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. Box 40658-00100, Nairobi, Kenya","active":true,"usgs":false}],"preferred":false,"id":822677,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dispoto, Janice H","contributorId":265382,"corporation":false,"usgs":false,"family":"Dispoto","given":"Janice","email":"","middleInitial":"H","affiliations":[{"id":54661,"text":"Department of Ornithology, Academy of Natural Sciences of Drexel University, Philadelphia, PA","active":true,"usgs":false}],"preferred":false,"id":822678,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Galen, Spencer C","contributorId":229671,"corporation":false,"usgs":false,"family":"Galen","given":"Spencer","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":822679,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kirchgatter, Karin","contributorId":265383,"corporation":false,"usgs":false,"family":"Kirchgatter","given":"Karin","email":"","affiliations":[{"id":54662,"text":"Laboratório de Bioquímica e Biologia Molecular, Superintendência de Controle de Endemias, São Paulo, SP 01027-000 and Instituto de Medicina Tropical, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP 05403-000, Brazil","active":true,"usgs":false}],"preferred":false,"id":822680,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sagario, M. Cecilia","contributorId":265385,"corporation":false,"usgs":false,"family":"Sagario","given":"M.","email":"","middleInitial":"Cecilia","affiliations":[{"id":54664,"text":"Grupo de Ecología Terrestre de Neuquén, Instituto de Investigaciones en Biodiversidad y Medioambiente (INIBIOMA–CONICET and UNComahue), and Centro de Ecología Aplicada del Neuquén (CEAN), Junín de los Andes, CP8371, Neuquén, Argentina","active":true,"usgs":false}],"preferred":false,"id":822682,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Cueto, Victor R","contributorId":265386,"corporation":false,"usgs":false,"family":"Cueto","given":"Victor R","affiliations":[{"id":54665,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CIEMEP), CONICET – Universidad Nacional de la Patagonia San Juan Bosco, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":822683,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Gonzalez-Acuna, Daniel","contributorId":265387,"corporation":false,"usgs":false,"family":"Gonzalez-Acuna","given":"Daniel","email":"","affiliations":[{"id":54666,"text":"Laboratorio de Parásitos y Enfermedades de Fauna Silvestre, Facultad de Ciencias Veterinarias, Universidad de Concepción, Avenida Vicente Méndez 595, Chillán, Chile","active":true,"usgs":false}],"preferred":false,"id":822684,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Inumaru, Mizue","contributorId":265460,"corporation":false,"usgs":false,"family":"Inumaru","given":"Mizue","email":"","affiliations":[{"id":34786,"text":"Nihon University, Japan","active":true,"usgs":false}],"preferred":false,"id":822763,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Sato, Yukita","contributorId":265461,"corporation":false,"usgs":false,"family":"Sato","given":"Yukita","email":"","affiliations":[{"id":34786,"text":"Nihon University, Japan","active":true,"usgs":false}],"preferred":false,"id":822764,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Schumm, Yvonne R.","contributorId":265462,"corporation":false,"usgs":false,"family":"Schumm","given":"Yvonne","email":"","middleInitial":"R.","affiliations":[{"id":38764,"text":"Justus Liebig University Giessen","active":true,"usgs":false}],"preferred":false,"id":822765,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Quillfeldt, Petra","contributorId":243493,"corporation":false,"usgs":false,"family":"Quillfeldt","given":"Petra","affiliations":[{"id":38764,"text":"Justus Liebig University Giessen","active":true,"usgs":false}],"preferred":false,"id":822766,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Pellegrino, Irene","contributorId":265463,"corporation":false,"usgs":false,"family":"Pellegrino","given":"Irene","email":"","affiliations":[],"preferred":false,"id":822767,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Dharmarajan, Guha","contributorId":265464,"corporation":false,"usgs":false,"family":"Dharmarajan","given":"Guha","email":"","affiliations":[{"id":33054,"text":"Savannah River Ecology Laboratory, University of Georgia, Aiken, South Carolina","active":true,"usgs":false}],"preferred":false,"id":822768,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Gupta, Pooja","contributorId":265465,"corporation":false,"usgs":false,"family":"Gupta","given":"Pooja","email":"","affiliations":[{"id":47860,"text":"University of Georgia Savannah River Ecology Laboratory, Aiken, SC, USA","active":true,"usgs":false}],"preferred":false,"id":822769,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Robin, V. V.","contributorId":265466,"corporation":false,"usgs":false,"family":"Robin","given":"V.","email":"","middleInitial":"V.","affiliations":[{"id":27952,"text":"Indian Institute of Science","active":true,"usgs":false}],"preferred":false,"id":822770,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Ciloglu, Arif","contributorId":260869,"corporation":false,"usgs":false,"family":"Ciloglu","given":"Arif","email":"","affiliations":[{"id":52698,"text":"Department of Parasitology, Faculty of Veterinary Medicine, Erciyes University, Kayseri, Turkey","active":true,"usgs":false}],"preferred":false,"id":822771,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Yildirim, Alparslan","contributorId":265467,"corporation":false,"usgs":false,"family":"Yildirim","given":"Alparslan","email":"","affiliations":[{"id":52698,"text":"Department of Parasitology, Faculty of Veterinary Medicine, Erciyes University, Kayseri, Turkey","active":true,"usgs":false}],"preferred":false,"id":822772,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Huang, Xi","contributorId":260871,"corporation":false,"usgs":false,"family":"Huang","given":"Xi","email":"","affiliations":[{"id":16866,"text":"Beijing Normal University","active":true,"usgs":false}],"preferred":false,"id":822773,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Chapa-Vargas, Leonardo","contributorId":265468,"corporation":false,"usgs":false,"family":"Chapa-Vargas","given":"Leonardo","email":"","affiliations":[{"id":50469,"text":"División de Ciencias Ambientales, Instituto Potosino de Investigación Científica y Tecnológica, San Luis Potosí, Mexico","active":true,"usgs":false}],"preferred":false,"id":822774,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Alvarez-Mendizabal, Paulina","contributorId":265469,"corporation":false,"usgs":false,"family":"Alvarez-Mendizabal","given":"Paulina","email":"","affiliations":[],"preferred":false,"id":822775,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Santiago-Alarcon, Diego","contributorId":265470,"corporation":false,"usgs":false,"family":"Santiago-Alarcon","given":"Diego","email":"","affiliations":[{"id":35220,"text":"University of South Florida, Tampa, FL, USA","active":true,"usgs":false}],"preferred":false,"id":822776,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":822685,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Hellgren, Olof","contributorId":140266,"corporation":false,"usgs":false,"family":"Hellgren","given":"Olof","email":"","affiliations":[{"id":13428,"text":"Lund University","active":true,"usgs":false}],"preferred":false,"id":822777,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Voelker, Gary","contributorId":229521,"corporation":false,"usgs":false,"family":"Voelker","given":"Gary","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":822686,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Ricklefs, Robert E","contributorId":265388,"corporation":false,"usgs":false,"family":"Ricklefs","given":"Robert","email":"","middleInitial":"E","affiliations":[{"id":54667,"text":"Department of Biology, University of Missouri-St. Louis, St. Louis, MO 63121, USA","active":true,"usgs":false}],"preferred":false,"id":822687,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Hackett, Shannon","contributorId":265389,"corporation":false,"usgs":false,"family":"Hackett","given":"Shannon","email":"","affiliations":[{"id":54668,"text":"The Richard and Jill Chaifetz Associate Curator of Birds, Life Sciences and Pritzker Lab Field Museum of Natural History, 1400 South Lake Shore Drive, Chicago, IL 60605, USA","active":true,"usgs":false}],"preferred":false,"id":822688,"contributorType":{"id":1,"text":"Authors"},"rank":38},{"text":"Collins, Michael D","contributorId":265390,"corporation":false,"usgs":false,"family":"Collins","given":"Michael","email":"","middleInitial":"D","affiliations":[{"id":54669,"text":"Department of Biology, Rhodes College, Memphis, TN 38112, USA","active":true,"usgs":false}],"preferred":false,"id":822689,"contributorType":{"id":1,"text":"Authors"},"rank":39},{"text":"Weckstein, Jason 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":70224627,"text":"70224627 - 2021 - Hotspot dune erosion on an intermediate beach","interactions":[],"lastModifiedDate":"2021-10-01T13:25:35.688431","indexId":"70224627","displayToPublicDate":"2021-09-08T08:21:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Hotspot dune erosion on an intermediate beach","docAbstract":"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.
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":70223775,"text":"sir20215093 - 2021 - A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","interactions":[],"lastModifiedDate":"2021-09-08T11:52:20.913559","indexId":"sir20215093","displayToPublicDate":"2021-09-07T19:13:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5093","displayTitle":"A Machine Learning Approach to Modeling Streamflow with Sparse Data in Ungaged Watersheds on the Wyoming Range, Wyoming, 2012–17","title":"A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","docAbstract":"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
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":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":70223753,"text":"70223753 - 2021 - A protocol for modelling generalised biological responses using latent variables in structural equation models","interactions":[],"lastModifiedDate":"2021-09-08T11:58:05.588979","indexId":"70223753","displayToPublicDate":"2021-09-07T09:21:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"A protocol for modelling generalised biological responses using latent variables in structural equation models","docAbstract":"In this paper we consider the problem of how to quantitatively characterize the degree to which a study object exhibits a generalized response. By generalized response, we mean a multivariate response where numerous individual properties change in concerted fashion due to some internal integration. In latent variable structural equation modeling (LVSEM), we would typically approach this situation using a latent variable to represent a general property of interest (e.g., performance) and multiple observed indicator variables that reflect the specific features associated with that general property. While ecologists have used LVSEM in a number of cases, there is substantial potential for its wider application. One obstacle is that LV models can be complex and easily over-specified, degrading their value as a means of generalization. It can also be challenging to diagnose causes of misspecification and understand which model modifications are sensible. In this paper we present a protocol, consisting of a series of questions, designed to guide the researchers through the evaluation process. These questions address (1) theoretical development, (2) data requirements, (3) whether responses to perturbation are general, (4) unique reactions by individual measures, and (5) how far generality can be extended. For this illustration, we reference a recent study considering the potential consequences of maintaining biodiversity as part of agricultural management on the overall quality of grapes used for wine making. We extend our presentation to include the complexities that occur when there are multiple species with unique reactions.","language":"English","publisher":"Pensoft Publishers","doi":"10.3897/oneeco.6.e67320","usgsCitation":"Grace, J., and Steiner, M., 2021, A protocol for modelling generalised biological responses using latent variables in structural equation models: One Ecosystem, v. 6, e67320, 20 p., https://doi.org/10.3897/oneeco.6.e67320.","productDescription":"e67320, 20 p.","ipdsId":"IP-128690","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450898,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.6.e67320","text":"Publisher Index Page"},{"id":388870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2021-07-08","publicationStatus":"PW","contributors":{"editors":[{"text":"Akomolafe, Gbenga","contributorId":265354,"corporation":false,"usgs":false,"family":"Akomolafe","given":"Gbenga","email":"","affiliations":[],"preferred":false,"id":822627,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Grace, James B. 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":220095,"corporation":false,"usgs":true,"family":"Grace","given":"James B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":822551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steiner, Magdalena","contributorId":265327,"corporation":false,"usgs":false,"family":"Steiner","given":"Magdalena","email":"","affiliations":[{"id":54645,"text":"University of Fribourg, Ecology and Evolution, Department of Biology","active":true,"usgs":false}],"preferred":false,"id":822552,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223756,"text":"70223756 - 2021 - Instrumental variable methods in structural equation models","interactions":[],"lastModifiedDate":"2021-09-07T14:17:22.994951","indexId":"70223756","displayToPublicDate":"2021-09-07T09:12:57","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":"Instrumental variable methods in structural equation models","docAbstract":"Accurate status assessments of long-lived, widely distributed taxa depend on the availability of long-term monitoring data from multiple populations. However, monitoring populations across large temporal and spatial scales is often beyond the scope of any one researcher or research group. Consequently, wildlife managers may be tasked with utilizing limited information from different sources to detect range-wide evidence of population declines and their causes. When assessments need to be made under such constraints, the research and management communities must determine how to extrapolate from variable population data to species-level inferences. Here, using three different approaches, we integrate and analyze data from the peer-reviewed literature and government agency reports to inform conservation for northwestern pond turtles (NPT) Actinemys marmorata and southwestern pond turtles (SPT) Actinemys pallida. Both NPT and SPT are long-lived freshwater turtles distributed along the west coast of the United States and Mexico. Conservation concerns exist for both species; however, SPT may face more severe threats and are thought to exist at lower densities throughout their range than NPT. For each species, we ranked the impacts of 13 potential threats, estimated population sizes, and modeled population viability with and without long-term droughts. Our results suggest that predation of hatchlings by invasive predators, such as American bullfrogs Lithobates catesbeianus and Largemouth Bass Micropterus salmoides, is a high-ranking threat for NPT and SPT. Southwestern pond turtles may also face more severe impacts associated with natural disasters (droughts, wildfires, and floods) than do NPT. Population size estimates from trapping surveys indicate that SPT have smaller population sizes on average than do NPT (P = 0.0003), suggesting they may be at greater risk of local extirpation. Population viability analysis models revealed that long-term droughts are a key environmental parameter; as the frequency of severe droughts increases with climate change, the likelihood of population recovery decreases, especially when census sizes are low. Given current population trends and vulnerability to natural disasters throughout their range, we suggest that conservation and recovery actions first focus on SPT to prevent further population declines.
The field of geospatial analytics and modeling has a long history coinciding with the physical and cultural evolution of humans. This history is analyzed relative to the four scientific paradigms: (1) empirical analysis through description, (2) theoretical explorations using models and generalizations, (3) simulating complex phenomena and (4) data exploration. Correlations among developments in general science and those of the geospatial sciences are explored. Trends identify areas ripe for growth and improvement in the fourth and current paradigm that has been spawned by the big data explosion, such as exposing the ‘black box’ of GeoAI training and generating big geospatial training datasets. Future research should focus on integrating both theory- and data-driven knowledge discovery.
About 14.5 months after the 2018 eruption and summit collapse of Kīlauea Volcano, Hawaiʻi, liquid water started accumulating in the deepened summit crater, forming a lake that attained 51 m depth before rapidly boiling off on December 20, 2020, when an eruption from the crater wall poured lava into the lake. Modeling the growth of the crater lake at Kīlauea summit is important for assessing the potential for explosive volcanism. Our current understanding of the past 2500 years of eruptive activity at Kīlauea suggests a slight dominance of explosive behavior over effusive. The deepened summit crater and presence of the crater lake in 2019 raised renewed concerns about explosive activity. Groundwater models using hydraulic-property data from a nearby drillhole successfully forecast the timing and rate of lake filling. Here we compare the groundwater-model predictions with observational data through the demise of the crater lake, examine the implications for local water-table configuration, consider the potential role of evaporation and recharge (neglected in previous models), and briefly discuss the energetics of the rapid boil-off. This post audit of groundwater-flow models of Kīlauea summit shows that simple models can sometimes be used effectively to simulate complex settings such as volcanoes.
Functional connectivity (i.e., the movement of individuals across a landscape) is essential for the maintenance of genetic variation and persistence of rare species. However, illuminating the processes influencing functional connectivity and ultimately translating this knowledge into management practice remains a fundamental challenge. Here, we combine various population structure analyses with pairwise, population-specific demographic modeling to investigate historical functional connectivity in Graham’s beardtongue (Penstemon grahamii), a rare plant narrowly distributed across a dryland region of the western US. While principal component and population structure analyses indicated an isolation-by-distance pattern of differentiation across the species’ range, spatial inferences of effective migration exposed an abrupt shift in population ancestry near the range center. To understand these seemingly conflicting patterns, we tested various models of historical gene flow and found evidence for recent admixture (~ 3400 generations ago) between populations near the range center. This historical perspective reconciles population structure patterns and suggests management efforts should focus on maintaining connectivity between these previously isolated lineages to promote the ongoing transfer of genetic variation. Beyond providing species-specific knowledge to inform management options, our study highlights how understanding demographic history may be critical to guide conservation efforts when interpreting population genetic patterns and inferring functional connectivity.
The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.
The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.
Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.
This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.87376937332855\n ],\n [\n -75.3826904296875,\n 37.83473402375478\n ],\n [\n -75.30441284179688,\n 37.88027325525864\n ],\n [\n -75.15335083007812,\n 38.11727165830543\n ],\n [\n -75.12039184570312,\n 38.29101446582335\n ],\n [\n -75.17120361328125,\n 38.22847167526397\n ],\n [\n -75.28793334960938,\n 38.0513353697269\n ],\n [\n -75.3826904296875,\n 37.93769926732864\n ],\n [\n -75.42388916015625,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Landscape features influence wildlife movements across spatial scales and have the potential to influence the spread of disease. Chronic wasting disease (CWD) is a fatal prion disease affecting members of the family Cervidae, particularly white-tailed deer (Odocoileus virginianus), and the first positive CWD case in a wild deer in Ohio, USA, was recorded in 2020. Landscape genetics approaches are increasingly used to better understand potential pathways for CWD spread in white-tailed deer, but little is known about genetic structure of white-tailed deer in Ohio. The objectives of our study were to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We collected genetic data from 619 individual deer from 24 counties across Ohio during 2007–2009. We used microsatellite genotypes from 619 individuals genotyped at 11 loci and haplotypes from a 547-base pair fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data. Non-spatial genetic clustering tests failed to detect spatial genetic structure, whereas spatial genetic clustering tests indicated subtle spatial genetic structure. The IBD model consistently outperformed IBR models that included land cover, traffic volume, and streams. Our results indicated widespread genetic connectivity of white-tailed deer across Ohio and negligible effects of landscape features. These patterns likely reflect some combination of minimal resistive effects of landscape features on white-tail deer movement in Ohio and the effects of regional recolonization or translocation. We encourage continued CWD surveillance in Ohio, particularly in the proximity of confirmed cases.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22120","usgsCitation":"Bauder, J., Anderson, C.S., Gibbs, H., Tonkovich, M., and Walter, W., 2021, Landscape features fail to explain spatial genetic structure in white-tailed deer across Ohio, USA: Journal of Wildlife Management, v. 85, no. 8, p. 1669-1684, https://doi.org/10.1002/jwmg.22120.","productDescription":"16 p.","startPage":"1669","endPage":"1684","ipdsId":"IP-128673","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-82.835118,41.708971],[-82.82572,41.72281],[-82.820409,41.724549],[-82.810487,41.720524],[-82.782719,41.694003],[-82.793069,41.664692],[-82.827011,41.633701],[-82.842099,41.628323],[-82.843602,41.647009],[-82.835118,41.708971]]],[[[-82.700208,41.61219],[-82.691123,41.611331],[-82.68015,41.61897],[-82.677772,41.617986],[-82.680669,41.594611],[-82.686033,41.587246],[-82.702027,41.585437],[-82.735766,41.600982],[-82.735707,41.603361],[-82.718802,41.619629],[-82.70731,41.619609],[-82.703438,41.617734],[-82.700208,41.61219]]],[[[-81.725583,39.215835],[-81.733357,39.205868],[-81.737085,39.193836],[-81.741533,39.189596],[-81.752754,39.184676],[-81.755754,39.180976],[-81.756254,39.177276],[-81.743565,39.141933],[-81.744838,39.130898],[-81.742153,39.116777],[-81.745453,39.098078],[-81.752353,39.089878],[-81.760753,39.084078],[-81.775554,39.078378],[-81.785554,39.078578],[-81.803055,39.083878],[-81.810655,39.083278],[-81.813855,39.079278],[-81.811655,39.059578],[-81.803355,39.047678],[-81.772854,39.026179],[-81.764253,39.015279],[-81.765153,39.002579],[-81.774062,38.993682],[-81.776723,38.985142],[-81.775734,38.980737],[-81.78182,38.964935],[-81.778845,38.955892],[-81.756975,38.937152],[-81.756131,38.933545],[-81.759995,38.925828],[-81.76976,38.92273],[-81.781248,38.924804],[-81.793372,38.930204],[-81.806137,38.942112],[-81.814235,38.946168],[-81.827354,38.945898],[-81.831516,38.943697],[-81.844486,38.928746],[-81.845312,38.910088],[-81.848653,38.901407],[-81.855971,38.892734],[-81.889233,38.874279],[-81.898541,38.874582],[-81.910312,38.879294],[-81.928,38.893492],[-81.926671,38.901311],[-81.90091,38.924338],[-81.89847,38.929603],[-81.900595,38.937671],[-81.933186,38.987659],[-81.941829,38.993295],[-81.951447,38.996032],[-81.967769,38.992955],[-81.979371,38.993193],[-81.982032,38.995697],[-81.987061,39.011978],[-81.994961,39.022478],[-82.002261,39.027878],[-82.017562,39.030078],[-82.035963,39.025478],[-82.041563,39.017878],[-82.045663,39.003778],[-82.051563,38.994378],[-82.091565,38.973778],[-82.094865,38.964578],[-82.109065,38.945579],[-82.111666,38.932579],[-82.128866,38.909979],[-82.143167,38.898079],[-82.145267,38.883479],[-82.139224,38.86502],[-82.144867,38.84048],[-82.16157,38.824632],[-82.179478,38.817376],[-82.191172,38.815137],[-82.20929,38.802672],[-82.217269,38.79568],[-82.221566,38.787187],[-82.220449,38.773739],[-82.216614,38.76835],[-82.198882,38.757725],[-82.195606,38.752441],[-82.193268,38.741182],[-82.188268,38.734082],[-82.182467,38.708782],[-82.190167,38.687382],[-82.190867,38.680383],[-82.186067,38.666783],[-82.185567,38.659583],[-82.179067,38.648883],[-82.172667,38.629684],[-82.172066,38.619284],[-82.177267,38.603784],[-82.188767,38.594984],[-82.205171,38.591719],[-82.222168,38.591384],[-82.245969,38.598483],[-82.26207,38.598183],[-82.27427,38.593683],[-82.291271,38.578983],[-82.293871,38.572683],[-82.293271,38.560283],[-82.295671,38.538483],[-82.303971,38.517683],[-82.304223,38.496308],[-82.310639,38.483172],[-82.318111,38.457876],[-82.323999,38.449268],[-82.330335,38.4445],[-82.34064,38.440948],[-82.381773,38.434783],[-82.389746,38.434355],[-82.404882,38.439347],[-82.529579,38.405182],[-82.549799,38.403202],[-82.569368,38.406258],[-82.588249,38.415489],[-82.596921,38.426705],[-82.600761,38.437425],[-82.604089,38.459841],[-82.610458,38.471457],[-82.618474,38.477089],[-82.637707,38.484449],[-82.657051,38.496816],[-82.675724,38.515504],[-82.689965,38.53592],[-82.700045,38.544336],[-82.730958,38.559264],[-82.763695,38.560399],[-82.779472,38.559023],[-82.800112,38.563183],[-82.820161,38.572703],[-82.844306,38.590862],[-82.854291,38.613454],[-82.856791,38.632878],[-82.856291,38.646078],[-82.859391,38.660378],[-82.863291,38.669277],[-82.874892,38.682827],[-82.877592,38.690177],[-82.870392,38.722077],[-82.871292,38.739376],[-82.879492,38.751476],[-82.889193,38.756076],[-82.894193,38.756576],[-82.923694,38.750076],[-82.943147,38.74328],[-82.968695,38.728776],[-82.979395,38.725976],[-83.011816,38.730057],[-83.030702,38.72572],[-83.053104,38.695831],[-83.064319,38.688976],[-83.084226,38.68109],[-83.102746,38.677316],[-83.112372,38.671685],[-83.122547,38.6592],[-83.128973,38.640231],[-83.135046,38.631719],[-83.142836,38.625076],[-83.156926,38.620547],[-83.202453,38.616956],[-83.211027,38.618578],[-83.232404,38.627569],[-83.245572,38.627936],[-83.254558,38.623403],[-83.264011,38.621535],[-83.26851,38.615104],[-83.286514,38.599241],[-83.294193,38.596588],[-83.307832,38.600824],[-83.317542,38.609242],[-83.322383,38.630615],[-83.327636,38.637489],[-83.356445,38.654009],[-83.384755,38.663171],[-83.420194,38.668428],[-83.446989,38.670143],[-83.468059,38.67547],[-83.493342,38.694187],[-83.504365,38.699256],[-83.520953,38.703045],[-83.533339,38.702105],[-83.569098,38.692842],[-83.615736,38.684145],[-83.626922,38.679387],[-83.636208,38.670584],[-83.642994,38.643273],[-83.649737,38.632753],[-83.663911,38.62793],[-83.679484,38.630036],[-83.704006,38.639724],[-83.713405,38.641591],[-83.720779,38.646704],[-83.74992,38.649613],[-83.769347,38.65522],[-83.775761,38.666748],[-83.78362,38.695641],[-83.787113,38.699489],[-83.798549,38.704668],[-83.821854,38.709575],[-83.836696,38.717857],[-83.841689,38.724264],[-83.846207,38.74229],[-83.852085,38.751433],[-83.859028,38.756793],[-83.873168,38.762418],[-83.926986,38.771562],[-83.928454,38.774583],[-83.943978,38.783616],[-83.962123,38.787384],[-83.978814,38.787104],[-84.044486,38.770572],[-84.071491,38.770475],[-84.108836,38.779247],[-84.135088,38.789485],[-84.155912,38.794909],[-84.198358,38.80092],[-84.212904,38.805707],[-84.2253,38.817665],[-84.231306,38.830552],[-84.233727,38.853576],[-84.232132,38.880483],[-84.234453,38.893226],[-84.25701,38.923208],[-84.279916,38.945168],[-84.288164,38.955789],[-84.295076,38.968295],[-84.297255,38.989694],[-84.304698,39.006455],[-84.31368,39.016981],[-84.326539,39.027463],[-84.346039,39.036963],[-84.360439,39.041362],[-84.38684,39.045162],[-84.406941,39.045662],[-84.42573,39.053059],[-84.429841,39.058262],[-84.432341,39.067561],[-84.432941,39.083961],[-84.434641,39.086861],[-84.432841,39.094261],[-84.435541,39.102261],[-84.445242,39.114461],[-84.455342,39.12036],[-84.470542,39.12146],[-84.476243,39.11916],[-84.487743,39.11076],[-84.496543,39.10026],[-84.509743,39.09366],[-84.524644,39.09216],[-84.541344,39.09916],[-84.550844,39.09936],[-84.572144,39.08206],[-84.607928,39.073238],[-84.620112,39.073457],[-84.632446,39.07676],[-84.657246,39.09546],[-84.684847,39.100459],[-84.718548,39.137059],[-84.732048,39.144458],[-84.744149,39.147458],[-84.754449,39.146658],[-84.766749,39.138558],[-84.78768,39.115297],[-84.820157,39.10548],[-84.819451,39.305152],[-84.814955,39.566251],[-84.814179,39.814212],[-84.803918,40.310094],[-84.804504,40.411555],[-84.802547,40.50181],[-84.803919,41.435531],[-84.806082,41.696089],[-84.360546,41.706621],[-83.453832,41.732647],[-83.455626,41.727445],[-83.446032,41.706847],[-83.409531,41.691247],[-83.39263,41.691947],[-83.37573,41.686647],[-83.357073,41.687763],[-83.341817,41.693518],[-83.337985,41.698682],[-83.337977,41.70341],[-83.326825,41.701562],[-83.293928,41.680846],[-83.29068,41.676794],[-83.238191,41.651167],[-83.23166,41.644218],[-83.194524,41.631008],[-83.145887,41.617904],[-83.103928,41.613558],[-83.086036,41.60668],[-83.066593,41.59534],[-83.043287,41.568205],[-83.028072,41.555656],[-82.999916,41.538534],[-82.96985,41.524327],[-82.934369,41.514353],[-82.897728,41.519241],[-82.875229,41.529684],[-82.85677,41.548262],[-82.855197,41.564114],[-82.859531,41.576371],[-82.852957,41.583327],[-82.834101,41.587587],[-82.820207,41.570664],[-82.794324,41.546486],[-82.785496,41.540675],[-82.77201,41.54058],[-82.749907,41.54647],[-82.717878,41.54193],[-82.711332,41.53695],[-82.711632,41.527201],[-82.721914,41.516677],[-82.713904,41.501697],[-82.710013,41.49759],[-82.687921,41.492324],[-82.658302,41.461878],[-82.617745,41.431833],[-82.616952,41.428425],[-82.55808,41.400005],[-82.513827,41.384257],[-82.499099,41.381541],[-82.481214,41.381342],[-82.460599,41.386316],[-82.431315,41.396866],[-82.398086,41.413945],[-82.361784,41.426644],[-82.334182,41.430243],[-82.29158,41.428442],[-82.268479,41.430842],[-82.254678,41.434441],[-82.193375,41.46454],[-82.184774,41.47404],[-82.181598,41.471634],[-82.165373,41.47444],[-82.094169,41.495039],[-82.011966,41.515639],[-81.994565,41.51444],[-81.964912,41.505446],[-81.958463,41.498642],[-81.937862,41.491443],[-81.87736,41.483445],[-81.860262,41.483841],[-81.810992,41.495592],[-81.794449,41.49663],[-81.782258,41.49605],[-81.744755,41.48715],[-81.738755,41.48855],[-81.710986,41.501734],[-81.664884,41.53143],[-81.633652,41.540458],[-81.599747,41.560649],[-81.529955,41.614374],[-81.50044,41.623448],[-81.48864,41.631348],[-81.466038,41.649148],[-81.441339,41.674074],[-81.353229,41.727743],[-81.301626,41.750543],[-81.286925,41.760243],[-81.264224,41.758143],[-81.248672,41.761291],[-81.112885,41.817571],[-81.092716,41.822988],[-81.05192,41.839557],[-81.024525,41.846469],[-81.01049,41.853962],[-80.936244,41.862352],[-80.900342,41.868912],[-80.814943,41.897694],[-80.799822,41.909749],[-80.784682,41.911525],[-80.782052,41.906635],[-80.757945,41.913352],[-80.720816,41.919744],[-80.581882,41.95761],[-80.519461,41.977513],[-80.518991,40.638801],[-80.551126,40.628847],[-80.56784,40.617552],[-80.576736,40.614224],[-80.583633,40.61552],[-80.592049,40.622496],[-80.598764,40.625263],[-80.627171,40.619936],[-80.634355,40.616095],[-80.651716,40.597744],[-80.662564,40.5916],[-80.667957,40.582496],[-80.666917,40.573664],[-80.652436,40.562544],[-80.633107,40.538705],[-80.627507,40.535793],[-80.618003,40.502049],[-80.609058,40.489506],[-80.599194,40.482566],[-80.595494,40.475266],[-80.596094,40.463366],[-80.612295,40.434867],[-80.612195,40.402667],[-80.615195,40.399867],[-80.632196,40.393667],[-80.633596,40.390467],[-80.631596,40.385468],[-80.619196,40.381768],[-80.609695,40.374968],[-80.607595,40.369568],[-80.612796,40.347668],[-80.610796,40.340868],[-80.602895,40.327869],[-80.599895,40.317669],[-80.602895,40.307069],[-80.614896,40.291969],[-80.616796,40.285269],[-80.616196,40.27227],[-80.619297,40.26517],[-80.622497,40.26177],[-80.644598,40.25127],[-80.652098,40.24497],[-80.661543,40.229798],[-80.6681,40.199671],[-80.6726,40.192371],[-80.686137,40.181607],[-80.704602,40.154823],[-80.710042,40.138311],[-80.710554,40.125271],[-80.706702,40.110872],[-80.709102,40.101472],[-80.713003,40.096872],[-80.730704,40.086472],[-80.738604,40.075672],[-80.737104,40.064972],[-80.733104,40.058772],[-80.730904,40.046672],[-80.731504,40.037472],[-80.737389,40.027593],[-80.741901,40.007929],[-80.738717,39.985113],[-80.740126,39.970793],[-80.758527,39.959241],[-80.764479,39.95025],[-80.761312,39.929098],[-80.756784,39.920586],[-80.756432,39.91393],[-80.759296,39.909466],[-80.762592,39.908906],[-80.772641,39.911306],[-80.782849,39.917162],[-80.795714,39.91969],[-80.806018,39.91713],[-80.809283,39.910314],[-80.809011,39.903226],[-80.802339,39.89161],[-80.793989,39.882787],[-80.790156,39.872252],[-80.790761,39.86728],[-80.799898,39.858912],[-80.821279,39.849982],[-80.826124,39.844238],[-80.826228,39.835802],[-80.82248,39.824971],[-80.822181,39.811708],[-80.826079,39.798584],[-80.835311,39.79069],[-80.863048,39.775197],[-80.869092,39.766364],[-80.869933,39.763555],[-80.865339,39.753251],[-80.854717,39.742592],[-80.846091,39.737812],[-80.831551,39.719475],[-80.829764,39.711839],[-80.831871,39.705655],[-80.839112,39.701033],[-80.854599,39.697473],[-80.863698,39.691724],[-80.86633,39.683167],[-80.866647,39.652616],[-80.876002,39.627084],[-80.88036,39.620706],[-80.892208,39.616756],[-80.91762,39.618703],[-80.925841,39.617396],[-80.936906,39.612616],[-80.970436,39.590127],[-80.993695,39.571253],[-81.020055,39.55541],[-81.030169,39.545211],[-81.044902,39.5363],[-81.070594,39.515991],[-81.100833,39.487175],[-81.114433,39.466275],[-81.132534,39.446275],[-81.138134,39.443775],[-81.152534,39.443175],[-81.170634,39.439175],[-81.185946,39.430731],[-81.190714,39.423562],[-81.208231,39.407147],[-81.211433,39.402031],[-81.211654,39.392977],[-81.217315,39.38759],[-81.223581,39.386062],[-81.24184,39.390276],[-81.270716,39.385914],[-81.281405,39.379258],[-81.297517,39.374378],[-81.347567,39.34577],[-81.375961,39.341697],[-81.384556,39.343449],[-81.393794,39.351706],[-81.406689,39.38809],[-81.412706,39.394618],[-81.435642,39.408474],[-81.446543,39.410374],[-81.456143,39.409274],[-81.473188,39.40017],[-81.489044,39.384074],[-81.503189,39.373242],[-81.542346,39.352874],[-81.557547,39.338774],[-81.565047,39.293874],[-81.565247,39.276175],[-81.570247,39.267675],[-81.585559,39.268747],[-81.59516,39.273387],[-81.608408,39.276043],[-81.621305,39.273643],[-81.656138,39.277355],[-81.678331,39.273755],[-81.689483,39.266043],[-81.69638,39.257035],[-81.696636,39.246123],[-81.691067,39.230139],[-81.692395,39.226443],[-81.700908,39.220844],[-81.725583,39.215835]]]]},\"properties\":{\"name\":\"Ohio\",\"nation\":\"USA \"}}]}","volume":"85","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan M.","contributorId":288641,"corporation":false,"usgs":false,"family":"Bauder","given":"Javan M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":838159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Christine S.","contributorId":288642,"corporation":false,"usgs":false,"family":"Anderson","given":"Christine","email":"","middleInitial":"S.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, H. Lisle","contributorId":288643,"corporation":false,"usgs":false,"family":"Gibbs","given":"H. Lisle","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tonkovich, Michael J.","contributorId":288644,"corporation":false,"usgs":false,"family":"Tonkovich","given":"Michael J.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":838162,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838158,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}