Water development has threatened the ecological integrity of riverine ecosystems. Increasing water demand, persistent drought, and climate change exacerbate the effects of habitat degradation and loss in altered systems such as the Colorado River basin. Today, biologists are challenged to identify management actions that benefit native fishes while not hindering water development or management. Herein, we discuss the importance of the natural flow regime for functioning riverine ecosystems and provide examples from four tributaries to the Green River, a major headwater branch of the Colorado River. These tributaries represent a gradient of impacts ranging from water abstraction to the point of complete seasonal desiccation to a relatively natural flow regime, and consequently have maintained different levels of instream habitat complexity and native fish persistence. Despite decades of management, endangered species lack self-sustaining populations and other native species have been extirpated from over half their ranges, which begs the question: can water development and fish conservation be balanced under current water laws and climate change-driven declines in runoff? Given the continued decline in freshwater biodiversity and abundance occurring across the globe, we contend that immediate designation of rivers with natural flow regimes as freshwater conservation areas will enhance native species recovery.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10703","usgsCitation":"Pennock, C., Budy, P., Macfarlane, W., Breen, M., Jimenez, J., and Schmidt, J., 2022, Native fish need a natural flow regime: Fisheries Magazine, v. 47, no. 3, p. 118-123, https://doi.org/10.1002/fsh.10703.","productDescription":"6 p.","startPage":"118","endPage":"123","ipdsId":"IP-130996","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.4907110693891,\n 42.5751470978968\n ],\n [\n -112.27098956249205,\n 42.5751470978968\n ],\n [\n -112.27098956249205,\n 36.331037654860665\n ],\n [\n -105.4907110693891,\n 36.331037654860665\n ],\n [\n -105.4907110693891,\n 42.5751470978968\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Pennock, Casey A.","contributorId":337700,"corporation":false,"usgs":false,"family":"Pennock","given":"Casey A.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":902623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Macfarlane, William W.","contributorId":337701,"corporation":false,"usgs":false,"family":"Macfarlane","given":"William W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":902624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breen, Matthew J.","contributorId":337702,"corporation":false,"usgs":false,"family":"Breen","given":"Matthew J.","affiliations":[{"id":81036,"text":"Northeastern Regional Office","active":true,"usgs":false}],"preferred":false,"id":902625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jimenez, Justin","contributorId":337704,"corporation":false,"usgs":false,"family":"Jimenez","given":"Justin","affiliations":[{"id":81037,"text":"U. 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Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":902626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, John C.","contributorId":337707,"corporation":false,"usgs":false,"family":"Schmidt","given":"John C.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":902627,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70231551,"text":"70231551 - 2022 - Aquatic vegetation dynamics in the Upper Mississippi River over 2 decades spanning vegetation recovery","interactions":[],"lastModifiedDate":"2022-05-13T11:47:38.827389","indexId":"70231551","displayToPublicDate":"2021-11-18T06:43:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic vegetation dynamics in the Upper Mississippi River over 2 decades spanning vegetation recovery","docAbstract":"Macrophytes have recovered in rivers across the world, but long-term data and studies are lacking regarding community assembly and diversity changes coincident with macrophyte recovery. We investigated patterns of aquatic vegetation species composition and diversity in thousands of sites in the Upper Mississippi River, USA, spanning 21 y of monitoring and a period of vegetation recovery. We analyzed site-level compositional dissimilarity and environmental associations using non-metric multidimensional scaling, compared stability of lake-level assemblages over time with convex hulls, and assessed shared trends in assemblage dissimilarity at the pool scale using dynamic factor analysis. Site-level differences in aquatic vegetation assemblage structure were associated with water depth and substrate, and a gradient of species abundance and diversity was apparent. A common trend in assemblage dissimilarity over time and across contiguous floodplain lakes indicate that assemblage composition changed and diversity increased with considerable synchrony within the past 21 y. Shared trends across the 400-km study reach are indicative of 1 or more widespread, common drivers; however, neither hydrologic extremes nor turbidity explained vegetation assemblage patterns. Following several years of strong changes in composition and increased diversity, the vegetation assemblage displayed signs of increasing stability in some pools but not others. Further research is needed to identify drivers and mechanisms of aquatic vegetation assemblage expansion, assembly, and resilience, all of which will be applicable to the recovery of aquatic vegetation in floodplain systems worldwide.
The biogeochemistry of tidal marsh sediments facilitates the transformation of mercury (Hg) into the biologically available form methylmercury (MeHg), resulting in elevated Hg exposures to tidal marsh wildlife. Saltmarsh and Acadian Nelson’s sparrows (Ammospiza caudacutua and A. nelsoni subvirgatus, respectively) exclusively inhabit tidal marshes, potentially experiencing elevated risk to Hg exposure, and have experienced range-wide population declines. To characterize spatial and temporal variation of Hg exposure in these species, we sampled total mercury (THg) in blood collected from 9 populations spanning 560 km of coastline, including individuals resampled within and among years. Using concurrent nesting studies, we tested whether THg was correlated with nest survival probabilities, an index of fecundity. Blood THg ranged from 0.074–3.373 µg/g ww across 170 samples from 127 individuals. We detected high spatial variability in Hg exposure, observing differences of more than 45-fold across all individuals and 8-fold in mean blood THg among all study plots, including 4-fold between study plots within 4 km. Intraindividual changes in blood Hg exposure did not vary systematically in time but were considerable, varying by up to 2-fold within and among years. Controlling for both species differences and maximum water level, the dominant driver of fecundity in this system, nest survival probability decreased by 10% across the full range of female blood THg concentrations observed. We conclude that Hg has the potential to impair songbird reproduction, potentially exacerbating known climate-change driven population declines from sea-level rise in saltmarsh and Acadian Nelson’s sparrows.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-021-02488-1","usgsCitation":"Ruskin, K.J., Herring, G., Eagles-Smith, C., Eiklor, A.B., Elphick, C.S., Etterson, M.A., Field, C.B., Longnecker, R.A., Kovach, A.I., Shriver, W.G., Walsh, J.F., and Olsen, B., 2022, Mercury exposure of tidal marsh songbirds in the northeastern United States and its association with nest survival: Ecotoxicology, v. 31, p. 208-220, https://doi.org/10.1007/s10646-021-02488-1.","productDescription":"13 p.","startPage":"208","endPage":"220","ipdsId":"IP-127238","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":397869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, New Hampshire, New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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B.","contributorId":289543,"corporation":false,"usgs":false,"family":"Eiklor","given":"Alyssa","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":839082,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elphick, Chris S","contributorId":289387,"corporation":false,"usgs":false,"family":"Elphick","given":"Chris","email":"","middleInitial":"S","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":839084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Etterson, Matthew A.","contributorId":108012,"corporation":false,"usgs":false,"family":"Etterson","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":839085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Field, Chrisopher B.","contributorId":199179,"corporation":false,"usgs":false,"family":"Field","given":"Chrisopher","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":839290,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Longnecker, Rebecca A.","contributorId":289544,"corporation":false,"usgs":false,"family":"Longnecker","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":839086,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kovach, Adrienne I. 0000-0002-6791-0610","orcid":"https://orcid.org/0000-0002-6791-0610","contributorId":218939,"corporation":false,"usgs":false,"family":"Kovach","given":"Adrienne","email":"","middleInitial":"I.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":839087,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shriver, W Greg","contributorId":289393,"corporation":false,"usgs":false,"family":"Shriver","given":"W","email":"","middleInitial":"Greg","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":839088,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Walsh, James F.","contributorId":214333,"corporation":false,"usgs":false,"family":"Walsh","given":"James","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":839089,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Olsen, Brian J.","contributorId":272508,"corporation":false,"usgs":false,"family":"Olsen","given":"Brian J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":839090,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70226614,"text":"70226614 - 2022 - Riverscape approaches in practice: Perspectives and applications","interactions":[],"lastModifiedDate":"2022-03-15T16:16:03.600707","indexId":"70226614","displayToPublicDate":"2021-11-10T06:49:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Riverscape approaches in practice: Perspectives and applications","docAbstract":"Landscape perspectives in riverine ecology have been undertaken increasingly in the last 30 years, leading aquatic ecologists to develop a diverse set of approaches for conceptualizing, mapping and understanding ‘riverscapes’. Spatiotemporally explicit perspectives of rivers and their biota nested within the socio-ecological landscape now provide guiding principles and approaches in inland fisheries and watershed management. During the last two decades, scientific literature on riverscapes has increased rapidly, indicating that the term and associated approaches are serving an important purpose in freshwater science and management. We trace the origins and theoretical foundations of riverscape perspectives and approaches and examine trends in the published literature to assess the state of the science and demonstrate how they are being applied to address recent challenges in the management of riverine ecosystems. We focus on approaches for studying and visualizing rivers and streams with remote sensing, modelling and sampling designs that enable pattern detection as seen from above (e.g. river channel, floodplain, and riparian areas) but also into the water itself (e.g. aquatic organisms and the aqueous environment). Key concepts from landscape ecology that are central to riverscape approaches are heterogeneity, scale (resolution, extent and scope) and connectivity (structural and functional), which underpin spatial and temporal aspects of study design, data collection and analysis. Mapping of physical and biological characteristics of rivers and floodplains with high-resolution, spatially intensive techniques improves understanding of the causes and ecological consequences of spatial patterns at multiple scales. This information is crucial for managing river ecosystems, especially for the successful implementation of conservation, restoration and monitoring programs. Recent advances in remote sensing, field-sampling approaches and geospatial technology are making it increasingly feasible to collect high-resolution data over larger scales in space and time. We highlight challenges and opportunities and discuss future avenues of research with emerging tools that can potentially help to overcome obstacles to collecting, analysing and displaying these data. This synthesis is intended to help researchers and resource managers understand and apply these concepts and approaches to address real-world problems in freshwater management.
A simplified low-cost approach to experimentally determine transport parameters in mountain rivers is described, with an emphasis on the longitudinal dispersion coefficient (DL). The approach is based on a slug injection of table salt (NaCl) as a tracer and specific conductance readings at different locations downstream of the injection spot. Observed specific conductance readings are fit using the advection-dispersion equation with OTIS-P, yielding estimates of cross-sectional area and longitudinal dispersion coefficient for various stream reaches. Estimates of the DL are used to assess the accuracy of several empirical equations reported in the literature. This allowed the determination of complementary transport parameters related to transient storage zones. The empirical equations yielded rather high DL values, with some reaching up an order of magnitude higher to those obtained from tracer additions and OTIS-P. Overall, the proposed approach seems reliable and pertinent for river reaches of ca. 150 m in length.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3890","usgsCitation":"Castillo, D., Runkel, R.L., Duhalde, D., Pasten, P., Arumí, J., Oyarzun, J., Núñez, J., Maturana, H., and Oyarzun, R., 2022, A simple low-cost approach for transport parameter determination in mountain rivers: River Research and Applications, v. 38, no. 1, p. 173-181, https://doi.org/10.1002/rra.3890.","productDescription":"9 p.","startPage":"173","endPage":"181","ipdsId":"IP-129771","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":392855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Coquimbo Region, upper Elqui River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.58441162109375,\n -30.27804437780013\n ],\n [\n -69.95819091796875,\n -30.27804437780013\n ],\n [\n -69.95819091796875,\n -29.807284450222504\n ],\n [\n -70.58441162109375,\n -29.807284450222504\n ],\n [\n -70.58441162109375,\n -30.27804437780013\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Castillo, Daniella","contributorId":270038,"corporation":false,"usgs":false,"family":"Castillo","given":"Daniella","email":"","affiliations":[{"id":56062,"text":"U. La Serena, Chile","active":true,"usgs":false}],"preferred":false,"id":828329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duhalde, Denisse","contributorId":267247,"corporation":false,"usgs":false,"family":"Duhalde","given":"Denisse","email":"","affiliations":[{"id":55453,"text":"U. 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La Serena","active":true,"usgs":false}],"preferred":false,"id":828335,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maturana, Hugo","contributorId":267248,"corporation":false,"usgs":false,"family":"Maturana","given":"Hugo","email":"","affiliations":[{"id":27795,"text":"Universidad Católica del Norte","active":true,"usgs":false}],"preferred":false,"id":828336,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oyarzun, Ricardo","contributorId":267252,"corporation":false,"usgs":false,"family":"Oyarzun","given":"Ricardo","email":"","affiliations":[{"id":55455,"text":"Universidad de La Serena","active":true,"usgs":false}],"preferred":false,"id":828337,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230212,"text":"70230212 - 2022 - Seasonality of solute flux and water source chemistry in a coastal glacierized watershed undergoing rapid change: Wolverine Glacier watershed, Alaska","interactions":[],"lastModifiedDate":"2022-04-05T15:04:10.048629","indexId":"70230212","displayToPublicDate":"2021-11-01T09:53:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Seasonality of solute flux and water source chemistry in a coastal glacierized watershed undergoing rapid change: Wolverine Glacier watershed, Alaska","docAbstract":"As glaciers around the world rapidly lose mass, the tight coupling between glaciers and downstream ecosystems is resulting in widespread impacts on global hydrologic and biogeochemical cycling. However, a range of challenges make it difficult to conduct research in glacierized systems and our knowledge of seasonally changing hydrologic processes and solute sources and signatures is limited. This in turn hampers our ability to make predictions on solute composition and flux. We conducted a broad water sampling campaign in order to understand the present-day partitioning of water sources and associated solutes in Alaska’s Wolverine Glacier watershed. We established a relationship between electrical conductivity (EC) and streamflow at the watershed outlet to divide the melt season into four hydroclimatic periods. Across hydroclimatic periods, we observed a shift in off-glacier source waters from snowmelt-dominated overland and shallow subsurface flow paths to deeper groundwater flow paths. We also observed the shift from a low- to high-efficiency subglacial drainage network and the associated flushing of water stored sub-glacially with higher solute loads. We used calcium, the dominant dissolved ion, from watershed outlet samples to estimate solute fluxes for each hydroclimatic period across two melt seasons. We found between 40 and 55 percent of Ca2+ export occurred during the late season rainy period. This partitioning of the melt season coupled with a characterization of the chemical makeup and magnitude of solute export provides new insight into a rapidly changing watershed and creates a framework to quantify and predict changes to solute fluxes across a melt season.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028725","usgsCitation":"Bergstrom, A., Koch, J.C., O'Neel, S., and Baker, E., 2022, Seasonality of solute flux and water source chemistry in a coastal glacierized watershed undergoing rapid change: Wolverine Glacier watershed, Alaska: Water Resources Research, v. 57, no. 11, e2020WR028725, 22 p., https://doi.org/10.1029/2020WR028725.","productDescription":"e2020WR028725, 22 p.","ipdsId":"IP-125760","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":489151,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://scholarworks.boisestate.edu/geo_facpubs/632","text":"Publisher Index Page"},{"id":436041,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LLXNAX","text":"USGS data release","linkHelpText":"Geochemistry of Water Sources in the Wolverine Glacier Watershed, Alaska in 2016 and 2017"},{"id":398114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Nellie Juan River watershed, Wolverine Glacier watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.14283752441406,\n 60.30892680397063\n ],\n [\n -149.31381225585938,\n 60.30110330199715\n ],\n [\n -149.34059143066406,\n 60.255144967945945\n ],\n [\n -149.20120239257812,\n 60.189329357896995\n ],\n [\n -149.1442108154297,\n 60.19922718654721\n ],\n [\n -148.99314880371094,\n 60.1835257946458\n ],\n [\n -148.9354705810547,\n 60.23163020858355\n ],\n [\n -148.84140014648438,\n 60.28340847828243\n ],\n [\n -148.85650634765625,\n 60.29974250173091\n ],\n [\n -148.80912780761716,\n 60.32252843308696\n ],\n [\n -148.7548828125,\n 60.31266781667306\n ],\n [\n -148.74114990234375,\n 60.32898722274035\n ],\n [\n -148.69995117187497,\n 60.337823495982015\n ],\n [\n -148.70681762695312,\n 60.354809605953754\n ],\n [\n -148.62579345703125,\n 60.37959344588718\n ],\n [\n -148.6003875732422,\n 60.39825375597505\n ],\n [\n -148.68072509765625,\n 60.43452591318082\n ],\n [\n -148.7164306640625,\n 60.44231745121464\n ],\n [\n -148.7164306640625,\n 60.44976847885747\n ],\n [\n -148.8365936279297,\n 60.477863792166026\n ],\n [\n -148.8750457763672,\n 60.47346492719931\n ],\n [\n -148.9581298828125,\n 60.43554230669233\n ],\n [\n -148.941650390625,\n 60.38739814916949\n ],\n [\n -149.04052734375,\n 60.372465778991284\n ],\n [\n -149.14283752441406,\n 60.30892680397063\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Bergstrom, Anna 0000-0002-9684-4018","orcid":"https://orcid.org/0000-0002-9684-4018","contributorId":289664,"corporation":false,"usgs":false,"family":"Bergstrom","given":"Anna","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":839566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":839567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":839568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baker, Emily 0000-0002-0938-3496 ehbaker@usgs.gov","orcid":"https://orcid.org/0000-0002-0938-3496","contributorId":200570,"corporation":false,"usgs":true,"family":"Baker","given":"Emily","email":"ehbaker@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":839569,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228802,"text":"70228802 - 2022 - Natural inactivation of MS2, poliovirus type 1 and Cryptosporidium parvum in an anaerobic and reduced aquifer","interactions":[],"lastModifiedDate":"2022-02-22T13:16:37.319102","indexId":"70228802","displayToPublicDate":"2021-11-01T07:13:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2169,"text":"Journal of Applied Microbiology","active":true,"publicationSubtype":{"id":10}},"title":"Natural inactivation of MS2, poliovirus type 1 and Cryptosporidium parvum in an anaerobic and reduced aquifer","docAbstract":"The study of microbial inactivation rates in aquifer systems has most often been determined in aerobic and oxidized systems. This study examined the inactivation (i.e. loss of infectivity) of MS2, poliovirus type 1 (PV1) and Cryptosporidium parvum in an anaerobic and reduced groundwater system that has been identified as storage zones for aquifer storage and recovery (ASR) facilities.
Anaerobic and reduced (ORP < −250 mV) groundwater from an artesian well was diverted to an above-ground, flow-through mesocosm that contained diffusion chambers filled with MS2, PV1 or Cryptosporidium parvum. The respective infectivity assays were performed on microorganisms recovered from the diffusion chambers during 30- to 58-day experiments. The net reduction in infectivity was 5.73 log10 over 30 days for MS2, 5.00 log10 over 58 days for PV1 and 4.07 log10 over 37 days for C. parvum. The best fit inactivation model for PV1 was the log-linear model and the Weibull model for MS2 and C. parvum, with respective inactivation rates (95% confidence interval) of 0.19 (0.17–0.21) log10 day−1, 0.31 (0.19–0.89) log10 day−1 and 0.20 (0.14–0.37) log10 day−1.
The groundwater geochemical conditions in this aquifer enhanced the inactivation of MS2, PV1, and C. parvum at rates approximately 2.0–5.3-fold, 1.2–17.0-fold, and 4.5–5.6-fold greater, respectively, than those from published studies that used diffusion chambers in aerobic-to-anoxic groundwater systems, with positive redox potentials.
Geochemical conditions like those in the aquifer zone in this study can naturally and significantly reduce concentrations of microbial indicators and pathogens of human health concern in injected surface water. Appropriate storage times for injected surface water could complement above-ground engineered processes for microorganism removal and inactivation (e.g. filtration, disinfection) by naturally increasing overall microorganism log-inactivation rates of ASR facilities.
","language":"English","publisher":"Wiley","doi":"10.1111/jam.15349","usgsCitation":"Lisle, J.T., and Lukasic, G., 2022, Natural inactivation of MS2, poliovirus type 1 and Cryptosporidium parvum in an anaerobic and reduced aquifer: Journal of Applied Microbiology, v. 132, no. 3, p. 2464-2474, https://doi.org/10.1111/jam.15349.","productDescription":"11 p.","startPage":"2464","endPage":"2474","ipdsId":"IP-131012","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":396232,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"132","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lisle, John T. 0000-0002-5447-2092 jlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-5447-2092","contributorId":2944,"corporation":false,"usgs":true,"family":"Lisle","given":"John","email":"jlisle@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":835536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lukasic, Geroge","contributorId":279834,"corporation":false,"usgs":false,"family":"Lukasic","given":"Geroge","email":"","affiliations":[{"id":57372,"text":"BCS Laboratories, Inc., Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":835537,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226137,"text":"70226137 - 2022 - Tree mortality response to drought-density interactions suggests opportunities to enhance drought resistance","interactions":[],"lastModifiedDate":"2022-02-15T16:08:05.671364","indexId":"70226137","displayToPublicDate":"2021-11-01T06:53:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9913,"text":"Journal of Applied Ecology.","active":true,"publicationSubtype":{"id":10}},"title":"Tree mortality response to drought-density interactions suggests opportunities to enhance drought resistance","docAbstract":"The future of dry forests around the world is uncertain given predictions that rising temperatures and enhanced aridity will increase drought-induced tree mortality. Using forest management and ecological restoration to reduce density and competition for water offers one of the few pathways that forests managers can potentially minimize drought-induced tree mortality. Competition for water during drought leads to elevated tree mortality in dense stands, although the influence of density on heat-induced stress, and the durations of hot or dry conditions that most impact mortality, remain unclear.
Understanding how competition interacts with hot-drought stress is essential to recognize how, where, and how much reducing density can help sustain dry forests in a rapidly changing world. Here, we integrated repeat measurements of 28,881 ponderosa pine trees across the western US (2000-2017) with soil moisture estimates from a water balance model to examine how annual mortality responds to competition, temperature and soil moisture conditions.
Tree mortality responded most strongly to basal area, and was elevated in places with high mean temperatures, unusually hot 7-year high temperature anomalies, and unusually dry 8-year low soil moisture anomalies. Mortality was also lower in places that experienced unusually wet 3-year soil moisture anomalies between measurements. Importantly, we found that basal area interacts with temperature and soil moisture, exacerbating mortality during times of stress imposed by high temperature or low moisture.
Synthesis and Applications: Our results imply that a 50% reduction in forest basal area could reduce drought-driven tree mortality by 20-80%. The largest impacts of density reduction are seen in areas with high current basal area and places that experience high temperatures and/or severe multiyear droughts. These interactions between competition and drought are critical to understand past and future patterns of tree mortality in the context of climate change, and provide information for resource managers seeking to enhance dry forest drought resistance.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14073","usgsCitation":"Bradford, J., Shriver, R.K., Robles, M.D., McCauley, L., Andrews, C.M., Crimmins, M.A., and Bell, D.M., 2022, Tree mortality response to drought-density interactions suggests opportunities to enhance drought resistance: Journal of Applied Ecology., v. 59, no. 2, p. 549-559, https://doi.org/10.1111/1365-2664.14073.","productDescription":"11 p.","startPage":"549","endPage":"559","ipdsId":"IP-126821","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449572,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.14073","text":"Publisher Index Page"},{"id":436042,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92HBML8","text":"USGS data release","linkHelpText":"Estimated tree mortality, basal area, climate, and drought conditions for ponderosa pine in forest inventory plots across the western U.S."},{"id":391609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":826597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shriver, Robert K 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":222834,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert","email":"","middleInitial":"K","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":826598,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robles, Marcos D.","contributorId":244893,"corporation":false,"usgs":false,"family":"Robles","given":"Marcos","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":826599,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCauley, Lisa A","contributorId":268774,"corporation":false,"usgs":false,"family":"McCauley","given":"Lisa A","affiliations":[{"id":55658,"text":"The Nature Conservancy, Center for Science and Public Policy, 1510 E Ft Lowell Road, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":826600,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":826601,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crimmins, Michael A.","contributorId":178238,"corporation":false,"usgs":false,"family":"Crimmins","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":826602,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":826603,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225740,"text":"70225740 - 2022 - Techniques to improve ecological interpretability of black box machine learning models","interactions":[],"lastModifiedDate":"2023-03-24T16:57:11.274452","indexId":"70225740","displayToPublicDate":"2021-10-28T08:45:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2151,"text":"Journal of Agricultural, Biological, and Environmental Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Techniques to improve ecological interpretability of black box machine learning models","docAbstract":"Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous USA, as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate–response relationships. Additionally, interaction effects were quantified according to interaction strength (IAS) and Friedman’s
Trees in the urban right-of-way areas have increasingly been considered part of a suite of green infrastructure practices used to manage stormwater runoff. A paired-catchment experimental design (with street tree removal as the treatment) was used to assess how street trees affect major hydrologic fluxes in a typical residential stormwater collection and conveyance network. The treatment consisted of removing 29 green ash (Fraxinus pennsylvanica) and two Norway maple (Acer platanoides) street trees from a medium-density residential area. Tree removal resulted in an estimated 198 m3 increase in surface runoff volume compared to the control catchment over the course of the study. This increase accounted for 4% of the total measured runoff after trees were removed. Despite significant changes to runoff volume (p ≤ 0.10), peak discharge was generally not affected by tree removal. On a per-tree basis, 66 L of rainfall per m2 of canopy was lost that would have otherwise been intercepted and stored. Runoff volume reduction benefit was estimated at 6376 L per tree. These values experimentally document per-capita retention services rendered by trees over a growing season with 42 storm events. These values are within the range reported by previous studies, which largely relied on simulation. This study provides catchment scale evidence that reducing stormwater runoff is one of many ecosystem services provided by street trees. This study quantifies these services, based on site conditions and a mix of deciduous species, and serves to improve our ability to account for this important yet otherwise poorly constrained hydrologic service. Engineers, city planners, urban foresters, and others involved with the management of urban stormwater can use this information to better understand tradeoffs involved in using green infrastructure to reduce urban runoff burden.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.151296","usgsCitation":"Selbig, W.R., Loheid, S., Schuster, W., Scharenbroch, B.C., Coville, R.C., Kruegler, J., Avery, W., Haefner, R.J., and Nowak, D., 2022, Quantifying the stormwater runoff volume reduction benefits of urban street tree canopy: Science of the Total Environment, v. 806, no. 3, 151296, 9 p., https://doi.org/10.1016/j.scitotenv.2021.151296.","productDescription":"151296, 9 p.","ipdsId":"IP-131647","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436043,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJHBVW","text":"USGS data release","linkHelpText":"Storm event data in the control and test catchments during the calibration and treatment phase of a urban tree canopy study in Fond du Lac, Wisconsin, from May 2018 through September 2020: U.S. Geological Survey data release"},{"id":392624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.516845703125,\n 43.70163689691259\n ],\n [\n -88.34930419921875,\n 43.70163689691259\n ],\n [\n -88.34930419921875,\n 43.85235516793534\n ],\n [\n -88.516845703125,\n 43.85235516793534\n ],\n [\n -88.516845703125,\n 43.70163689691259\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"806","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loheid, Steven P. II 0000-0003-1897-0163","orcid":"https://orcid.org/0000-0003-1897-0163","contributorId":269846,"corporation":false,"usgs":false,"family":"Loheid","given":"Steven P.","suffix":"II","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":828022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schuster, William","contributorId":117899,"corporation":false,"usgs":true,"family":"Schuster","given":"William","email":"","affiliations":[],"preferred":false,"id":828040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scharenbroch, Bryant C. 0000-0002-9342-7550","orcid":"https://orcid.org/0000-0002-9342-7550","contributorId":269849,"corporation":false,"usgs":false,"family":"Scharenbroch","given":"Bryant","email":"","middleInitial":"C.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":828041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coville, Robert C. 0000-0002-6895-2564","orcid":"https://orcid.org/0000-0002-6895-2564","contributorId":269851,"corporation":false,"usgs":false,"family":"Coville","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":828042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kruegler, James 0000-0002-2671-0807","orcid":"https://orcid.org/0000-0002-2671-0807","contributorId":269853,"corporation":false,"usgs":false,"family":"Kruegler","given":"James","email":"","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":828043,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Avery, William 0000-0002-2651-9906","orcid":"https://orcid.org/0000-0002-2651-9906","contributorId":269858,"corporation":false,"usgs":false,"family":"Avery","given":"William","email":"","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":828044,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haefner, Ralph J. 0000-0002-4363-9010 rhaefner@usgs.gov","orcid":"https://orcid.org/0000-0002-4363-9010","contributorId":1793,"corporation":false,"usgs":true,"family":"Haefner","given":"Ralph","email":"rhaefner@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828045,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nowak, David 0000-0002-2043-0062","orcid":"https://orcid.org/0000-0002-2043-0062","contributorId":269856,"corporation":false,"usgs":false,"family":"Nowak","given":"David","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":828046,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70226804,"text":"70226804 - 2022 - Sediment-ecological connectivity in a large river network","interactions":[],"lastModifiedDate":"2022-02-15T16:11:44.987675","indexId":"70226804","displayToPublicDate":"2021-10-26T08:24:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Sediment-ecological connectivity in a large river network","docAbstract":"Sediment eroded from the headwaters of a large basin strongly influences channels and ecosystems far downstream, but the connection is often difficult to trace. Disturbance-dependent riparian trees are thought to rely primarily on floods for formation of the sand bars necessary for seedling establishment, but pulses of sediment should also promote formation of such features. In order to expand understanding of the role of sediment connectivity in governing ecological processes, here we explore the hypothesis that cottonwood forest along the Green and Yampa Rivers in Utah and Colorado are dominated by trees established a century ago during a period of extensive channel migration caused by significant headwater erosion. Analysis of historical documents and aerial photographs suggests that three key tributaries of the Yampa River underwent significant historical erosion from roughly 1880 to 1940. Average width and depth of tributaries with defined arroyos increased two to six times from historical surveys, resulting in the export of ~30 million metric tons of sediment, sizably increasing the sediment load and channel migration rate of the Yampa and Green Rivers. Establishment of major portions of several downstream cottonwood forests occurred during this period of historical erosion, increased sediment loads, and heightened channel migration rates, and the area of forest dating to that time is much greater than can be explained by high flows alone. Viewed collectively, our findings suggest tributary erosion played a vital role in successful downstream forest establishment, a link we contend is best illustrated through a sediment-ecological connectivity framework. Broadly, this framework facilitates consideration of linkages between morphological and ecological processes at the watershed-scale. Development and utilization of a watershed-scale sediment-ecological connectivity perspective highlights the value of sediment as a critical ecological resource to be managed jointly with flow to ensure the maintenance of vital riverine ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5277","usgsCitation":"Kemper, J.T., Thaxton, R.D., Rathburn, S.L., Friedman, J.M., Mueller, E., and Scott, M.L., 2022, Sediment-ecological connectivity in a large river network: Earth Surface Processes and Landforms, v. 47, no. 2, p. 639-657, https://doi.org/10.1002/esp.5277.","productDescription":"19 p.","startPage":"639","endPage":"657","ipdsId":"IP-127871","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":392853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Green River, Yampa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.11572265625,\n 38.013476231041935\n ],\n [\n -105.633544921875,\n 38.013476231041935\n ],\n [\n -105.633544921875,\n 40.93841495689795\n ],\n [\n -111.11572265625,\n 40.93841495689795\n ],\n [\n -111.11572265625,\n 38.013476231041935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kemper, John T.","contributorId":270040,"corporation":false,"usgs":false,"family":"Kemper","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":828338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thaxton, R. D.","contributorId":270041,"corporation":false,"usgs":false,"family":"Thaxton","given":"R.","email":"","middleInitial":"D.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":828339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rathburn, Sara L.","contributorId":140606,"corporation":false,"usgs":false,"family":"Rathburn","given":"Sara","email":"","middleInitial":"L.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":828340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":828341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Erich R. 0000-0001-8202-154X","orcid":"https://orcid.org/0000-0001-8202-154X","contributorId":207750,"corporation":false,"usgs":false,"family":"Mueller","given":"Erich R.","affiliations":[{"id":37626,"text":"Department of Geography, University of Wyoming, Laramie, WY, USA","active":true,"usgs":false}],"preferred":false,"id":828342,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Michael L.","contributorId":204827,"corporation":false,"usgs":false,"family":"Scott","given":"Michael","email":"","middleInitial":"L.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":828343,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229388,"text":"70229388 - 2022 - Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors","interactions":[],"lastModifiedDate":"2022-03-04T17:22:20.925497","indexId":"70229388","displayToPublicDate":"2021-10-25T11:12:36","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors","docAbstract":"The advent of Low Power Wide Area Networks (LPWAN) has improved the feasibility of wireless sensor networks for environmental sensing across wide areas. We have built EnviSense, an ultra-low power environmental sensing system, and deployed over a dozen of them across two locations in Northern California for hydrological monitoring applications with the U.S. Geological Survey (USGS). This paper details our experiences with the design and implementation of this system across two years, including six months of continuous measurement in the field. We describe the lessons learned for deployment planning, remote device management and programming, and system co-design with a domain-expert from the USGS.
","largerWorkTitle":"LP-IoT '21: Proceedings of the 1st ACM Workshop on No Power and Low Power Internet-of-Things","language":"English","publisher":"Association of Computing Machinery","doi":"10.1145/3477085.3478988","usgsCitation":"Grimsley, R., Marineau, M.D., and Iannucci, R.A., 2022, Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors, in LP-IoT '21: Proceedings of the 1st ACM Workshop on No Power and Low Power Internet-of-Things, p. 1-7, https://doi.org/10.1145/3477085.3478988.","productDescription":"7 p.","startPage":"1","endPage":"7","ipdsId":"IP-130093","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":449595,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1145/3477085.3478988","text":"Publisher Index Page"},{"id":396759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Beale Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.42982482910158,\n 39.066380823434486\n ],\n [\n -121.36528015136717,\n 39.06718050308463\n ],\n [\n -121.31996154785158,\n 39.087169549791966\n ],\n [\n -121.31927490234376,\n 39.13325601865834\n ],\n [\n -121.34056091308594,\n 39.14949897356036\n ],\n [\n -121.3985824584961,\n 39.176650950983294\n ],\n [\n -121.47239685058592,\n 39.16227768020765\n ],\n [\n -121.48097991943358,\n 39.14630393428414\n ],\n [\n -121.4813232421875,\n 39.12633165289992\n ],\n [\n -121.42982482910158,\n 39.066380823434486\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Grimsley, Reese 0000-0002-3458-0707","orcid":"https://orcid.org/0000-0002-3458-0707","contributorId":287982,"corporation":false,"usgs":false,"family":"Grimsley","given":"Reese","email":"","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":837249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marineau, Mathieu D. 0000-0002-6568-0743 mmarineau@usgs.gov","orcid":"https://orcid.org/0000-0002-6568-0743","contributorId":4954,"corporation":false,"usgs":true,"family":"Marineau","given":"Mathieu","email":"mmarineau@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iannucci, Robert A.","contributorId":202339,"corporation":false,"usgs":false,"family":"Iannucci","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":36393,"text":"Carnegie Mellon University - Silicon Valley","active":true,"usgs":false}],"preferred":false,"id":837251,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230213,"text":"70230213 - 2022 - Temperature-based modeling of incubation period to protect loggerhead hatchlings on an urban beach in Northwest Florida","interactions":[],"lastModifiedDate":"2022-04-05T15:19:54.312558","indexId":"70230213","displayToPublicDate":"2021-10-25T10:16:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2277,"text":"Journal of Experimental Marine Biology and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Temperature-based modeling of incubation period to protect loggerhead hatchlings on an urban beach in Northwest Florida","docAbstract":"Sea turtle hatchlings face many natural and anthropogenic threats during their short journey to the water after emerging from nests. Reducing hatchling mortality is critical to population recovery of imperiled sea turtle species; however, protecting hatchlings is particularly challenging on beaches degraded by human development and disturbances, including artificial lighting. Managers need practical methods to reduce hatchling mortality without harming their natural behavior or development. To address this need, we describe an approach to reduce mortality of loggerhead hatchlings that relies on prediction of clutch incubation length and knowledge of hatchling emergence patterns. We developed models to predict incubation length utilizing sand temperature and nest depth data from 133 loggerhead nests laid on an urban beach in Northwest Florida from 2013 to 2020. Incubation length was predicted to within 2.2 days using mean sand temperatures measured just outside of the clutch. Predicted accuracy improved to 1.9 days using a 2-parameter model incorporating sand temperature and measured depth to the topmost eggs. Hatchlings emerged almost exclusively at night in a single large group with no evidence of asynchronous emergences. Emergence times were skewed toward the early evening, in contrast to loggerhead nests on the Florida Atlantic coast which tend to hatch near midnight. Using these prediction tools, monitoring efforts could be focused on days and times of expected emergence to enable protection of hatchlings emerging naturally from nests left in situ. The method used here, while not a substitute for recovery of degraded nesting habitat, provides a way to protect hatchlings that avoids disturbing the eggs with instruments or restraining the hatchlings with cages or screens.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jembe.2021.151647","usgsCitation":"Watson, K.P., and Lamont, M., 2022, Temperature-based modeling of incubation period to protect loggerhead hatchlings on an urban beach in Northwest Florida: Journal of Experimental Marine Biology and Ecology, v. 546, 151647, 10 p., https://doi.org/10.1016/j.jembe.2021.151647.","productDescription":"151647, 10 p.","ipdsId":"IP-127739","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","county":"Bay County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.528564453125,\n 30.023921574501376\n ],\n [\n -85.8856201171875,\n 30.235340577517942\n ],\n [\n -85.92819213867188,\n 30.22466172703242\n ],\n [\n -85.59860229492188,\n 30.0286775329042\n ],\n [\n -85.54504394531249,\n 30.00013836058068\n ],\n [\n -85.528564453125,\n 30.023921574501376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"546","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Watson, Kennard P.","contributorId":289668,"corporation":false,"usgs":false,"family":"Watson","given":"Kennard","email":"","middleInitial":"P.","affiliations":[{"id":62225,"text":"Panama City Beach Turtle Watch","active":true,"usgs":false}],"preferred":false,"id":839570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839571,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227255,"text":"70227255 - 2022 - Fatty acid profiles of feeding and fasting bears: Estimating calibration coefficients, the timeframe of diet estimates, and selective mobilization during hibernation","interactions":[],"lastModifiedDate":"2022-03-15T16:44:22.084218","indexId":"70227255","displayToPublicDate":"2021-10-23T07:27:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2226,"text":"Journal of Comparative Physiology B: Biochemical, Systemic, and Environmental Physiology","active":true,"publicationSubtype":{"id":10}},"title":"Fatty acid profiles of feeding and fasting bears: Estimating calibration coefficients, the timeframe of diet estimates, and selective mobilization during hibernation","docAbstract":"Accurate information on diet composition is central to understanding and conserving carnivore populations. Quantitative fatty acid signature analysis (QFASA) has emerged as a powerful tool for estimating the diets of predators, but ambiguities remain about the timeframe of QFASA estimates and the need to account for species-specific patterns of metabolism. We conducted a series of feeding experiments with four juvenile male brown bears (Ursus arctos) to (1) track the timing of changes in adipose tissue composition and QFASA diet estimates in response to a change in diet and (2) quantify the relationship between consumer and diet FA composition (i.e., determine “calibration coefficients”). Bears were fed three compositionally distinct diets for 90–120 days each. Two marine-based diets were intended to approximate the lipid content and composition of the wild diet of polar bears (U. maritimus). Bear adipose tissue composition changed quickly in the direction of the diet and showed evidence of stabilization after 60 days. During hibernation, FA profiles were initially stable but diet estimates after 10 weeks were sensitive to calibration coefficients. Calibration coefficients derived from the marine-based diets were broadly similar to each other and to published values from marine-fed mink (Mustela vison), which have been used as a model for free-ranging polar bears. For growing bears on a high-fat diet, the temporal window for QFASA estimates was 30–90 days. Although our results reinforce the importance of accurate calibration, the similarities across taxa and diets suggest it may be feasible to develop a generalized QFASA approach for mammalian carnivores.
Microbial metabolisms and interactions that facilitate subsurface conversions of recalcitrant carbon to methane are poorly understood. We deployed an in situ enrichment device in a subsurface coal seam in the Powder River Basin (PRB), USA, and used BONCAT-FACS-Metagenomics to identify translationally active populations involved in methane generation from a variety of coal-derived aromatic hydrocarbons. From the active fraction, high-quality metagenome-assembled genomes (MAGs) were recovered for the acetoclastic methanogen, Methanothrix paradoxum, and a novel member of the Chlorobi with the potential to generate acetate via the Pta-Ack pathway. Members of the Bacteroides and Geobacter also encoded Pta-Ack and together, all four populations had the putative ability to degrade ethylbenzene, phenylphosphate, phenylethanol, toluene, xylene, and phenol. Metabolic reconstructions, gene analyses, and environmental parameters also indicated that redox fluctuations likely promote facultative energy metabolisms in the coal seam. The active “Chlorobi PRB” MAG encoded enzymes for fermentation, nitrate reduction, and multiple oxygenases with varying binding affinities for oxygen. “M. paradoxum PRB” encoded an extradiol dioxygenase for aerobic phenylacetate degradation, which was also present in previously published Methanothrix genomes. These observations outline underlying processes for bio-methane from subbituminous coal by translationally active populations and demonstrate activity-based metagenomics as a powerful strategy in next generation physiology to understand ecologically relevant microbial populations.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41396-021-01139-x","usgsCitation":"McKay, L.J., Smith, H.J., Barnhart, E.P., Schweitzer, H.S., Malmstrom, R.R., Goudeau, D., and Fields, M.W., 2022, Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane: The ISME Journal, v. 16, p. 915-926, https://doi.org/10.1038/s41396-021-01139-x.","productDescription":"12 p.","startPage":"915","endPage":"926","ipdsId":"IP-126752","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":449609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41396-021-01139-x","text":"Publisher Index Page"},{"id":391508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Powder River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.75439453125,\n 41.409775832009565\n ],\n [\n -104.765625,\n 41.83682786072714\n ],\n [\n -104.23828125,\n 44.59046718130883\n ],\n [\n -104.9853515625,\n 46.649436163350245\n ],\n [\n -106.58935546875,\n 46.7549166192819\n ],\n [\n -108.1494140625,\n 46.51351558059737\n ],\n [\n -108.12744140625,\n 45.38301927899065\n ],\n [\n -106.41357421875,\n 43.6599240747891\n ],\n [\n -105.99609375,\n 41.83682786072714\n ],\n [\n -105.75439453125,\n 41.409775832009565\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2021-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"McKay, Luke J.","contributorId":268349,"corporation":false,"usgs":false,"family":"McKay","given":"Luke","email":"","middleInitial":"J.","affiliations":[{"id":55631,"text":"Center for Biofilm Engineering, Montana State University, Bozeman","active":true,"usgs":false}],"preferred":false,"id":826471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Heidi J.","contributorId":268344,"corporation":false,"usgs":false,"family":"Smith","given":"Heidi","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schweitzer, Hannah S.","contributorId":268345,"corporation":false,"usgs":false,"family":"Schweitzer","given":"Hannah","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826474,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Malmstrom, Rex R.","contributorId":268350,"corporation":false,"usgs":false,"family":"Malmstrom","given":"Rex","email":"","middleInitial":"R.","affiliations":[{"id":55632,"text":"DOE Joint Genome Institute","active":true,"usgs":false}],"preferred":false,"id":826475,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goudeau, Danielle","contributorId":268351,"corporation":false,"usgs":false,"family":"Goudeau","given":"Danielle","email":"","affiliations":[{"id":55632,"text":"DOE Joint Genome Institute","active":true,"usgs":false}],"preferred":false,"id":826476,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":826479,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225555,"text":"70225555 - 2022 - Snow depth retrieval with an autonomous UAV-mounted software-defined radar","interactions":[],"lastModifiedDate":"2025-09-05T18:45:03.535956","indexId":"70225555","displayToPublicDate":"2021-10-19T07:20:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9538,"text":"Transactions on Geoscience and Remote Sensing (TGARS)","active":true,"publicationSubtype":{"id":10}},"title":"Snow depth retrieval with an autonomous UAV-mounted software-defined radar","docAbstract":"Worldwide, many productive rivers are dam-regulated and rely on flow management strategies that must balance support of ecological processes with human water use. One component of evaluating this balance is to understand ecological consequences of alternative flow management strategies, which has often been accomplished by coupling population dynamics models with models that relate streamflow to habitat availability and quality. Numerous methods assign habitat availability to locations within a river basin: These include fine-scale field-measured values that are extrapolated to other locations within the basin having similar physical characteristics or equation-driven values created by functions of model-predicted values of physical characteristics. The array of options for creating habitat models is evolving rapidly as high-resolution remote-sensing data becomes more accessible and computational capacity improves. Our objective was to identify trade-offs among approaches that assign habitat relationships to large rivers and to create a decision support tool to supplement choices of extent and granularity. Using a selection of case studies that represent a breadth of scales and diverse trade-offs, we demonstrate the need for a transparent process of data evaluation and assessment to determine the appropriate fit for model scope or context that best supports management needs and recognize sources of uncertainty. The structured approach proposed here aims at improving future model development and refine population dynamics models that inform the management of rivers.
Tidal marsh restorations may result in transitional mudflat habitats depending on hydrological and geomorphological conditions. Compared to tidal marsh, mudflats are thought to have limited value for carbon sequestration, carbon storage, and foraging benefits for salmon. We evaluated greenhouse gas exchange, sediment carbon storage, and invertebrate production at restoration and reference tidal marsh sites within the Nisqually River Delta, Puget Sound, Washington. Within the first seven years, the restoration site was a sparsely vegetated mudflat that didn't sequester atmospheric CO2; but, had sediment carbon accumulation rates similar to the reference site due to allochthonous carbon subsidies from nearby mature marshes. Compared to other estuarine habitat types, the tidal marsh supported the greatest production of energy-rich insect prey for juvenile salmon; yet, the restoration site produced similar, and at times elevated, invertebrate prey resources and prey energy compared to the reference site. As a result, salmon that foraged within the restoration site gained measurable benefits as indicated by their bioenergetic growth potential. The restoration site received carbon subsidies from the broader estuarine landscape for both sediment carbon accumulation and invertebrate prey production. These findings demonstrate the importance of habitat connectivity and show how blue carbon and wildlife co-benefits are closely intertwined.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wetland Carbon and Environmental Management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/9781119639305.ch5","usgsCitation":"Woo, I., Davis, M.J., De La Cruz, S.E., Windham-Myers, L., Drexler, J.Z., Byrd, K.B., Stuart-Haëntjens, E., Anderson, F.E., Bergamaschi, B.A., Nakai, G., Ellings, C.S., and Hodgson, S., 2022, Carbon flux, storage, and wildlife co-benefits in a restoring estuary, chap. 5 of Wetland Carbon and Environmental Management, p. 105-125, https://doi.org/10.1002/9781119639305.ch5.","productDescription":"21 p.","startPage":"105","endPage":"125","ipdsId":"IP-124300","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":395626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.73599624633789,\n 47.06462634563797\n ],\n [\n -122.68054962158202,\n 47.06462634563797\n ],\n [\n -122.68054962158202,\n 47.10319964190176\n ],\n [\n -122.73599624633789,\n 47.10319964190176\n ],\n [\n -122.73599624633789,\n 47.06462634563797\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-10-15","publicationStatus":"PW","contributors":{"editors":[{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":833746,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":833747,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Stagg, Camille L. 0000-0002-1125-7253 staggc@usgs.gov","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":4111,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","email":"staggc@usgs.gov","middleInitial":"L.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833748,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833699,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833700,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833701,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - 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2022 - Predicting regional fluoride concentrations at public and domestic supply depths in basin-fill aquifers of the western United States using a random forest model","interactions":[],"lastModifiedDate":"2021-12-10T17:13:59.830723","indexId":"70225701","displayToPublicDate":"2021-10-14T07:37:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Predicting regional fluoride concentrations at public and domestic supply depths in basin-fill aquifers of the western United States using a random forest model","docAbstract":"A random forest regression (RFR) model was applied to over 12,000 wells with measured fluoride (F) concentrations in untreated groundwater to predict F concentrations at depths used for domestic and public supply in basin-fill aquifers of the western United States. The model relied on twenty-two regional-scale environmental and surficial predictor variables selected to represent factors known to control F concentrations in groundwater. The testing model fit R2 and RMSE were 0.52 and 0.78 mg/L. Comparisons of measured to predicted proportions of four F-concentrations categories (<0.7 mg/L, 0.7–2 mg/L, >2 mg/L – 4 mg/L, and > 4 mg/L) indicate that the model performed well at making regional-scale predictions. Differences between measured and predicted proportions indicate underprediction of measured F at values by between 4 and 20 mg/L, representing less than 1% of the regional scale predicted values. These residuals most often map to geographic regions where local-scale processes including evaporative discharge in closed basins or intermittent streams concentrate fluoride in shallow groundwater. Despite this, the RFR model provides spatially continuous F predictions across the basin-fill aquifers where discrete samples are missing. Further, the predictions capture documented areas that exceed the F maximum contaminant level for drinking water of 4 mg/L and areas that are below the oral-health benchmark of 0.7 mg/L. These predictions can be used to estimate fluoride concentrations in unmonitored areas and to aid in identifying geographic areas that may require further investigation at localized scales.
Pesticides are widely recognized as important biological stressors in streams, especially in heavily developed urban and agricultural areas like the Central California Coast region. We assessed occurrence and potential toxicity of pesticides in small streams in the region using two analytical methods: a broad-spectrum (223 compounds) method in use since 2012 and a newly developed method for 30 additional new-generation fungicides and insecticides. At least one pesticide compound was identified in 83 of the 85 streams sampled. About one-half (48%) of the 253 pesticides measured were detected at least once and 27 were detected in 10% or more of samples. Three of the top 4, and 6 of the top 10 most frequently detected compounds (chlorantraniliprole, dinotefuran, boscalid, thiamethoxam, clothianidin and the fluopicolide degradate 2,6-dichlorobenzamide) were analyzed by the new method. Pesticide mixtures were common, with two or more pesticide compounds detected in 81% of samples and 10 or more in 32% of samples. The pesticide count at a site was relatively consistent over the 6-week study. Four sites with mixed land-use in the lower basin (<5 km from the sampling site) tended to have the highest pesticide counts and the highest concentrations. Potential toxicity (assessed by comparison to benchmarks) to invertebrates was much more common than potential toxicity to fish or plants and was associated with a wide array of insecticides. The common occurrence of new-generation pesticides highlights the need to continuously update analytical methods to keep pace with changing pesticide use for a fuller assessment of pesticide occurrence and effects on the environment.
Submerged aquatic vegetation (SAV) creates highly productive habitats in coastal areas, providing support for many important species of fish and wildlife. Despite the importance and documented loss of SAV across fresh to marine habitats globally, we lack consistent baseline data on estuarine SAV resources, particularly in the northern Gulf of Mexico (NGOM) estuaries. To understand SAV distribution in the NGOM, SAV biomass and species identity were collected at 384 sites inter-annually (June–September; 2013–2015) from Mobile Bay, Alabama, to San Antonio Bay, Texas, USA. Coastwide, SAV distribution and biomass were consistent across years, covering an estimated 87,000 ha, and supporting approximately 16 ± 1% total cover with an average biomass of 24.5 ± 1.9 g m−2. Differences in hydrology (i.e., precipitation, freshwater input, water depth) and exposure (i.e., wave and wind energy) manifested in unique SAV assemblages and biomass distributions across the region (i.e., Coastal Mississippi-Alabama, Mississippi River Coastal Wetlands, Chenier Plain, Texas Mid-Coast) and estuarine gradient (i.e., marsh zones defined as fresh, intermediate, brackish, saline). Descriptive cluster analyses identified indicator SAV species, known as medoid observations that represented combined salinity, turbidity, and depth conditions unique to different region and marsh zone combinations. While the presence of SAV is often used as an indicator of ecological health, identifying a medoid-based SAV indicator species in aquatic habitats can be used to describe estuarine conditions in more detail and develop aquatic habitat zones. Exploration and the use of this type of field data could be developed as a means to track, manage, and define aquatic habitats across regional and estuarine gradients and further develop ecosystem-based assessment and restoration activities. Identifying aquatic zones through a representative medoid associates SAV species with locations defined by both long-term salinity and salinity variability, water depth, and exposure, which is a powerful potential tool for managers and restoration decision-makers.
","language":"English","publisher":"Springer Link","doi":"10.1007/s12237-021-00958-7","usgsCitation":"DeMarco, K., Hillmann, E., Nyman, J.A., Couvillion, B., and La Peyre, M., 2022, Defining aquatic habitat zones across northern Gulf of Mexico estuarine gradients through submerged aquatic vegetation species assemblage and biomass data: Estuaries and Coasts, v. 45, p. 148-167, https://doi.org/10.1007/s12237-021-00958-7.","productDescription":"20 p.","startPage":"148","endPage":"167","ipdsId":"IP-121908","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":500008,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/agrnr_pubs/598","text":"External Repository"},{"id":396544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.822265625,\n 26.60817437403311\n ],\n [\n -87.47314453125,\n 26.60817437403311\n ],\n [\n -87.47314453125,\n 31.034108344903512\n ],\n [\n -97.822265625,\n 31.034108344903512\n ],\n [\n -97.822265625,\n 26.60817437403311\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2021-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"DeMarco, K. E.","contributorId":287038,"corporation":false,"usgs":false,"family":"DeMarco","given":"K. E.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":836446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hillmann, E. R.","contributorId":287039,"corporation":false,"usgs":false,"family":"Hillmann","given":"E. R.","affiliations":[{"id":28058,"text":"Southeastern Louisiana University","active":true,"usgs":false}],"preferred":false,"id":836447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nyman, J. A.","contributorId":275213,"corporation":false,"usgs":false,"family":"Nyman","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":836449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Couvillion, Brady 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":222810,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":836448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836450,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226957,"text":"70226957 - 2022 - Estimating urban air pollution contribution to South Platte River nitrogen loads with National Atmospheric Deposition Program data and SPARROW model","interactions":[],"lastModifiedDate":"2021-12-22T13:00:51.139878","indexId":"70226957","displayToPublicDate":"2021-10-01T06:57:24","publicationYear":"2022","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":"Estimating urban air pollution contribution to South Platte River nitrogen loads with National Atmospheric Deposition Program data and SPARROW model","docAbstract":"Air pollution is commonly disregarded as a source of nutrient loading to impaired surface waters managed under the Clean Water Act per states’ 303(d) list programs. The contribution of air pollution to 2017–2018 South Platte River nitrogen (N) loads was estimated from the headwaters to the gage at Weldona, Colorado, USA (100 km downstream of Denver), using data from the National Atmospheric Deposition Program (NADP) and the SPAtially Referenced Regressions On Watershed attributes (SPARROW) model. The NADP offers wet-deposition raster created by spatial interpolation of data collected from regionally representative monitoring sites, excluding the influences from urban site data. For this study, NADP wet-deposition data obtained from sites within the Denver-Boulder, Colorado, urban corridor were included and excluded in new spatial interpolations of wet-deposition raster, which were used as input for SPARROW to model the influence of urban air pollution sources on South Platte River loads. Because urban air pollution is already incorporated into the NADP Total Deposition modeling methodology, dry N deposition was held constant for each SPARROW modeling scenario when dry deposition was included. By including the urban wet-deposition data in the model, estimated N loading to the South Platte River at Denver increased by 9–11 percent. Factoring in dry deposition at a 1:1.8 dry:wet ratio obtained from the results, urban air pollution was estimated to contribute as much as 20 percent of the nitrate Total Maximum Daily Load for Segment 14 of the South Platte River.
Most Americans receive their drinking water from publicly supplied sources, a large portion of it from groundwater. Mapping these populations consistently and at a high resolution is important for understanding where the resource is used and needs to be protected. The results show that 269 million people are supplied by public supply, 107 million are supplied by groundwater and 162 million are supplied by surface water. The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across populated census blocks, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations. In addition, a two-dimensional polygon dataset was created for the conterminous U.S. that identifies 177 unique Hydrogeologic Mapping Units (HMUs) with similar hydrogeologic characteristics. The HMUs do not overlap, but they can delineate areas where stacked hydrogeologic regions (HRs) contribute drinking water from below the surface. HRs are waterbearing geologic regions identified as either a principal aquifers (PA) or secondary hydrogeologic regions (SHR). Within each HMU, the wells were used to determine the proportion of each HR that is providing groundwater to the HMU. In 63% of the HMUs, a single HR is providing water to the public supply wells located within it, while the rest of the HMUs show that the wells are tapping up to a maximum of four stacked HRs. In total, groundwater from 108 HRs provide drinking water for public supply, six of which provide more than 50% of the groundwater used for public supply drinking water. The aquifer serving the largest number of equivalent people (>17 million) is the glacial aquifer. The HR providing the greatest number of people per km2 is the Biscayne aquifer in Florida at nearly 453 people per km2.