Siliceous sinter is formed by biogenic and abiogenic opal deposition around hot springs and geysers. Using Structure-from-Motion photogrammetry we generated three-dimensional models of Giant and Castle Geysers from the Upper Geyser Basin of Yellowstone National Park. We use these models to calculate an approximate mass of sinter for each (~2 and ~ 5 kton, respectively) and estimate a range of plausible long-term deposition rates for Castle Geyser (470 to 940 kg·yr−1). We estimate ~2% of the silica discharged from Castle Geyser is deposited as sinter in the cone and proximal terraces. We collected 15 sinter samples following the stratigraphy of each geyser from an older terrace to a younger cone and examined them using a variety of analytical methods. We find that young opaline sinter with a water content of <12 wt% (from loss on ignition) contains higher concentrations of major and trace elements, notably As, Sb, Rb, Ga and Cs, relative to older dehydrated sinter. Rare earth element (REE) concentrations in sinter are 2–3 orders of magnitude higher than in the thermal water from which they are deposited. Sinter deposits are enriched in light REE, Gd and Yb when normalized to concentrations in thermal water and enriched in Eu, Tm, and Yb when normalized to the underlying rhyolite. Sinter samples with the highest REE concentrations are also enriched in organic material, implying either microbial uptake of REE, or that organic molecules are efficient ligands that form metal complexes.
We describe recent improvements to the Third Uniform California Earthquake Rupture Forecast ETAS Model (UCERF3‐ETAS), which continues to represent our most advanced and complete earthquake forecast in terms of relaxing segmentation assumptions and representing multifault ruptures, elastic‐rebound effects, and spatiotemporal clustering (the latter to represent aftershocks and otherwise triggered events). The two main improvements include adding aleatory variability in aftershock productivity and the option to represent off‐fault events with finite‐rupture surfaces. We also summarize the studies that led to these modifications, and reflect on how past and future uses of the model can improve our understanding of earthquake processes and the hazards and risks they pose.
Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real-time to estimate time-varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base-flow recession, unsaturated infiltration models, or Water-Table Fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real-time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics-based State-Space (SS) model of one-dimensional infiltration to estimate recharge, which includes preferential and diffuse-flow to the water table. The model can take advantage of real-time data for water-table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water-table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily versus sub-daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.
","language":"English","publisher":"Wiley","doi":"10.1029/2020WR029110","usgsCitation":"Shapiro, A.M., and Day-Lewis, F., 2021, Estimating and forecasting time-varying groundwater recharge in fractured rock: A state-space formulation with preferential and diffuse flow to the water table: Water Resources Research, v. 57, no. 9, e2020WR029110, 30 p., https://doi.org/10.1029/2020WR029110.","productDescription":"e2020WR029110, 30 p.","ipdsId":"IP-122279","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450863,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr029110","text":"Publisher Index Page"},{"id":436205,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VBR9V8","text":"USGS data release","linkHelpText":"Algorithms for model parameter estimation, state estimation, and forecasting applied to a State-Space model coupled with the Kalman Filter for one-dimensional vertical infiltration to fractured rock aquifers"},{"id":436204,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LLXCIC","text":"USGS data release","linkHelpText":"Water Level Altitude in Bedrock Wells and Meteorological Data at the Masser Groundwater Recharge Site between February 1 and December 31, 1999"},{"id":389205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":823234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823235,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223784,"text":"70223784 - 2021 - Global drivers of avian haemosporidian infections vary across zoogeographical regions","interactions":[],"lastModifiedDate":"2021-11-16T15:41:14.844104","indexId":"70223784","displayToPublicDate":"2021-09-08T14:05:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Global drivers of avian haemosporidian infections vary across zoogeographical regions","docAbstract":"Aim: Macroecological analyses provide valuable insights into factors that influence how parasites are distributed across space and among hosts. Amid large uncertainties that arise when generalizing from local and regional findings, hierarchical approaches applied to global datasets are required to determine whether drivers of parasite infection patterns vary across scales. We assessed global patterns of haemosporidian infections across a broad diversity of avian host clades and zoogeographical realms to depict hotspots of prevalence and to identify possible underlying drivers.
Location: Global.
Time period: 1994–2019.
Major taxa studied: Avian haemosporidian parasites (genera Plasmodium, Haemoproteus, Leucocytozoon and Parahaemoproteus).
Methods: We amalgamated infection data from 53,669 individual birds representing 2,445 species world-wide. Spatio-phylogenetic hierarchical Bayesian models were built to disentangle potential landscape, climatic and biotic drivers of infection probability while accounting for spatial context and avian host phylogenetic relationships.
Results: Idiosyncratic responses of the three most common haemosporidian genera to climate, habitat, host relatedness and host ecological traits indicated marked variation in host infection rates from local to global scales. Notably, host ecological drivers, such as migration distance for Plasmodium and Parahaemoproteus, exhibited predominantly varying or even opposite effects on infection rates across regions, whereas climatic effects on infection rates were more consistent across realms. Moreover, infections in some low-prevalence realms were disproportionately concentrated in a few local hotspots, suggesting that regional-scale variation in habitat and microclimate might influence transmission, in addition to global drivers.
Main conclusions: Our hierarchical global analysis supports regional-scale findings showing the synergistic effects of landscape, climate and host ecological traits on parasite transmission for a cosmopolitan and diverse group of avian parasites. Our results underscore the need to account for such interactions, in addition to possible variation in drivers across regions, to produce the robust inference required to predict changes in infection risk under future scenarios.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1111/geb.13390","usgsCitation":"Fecchio, A., Clark, N.J., Bell, J.A., Skeen, H., Lutz, H.L., De La Torre, G.M., Vaughan, J.A., Tkach, V.V., Schunck, F., Ferreira, F.C., Braga, E.M., Lugarini, C., Wamiti, W., Dispoto, J.H., Galen, S.C., Kirchgatter, K., Sagario, M.C., Cueto, V., Gonzalez-Acuna, D., Inumaru, M., Sato, Y., Schumm, Y.R., Quillfeldt, P., Pellegrino, I., Dharmarajan, G., Gupta, P., Robin, V.V., Ciloglu, A., Yildirim, A., Huang, X., Chapa-Vargas, L., Alvarez-Mendizabal, P., Santiago-Alarcon, D., Drovetski, S.V., Hellgren, O., Voelker, G., Ricklefs, R.E., Hackett, S., Collins, M.D., Weckstein, J.D., and Wells, K., 2021, Global drivers of avian haemosporidian infections vary across zoogeographical regions: Global Ecology and Biogeography, v. 30, no. 12, p. 2393-2406, https://doi.org/10.1111/geb.13390.","productDescription":"14 p.","startPage":"2393","endPage":"2406","temporalStart":"1994-01-01","temporalEnd":"2019-12-31","ipdsId":"IP-126030","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450866,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1980622","text":"External Repository"},{"id":388968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"editors":[{"text":"Kamath, Pauline","contributorId":198306,"corporation":false,"usgs":false,"family":"Kamath","given":"Pauline","affiliations":[],"preferred":false,"id":822778,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Fecchio, Alan 0000-0002-7319-0234","orcid":"https://orcid.org/0000-0002-7319-0234","contributorId":265372,"corporation":false,"usgs":false,"family":"Fecchio","given":"Alan","email":"","affiliations":[{"id":54651,"text":"Programa de Pós-Graduação em Ecologia e Conservação da Biodiversidade, Universidade Federal de Mato Grosso, Avenida Fernando Corrêa da Costa 2367, Cuiabá, MT, 78060900, Brazil","active":true,"usgs":false}],"preferred":false,"id":822666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Nicholas J.","contributorId":204867,"corporation":false,"usgs":false,"family":"Clark","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":16755,"text":"University of Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":822667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Jeffrey A","contributorId":265373,"corporation":false,"usgs":false,"family":"Bell","given":"Jeffrey","email":"","middleInitial":"A","affiliations":[{"id":52695,"text":"Department of Biology, University of North Dakota, Grand Forks, ND 58201, USA","active":true,"usgs":false}],"preferred":false,"id":822668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skeen, Heather","contributorId":265374,"corporation":false,"usgs":false,"family":"Skeen","given":"Heather","email":"","affiliations":[{"id":54652,"text":"Committee on Evolutionary Biology, University of Chicago, Chicago, IL, 6063 and Negaunee Integrative Research Center, The Field Museum, Chicago, IL, 60605 USA","active":true,"usgs":false}],"preferred":false,"id":822669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lutz, Holly L","contributorId":265375,"corporation":false,"usgs":false,"family":"Lutz","given":"Holly","email":"","middleInitial":"L","affiliations":[{"id":54653,"text":"Department of Surgery, University of Chicago, 5812 S. Ellis Ave., Chicago, IL 60637 and Integrative Research Center, Field Museum of Natural History, 1400 South Lake Shore Drive, Chicago, IL 60605 USA","active":true,"usgs":false}],"preferred":false,"id":822670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"De La Torre, Gabriel M","contributorId":265384,"corporation":false,"usgs":false,"family":"De La Torre","given":"Gabriel","email":"","middleInitial":"M","affiliations":[{"id":54663,"text":"Programa de Pós-graduação em Ecologia e Conservação, Universidade Federal do Paraná, Curitiba, PR, Brazil","active":true,"usgs":false}],"preferred":false,"id":822681,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vaughan, Jefferson A","contributorId":265376,"corporation":false,"usgs":false,"family":"Vaughan","given":"Jefferson","email":"","middleInitial":"A","affiliations":[{"id":52695,"text":"Department of Biology, University of North Dakota, Grand Forks, ND 58201, USA","active":true,"usgs":false}],"preferred":false,"id":822671,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tkach, Vasyl V.","contributorId":190351,"corporation":false,"usgs":false,"family":"Tkach","given":"Vasyl","email":"","middleInitial":"V.","affiliations":[{"id":52695,"text":"Department of Biology, University of North Dakota, Grand Forks, ND 58201, USA","active":true,"usgs":false}],"preferred":false,"id":822672,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schunck, Fabio","contributorId":265377,"corporation":false,"usgs":false,"family":"Schunck","given":"Fabio","email":"","affiliations":[{"id":54654,"text":"Brazilian Committee for Ornithological Records – CBRO","active":true,"usgs":false}],"preferred":false,"id":822673,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ferreira, Francisco C","contributorId":265378,"corporation":false,"usgs":false,"family":"Ferreira","given":"Francisco","email":"","middleInitial":"C","affiliations":[{"id":54655,"text":"Center for Conservation Genomics, Smithsonian Conservation Biology Institute, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":822674,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Braga, Erika M","contributorId":265379,"corporation":false,"usgs":false,"family":"Braga","given":"Erika","email":"","middleInitial":"M","affiliations":[{"id":54658,"text":"Departamento de Parasitologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil","active":true,"usgs":false}],"preferred":false,"id":822675,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lugarini, Camile","contributorId":265380,"corporation":false,"usgs":false,"family":"Lugarini","given":"Camile","email":"","affiliations":[{"id":54659,"text":"Centro Nacional de Pesquisa e Conservação de Aves Silvestres, Instituto Chico Mendes de Conservação da Biodiversidade, Florianópolis, SC, Brazil","active":true,"usgs":false}],"preferred":false,"id":822676,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wamiti, Wanyoike","contributorId":265381,"corporation":false,"usgs":false,"family":"Wamiti","given":"Wanyoike","email":"","affiliations":[{"id":54660,"text":"Zoology Department, National Museums of Kenya, P.O. 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D","contributorId":265391,"corporation":false,"usgs":false,"family":"Weckstein","given":"Jason","email":"","middleInitial":"D","affiliations":[{"id":54670,"text":"Department of Ornithology, Academy of Natural Sciences of Drexel University, Philadelphia, PA 19103, USA and Department of Biodiversity, Earth, and Environmental Sciences, Drexel University, Philadelphia, PA 19103, USA","active":true,"usgs":false}],"preferred":false,"id":822690,"contributorType":{"id":1,"text":"Authors"},"rank":40},{"text":"Wells, Konstans","contributorId":265392,"corporation":false,"usgs":false,"family":"Wells","given":"Konstans","email":"","affiliations":[{"id":54671,"text":"Department of Biosciences, Swansea University, Swansea, SA2 8PP UK","active":true,"usgs":false}],"preferred":false,"id":822691,"contributorType":{"id":1,"text":"Authors"},"rank":41}]}} ,{"id":70223785,"text":"70223785 - 2021 - Thermal stability of an adaptable, invasive ectotherm: Argentine giant tegus in the Greater Everglades ecosystem, USA","interactions":[],"lastModifiedDate":"2021-09-08T19:02:29.353507","indexId":"70223785","displayToPublicDate":"2021-09-08T11:53:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Thermal stability of an adaptable, invasive ectotherm: Argentine giant tegus in the Greater Everglades ecosystem, USA","docAbstract":"Invasive species globally threaten biodiversity and economies, but the ecophysiological mechanisms underlying their success are often understudied. For those alien species that also exhibit high phenotypic plasticity, such as habitat generalists, adaptations in response to environmental pressures can take place relatively quickly. The Argentine giant tegu (Salvator merianae; tegu) is a large omnivorous lizard from South America that is prolific, long-lived, vagile, and highly adaptable to disturbed environments. They are well suited to the climate of southeastern United States, introduced to several disjunct areas, including the Everglades, where their voracious appetite threatens native wildlife. Tegus undergo winter dormancy (hibernation) to cope with colder temperatures, and while this behavior may facilitate invasion into more temperate regions, it may also present management opportunities. We studied the thermal habits of wild S. merianae within their invaded range in southern Florida, USA. We used radiotelemetry and trail cameras to verify aboveground behaviors, and temperature dataloggers to monitor surface (sun-exposed [Te] and shaded [Ts]), ambient (Ta), subsurface ground (Th), and internal body (Tb) temperatures of a population of free-ranging tegus over several seasons. We evaluated thermal and behavioral data and identified five biologically significant periods: pre-hibernal, hibernal, cold snaps, hibernal-basking, and post-hibernal. We found tegus maintained thermal stability throughout the hibernal period, frequently at temperatures above available thermal microhabitats. Variation in Tb was lowest during hibernation and cold snaps and was less variable than subsurface temperatures despite not leaving their hibernaculum. Hibernal ingress and egress were best predicted by temperature differentials between exposed soil and ambient daily mean temperatures (Te − Ta) and daylength. Though we detected no sex differences, larger animals started hibernation sooner, stayed in hibernation longer, and retained higher fat stores over the study period. One individual did not hibernate, representing only the second record of this behavior. Despite limitations of these descriptive data, this is the first study finely detailing Tb of a population of wild, free-ranging S. merianae over multiple biologically significant time periods and to associate Tb with thermal habitats within its invasive range. Tegus' apparent ability for thermal stability expands the adaptability breadth of this species and underscores the invasion threat.
A large, low pressure Nor’easter storm and Hurricane Joaquin contributed to multiple weeks of sustained, elevated wave and water level conditions along the southeastern Atlantic coast of the United States in Fall 2015. Sea level anomalies in excess of 1 m and offshore wave heights of up to 4 m were recorded during these storms, as observed at the U.S. Army Corps of Engineers’ Field Research Facility in Duck, NC, USA. In response to these energetic oceanographic conditions, there were highly variable morphologic changes to the dune over short spatial scales (<km) which included a range of responses from vertical dune scarping to no measureable response. The portion of the study area with the largest dune erosion occurred at a location fronted by an abnormally deep nearshore bathymetric feature, which altered surf-zone waves and hydrodynamics. The pre-storm beach and dune topography also varied throughout the study area, additionally influencing the frequency of dune collision and contributing to the spatially variable erosion patterns. This work uses field datasets and numerical modeling tools to investigate the causation of hotspot dune erosion at the Field Research Facility. Three different numerical models were tested against the available data in order to assess model skill at resolving complex spatial dune erosion patterns. The three models successfully reproduce the general spatial trends in alongshore variable responses, although not necessarily the details of profile response or net erosion magnitude. Analysis of the model outputs, in conjunction with the available field data, suggests that the observed hotspot dune erosion is related to a complex combination of both topographic and bathymetric controls on the processes driving dune erosion. Therefore, the most simplistic model tested, which only accounts for alongshore variations in topographic profile details, can only predict hotspot dune erosion in locations where steep beach and/or dune topography is the primary control on collisional dune impacts. The higher fidelity models, which account for feedback effects from subaqueous morphology, are similarly able to predict the locations of maximum hotspot erosion, but are sensitive to beach over-steepening and/or errors in wave runup calculations that can lead to over-prediction of simulated dune erosion. This work highlights that numerous existing tools are capable of identifying the foredune regions at most risk from hotspot erosion, as well as the need for continued research to improve representation of all relevant intra-storm morphodynamic processes.
Coastal erosion, including sea-cliff retreat, represents both an important component of some sediment budgets and a significant threat to coastal communities in the face of rising sea level. Despite the importance of predicting future rates of coastal erosion, few prehistoric constraints exist on the relative importance of sediment supplied by coastal erosion versus rivers with respect to past sea-level change. We used detrital zircon U-Pb geochronology as a provenance tracer of river and deep-sea fan deposits from the Southern California Borderland (United States) to estimate relative sediment contributions from rivers and coastal erosion from late Pleistocene to present. Mixture modeling of submarine canyon and fan samples indicates that detrital zircon was dominantly (55%–86%) supplied from coastal erosion during latest Pleistocene (ca. 13 ka) sea-level rise, with lesser contributions from rivers, on the basis of unique U-Pb age modes relative to local Peninsular Ranges bedrock sources. However, sediment that was deposited when sea level was stable at its highest and lowest points since the Last Glacial Maximum was dominantly supplied by rivers, suggesting decreased coastal erosion during periods of sea-level stability. We find that relative sediment supply from coastal erosion is strongly dependent on climate state, corroborating predictions of enhanced coastal erosion during future sea-level rise.
Critical mineral resources titanium, zirconium, and rare earth elements occur in placer deposits over vast parts of the U.S. Atlantic Coastal Plain. Key questions regarding provenance, pathways of minerals to deposit sites, and relations to geologic features remain unexplained. As part of a national effort to collect data over regions prospective for critical minerals, the first public high-resolution aeroradiometric survey over the U.S. Atlantic Coastal Plain was conducted over Quaternary sediments in South Carolina. The new data provide an unprecedented view of potential deposits by imaging Th-bearing minerals in the heavy mineral assemblage. Sand ridges show the highest radiometric Th values with localized, linear anomalies, especially along the shoreface and in areas reworked by multiple processes and/or during multiple episodes. Estuarine areas with finer-grained sediments show lower, distributed Th anomalies. Th values averaged over geologic unit areas are similar for both environments, suggesting that heavy minerals are present but have not been locally concentrated in the lower-energy estuarine environments. Radiometric K highlights immature minerals such as mica and potassium feldspar. K is elevated within shallow sediments younger than ca. 130 ka, an attribute that persists in regional data from northern South Carolina to northern Florida. Both K and Th are elevated over the floodplains of the Santee River and other rivers with headwaters in the igneous and metamorphic Piedmont Terrane. The persistence of K anomalies for distances of more than 100 km from the Santee River floodplain suggests that heavy minerals are delivered from the Piedmont to offshore areas by major rivers, transported along the coast by the longshore current, and redeposited onshore by marine processes.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GSATG512A.1","usgsCitation":"Shah, A.K., Morrow, R., Pace, M., Harris, M., and Doar, W., 2021, Mapping critical minerals from the sky: GSA Today, v. 31, 7 p., https://doi.org/10.1130/GSATG512A.1.","productDescription":"7 p.","ipdsId":"IP-122675","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":450884,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/gsatg512a.1","text":"Publisher Index Page"},{"id":389590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.189453125,\n 34.288991865037524\n ],\n [\n -80.4638671875,\n 33.90689555128866\n ],\n [\n -81.58447265624999,\n 32.861132322810946\n ],\n [\n -82.11181640625,\n 31.93351676190369\n ],\n [\n -82.28759765625,\n 30.95876857077987\n ],\n [\n -82.02392578125,\n 30.050076521698735\n ],\n [\n -81.6943359375,\n 29.286398892934763\n ],\n [\n -80.9912109375,\n 29.209713225868185\n ],\n [\n -80.7275390625,\n 29.592565403314087\n ],\n [\n -80.96923828125,\n 30.732392734006083\n ],\n [\n -80.35400390625,\n 31.914867503276223\n ],\n [\n -79.21142578125,\n 32.37996146435729\n ],\n [\n -78.24462890625,\n 33.119150226768866\n ],\n [\n -78.15673828125,\n 33.63291573870479\n ],\n [\n -79.013671875,\n 34.30714385628804\n ],\n [\n -79.189453125,\n 34.288991865037524\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":823741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrow, Robert 0000-0001-5282-2389","orcid":"https://orcid.org/0000-0001-5282-2389","contributorId":265921,"corporation":false,"usgs":false,"family":"Morrow","given":"Robert","email":"","affiliations":[{"id":54824,"text":"SC Geological Survey","active":true,"usgs":false}],"preferred":false,"id":823742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pace, Michael 0000-0003-2770-5724","orcid":"https://orcid.org/0000-0003-2770-5724","contributorId":216678,"corporation":false,"usgs":true,"family":"Pace","given":"Michael","email":"","affiliations":[],"preferred":true,"id":823743,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, M.Scott 0000-0002-9220-788X","orcid":"https://orcid.org/0000-0002-9220-788X","contributorId":265922,"corporation":false,"usgs":false,"family":"Harris","given":"M.Scott","email":"","affiliations":[{"id":35839,"text":"College of Charleston","active":true,"usgs":false}],"preferred":false,"id":823744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doar, William 0000-0002-9895-8422","orcid":"https://orcid.org/0000-0002-9895-8422","contributorId":265923,"corporation":false,"usgs":false,"family":"Doar","given":"William","email":"","affiliations":[{"id":54824,"text":"SC Geological Survey","active":true,"usgs":false}],"preferred":false,"id":823745,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223848,"text":"70223848 - 2021 - The application of metacommunity theory to the management of riverine ecosystems","interactions":[],"lastModifiedDate":"2021-10-18T15:04:59.504944","indexId":"70223848","displayToPublicDate":"2021-09-08T07:01:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"The application of metacommunity theory to the management of riverine ecosystems","docAbstract":"River managers strive to use the best available science to sustain biodiversity and ecosystem function. To achieve this goal requires consideration of processes at different scales. Metacommunity theory describes how multiple species from different communities potentially interact with local-scale environmental drivers to influence population dynamics and community structure. However, this body of knowledge has only rarely been used to inform management practices for river ecosystems. In this article, we present a conceptual model outlining how the metacommunity processes of local niche sorting and dispersal can influence the outcomes of management interventions and provide a series of specific recommendations for applying these ideas as well as research needs. In all cases, we identify situations where traditional approaches to riverine management could be enhanced by incorporating an understanding of metacommunity dynamics. A common theme is developing guidelines for assessing the metacommunity context of a site or region, evaluating how that context may affect the desired outcome, and incorporating that understanding into the planning process and methods used. To maximize the effectiveness of management activities, scientists, and resource managers should update the toolbox of approaches to riverine management to reflect theoretical advances in metacommunity ecology.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1557","usgsCitation":"Patrick, C.J., Anderson, K.E., Brown, B.L., Hawkins, C.P., Metcalfe, A.N., Saffarinia, P., Siqueira, T., Swan, C.M., Tonkin, J.D., and Yuan, L.L., 2021, The application of metacommunity theory to the management of riverine ecosystems: WIREs Water, v. 8, no. 6, e1557, 21 p., https://doi.org/10.1002/wat2.1557.","productDescription":"e1557, 21 p.","ipdsId":"IP-119036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450888,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1557","text":"Publisher Index Page"},{"id":389050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Patrick, Christopher J.","contributorId":199778,"corporation":false,"usgs":false,"family":"Patrick","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":822932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kurt E.","contributorId":265545,"corporation":false,"usgs":false,"family":"Anderson","given":"Kurt","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":822933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Brown L.","contributorId":265546,"corporation":false,"usgs":false,"family":"Brown","given":"Brown","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":822934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawkins, Charles P.","contributorId":198331,"corporation":false,"usgs":false,"family":"Hawkins","given":"Charles","email":"","middleInitial":"P.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":822935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Metcalfe, Anya N. 0000-0002-6286-4889 ametcalfe@usgs.gov","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":5271,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","email":"ametcalfe@usgs.gov","middleInitial":"N.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saffarinia, Parsa","contributorId":265547,"corporation":false,"usgs":false,"family":"Saffarinia","given":"Parsa","email":"","affiliations":[],"preferred":false,"id":822937,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Siqueira, Tadeu","contributorId":265548,"corporation":false,"usgs":false,"family":"Siqueira","given":"Tadeu","email":"","affiliations":[],"preferred":false,"id":822938,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Swan, Christopher M.","contributorId":265549,"corporation":false,"usgs":false,"family":"Swan","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":822939,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tonkin, Jonathan D.","contributorId":260624,"corporation":false,"usgs":false,"family":"Tonkin","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":822940,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yuan, Lester L.","contributorId":198316,"corporation":false,"usgs":false,"family":"Yuan","given":"Lester","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":822941,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223780,"text":"fs20213046 - 2021 - Streamflow—Water year 2020","interactions":[],"lastModifiedDate":"2026-06-23T20:57:22.793043","indexId":"fs20213046","displayToPublicDate":"2021-09-07T19:14:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3046","displayTitle":"Streamflow—Water Year 2020","title":"Streamflow—Water year 2020","docAbstract":"The maps and graphs in this summary describe national streamflow conditions for water year 2020 (October 1, 2019, to September 30, 2020) in the context of streamflow ranks relative to the 91-year period of water years 1930–2020. Annual runoff in the Nation’s rivers and streams during water year 2020 (11.10 inches) was higher than the long-term (1930–2020) mean annual runoff of 9.40 inches for the contiguous United States. Nationwide, the 2020 streamflow ranked the 10th highest out of the 91 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213046","usgsCitation":"Jian, X., Wolock, D.M., Lins, H.F., Henderson, R.J., and Brady, S.J., 2021, Streamflow—Water year 2020: U.S. Geological Survey Fact Sheet 2021–3046, 6 p., https://doi.org/10.3133/fs20213046.","productDescription":"Report: 6 p.; Dataset","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-129901","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":388902,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3046/fs20213046.pdf","text":"Report","size":"3.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3046"},{"id":388903,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the 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415 National Center
Reston, Virginia 20192
Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse.
To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow.
The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [R2]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period.
The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature.
The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance (R2=0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance (R2=0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent (R2=0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations.
The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215093","usgsCitation":"McShane, R.R., and Eddy-Miller, C.A., 2021, A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17: U.S. Geological Survey Scientific Investigations Report 2021–5093, 29 p., https://doi.org/10.3133/sir20215093.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-117330","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388893,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCP1AE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input data, model output, and R scripts for a machine learning streamflow model on the Wyoming Range, Wyoming, 2012–17"},{"id":388895,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.xml","text":"Report","size":"219 kB","linkFileType":{"id":8,"text":"xml"}},{"id":388896,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5093/images"},{"id":388894,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5093/coverthb.jpg"},{"id":388892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5093"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.90972900390625,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.09618442380296\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The restoration of coastal habitats, particularly coral reefs, can reduce risks by decreasing the exposure of coastal communities to flooding hazards. In the United States, the protective services provided by coral reefs were recently assessed in social and economic terms, with the annual protection provided by U.S. coral reefs off the coasts of the State of Florida and the Commonwealth of Puerto Rico estimated to be more than 9,800 people and $859 million (2010 U.S. dollars). Hurricanes Irma and Maria in 2017 caused widespread damage to coral reefs in the State of Florida and the Commonwealth of Puerto Rico. Here we combine engineering, ecologic, geospatial, social, and economic data and tools to provide a rigorous valuation of where potential coral reef restoration could decrease the hazard faced by Florida and Puerto Rico’s reef-fronted coastal communities. The three restoration scenarios considered: (1) Ecological restoration, ‘E25’, which assumes planting 0.25-meter (m)-high corals on a (cross-shore) 25-m-wide reef; (2) Structural plus ecological, ‘S25’, which assumes emplacing a 1.00-m high structure with 0.25-m high corals on top on a 25 m wide reef; and (3) structural plus ecological, ‘S05’, which assumes emplacing a 1.00-m high structure with 0.25-m high corals on top on a 5 m wide reef. Planted corals are assumed to increase hydrodynamic roughness, thereby dissipating incident wave energy and decreasing flooding potential. We used a standardized approach to ‘place’ potential restoration projects throughout the whole (linear) extent of reefs bordering Florida and Puerto Rico to identify where coral reef restoration could be useful for meeting flood reduction benefits. We always sited potential restoration projects within the existing distribution of reefs even though many sites were far (kilometers [km]) offshore and some sites were relatively deep (up to 7 m depth). We followed risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 980 km of Florida and Puerto’s Rico reef-lined shorelines for the three potential coral reef restoration scenarios and compare them to the flood zones without coral reef restoration. We quantified the potential coastal flood risk reduction provided by coral reef restoration using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculate the change in annual expected damages, a measure of the annual protection gained because of coral reef restoration. We found that the benefits of reef restoration off Florida and Puerto Rico are spatially highly variable. In most areas, we found little or no benefit from reef restoration (for example, restoration sites were far offshore or deep). However, there were a number of key areas where reef restoration could have substantial benefits for flood risk reduction. In particular, we estimated the protection gained by Florida and Puerto Rico’s coral reefs from coral reef restoration to result in:
Thus, the annual value of flood risk reduction provided by potential coral reef restoration in Florida and Puerto Rico is more than 3,100 people and $272.9 million (2010 U.S. dollars) in economic activity. These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when potential coral reef restoration in Florida and Puerto Rico can increase critical coastal storm flood reduction benefits. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida and Puerto Rico’s coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211054","collaboration":"Prepared in cooperation with the University of California, Santa Cruz","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Cumming, K.A., Cole, A.D., Shope, J.B., Gaido L., C., Viehman, T.S., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the coastal hazard risks reduction provided by potential coral reef restoration in Florida and Puerto Rico: U.S. Geological Survey Open-File Report 2021–1054, 35 p., https://doi.org/10.3133/ofr20211054.","productDescription":"Report: vi, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-125062","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386211,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1054/covrthb.jpg"},{"id":386212,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1054/ofr20211054.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386214,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZQKZR9","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida, the Commonwealth of Puerto Rico, and the Territory of the U.S. Virgin Islands for current and potentially restored coral reefs"},{"id":386215,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211055","text":"Open-File Report 2021-1055","linkHelpText":"- Rigorously Valuing the Impact of Projected Coral Reef Degradation on Coastal Hazard Risk in Florida"},{"id":386216,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211056","text":"Open-File Report 2021-1056","linkHelpText":"- Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico"}],"country":"United States","state":"Florida","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.390380859375,\n 18.594188856740413\n ],\n [\n -66.7529296875,\n 18.70869162255995\n ],\n [\n -67.08251953125,\n 18.729501999072138\n ],\n [\n -67.32421875,\n 18.510865709091377\n ],\n [\n -67.401123046875,\n 18.25021997706561\n ],\n [\n -67.30224609375,\n 17.916022703877665\n ],\n [\n -66.895751953125,\n 17.853290114098012\n ],\n [\n -66.42333984375,\n 17.8742034396575\n ],\n [\n -65.885009765625,\n 17.821915515968854\n ],\n [\n -65.467529296875,\n 17.926475979176438\n ],\n [\n -65.32470703125,\n 18.218916080017465\n ],\n [\n -65.511474609375,\n 18.46918890441719\n ],\n [\n -66.390380859375,\n 18.594188856740413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
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Santa Cruz, CA 95060
The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. In the United States, the physical protective services provided by coral reefs were recently assessed, in social and economic terms, with the annual protection provided by U.S. coral reefs off the coast of the State of Florida estimated to be more than 5,600 people and $675 million (2010 U.S. dollars). Degradation of coral reef ecosystems over the past several decades and during tropical storm events has caused regional-scale erosion of the shallow seafloor that serves as a protective barrier against coastal hazards along Southeast Florida, increasing risks to coastal populations. Here we combine engineering, ecologic, geospatial, social, and economic data and tools to provide a rigorous valuation of the increased hazard faced by Florida’s reef-fronted coastal communities because of the projected degradation of its adjacent coral reefs. We followed risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 430 kilometers of Florida’s reef-lined shorelines for both the current and projected future coral reef conditions. We quantified the coastal flood risk increase caused by coral reef degradation using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculated the change in annual expected damages, a measure of the annual protection lost because of projected coral reef degradation. We found that degradation of the coral reefs off Florida increases future risks significantly. In particular, we estimated the protection lost by Florida’s coral reefs from projected coral reef degradation will result in:
Thus, the annual value of increased flood risk caused by the projected degradation of Florida’s coral reefs is more than 7,300 people and $823.6 million (2010 U.S. dollars). These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when degradation of Florida’s coral reefs will decrease critical coastal storm flood reduction benefits. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida’s coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211055","collaboration":"Prepared in cooperation with the University of California, Santa Cruz","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Yates, K.K., Cumming, K.A., Cole, A.D., Shope, J.B., Gaido L., C., Zawada, D.G., Arsenault, S.R., Fehr, Z.W., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the impact of projected coral reef degradation on coastal hazard risk in Florida: U.S. Geological Survey Open-File Report 2021–1055, 27 p., https://doi.org/10.3133/ofr20211055.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-125063","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386221,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211054","text":"Open-File Report 2021-1054","linkHelpText":"- Rigorously Valuing the Potential Coastal Hazard Risk Reduction Provided by Coral Reef Restoration in Florida and Puerto Rico"},{"id":386222,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211056","text":"Open-File Report 2021-1056","linkHelpText":"- Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico"},{"id":386220,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D9LDEP","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida with and without projected coral reef degradation"},{"id":386218,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1055/covrthb.jpg"},{"id":386219,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1055/ofr20211055.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
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Santa Cruz, CA 95060
The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. In the United States, the physical protective services provided by coral reefs were recently assessed in social and economic terms, with the annual protection provided by U.S. coral reefs off the coasts of the State of Florida and the Commonwealth of Puerto Rico estimated to be more than 9,800 people and $859 million (2010 U.S. dollars). Hurricanes Irma and Maria in 2017 caused widespread damage to coral reefs in the State of Florida and the Commonwealth of Puerto Rico. These damages were measured in post-storm surveys of reefs and assessed in terms of their impact on reef condition and height, which are critical parameters for evaluating the coastal defense benefits of reefs. We combined engineering, ecologic, geospatial, social, and economic data and tools to value the increased risks in Florida and Puerto Rico from hurricane-induced damages to their adjacent coral reefs. We followed risk-based valuation approaches to map flooding at 10-square-meter resolution along all 980 kilometers of Florida and Puerto Rico’s reef-lined shorelines considering reef condition before (undamaged) and after (damaged) the 2017 hurricanes. We quantified the coastal flood risk increase caused by the hurricane-induced damage to the coral reefs using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculated the change in annual expected damages, a measure of the annual protection lost because of the reef damage caused by the 2017 hurricanes. We found that the damages to the coral reefs off Florida and Puerto Rico from Hurricanes Irma and Maria increased future risks significantly. In particular, we estimated the protection lost by Florida and Puerto Rico’s coral reefs from the 2017 hurricanes to result in:
Thus, the annual value of increased flood risk caused by the damage to Florida and Puerto Rico’s coral reefs from hurricanes in 2017 is more than 4,300 people and $181.5 mil-lion (2010 U.S. dollars) in economic impacts. These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when the damage from the 2017 hurricanes decreased critical coastal storm flood reduction benefits to Florida and Puerto Rico’s coral reefs. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida and Puerto Rico’s coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211056","collaboration":"Prepared in cooperation with the University of California, Santa Cruz and the National Oceanic and Atmospheric Administration","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Viehman, T.S., Cumming, K.A., Cole, A.D., Shope, J.B., Groves, S.H., Gaido L., C., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the impact of Hurricanes Irma and Maria on coastal hazard risks in Florida and Puerto Rico: U.S. Geological Survey Open-File Report 2021–1056, 29 p., https://doi.org/10.3133/ofr20211056.","productDescription":"Report: v, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-125064","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386227,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EHOBKO","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida and the Commonwealth of Puerto Rico before and after Hurricanes Irma and Maria due to the storms' damage to the coral reefs"},{"id":386229,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211055","text":"Open-File Report 2021-1055","linkHelpText":"- Rigorously Valuing the Impact of Projected Coral Reef Degradation on Coastal Hazard Risk in Florida"},{"id":386225,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1056/covrthb.jpg"},{"id":386226,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1056/ofr20211056.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386228,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211054","text":"Open-File Report 2021-1054","linkHelpText":"- Rigorously Valuing the Potential Coastal Hazard Risk Reduction Provided by Coral Reef Restoration in Florida and Puerto Rico"}],"country":"United States","state":"Florida","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.368408203125,\n 18.542116654448996\n ],\n [\n -66.961669921875,\n 18.646245142670608\n ],\n [\n -67.269287109375,\n 18.552532366385577\n ],\n [\n -67.357177734375,\n 18.198043686762652\n ],\n [\n -67.236328125,\n 17.916022703877665\n ],\n [\n -66.59912109375,\n 17.832374329567518\n ],\n [\n -65.643310546875,\n 17.95783210227242\n ],\n [\n -65.291748046875,\n 18.22935133838668\n ],\n [\n -65.54443359375,\n 18.500447458475094\n ],\n [\n -66.368408203125,\n 18.542116654448996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060
Interactions between neighboring plants are critical for biodiversity maintenance in plant populations and communities. Intraspecific trait variation and genome duplication are common in plant species and can drive eco-evolutionary dynamics through genotype-mediated plant–plant interactions. However, few studies have examined how species-wide intraspecific variation may alter interactions between neighboring plants. We investigate how subspecies and ploidy variation in a genetically diverse species, big sagebrush (Artemisia tridentata), can alter the demographic outcomes of plant interactions. Using a replicated, long-term common garden experiment that represents range-wide diversity of A. tridentata, we ask how intraspecific variation, environment, and stand age mediate neighbor effects on plant growth and survival. Spatially explicit models revealed that ploidy variation and subspecies identity can mediate plant–plant interactions but that the effect size varied in time and across experimental sites. We found that demographic impacts of neighbor effects were strongest during early stages of stand development and in sites with greater growth rates. Within subspecies, tetraploid populations showed greater tolerance to neighbor crowding compared to their diploid variants. Our findings provide evidence that intraspecific variation related to genome size and subspecies identity impacts spatial demography in a genetically diverse plant species. Accounting for intraspecific variation in studies of conspecific density dependence will improve our understanding of how local populations will respond to novel genotypes and biotic interaction regimes. As introduction of novel genotypes into local populations becomes more common, quantifying demographic processes in genetically diverse populations will help predict long-term consequences of plant–plant interactions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3502","usgsCitation":"Zaiats, A., Germino, M., Serpe, M.D., Richardson, B., and Caughlin, T., 2021, Intraspecific variation mediates density dependence in a genetically diverse plant species: Ecology, v. 102, no. 11, e03502, 11 p., https://doi.org/10.1002/ecy.3502.","productDescription":"e03502, 11 p.","ipdsId":"IP-122281","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":388887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Utah","city":"Ephraim, Majors Flat, Orchard","otherGeospatial":"Great Basin Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.861572265625,\n 43.46886761482925\n ],\n [\n -114.04907226562499,\n 41.9921602333763\n ],\n [\n -114.06005859375,\n 37.00255267215955\n ],\n [\n -113.148193359375,\n 37.59682400108367\n ],\n [\n -112.06054687499999,\n 39.39375459224348\n ],\n [\n -111.64306640625,\n 40.10328591293439\n ],\n [\n -111.90673828125,\n 40.83874913796459\n ],\n [\n -112.071533203125,\n 42.00032514831621\n ],\n [\n -115.8837890625,\n 43.492782808225\n ],\n [\n -116.861572265625,\n 43.46886761482925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii","contributorId":257073,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":822590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":822591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Serpe, Marcelo D.","contributorId":257074,"corporation":false,"usgs":false,"family":"Serpe","given":"Marcelo","email":"","middleInitial":"D.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":822592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richardson, Bryce 0000-0001-9521-4367","orcid":"https://orcid.org/0000-0001-9521-4367","contributorId":195702,"corporation":false,"usgs":false,"family":"Richardson","given":"Bryce","email":"","affiliations":[],"preferred":false,"id":822593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":822594,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223771,"text":"70223771 - 2021 - Pedigree accumulation analysis: Combining methods from community ecology and population genetics for breeding adult estimation","interactions":[],"lastModifiedDate":"2021-12-10T16:43:01.819527","indexId":"70223771","displayToPublicDate":"2021-09-07T10:51:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Pedigree accumulation analysis: Combining methods from community ecology and population genetics for breeding adult estimation","docAbstract":"The basic stratigraphic and structural framework of Mount Diablo is described using a revised geologic map, gravity data, and aeromagnetic data. The mountain is made up of two distinct stratigraphic assemblages representing different depocenters that were juxtaposed by ~20 km of late Pliocene and Quaternary right-lateral offset on the Greenville-Diablo-Concord fault. Both assemblages are composed of Cretaceous and Cenozoic strata overlying a compound basement made up of the Franciscan and Great Valley complexes. The rocks are folded and faulted by late Neogene and Quaternary compressional structures related to both regional plate-boundary–normal compression and a restraining step in the strike-slip fault system. The core of the mountain is made up of uplifted basement rocks. Late Neogene and Quaternary deformation is overprinted on Paleogene extensional deformation that is evidenced at Mount Diablo by significant attenuation in the basement rocks and by an uptilted stepped graben structure on the northeast flank. Retrodeformation of the northeast flank suggests that late Early to early Late Cretaceous strata may have been deposited against and across a steeply west-dipping basement escarpment. The location of the mountain today was a depocenter through the Late Cretaceous and Paleogene and received shallow-marine deposits periodically into the late Miocene. Uplift of the mountain itself happened mostly in the Quaternary.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Regional geology of Mount Diablo, California: Its tectonic evolution on the North America plate boundary","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.1217(01)","usgsCitation":"Graymer, R., and Langenheim, V., 2021, Geologic framework of Mount Diablo, California, chap. of Regional geology of Mount Diablo, California: Its tectonic evolution on the North America plate boundary, v. 217, p. 1-34, https://doi.org/10.1130/2021.1217(01).","productDescription":"34 p.","startPage":"1","endPage":"34","ipdsId":"IP-113894","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":450894,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/figure/Supplemental_Material_Geologic_framework_of_Mount_Diablo_California/15148989","text":"External Repository"},{"id":388591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mount Diablo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.025146484375,\n 37.7897092979573\n ],\n [\n -121.80198669433592,\n 37.7897092979573\n ],\n [\n -121.80198669433592,\n 37.92632597629602\n ],\n [\n -122.025146484375,\n 37.92632597629602\n ],\n [\n -122.025146484375,\n 37.7897092979573\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"217","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Graymer, Russell 0000-0003-4910-5682","orcid":"https://orcid.org/0000-0003-4910-5682","contributorId":207816,"corporation":false,"usgs":true,"family":"Graymer","given":"Russell","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":822042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":206990,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[],"preferred":true,"id":822043,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229777,"text":"70229777 - 2021 - Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","interactions":[],"lastModifiedDate":"2022-03-17T15:32:45.548565","indexId":"70229777","displayToPublicDate":"2021-09-07T10:17:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","docAbstract":"We develop a self‐consistent scaling model relating magnitude Mw to surface rupture length (LE), surface displacement DE, and rupture width WE, for strike‐slip faults. Knowledge of the long‐term fault‐slip rate SF improves magnitude estimates. Data are collected for 55 ground‐rupturing strike‐slip earthquakes that have geological estimates of LE, DE, and SF, and geophysical estimates of WE. We begin with the model of Anderson et al. (2017), which uses a closed form equation for the seismic moment of a surface‐rupturing strike‐slip fault of arbitrary aspect ratio and given stress drop, ΔτC. Using WE estimates does not improve Mw estimates. However, measurements of DE plus the relationship between ΔτC and surface slip provide an alternate approach to study WE. A grid of plausible stress drop and width pairs were used to predict displacement and earthquake magnitude. A likelihood function was computed from within the uncertainty ranges of the corresponding observed Mw and DE values. After maximizing likelihoods over earthquakes in length bins, we found the most likely values of WE for constant stress drop; these depend on the rupture length. The best‐fitting model has the surprising form WE∝logLE—a gentle increase in width with rupture length. Residuals from this model are convincingly correlated to the fault‐slip rate and also show a weak correlation with the crustal thickness. The resulting model thus supports a constant stress drop for ruptures of all lengths, consistent with teleseismic observation. The approach can be extended to test other observable factors that might improve the predictability of magnitude from a mapped fault for seismic hazard analyses.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210113","usgsCitation":"Anderson, J.G., Biasi, G., Angster, S.J., and Wesnousky, S., 2021, Improved scaling relationships for seismic moment and average slip of strike-slip earthquakes incorporating fault slip rate, fault width and stress drop: Bulletin of the Seismological Society of America, v. 111, no. 5, p. 2379-2392, https://doi.org/10.1785/0120210113.","productDescription":"14 p.","startPage":"2379","endPage":"2392","ipdsId":"IP-117223","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, John G.","contributorId":140379,"corporation":false,"usgs":false,"family":"Anderson","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":929142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angster, Stephen J. 0000-0001-9250-8415","orcid":"https://orcid.org/0000-0001-9250-8415","contributorId":225610,"corporation":false,"usgs":true,"family":"Angster","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wesnousky, Stephen G.","contributorId":351624,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Stephen G.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":929145,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223483,"text":"sim3470 - 2021 - Geologic map of Olympus Mons caldera, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:13:31.216229","indexId":"sim3470","displayToPublicDate":"2021-09-07T10:02:40","publicationYear":"2021","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":"3470","displayTitle":"Geologic Map of Olympus Mons Caldera, Mars","title":"Geologic map of Olympus Mons caldera, Mars","docAbstract":"The Mars volcano, Olympus Mons, is probably the best known extraterrestrial volcano. The summit forms a nested caldera with six overlapping collapse pits that collectively measure ~65 x ~80 kilometers (km). Numerous wrinkle ridges and graben occur on the caldera floor, and topographic data indicate >1.2 km of elevation change since the formation of the floor as a series of lava lakes. The paths of lava flows on the south and southeast flanks do not conform to present-day topography. Mapping at a scale of 1:200,000 shows that the summit area displays a complex volcanic history that has numerous similarities to terrestrial shield volcanoes. Pangboche crater is a large (~10-km-diameter) crater of impact origin that lies on the south flank of the caldera and, because of the elevation and lack of volatiles, it displays numerous features more similar to fresh lunar craters than to impact craters on Mars.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3470","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Mouginis-Mark, P.J., 2021, Geologic map of Olympus Mons caldera, Mars: U.S. Geological Survey Scientific Investigations Map 3470, 6 p., 1 sheet, scale 1:200,000, https://doi.org/10.3133/sim3470.","productDescription":"Report: iv, 6 p.; Metadata; Read Me; 1 Sheet: 38.06 x 40.11 inches","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-107079","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436209,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95C2UHD","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3470 Geologic Map of Olympus Mons Caldera, Mars"},{"id":388840,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_gis_files.zip","text":"SIM 3470 GIS Files","size":"260 MB","linkFileType":{"id":6,"text":"zip"}},{"id":388595,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_sheet.pdf","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388596,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_metadata.txt","size":"20 KB","linkFileType":{"id":2,"text":"txt"}},{"id":388592,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3470/covrthb.jpg"},{"id":388593,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_pamphlet.pdf","text":"Pamphlet to Accompany Map Sheet","size":"1 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388594,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_readme.docx","text":"Read Me","size":"25 KB docx"},{"id":405427,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P95C2UHD","text":"Interactive map","description":"Mouginis-Mark, P.J., 2021, Geologic map of Olympus Mons caldera, Mars: U.S. Geological Survey Scientific Investigations Map 3470, 6 p., 1 sheet, scale 1:200,000, https://doi.org/10.3133/sim3470.","linkHelpText":"- Geologic Map of Olympus Mons Caldera, Mars, 1:200K. Mouginis-Mark (2021)"},{"id":388598,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_metadata.xml","size":"20 KB","linkFileType":{"id":8,"text":"xml"}}],"contact":"Contact Astrogeology Research Program staff
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U.S. Geological Survey
2255 N. Gemini Dr.
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Sea ducks exhibit complex movement patterns throughout their annual cycle; most species use distinct molting and staging sites during migration and disjunct breeding and wintering sites. Although research on black scoters (Melanitta americana) has investigated movements and habitat selection during winter, little is known about their annual-cycle movements. We used satellite telemetry to identify individual variation in migratory routes and breeding areas for black scoters wintering along the Atlantic Coast, to assess migratory connectivity among wintering, staging, breeding, and molt sites, and to examine effects of breeding site attendance on movement patterns and phenology. Black scoters occupied wintering areas from Canadian Maritime provinces to the southeastern United States. Males used an average of 2.5 distinct winter areas compared to 1.1 areas for females, and within-winter movements averaged 1,256 km/individual. Individuals used an average of 2.1 staging sites during the 45-day pre-breeding migration period, and almost all were detected in the Gulf of St. Lawrence. Males spent less time at breeding sites and departed them earlier than females. During post-breeding migration, females took approximately 25 fewer days than males to migrate from breeding sites to molt and staging sites, and then wintering areas. Most individuals used molt sites in James and Hudson bays before migrating directly to coastal wintering sites, which took approximately 11 days and covered 1,524 km. Males tended to arrive at wintering areas 10 days earlier than females. Individuals wintering near one another did not breed closer together than expected by chance, suggesting weak spatial structuring of the Atlantic population. Females exhibited greater fidelity (4.5 km) to previously used breeding sites compared to males (60 km). A substantial number of birds bred west of Hudson Bay in the Barrenlands, suggesting this area is used more widely than believed previously. Hudson and James bays provided key habitat for black scoters that winter along the Atlantic Coast, with most individuals residing for >30% of their annual cycle in these bays. Relative to other species of sea duck along the Atlantic Coast, the Atlantic population of black scoter is more dispersed and mobile during winter but is more concentrated during migration. These results could have implications for future survey efforts designed to assess population trends of black scoters.
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