On sea turtle nesting beaches, artificial lighting associated with human development interferes with hatchling orientation from nest to sea. Although hatchling disorientation has been documented for many beaches, data that managers can use in understanding, predicting, and managing the issue are of limited detail. The present study provides baseline hatchling orientation data that can be compared to those from beaches with artificial lighting to prioritize light-management efforts there. In 2014, the precision of hatchling orientation was quantified for 87 nests on a naturally lighted beach that had little to no artificial lighting. Precision of hatchling orientation was regressed against seven environmental variables: beach slope, distance from nest to dune, dune height, apparent dune silhouette height relative to nest site, moon illumination percentage, cloud cover percentage, and relative humidity. Results favored a regression model that included distance from nest to dune, with shorter distances from the dune predicting a narrower angular range (i.e., greater precision) of hatchling orientation. The study confirmed findings of an earlier laboratory experiment that highlighted the importance to accurate hatchling orientation of a dark silhouette (dune) on the side of the nest site opposite the ocean side. Reducing artificial light and promoting the planting of pioneer plants that assist dune formation can increase hatchling survival.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jembe.2021.151568","usgsCitation":"Hirama, S., Witherington, B., Kneifl, K., Sylvai, A., Wideroff, M., and Carthy, R., 2021, Environmental factors predicting the orientation of sea turtle hatchlings on a naturally lighted beach: A baseline for light-management goals: Journal of Experimental Marine Biology and Ecology, v. 541, 151568, 7 p., https://doi.org/10.1016/j.jembe.2021.151568.","productDescription":"151568, 7 p.","ipdsId":"IP-128875","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jembe.2021.151568","text":"Publisher Index Page"},{"id":397164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","county":"Brevard County","otherGeospatial":"Canaveral National Seashore, Playalinda","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.71956,\n 28.77088\n ],\n [\n -80.62646,\n 28.77088\n ],\n [\n -80.62646,\n 28.64748\n ],\n [\n -80.71956,\n 28.64748\n ],\n [\n -80.71956,\n 28.77088\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"541","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hirama, S.","contributorId":288634,"corporation":false,"usgs":false,"family":"Hirama","given":"S.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":838152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Witherington, B.","contributorId":288637,"corporation":false,"usgs":false,"family":"Witherington","given":"B.","affiliations":[{"id":61821,"text":"Inwater Research Group, Inc","active":true,"usgs":false}],"preferred":false,"id":838153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kneifl, K.","contributorId":288638,"corporation":false,"usgs":false,"family":"Kneifl","given":"K.","email":"","affiliations":[{"id":61824,"text":"Canaveral National Seashore","active":true,"usgs":false}],"preferred":false,"id":838154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sylvai, A.","contributorId":288639,"corporation":false,"usgs":false,"family":"Sylvai","given":"A.","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":838155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wideroff, M.","contributorId":288640,"corporation":false,"usgs":false,"family":"Wideroff","given":"M.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":838156,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838157,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229060,"text":"70229060 - 2021 - Water quality associations and spatiotemporal distribution of the harmful alga Prymnesium parvum in an impounded urban stream system","interactions":[],"lastModifiedDate":"2022-02-28T16:25:30.829318","indexId":"70229060","displayToPublicDate":"2021-05-04T10:18:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10138,"text":"Journal of Urban Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Water quality associations and spatiotemporal distribution of the harmful alga Prymnesium parvum in an impounded urban stream system","title":"Water quality associations and spatiotemporal distribution of the harmful alga Prymnesium parvum in an impounded urban stream system","docAbstract":"The Jim Bertram Lake System consists of several stream impoundments within the City of Lubbock, Texas (USA). Baseflow in the upstream reach is dominated by nitrogen-rich-treated wastewater. While toxic blooms of Prymnesium parvum have occurred in this system for ∼2 decades during fall or winter-spring, little is known about water quality variables that facilitate blooms or the alga’s spatiotemporal distribution. Water quality associations were examined monthly over a 1-year period. Total phosphorus was largely below the detection limit, suggesting that the system is phosphorus limited. Algal abundance was low during the assessment period and associations were determined using multiple logistic regression. Algal incidence was negatively associated with temperature and positively with organic nitrogen and calcium hardness. These findings conform with earlier reports but positive associations with the latter two variables are noteworthy because they have not been widely confirmed. Spatiotemporal distribution was evaluated in fall and winter-spring of three consecutive years. Prymnesium parvum incidence was higher in the upper than in the lower reach, and detections in the lower reach occurred only after a dense bloom developed in the upper reach contemporaneously with stormwater runoff-associated flooding. Thus, the upstream reach is a major source of propagules for downstream sites. Because urban runoff is a source of phosphorus and its nitrogen: phosphorus ratio is lower than prevailing ratios in the upper reach, what triggered the bloom was likely relief from phosphorus limitation. This study provided water quality, geographic and hydrological indices that may inform prevention and control methods for harmful algae in nitrogen-enriched urban systems.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jue/juab011","usgsCitation":"Clayton, J.B., Patino, R., Rashel, R.H., and Tábora-Sarmiento, S., 2021, Water quality associations and spatiotemporal distribution of the harmful alga Prymnesium parvum in an impounded urban stream system: Journal of Urban Ecology, v. 7, no. 4, juab011, 13 p., https://doi.org/10.1093/jue/juab011.","productDescription":"juab011, 13 p.","ipdsId":"IP-120920","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jue/juab011","text":"Publisher Index Page"},{"id":396561,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","city":"Lubbock","otherGeospatial":"Jim Bertram Lake System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.02110290527344,\n 33.47498122050127\n ],\n [\n -101.76429748535156,\n 33.47498122050127\n ],\n [\n -101.76429748535156,\n 33.714630486382156\n ],\n [\n -102.02110290527344,\n 33.714630486382156\n ],\n [\n -102.02110290527344,\n 33.47498122050127\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Clayton, J. B.","contributorId":286959,"corporation":false,"usgs":false,"family":"Clayton","given":"J.","email":"","middleInitial":"B.","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":836384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rashel, R. H.","contributorId":286960,"corporation":false,"usgs":false,"family":"Rashel","given":"R.","email":"","middleInitial":"H.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":836386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tábora-Sarmiento, S.","contributorId":286963,"corporation":false,"usgs":false,"family":"Tábora-Sarmiento","given":"S.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":836387,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236992,"text":"70236992 - 2021 - Structure and Qp-Qs relations in the Seattle and Tualatin basins from converted seismic phases","interactions":[],"lastModifiedDate":"2022-09-27T13:44:16.595597","indexId":"70236992","displayToPublicDate":"2021-05-04T08:31:35","publicationYear":"2021","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}},"displayTitle":"Structure and Qp-Qs relations in the Seattle and Tualatin basins from converted seismic phases","title":"Structure and Qp-Qs relations in the Seattle and Tualatin basins from converted seismic phases","docAbstract":"We use converted body‐wave phases from local earthquakes to constrain depth to basement and average attenuation relations for the Seattle basin in Washington and the Tualatin basin in Oregon. P‐, P‐to‐S‐(Ps), S‐to‐P‐(Sp), and S‐wave arrivals are present in three‐component recordings of magnitude 2.5–4.0 earthquakes at seismic stations located in these basins. Based on their relative travel times, these phases are attributed to body‐wave conversions at the basement‐to‐basin contact or to high‐impedance interfaces within the basins. Depth to basement values are calculated using the differential travel times between direct and converted phases, as well as average P‐ and S‐wave velocity values. We also identify a high‐impedance layer in the Tualatin basin that likely represents a laterally extensive deposit of volcanic materials embedded between the basement contact and the Columbia River Basalt Group. In addition, the average
Long-term studies of stream ecosystems are essential for assessing restoration success because they allow researchers to quantify recovery trajectories, gauge the relative influence of episodic events, and determine the time required to achieve clean-up objectives. To quantify responses of benthic macroinvertebrate assemblages to stream remediation, we integrated results of 4 long-term (20–29 y) assessments of mining-impacted watersheds that were broadly distributed across the western US (California, Colorado, Idaho, Montana). Using a before–after control–impact (BACI) study design, we observed substantial reductions in metal concentrations and corresponding improvements of benthic assemblages following remediation. Recovery rates were relatively consistent, and streams typically recovered within 10 to 15 y after remediation was initiated (mean = 10.25 y), although episodic events changed trajectories at some sites. Differences in recovery among watersheds were likely determined by a number of factors, including the severity of contamination, effectiveness of remediation, proximity to upstream sources of colonization, and hydrologic variation. We also observed considerable variation in the rate and extent of recovery among assemblage metrics. For example, total abundance and richness recovered rapidly at most sites, but the composition of benthic macroinvertebrate assemblages remained substantially altered compared with reference sites. Using piecewise linear regression, we estimated a threshold response of Ephemeroptera, Plecoptera, and Trichoptera (EPT) species richness at ~1 cumulative criteria unit (CCU), which is the sum of the fractions of chronic water-quality criteria for metals measured, suggesting this value was protective of benthic assemblages. However, EPT richness was reduced by ~20% at 2× this CCU value, indicating that moderate exceedances of water-quality criteria could substantially affect stream biodiversity. Non-metric multidimensional scaling analyses identified common sets of species trait states across the 4 watersheds that were associated with either metal contamination or with recovering and intact reference stream assemblages. Our study illustrates the importance of long-term studies for quantifying responses to stream restoration and the usefulness of BACI designs for demonstrating cause-and-effect relationships between restoration treatments and community recovery. Because these 4 watersheds were among the most severely polluted sites in the western US, our study demonstrates the value of these investments in watershed restoration and the potential for success under the most extreme conditions.
The development and installation of renewable energy comes with environmental cost, including the death of wildlife. These costs occur locally, and seem small compared to the global loss of biodiversity. However, failure to acknowledge uncertainties around these costs affects local conservation, and may lead to the loss of populations or species. Working with these uncertainties can result in adaptive management plans designed to benefit renewable energy development and conservation. An example is the U.S. government's policy for managing bald (Haliaeetus leucocephalus) and golden (Aquila chrysaetos) eagle deaths at terrestrial wind facilities. Using records from 422 U.S. wind facilities we improved the precision of estimates of exposure (8.79 eagle minutes hr−1 km−3, SD: 13.64) and collision probability (0.0058 birds per minute of exposure, SD: 0.0038) currently used in U.S. policy. The new estimates for bald (exposure: 3.19 eagle minutes hr−1 km−3, SD: 2.583; collision probability: 0.007025 eagles per minute of exposure, SD: 0.004379) and golden (exposure: 1.21 eagle minutes hr−1 km−3, SD: 0.352; collision probability: 0.005648 birds per minute of exposure, SD: 0.004413) eagles had a smaller mean and standard deviation. Thus, their implementation within the government's adaptive management framework could help refine the balance between energy consumption and conservation.
Understanding trophic interactions is critical for successful resource management. However, studying diet patterns (e.g., spatial and seasonal changes) can require extensive effort. Using individual analyses to interpret patterns may be further complicated by assumptions and limitations of the analytical approach. We investigated and compared predicted adult lake trout (Salvelinus namaycush) diet composition and patterns using stomach content analysis (SCA), fatty acid analysis (FAS), and stable isotope analysis (SIA) individually and simultaneously. The three analyses were conducted for fall-captured fish in Lake Ontario and provided different diet composition estimates; SCA suggested alewife (Alosa pseudoharengus) was dominant by frequency and mass, while FAA and SIA suggested rainbow smelt (Osmerus mordax) contributed the most based on similarity among fatty acid signatures and two-stable isotope (carbon and nitrogen) mixing models, respectively. We hypothesize the disagreement among diet estimates is a result of a seasonal shift in diet variably expressed due to differing extent of time reflected by the diet metric: hours to days for SCA, weeks to months for FAA and several months for SIA. Despite variability in diet composition estimates among methods, similar patterns in lake trout diet were observed among the three diet analyses; the contribution of alewife in lake trout diet was greater for larger individuals and for males compared to females, particularly in the east and northeast regions of the lake where alewife density was relatively low. Thus, the complementary results from the three analyses suggest that length, location, sex, and season all influence lake trout diet. Individually, analyses often failed to identify these patterns in lake trout diet with significance, and some of the patterns have not been observed in previous studies of lake trout diet in Lake Ontario. The thorough description of lake trout diet obtained from a single sampling season demonstrates how simultaneous use of multiple diet analyses may allow investigation of spatial and seasonal diet composition and with reduced sampling effort.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107728","usgsCitation":"Futia, M.H., Colborne, S.F., Fisk, A., Gorsky, D., Johnson, T.B., Lantry, B.F., Lantry, J., and Rinchard, J., 2021, Comparisons among three diet analyses demonstrate multiple patterns in the estimated adult diet of a freshwater piscivore, Salvelinus namaycush: Ecological Indicators, v. 127, 107728, 12 p., https://doi.org/10.1016/j.ecolind.2021.107728.","productDescription":"107728, 12 p.","ipdsId":"IP-120076","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452430,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107728","text":"Publisher Index Page"},{"id":412729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.67051642015157,\n 43.10662975810362\n ],\n [\n -75.13575754812497,\n 43.10662975810362\n ],\n [\n -75.13575754812497,\n 44.79831104261547\n ],\n [\n -80.67051642015157,\n 44.79831104261547\n ],\n [\n -80.67051642015157,\n 43.10662975810362\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Futia, Matthew H.","contributorId":208498,"corporation":false,"usgs":false,"family":"Futia","given":"Matthew","email":"","middleInitial":"H.","affiliations":[{"id":37810,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, 350 New Campus Drive, Brockport, New York","active":true,"usgs":false}],"preferred":false,"id":863418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colborne, Scott F.","contributorId":174737,"corporation":false,"usgs":false,"family":"Colborne","given":"Scott","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":863419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisk, Aaron T.","contributorId":51604,"corporation":false,"usgs":false,"family":"Fisk","given":"Aaron T.","affiliations":[],"preferred":false,"id":863420,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorsky, Dimitry","contributorId":251650,"corporation":false,"usgs":false,"family":"Gorsky","given":"Dimitry","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":863421,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Timothy B.","contributorId":49753,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":863422,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863423,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lantry, Jana","contributorId":141102,"corporation":false,"usgs":false,"family":"Lantry","given":"Jana","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":863424,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rinchard, Jacques","contributorId":58161,"corporation":false,"usgs":true,"family":"Rinchard","given":"Jacques","affiliations":[],"preferred":false,"id":863425,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70260186,"text":"70260186 - 2021 - Rapid metal pollutant deposition from the volcanic plume of Kīlauea, Hawai’i","interactions":[],"lastModifiedDate":"2024-10-30T12:03:05.610046","indexId":"70260186","displayToPublicDate":"2021-05-04T07:01:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11444,"text":"Nature Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Rapid metal pollutant deposition from the volcanic plume of Kīlauea, Hawai’i","docAbstract":"Long-lived basaltic volcanic eruptions are a globally important source of environmentally reactive, volatile metal pollutant elements such as selenium, cadmium and lead. The 2018 eruption of Kīlauea, Hawai’i produced exceptionally high discharge of metal pollutants, and was an unprecedented opportunity to track them from vent to deposition. Here we show, through geochemical sampling of the plume that volatile metal pollutants were depleted in the plume up to 100 times faster than refractory species, such as magnesium and iron. We propose that this rapid wet deposition of complexes containing reactive and potentially toxic volatile metal pollutants may disproportionately impact localised areas close to the vent. We infer that the relationship between volatility and solubility is an important control on the atmospheric behaviour of elements. We suggest that assessment of hazards from volcanic emissions should account for heterogeneous plume depletion of metal pollutants.
Volcanoes represent one of the largest natural sources of metals to the Earth’s surface. Emissions of these metals can have important impacts on the biosphere as pollutants or nutrients. Here we use ground- and drone-based direct measurements to compare the gas and particulate chemistry of the magmatic and lava–seawater interaction (laze) plumes from the 2018 eruption of Kīlauea, Hawai’i. We find that the magmatic plume contains abundant volatile metals and metalloids whereas the laze plume is further enriched in copper and seawater components, like chlorine, with volatile metals also elevated above seawater concentrations. Speciation modelling of magmatic gas mixtures highlights the importance of the S2− ligand in highly volatile metal/metalloid degassing at the magmatic vent. In contrast, volatile metal enrichments in the laze plume can be explained by affinity for chloride complexation during late-stage degassing of distal lavas, which is potentially facilitated by the HCl gas formed as seawater boils.
The mission of Northern Prairie Wildlife Research Center is to provide scientific information needed to conserve and manage the Nation’s natural capital for current and future generations, with an emphasis on migratory birds, Department of the Interior trust resources, and ecosystems of the Nation’s interior. This report provides an overview of the studies conducted at Northern Prairie during fiscal years 2019–20 in pursuit of this mission. Studies are organized under a framework developed by the U.S. Geological Survey Ecosystems Mission Area, identifying primary and secondary alignment with focal areas of research, and summarizing recent scientific products resulting from these studies. Partnerships with Federal, State, and non-Governmental organizations are essential to a robust program of applied ecological research, and we thank our many collaborators and colleagues whose contributions made this work possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1481","usgsCitation":"Sherfy, M.H., ed., 2021, U.S. Geological Survey—Northern Prairie Wildlife Research Center 2019–20 research activity report: U.S. Geological Survey Circular 1481, 67 p., https://doi.org/10.3133/cir1481.","productDescription":"ix, 67 p.","numberOfPages":"81","onlineOnly":"Y","ipdsId":"IP-121611","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":385425,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1481/coverthb.jpg"},{"id":385426,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1481/cir1481.pdf","text":"Report","size":"33.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1481"}],"country":"United States","state":"North 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Dakota\",\"nation\":\"USA \"}}]}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Deadly and destructive debris flows often follow wildfire, but understanding of changes in the hazard potential with time since fire is poor. We develop a simulation‐based framework to quantify changes in the hydrologic triggering conditions for debris flows as postwildfire infiltration properties evolve through time. Our approach produces time‐varying rainfall intensity‐duration thresholds for runoff‐ and infiltration‐generated debris flows with physics‐based hydrologic simulations that are parameterized with widely available hydroclimatic, vegetation reflectance, and soil texture data. When we apply our thresholding protocol to a test case in the San Gabriel Mountains (California, USA), the results are consistent with existing regional empirical thresholds and rainstorms that caused runoff‐ and infiltration‐generated debris flows soon after and three years following a wildfire, respectively. We find that the hydrologic triggering mechanisms for the two observed debris flow types are coupled with the effects of fire on the soil saturated hydraulic conductivity. Specifically, the rainfall intensity needed to generate debris flows via runoff increases with time following wildfire while the rainfall duration needed to produce debris flows via subsurface pore‐water pressures decreases. We also find that variations in soil moisture, rainfall climatology, median grain size, and root reinforcement could impact the median annual probability of postwildfire debris flows. We conclude that a simulation‐based method for calculating rainfall thresholds is a tractable approach to improve situational awareness of debris flow hazard in the years following wildfire. Further development of our framework will be important to quantify postwildfire hazard levels in variable climates, vegetation types, and fire regimes.
","language":"English","publisher":"Wiley","doi":"10.1029/2021JF006091","usgsCitation":"Thomas, M.A., Rengers, F.K., Kean, J.W., McGuire, L.A., Staley, D.M., Barnhart, K.R., and Ebel, B., 2021, Postwildfire soil‐hydraulic recovery and the persistence of debris flow hazards: Journal of Geophysical Research: Earth Surface, v. 126, no. 6, e2021JF006091, 25 p., https://doi.org/10.1029/2021JF006091.","productDescription":"e2021JF006091, 25 p.","ipdsId":"IP-126218","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":452437,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021jf006091","text":"External Repository"},{"id":436384,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLP6XG","text":"USGS data release","linkHelpText":"Soil moisture monitoring following the 2009 Station Fire, California, USA, 2016-2019"},{"id":385458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":815192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":815195,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70222952,"text":"70222952 - 2021 - Late Pleistocene baldcypress (Taxodium distichum) forest deposit on the continental shelf of the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2021-08-10T13:45:44.888124","indexId":"70222952","displayToPublicDate":"2021-05-03T08:40:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1068,"text":"Boreas","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Late Pleistocene baldcypress (Taxodium distichum) forest deposit on the continental shelf of the northern Gulf of Mexico","title":"Late Pleistocene baldcypress (Taxodium distichum) forest deposit on the continental shelf of the northern Gulf of Mexico","docAbstract":"Approximately 13 km south of Gulf Shores, Alabama (United States), divers found in situ baldcypress (Taxodium distichum) stumps 18 m below the ocean surface. These trees could have only lived when sea level fell during the Pleistocene subaerially exposing the tectonically stable continental shelf. Here we investigate the geophysical properties along with microfossil and stratigraphical analyses of sediment cores to understand the factors that lead to this wood’s preservation. The stumps are exposed in an elongated depression (~100 m long, ~1 m deep) nested in a trough of the northwest–southeast trending Holocene sand ridges and troughs with 2–5 m vertical relief and ~0.5 km wavelength. Radiocarbon ages of the wood were infinite thus optically stimulated luminescence (OSL) dating was used to constrain the site’s age. Below the Holocene sands (~0.1–4 m thick), separated by a regional erosional unconformity, are Late Pleistocene mud-peat (72±8 ka OSL), mud-sand (63±5, 73±6 ka OSL), and palaeosol (56±5 ka OSL) facies that grade laterally from west to east, respectively. Foraminiferal analysis reveals the location of the terrestrial-marine transitional layer above the Pleistocene facies in an interbedded sand and mud facies (3940±30 (1σ) 14C a BP), which is part of a lower shoreface or marine-dominated estuarine environment. The occurrence of palaeosol and swamp facies of broadly similar ages and elevation suggests the glacial landscape possessed topographic relief that allowed wood, mud and peats to be preserved for ~50 ka of subaerial exposure before transitioning to the modern marine environment. We hypothesize that rapid sea-level rise occurring ~60 or ~40 ka ago provided opportunities for local flood-plain aggradation to bury the swamp thus preserving the stumps and that other sites may exist in the northern Gulf of Mexico shelf.
","language":"English","publisher":"Wiley","doi":"10.1111/bor.12524","usgsCitation":"DeLong, K., Gonzalez, S., Obelcz, J., Truong, J.T., Bentley, S.J., Xu, K., Reese, C.A., Harley, G.L., Caporaso, A., Shen, Z., and Middleton, B., 2021, Late Pleistocene baldcypress (Taxodium distichum) forest deposit on the continental shelf of the northern Gulf of Mexico: Boreas, v. 50, no. 3, p. 871-892, https://doi.org/10.1111/bor.12524.","productDescription":"22 p.","startPage":"871","endPage":"892","ipdsId":"IP-109473","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452440,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/geo_pubs/1946","text":"Publisher Index Page"},{"id":387806,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi","otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.38427734374999,\n 27.0982539061379\n ],\n [\n -84.19921875,\n 27.0982539061379\n ],\n [\n -84.19921875,\n 31.034108344903512\n ],\n [\n -91.38427734374999,\n 31.034108344903512\n ],\n [\n -91.38427734374999,\n 27.0982539061379\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"DeLong, Kristine L.","contributorId":263459,"corporation":false,"usgs":false,"family":"DeLong","given":"Kristine L.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":820886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gonzalez, Suyapa","contributorId":263462,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Suyapa","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":820887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Obelcz, Jeffrey B.","contributorId":263465,"corporation":false,"usgs":false,"family":"Obelcz","given":"Jeffrey B.","affiliations":[{"id":53993,"text":"U.S. Naval Research Lab, Stennis Space Center","active":true,"usgs":false}],"preferred":false,"id":820888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Truong, Jonathan T.","contributorId":263466,"corporation":false,"usgs":false,"family":"Truong","given":"Jonathan","email":"","middleInitial":"T.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":820889,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bentley, Samuel J. Sr.","contributorId":263467,"corporation":false,"usgs":false,"family":"Bentley","given":"Samuel","suffix":"Sr.","email":"","middleInitial":"J.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":820890,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xu, Kehui","contributorId":223696,"corporation":false,"usgs":false,"family":"Xu","given":"Kehui","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":820891,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reese, Carl A.","contributorId":263468,"corporation":false,"usgs":false,"family":"Reese","given":"Carl","email":"","middleInitial":"A.","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":820892,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harley, Grant L.","contributorId":204186,"corporation":false,"usgs":false,"family":"Harley","given":"Grant","email":"","middleInitial":"L.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":820893,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Caporaso, Alicia","contributorId":263469,"corporation":false,"usgs":false,"family":"Caporaso","given":"Alicia","email":"","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":false,"id":820894,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shen, Zhixiong","contributorId":263470,"corporation":false,"usgs":false,"family":"Shen","given":"Zhixiong","email":"","affiliations":[{"id":24750,"text":"Coastal Carolina University","active":true,"usgs":false}],"preferred":false,"id":820895,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Middleton, Beth 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":206922,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":820896,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70221398,"text":"70221398 - 2021 - Refining the coarse filter approach: Using habitat-based species models to identify rarity and vulnerabilities in the protection of U.S. biodiversity","interactions":[],"lastModifiedDate":"2021-06-15T10:28:49.88002","indexId":"70221398","displayToPublicDate":"2021-05-03T07:59:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Refining the coarse filter approach: Using habitat-based species models to identify rarity and vulnerabilities in the protection of U.S. biodiversity","docAbstract":"Preserving biodiversity and its many components is a priority of conservation science and how to efficiently allocate resources to preserve healthy populations of as many species, habitats, and ecosystems as possible. We used the U.S. Geological Survey (USGS) Gap Analysis Project (GAP) species models released in 2018, which identify predicted habitats for terrestrial vertebrates in the conterminous United States, to illustrate hotspots of biodiversity for the major taxonomic groups. This collection represents the first complete compilation of terrestrial vertebrate species models for the conterminous United States (U.S. Geological Survey (USGS), 2018a). We used the species models but not the available subspecies models; this resulted in the inclusion of 282 amphibian models, 621 bird models, 365 mammal models, and 322 reptiles in our analysis. We also used population trend information and made spatial queries to characterize species in three dimensions: geographic range (small or large), habitat breadth (narrow or wide), and population trend (decreasing vs stable or increasing). This characterization allowed us to divide the species into eight groups (A-H) with similar characteristics. Group A species (large geographic range, wide habitat breadth, and stable or increasing population trend) are species that are common now with no indication of becoming rare. Species B-H have theoretical or known characteristics that could lead them to become rare with the H species exhibiting small geographic range, narrow habitat breadth, and decreasing population trend. Finally, we evaluated the prevalence of mapped habitat on protected lands for each species, exploring the patterns of representation in the rare species groups by ecoregion. The species we identified with population and habitat use characteristics that potentially predispose them to being or becoming rare represented a large percentage of each taxon. Potentially rare species were widely distributed among ecoregions. Of the 20 ecoregions in the country, 14 have a greater number of rare species than the national average for at least one taxon. Protection of the habitat for the majority of these rare species is below that recommended (17% of available habitat) by the Convention on Biological Diversity (CBD). The Everglades ecoregion was the only ecoregion that protected more than half of its rare or potentially rare species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01598","usgsCitation":"Davidson, A., Dunn, L., Gergely, K., McKerrow, A., Williams, S.G., and Case, M., 2021, Refining the coarse filter approach: Using habitat-based species models to identify rarity and vulnerabilities in the protection of U.S. biodiversity: Global Ecology and Conservation, v. 28, e01598, 19 p., https://doi.org/10.1016/j.gecco.2021.e01598.","productDescription":"e01598, 19 p.","ipdsId":"IP-101927","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":452441,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01598","text":"Publisher Index Page"},{"id":386468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.61718749999999,\n 25.16517336866393\n ],\n [\n -63.984375,\n 25.16517336866393\n ],\n [\n -63.984375,\n 51.83577752045248\n ],\n [\n -127.61718749999999,\n 51.83577752045248\n ],\n [\n -127.61718749999999,\n 25.16517336866393\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Davidson, Anne","contributorId":197967,"corporation":false,"usgs":false,"family":"Davidson","given":"Anne","email":"","affiliations":[],"preferred":false,"id":817517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunn, Leah","contributorId":217944,"corporation":false,"usgs":false,"family":"Dunn","given":"Leah","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":817518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gergely, Kevin 0000-0002-4379-2189","orcid":"https://orcid.org/0000-0002-4379-2189","contributorId":208371,"corporation":false,"usgs":true,"family":"Gergely","given":"Kevin","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":817519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":817520,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Steven G. 0000-0003-3760-6818","orcid":"https://orcid.org/0000-0003-3760-6818","contributorId":215501,"corporation":false,"usgs":false,"family":"Williams","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":39268,"text":"North Carolina State University, NC Cooperative Fish & Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":817521,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Case, Mackenzie 0000-0002-5657-9133","orcid":"https://orcid.org/0000-0002-5657-9133","contributorId":260200,"corporation":false,"usgs":false,"family":"Case","given":"Mackenzie","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":817522,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222517,"text":"70222517 - 2021 - Lipidomics reveals specific lipid molecules associated with cold stress syndrome in the Florida manatee (Trichechus manatus latirostris)","interactions":[],"lastModifiedDate":"2021-08-02T12:55:06.574686","indexId":"70222517","displayToPublicDate":"2021-05-03T07:46:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2660,"text":"Marine Biology","active":true,"publicationSubtype":{"id":10}},"title":"Lipidomics reveals specific lipid molecules associated with cold stress syndrome in the Florida manatee (Trichechus manatus latirostris)","docAbstract":"Cold stress syndrome (CSS) in the Florida manatee (Trichechus manatus latirostris) results in perturbations to many physiological pathways, often leading to further illness or death. In this study, we applied a non-targeted lipidomics approach with ultra-high performance liquid chromatography and high-resolution tandem mass spectrometry to characterize changes related to CSS in the lipidomic profiles of manatee plasma. Lipidomic analyses were conducted on healthy manatee (control) and cold-exposed manatee plasma samples with varying concentrations of Serum Amyloid A (SAA), an acute-phase protein that is associated with inflammatory disease. Control manatees (n = 10) were compared to all manatees exposed to cold temperatures (n = 17), and a subset of those manatees with SAA values > 120 μg/mL (n = 9). Increased SAA values were associated with higher levels of various acylcarnitine lipids, while several triacylglycerols and oxidized triacylglycerols were significantly lower in manatees with cold exposure. These identified lipids are critical molecules involved in the maintenance of energy homeostasis and could potentially be examined in conjunction with current physical parameters to characterize cold stress. The ability to detect such differences highlights the addition of lipidomics as a valuable tool in understanding cold stress and potentially other illnesses in manatees. Further investigation into the function of the altered lipids could greatly increase our understanding of lipid metabolism in physiologically stressed manatees as well as other marine mammals and inform future management recovery strategies.
Frequency-dependent horizontal-to-vertical spectral ratios (HVSR) of Fourier amplitudes from three-component recordings can provide information on one or more site resonant frequencies and relative levels of amplification at those frequencies. Such information is potentially useful for predicting site amplification but is not present in site databases that have been developed over the last 15–20 years for the Next-Generation Attenuation (NGA) projects, which instead use the time-averaged shear-wave velocity (VS) in the upper 30 m of the site (VS30) as the primary site parameter and are supplemented with basin depth terms where available. As a consequence, HVSR parameters are also not used in NGA ground motion models.
In order for HVSR-based parameters to be used in future versions of site databases, a publicly accessible repository of this information is needed. We adapt a relational database developed to archive and disseminate VS data to also include HVSR. The database provides relevant microtremor-based HVSR data (mHVSR) and supporting metadata. We consider the most relevant data to be the frequency-dependent mHVSR, where the horizontal is taken as the median component and also as a function of horizontal azimuth (referred to as polar plots). Relevant metadata includes site location information, details about the equipment used to make the measurements, and processing details related to windowing, anti-trigger routines, and filtering. We describe the database schema developed to organize and present this information.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"GIRS 2021-06","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"California Geological Survey","doi":"10.34948/N3KW20","usgsCitation":"Wang, P., Zimmaro, P., Gospe, T., Ahdi, S.K., Yong, A., and Stewart, J.P., 2021, Horizontal-to-vertical spectral ratios from California sites: Open-source database and data interpretation to establish site parameters, xi, 64 p., https://doi.org/10.34948/N3KW20.","productDescription":"xi, 64 p.","ipdsId":"IP-128357","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":388720,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0003-0274-5180","orcid":"https://orcid.org/0000-0003-0274-5180","contributorId":265143,"corporation":false,"usgs":true,"family":"Ahdi","given":"Sean","email":"","middleInitial":"Kamran","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":822311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":822312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":822313,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221327,"text":"70221327 - 2021 - Relating Tmax and hydrogen index to vitrinite and solid bitumen reflectance in hydrous pyrolysis residues: Comparisons to natural thermal indices","interactions":[],"lastModifiedDate":"2021-06-10T12:34:40.076535","indexId":"70221327","displayToPublicDate":"2021-05-03T07:33:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Relating Tmax and hydrogen index to vitrinite and solid bitumen reflectance in hydrous pyrolysis residues: Comparisons to natural thermal indices","docAbstract":"Vitrinite reflectance (VRo; %) generally is considered the most reliable technique to determine the thermal maturity of sedimentary rocks. However, it is a time-consuming process to collect reflectance (Ro; %) measurements and is subjective to the interpretation of each trained technician, who must be able to discern between vitrinite and solid bitumen and other organic matter types. Inadvertent misidentification of solid bitumen for vitrinite can lead to reports of ‘suppressed’ VRo, especially at lower thermal maturities (< 1.0% Ro). Programmed pyrolysis data, such as Tmax and hydrogen index (HI), are comparatively inexpensive and more time-efficient to obtain than Ro data and are determined by instrument settings, rather than by operator decision, and are therefore independent of operator-based training or experience bias. This study uses hydrous pyrolysis (HP) residues from various coals and shales to relate measured VRo and solid bitumen reflectance (BRo; %) values to their respective Tmax and HI values and determines whether these relationships can be used as a proxy to calculate Ro in naturally matured samples. Although the estimation of Ro is not always accurate, the results demonstrate that relational equations for shales and coals derived from the Tmax and HI data of HP residues can effectively calculate Ro in natural series. Approximately 60% of calculated Ro from Tmax and 83% of calculated Ro from HI relational equations are within interlaboratory reproducibility limits (± 0.2% shale BRo; ± 0.06% coal VRo) when compared to their respective measured Ro values from natural series. Variables that may affect accuracy of the applied relational equations include variable sedimentary organic matter composition of samples, differences of maturation reaction kinetics of the sedimentary organic matter in experimental versus natural settings, and decreasing reliability of all thermal proxy measurements at higher maturities.
Wildlife populations in southeast Asia are increasingly experiencing a broad array of anthropogenic threats, and mammalian carnivores are particularly vulnerable. Populations of the Malayan sun bear Helarctos malayanus are estimated to have declined by 30% over the last 30 years from forest conversion to industrial plantations and mortality associated with human–bear conflicts and illegal wildlife trade. However, the effects of industrial plantations on habitat selection and activity patterns of mammals that live at the protected area-plantation interface, including sun bears, are not well known. We investigated habitat selection and activity patterns of sun bears in Tabin Wildlife Reserve in Sabah, Malaysia. We deployed 83 remote camera sites to record sun bear detections during two sampling periods (2012–2013 and 2017). We used generalized linear models to examine relationships between sun bear presence and site covariates representing physical, environmental and anthropogenic elements of the landscape. Relative probability of sun bear presence was positively associated with distance to roads and elevation. Because most roads were on the reserve boundary and often associated with oil palm plantations, proximity to roads likely served as a surrogate measure of human accessibility and activity in peripheral areas of the reserve. Supporting that interpretation, sun bears close to the reserve boundary were primarily active at night, whereas daytime activity was more common for bears in the interior. Our findings indicate that sun bears alter behaviour and habitat selection likely in response to anthropogenic activities at the edges of Tabin Wildlife Reserve (112 200 ha). Because the ratio of edge to interior increases steeply with declining habitat area, smaller protected areas bordered by plantations are predicted to have greater impacts on sun bear behaviour and, potentially, population persistence. Effective conservation actions may benefit from management to improve the security of edge habitats for sun bears and other vulnerable species.
Control of invasive sea lamprey in the Great Lakes with a selective pesticide (lampricide) that targeted larval sea lamprey began in the late 1950's and continues to be one of the main methods for control. Although the Great Lakes Fishery Commission, which was formed with the mandate of controlling sea lamprey, often expresses the success of the sea lamprey control program in terms of percent reduction from lake-wide pre-lampricide control adult sea lamprey abundances, there remains a large amount of uncertainty surrounding these estimates. In this study, we gathered historical data on adult sea lamprey captures from trapping efforts from the mid-1950's through the late 1970's to better understand pre-control abundance. We used this information to estimate lake-wide population abundances of adult sea lamprey using a weighted linear regression that includes environmental and lampricide treatment predictor variables. We varied trapping efficiency for early trapping data to evaluate the uncertainty in abundance estimates. Pre-control adult sea lamprey abundances in all lakes were much greater than current population sizes, but estimates were quite sensitive to trapping efficiency. In Lake Superior, declines in abundance aligned with increases in control efforts, but in other lakes, declines were occurring prior to the onset of lampricide application, perhaps because of a loss of prey. We suggest that previous estimates of pre-control adult sea lamprey abundance may have been underestimated unless trapping efficiency was greater than what is currently achieved in the basin.
The predominant grazing-management practice of the Kansas Flint Hills involves annual prescribed burning in March or April with postfire grazing by yearling beef cattle at a high stocking density from April to August. There has been a dramatic increase in sericea lespedeza (Lespedeza cuneata [Dumont] G. Don) coincident with this temporally focused use of prescribed fire in the Flint Hills region. The species is an aggressive invader and a statewide noxious weed in Kansas. Control has generally been attempted using repeated herbicide applications. This approach has not limited proliferation of sericea lespedeza and resulted in collateral damage to nontarget flora and fauna. Alternative timing of prescribed fire has not been evaluated for its control. Our objectives for this 4-yr experiment were to (1) document the effects of prescribed burning during early April, early August, or early September on vigor of sericea lespedeza, standing forage biomass, and basal cover of native graminoids, forbs, and shrubs and (2) measure responses to fire regimes by grassland bird and butterfly communities. Whole-plant dry mass, basal cover, and seed production of sericea lespedeza were markedly less (P < 0.01) in areas treated with prescribed fire in August or September compared with April. Forage biomass did not differ (P ≥ 0.43) among treatments when measured during July; moreover, frequencies of bare soil, litter, and total basal plant cover were not different (P ≥ 0.29) among treatments. Combined basal covers of C4 grasses, C3 grasses, annual grasses, forbs, and shrubs also did not differ (P ≥ 0.11) between treatments. Densities of grasshopper sparrow (Ammodramus savannarum), dickcissel (Spiza americana), and eastern meadowlark (Sturnella magna) were not negatively affected (P > 0.10) by midsummer or late-summer fires relative to early-spring fires. There were no differences (P > 0.10) in densities of grassland-specialist butterfly species across fire regimes. Under the conditions of our experiment, prescribed burning during summer produced no detrimental effects on forage production, desirable nontarget plant species, grassland birds, or butterfly communities but had strong suppressive effects on sericea lespedeza. Additional research is warranted to investigate how to best incorporate late-summer prescribed fire into common grazing-management practices in the Kansas Flint Hills.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/tas/txab079","usgsCitation":"Alexander, J., Fick, W.H., Ogden, S., Haukos, D.A., Lemmon, J., Gatson, G.A., and Olson, K.C., 2021, Effects of prescribed fire timing on vigor of the invasive forb sericea lespedeza (Lespedeza cuneata), total forage biomass accumulation, plant-community composition, and native fauna on tallgrass prairie in the Kansas Flint Hills: Translational Animal Science, v. 5, no. 2, txab079, 16 p., https://doi.org/10.1093/tas/txab079.","productDescription":"txab079, 16 p.","ipdsId":"IP-095662","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":452452,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/tas/txab079","text":"Publisher Index Page"},{"id":396765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","county":"Geary County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.99142456054688,\n 39.22693426244916\n ],\n [\n -96.95571899414062,\n 39.22693426244916\n ],\n [\n -96.95571899414062,\n 39.254588032219935\n ],\n [\n -96.99142456054688,\n 39.254588032219935\n ],\n [\n -96.99142456054688,\n 39.22693426244916\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Alexander, Jonathan","contributorId":273845,"corporation":false,"usgs":false,"family":"Alexander","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":837057,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fick, Walter H.","contributorId":273077,"corporation":false,"usgs":false,"family":"Fick","given":"Walter","email":"","middleInitial":"H.","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":837056,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ogden, Sarah","contributorId":273076,"corporation":false,"usgs":false,"family":"Ogden","given":"Sarah","email":"","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":837055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837052,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lemmon, Jack","contributorId":273844,"corporation":false,"usgs":false,"family":"Lemmon","given":"Jack","email":"","affiliations":[],"preferred":false,"id":837054,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gatson, Garth A.","contributorId":273846,"corporation":false,"usgs":false,"family":"Gatson","given":"Garth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":837053,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olson, K. C.","contributorId":273843,"corporation":false,"usgs":false,"family":"Olson","given":"K.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":837264,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223879,"text":"70223879 - 2021 - Intact landscape promotes gene flow and low genetic structuring in the threatened Eastern Massasauga Rattlesnake","interactions":[],"lastModifiedDate":"2021-09-13T13:20:31.409068","indexId":"70223879","displayToPublicDate":"2021-05-02T08:10:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Intact landscape promotes gene flow and low genetic structuring in the threatened Eastern Massasauga Rattlesnake","docAbstract":"Genetic structuring of wild populations is dependent on environmental, ecological, and life-history factors. The specific role environmental context plays in genetic structuring is important to conservation practitioners working with rare species across areas with varying degrees of fragmentation. We investigated fine-scale genetic patterns of the federally threatened Eastern Massasauga Rattlesnake (Sistrurus catenatus) on a relatively undisturbed island in northern Michigan, USA. This species often persists in habitat islands throughout much of its distribution due to extensive habitat loss and distance-limited dispersal. We found that the entire island population exhibited weak genetic structuring with spatially segregated variation in effective migration and genetic diversity. The low level of genetic structuring contrasts with previous studies in the southern part of the species’ range at comparable fine scales (~7 km), in which much higher levels of structuring were documented. The island population's genetic structuring more closely resembles that of populations from Ontario, Canada, that occupy similarly intact habitats. Intrapopulation variation in effective migration and genetic diversity likely corresponds to the presence of large inland lakes acting as barriers and more human activity in the southern portion of the island. The observed genetic structuring in this intact landscape suggests that the Eastern Massasauga is capable of sufficient interpatch movements to reduce overall genetic structuring and colonize new habitats. Landscape mosaics with multiple habitat patches and localized barriers (e.g., large water bodies or roads) will promote gene flow and natural colonization for this declining species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.7480","usgsCitation":"Kudla, N., McCluskey, E.M., Lulla, V., Grundel, R., and Moore, J.A., 2021, Intact landscape promotes gene flow and low genetic structuring in the threatened Eastern Massasauga Rattlesnake: Ecology and Evolution, v. 11, no. 11, p. 6276-6288, https://doi.org/10.1002/ece3.7480.","productDescription":"13 p.","startPage":"6276","endPage":"6288","ipdsId":"IP-120488","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452455,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.7480","text":"Publisher Index Page"},{"id":436385,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HJW59U","text":"USGS data release","linkHelpText":"Genotype Data for Eastern Massasauga Rattlesnakes (Sistrurus catenatus) from Bois Blanc Island, Michigan at 15 Microsatellite DNA Loci"},{"id":389141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Bois Blanc Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.39697265625,\n 45.72152152227954\n ],\n [\n -84.34890747070312,\n 45.774707263032546\n ],\n [\n -84.40177917480469,\n 45.78907308856107\n ],\n [\n -84.42649841308594,\n 45.81827218518002\n ],\n [\n -84.42924499511719,\n 45.807743127853776\n ],\n [\n -84.4244384765625,\n 45.79338211440398\n ],\n [\n -84.43267822265625,\n 45.79003067864973\n ],\n [\n -84.49928283691406,\n 45.81157210628936\n ],\n [\n -84.51507568359375,\n 45.81300790534134\n ],\n [\n -84.53361511230469,\n 45.80965764997408\n ],\n [\n -84.58786010742188,\n 45.821621922335794\n ],\n [\n -84.59609985351562,\n 45.80917902561322\n ],\n [\n -84.57344055175781,\n 45.79816953017265\n ],\n [\n -84.54391479492188,\n 45.77901739936284\n ],\n [\n -84.5240020751953,\n 45.754109791149894\n ],\n [\n -84.51576232910155,\n 45.74883944887109\n ],\n [\n -84.50065612792967,\n 45.72583576754234\n ],\n [\n -84.43611145019531,\n 45.7205627558654\n ],\n [\n -84.41688537597656,\n 45.71480981187499\n ],\n [\n -84.39697265625,\n 45.72152152227954\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Kudla, Nathan","contributorId":265592,"corporation":false,"usgs":false,"family":"Kudla","given":"Nathan","email":"","affiliations":[{"id":15305,"text":"Grand Valley State University","active":true,"usgs":false}],"preferred":false,"id":823068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCluskey, Eric M.","contributorId":265593,"corporation":false,"usgs":false,"family":"McCluskey","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":15305,"text":"Grand Valley State University","active":true,"usgs":false}],"preferred":false,"id":823069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lulla, Vijay","contributorId":265594,"corporation":false,"usgs":false,"family":"Lulla","given":"Vijay","email":"","affiliations":[{"id":54727,"text":"Indiana University Purdue University Indianapolis","active":true,"usgs":false}],"preferred":false,"id":823070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":823071,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Jennifer A.","contributorId":265595,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":15305,"text":"Grand Valley State University","active":true,"usgs":false}],"preferred":false,"id":823072,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222086,"text":"70222086 - 2021 - Polar bear foraging behavior","interactions":[],"lastModifiedDate":"2021-07-19T23:42:46.043117","indexId":"70222086","displayToPublicDate":"2021-05-01T18:40:13","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Polar bear foraging behavior","docAbstract":"Polar bears forage in the marine environment, primarily on the sea ice over the shallow waters of the continental shelf. They are solitary, ambush hunters that catch ringed and bearded seals when they surface to breathe in ice holes or haul out on the ice to rest and molt. In most parts of their range, polar bears experience dramatic seasonal variability in their ability to catch seals, with foraging success peaking in late spring and early summer when seal pups are weaned. During this time, the body mass of polar bears can nearly double, especially in pregnant females, such that body composition may reach 49% body fat. The accumulation of body fat is vital for these bears to survive through the autumn and winter when seals are less accessible or when pregnant adult female bears enter dens and fast. When the sea ice retreats in summer, some bears exhibit a temporary switch to omnivory, feeding on a variety of terrestrial food. However, the energetic benefit of most terrestrial food is small relative to their marine mammal prey and, in some regions, increased land use has been associated with declines in body condition. Reduced accessibility of seal prey to polar bears as a result of global climate change threatens the long-term sustainability of this Arctic predator.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Ethology and behavioral ecology of sea otters and polar bears","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-66796-2_13","usgsCitation":"Pagano, A.M., 2021, Polar bear foraging behavior, chap. of Ethology and behavioral ecology of sea otters and polar bears, p. 247-267, https://doi.org/10.1007/978-3-030-66796-2_13.","productDescription":"21 p.","startPage":"247","endPage":"267","ipdsId":"IP-112145","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":452458,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-030-66796-2_13","text":"Publisher Index Page"},{"id":387260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":819458,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222087,"text":"70222087 - 2021 - Sea otter predator avoidance behavior","interactions":[],"lastModifiedDate":"2021-07-19T23:38:55.532952","indexId":"70222087","displayToPublicDate":"2021-05-01T18:34:28","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Sea otter predator avoidance behavior","docAbstract":"Predators directly affect their prey as a source of mortality, and prey respond by employing antipredator strategies. Sea otters are a keystone predator within the nearshore community, but higher trophic level avian, terrestrial, and pelagic predators (e.g., bald eagles, brown bears, wolves, white sharks, and killer whales) prey on them. Three antipredator strategies used by sea otters are vigilance (group or sentinel detection of danger), avoidance (seeking a location that is inaccessible to predators), and crypsis (the ability to avoid observation or detection). Vigilant behavior allowed sea otters to escape total extinction during the Maritime Fur Trade of the eighteenth and nineteenth centuries. Female otters with pups practice vigilance when they reduce their foraging time and move along meandering paths. Sea otters usually rest at sea, and when they rest on shore, they usually haul out on offshore rocks, reefs, and small islands—possibly a behavioral response to terrestrial predators (brown bears and wolves can kill non-vigilant sea otters on shore). In areas where many sea otters haul out together, group vigilance may be important in detecting an approaching threat. Along the coast of central California, white sharks are a significant source of sea otter mortality, and the only antipredator strategy is avoidance or crypsis by resting in kelp beds. Despite the threat, sea otters still forage in open water, so the perception of risk may be low. In the western Aleutian Islands, killer whale predation is believed to be the cause of a > 90% decline in sea otters. As a result, sea otters perceive killer whales as a threat and limit their movements to shallow, complex habitats where the risk of attack is low. This behavioral response is so strong in the western Aleutian Islands that it may it limit sea otter dispersal among islands, with implications for the connectivity and genetic health of the small, isolated populations that remain.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Ethology and behavioral ecology of sea otters and polar bears","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-66796-2_9","usgsCitation":"Monson, D., 2021, Sea otter predator avoidance behavior, chap. of Ethology and behavioral ecology of sea otters and polar bears, p. 161-172, https://doi.org/10.1007/978-3-030-66796-2_9.","productDescription":"12 p.","startPage":"161","endPage":"172","ipdsId":"IP-117074","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":452459,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-030-66796-2_9","text":"Publisher Index Page"},{"id":387259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":819459,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222049,"text":"70222049 - 2021 - 2020 National Park Visitor Spending Effects Economic Contributions to Local Communities, States,and the Nation","interactions":[],"lastModifiedDate":"2021-07-15T21:52:15.890092","indexId":"70222049","displayToPublicDate":"2021-05-01T16:49:09","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/EQD/NRR--2021/2259","title":"2020 National Park Visitor Spending Effects Economic Contributions to Local Communities, States,and the Nation","docAbstract":"The National Park Service (NPS) manages the Nation’s most iconic destinations that attract millions of visitors from across the Nation and around the world. Trip-related spending by NPS visitors generates and supports economic activity within park gateway communities. This report summarizes the annual economic contribution analysis that measures how NPS visitor spending cycles through local economies, generating business sales and supporting jobs and income. In 2020, the National Park System received over 237 million recreation visits (down 28% from 2019). Visitors to national parks spent an estimated $14.5 billion in local gateway regions (down 31% from 2019). The estimated contribution of this spending to the national economy was 234,000 jobs, $9.7 billion in labor income, $16.7 billion in value added, and $28.6 billion in economic output. The lodging sector saw the highest direct effects, with $5 billion in economic output directly contributed to this sector nationally. The restaurants sector saw the next greatest effects, with $3 billion in economic output directly contributed to this sector nationally. Results from the Visitor Spending Effects report series are available online via an interactive tool. Users can view year-by-year trend data and explore current year visitor spending, jobs, labor income, value added, and economic output effects by sector for national, state, and local economies. The interactive tool is available at https://www.nps.gov/subjects/socialscience/vse.htm.
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Yet, little is known about the extent of this vulnerability and the estuary-specific drivers that contribute to acidification, such as nutrient enrichment from stormwater, agriculture and wastewater discharges, upwelling of CO2 -rich seawater, elevated atmospheric CO2 from urban and agricultural activities, benthic and marsh-driven processes, and alkalinity and carbon content of freshwater flows. Comprehensive, high resolution monitoring data are needed at varying spatial and temporal scales to provide actionable information tailored to each estuary. Because carbonate chemistry in the coastal environment can be affected by nutrient dynamics, understanding how nutrient inputs exacerbate acidification impacts is essential for the formulation of estuary-specific actions.
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