-8499-5743","contributorId":223707,"corporation":false,"usgs":true,"family":"Langtimm","given":"Catherine","email":"","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":786040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Obeysekera, Jayantha 0000-0002-9261-1268","orcid":"https://orcid.org/0000-0002-9261-1268","contributorId":223708,"corporation":false,"usgs":false,"family":"Obeysekera","given":"Jayantha","affiliations":[{"id":40755,"text":"South Florida WMD West Palm Beach, FL","active":true,"usgs":false}],"preferred":false,"id":786041,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211848,"text":"sir20205079 - 2020 - Water-quality trends for selected sites and constituents in the international Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970–2017","interactions":[],"lastModifiedDate":"2020-08-11T12:18:59.556966","indexId":"sir20205079","displayToPublicDate":"2020-08-10T12:46:54","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5079","displayTitle":"Water-Quality Trends for Selected Sites and Constituents in the International Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970–2017","title":"Water-quality trends for selected sites and constituents in the international Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970–2017","docAbstract":"

A comprehensive study to evaluate water-quality trends, while considering natural hydroclimatic variability, in the Red River of the North Basin and assess water-quality conditions for the Red River of the North crossing the international boundary near Emerson, Manitoba, Canada (the binational site), was completed by the U.S. Geological Survey in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada. The international Red River of the North Basin encompasses 3 U.S. States (South Dakota, North Dakota, and Minnesota) and 1 Canadian Province (Manitoba). Water quality in the Red River of the North Basin is of concern for both Federal governments and State and Provincial governments. Water-quality objectives have been previously established for selected dissolved ions and recently (2019) proposed for selected nutrients for the binational site.

In the current (2020) study, water-quality data from State, Provincial, and Federal agencies in the United States and Canada for sites in the Red River of the North Basin from 1970 to 2017 were compiled and used for trend analysis. Trend analysis using a water-quality dataset from multiple agencies that collect water-quality data for various objectives presented multiple challenges. The trend-analysis approach was able to accommodate differences in water-quality data caused by field-collection and laboratory-analytical method differences, disparities in sampling frequencies, and spatial and temporal gaps in data. Most of these challenges were overcome by the statistical tool, R–QWTREND, which identifies trends in concentration unrelated to variability in streamflow.

The integrated basin approach used in the current study, combined with comparing current data trends with historical trends, provided valuable insights into understanding how water quality is changing spatially (34 sites analyzed for a recent period, 2000–15) and temporally (5 sites analyzed for a 45-year historical period, 1970–2015) within the Red River of the North Basin. One of the most consistent spatial and temporal changes observed in the current study was increasing concentrations of sulfate among tributary and main-stem sites since 2000. For some sites, increases were detected starting as early as 1985. Total dissolved solids and chloride concentrations had spatial and temporal patterns like sulfate. Although R–QWTREND removes the variability in constituent concentration caused by natural streamflow variability, all variability in sulfate caused by hydroclimatic variability may not be captured because of changes in hydrologic pathways and changes in the contributions of sulfate from various natural sources.

Nutrient concentrations demonstrated less consistent spatial and temporal changes than sulfate, and changes in nutrient concentrations were assumed to be more closely tied to human-induced rather than natural changes. Nitrate-plus-nitrite concentrations were mostly increasing in the upper Red River of the North subbasin, and for nitrate plus nitrite and total nitrogen, the Sheyenne River subbasin had consistent decreasing concentrations. Since 2000, total phosphorus has decreased in the upper Red River of the North subbasin, but total phosphorus concentration has increased for sites in the lower Red River of the North subbasin, and for some main-stem sites, concentrations have been increasing since 1985. Unlike sulfate, the pattern in historical trends for total phosphorus for the main-stem sites differed from tributary sites, indicating that human-induced changes affected tributaries and main-stem sites differently.

The more detailed evaluation of flow-averaged water-quality conditions for the binational site provided an understanding of how loads have changed over time and what proportion of the year and season concentrations are expected to exceed water-quality objectives. In a basin with highly variable streamflow like the Red River of the North, the trend in flow-averaged load (assuming streamflow conditions are the same year after year) provided a robust measure of change over time. Increasing concentrations of sulfate, chloride, total dissolved solids, and total phosphorus since 1985 for the binational site resulted in longer periods of exceedance of water-quality objectives per year occurring over time. For total nitrogen, decreasing concentrations resulted in shorter periods of exceedance per year during 1980 to 2015, but concentrations were still expected to exceed the water-quality objective about half the year. Periods of when exceedances were likely to occur during the year were affected by the source and transport mechanisms of the constituent.

Trend results from this effort identified how water quality has changed across the basin, and further investigation would help to identify causes for the trends observed here. Information from the current study provides a basis for future trend attribution studies, evaluation of water-quality objectives, and development of comprehensive strategies for reducing nutrients to desired targets and establishes a baseline for tracking future progress in the Red River of the North Basin.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205079","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada","usgsCitation":"Nustad, R.A., and Vecchia, A.V., 2020, Water-quality trends for selected sites and constituents in the international Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970–2017: U.S. Geological Survey Scientific Investigations Report 2020–5079, 75 p., https://doi.org/10.3133/sir20205079.","productDescription":"Report: ix, 75 p.; 2 Tables; Data Release; Dataset","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-113881","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":377257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5079/coverthb.jpg"},{"id":377260,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C9JAMY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality and streamflow data for United States and Canadian sites in the Red River Basin and scripts for trend analysis—Data supporting water-quality trend analysis in the Red River of the North basin, 1970–2017"},{"id":377258,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079.pdf","text":"Report","size":"11.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5079"},{"id":377259,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079_tables_2_and_3.xlsx","text":"Tables 2 and 3","size":"60.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5079 Tables 2 and 3"},{"id":377261,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","description":"USGS Data Release","linkHelpText":"— U.S. Geological Survey National Water Information System database"}],"country":"United States, Canada","state":"Minnesota, North Dakota, South Dakota, Manitoba","otherGeospatial":"Red River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.27294921875,\n 50.14874640066278\n ],\n [\n -98.85498046875,\n 49.710272582105695\n ],\n [\n -100.81054687499999,\n 49.38237278700955\n ],\n [\n -100.7666015625,\n 48.58932584966975\n ],\n [\n -99.86572265625,\n 47.040182144806664\n ],\n [\n -98.525390625,\n 46.7248003746672\n ],\n [\n -98.76708984374999,\n 46.37725420510028\n ],\n [\n -98.63525390624999,\n 45.96642454131025\n ],\n [\n -97.91015624999999,\n 45.55252525134013\n ],\n [\n -97.14111328125,\n 45.321254361171476\n ],\n [\n -95.77880859375,\n 45.89000815866184\n ],\n [\n -95.2294921875,\n 46.28622391806706\n ],\n [\n -95.1416015625,\n 46.73986059969267\n ],\n [\n -95.0537109375,\n 47.68018294648414\n ],\n [\n -94.59228515625,\n 47.79839667295524\n ],\n [\n -94.306640625,\n 48.07807894349862\n ],\n [\n -94.54833984375,\n 48.29781249243716\n ],\n [\n -95.1416015625,\n 48.23930899024907\n ],\n [\n -95.2734375,\n 48.850258199721495\n ],\n [\n -95.42724609375,\n 49.1242192485914\n ],\n [\n -96.7236328125,\n 50.02185841773444\n ],\n [\n -97.27294921875,\n 50.14874640066278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503 1608
Mountain View Road,
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-08-10","noUsgsAuthors":false,"publicationDate":"2020-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Nustad, Rochelle A. 0000-0002-4713-5944 ranustad@usgs.gov","orcid":"https://orcid.org/0000-0002-4713-5944","contributorId":1811,"corporation":false,"usgs":true,"family":"Nustad","given":"Rochelle","email":"ranustad@usgs.gov","middleInitial":"A.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795353,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401 avecchia@usgs.gov","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":1173,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"avecchia@usgs.gov","middleInitial":"V.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795354,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211861,"text":"70211861 - 2020 - Distribution of deep-water scleractinian and stylasterid corals across abiotic environmental gradients on three seamounts in the Anegada Passage","interactions":[],"lastModifiedDate":"2020-08-11T12:52:18.642625","indexId":"70211861","displayToPublicDate":"2020-08-10T11:45:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of deep-water scleractinian and stylasterid corals across abiotic environmental gradients on three seamounts in the Anegada Passage","docAbstract":"In the Caribbean Basin the distribution and diversity patterns of deep-sea scleractinian corals and stylasterid hydrocorals are poorly known compared to their shallow-water relatives. In this study, we examined species distribution and community assembly patterns of scleractinian and stylasterid corals on three high-profile seamounts within the Anegada Passage, a deep-water throughway linking the Caribbean Sea and western North Atlantic. Using remotely operated vehicle surveys conducted on the E/V Nautilus by the ROV Hercules in 2014, we characterized coral assemblages and seawater environmental variables between 162 and 2,157 m on Dog Seamount, Conrad Seamount, and Noroît Seamount. In all, 13 morphospecies of scleractinian and stylasterid corals were identified from video with stylasterids being numerically more abundant than both colonial and solitary scleractinians. Cosmopolitan framework-forming species including Madrepora oculata and Solenosmilia variabilis were present but occurred in patchy distributions among the three seamounts. Framework-forming species occurred at or above the depth of the aragonite saturation horizon with stylasterid hydrocorals being the only coral taxon observed below Ωarag values of 1. Coral assemblage variation was found to be strongly associated with depth and aragonite saturation state, while other environmental variables exerted less influence. This study enhances our understanding of the factors that regulate scleractinian and stylasterid coral distribution in an underreported marginal sea and establishes a baseline for monitoring future environmental changes due to ocean acidification and deoxygenation in the tropical western Atlantic.","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.9523","usgsCitation":"Auscavitch, S.R., Lunden, J.J., Barkman, A., Quattrini, A., Demopoulos, A., and Cordes, E.E., 2020, Distribution of deep-water scleractinian and stylasterid corals across abiotic environmental gradients on three seamounts in the Anegada Passage: PeerJ, v. 8, e9523, 7 p., https://doi.org/10.7717/peerj.9523.","productDescription":"e9523, 7 p.","ipdsId":"IP-112275","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455687,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.9523","text":"Publisher Index Page"},{"id":377286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, United Kingdom","state":"Puerto Rico","otherGeospatial":"St. Croix, British Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.841064453125,\n 17.602139123350838\n ],\n [\n -63.73718261718749,\n 17.602139123350838\n ],\n [\n -63.73718261718749,\n 18.750309813140653\n ],\n [\n -65.841064453125,\n 18.750309813140653\n ],\n [\n -65.841064453125,\n 17.602139123350838\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Auscavitch, Steven R.","contributorId":237839,"corporation":false,"usgs":false,"family":"Auscavitch","given":"Steven","email":"","middleInitial":"R.","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":795448,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lunden, Jay J.","contributorId":146489,"corporation":false,"usgs":false,"family":"Lunden","given":"Jay","email":"","middleInitial":"J.","affiliations":[{"id":16710,"text":"Temple University, Department of Biology","active":true,"usgs":false}],"preferred":false,"id":795449,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barkman, Alexandria","contributorId":237840,"corporation":false,"usgs":false,"family":"Barkman","given":"Alexandria","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":795450,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quattrini, Andrea","contributorId":189928,"corporation":false,"usgs":false,"family":"Quattrini","given":"Andrea","affiliations":[],"preferred":false,"id":795451,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":210316,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795452,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cordes, Erik E.","contributorId":37623,"corporation":false,"usgs":false,"family":"Cordes","given":"Erik","email":"","middleInitial":"E.","affiliations":[{"id":16710,"text":"Temple University, Department of Biology","active":true,"usgs":false}],"preferred":false,"id":795453,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211860,"text":"70211860 - 2020 - The importance of the Northeastern Gulf of Mexico to foraging loggerhead sea turtles","interactions":[],"lastModifiedDate":"2020-08-11T12:57:45.027359","indexId":"70211860","displayToPublicDate":"2020-08-10T11:32:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"The importance of the Northeastern Gulf of Mexico to foraging loggerhead sea turtles","docAbstract":"Identification of high-use foraging sites where imperiled sea turtles are resident remains a globally-recognized conservation priority. In the biodiverse Gulf of Mexico (GoM), recent telemetry studies highlighted post-nesting foraging sites for federally threatened loggerhead turtles (Caretta caretta). Our aim here was to discern loggerhead use of additional northern GoM regions that may serve as high-use foraging sites. Thus, we used satellite tracking and switching state-space modeling to show that the Big Bend region off the northwest Florida coast is a coastal foraging area that supports imperiled adult female loggerhead turtles tracked from different nesting subpopulations. From 2011 to 2016, we satellite-tagged 15 loggerheads that nested on four distinct beaches around the GoM: Dry Tortugas National Park, FL; Everglades National Park, FL; St. Joseph Peninsula, FL; and Gulf Shores, AL. Turtles arrived at their foraging ground in the Big Bend region between June and September and remained resident in their respective foraging sites for an average of 198 tracking days, where they established mean home ranges (95% kernel density estimate) 232.7 km2. Larger home ranges were in deeper water; 50% kernel density estimate centroid values were a mean 26.4 m deep and 52.7 km from shore. The Big Bend region provides a wide area of suitable year-round foraging habitat for loggerheads from at least 3 different nesting subpopulations. Understanding where and when threatened loggerheads forage and remain resident is key for designing both surveys of foraging resources and additional protection strategies that can impact population recovery trajectories for this imperiled species.","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2020.00330","usgsCitation":"Hart, K.M., Lamont, M.M., Iverson, A., and Smith, B., 2020, The importance of the Northeastern Gulf of Mexico to foraging loggerhead sea turtles: Frontiers in Marine Science, v. 7, 330, 7 p., https://doi.org/10.3389/fmars.2020.00330.","productDescription":"330, 7 p.","ipdsId":"IP-112386","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455691,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.00330","text":"Publisher Index Page"},{"id":377283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park, St. Joseph Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.3802490234375,\n 29.912090918781505\n ],\n [\n -85.30334472656249,\n 29.6594160549124\n ],\n [\n -82.64465332031249,\n 28.405896722414823\n ],\n [\n -82.430419921875,\n 28.502488316130417\n ],\n [\n -82.474365234375,\n 29.008140362978157\n ],\n [\n -82.75451660156249,\n 29.377388403478992\n ],\n [\n -83.1884765625,\n 29.640320395351402\n ],\n [\n -83.6224365234375,\n 30.130875412002318\n ],\n [\n -84.0618896484375,\n 30.420256142845158\n ],\n [\n -84.5562744140625,\n 30.467614102257855\n ],\n [\n -85.078125,\n 30.282788098216884\n ],\n [\n -85.3802490234375,\n 29.912090918781505\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2020-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamont, Margaret M. 0000-0001-7520-6669 mlamont@usgs.gov","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":4525,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"mlamont@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iverson, Autumn 0000-0002-8353-6745 ariverson@usgs.gov","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":179150,"corporation":false,"usgs":true,"family":"Iverson","given":"Autumn","email":"ariverson@usgs.gov","affiliations":[],"preferred":true,"id":795446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Brian 0000-0002-0531-0492 bjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":202305,"corporation":false,"usgs":true,"family":"Smith","given":"Brian","email":"bjsmith@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795447,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215620,"text":"70215620 - 2020 - Regime change in a large-floodplain river ecosystem: Patterns in body-size and functional biomass indicate a shift in fish communities","interactions":[],"lastModifiedDate":"2020-10-26T14:17:03.523653","indexId":"70215620","displayToPublicDate":"2020-08-10T09:12:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Regime change in a large-floodplain river ecosystem: Patterns in body-size and functional biomass indicate a shift in fish communities","docAbstract":"

Changes in species dominance may drive regime shifts because dominant biotic feedbacks reflect functional traits of a community. Changes in species dominance has been documented by a 25-year fish community dataset encompassing six reaches of the Upper Mississippi and Illinois Rivers. Specifically, common carp (Cyprinus carpio) abundance has declined across all reaches, whereas silver carp (Hypophthalmichthys molitrix) and bighead carp (Hypophthalmichthys nobilis) have increased in abundance in the southern three reaches. To test whether signals in the data were consistent with regime transitions, changes in body-size patterns and trends in functional biomass and variance of functional biomass of the fish community were assessed. I further explored biomass thresholds relative to transitions. Shifts in body-size aggregations and trends in functional biomass support hypotheses that transitions from common carp dominance to a more functional diverse community represent alternate regimes. Results indicate such transitions occurred in the early 2000s for the two most northern reaches, and that the third most northern reach is nearing this transition. In the southern reaches, results indicate that transitions from common carp dominance to silver and bighead carp dominance also represent alternate regimes. Regime transitions support biomass thresholds between 8000 and 10,000 g per unit of day electrofishing effort of common carp and between 20 and 2000 g per unit effort in silver and bighead carp. Consistencies across reaches with similar dominance patterns provide support that reinforcing feedbacks, unique to the functional attributes of the dominant species, occur at similar biomass levels. Building knowledge of such feedback mechanisms is imperative to effectively manage for desired regimes.


","language":"English","publisher":"Springer","doi":"10.1007/s10530-020-02330-5","usgsCitation":"Bouska, K.L., 2020, Regime change in a large-floodplain river ecosystem: Patterns in body-size and functional biomass indicate a shift in fish communities: Biological Invasions, v. 22, no. 12, p. 3371-3389, https://doi.org/10.1007/s10530-020-02330-5.","productDescription":"19 p.","startPage":"3371","endPage":"3389","ipdsId":"IP-112866","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":379753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.6474609375,\n 45.30580259943578\n ],\n [\n -93.88916015625,\n 44.933696389694674\n ],\n [\n -92.52685546875,\n 44.38669150215206\n ],\n [\n -91.5380859375,\n 43.29320031385282\n ],\n [\n -91.12060546875,\n 42.47209690919285\n ],\n [\n -91.40625,\n 41.68932225997044\n ],\n [\n -92.04345703125,\n 40.713955826286046\n ],\n [\n -91.56005859375,\n 39.740986355883564\n ],\n [\n -90.85693359375,\n 38.788345355085625\n ],\n [\n -90.32958984375,\n 37.75334401310656\n ],\n [\n -89.5166015625,\n 36.932330061503144\n ],\n [\n -89.05517578125,\n 37.03763967977139\n ],\n [\n -89.296875,\n 37.77071473849609\n ],\n [\n -89.736328125,\n 38.54816542304656\n ],\n [\n -90.23071289062499,\n 39.33429742980725\n ],\n [\n -91.153564453125,\n 39.78321267821705\n ],\n [\n -91.03271484375,\n 40.38002840251183\n ],\n [\n -90.439453125,\n 41.09591205639546\n ],\n [\n -89.82421875,\n 41.96765920367816\n ],\n [\n -90.68115234375,\n 43.24520272203356\n ],\n [\n -91.20849609375,\n 44.22945656830167\n ],\n [\n -92.57080078125,\n 44.94924926661153\n ],\n [\n -93.6474609375,\n 45.30580259943578\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":803010,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211128,"text":"70211128 - 2020 - 2019 fiscal year state of the Survey","interactions":[],"lastModifiedDate":"2020-08-10T16:17:55.238546","indexId":"70211128","displayToPublicDate":"2020-08-10T09:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"seriesTitle":{"id":5974,"text":"State of the Survey","active":true,"publicationSubtype":{"id":6}},"displayTitle":"2019 Fiscal Year State of the Survey","title":"2019 fiscal year state of the Survey","docAbstract":"

This first of its kind publication, the USGS “2019 Fiscal Year State of the Survey” report, highlights notable USGS accomplishments over the past year that have contributed to meeting our goals and priorities in support of the Department of the Interior Strategic Plan. The activities summarized in this report demonstrate how integrated science and administrative support contribute to fulfilling our vision of being a world leader in the natural sciences through our scientific excellence and responsiveness to society’s need. And it celebrates the great work and dedication of our employees in helping advance our scientific knowledge and understanding of the Earth and its processes.

The \"State of the Survey\" is much more than a simple listing of accomplishments. It is a colorful visual tour through our diverse scientific endeavors. Within its pages you will find short videos on algal blooms, ShakeAlert, flood prediction, and more. You will find maps of where our centers and stations are located, charts of our funding sources, and pictures of our researchers at work.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ss2019","usgsCitation":"U.S. Geological Survey, Office of Budget, Planning, and Integration, 2020, 2019 fiscal year state of the Survey: State of the Survey, 36 p., https://doi.org/10.3133/ss2019.","productDescription":"36 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120325","costCenters":[{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"links":[{"id":377221,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/ss2019/ss2019.pdf","text":"Report","size":"142 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376422,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/ss2019/coverthb2.jpg"},{"id":376845,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/ss2019/ss2019.html","text":"Web version of report","linkFileType":{"id":5,"text":"html"}},{"id":377203,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://indd.adobe.com/view/4efaf9c0-b1d0-4781-bb04-357146085cda","text":"Interactive version of report"}],"contact":"

Office of Budget, Planning, and Integration
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U.S. Geological Survey
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","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-08-04","noUsgsAuthors":false,"publicationDate":"2020-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey, Office of Budget, Planning, and Integration","contributorId":236850,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey, Office of Budget, Planning, and Integration","id":794473,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70248820,"text":"70248820 - 2020 - The remarkable volcanism of Shastina, a stratocone segment of Mount Shasta, California","interactions":[],"lastModifiedDate":"2023-09-22T14:09:33.851825","indexId":"70248820","displayToPublicDate":"2020-08-10T08:56:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"The remarkable volcanism of Shastina, a stratocone segment of Mount Shasta, California","docAbstract":"

Mount Shasta, a 400 km3 volcano in northern California (United States), is the most voluminous stratocone of the Cascade arc. Most Mount Shasta lavas vented at or near the present summit; relatively smaller volumes erupted from scattered vents on the volcano’s flanks. An apron of pyroclastic and debris flows surrounds it.

Shastina, a large and distinct cone on the west side of Mount Shasta, represents a brief but exceptionally vigorous period of eruptive activity. Its volume of ∼13.5 km3 would make Shastina itself one of the larger Holocene Cascade stratovolcanoes. Its andesite-dacite lavas average 63 wt% SiO2 and have little compositional or petrographic variation; they erupted almost entirely from one central vent, although a single vent below Shastina’s north side erupted a flow of the same composition. Eruptions ended with explosive enlargement and breaching of the central crater and successive emplacement of four, more-silicic dacite domes within the crater and pyroclastic flows down its flank. Black Butte, a large volcanic dome and pyroclastic complex below the west flank of Shastina, is petrographically and chemically distinct but only slightly younger than Shastina itself, part of a nearly continuous Shastina–Black Butte eruptive episode.

Shastina overlies the widespread pumice of Red Banks, erupted from the Mount Shasta summit area and 14C dated at ca. 10,900 yr B.P. (calibrated). Shastina and Black Butte pyroclastic deposits have calibrated 14C ages indistinguishable from one another at ca. 10,700 cal. yr B.P. A cognate granitic-textured inclusion in a late Shastina lava flow yields a 238U-230Th date on zircons within error of those ages. Our conclusion that the entire, voluminous Shastina–Black Butte episode lasted no more than a few hundred years is confirmed by almost identical remanent magnetic directions of all of the lavas and pyroclastic deposits. Although extremely similar, the remanent magnetic directions do reveal a short path of secular variation through the eruptive sequence. We conclude that the entire Shastina–Black Butte eruptive episode lasted no more than ∼200 yr.

The magmas that produced the Shastina and Black Butte eruptions were separate individual bodies at different crustal levels. Each of these eruptive sequences probably represents magma approximating a liquid composition that experienced only minimal differentiation or crustal contamination and remained separated from the main central conduit for most eruptions of Mount Shasta. The probability of another rapidly developing, brief but voluminous eruptive episode at Mount Shasta is low but should not be ignored in evaluating future possible eruptive hazards.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02080.1","usgsCitation":"Christiansen, R.L., Calvert, A.T., Champion, D.E., Gardner, C.A., Fierstein, J., and Vazquez, J.A., 2020, The remarkable volcanism of Shastina, a stratocone segment of Mount Shasta, California: Geosphere, v. 16, no. 5, p. 1153-1178, https://doi.org/10.1130/GES02080.1.","productDescription":"23 p.","startPage":"1153","endPage":"1178","ipdsId":"IP-096141","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":455696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02080.1","text":"Publisher Index Page"},{"id":421072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Shastina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.24191149164602,\n 41.4216022165275\n ],\n [\n -122.24191149164602,\n 41.398796198932615\n ],\n [\n -122.20943661446428,\n 41.398796198932615\n ],\n [\n -122.20943661446428,\n 41.4216022165275\n ],\n [\n -122.24191149164602,\n 41.4216022165275\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Christiansen, Robert L. 0000-0002-8017-3918 rchris@usgs.gov","orcid":"https://orcid.org/0000-0002-8017-3918","contributorId":4412,"corporation":false,"usgs":true,"family":"Christiansen","given":"Robert","email":"rchris@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":883777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calvert, Andrew T. 0000-0001-5237-2218 acalvert@usgs.gov","orcid":"https://orcid.org/0000-0001-5237-2218","contributorId":2694,"corporation":false,"usgs":true,"family":"Calvert","given":"Andrew","email":"acalvert@usgs.gov","middleInitial":"T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":883778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Champion, Duane E. 0000-0001-7854-9034 dchamp@usgs.gov","orcid":"https://orcid.org/0000-0001-7854-9034","contributorId":2912,"corporation":false,"usgs":true,"family":"Champion","given":"Duane","email":"dchamp@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":883779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gardner, Cynthia A. 0000-0002-6214-6182 cgardner@usgs.gov","orcid":"https://orcid.org/0000-0002-6214-6182","contributorId":1959,"corporation":false,"usgs":true,"family":"Gardner","given":"Cynthia","email":"cgardner@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":883780,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fierstein, Judith E. 0000-0001-8024-1426","orcid":"https://orcid.org/0000-0001-8024-1426","contributorId":329988,"corporation":false,"usgs":true,"family":"Fierstein","given":"Judith E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":883781,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":883782,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215127,"text":"70215127 - 2020 - Plant biomass and rates of carbon dioxide uptake are enhanced by successful restoration of tidal connectivity in salt marshes","interactions":[],"lastModifiedDate":"2020-10-08T13:13:42.958491","indexId":"70215127","displayToPublicDate":"2020-08-10T08:08:45","publicationYear":"2020","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":"Plant biomass and rates of carbon dioxide uptake are enhanced by successful restoration of tidal connectivity in salt marshes","docAbstract":"

Salt marshes, due to their capability to bury soil carbon (C), are potentially important regional C sinks. Efforts to restore tidal flow to former salt marshes have increased in recent decades in New England (USA), as well as in some other parts of the world. In this study, we investigated plant biomass and carbon dioxide (CO2) fluxes at four sites where restoration of tidal flow occurred five to ten years prior to the study. Site elevation, aboveground biomass, CO2 flux, and porewater chemistry were measured in 2015 and 2016 in both restored marshes and adjacent marshes where tidal flow had never been restricted. We found that the elevation in restored marsh sites was 2–16 cm lower than their natural references. Restored marshes where porewater chemistry was similar to the natural reference had greater plant aboveground biomass, gross ecosystem production, ecosystem respiration, as well as net ecosystem CO2 exchange (NEE) than the natural reference, even though they had the same plant species. We also compared respiration rates in aboveground biomass (AR) and soil (BR) during July 2016, and found that restored marshes had higher AR and BR fluxes than natural references. Our findings indicated that well-restored salt marshes can result in greater plant biomass and NEP, which has the potential to enhance rates of C sequestration at 10-yrs post restoration. Those differences were likely due to lower elevation and greater flooding frequency in the recently restored marshes than the natural marsh. The inverse relationship between elevation and productivity further suggests that, where sea-level rise rate does not surpass the threshold of plant survival, the restoration of these salt marshes may lead to enhanced organic and mineral sedimentation, extending marsh survival under increased sea level, and recouping carbon stocks that were lost during decades of tidal restriction.


","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.141566","usgsCitation":"Wang, F., Eagle, M.J., Kroeger, K.D., Spivak, A.C., and Tang, J., 2020, Plant biomass and rates of carbon dioxide uptake are enhanced by successful restoration of tidal connectivity in salt marshes: Science of the Total Environment, v. 750, 141566, 11 p., https://doi.org/10.1016/j.scitotenv.2020.141566.","productDescription":"141566, 11 p.","ipdsId":"IP-121642","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455699,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.141566","text":"Publisher Index Page"},{"id":379222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.7464599609375,\n 41.679066225164114\n ],\n [\n -69.884033203125,\n 41.679066225164114\n ],\n [\n -69.884033203125,\n 42.07987816698549\n ],\n [\n -70.7464599609375,\n 42.07987816698549\n ],\n [\n -70.7464599609375,\n 41.679066225164114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"750","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Fanning","contributorId":242836,"corporation":false,"usgs":false,"family":"Wang","given":"Fanning","email":"","affiliations":[{"id":48547,"text":"Xiaoliang Research Station of Tropical Coastal Ecosystems, the CAS Engineering Laboratory for Ecological Restoration of Island and Coastal Ecosystems, South China","active":true,"usgs":false}],"preferred":false,"id":800946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":801016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":800948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spivak, Amanda C.","contributorId":191376,"corporation":false,"usgs":false,"family":"Spivak","given":"Amanda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":800949,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tang, Jianwu","contributorId":174890,"corporation":false,"usgs":false,"family":"Tang","given":"Jianwu","email":"","affiliations":[{"id":27818,"text":"The Ecosystems Center, Marine Biological Laboratory. Woods Hole, MA 02543.","active":true,"usgs":false}],"preferred":false,"id":800950,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228364,"text":"70228364 - 2020 - Risk of predation on offspring reduces parental provisioning, but not flight performance or survival across early life stages","interactions":[],"lastModifiedDate":"2022-02-10T12:08:04.765156","indexId":"70228364","displayToPublicDate":"2020-08-09T12:45:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Risk of predation on offspring reduces parental provisioning, but not flight performance or survival across early life stages","docAbstract":"
  1. Developmental responses can help young animals reduce predation risk but can also yield costs to performance and survival in subsequent life stages with major implications for lifetime fitness. Compensatory mechanisms may evolve to offset such costs, but evidence from natural systems is largely lacking.
  2. In songbirds, increased nest predation risk should favour reduced provisioning, but also young that fledge (leave their nest) at an earlier age. Both responses can result in fledglings with shorter wings, reduced mobility and decreased survival. Young may compensate for shorter wings developmentally by reallocating resources towards feather development or behaviourally by adjusting flight kinematics or habitat use. However, underfed young may lack the capacity to express these phenotypes due to insufficient resources or an inability to adjust allocation of resources.
  3. Using predation risk experiments and 29 years of observational field data, we test whether increased nest predation risk reduces flight performance and survival during the fledgling stage and explore potential mechanisms that might underlie these effects. We show that young from high-risk nests did not leave the nest earlier on average, but wing growth was slower likely due to observed reductions in parental feeding rates. Wings were shorter in high-risk nests when fledglings left the nest early. Yet, fledglings from high-risk nests showed improved flight performance for a given wing length such that flight performance at fledging did not differ between young from high-risk and low-risk nests. Young from high-risk nests may have offset the costs of shorter wings on flight performance by accelerating the emergence of flight feathers from their sheaths to reduce wing porosity, though evidence for this mechanism was mixed. Fledglings from high-risk nests also selected habitat with denser woody vegetation compared with young from low-risk nests.
  4. Together, these developmental and behavioural responses seem to mitigate the expected effects of increased nest predation risk on fledgling survival. Ultimately, our results show that offspring predation risk can affect parental provisioning and offspring morphology without major implications for performance and survival in subsequent life stages.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2435.13650","usgsCitation":"Mouton, J.C., Tobalske, B.W., Wright, N.A., and Martin, T.E., 2020, Risk of predation on offspring reduces parental provisioning, but not flight performance or survival across early life stages: Functional Ecology, v. 34, no. 10, p. 2147-2157, https://doi.org/10.1111/1365-2435.13650.","productDescription":"11 p.","startPage":"2147","endPage":"2157","ipdsId":"IP-111012","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455703,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2435.13650","text":"Publisher Index Page"},{"id":395709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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