{"pageNumber":"130","pageRowStart":"3225","pageSize":"25","recordCount":40783,"records":[{"id":70246241,"text":"70246241 - 2023 - Rapid active thrust faulting at the northern Alaska Range front","interactions":[],"lastModifiedDate":"2023-06-28T14:06:04.047682","indexId":"70246241","displayToPublicDate":"2023-03-29T09:02:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Rapid active thrust faulting at the northern Alaska Range front","docAbstract":"

Plate convergence rates strongly influence seismicity and mountain building inboard of convergent margins, but the distribution and kinematics of structures accommodating farfield convergence can be elusive. In interior Alaska, Yakutat microplate convergence drives late Pleistocene–recent right slip on the Denali fault, but westward-decreasing slip rates leave substantial residual Yakutat motion unaccounted for. Here, we show that Northern Foothills thrust slip beneath the northern Alaska Range absorbs a modern 4.4 mm/yr geodetic velocity gradient equivalent to ~78% of the 5.6 mm/yr residual Yakutat convergence along the central Denali fault. Infrared-stimulated luminescence ages of strath terrace deposits (67–4 ka; six sites) quantify Totatlanika River bedrock incision across the 1947 Mw 7.1 thrust earthquake epicentral region. Incision rates increase abruptly from <1 mm/yr to 4.8–5.6 mm/yr above the blind thrust tip near the range front. Rapid slip at 6.7 mm/yr on a steep thrust ramp beneath the northern Alaska Range front accommodates the geodetic gradient, drives rock uplift at rates matching measured incision rates, and implies that large earthquakes like the 1947 event may recur with 500–1400 yr frequency. Results illuminate focused seismogenic strain inboard of a complex convergent margin and prompt reevaluation of Alaska’s neotectonic framework.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/G51049.1","usgsCitation":"Bender, A., Lease, R.O., Rittenour, T.M., and Jones, J.V., 2023, Rapid active thrust faulting at the northern Alaska Range front: Geology, v. 51, no. 6, p. 527-531, https://doi.org/10.1130/G51049.1.","productDescription":"5 p.","startPage":"527","endPage":"531","ipdsId":"IP-146057","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":444023,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1130/g51049.1","text":"Publisher Index Page"},{"id":418586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.6301808346148,\n 64.19458360416576\n ],\n [\n -160.6301808346148,\n 57.461011714876776\n ],\n [\n -135.6483845452334,\n 57.461011714876776\n ],\n [\n -135.6483845452334,\n 64.19458360416576\n ],\n [\n -160.6301808346148,\n 64.19458360416576\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Bender, Adrian 0000-0001-7469-1957","orcid":"https://orcid.org/0000-0001-7469-1957","contributorId":219952,"corporation":false,"usgs":true,"family":"Bender","given":"Adrian","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":876378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lease, Richard O. 0000-0003-2582-8966 rlease@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-8966","contributorId":5098,"corporation":false,"usgs":true,"family":"Lease","given":"Richard","email":"rlease@usgs.gov","middleInitial":"O.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":876379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rittenour, Tammy M.","contributorId":140755,"corporation":false,"usgs":false,"family":"Rittenour","given":"Tammy","email":"","middleInitial":"M.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":876380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":876381,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241852,"text":"70241852 - 2023 - Synthesizing professional opinion and published science to build a conceptual model of Walleye recruitment","interactions":[],"lastModifiedDate":"2023-04-12T14:31:50.746615","indexId":"70241852","displayToPublicDate":"2023-03-29T08:28:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Synthesizing professional opinion and published science to build a conceptual model of Walleye recruitment","docAbstract":"

Understanding and predicting recruitment, longstanding goals in fisheries science and ecology, are complicated by variation in the importance of environmental drivers coupled with the dynamic nature of individual ecosystems. Developing an understanding of recruitment from well-monitored stocks offers an opportunity to overcome these complexities. We used a systematic literature review, a survey, and a workshop attended by professionals with expertise in recruitment of Walleye Sander vitreus to identify common environmental drivers of Walleye recruitment and additional sources of variation (i.e., context dependencies) among populations. The importance of individual environmental drivers, as well as the direction of their influence, differed as a function of geographic region, lake surface area, and Walleye life stage. The literature suggested abiotic conditions (e.g., temperature) during the first year of life were influential in determining recruitment. Professional opinion noted the importance of biotic factors, with prey availability and predation risk having the most consistent relationships with recruitment. We synthesized this information to propose a conceptual model that illustrates the suite of characteristics that shape Walleye recruitment over large spatial and temporal scales. Our findings emphasize the importance of first-year growth and system-specific contextual factors, which can alter the relative importance of the environmental drivers of recruitment.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10884","usgsCitation":"Krabbenhoft, C., Ludsin, S.A., Marschall, E., Budnik, R., Almeida, Z., Cahill, C., Embke, H.S., Feiner, Z.S., Schmalz, P.J., Thorstensen, M., Weber, M., Wuellner, M.R., and Hansen, G., 2023, Synthesizing professional opinion and published science to build a conceptual model of Walleye recruitment: Fisheries Magazine, v. 48, no. 4, p. 141-156, https://doi.org/10.1002/fsh.10884.","productDescription":"16 p.","startPage":"141","endPage":"156","ipdsId":"IP-143215","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":444028,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fsh.10884","text":"Publisher Index Page"},{"id":414891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Krabbenhoft, Corey 0000-0002-2630-8287","orcid":"https://orcid.org/0000-0002-2630-8287","contributorId":225163,"corporation":false,"usgs":false,"family":"Krabbenhoft","given":"Corey","email":"","affiliations":[{"id":41059,"text":"College of Arts and Sciences and Research and Education in Energy, Environment and Water (RENEW) Institute, University at Buffalo, Buffalo, NY 14228","active":true,"usgs":false}],"preferred":false,"id":867936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ludsin, Stuart A. 0000-0002-3866-2216","orcid":"https://orcid.org/0000-0002-3866-2216","contributorId":175425,"corporation":false,"usgs":false,"family":"Ludsin","given":"Stuart","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":867937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marschall, Elizabeth A.","contributorId":270538,"corporation":false,"usgs":false,"family":"Marschall","given":"Elizabeth A.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":867938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budnik, Richard","contributorId":303727,"corporation":false,"usgs":false,"family":"Budnik","given":"Richard","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":867939,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Almeida, Zoe","contributorId":303728,"corporation":false,"usgs":false,"family":"Almeida","given":"Zoe","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":867940,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cahill, Chris","contributorId":303730,"corporation":false,"usgs":false,"family":"Cahill","given":"Chris","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":867941,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":867942,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Feiner, Zachary S.","contributorId":150494,"corporation":false,"usgs":false,"family":"Feiner","given":"Zachary","email":"","middleInitial":"S.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":867943,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schmalz, Patrick J","contributorId":300821,"corporation":false,"usgs":false,"family":"Schmalz","given":"Patrick","email":"","middleInitial":"J","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":867944,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thorstensen, Matt","contributorId":303736,"corporation":false,"usgs":false,"family":"Thorstensen","given":"Matt","email":"","affiliations":[{"id":16603,"text":"University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":867945,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Weber, Michael","contributorId":213318,"corporation":false,"usgs":false,"family":"Weber","given":"Michael","affiliations":[],"preferred":false,"id":867946,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wuellner, Melissa R.","contributorId":172322,"corporation":false,"usgs":false,"family":"Wuellner","given":"Melissa","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":867947,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hansen, Gretchen","contributorId":174810,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":867948,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70249688,"text":"70249688 - 2023 - Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations","interactions":[],"lastModifiedDate":"2023-10-25T13:30:03.270824","indexId":"70249688","displayToPublicDate":"2023-03-28T08:24:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2172,"text":"Journal of Applied Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations","docAbstract":"

This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points).


","language":"English","publisher":"SPIE","doi":"10.1117/1.JRS.17.014520","usgsCitation":"Bjerklie, D.M., Durand, M., LeNoir, J.M., Dudley, R., Birkett, C., Jones, J., and Harlan, M.E., 2023, Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations: Journal of Applied Remote Sensing, v. 17, no. 1, 014520, 47 p., https://doi.org/10.1117/1.JRS.17.014520.","productDescription":"014520, 47 p.","ipdsId":"IP-144814","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":444037,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.17.014520","text":"Publisher Index Page"},{"id":435401,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWP4W0","text":"USGS data release","linkHelpText":"Data from Across the USA Used to Assess the Uncertainty of Discharge Estimates Using a Modified Manning's Equation"},{"id":435400,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZUUVPS","text":"USGS data release","linkHelpText":"Water Surfaces Elevations from Pressure Transducers on the Tanana River near Fairbanks, Alaska, 2016"},{"id":435399,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TDI0A6","text":"USGS data release","linkHelpText":"Water Surface Elevation Data Derived from Submersible Pressure Transducers Deployed in the Susitna, Yukon, and Nushagak Rivers, Alaska, Summers 2020-2021"},{"id":422097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bjerklie, David M. 0000-0002-9890-4125 dmbjerkl@usgs.gov","orcid":"https://orcid.org/0000-0002-9890-4125","contributorId":3589,"corporation":false,"usgs":true,"family":"Bjerklie","given":"David","email":"dmbjerkl@usgs.gov","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durand, Michael","contributorId":331079,"corporation":false,"usgs":false,"family":"Durand","given":"Michael","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":886725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeNoir, James M. 0000-0003-3295-4901","orcid":"https://orcid.org/0000-0003-3295-4901","contributorId":302690,"corporation":false,"usgs":true,"family":"LeNoir","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dudley, Robert W. 0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886727,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birkett, Charon","contributorId":331081,"corporation":false,"usgs":false,"family":"Birkett","given":"Charon","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":886728,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":886729,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harlan, Merritt Elizabeth 0000-0002-4019-4888","orcid":"https://orcid.org/0000-0002-4019-4888","contributorId":302672,"corporation":false,"usgs":true,"family":"Harlan","given":"Merritt","email":"","middleInitial":"Elizabeth","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":886730,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247519,"text":"70247519 - 2023 - Land cover differentially affects abundance of common and rare birds","interactions":[],"lastModifiedDate":"2023-08-10T12:04:06.822719","indexId":"70247519","displayToPublicDate":"2023-03-28T07:03:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Land cover differentially affects abundance of common and rare birds","docAbstract":"

While rare species are vulnerable to global change, large declines in common species (i.e., those with large population sizes, large geographic distributions, and/or that are habitat generalists) also are of conservation concern. Understanding if and how commonness mediates species' responses to global change, including land cover change, can help guide conservation strategies. We explored avian population responses to land cover change along a gradient from common to rare species using avian data from the North American Breeding Bird Survey (BBS) and land cover data from the National Land Cover Database for the conterminous United States. Specifically, we used generalized linear mixed effects models to ask if species' commonness affected the relationship between land cover and counts, using the initial amount of and change in land cover surrounding each North American BBS route from 2001 to 2016. We quantified species' commonness as a continuous metric at the national scale using the logarithm (base 10) of each species' total count across all routes in the conterminous United States in 2001. For our focal 15-year period, we found that higher proportions of initial natural land cover favored (i.e., were correlated with higher) counts of rare but not common species. We also found that commonness mediated how change in human land cover, but not natural land cover, was associated with species' counts at the end of the study period. Increases in developed lands did not favor counts of any species. Increases in agriculture and declines in pasture favored counts of common but not rare species. Our findings show a signal of commonness in how species respond to a major dimension of global change. Evaluating how and why commonness mediates species' responses to land cover change can help managers design conservation portfolios that sustain the spectrum of common to rare species.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16700","usgsCitation":"Davis, K., Banko, P.C., and Pejchar, L., 2023, Land cover differentially affects abundance of common and rare birds: Global Change Biology, v. 29, no. 11, p. 2999-3009, https://doi.org/10.1111/gcb.16700.","productDescription":"11 p.","startPage":"2999","endPage":"3009","ipdsId":"IP-139930","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":444041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.16700","text":"Publisher Index Page"},{"id":419697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Kristin P.","contributorId":175448,"corporation":false,"usgs":false,"family":"Davis","given":"Kristin P.","affiliations":[{"id":27570,"text":"Natural Resource Ecology Lab, Colorado State U, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":879974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":879975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pejchar, Liba","contributorId":225494,"corporation":false,"usgs":false,"family":"Pejchar","given":"Liba","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":879976,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241886,"text":"70241886 - 2023 - Forage fish species prefer habitat within designated offshore wind energy areas on the US Northeast Shelf ecosystem","interactions":[],"lastModifiedDate":"2023-03-30T12:17:21.833891","indexId":"70241886","displayToPublicDate":"2023-03-27T07:10:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Forage fish species prefer habitat within designated offshore wind energy areas on the US Northeast Shelf ecosystem","docAbstract":"

As the world develops sources of renewable energy, there is an intensifying interest in offshore wind energy production. The Northeast U.S. Continental Shelf (NES) ecosystem has favorable wind dynamics, with active development of wind energy. In this study, we present species distribution models that consider both occupancy and biomass responses for a broad spectrum of fish and macroinvertebrate taxa (n = 177). Building upon prior analyses, habitat was differentiated into overall and core habitats based on statistical distributions of habitat scores. Overall habitat was used to show each species' regional distribution based on fishery-independent survey captures between 1976 and 2019, whereas core habitat represented where the focus of the species' abundance was located as a subset of overall habitat. Wind energy developments may modify the water column in ways that impact lower-trophic-level productivity; therefore, added attention was given to the response of forage species. Over 20% of species showed preferential use of putative and potential wind development areas, including a disproportionate number of forage taxa. Principal usage varied by season, with forage species like Atlantic Menhaden Brevoortia tyrannus and Atlantic Mackerel Scomber scombrus preferentially using the lease areas in spring and Round Herring Etrumeus teres and longfin inshore squid Doryteuthis pealeii using lease areas in autumn. For species with relatively low usage of the lease areas, there was a tendency for the usage related to overall habitat to be lower than usage for core habitat; in contrast, for species with high usage of the lease areas, that usage was higher for overall habitat than for core habitat. The area of habitat tended to have positive trends across species, with these positive trends being disproportionately higher among forage taxa. These results frame the importance of wind lease areas for species in the NES, particularly forage taxa that fulfill many important ecological functions.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10230","usgsCitation":"Friedland, K., Adams, E.M., Goetsch, C., Gulka, J., Brady, D., Rzeszowski, E., Crear, D., Gaichas, S., Gill, A., McManus, M.C., Methratta, E.T., Morano, J.L., and Staudinger, M., 2023, Forage fish species prefer habitat within designated offshore wind energy areas on the US Northeast Shelf ecosystem: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 15, no. 2, e10230, 20 p., https://doi.org/10.1002/mcf2.10230.","productDescription":"e10230, 20 p.","ipdsId":"IP-147199","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":444058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10230","text":"Publisher Index Page"},{"id":414954,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.65376319213254,\n 35.30999369780926\n ],\n [\n -75.21431006713294,\n 34.58447631749944\n ],\n [\n -73.89595069213301,\n 34.86927742242225\n ],\n [\n -71.34712256713316,\n 36.72059131388109\n ],\n [\n -65.01899756713287,\n 41.33704105044268\n ],\n [\n -65.01899756713287,\n 42.18927475712735\n ],\n [\n -65.98579444213274,\n 43.31860455413721\n ],\n [\n -66.3813022546329,\n 44.33310290134406\n ],\n [\n -67.1283725671327,\n 44.490060305276444\n ],\n [\n -69.85298194213277,\n 43.28662372913982\n ],\n [\n -70.38032569213298,\n 42.57879164411301\n ],\n [\n -69.2377475671325,\n 41.8300959347126\n ],\n [\n -71.47895850463293,\n 41.00624791566659\n ],\n [\n -74.15962256713298,\n 39.395262496594825\n ],\n [\n -75.21431006713289,\n 37.5722517789458\n ],\n [\n -75.65376319213254,\n 35.30999369780926\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Friedland, Kevin","contributorId":292483,"corporation":false,"usgs":false,"family":"Friedland","given":"Kevin","affiliations":[],"preferred":false,"id":868075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Evan M.","contributorId":139994,"corporation":false,"usgs":false,"family":"Adams","given":"Evan","email":"","middleInitial":"M.","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":868076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goetsch, Chandra","contributorId":214868,"corporation":false,"usgs":false,"family":"Goetsch","given":"Chandra","email":"","affiliations":[],"preferred":false,"id":868193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gulka, Julia","contributorId":303827,"corporation":false,"usgs":false,"family":"Gulka","given":"Julia","email":"","affiliations":[],"preferred":false,"id":868194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brady, Damien","contributorId":303781,"corporation":false,"usgs":false,"family":"Brady","given":"Damien","email":"","affiliations":[],"preferred":false,"id":868077,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rzeszowski, Everett","contributorId":303828,"corporation":false,"usgs":false,"family":"Rzeszowski","given":"Everett","email":"","affiliations":[],"preferred":false,"id":868195,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crear, Daniel","contributorId":303782,"corporation":false,"usgs":false,"family":"Crear","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":868078,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gaichas, Sarah","contributorId":303783,"corporation":false,"usgs":false,"family":"Gaichas","given":"Sarah","affiliations":[],"preferred":false,"id":868079,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gill, Andrew","contributorId":303784,"corporation":false,"usgs":false,"family":"Gill","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":868080,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McManus, M. Conor","contributorId":303785,"corporation":false,"usgs":false,"family":"McManus","given":"M.","email":"","middleInitial":"Conor","affiliations":[],"preferred":false,"id":868081,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Methratta, Elizabeth T.","contributorId":303786,"corporation":false,"usgs":false,"family":"Methratta","given":"Elizabeth","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":868082,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Morano, Janelle L.","contributorId":303787,"corporation":false,"usgs":false,"family":"Morano","given":"Janelle","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":868083,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Staudinger, Michelle 0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":206655,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":868084,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70242144,"text":"70242144 - 2023 - Increased utilization of storm surge barriers: A research agenda on estuary impacts","interactions":[],"lastModifiedDate":"2023-04-10T12:15:58.113952","indexId":"70242144","displayToPublicDate":"2023-03-27T07:06:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Increased utilization of storm surge barriers: A research agenda on estuary impacts","docAbstract":"

Rising coastal flood risk and recent disasters are driving interest in the construction of gated storm surge barriers worldwide, with current studies recommending barriers for at least 11 estuaries in the United States alone. Surge barriers partially block estuary-ocean exchange with infrastructure across an estuary or its inlet and include gated areas that are closed only during flood events. They can alter the stratification and salt intrusion, change sedimentary systems, and curtail animal migration and ecosystem connectivity, with impacts growing larger with increasing gate closures. Existing barriers are being used with increasing frequency due to sea level rise. New barrier proposals typically come with maximum closure frequency recommendations, yet the future adherence to them is uncertain. Given that the broader environmental effects and coupled-human dynamics of surge barriers are not well-understood, we present an interdisciplinary research agenda for this increasingly prevalent modification to our coastal zone.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022EF002991","usgsCitation":"Orton, P.M., Ralston, D.K., van Prooijen, B.C., Secor, D., Ganju, N., Chen, Z., Fernald, S., Brooks, B., and Marcell, K., 2023, Increased utilization of storm surge barriers: A research agenda on estuary impacts: Earth's Future, v. 11, no. 3, e2022EF002991, 10 p., https://doi.org/10.1029/2022EF002991.","productDescription":"e2022EF002991, 10 p.","ipdsId":"IP-142008","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444060,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022ef002991","text":"Publisher Index Page"},{"id":415494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Orton, Philip M.","contributorId":179354,"corporation":false,"usgs":false,"family":"Orton","given":"Philip","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":868995,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralston, David K. 0000-0002-0774-3101","orcid":"https://orcid.org/0000-0002-0774-3101","contributorId":195909,"corporation":false,"usgs":false,"family":"Ralston","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":868996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Prooijen, Bram C.","contributorId":245647,"corporation":false,"usgs":false,"family":"van Prooijen","given":"Bram","email":"","middleInitial":"C.","affiliations":[{"id":17614,"text":"Delft University of Technology","active":true,"usgs":false}],"preferred":false,"id":868997,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Secor, David","contributorId":272560,"corporation":false,"usgs":false,"family":"Secor","given":"David","affiliations":[],"preferred":false,"id":868998,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868999,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Ziyu","contributorId":304040,"corporation":false,"usgs":false,"family":"Chen","given":"Ziyu","email":"","affiliations":[{"id":65953,"text":"Stevens Institute of Technology, Hoboken,NJ","active":true,"usgs":false}],"preferred":false,"id":869000,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fernald, Sarah","contributorId":208050,"corporation":false,"usgs":false,"family":"Fernald","given":"Sarah","email":"","affiliations":[{"id":37690,"text":"New York State, Department of Environmental Conservation, Hudson River National Estuarine Research Reserve, Staatsburg, NY","active":true,"usgs":false}],"preferred":false,"id":869001,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brooks, Bennett","contributorId":304041,"corporation":false,"usgs":false,"family":"Brooks","given":"Bennett","email":"","affiliations":[{"id":65954,"text":"Consensus Building Institute","active":true,"usgs":false}],"preferred":false,"id":869002,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Marcell, Kristin","contributorId":304042,"corporation":false,"usgs":false,"family":"Marcell","given":"Kristin","email":"","affiliations":[{"id":65954,"text":"Consensus Building Institute","active":true,"usgs":false}],"preferred":false,"id":869003,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241815,"text":"70241815 - 2023 - Drought survival strategies differ between coastal and montane conifers in northern California","interactions":[],"lastModifiedDate":"2023-03-28T11:52:01.349304","indexId":"70241815","displayToPublicDate":"2023-03-26T06:49:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Drought survival strategies differ between coastal and montane conifers in northern California","docAbstract":"

Increasingly severe and prolonged droughts are contributing to tree stress and forest mortality across western North America. However, in many cases, we currently have poor information concerning how drought responses in forests vary in relation to competition, climate, and site and tree characteristics. We used annual tree ring evidence of 13C discrimination (Δ13C) and growth metrics to assess drought resistance and resilience for six conifer species at the intersection of several bioregions in northern California. Within each species' range in northern California, we collected competition and tree characteristics from 270 focal trees across sites that varied from wetter to drier habitat conditions (54 sites). Across sites, all six conifer species weathered the severe 2013–2015 drought with reasonably high resistance and post-drought resilience. However, we found important differences in drought responses between coastal and montane species based on annual growth and Δ13C metrics. Broadly, the two coastal species showed consistent declines in drought resistance across successive drought years, whereas the four montane species maintained high drought resistance across drought years. More specifically, we found lower Δ13C and growth during drought years in coastal species, suggesting stomatal closure during drought with the potential for vulnerability to carbon depletion during long-term drought. Conversely, Δ13C and growth were stable in montane species throughout the drought, which may contribute to hydraulic failure under increased drought frequency and/or severity. We also evaluated environmental factors that affect Δ13C using data from before and during the drought. These physiological models were consistent for the two coastal species, with a positive relationship between annual precipitation and Δ13C and a negative relationship between tree density and Δ13C. Conversely, the four montane models illustrated a greater importance of site conditions on drought responses for these species. Our findings show differential risk for drought stress across diverse conifers during severe drought. This work highlights the importance of site and tree characteristics in determining drought responses across cool, annually humid coastal habitats to seasonally dry montane habitats.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4480","usgsCitation":"Robinson, W., Kerhoulas, L.P., Sherriff, R., Roletti, G., and van Mantgem, P., 2023, Drought survival strategies differ between coastal and montane conifers in northern California: Ecosphere, v. 14, no. 3, e4480, 14 p., https://doi.org/10.1002/ecs2.4480.","productDescription":"e4480, 14 p.","ipdsId":"IP-144930","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":444075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4480","text":"Publisher Index Page"},{"id":414809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.84004774452916,\n 42.140988870056304\n ],\n [\n -124.84004774452916,\n 38.560468860808896\n ],\n [\n -119.65673052542093,\n 38.560468860808896\n ],\n [\n -119.65673052542093,\n 42.140988870056304\n ],\n [\n -124.84004774452916,\n 42.140988870056304\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Wallis","contributorId":303695,"corporation":false,"usgs":false,"family":"Robinson","given":"Wallis","email":"","affiliations":[{"id":65879,"text":"California State Polytechnic University, Humboldt","active":true,"usgs":false}],"preferred":false,"id":867806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kerhoulas, Lucy P. 0000-0002-8530-8287","orcid":"https://orcid.org/0000-0002-8530-8287","contributorId":303696,"corporation":false,"usgs":false,"family":"Kerhoulas","given":"Lucy","email":"","middleInitial":"P.","affiliations":[{"id":65879,"text":"California State Polytechnic University, Humboldt","active":true,"usgs":false}],"preferred":false,"id":867807,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherriff, Rosemary L.","contributorId":243263,"corporation":false,"usgs":false,"family":"Sherriff","given":"Rosemary L.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":867808,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roletti, Gabriel","contributorId":303697,"corporation":false,"usgs":false,"family":"Roletti","given":"Gabriel","email":"","affiliations":[{"id":65879,"text":"California State Polytechnic University, Humboldt","active":true,"usgs":false}],"preferred":false,"id":867809,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867810,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241876,"text":"70241876 - 2023 - Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains","interactions":[],"lastModifiedDate":"2023-05-25T15:54:56.920665","indexId":"70241876","displayToPublicDate":"2023-03-25T08:44:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains","docAbstract":"

Context

Yellow sweetclover (Melilotus officinalis; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions causing substantial widespread blooms of YSC across western South Dakota (SD).

Objectives

We aimed to develop a generalized prediction model to predict the relative abundance of YSC in suitable habitats across rangelands of western South Dakota for 2019. Our research questions are: (1) What is the spatial extent of YSC across western South Dakota? (2) Which model can accurately predict the habitat and percent cover of YSC? and (3) What significant biophysical drivers affect its presence across western South Dakota?

Methods

We trained machine learning models with in situ data (2016–2021), Sentinel 2A-derived surface reflectance and indices (10 m, 20 m) and site-specific variables of climate, topography, and edaphic factors to optimize model performance.

Results

We identified moisture proxies (Shortwave Infrared reflectance and variability in Tasseled Cap Wetness) as the important predictors to explain the YSC presence. Land Surface Water Index and variability in summer temperature were the top predictors in explaining the YSC abundance. We demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of this invasive plant. We delineated major YSC hotspots in Butte, Pennington, and Corson Counties of South Dakota. The floodplains of major rivers, including White and Bad Rivers, and areas around Badlands National Park also showed a higher occurrence probability and cover percentage.

Conclusions

These prediction maps could aid land managers in devising management strategies for the regions that are prone to YSC outbreaks. The management workflow can also serve as a prototype for mapping other invasive plant species in similar regions.

","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01613-1","usgsCitation":"Saraf, S., John, R., Amirkhiz, R.G., Kolluru, V., Jain, K., Rigge, M.B., Giannico, V., Boyte, S., Chen, J., Henebry, G.M., Jarchow, M., and Lafortezza, R., 2023, Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains: Landscape Ecology, v. 38, p. 1463-1479, https://doi.org/10.1007/s10980-023-01613-1.","productDescription":"17 p.","startPage":"1463","endPage":"1479","ipdsId":"IP-147289","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489774,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11586/429913","text":"External Repository"},{"id":414967,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":868045,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jarchow, Meghann","contributorId":303764,"corporation":false,"usgs":false,"family":"Jarchow","given":"Meghann","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":868046,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lafortezza, Raffaele","contributorId":303767,"corporation":false,"usgs":false,"family":"Lafortezza","given":"Raffaele","email":"","affiliations":[{"id":65904,"text":"University of Bari “Aldo Moro”","active":true,"usgs":false}],"preferred":false,"id":868047,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70241881,"text":"70241881 - 2023 - Genesis of the Questa Mo porphyry deposit and nearby polymetallic mineralization, New Mexico, USA","interactions":[],"lastModifiedDate":"2023-09-20T16:13:30.618656","indexId":"70241881","displayToPublicDate":"2023-03-25T08:17:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Genesis of the Questa Mo porphyry deposit and nearby polymetallic mineralization, New Mexico, USA","docAbstract":"

The Oligocene Latir magmatic center in northern New Mexico is an exceptionally well-exposed volcanoplutonic complex that hosts a variety of magmatic-hydrothermal deposits, ranging from relatively deep, F-rich porphyry Mo mineralization to shallower epithermal deposits. We present new whole-rock chemical and isotopic data for plutonic rocks from the Latir magmatic center, including extensive sampling of drill core samples of intrusive rocks from the Questa porphyry Mo deposit. These data document temporal chemical trends of porphyry-related mineralization that occurred after caldera-forming magmatism and during postcaldera batholith assembly. Silicic magmas were generated multiple times throughout the history of the Latir magmatic center, but few are associated with the formation of a mineral deposit. Whole-rock trace element ratios and Sr, Nd, and Pb isotope compositions vary throughout the protracted history of silicic magmatism. The caldera-forming ignimbrite and early phase of postcaldera intrusions are unmineralized, more enriched in high field strength elements, and generally contain less radiogenic Sr and Pb and more radiogenic Nd than later intrusions. The Questa porphyry Mo deposit formed immediately after the most isotopically primitive phase of the batholith was assembled, ruling out simple reworking of juvenile mantle-derived crust as the source for mineralizing magmas. Rhyolite dikes associated with polymetallic sulfide deposits intruded ~800 k.y. after Mo mineralization, and Nd isotope data indicate that these dikes are associated with different batches of magma and are unrelated to the Mo-mineralizing intrusions at the Questa mine. Together, these data indicate that the source of magmas changed significantly throughout the 10-m.y. history of the magmatic center. We assess multiple genetic models for porphyry-related magmatism against this data set, favoring models with discrete periods of magma genesis from a deep hybridized zone in the lower crust giving rise to the punctuated periods of mineralization. These observations suggest that the formation of mineral deposits within a central magmatic locus is likely the result of the piecemeal assembly of individual hydrothermal-magmatic systems, and that distal and younger polymetallic mineralization commonly observed near known porphyry deposits represents decoupled processes.

","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.5011","usgsCitation":"Gaynor, S., Rosera, J.M., and Coleman, D.S., 2023, Genesis of the Questa Mo porphyry deposit and nearby polymetallic mineralization, New Mexico, USA: Economic Geology, v. 118, no. 6, p. 1319-1339, https://doi.org/10.5382/econgeo.5011.","productDescription":"21 p.","startPage":"1319","endPage":"1339","ipdsId":"IP-138609","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":502418,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://archive-ouverte.unige.ch/unige:167974","text":"External Repository"},{"id":414955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.625,\n 37\n ],\n [\n -105.625,\n 36.5\n ],\n [\n -105.375,\n 36.5\n ],\n [\n -105.375,\n 37\n ],\n [\n -105.625,\n 37\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"118","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gaynor, Sean P.","contributorId":297927,"corporation":false,"usgs":false,"family":"Gaynor","given":"Sean P.","affiliations":[],"preferred":false,"id":868058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosera, Joshua Mark 0000-0003-3807-5000","orcid":"https://orcid.org/0000-0003-3807-5000","contributorId":270284,"corporation":false,"usgs":true,"family":"Rosera","given":"Joshua","email":"","middleInitial":"Mark","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":868059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coleman, Drew S.","contributorId":303771,"corporation":false,"usgs":false,"family":"Coleman","given":"Drew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":868060,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242148,"text":"70242148 - 2023 - Dense geophysical observations reveal a triggered, concurrent multi-fault rupture at the Mendocino Triple Junction","interactions":[],"lastModifiedDate":"2023-04-10T12:01:29.54412","indexId":"70242148","displayToPublicDate":"2023-03-25T06:58:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13795,"text":"Nature Communications Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Dense geophysical observations reveal a triggered, concurrent multi-fault rupture at the Mendocino Triple Junction","docAbstract":"

A central question of earthquake science is how far ruptures can jump from one fault to another, because cascading ruptures can increase the shaking of a seismic event. Earthquake science relies on earthquake catalogs and therefore how complex ruptures get documented and cataloged has important implications. Recent investments in geophysical instrumentation allow us to resolve increasingly complex, multi-fault ruptures for even moderate-sized earthquakes. We combine dense seismic and geodetic measurements to reveal an enigmatic rupture in late 2021 at the Mendocino Triple Junction in northern California. We show that rupture was dynamically triggered, yet concurrent, on two distinct faults roughly 30 km apart. Thus, this rupture combines features of complex ruptures usually considered to be single earthquakes, and triggered ruptures considered as multiple earthquakes. This event illustrates that moderate-sized earthquakes can exhibit similar complexity to that more commonly documented for large earthquakes.

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Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city-scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 United States cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman's correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g., impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the EPA tended to include filters more frequently in their SCM inventories. 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Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

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Victoria","contributorId":303717,"corporation":false,"usgs":false,"family":"Hill","given":"Victoria","email":"","affiliations":[{"id":36518,"text":"Old Dominion University","active":true,"usgs":false}],"preferred":false,"id":867927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lebrasse, Marie Cindy","contributorId":303719,"corporation":false,"usgs":false,"family":"Lebrasse","given":"Marie","email":"","middleInitial":"Cindy","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":867928,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Li, Jiang","contributorId":167428,"corporation":false,"usgs":false,"family":"Li","given":"Jiang","email":"","affiliations":[],"preferred":false,"id":867929,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Darryl, Keith","contributorId":303721,"corporation":false,"usgs":false,"family":"Darryl","given":"Keith","email":"","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":867930,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kaldy, Jim","contributorId":303723,"corporation":false,"usgs":false,"family":"Kaldy","given":"Jim","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":867931,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Colarusso, Philip D.","contributorId":218700,"corporation":false,"usgs":false,"family":"Colarusso","given":"Philip D.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":867932,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Raulerson, Gary","contributorId":303725,"corporation":false,"usgs":false,"family":"Raulerson","given":"Gary","email":"","affiliations":[{"id":65891,"text":"Largo, FL","active":true,"usgs":false}],"preferred":false,"id":867933,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":867934,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Kenworthy, Judson","contributorId":303726,"corporation":false,"usgs":false,"family":"Kenworthy","given":"Judson","email":"","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":867935,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70241841,"text":"70241841 - 2023 - Foraging behavior of Raramuri Criollo vs. Angus cattle grazing California Chaparral and Colorado Plateau shrublands","interactions":[],"lastModifiedDate":"2023-03-29T11:58:33.655633","indexId":"70241841","displayToPublicDate":"2023-03-23T06:54:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Foraging behavior of Raramuri Criollo vs. Angus cattle grazing California Chaparral and Colorado Plateau shrublands","docAbstract":"

Selecting livestock genetics adapted to arid environments, such as Criollo cattle, is one of several strategies recommended for decreasing the vulnerability to climate change of ranching in the southwestern USA. Our objective was to determine whether desirable foraging traits of Criollo cattle previously documented in the Chihuahuan Desert, held true in two of the most climate-vulnerable ecosystems of the Southwest. We conducted a study at Rancho Corta Madera (RCM) in southern California and Dugout Ranch (DR) in southeast Utah. Twenty mature cows, 10 Raramuri Criollo and 10 Red or Black Angus, were monitored with GPS collars during multiple seasons between 2018 and 2021. Geolocation data were used to compute daily distance traveled (km*d−1), movement velocity (m*min−1), path sinuosity (SI), time spent grazing, resting, or traveling (h*d−1), and area of the pasture explored (ha*d−1) as well as to calculate selection of vegetation cover types (E, Ivlev's Electivity Index) by cows of each breed. The effects of breed, season, year, and pasture on each of these metrics were modeled with repeated measures analyses of variance. At both ranches, statistically detectable differences (P ≤ 0.05) between breeds were observed for most behavior metrics during the dormant season. Conversely, few breed differences were observed during the growing season. Criollo cattle exhibited greater relative preference for a number of shrub dominated vegetation types at both ranches, and similar relative selection of grassland dominated sites compared to Angus counterparts. At both ranches, Criollo cattle exhibited similar or less relative preference for riparian areas vs. Angus counterparts. Breed divergence vs. convergence of foraging behaviors during the dormant vs. growing seasons, previously observed in the Chihuahuan Desert, was documented at both sites. Positive system outcomes associated with foraging traits of Criollo cattle could be expected to occur more broadly across the Southwest.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2023.104975","usgsCitation":"Duni, D.M., McIntosh, M.M., Nyamuryekung’e, S., Cibils, A.F., Duniway, M.C., Estell, R.E., Spiegal, S.A., Gonzalez, A.L., Gedefaw, M.G., Redd, M., Paulin, R., Steele, C.M., Utsumi, S.A., and Perea, A.R., 2023, Foraging behavior of Raramuri Criollo vs. Angus cattle grazing California Chaparral and Colorado Plateau shrublands: Journal of Arid Environments, v. 213, 104975, 12 p., https://doi.org/10.1016/j.jaridenv.2023.104975.","productDescription":"104975, 12 p.","ipdsId":"IP-147637","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":444108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2023.104975","text":"Publisher Index Page"},{"id":414884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"213","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Duni, Danielle M.","contributorId":300506,"corporation":false,"usgs":false,"family":"Duni","given":"Danielle","email":"","middleInitial":"M.","affiliations":[{"id":65180,"text":"Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":867890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McIntosh, Matthew M.","contributorId":300505,"corporation":false,"usgs":false,"family":"McIntosh","given":"Matthew","email":"","middleInitial":"M.","affiliations":[{"id":65181,"text":"United States Department of Agriculture, Agricultural Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":867891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nyamuryekung’e, Shelemia","contributorId":300504,"corporation":false,"usgs":false,"family":"Nyamuryekung’e","given":"Shelemia","email":"","affiliations":[{"id":65180,"text":"Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":867892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cibils, Andres F.","contributorId":300502,"corporation":false,"usgs":false,"family":"Cibils","given":"Andres","email":"","middleInitial":"F.","affiliations":[{"id":65180,"text":"Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":867893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":867894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Estell, Richard E.","contributorId":303705,"corporation":false,"usgs":false,"family":"Estell","given":"Richard","email":"","middleInitial":"E.","affiliations":[{"id":65883,"text":"USDA - Agricultural Research Service Jornada Experimental Range, Las Cruces, NM, 88003","active":true,"usgs":false}],"preferred":false,"id":867895,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spiegal, Sheri A.","contributorId":303706,"corporation":false,"usgs":false,"family":"Spiegal","given":"Sheri","email":"","middleInitial":"A.","affiliations":[{"id":65883,"text":"USDA - Agricultural Research Service Jornada Experimental Range, Las Cruces, NM, 88003","active":true,"usgs":false}],"preferred":false,"id":867896,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzalez, Alfredo L.","contributorId":300512,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Alfredo","email":"","middleInitial":"L.","affiliations":[{"id":65181,"text":"United States Department of Agriculture, Agricultural Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":867897,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gedefaw, Melakeneh G.","contributorId":303707,"corporation":false,"usgs":false,"family":"Gedefaw","given":"Melakeneh","email":"","middleInitial":"G.","affiliations":[{"id":65884,"text":"Northern Arizona University, School of Informatics, Computing, and Cyber Systems, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":867898,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Redd, Matthew","contributorId":303708,"corporation":false,"usgs":false,"family":"Redd","given":"Matthew","email":"","affiliations":[{"id":65885,"text":"Dugout Ranch/Canyonlands Research Center, The Nature Conservancy, Monticello UT 84535","active":true,"usgs":false}],"preferred":false,"id":867899,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Paulin, Robert","contributorId":303709,"corporation":false,"usgs":false,"family":"Paulin","given":"Robert","email":"","affiliations":[{"id":65886,"text":"Rancho Corta Madera, Pine Valley, CA, 91962","active":true,"usgs":false}],"preferred":false,"id":867900,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Steele, Caitriana M.","contributorId":303710,"corporation":false,"usgs":false,"family":"Steele","given":"Caitriana","email":"","middleInitial":"M.","affiliations":[{"id":65887,"text":"USDA Southwest Climate Hub, USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":867901,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Utsumi, Santiago A.","contributorId":300511,"corporation":false,"usgs":false,"family":"Utsumi","given":"Santiago","email":"","middleInitial":"A.","affiliations":[{"id":65180,"text":"Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":867902,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Perea, Andres R.","contributorId":303711,"corporation":false,"usgs":false,"family":"Perea","given":"Andres","email":"","middleInitial":"R.","affiliations":[{"id":65888,"text":"New Mexico State University Department of Animal and Range Sciences, Las Cruces, NM, 88003","active":true,"usgs":false}],"preferred":false,"id":867903,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70242917,"text":"70242917 - 2023 - Seed dispersal and tree legacies influence spatial patterns of plant invasion dynamics","interactions":[],"lastModifiedDate":"2023-04-24T11:40:01.575364","indexId":"70242917","displayToPublicDate":"2023-03-23T06:37:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5523,"text":"Frontiers in Applied Mathematics and Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Seed dispersal and tree legacies influence spatial patterns of plant invasion dynamics","docAbstract":"

Invasive plant species alter community dynamics and ecosystem properties, potentially leading to regime shifts. Here, the invasion of a non-native tree species into a stand of native tree species is simulated using an agent-based model. The model describes an invasive tree with fast growth and high seed production that produces litter with a suppressive effect on native seedlings, based loosely on Melaleuca quinquenervia, invasive to southern Florida. The effect of a biocontrol agent, which reduces the invasive tree's growth and reproductive rates, is included to study how effective biocontrol is in facilitating the recovery of native trees. Even under biocontrol, the invader has some advantages over native tree species, such as the ability to tolerate higher stem densities than the invaded species and its litter's seedling suppression effect. We also include a standing dead component of both species, where light interception from dead canopy trees influences neighboring tree demographics. The model is applied to two questions. The first is how the mean seedling dispersal rate affects the spread of the invading species into a pure stand of natives, assuming the same mean dispersal distance for both species. For assumed litter seedling suppression that roughly balances the fitness levels of the two species, which species dominates depends on the mean dispersal distance. The invader dominates at both very high and very low mean seedling dispersal distances, while the native tree dominates for dispersal distances in the intermediate range. The second question is how standing dead trees affect either the rate of spread of the invader or the rate of recovery of the native species. The legacy of standing dead invasive trees may delay the recovery of native vegetation. The results here are novel and show that agent-based modeling is essential in illustrating how the fine-scale modeling of local interactions of trees leads to effects at the population level.

","language":"English","publisher":"Frontiers","doi":"10.3389/fams.2023.1086781","usgsCitation":"Lu, Y., Xia, J., Magee, L.J., and DeAngelis, D., 2023, Seed dispersal and tree legacies influence spatial patterns of plant invasion dynamics: Frontiers in Applied Mathematics and Statistics, v. 9, 1086781, 13 p., https://doi.org/10.3389/fams.2023.1086781.","productDescription":"1086781, 13 p.","ipdsId":"IP-146473","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444116,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fams.2023.1086781","text":"Publisher Index Page"},{"id":416168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Lu, Yuanming","contributorId":298492,"corporation":false,"usgs":false,"family":"Lu","given":"Yuanming","email":"","affiliations":[{"id":35560,"text":"Department of Biology, University of Florida","active":true,"usgs":false}],"preferred":false,"id":870197,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xia, Junfei","contributorId":298493,"corporation":false,"usgs":false,"family":"Xia","given":"Junfei","email":"","affiliations":[{"id":64593,"text":"Rosenstiel School of Marine and Atmospheric Science, University of Miami","active":true,"usgs":false}],"preferred":false,"id":870198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magee, Lukas J.","contributorId":304341,"corporation":false,"usgs":false,"family":"Magee","given":"Lukas","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":870199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":870200,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241125,"text":"sir20225132 - 2023 - Evaluation of potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada","interactions":[],"lastModifiedDate":"2026-02-24T18:04:17.753698","indexId":"sir20225132","displayToPublicDate":"2023-03-22T14:27:54","publicationYear":"2023","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":"2022-5132","displayTitle":"Evaluation of Potential Stresses and Hydrologic Conditions Driving Water-Level Fluctuations in Well ER-5-3-2, Frenchman Flat, Southern Nevada","title":"Evaluation of potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada","docAbstract":"

Well ER-5-3-2 is part of a well network designed to monitor long-term water levels and radionuclide concentrations downgradient from underground nuclear tests that occurred in Frenchman Flat, an area of the U.S. Department of Energy Nevada National Security Site in southern Nevada. Interpretation of monitoring records for well ER-5-3-2 was confounded by previously unexplained water-level fluctuations in the well hydrograph. This study integrated geologic, hydrologic, and water-chemistry data to evaluate potential stresses and hydrologic conditions that likely affected the well ER-5-3-2 hydrograph. Numerical groundwater models were applied to evaluate four model scenarios: (1) wellbore leakage without recharge, (2) wellbore leakage with recharge, (3) equilibration to vertical heterogeneities between shallow (low transmissivity) and deep (higher transmissivity) carbonate zones, and (4) equilibration to lateral heterogeneities in carbonate rocks.

Meteoric recharge was not the cause of the 21-foot (ft) water-level rise in well ER-5-3-2 from 2001 to 2011 or the 4-ft decline from 2012 to 2016. Based on observed water-level fluctuations in nearby wells, the water-level rise and decline from recharge for these periods was less than 3 and 1 ft, respectively. The lateral-heterogeneity scenario is based on the assumption that the 21-ft water-level rise from 2001 to 2011 was a natural water-level reequilibration following the pumping-induced depressurization of a large volume of high transmissivity and low-storage carbonate rock that is surrounded by low transmissivity and high-storage carbonate rock. The lateral-heterogeneity scenario was discounted because simulated water levels cannot match the well ER-5-3-2 hydrograph. Underground nuclear testing and temperature effects were discounted based on hydraulic connections and water-temperature data.

Wellbore-leakage scenarios are based on the assumption that the water-level rise was sustained from leakage rates required to cause a localized mounding in the carbonate system near well ER-5-3-2, where the carbonate transmissivity is 530 square feet per day. Even though simulated and measured water levels compare favorably for scenarios of wellbore leakage with and without recharge, large volumes (178–184 million gallons) of groundwater from volcanic rocks would be required to leak into the carbonate system, which is not supported by water-chemistry data.

An alternative conceptualization of wellbore leakage is based on the assumption that the 21-ft water-level rise from 2001 to 2011 was sustained by the hydraulic disconnection of well ER-5-3-2 from the carbonate system. The disconnection occurred several months after a constant-rate test in well ER-5-3-2 when carbonate rocks were hydraulically disconnected from the well by either (1) the shifting of sloughed fill in the open hole or (2) the encrusting of carbonate precipitate in the well screen. The hydraulic disconnection effectively sealed the well and caused a 21-ft water-level rise from wellbore leakage during 2001–11. In this case, total wellbore leakage from 2001 to 2011 was about 50 gallons. The 4-ft water-level decline from 2012 to 2016 was conceptualized to have occurred from the slow breaking of the seal and reconnection of the well to the carbonate system. This alternative conceptualization of wellbore leakage was consistent with water-chemistry analyses because the computed wellbore leakage (50 gallons) was small relative to purged volumes (30,000–40,000 gallons) for sampling, and the water chemistry would not be expected to change.

The shallow-deep carbonate scenario provided another explanation for the well ER-5-3-2 hydrograph. This scenario is based on the assumption that well-construction effects and vertical heterogeneity of the carbonate system explain the ER-5-3-2 water-level trend. Well-construction effects are attributed to a temporary clogging of the open interval below the well screen that was opened during pumping events, which affected the hydraulic connection of deep transmissive carbonate rocks to the wellbore. The 21-ft water-level rise from 2001 to 2011 was a natural equilibration to shallow, low-transmissivity carbonate rocks during a period when the lower open interval was clogged. The 4-ft decline from 2012 to 2016 represents equilibration between the shallow and deep intervals, because of a partial unclogging of the connection between the two intervals. The low water levels from 2016 to 2021 resulted from pumping for sampling and an unclogging of the open interval so that the low head in the deep carbonate dominated the water level. Despite potential well-construction effects, from either a wellbore leakage or shallow-deep carbonate scenario, samples collected from well ER-5-3-2 are representative of the carbonate system.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225132","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-EM0004969","usgsCitation":"Jackson, T.R., and Frus, R.J., 2023, Evaluation of potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada: U.S. Geological Survey Scientific Investigations Report 2022–5132, 35 p., https://doi.org/10.3133/sir20225132.","productDescription":"Report: viii, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-139917","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":413963,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95C0NG5","text":"MODFLOW 6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada","description":"Jackson, T.R., and Frus, R.J., 2023, MODFLOW 6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada: U.S. Geological Survey data release, available at https://doi.org/10.5066/P95C0NG5."},{"id":500485,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114613.htm","linkFileType":{"id":5,"text":"html"}},{"id":413973,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225132/full"},{"id":413962,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5132/images"},{"id":413961,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5132/sir20225132.xml"},{"id":413960,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5132/sir20225132.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":413959,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5132/covrthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Frenchman Flat","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.05668643871044,\n 36.549912612507626\n ],\n [\n -116.05668643871044,\n 35.84481987187543\n ],\n [\n -115.27945901304658,\n 35.84481987187543\n ],\n [\n -115.27945901304658,\n 36.549912612507626\n ],\n [\n -116.05668643871044,\n 36.549912612507626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2023-03-22","noUsgsAuthors":false,"publicationDate":"2023-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Jackson, Tracie R. 0000-0001-8553-0323","orcid":"https://orcid.org/0000-0001-8553-0323","contributorId":215365,"corporation":false,"usgs":true,"family":"Jackson","given":"Tracie R.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frus, Rebecca J. 0000-0002-2435-7202","orcid":"https://orcid.org/0000-0002-2435-7202","contributorId":206261,"corporation":false,"usgs":true,"family":"Frus","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866170,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241482,"text":"sir20225120 - 2023 - Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York","interactions":[],"lastModifiedDate":"2026-02-23T20:41:49.639474","indexId":"sir20225120","displayToPublicDate":"2023-03-22T12:18:00","publicationYear":"2023","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":"2022-5120","displayTitle":"Preliminary Machine Learning Models of Manganese and 1,4-Dioxane in Groundwater on Long Island, New York","title":"Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York","docAbstract":"

Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil properties, groundwater flow, land use, and other features that describe the hydrogeology and geochemistry of the aquifer system. Four models were developed to predict the probability of manganese concentrations greater than a detection level of 10 micrograms per liter (μg/L) and greater than three threshold concentrations (50, 150, and 300 μg/L) relevant to drinking-water quality. One model was developed to predict the probability of 1,4-dioxane concentrations greater than a detection level of 0.07 μg/L. The 1,4-dioxane model was limited geographically to Suffolk County because of data availability. Predictions were made for two layers in the upper glacial aquifer and three layers in the Magothy aquifer, which are the upper two of the three major aquifers of the Long Island aquifer system.

The objective of the study described in this report was to demonstrate the application of the methods rather than to develop precise estimates of manganese or 1,4-dioxane concentrations at any given location. The predictive models developed in the study are considered preliminary in the sense that they are an initial effort at developing these kinds of models specifically for Long Island. The models could be improved by the inclusion of additional data, by the use of methods to improve the modeling of infrequent high concentrations of manganese and 1,4-dioxane (above threshold concentrations), and by including more explanatory variables that specifically describe conditions and contaminant sources on Long Island. Nonetheless, the distribution of model predictions and the influence of explanatory variables in the models were consistent with the expected relations between contaminant concentrations and groundwater-flow-system characteristics and the distribution of manmade sources.

Mapped predictions indicated that manganese detections were more probable in the upper glacial aquifer and along the southern shore of Long Island, consistent with the distribution of anoxic conditions in groundwater in the Long Island aquifer system. Manganese was infrequently predicted at concentrations greater than thresholds of concern for drinking-water quality in any of the aquifer layers. Detections of 1,4-dioxane were predicted in the western, more highly developed parts of Suffolk County, in the upper glacial aquifer and the top and middle layers of the Magothy aquifer, and in northwestern Suffolk County in the bottom layer of the Magothy aquifer. Although preliminary in nature and based on limited data, these mapped predictions can be used to generally identify areas where manganese and 1,4-dioxane may be present at concentrations of concern to prioritize areas for future monitoring and to guide future modeling and mapping efforts.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225120","programNote":"National Water Quality Program","usgsCitation":"DeSimone, L.A., 2023, Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5120, 34 p., https://doi.org/10.3133/sir20225120.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-133571","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":414438,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5120/images/"},{"id":414437,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.XML"},{"id":414436,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225120/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5120"},{"id":500463,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114612.htm","linkFileType":{"id":5,"text":"html"}},{"id":414439,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90AT9YG","text":"USGS data release","linkHelpText":"Data and model archive for preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York"},{"id":414434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5120/coverthb.jpg"},{"id":414435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.pdf","text":"Report","size":"5.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5120"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.05146015978082,\n 40.628474760922984\n ],\n [\n -73.96502494019428,\n 40.542103435896706\n ],\n [\n -73.54649650851006,\n 40.545560430280744\n ],\n [\n -73.20985407432973,\n 40.61811609149555\n ],\n [\n -72.74128419972719,\n 40.738867255336714\n ],\n [\n -72.19082832762075,\n 40.90411303840304\n ],\n [\n -71.79504600635465,\n 41.08266452105815\n ],\n [\n -72.259066658874,\n 41.20257103045407\n ],\n [\n -72.71853808930965,\n 41.00374947032347\n ],\n [\n -73.1643618534946,\n 41.010615404965876\n ],\n [\n -73.52375039809249,\n 40.948796241204036\n ],\n [\n -73.76485916851937,\n 40.873160815851264\n ],\n [\n -73.87858972060721,\n 40.79055078444986\n ],\n [\n -74.01961560519632,\n 40.72163048139012\n ],\n [\n -74.05146015978082,\n 40.68714353955147\n ],\n [\n -74.05146015978082,\n 40.628474760922984\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2023-03-22","noUsgsAuthors":false,"publicationDate":"2023-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"DeSimone, Leslie A. 0000-0003-0774-9607 ldesimon@usgs.gov","orcid":"https://orcid.org/0000-0003-0774-9607","contributorId":195635,"corporation":false,"usgs":true,"family":"DeSimone","given":"Leslie","email":"ldesimon@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866989,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70261885,"text":"70261885 - 2023 - Advances in our understanding of pyroclastic current behavior from the 1980 eruption sequence of Mount St. Helens volcano (Washington), USA","interactions":[],"lastModifiedDate":"2024-12-31T16:53:23.631087","indexId":"70261885","displayToPublicDate":"2023-03-22T10:28:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Advances in our understanding of pyroclastic current behavior from the 1980 eruption sequence of Mount St. Helens volcano (Washington), USA","docAbstract":"This review summarizes what the volcanology community has learned thus far from studying the deposits of pyroclastic currents (PC) from the 1980 eruption sequence at Mount St. Helens. The review includes mass flow events during the May 18 eruption, including the lateral blast, the afternoon column collapse and boil-over PC activity, and some aspects of the debris avalanche. We also include a summary of PCs generated in the smaller eruptions following the climactic May 18 event. Our objective is to summarize the state of our understanding of PC transport and emplacement mechanisms from the combination of field and laboratory observations, granular flow experiments, and numerical modeling techniques. Specifically, we couple deposit characteristics, experiments, and numerical modeling techniques to critically address the problems of (1) constraining conditions in the flow boundary zone at the time of deposition, (2) the influence of substrate roughness and topography on PC behavior, (3) the prevalence, causes, and consequences of substrate erosion by PCs, and (4) the reconstruction of PC transportation and sedimentation processes from a combination of geophysical and sedimentological observations. We conclude by providing opportunities for future research as our field, experimental, and numerical research techniques advance.","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01617-w","usgsCitation":"Brand, B.D., Pollock, N., Vallance, J.W., Ongaro, T., Roche, O., Trolese, M., Geordano, G., Marshall, A., and Criswell, C., 2023, Advances in our understanding of pyroclastic current behavior from the 1980 eruption sequence of Mount St. Helens volcano (Washington), USA: Bulletin of Volcanology, v. 85, no. 24, 33 p., https://doi.org/10.1007/s00445-022-01617-w.","productDescription":"33 p.","ipdsId":"IP-138509","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467116,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-022-01617-w","text":"Publisher Index Page"},{"id":465579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.21517562866211,\n 46.171866354392314\n ],\n [\n -122.18616485595703,\n 46.159501957257795\n ],\n [\n -122.1602439880371,\n 46.16651671595163\n ],\n [\n -122.14839935302733,\n 46.18090011791342\n ],\n [\n -122.14668273925783,\n 46.196111522993\n ],\n [\n -122.15183258056639,\n 46.21595132425726\n ],\n [\n -122.16590881347656,\n 46.22759050621931\n ],\n [\n -122.1932029724121,\n 46.23637764124692\n ],\n [\n -122.2319984436035,\n 46.229253045075275\n ],\n [\n -122.23148345947267,\n 46.19872555930486\n ],\n [\n -122.23131179809569,\n 46.18708025892558\n ],\n [\n -122.21517562866211,\n 46.171866354392314\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"24","noUsgsAuthors":false,"publicationDate":"2023-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Brand, Brittany D. brittanybrand@boisestate.edu","contributorId":193853,"corporation":false,"usgs":false,"family":"Brand","given":"Brittany","email":"brittanybrand@boisestate.edu","middleInitial":"D.","affiliations":[],"preferred":false,"id":922134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollock, Nicholas","contributorId":193854,"corporation":false,"usgs":false,"family":"Pollock","given":"Nicholas","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":922135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vallance, James W. 0000-0002-3083-5469 jvallance@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5469","contributorId":547,"corporation":false,"usgs":true,"family":"Vallance","given":"James","email":"jvallance@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":922136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ongaro, Tomaso Esposti","contributorId":347669,"corporation":false,"usgs":false,"family":"Ongaro","given":"Tomaso Esposti","affiliations":[{"id":83198,"text":"Instituto Nazionale di Geofisica e Vulcanologia Sezione di Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":922137,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roche, Olivier","contributorId":167382,"corporation":false,"usgs":false,"family":"Roche","given":"Olivier","email":"","affiliations":[{"id":24702,"text":"Laboratoire Magmas et Volcans, Université Blaise Pascal-CNRS-IRD, OPGC, F-63038 6 Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":922138,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Trolese, Matteo","contributorId":347687,"corporation":false,"usgs":false,"family":"Trolese","given":"Matteo","affiliations":[],"preferred":false,"id":922174,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Geordano, Guido","contributorId":347670,"corporation":false,"usgs":false,"family":"Geordano","given":"Guido","affiliations":[{"id":83199,"text":"Dipartimento di Scienze, Universita degli Studi Roma, Italy","active":true,"usgs":false}],"preferred":false,"id":922139,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Marshall, Aaron A.","contributorId":347689,"corporation":false,"usgs":false,"family":"Marshall","given":"Aaron A.","affiliations":[],"preferred":false,"id":922175,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Criswell, C. William","contributorId":347688,"corporation":false,"usgs":false,"family":"Criswell","given":"C. William","affiliations":[],"preferred":false,"id":922176,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70247889,"text":"70247889 - 2023 - Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases","interactions":[],"lastModifiedDate":"2024-01-26T18:04:40.163463","indexId":"70247889","displayToPublicDate":"2023-03-22T07:07:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases","docAbstract":"

Emerging diseases can have devastating consequences for wildlife and require a rapid response. A critical first step towards developing appropriate management is identifying the etiology of the disease, which can be difficult to determine, particularly early in emergence. Gathering and synthesizing existing information about potential disease causes, by leveraging expert knowledge or relevant existing studies, provides a principled approach to quickly inform decision-making and management efforts. Additionally, updating the current state of knowledge as more information becomes available over time can reduce scientific uncertainty and lead to substantial improvement in the decision-making process and the application of management actions that incorporate and adapt to newly acquired scientific understanding. Here we present a rapid prototyping method for quantifying belief weights for competing hypotheses about the etiology of disease using a combination of formal expert elicitation and Bayesian hierarchical modeling. We illustrate the application of this approach for investigating the etiology of stony coral tissue loss disease (SCTLD) and discuss the opportunities and challenges of this approach for addressing emergent diseases. Lastly, we detail how our work may apply to other pressing management or conservation problems that require quick responses. We found the rapid prototyping methods to be an efficient and rapid means to narrow down the number of potential hypotheses, synthesize current understanding, and help prioritize future studies and experiments. This approach is rapid by providing a snapshot assessment of the current state of knowledge. It can also be updated periodically (e.g., annually) to assess changes in belief weights over time as scientific understanding increases. Synthesis and applications: The rapid prototyping approaches demonstrated here can be used to combine knowledge from multiple experts and/or studies to help with fast decision-making needed for urgent conservation issues including emerging diseases and other management problems that require rapid responses. These approaches can also be used to adjust belief weights over time as studies and expert knowledge accumulate and can be a helpful tool for adapting management decisions.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.117668","usgsCitation":"Robertson, E.P., Walsh, D.P., Martin, J., Work, T.M., Kellogg, C.A., Evans, J.S., Hawthorn, A.C., Aeby, G., Paul, V.J., Walker, B., Kiryu, Y., Woodley, C., Meyer, J.L., Rosales, S.M., Studivan, M.S., Moore, J., Brandt, M.E., and Bruckner, A., 2023, Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases: Journal of Environmental Management, v. 337, 117668, 9 p., https://doi.org/10.1016/j.jenvman.2023.117668.","productDescription":"117668, 9 p.","ipdsId":"IP-146770","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444129,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2023.117668","text":"Publisher Index Page"},{"id":435408,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DLNEBY","text":"USGS data release","linkHelpText":"Expert assessments of hypotheses concerning the etiological agent(s) of Stony Coral Tissue Loss Disease collected during a rapid prototyping project"},{"id":435407,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S9JDVB","text":"USGS data release","linkHelpText":"Code for Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases"},{"id":420068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"337","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Ellen Padgett 0000-0002-8135-0816","orcid":"https://orcid.org/0000-0002-8135-0816","contributorId":328644,"corporation":false,"usgs":true,"family":"Robertson","given":"Ellen","email":"","middleInitial":"Padgett","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":880877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":880878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":218445,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":880879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":880880,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 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0000-0002-8029-1383","orcid":"https://orcid.org/0000-0002-8029-1383","contributorId":292709,"corporation":false,"usgs":true,"family":"Hawthorn","given":"Aine","email":"","middleInitial":"C.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":880883,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aeby, Greta","contributorId":252909,"corporation":false,"usgs":false,"family":"Aeby","given":"Greta","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":880884,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Paul, Valerie J. 0000-0002-4691-1569","orcid":"https://orcid.org/0000-0002-4691-1569","contributorId":279530,"corporation":false,"usgs":false,"family":"Paul","given":"Valerie","email":"","middleInitial":"J.","affiliations":[{"id":57268,"text":"Smithsonian Marine 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S.","contributorId":297510,"corporation":false,"usgs":false,"family":"Studivan","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":64418,"text":"University of Miami, NOAA","active":true,"usgs":false}],"preferred":false,"id":880891,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Moore, Jennifer","contributorId":328646,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","affiliations":[{"id":78438,"text":"Moore Ecological Analysis and Management, LLC","active":true,"usgs":false}],"preferred":false,"id":880892,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Brandt, Marilyn E.","contributorId":171794,"corporation":false,"usgs":false,"family":"Brandt","given":"Marilyn","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":880893,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Bruckner, Andrew","contributorId":305643,"corporation":false,"usgs":false,"family":"Bruckner","given":"Andrew","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":880894,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70255238,"text":"70255238 - 2023 - Why hibernate? Tests of four hypotheses to explain intraspecific variation in hibernation phenology","interactions":[],"lastModifiedDate":"2024-06-13T15:29:53.521826","indexId":"70255238","displayToPublicDate":"2023-03-21T10:27:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Why hibernate? Tests of four hypotheses to explain intraspecific variation in hibernation phenology","docAbstract":"
  1. Hibernation is a remarkable behaviour deployed by a diverse array of endotherms within many clades that greatly reduces metabolic need, but also has somatic costs. Hibernation in modern endotherms is often assumed to be an adaptation allowing animals to avoid extreme thermal conditions or food shortages in seasonal environments. However, many animals hibernate when foraging conditions are energetically profitable, suggesting other causal factors influence hibernation behaviour.
  2. Understanding the selection pressures responsible for intraspecific variation in the timing and duration of hibernation can help elucidate the relative evolutionary influences of the ultimate ecological causes of hibernation. We tested four previously proposed mechanistic hypotheses to explain intraspecific variation in hibernation phenology in the federally threatened northern Idaho ground squirrel (Urocitellus brunneus): (1) thermal tolerance, (2) food limitation, (3) predation avoidance and (4) sexual selection.
  3. The predation avoidance and sexual selection hypotheses received the most support, although we also found some support for the thermal tolerance and food limitation hypotheses. Heavy squirrels increased hibernation duration regardless of environmental conditions, as predicted solely by the predation avoidance hypothesis. Reproductive males emerged from hibernation earlier in spring than other sex–age classes, a pattern predicted by the sexual selection hypothesis. Temperature and food availability explained a much smaller amount of the variation in hibernation behaviour, only partially supporting predictions of the thermal tolerance and food limitation hypotheses.
  4. Our results indicate that animals navigate life-history trade-offs between energetic allocation to survival and reproduction via state-dependent optimization of hibernation phenology. Consequently, any future environmental changes that influence body condition will have implications for population ecology and life-history evolution of hibernating animals due to stark differences in daily survival probability between hibernation and the active season.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2435.14322","usgsCitation":"Allison, A., Conway, C.J., and Morris, A.E., 2023, Why hibernate? Tests of four hypotheses to explain intraspecific variation in hibernation phenology: Functional Ecology, v. 37, no. 6, p. 1580-1593, https://doi.org/10.1111/1365-2435.14322.","productDescription":"15 p.","startPage":"1580","endPage":"1593","ipdsId":"IP-144984","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2435.14322","text":"Publisher Index Page"},{"id":430143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Allison, Austin A Z.","contributorId":337876,"corporation":false,"usgs":false,"family":"Allison","given":"Austin A Z.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":903819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morris, Alice E","contributorId":339157,"corporation":false,"usgs":false,"family":"Morris","given":"Alice","email":"","middleInitial":"E","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":903820,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241601,"text":"70241601 - 2023 - Evolving radon diffusion through earthen barriers at uranium waste disposal sites","interactions":[],"lastModifiedDate":"2023-03-27T10:54:56.625544","indexId":"70241601","displayToPublicDate":"2023-03-21T09:15:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2263,"text":"Journal of Environmental Radioactivity","active":true,"publicationSubtype":{"id":10}},"title":"Evolving radon diffusion through earthen barriers at uranium waste disposal sites","docAbstract":"

Field measurements of Rn-222 fluxes from the tops and bottoms of compacted clay radon barriers were used to calculate effective Rn diffusion coefficients (DRn) at four uranium waste disposal sites in the western United States to assess cover performance after more than 20 years of service. Values of DRn ranged from 7.4 × 10−7 to 6.0 × 10−9 m2/s, averaging 1.42 × 10−7. Water saturation (SW) from soil cores indicated that there was relatively little control of DRn by SW, especially at higher moisture levels, in contrast to estimates from most steady-state diffusion models. This is attributed to preferential pathways intrinsic to construction of the barriers or to natural process that have developed over time including desiccation cracks, root channels, and insect burrows in the engineered earthen barriers. A modification to some models in which fast and slow pathway DRn values are partitioned appears to give a good representation of the data; 4% of the fast pathway was needed to fit the data regression. For locations with high Sw and highest DRn (and fluxes) at each site, the proportion of fast pathway ranged from 1.7% to 34%, but for many locations with lower fluxes, little if any fast pathway was needed.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvrad.2023.107140","usgsCitation":"Fuhrmann, M., Caldwell, T., Likos, W.J., Waugh, W.J., Williams, M.M., and Benson, C.H., 2023, Evolving radon diffusion through earthen barriers at uranium waste disposal sites: Journal of Environmental Radioactivity, v. 262, 107140, 7 p., https://doi.org/10.1016/j.jenvrad.2023.107140.","productDescription":"107140, 7 p.","ipdsId":"IP-140005","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":444139,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2424456","text":"External Repository"},{"id":414702,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"262","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fuhrmann, Mark","contributorId":293204,"corporation":false,"usgs":false,"family":"Fuhrmann","given":"Mark","email":"","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":867453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Todd 0000-0003-4068-0648","orcid":"https://orcid.org/0000-0003-4068-0648","contributorId":217924,"corporation":false,"usgs":true,"family":"Caldwell","given":"Todd","email":"","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867454,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Likos, William J. 0000-0001-8177-6625","orcid":"https://orcid.org/0000-0001-8177-6625","contributorId":303390,"corporation":false,"usgs":false,"family":"Likos","given":"William","email":"","middleInitial":"J.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":867455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waugh, W. Jodi","contributorId":303391,"corporation":false,"usgs":false,"family":"Waugh","given":"W.","email":"","middleInitial":"Jodi","affiliations":[{"id":65785,"text":"RSI Entech","active":true,"usgs":false}],"preferred":false,"id":867456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Morgan M.","contributorId":303392,"corporation":false,"usgs":false,"family":"Williams","given":"Morgan","email":"","middleInitial":"M.","affiliations":[{"id":65785,"text":"RSI Entech","active":true,"usgs":false}],"preferred":false,"id":867457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Benson, Craig H. 0000-0001-8871-382X","orcid":"https://orcid.org/0000-0001-8871-382X","contributorId":303394,"corporation":false,"usgs":false,"family":"Benson","given":"Craig","email":"","middleInitial":"H.","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":867458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255298,"text":"70255298 - 2023 - Understanding the spatiotemporal distribution of snow refugia in the rain-snow transition zone of north-central Idaho","interactions":[],"lastModifiedDate":"2024-06-14T12:24:56.984299","indexId":"70255298","displayToPublicDate":"2023-03-21T07:19:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the spatiotemporal distribution of snow refugia in the rain-snow transition zone of north-central Idaho","docAbstract":"

Knowledge of snow cover distribution and disappearance dates over a wide range of scales is imperative for understanding hydrological dynamics and for habitat management of wildlife species that rely on snow cover. Identification of snow refugia, or places with relatively late snow disappearance dates (SDDs) compared to surrounding areas, is especially important as climate change alters snow cover timing and duration. The purpose of this study was to increase understanding of snow refugia in complex terrain spanning the rain-snow transition zone at fine spatial and temporal scales. To accomplish this objective, we used remote cameras to provide relatively high temporal and spatial resolution measurements on snowpack conditions. We built linear models to relate SDDs at the monitoring sites to topoclimatic and canopy cover metrics. One model to quantify SDDs included elevation, aspect, and an interaction between canopy cover and cold-air pooling potential. High-elevation, north-facing sites in cold-air pools (CAPs) had the latest SDDs, but isolated lower-elevation points also exhibited relatively late potential SDDs. Importantly, canopy cover had a much stronger effect on SDDs in CAPs than in non-CAPs, indicating that best practices in forest management for snow refugia could vary across microtopography. A second model that included in situ hydroclimate observations (December–February (DJF) temperature and March 1 snow depth) indicated that March 1 snow depth had little impact on SDD at the coldest winter temperatures, and that DJF temperatures had a stronger effect on SDD at lower snow depths, implying that the relative importance of snowfall and temperature could vary across hydroclimatic contexts in their impact on snow refugia. This new understanding of factors influencing snow refugia can guide forest management actions to increase snow retention and inform management of snow-dependent wildlife species in complex terrain.

","language":"English","publisher":"IOPScience","doi":"10.1088/1748-9326/acbb90","usgsCitation":"Strickfaden, K.M., Marshall, A.M., Svancara, L.K., Dugger, K., and Link, T.E., 2023, Understanding the spatiotemporal distribution of snow refugia in the rain-snow transition zone of north-central Idaho: Environmental Research Letters, v. 18, 044014, 11 p., https://doi.org/10.1088/1748-9326/acbb90.","productDescription":"044014, 11 p.","ipdsId":"IP-147918","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444142,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/acbb90","text":"Publisher Index Page"},{"id":430200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationDate":"2023-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Strickfaden, Kaitlyn M.","contributorId":339386,"corporation":false,"usgs":false,"family":"Strickfaden","given":"Kaitlyn","email":"","middleInitial":"M.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":904127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Adrienne M.","contributorId":339387,"corporation":false,"usgs":false,"family":"Marshall","given":"Adrienne","email":"","middleInitial":"M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":904128,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Svancara, Leona K.","contributorId":339389,"corporation":false,"usgs":false,"family":"Svancara","given":"Leona","email":"","middleInitial":"K.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":904129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":904130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Link, Timothy E.","contributorId":339393,"corporation":false,"usgs":false,"family":"Link","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":904131,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249179,"text":"70249179 - 2023 - Extensive regional variation in the phenology of insects and their response to temperature across North America","interactions":[],"lastModifiedDate":"2023-09-29T12:29:08.333477","indexId":"70249179","displayToPublicDate":"2023-03-21T07:02:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Extensive regional variation in the phenology of insects and their response to temperature across North America","docAbstract":"

Climate change models often assume similar responses to temperatures across the range of a species, but local adaptation or phenotypic plasticity can lead plants and animals to respond differently to temperature in different parts of their range. To date, there have been few tests of this assumption at the scale of continents, so it is unclear if this is a large-scale problem. Here, we examined the assumption that insect taxa show similar responses to temperature at 96 sites in grassy habitats across North America. We sampled insects with Malaise traps during 2019–2021 (N = 1041 samples) and examined the biomass of insects in relation to temperature and time of season. Our samples mostly contained Diptera (33%), Lepidoptera (19%), Hymenoptera (18%), and Coleoptera (10%). We found strong regional differences in the phenology of insects and their response to temperature, even within the same taxonomic group, habitat type, and time of season. For example, the biomass of nematoceran flies increased across the season in the central part of the continent, but it only showed a small increase in the Northeast and a seasonal decline in the Southeast and West. At a smaller scale, insect biomass at different traps operating on the same days was correlated up to ~75 km apart. Large-scale geographic and phenological variation in insect biomass and abundance has not been studied well, and it is a major source of controversy in previous analyses of insect declines that have aggregated studies from different locations and time periods. Our study illustrates that large-scale predictions about changes in insect populations, and their causes, will need to incorporate regional and taxonomic differences in the response to temperature.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.4036","usgsCitation":"Dunn, P., Ahmed, I., Armstrong, E., Barlow, N., Barnard, M., Belisle, M., Benson, T., Berzins, L., Boynton, C., Brown, T.A., Cady, M., Cameron, K., Chen, X., Clark, B., Clotfelter, E., Cromwell, K., Dawson, R., Denton, E., Forbes, A., Fowler, K., Gandhi, K., Garant, D., Hiebert, M., Houchen, C., Houtz, J., Imlay, T., Inouye, B., Inouye, D., Jackson, M., Jacobson, A., Jayd, K., Juteau, C., Kautz, A., Killian, C., Komatsu, K.J., Larsen, K., Laughlin, A., Levesque-Beaudin, V., Leys, R., Long, E., Lougheed, S., Mackenzie, S., Marangelo, J., Miller, C., Molano-Flores, B., Morrissey, C., Nicholls, E., Orlofske, J., Pearse, I., Peck, K., Pelletier, F., Pitt, A., Poston, J., Racke, D., Randall, J.A., Richardson, M., Rooney, O., Ruegg, A.R., Rush, S., Ryan, S.J., Sadowski, M., Schoepf, I., Schulz, L., Shea, B., Sheehan, T., Siefferman, L., Sikes, D., Stanback, M., Styrsky, J., Styrsky, J., Taff, C., Uehling, J., Uvino, K., Wassmer, T., Weglarz, K., Weinberger, M., Wenzel, J., and Whittingham, L., 2023, Extensive regional variation in the phenology of insects and their response to temperature across North America: Ecology, v. 104, no. 5, e4036, 17 p., https://doi.org/10.1002/ecy.4036.","productDescription":"e4036, 17 p.","ipdsId":"IP-116242","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444143,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.4036","text":"Publisher Index Page"},{"id":421386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United 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Landslides pose a threat to life and infrastructure and are influenced by anthropogenic modifications associated with land development. These modifications can affect susceptibility to landslides, and thus quantifying their influence on landslide occurrence can help design sustainable development efforts. Although landslide susceptibility has been shown to increase following urban expansion, the long-lasting effect of urbanization on landslide susceptibility remains largely unquantified. Hence, susceptibility maps developed based on inventories from non-urbanized areas may incorrectly evaluate the hazard in urbanized areas. To quantify this effect, we analyzed a landslide inventory from southwestern Pennsylvania, where the pulse of urbanization occurred more than a decade before the inventory was created. Using road density as a proxy for urbanization, the study area was divided into urbanized and non-urbanized areas. Susceptibility patterns were computed using statistical analyses of a post-urbanization landslide inventory together with maps of topographic, land cover, and geologic factors. A pre-urbanization landslide inventory was used as a control. Our findings indicate that urbanization has a decades-long effect on landslide susceptibility, where urbanized areas are generally more susceptible to landslides. In urbanized areas landslides are strongly associated with distance from roads and topographic curvature, whereas in non-urbanized landslides are strongly associated with stratigraphic formation and distance from streams. The consistent differences in susceptibility patterns between urbanized and non-urbanized areas indicate that urbanization has a long-lasting effect on landslide susceptibility and that susceptibility estimates should be made separately for these different environments to account for the persistent influence of urbanization.

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This research builds upon the extensive body of work to model exotic annual grass (EAG) characteristics and invasion. EAGs increase wildland fire risk and intensifies wildland fire behavior in western U.S. rangelands. Therefore, understanding characteristics of EAG growth increases understanding of its dynamics and can inform rangeland management decisions. To better understand EAG phenology and spatial distribution, monthly weather (precipitation, minimum and maximum temperature) variables were analyzed for 24 level III ecoregions. This research characterizes EAGs’ phenology identified by a normalized difference vegetation index (NDVI) threshold-based interpolation technique. An EAG phenology metric model was used to estimate a growing season dynamic for the years 2017–2021 for shrub and herbaceous land cover types in the western conterminous United States (66% of the area). The EAG phenology metrics include six growing season metrics such as start of season time, end of season time, and time of maximum NDVI during the growing season. The models’ cross validation results for Pearson’s r ranged from 0.88 to 0.95. Increased understanding of the effects that weather conditions have on EAG growth and spatial distribution can help land managers develop time-sensitive plans to protect entities deemed valuable to society like native habitat, wildlife, recreational areas, and air quality.

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Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual-level data. A variety of implementations exist for such models, including integrated likelihood, parameter-expanded data augmentation, and combinations of those. Capture–recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture–recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two datasets resulting from capture–recapture studies of different species.

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