Climate change is causing rapid warming and altered precipitation patterns in mountain watersheds, both of which influence the timing of ice breakup in mountain lakes. To enable predictions of ice breakup in the future, we analyzed a dataset of mountain lake ice breakup dates derived from remote sensing and historical downscaled climate data. We evaluated drivers of ice breakup, constructed a predictive statistical model, and developed projections of mountain lake ice breakup date with global climate models. Using Random Forest analysis, we determined that winter and spring cumulative snow fraction (portion of precipitation falling as snow) and air temperature are the strongest predictors of ice breakup on mountain lakes. Interactions between precipitation, cumulative winter air temperature and lake surface area indicate that shifts in air temperature and precipitation affect smaller lakes (< 2 km2) more than larger lakes (> 2–10 km2). A linear mixed effects model (RMSE of 18 d), applied with an ensemble of 15 global climate models, projected that end-of-century ice breakup in mountain lakes will be earlier by 25 ± 4 and 61 ± 5 (mean ± SE) days for representative concentration pathways 4.5 and 8.5, respectively.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.11656","usgsCitation":"Caldwell, T.J., Chandra, S., Albright, T., Harpold, A., Dills, T., Greenberg, J., Sadro, S., and Dettinger, M.D., 2021, Drivers and projections of ice phenology in mountain lakes in the western United States: Limnology and Oceanography, v. 66, no. 3, p. 995-1008, https://doi.org/10.1002/lno.11656.","productDescription":"14 p.","startPage":"995","endPage":"1008","ipdsId":"IP-104573","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.11656","text":"Publisher Index Page"},{"id":387042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Oregon, Washington","otherGeospatial":"Cascade Mountains, northern Rocky Mountains, Sierra Nevada Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.136474609375,\n 48.07807894349862\n ],\n [\n -116.378173828125,\n 49.001843917978526\n ],\n [\n -119.36645507812499,\n 49.001843917978526\n ],\n [\n -119.20166015625,\n 48.52388120259336\n ],\n [\n -117.191162109375,\n 47.66538735632654\n ],\n [\n -116.42211914062499,\n 47.65058757118734\n ],\n [\n -116.114501953125,\n 47.71715357016648\n ],\n [\n -116.136474609375,\n 48.07807894349862\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.76171875,\n 46.475699386607516\n ],\n [\n -119.84985351562499,\n 48.96579381461063\n ],\n [\n -121.78344726562499,\n 49.01625665778159\n ],\n [\n -122.1240234375,\n 47.04766864046083\n ],\n [\n -122.58544921875,\n 45.506346901083425\n ],\n [\n -123.1787109375,\n 42.73087427928485\n ],\n [\n -122.54150390625,\n 42.04113400940807\n ],\n [\n -122.27783203125,\n 41.1290213474951\n ],\n [\n -120.87158203125,\n 41.393294288784865\n ],\n [\n -120.9814453125,\n 42.08191667830631\n ],\n [\n -121.62963867187499,\n 43.98491011404692\n ],\n [\n -121.14624023437499,\n 45.18978009667531\n ],\n [\n -120.76171875,\n 46.475699386607516\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.45507812500001,\n 36.19109202182454\n ],\n [\n -119.794921875,\n 39.01064750994083\n ],\n [\n -120.498046875,\n 40.49709237269567\n ],\n [\n -120.52001953124999,\n 40.94671366508002\n ],\n [\n -121.6845703125,\n 40.56389453066509\n ],\n [\n -120.73974609374999,\n 38.47939467327645\n ],\n [\n -118.23486328125,\n 35.47856499535729\n ],\n [\n -116.87255859374999,\n 35.65729624809628\n ],\n [\n -116.45507812500001,\n 36.19109202182454\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell, Timothy J","contributorId":146463,"corporation":false,"usgs":false,"family":"Caldwell","given":"Timothy","email":"","middleInitial":"J","affiliations":[{"id":16704,"text":"University of Nevada - Reno","active":true,"usgs":false}],"preferred":false,"id":818862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandra, Sudeep 0000-0002-9297-8211","orcid":"https://orcid.org/0000-0002-9297-8211","contributorId":224786,"corporation":false,"usgs":false,"family":"Chandra","given":"Sudeep","email":"","affiliations":[{"id":32871,"text":"University of Nevada at Reno","active":true,"usgs":false}],"preferred":false,"id":818863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Albright, Thomas","contributorId":260809,"corporation":false,"usgs":false,"family":"Albright","given":"Thomas","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":818864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harpold, Adrian","contributorId":207118,"corporation":false,"usgs":false,"family":"Harpold","given":"Adrian","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":818865,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dills, Thomas","contributorId":260810,"corporation":false,"usgs":false,"family":"Dills","given":"Thomas","email":"","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":818866,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greenberg, Jonathan","contributorId":260811,"corporation":false,"usgs":false,"family":"Greenberg","given":"Jonathan","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":818867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sadro, Steven 0000-0002-6416-3840","orcid":"https://orcid.org/0000-0002-6416-3840","contributorId":139662,"corporation":false,"usgs":false,"family":"Sadro","given":"Steven","email":"","affiliations":[{"id":12871,"text":"Marine Science Institute, University of California, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":818868,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":149896,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael","email":"mddettin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":818869,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70217180,"text":"70217180 - 2021 - Is there enough water? How bearish and bullish outlooks are linked to decision-maker perspectives on environmental flows","interactions":[],"lastModifiedDate":"2021-01-11T14:32:11.466282","indexId":"70217180","displayToPublicDate":"2020-11-26T08:27:56","publicationYear":"2021","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":"Is there enough water? How bearish and bullish outlooks are linked to decision-maker perspectives on environmental flows","docAbstract":"Policies that mandate environmental flows (e-flows) can be powerful tools for freshwater conservation, but implementation of these policies faces many hurdles. To better understand these challenges, we explored two key questions: (1) What additional data are needed to implement e-flows? and (2) What are the major socio-political barriers to implementing e-flows? We surveyed water and natural resource decision makers in the semi-arid Red River basin, Texas-Oklahoma, USA, and used social network analysis to analyze their communication patterns. Most respondents agreed that e-flows can provide important benefits and identified the same data needs. However, respondents sharply in their beliefs on other issues, and a clustering analysis revealed two distinct groups of decision makers. One cluster of decision makers tended to be bearish, or pessimistic, and believed that: current flow conditions are not adequate, there are many serious socio-political barriers to implementation, water conflicts will likely increase in the future, and climate change is likely to exacerbate these issues. The other cluster of respondents was bullish, or optimistic: they foresaw fewer future water conflicts and fewer socio-political barriers to implementation. Despite these differences, both clusters largely identified the same data needs and barriers to e-flows implementation. Our social network analysis revealed that the frequency of communication between clusters was not significantly different than the frequency of communication within clusters. Overall, our results suggest that the different perspectives of decision-makers could complicate efforts to implement e-flows and proactively plan for climate change. However, there are opportunities for collaboration on addressing common data needs and barriers to implementation. Overall, our study provides a key socio-environmental perspective on e-flows implementation from a semi-arid and socio-politically complex river basin and contextualizes the many challenges facing e-flows implementation in river basins globally.
Freshwater mussels of the order Unionida are a widely distributed taxon that are important in maintaining freshwater ecosystems and are also highly imperiled throughout the world. Monitoring of mussel populations with environmental DNA (eDNA) is an attractive alternative to traditional methods because it is noninvasive and requires less labor and taxonomic knowledge from field personnel. We developed eDNA metabarcoding assays specific to freshwater mussels and tested them at six sites in the Clinch River, located in the southeastern United States. Our objective was to determine the utility of eDNA metabarcoding for future monitoring of mussel populations and restoration efforts in this watershed. Two metabarcoding assays that target the mitochondrial DNA regions of the cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunit (ND1) genes were developed and tested. Our assays appear to be order specific, amplifying members from the two families found in North America, Unionidae and Margaritiferidae, while not amplifying nontarget fish or other bivalve species. From the field collected samples, our assays together detected 19 species, eight of which are listed as federally endangered. The assays also detected 42%, 58%, and 54% of the species identified by recent quantitative visual mussel surveys at three sampling sites. Increased sampling effort by processing a greater water volume or number of samples will likely increase species detections. These eDNA metabarcoding assays may enable enhanced monitoring of freshwater mussel assemblages and subsequently inform conservation efforts.
In the past two decades, Salt Plains National Wildlife Refuge has been increasingly recognized as important habitat for both breeding and migratory shorebirds. North American snowy plovers Charadrius nivosus in particular rely on the nearly 5,000-ha salt flat at Salt Plains National Wildlife Refuge, which thousands use as breeding and stopover habitat. Elsewhere on the Southern Great Plains, decadal declines up to 75% within snowy plover subpopulations have been documented and attributed to vegetation encroachment, increased rates of nest predation, and decreased availability of fresh surface water. Despite many attempts to estimate this species' abundance across the continent, to date, no known attempt at distance sampling of snowy plovers has occurred. To address this paucity of data, we assessed feasibility of distance sampling methods to accurately estimate snowy plover abundance and detectability. Distance sampling surveys (2017–2018) indicated high detection probability (P = 0.80) and the population abundance estimate across the salt flat extrapolated to 3,307 individuals. The distance-sampling population abundance estimate is lower than population abundance estimates determined by two previous studies within the past decade but far greater than 2,105 estimated for a study in 2006. Overall, distance sampling snowy plovers at Salt Plains National Wildlife Refuge proved to be an effective addition to pre-established survey protocols but further investigation is needed to compare accuracy and precision of methods used in this study, annual surveys conducted by Salt Plains National Wildlife Refuge, and other potential snowy plover surveys.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-20-041","usgsCitation":"Heath-Acre, K., Conway, W.C., Boal, C.W., Collins, D., Hensley, G., Johnson, W., and Schmidt, P.M., 2021, Detectability and abundance of snowy plovers at Salt Plains National Wildlife Refuge, Oklahoma: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 50-60, https://doi.org/10.3996/JFWM-20-041.","productDescription":"11 p.","startPage":"50","endPage":"60","ipdsId":"IP-119304","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454211,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-041","text":"Publisher Index Page"},{"id":396436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Salt Plains National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.30841064453125,\n 36.67172341847759\n ],\n [\n -98.12713623046875,\n 36.67172341847759\n ],\n [\n -98.12713623046875,\n 36.85984517196147\n ],\n [\n -98.30841064453125,\n 36.85984517196147\n ],\n [\n -98.30841064453125,\n 36.67172341847759\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Heath-Acre, K. 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C.","contributorId":280037,"corporation":false,"usgs":false,"family":"Conway","given":"W.","email":"","middleInitial":"C.","affiliations":[{"id":57413,"text":"Texas Tech University Lubbock","active":true,"usgs":false}],"preferred":false,"id":835925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, D. P.","contributorId":276303,"corporation":false,"usgs":false,"family":"Collins","given":"D. P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hensley, G.","contributorId":280038,"corporation":false,"usgs":false,"family":"Hensley","given":"G.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, W. P.","contributorId":280039,"corporation":false,"usgs":false,"family":"Johnson","given":"W. P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835929,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidt, P. M.","contributorId":280040,"corporation":false,"usgs":false,"family":"Schmidt","given":"P.","email":"","middleInitial":"M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835930,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70221860,"text":"70221860 - 2021 - Influence of water temperature and biotic interactions on the distribution of westslope cutthroat trout (Oncorhynchus clarkii lewisi) in a population stronghold under climate change","interactions":[],"lastModifiedDate":"2021-07-12T17:19:10.604507","indexId":"70221860","displayToPublicDate":"2020-11-22T12:14:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Influence of water temperature and biotic interactions on the distribution of westslope cutthroat trout (Oncorhynchus clarkii lewisi) in a population stronghold under climate change","title":"Influence of water temperature and biotic interactions on the distribution of westslope cutthroat trout (Oncorhynchus clarkii lewisi) in a population stronghold under climate change","docAbstract":"Climate warming is expected to have substantial impacts on native trout across the Rocky Mountains, but there is little understanding of how these changes affect future distributions of co-occurring native fishes within population strongholds. We used mixed-effects logistic regression to investigate the role of abiotic (e.g., temperature) and biotic factors (bull trout presence, Salvelinus confluentus) on distributions of westslope cutthroat trout (Oncorhynchus clarkii lewisi; WCT) in the North Fork Flathead River, USA and Canada. The probability of WCT presence increased with stream temperature and decreased with channel gradient and bull trout presence, yet the effect of bull trout was reduced with increasing pool densities. Combining this model with spatially explicit stream temperature projections, we predict a 29% increase in suitable habitat under high emissions through 2075, with gains at mid-elevation sites predicted to exceed bull trout thermal tolerances and high-elevation sites expected to become more thermally suitable for WCT. Our study illustrates the importance of considering abiotic and biotic drivers to assess species response to climate change, helping to guide local-scale climate adaptation and management.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2020-0099","usgsCitation":"Heinle, K., Eby, L., Muhlfeld, C.C., Steed, A., Jones, L., D’Angelo, V.S., Whiteley, A.R., and Hubblewhite, M., 2021, Influence of water temperature and biotic interactions on the distribution of westslope cutthroat trout (Oncorhynchus clarkii lewisi) in a population stronghold under climate change: Canadian Journal of Fisheries and Aquatic Sciences, v. 78, no. 4, p. 444-456, https://doi.org/10.1139/cjfas-2020-0099.","productDescription":"13 p.","startPage":"444","endPage":"456","ipdsId":"IP-111700","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":387127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, British Columbia, Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.9996337890625,\n 47.98256841921405\n ],\n [\n -113.49426269531249,\n 47.97889140226657\n ],\n [\n -113.6700439453125,\n 48.34894812401375\n ],\n [\n -113.895263671875,\n 48.669198799260045\n ],\n [\n -114.730224609375,\n 49.57510247172322\n ],\n [\n -114.993896484375,\n 49.61070993807422\n ],\n [\n -115.103759765625,\n 49.396675075193976\n ],\n [\n -114.4940185546875,\n 48.585692256886624\n ],\n [\n -113.9996337890625,\n 47.98256841921405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Heinle, Kadie","contributorId":260877,"corporation":false,"usgs":false,"family":"Heinle","given":"Kadie","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":819032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eby, Lisa A","contributorId":251751,"corporation":false,"usgs":false,"family":"Eby","given":"Lisa A","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":819033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":819034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steed, Amber","contributorId":124596,"corporation":false,"usgs":false,"family":"Steed","given":"Amber","affiliations":[{"id":5133,"text":"Montana Fish Wildlife and Parks, Kalispell, Montana 59901","active":true,"usgs":false}],"preferred":false,"id":819035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Leslie","contributorId":260953,"corporation":false,"usgs":false,"family":"Jones","given":"Leslie","affiliations":[],"preferred":false,"id":819200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"D’Angelo, Vincent S. 0000-0003-1244-8091 vdangelo@usgs.gov","orcid":"https://orcid.org/0000-0003-1244-8091","contributorId":224823,"corporation":false,"usgs":true,"family":"D’Angelo","given":"Vincent","email":"vdangelo@usgs.gov","middleInitial":"S.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":819037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hubblewhite, Mark","contributorId":260878,"corporation":false,"usgs":false,"family":"Hubblewhite","given":"Mark","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":819038,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216733,"text":"70216733 - 2021 - Small mammal responses to wetland restoration in the Greater Everglades ecosystem","interactions":[],"lastModifiedDate":"2021-04-08T14:17:17.479018","indexId":"70216733","displayToPublicDate":"2020-11-22T07:56:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Small mammal responses to wetland restoration in the Greater Everglades ecosystem","docAbstract":"Wetlands have experienced dramatic losses in extent around the world, disrupting ecosystem function, habitat, and biodiversity. In Florida’s Greater Everglades, a massive restoration effort costing billions of dollars and spanning multiple decades is underway. As Everglades restoration is implemented in incremental projects, scientists and planners monitor the outcomes of projects. In this study, we evaluated the progress of a restoration project in the southwestern Everglades. We aimed to determine whether the presence and density of small mammals differed between areas with hydrologic restoration of the ecosystem and areas without restoration. Our three focal species were: marsh rice rat (Oryzomys palustris), hispid cotton rat (Sigmodon hispidus), and cotton mouse (Peromyscus gossypinus). Using spatially explicit capture‐recapture models, we found greater densities of cotton mouse in restored habitat and lower densities of hispid cotton rat in sites with higher water levels. Additionally, we found an increase in the presence of the marsh rice rat in restored areas compared to unrestored, but captures were too low to reliably assess significance. Our study provides evidence that ongoing restoration in the southwestern Everglades is already impacting the small mammal community.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13332","usgsCitation":"Romanach, S., D’Acunto, L., Chapman, J., and Hanson, M., 2021, Small mammal responses to wetland restoration in the Greater Everglades ecosystem: Restoration Ecology, v. 29, no. 3, e13332, 9 p., https://doi.org/10.1111/rec.13332.","productDescription":"e13332, 9 p.","ipdsId":"IP-114410","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":454216,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13332","text":"Publisher Index Page"},{"id":436633,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BWA7RD","text":"USGS data release","linkHelpText":"Small mammal captures at the Picayune Strand State Forest, October 2014 - April 2016"},{"id":380947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.97448730468749,\n 25.035838555635017\n ],\n [\n -79.903564453125,\n 25.035838555635017\n ],\n [\n -79.903564453125,\n 26.59343927024179\n ],\n [\n -81.97448730468749,\n 26.59343927024179\n ],\n [\n -81.97448730468749,\n 25.035838555635017\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":220761,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":806009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":806010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapman, Julia","contributorId":245353,"corporation":false,"usgs":false,"family":"Chapman","given":"Julia","affiliations":[],"preferred":false,"id":806011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, Matthew R 0000-0002-2859-3878","orcid":"https://orcid.org/0000-0002-2859-3878","contributorId":245354,"corporation":false,"usgs":false,"family":"Hanson","given":"Matthew R","affiliations":[],"preferred":false,"id":806012,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216934,"text":"70216934 - 2021 - Evaluation of a roughness length parametrization accounting for wind–wave alignment in a coupled atmosphere–wave model","interactions":[],"lastModifiedDate":"2021-03-05T21:07:22.044536","indexId":"70216934","displayToPublicDate":"2020-11-21T12:54:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7443,"text":"Quarterly Journal of the Royal Meteorological Society","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of a roughness length parametrization accounting for wind–wave alignment in a coupled atmosphere–wave model","docAbstract":"The importance of wind energy as an alternative energy source has increased over the latest years with more focus on offshore winds. A good estimation of the offshore winds is thus of major importance for this industry. Up to now the effect of the wind–wave (mis)alignment has not yet been taken into account in coupled atmosphere–wave models to study the vertical wind profile and power production estimations of offshore wind farms. In this study the roughness length parametrization of Drennan et al. in 2003, and its extension addressing the wind–wave (mis)alignment proposed by Porchetta et al. in 2019, are investigated in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This study shows that the yearly mean wind estimation at hub height (100 m) is improved by the roughness length parametrization of Porchetta et al. compared to Drennan. This is mainly due to the increased roughness of the former parametrization compare to the latter, even in aligned wind–wave conditions. This difference in roughness is caused by the dataset used to obtain the constants, deep‐water conditions versus mixed offshore conditions. Moreover, the roughness length parametrization of Porchetta et al. performs better in two of three alignment categories. Furthermore, similar model performances are obtained if we exclude the wind directions from the wind shadow zone of the measurement mast or the wind directions from the recently built Alpha Ventus wind farm, which is in close vicinity of the measurement mast. Investigating different wind conditions shows that the new roughness length parametrization of Porchetta et al. performs best for both offshore and onshore winds. Additionally, we show that the coupled model estimations of the vertical wind are only slightly affected by significant wave height estimations. Similar model performances for different accuracies of significant wave height estimations are presented. One exception is the perpendicular alignment category where the new roughness length of Porchetta et al. outperforms the roughness length of Drennan when investigating the wind estimations related to significant wave heights with a higher accuracy. The roughness length parametrization of Porchetta et al. reduced the power production overestimation of the coupled model from 5.7 to 2.8%. We also show that the standalone atmospheric model including the roughness length of Charnock in 1955 has a degraded performance compared to the coupled model including the roughness length parametrization of Porchetta et al. for yearly average wind profiles.
","language":"English","publisher":"Royal Meteorological Society","doi":"10.1002/qj.3948","usgsCitation":"Porchetta, S., Temel, O., Warner, J., Munoz-Esparza, J., Monbaliu, J., van Beeck, J., and van Lipzig, N., 2021, Evaluation of a roughness length parametrization accounting for wind–wave alignment in a coupled atmosphere–wave model: Quarterly Journal of the Royal Meteorological Society, v. 147, no. 735, p. 825-846, https://doi.org/10.1002/qj.3948.","productDescription":"22 p.","startPage":"825","endPage":"846","ipdsId":"IP-117950","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454221,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lirias.kuleuven.be/bitstream/123456789/685815/2/COAWST_QJRMetS_rkul.docx","text":"External Repository"},{"id":381447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"147","issue":"735","noUsgsAuthors":false,"publicationDate":"2020-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Porchetta, Sara","contributorId":245775,"corporation":false,"usgs":false,"family":"Porchetta","given":"Sara","email":"","affiliations":[{"id":49315,"text":"KU Leuven, Department Earth and Environmental Sciences, Leuven, Belgium","active":true,"usgs":false}],"preferred":false,"id":807016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Temel, O.","contributorId":245776,"corporation":false,"usgs":false,"family":"Temel","given":"O.","email":"","affiliations":[{"id":49316,"text":"Royal Observatory of Belgium, Brussels, Belgium","active":true,"usgs":false}],"preferred":false,"id":807017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":807018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munoz-Esparza, J.C.","contributorId":245777,"corporation":false,"usgs":false,"family":"Munoz-Esparza","given":"J.C.","email":"","affiliations":[{"id":16785,"text":"National Center for Atmospheric Research, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":807019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monbaliu, J","contributorId":245778,"corporation":false,"usgs":false,"family":"Monbaliu","given":"J","email":"","affiliations":[{"id":49317,"text":"KULeuven, Department of Civil Engineering, Leuven, Belgium","active":true,"usgs":false}],"preferred":false,"id":807020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Beeck, J.","contributorId":245779,"corporation":false,"usgs":false,"family":"van Beeck","given":"J.","email":"","affiliations":[{"id":49319,"text":"KULeuven, Department Earth and Environmental Sciences, Leuven, Belgium","active":true,"usgs":false}],"preferred":false,"id":807021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"van Lipzig, N.","contributorId":245780,"corporation":false,"usgs":false,"family":"van Lipzig","given":"N.","email":"","affiliations":[{"id":49321,"text":"von Karman Institute for Fluid Dynamics, Sint-Genesius-Rode, Belgium","active":true,"usgs":false}],"preferred":false,"id":807022,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219218,"text":"70219218 - 2021 - Variable seepage meter efficiency in high-permeability settings","interactions":[],"lastModifiedDate":"2021-04-01T11:25:55.835244","indexId":"70219218","displayToPublicDate":"2020-11-21T06:47:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Variable seepage meter efficiency in high-permeability settings","docAbstract":"The efficiency of seepage meters, long considered a fixed property associated with the meter design, is not constant in highly permeable sediments. Instead, efficiency varies substantially with seepage bag fullness, duration of bag attachment, depth of meter insertion into the sediments, and seepage velocity. Tests conducted in a seepage test tank filled with isotropic sand with a hydraulic conductivity of about 60 m/d indicate that seepage meter efficiency varies widely and decreases unpredictably when the volume of the seepage bag is greater than about 65 to 70 percent full or less than about 15 to 20 percent full. Seepage generally decreases with duration of bag attachment even when operated in the mid-range of bag fullness. Stopping flow through the seepage meter during bag attachment or removal also results in a decrease in meter efficiency. Numerical modeling indicates efficiency is inversely related to hydraulic conductivity in highly permeable sediments. An efficiency close to 1 for a meter installed in sediment with a hydraulic conductivity of 1 m/d decreases to about 60 and then 10 percent when hydraulic conductivity is increased to 10 and 100 m/d, respectively. These large efficiency reductions apply only to high-permeability settings, such as wave- or tidally washed coarse sand or gravel, or fluvial settings with an actively mobile sand or gravel bed, where low resistance to flow through the porous media allows bypass flow around the seepage cylinder to readily occur. In more typical settings, much greater resistance to bypass flow suppresses small changes in meter resistance during inflation or deflation of seepage bags.
","language":"English","publisher":"MPDI","doi":"10.3390/w12113267","usgsCitation":"Rosenberry, D.O., Nieto-Lopez, J.M., Webb, R.M., and Muller, S., 2021, Variable seepage meter efficiency in high-permeability settings: Water, v. 12, no. 11, 3267, 22 p., https://doi.org/10.3390/w12113267.","productDescription":"3267, 22 p.","ipdsId":"IP-119819","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454229,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12113267","text":"Publisher Index Page"},{"id":436637,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93N8B2N","text":"USGS data release","linkHelpText":"Webb and Rosenberry, 2020, MODFLOW 2005 and MODPATH 5 model data sets used to evaluate seepage-meter efficiency in high-permeability settings"},{"id":436636,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93N8B2N","text":"USGS data release","linkHelpText":"Webb and Rosenberry, 2020, MODFLOW 2005 and MODPATH 5 model data sets used to evaluate seepage-meter efficiency in high-permeability settings"},{"id":436635,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SO2FVM","text":"USGS data release","linkHelpText":"Seepage meter efficiency in highly permeable settings source data (2020)"},{"id":436634,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SO2FVM","text":"USGS data release","linkHelpText":"Seepage meter efficiency in highly permeable settings source data (2020)"},{"id":384775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":813261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nieto-Lopez, Jose M 0000-0002-2596-6368","orcid":"https://orcid.org/0000-0002-2596-6368","contributorId":256817,"corporation":false,"usgs":false,"family":"Nieto-Lopez","given":"Jose","email":"","middleInitial":"M","affiliations":[{"id":51863,"text":"University of Malaga","active":true,"usgs":false}],"preferred":false,"id":813262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webb, Richard M. 0000-0001-9531-2207 rmwebb@usgs.gov","orcid":"https://orcid.org/0000-0001-9531-2207","contributorId":1570,"corporation":false,"usgs":true,"family":"Webb","given":"Richard","email":"rmwebb@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muller, Sascha","contributorId":256818,"corporation":false,"usgs":false,"family":"Muller","given":"Sascha","email":"","affiliations":[{"id":12672,"text":"University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":813264,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227051,"text":"70227051 - 2021 - Suspended-sediment Flux in the San Francisco Estuary; Part II: the Impact of the 2013–2016 California Drought and Controls on Sediment Flux","interactions":[],"lastModifiedDate":"2021-12-28T14:48:59.650241","indexId":"70227051","displayToPublicDate":"2020-11-20T08:46:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Suspended-sediment Flux in the San Francisco Estuary; Part II: the Impact of the 2013–2016 California Drought and Controls on Sediment Flux","docAbstract":"Recent modeling has demonstrated that sediment supply is one of the primary environmental variables that will determine the sustainability of San Francisco Estuary tidal marshes over the next century as sea level rises. Therefore, understanding the environmental controls on sediment flux within the San Francisco Estuary is crucial for optimal planning and management of tidal marsh restoration. Herein, we present suspended-sediment flux estimates from water year (WY) 2009–2016 from the San Francisco Estuary to investigate the environmental controls and impact of the record 2013–2016 California drought. During the recent drought, sediment flux into Lower South Bay, the southernmost subembayment of the San Francisco Estuary, increased by 345% from 114 kt/year from WY 2009 to 2011 to 508 kt/year from WY 2014 to 2016, while local tributary sediment flux declined from 209 to 51 kt/year. Total annual sediment flux from WY 2009 to 2011 and 2014 to 2016 can be predicted by total annual freshwater inflow from the Sacramento-San Joaquin Delta (R2 = 0.83, p < 0.01), the primary source of freshwater input into the San Francisco Estuary. The volume of freshwater inflow from the Sacramento-San Joaquin Delta is hypothesized to affect shoal-to-channel density gradients that affect sediment flux from broad, typically more saline and turbid shoals, to the main tidal-channel seaward of Lower South Bay. During the drought, freshwater inflow from the Sacramento-San Joaquin Delta decreased, and replacement of typically more saline shoal water was reduced. As a result, landward-increasing cross-channel density gradients enhanced shoal-to-channel advective flux that increased sediment available for tidal dispersion and drove an increase in net-landward sediment flux into Lower South Bay.
Geologists and petroleum engineers have struggled to identify the mechanisms that drive productivity in horizontal hydraulically fractured oil wells. The machine learning algorithms of Random Forest (RF), gradient boosting trees (GBT) and extreme gradient boosting (XGBoost) were applied to a dataset containing 7311 horizontal hydraulically fractured wells drilled into the middle member of the Bakken Formation from 2010 through 2017. The initial goal is to use these data‐driven machine learning algorithms to identify the most important explanatory predictors of well productivity within nine subareas and the composite area. Predictor variables representing initial gas production, the initial 180‐day water cut, and vertical depth vary spatially and are identified with geologically favorable areas. Well‐completion predictors include the well lateral length, number of fracture stages, volume of proppant per stage, and the volume of injected fluids per stage. The performance of methods is compared based on a common test sample. The analysis then examines the comparative predictive performance of the three algorithms for 1330 wells that had initiated production after the initial 7311 well sample had been producing. The computations of predictor importance identified the initial 180‐day water cut and the 30‐day initial gas production predictors as having a dominant influence in most subareas and for the composite area. The relative importance of well completion predictor variables, that is, the number of fracture stages per well, volume of injected proppant per stage, volume of injected fluids per stage, and lateral length, varied considerably across the subareas. For the common test or holdout sample, the models calibrated with the XGBoost algorithm had superior predictive power. The predictive power of all the algorithms trained on the data from the original sample suffered some loss when tested with a sample of wells that had started production after the end of that period. Implications of the empirical findings and strategies to mitigate loss of predictive power are discussed in the concluding section.
The U.S. Department of the Interior recently included uranium (U) on a list of mineral commodities that are considered critical to economic and national security. The uses of U for commercial and residential energy production, defense applications, medical device technologies, and energy generation for space vehicles and satellites are known, but the environmental impacts of uranium extraction are not always well quantified. We conducted a screening-level ecological risk analysis based on exposure to mining-related elements via diets and incidental soil ingestion for terrestrial biota to provide context to chemical characterization and exposures at breccia pipe U mines in northern Arizona. Relative risks, calculated as hazard quotients (HQs), were generally low for all biological receptor models. Our models screened for risk to omnivores and insectivores (HQs>1) but not herbivores and carnivores. Uranium was not the driver of ecological risk; arsenic, cadmium, copper, and zinc were of concern for biota consuming ground-dwelling invertebrates. Invertebrate species composition should be considered when applying these models to other mining locations or future sampling at the breccia pipe mine sites. Dietary concentration thresholds (DCTs) were also calculated to understand food concentrations that may lead to ecological risk. The DCTs indicated that critical concentrations were not approached in our model scenarios, as evident in the very low HQs for most models. The DCTs may be used by natural resource and land managers as well as mine operators to screen or monitor for potential risk to terrestrial receptors as mine sites are developed and remediated in the future.
The Chesapeake Bay is the largest estuary in the United States and its watershed includes river drainages in six states and the District of Columbia. Sportfishing is of major economic interest, however, the rivers within the watershed provide numerous other ecological, recreational, cultural and economic benefits, as well as serving as a drinking water source for millions of people. Consequently, major fish kills and the subsequent finding of estrogenic endocrine disruption (intersex or testicular oocytes and plasma vitellogenin in male fishes) raised public and management concerns. Studies have occurred at various sites within the Bay watershed to document the extent and severity of endocrine disruption, identify risk factors and document temporal and spatial variability. Data from these focal studies, which began in 2004, were used in CART (classification and regression trees) analyses to better identify land use associations and potential management practices that influence estrogenic endocrine disruption. These analyses emphasized the importance of scale (immediate versus upstream catchment) and the complex mixtures of stressors which can contribute to surface water estrogenicity and the associated adverse effects of exposure. Both agricultural (percent cultivated, pesticide application, phytoestrogen cover crops) and developed (population density, road density, impervious surface) land cover showed positive relationships to estrogenic indicators, while percent forest and shrubs generally had a negative association. The findings can serve as a baseline for assessing ongoing restoration and management practices.
Steamboat Geyser in Yellowstone National Park’s Norris Geyser Basin began a prolific sequence of eruptions in March 2018 after 34 y of sporadic activity. We analyze a wide range of datasets to explore triggering mechanisms for Steamboat’s reactivation and controls on eruption intervals and height. Prior to Steamboat’s renewed activity, Norris Geyser Basin experienced uplift, a slight increase in radiant temperature, and increased regional seismicity, which may indicate that magmatic processes promoted reactivation. However, because the geothermal reservoir temperature did not change, no other dormant geysers became active, and previous periods with greater seismic moment release did not reawaken Steamboat, the reason for reactivation remains ambiguous. Eruption intervals since 2018 (3.16 to 35.45 d) modulate seasonally, with shorter intervals in the summer. Abnormally long intervals coincide with weakening of a shallow seismic source in the geyser basin’s hydrothermal system. We find no relation between interval and erupted volume, implying unsteady heat and mass discharge. Finally, using data from geysers worldwide, we find a correlation between eruption height and inferred depth to the shallow reservoir supplying water to eruptions. Steamboat is taller because water is stored deeper there than at other geysers, and, hence, more energy is available to power the eruptions.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2020943118","usgsCitation":"Reed, M., Munoz-Saez, C., Hajimirza, S., Wu, S., Barth, A., Girona, T., Rasht-Behesht, M., Karplus, M., Hurwitz, S., and Manga, M., 2021, The 2018 reawakening and eruption dynamics of Steamboat Geyser, the world’s tallest active geyser: PNAS, v. 118, no. 2, e2020943118, 10 p., https://doi.org/10.1073/pnas.2020943118.","productDescription":"e2020943118, 10 p.","ipdsId":"IP-123913","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":454249,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2020943118","text":"Publisher Index Page"},{"id":382060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Norris Geyser Basin, Steamboat Geyser, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.71465730667114,\n 44.71879196233473\n ],\n [\n -110.6957745552063,\n 44.71879196233473\n ],\n [\n -110.6957745552063,\n 44.73068351783913\n ],\n [\n -110.71465730667114,\n 44.73068351783913\n ],\n [\n -110.71465730667114,\n 44.71879196233473\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"118","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Mara","contributorId":247557,"corporation":false,"usgs":false,"family":"Reed","given":"Mara","affiliations":[],"preferred":false,"id":807890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munoz-Saez, Carolina","contributorId":131167,"corporation":false,"usgs":false,"family":"Munoz-Saez","given":"Carolina","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. 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Engineering","active":true,"usgs":false}],"preferred":false,"id":807899,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70217217,"text":"70217217 - 2021 - Stream dissolved organic matter in permafrost regions shows surprising compositional similarities but negative priming and nutrient effects","interactions":[],"lastModifiedDate":"2021-01-13T13:39:59.317052","indexId":"70217217","displayToPublicDate":"2020-11-18T07:36:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Stream dissolved organic matter in permafrost regions shows surprising compositional similarities but negative priming and nutrient effects","docAbstract":"Permafrost degradation is delivering bioavailable dissolved organic matter (DOM) and inorganic nutrients to surface water networks. While these permafrost subsidies represent a small portion of total fluvial DOM and nutrient fluxes, they could influence food webs and net ecosystem carbon balance via priming or nutrient effects that destabilize background DOM. We investigated how addition of biolabile carbon (acetate) and inorganic nutrients (nitrogen and phosphorus) affected DOM decomposition with 28‐day incubations. We incubated late‐summer stream water from 23 locations nested in seven northern or high‐altitude regions in Asia, Europe, and North America. DOM loss ranged from 3% to 52%, showing a variety of longitudinal patterns within stream networks. DOM optical properties varied widely, but DOM showed compositional similarity based on Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR MS) analysis. Addition of acetate and nutrients decreased bulk DOM mineralization (i.e., negative priming), with more negative effects on biodegradable DOM but neutral or positive effects on stable DOM. Unexpectedly, acetate and nutrients triggered breakdown of colored DOM (CDOM), with median decreases of 1.6% in the control and 22% in the amended treatment. Additionally, the uptake of added acetate was strongly limited by nutrient availability across sites. These findings suggest that biolabile DOM and nutrients released from degrading permafrost may decrease background DOM mineralization but alter stoichiometry and light conditions in receiving waterbodies. We conclude that priming and nutrient effects are coupled in northern aquatic ecosystems and that quantifying two‐way interactions between DOM properties and environmental conditions could resolve conflicting observations about the drivers of DOM in permafrost zone waterways.
Despite agreement that calc-alkaline volcanism occurs at subduction zones and is responsible for the genesis of continental landmasses, there is no consensus on the source of the Fe-depleted signature hallmark to calc-alkaline volcanism. In this study, we utilize mafic tephras collected from Buldir Volcano to address the genesis of strongly calc-alkaline volcanic rocks (those with a low Tholeiitic Index; ≤0·7) in a segment of the western Aleutian Arc to determine if the eruptions are plausibly part of a liquid line of descent, if they are mixtures of crustal melts and parental magmas, or if they are mixtures of melts of the mantle and the subducting slab. We conducted a series of H2O-saturated phase equilibrium experiments (1175–1000°C; 100 MPa) in a rapid-quench cold-seal (MHC) apparatus on the most primitive natural lava from Buldir (9·34 wt % MgO) at oxidizing conditions near the Re–ReO2 buffer. We confirmed that all experiments equilibrated 0·3 ± 0·23 log units above the Re–ReO2 buffer (ΔQFM ∼ +2·8) using X-ray Absorption Near Edge Structure (XANES) spectroscopy. Chromite is the liquidus phase, followed by olivine, then plagioclase, then clinopyroxene, and finally hornblende. Once clinopyroxene saturates, spinel composition shifts to magnetite. We compared our experimental results to the major element geochemistry and petrology of six tephras (51·9–54·8 wt % SiO2) from Buldir collected during the 2015 field season of the GeoPRISMS shared platform. Tephras contain olivine + plagioclase + clinopyroxene + spinel ± hornblende; plagioclase comprises most of the crystalline volume, followed by either olivine or hornblende. Spinel is ubiquitous; with Cr-rich spinel inclusions in olivine and hornblende, and magnetite in the groundmass.
Variations in phenocryst assemblages and compositions between samples can be attributed to differences in pre-eruptive temperatures, where hotter samples are devoid of hornblende, and contain Fo-rich olivine and plagioclase with lower An-contents, owing to the position of the mineral-in curves at fluid-saturated conditions. Experimental glasses match the depletion in FeOT observed in the tephra whole rock compositions. The continuous depletion in FeOT is attributable to saturation of spinel as a liquidus phase (initially as chromite) and continuous crystallization through the experimental series (changing to magnetite at colder temperatures). In contrast to the natural samples, the experiments show enrichment in TiO2 with decreasing MgO, suggesting that differentiation did not occur at 100 MPa on Buldir. The TiO2 depletion in volcanic rocks from Buldir can be accounted for if hornblende crystallization occurs close to the liquidus of a parental magma; a condition that is met at higher pressures and hydrous conditions.
The emerging picture for Buldir Island is that (1) oxidizing conditions are required to drive the observed depletions in FeOT via crystallization of spinel, and (2) elevated H2O contents and high pressures are required to saturate hornblende close to the liquidus to reproduce the entire suite of major elements. Our study provides a mechanism to generate the calc-alkaline trends observed at Buldir without requiring mixing of slab and mantle melts. We conclude that calc-alkaline volcanic rocks with extremely low Tholeiitic Indices (0·7), like those from Buldir, cannot be generated in absence of high oxygen fugacity, even at high pressure and/or elevated water pressures.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egaa104","usgsCitation":"Waters, L., Cottrell, E., Coombs, M.L., and Kelley, K.A., 2021, Generation of calc-alkaline magmas during crystallization at high oxygen fugacity: An experimental and petrologic study of tephras from Buldir Volcano, western Aleutian Arc, Alaska, USA: Journal of Petrology, v. 62, no. 3, egaa104, 36 p., https://doi.org/10.1093/petrology/egaa104.","productDescription":"egaa104, 36 p.","ipdsId":"IP-112037","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":499913,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/gsofacpubs/1581","text":"External Repository"},{"id":386115,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian arc","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.67578125,\n 58.768200159239576\n ],\n [\n -171.650390625,\n 54.1109429427243\n ],\n [\n -179.47265625,\n 52.26815737376817\n ],\n [\n -177.45117187499997,\n 49.61070993807422\n ],\n [\n -155.7421875,\n 55.32914440840507\n ],\n [\n -152.9296875,\n 57.27904276497778\n ],\n [\n -157.67578125,\n 58.768200159239576\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Waters, Laura","contributorId":259192,"corporation":false,"usgs":false,"family":"Waters","given":"Laura","affiliations":[{"id":36475,"text":"Sonoma State University","active":true,"usgs":false}],"preferred":false,"id":816778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cottrell, Elizabeth","contributorId":192904,"corporation":false,"usgs":false,"family":"Cottrell","given":"Elizabeth","email":"","affiliations":[],"preferred":false,"id":816779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coombs, Michelle L. 0000-0002-6002-6806 mcoombs@usgs.gov","orcid":"https://orcid.org/0000-0002-6002-6806","contributorId":2809,"corporation":false,"usgs":true,"family":"Coombs","given":"Michelle","email":"mcoombs@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":816780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelley, Katherine A.","contributorId":192905,"corporation":false,"usgs":false,"family":"Kelley","given":"Katherine","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816781,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243772,"text":"70243772 - 2021 - Behavioral responses of sea lamprey (Petromyzon marinus) and white sucker (Catostomus commersonii) to turbulent flow during fishway passage attempts","interactions":[],"lastModifiedDate":"2023-05-19T11:43:02.869837","indexId":"70243772","displayToPublicDate":"2020-11-18T06:33:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Behavioral responses of sea lamprey (Petromyzon marinus) and white sucker (Catostomus commersonii) to turbulent flow during fishway passage attempts","title":"Behavioral responses of sea lamprey (Petromyzon marinus) and white sucker (Catostomus commersonii) to turbulent flow during fishway passage attempts","docAbstract":"An understanding of how undesirable and desirable fish species respond behaviorally to turbulent flow in fishways would guide development of selective fish passage techniques. We applied high-resolution computational fluid dynamics modeling and competing risks analysis towards the development of predictive selective passage models. Sea lamprey (Petromyzon marinus; an invasive fish in the Great Lakes Basin, North America) upstream passage probability declined from 0.73 to 0.03 as flow conditions became increasingly turbulent, while declines in white sucker (Catostomus commersonii, a native fish in the region) upstream passage probability were less substantial (0.53 to 0.44). Deploying a sea lamprey trap in the fishway did not effectively reduce sea lamprey upstream passage probability, though capture rate increased during trials with cooler water temperature and low total kinetic energy. Bifurcated fishways that maintain low turbulent flow in the entrapment route and high turbulent flow in the upstream passage route could increase the effectiveness of trapping sea lamprey in fishways as a means to advance selective passage goals.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2020-0223","usgsCitation":"Lewandoski, S.A., Hrodey, P.J., Miehls, S.M., Piszczek, P., and Zielinski, D., 2021, Behavioral responses of sea lamprey (Petromyzon marinus) and white sucker (Catostomus commersonii) to turbulent flow during fishway passage attempts: Canadian Journal of Fisheries and Aquatic Sciences, v. 78, no. 4, p. 409-421, https://doi.org/10.1139/cjfas-2020-0223.","productDescription":"13 p.","startPage":"409","endPage":"421","ipdsId":"IP-120067","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":417234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bois Brule River, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.61913799037413,\n 46.746319413615765\n ],\n [\n -91.61109265501442,\n 46.697600103611876\n ],\n [\n -91.60349428273015,\n 46.66908351070549\n ],\n [\n -91.610645691939,\n 46.6396312386392\n ],\n [\n -91.59455502121916,\n 46.61568945646317\n ],\n [\n -91.59008539046347,\n 46.5831713812953\n ],\n [\n -91.59768376274775,\n 46.54414318194961\n ],\n [\n -91.61735013807177,\n 46.5241572565657\n ],\n [\n -91.60751695040997,\n 46.48507053273994\n ],\n [\n -91.63612258724542,\n 46.45367061381313\n ],\n [\n -91.68126585787567,\n 46.44011995829479\n ],\n [\n -91.75201200283867,\n 46.40437245435308\n ],\n [\n -91.75201200283867,\n 46.39820791069087\n ],\n [\n -91.64652871700919,\n 46.43980506395201\n ],\n [\n -91.6128335861273,\n 46.45168996471648\n ],\n [\n -91.5878036538965,\n 46.483091025906276\n ],\n [\n -91.59853076771002,\n 46.527698611965775\n ],\n [\n -91.576629577008,\n 46.54522398535883\n ],\n [\n -91.57081905702584,\n 46.589166256606916\n ],\n [\n -91.57528868778131,\n 46.62048794471005\n ],\n [\n -91.59137935850114,\n 46.64381389971618\n ],\n [\n -91.58512187544464,\n 46.68098665401865\n ],\n [\n -91.59629595233312,\n 46.712255228466375\n ],\n [\n -91.5954020261823,\n 46.726657203488514\n ],\n [\n -91.60389432461741,\n 46.75361226678078\n ],\n [\n -91.61913799037413,\n 46.746319413615765\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"78","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lewandoski, Sean A.","contributorId":221007,"corporation":false,"usgs":false,"family":"Lewandoski","given":"Sean","email":"","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":873209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hrodey, Peter J.","contributorId":205578,"corporation":false,"usgs":false,"family":"Hrodey","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":6599,"text":"U.S. Fish and Wildlife Service, Marquette Biological Station","active":true,"usgs":false}],"preferred":false,"id":873210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":873211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Piszczek, Paul","contributorId":305569,"corporation":false,"usgs":false,"family":"Piszczek","given":"Paul","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":873212,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zielinski, Daniel","contributorId":245798,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":873213,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216698,"text":"70216698 - 2021 - Mainstems: A logical data model implementing mainstem and drainage basin feature types based on WaterML2 Part 3: HY Features concepts","interactions":[],"lastModifiedDate":"2020-12-01T13:34:28.581683","indexId":"70216698","displayToPublicDate":"2020-11-13T07:32:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Mainstems: A logical data model implementing mainstem and drainage basin feature types based on WaterML2 Part 3: HY Features concepts","docAbstract":"The Mainstems data model implements the catchment and flowpath concepts from WaterML2 Part 3: Surface Hydrology Features (HY_Features) for persistent, cross-scale, identification of hydrologic features. The data model itself provides a focused and lightweight method to describe hydrologic networks with minimum but sufficient information. The design is intended to provide a model for data integration that can be used for network navigation and persistent hydrologic indexing (hydrographic addressing) functionality. Mainstems is designed to provide long-term stability with minimal maintenance requirements. The data model is not meant to advance hydrologic process representation or uniquely represent geomorphic characteristics. The principle assumption in Mainstems is that all drainage basins have one - and only one - headwater source area and a single mainstem that flows to a single outlet. Using these base feature types, (headwater, outlet, mainstem, and drainage basin) a nested set of drainage basins - and the associated dendritic network of mainstems - can be identified.
Increasing frequency and severity of droughts have motivated natural resource managers to mitigate harmful ecological and hydrological effects of drought, but drought mitigation is an emerging science and evaluating its effectiveness is difficult. We examined ecohydrological responses of drought mitigation actions aimed at conserving populations of the Columbia spotted frog (Rana luteiventris) in a semi-arid valley in Nevada, USA. Abundance of this rare frog had declined precipitously after multiple droughts. Mitigation included excavating ponds to increase available surface water and installing earthen dams to raise water tables. We assessed responses of riparian vegetation to mitigation using a 30-year time series of satellite-derived Normalized Difference Vegetation Index (NDVI) and gridded weather data. We then analyzed a 23-year mark-recapture dataset to evaluate the effects of drought mitigation and NDVI on the probability of frog survival and rates of recruitment. After accounting for interannual precipitation variability, we found that NDVI increased significantly from before to after drought mitigation, suggesting that mitigation influenced the hydrology and vegetation of the meadows. Frog survival increased with NDVI, but mitigation had a stronger effect than NDVI suggesting that excavated mitigation ponds were particularly important for frog survival during drought. In contrast, frog recruitment was associated with NDVI more than mitigation, but only in meadows where NDVI was dependent on precipitation. At meadows with available groundwater, recruitment was associated with mitigation ponds. These findings suggest that mitigation ponds are critical for juvenile frogs to recruit into the adult population, but recruitment can also be increased by raising water tables in meadows lacking groundwater sources. Lagged recruitment (i.e., effects on larvae and juveniles) was negatively associated with NDVI. This study illustrates the ecohydrological complexity of drought mitigation and demonstrates novel ways to assess the effectiveness of drought mitigation using time series of readily available satellite imagery and organismal data.
Maximum densities of juvenile river herring (Alewife Alosa pseudoharengus and Blueback Herring A. aestivalis) vary among freshwater lakes, likely due to densities of adult spawners. Differences in habitat availability and lake water quality may also contribute to variation in juvenile river herring productivity between populations, yet these relationships have not been tested across a large geographic scope. In this study we investigated relationships between juvenile river herring densities and (1) spawning adult river herring densities, (2) lake habitat availability, and (3) lake water quality in 29 freshwater lakes in the northeastern USA. Purse seines were used at night to sample juvenile river herring monthly in June–August 2014 and 2015, with concurrent collection of lake-specific physical (e.g., lake surface area, mean depth, depth to thermocline), chemical (e.g., nitrogen, phosphorus, dissolved organic carbon [DOC]), and biological (chlorophyll a, adult spawning density) data. Spawning adult density (number of adults per surface area of lake) explained 66.6% of the variation in juvenile densities using a generalized additive model. Juvenile densities increased with increasing adult density, peaking at roughly 1,000 adults/ha, and then declined at higher adult densities, suggesting a limit to carrying capacity in juvenile production. Linear mixed-effects models revealed that differences in water quality and habitat across lakes explained additional variation in juvenile densities. Specifically, DOC was negatively related to juvenile densities, suggesting that DOC limits the amount of suitable, well-oxygenated epilimnion habitat available to juvenile river herring in late summer. Our results can be used to help understand expected juvenile production based on adult density within a lake, to inform expectations about juvenile growth and survival, and to understand the mechanisms for how changes in habitat availability and water quality affect river herring populations.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10282","usgsCitation":"Devine, M.T., Rosset, J., Roy, A.H., Gahagan, B.I., Armstrong, M.P., Whiteley, A., and Jordaan, A., 2021, Feeling the squeeze: Adult run size and habitat availability limit juvenile river herring densities in lakes: Transactions of the American Fisheries Society, v. 150, no. 2, p. 207-221, https://doi.org/10.1002/tafs.10282.","productDescription":"16 p.","startPage":"207","endPage":"221","ipdsId":"IP-120456","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":396569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.66455078125,\n 44.6061127451739\n ],\n [\n -70.927734375,\n 44.62175409623324\n ],\n [\n -72.1142578125,\n 43.723474896114794\n ],\n [\n -73.4326171875,\n 41.31082388091818\n ],\n [\n -71.103515625,\n 41.1290213474951\n ],\n [\n -70.0048828125,\n 41.32732632036622\n ],\n [\n -69.54345703125,\n 41.88592102814744\n ],\n [\n -70.400390625,\n 42.827638636242284\n ],\n [\n -69.78515625,\n 43.48481212891603\n ],\n [\n -68.84033203125,\n 44.02442151965934\n ],\n [\n -68.66455078125,\n 44.6061127451739\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Devine, Matthew T.","contributorId":204986,"corporation":false,"usgs":false,"family":"Devine","given":"Matthew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":836323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosset, Julianne","contributorId":197446,"corporation":false,"usgs":false,"family":"Rosset","given":"Julianne","email":"","affiliations":[],"preferred":false,"id":836324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gahagan, Benjamin I.","contributorId":200168,"corporation":false,"usgs":false,"family":"Gahagan","given":"Benjamin","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":836325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Michael P.","contributorId":286850,"corporation":false,"usgs":false,"family":"Armstrong","given":"Michael","email":"","middleInitial":"P.","affiliations":[{"id":40132,"text":"Massachusetts Division of Marine Resources","active":true,"usgs":false}],"preferred":false,"id":836326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whiteley, Andrew R.","contributorId":286853,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew R.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":836327,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jordaan, Adrian","contributorId":210892,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":836328,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216363,"text":"70216363 - 2021 - A lagrangian-to-eulerian metric to identify estuarine pelagic habitats","interactions":[],"lastModifiedDate":"2021-06-01T17:01:46.95513","indexId":"70216363","displayToPublicDate":"2020-11-11T09:23:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"A lagrangian-to-eulerian metric to identify estuarine pelagic habitats","docAbstract":"Estuaries are among the world’s most productive ecosystems, but recent natural and anthropogenic changes have stressed these ecosystems. Tools to assess estuarine pelagic habitats are important to support and maintain healthy ecosystem function. In this work, we demonstrate that estuarine pelagic habitats can be identified by a simple ratio, termed the LE ratio, that takes into account the tidal excursion along a channel (a Lagrangian length scale) and the distance along that channel (an Eulerian length scale). To develop and assess this concept, numerical simulations of the 1D advection–dispersion equation of a conservative tracer and tidal excursion estimates based on data were used to formulize a conceptual model and to define exchange zones within a tidal channel. This conceptual model was then used to predict the extent of pelagic habitats in a terminal channel network in the Sacramento–San Joaquin Delta. Exchange zones mapped onto these channels were found to be in good agreement with independent estimates of residence time. Sensitivity analyses of the numerical model suggest that productive pelagic habitats can be expanded by a factor of 2 by either increasing dispersion or increasing spring–neap variability in mean tidal velocity. Such changes can also enhance flushing in upper channel reaches. These findings are relevant for tidal marsh restoration projects that aim to expand beneficial aquatic habitats by varying exchange or residence time over the spring–neap cycle, because this variability may interact synergistically with varying rates of phytoplankton growth due to spatiotemporal changes in environmental conditions.
A 39-cm sediment core from Castle Lake, California (USA) spans the last ~ 450 years and was analyzed for diatoms and organic geochemistry (δ15N, δ13C, and C:N), with the goal of determining sensitivity to natural climate variation and twentieth century anthropogenic effects. Castle Lake is a subalpine, nitrogen-limited lake with ~ 5 months of annual ice cover. Human impacts include light recreational use, past fish stocking, and experimental use by the Castle Lake Research Station. The base of the core (below 32 cm; pre mid-1700s) represents the period of maximum ice cover. In contrast, the end of the Little Ice Age (mid 1700s–early 1800s) is dominated by cyclotelloids (mostly Discostella stelligera), indicating significant open-water periods, a condition that persisted into the early 1900s. Cyclotelloids began to decline in the 1960s and were replaced by the Fragilaria tenera grp. (peak in 1970s), succeeded by Asterionella formosa (peak ~ 2010), and accompanied by a reduction in δ15N values and a decrease in C:N that may represent increased atmospheric nitrogen deposition. Another anthropogenic signal was discerned in the core and was interpreted to be the result of an ammonium nitrate fertilization experiment of the epilimnion that was conducted in 1980 and 1981. This signal was manifested in the core largely by a negative excursion in δ15N, possibly caused by fractionation during denitrification in surface sediment. A phytoplankton monitoring dataset collected by the Castle Lake Research Station from 1967 to 1984 corroborates the timing of increased araphid euplanktonic species in the 1970s, and increases in two benthic diatoms (Staurosirella pinnata and Tabellaria fenestrata), entrained in the phytoplankton tows during the experimentation years. Both ice cover and nitrogen addition appear to be strong drivers that affected the lake diatoms, although additional drivers, such as fish stocking and associated cascade effects need further exploration. These data will be helpful for interpreting longer core records from Castle Lake, should the opportunity arise, as well as cores from similar systems in the region.
Anglers face constraints that influence participation and dropout rates. Some recreational anglers may be able to negotiate constraints by altering the timing or frequency of participation, acquiring new skills, or modifying nonrecreational aspects such as family or work responsibilities. We consider data collected via a mail survey from Georgia-resident trout license holders to identify both perceived constraints and strategies used to negotiate them. To capture variation among anglers, survey responses were grouped by level of angler specialization using K-means cluster analysis, which resulted in a three-cluster solution of most, moderately, and least specialized anglers. Analyses of variance were used to detect potential differences among the three specialization clusters. Tests revealed that the least specialized anglers experienced constraints more intensely than the most or moderately specialized anglers. Likewise, least specialized anglers were less able to negotiate constraints when compared to the most or moderately specialized anglers. However, the least specialized anglers used negotiation strategies involving overcoming perceived lack of skill more intensely than their counterparts. The most intensely experienced constraints overall were lack of time due to work or family obligations and distance to Georgia’s trout waters from home. The most intensely used negotiation strategies overall were “learn to enjoy being outside and stress less about catching fish” and “encourage family or friends to go fishing with me.” This research benefits fishery managers by providing a method of identifying angling groups that perceive more constraints and are less likely to overcome these constraints through constraint negotiation strategies. With this information, managers may choose to tailor efforts towards reducing constraints for angling groups that have low participation and may drop out of the activity all together.
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Although the species of As significantly affects its fate, transport, toxicity, and As treatment technology efficacy, reported effectiveness of As species preservation methods varies widely with preservation methods and natural water geochemistry. Our study 1) evaluates the shelf life of As(III), As(V), dimethylarsinate (DMA), and monomethylarsonate (MMA) in standards prepared with ultrapure water; 2) establishes a hold time for these As species in low-iron (Fe) groundwater and surface water samples preserved with a concentration of EDTA that exceeded the sum of the molar concentrations of Al, Fe, Mn, Ca, Mg, and Sr (molar excess of EDTA); and 3) evaluates As(III) species stability in groundwater samples with detectable SO4 and up to 6.5 mg/L Fe concentrations preserved in 3 ways: less than molar excess EDTA, molar excess EDTA, and Vacuette® tubes with an unknown (proprietary) amount of EDTA. Arsenic species standards prepared with 2.5 mM EDTA in ultrapure water and stored at 4 °C had a shelf life of at least 180 days. As(III) was stable for at least 15 days and DMA and MMA were stable for at least 90 days in environmental samples with Fe less than 1 mg/L that were preserved with a molar excess of EDTA and stored in opaque containers at 4 °C. As(III) species were not stable for any holding time in samples with Fe greater than 1 mg/L and detectable SO4 when preserved with a molar excess of EDTA and stored in white high density polyethylene bottles at room temperature, or when preserved by storage in EDTA containing Vacuette® tubes at 4 °C. For geochemical or water quality studies where the distribution of As(III) and As(V) is a critical factor, an understanding of the sample chemistry, rapid As speciation analysis after sample collection, and collecting a field spike with the sample can be helpful for collecting accurate inorganic As species data.
Neonicotinoid pesticides can persist in soils for extended time periods; however, they also have a high potential to contaminate ground and surface waters. Studies have reported negative effects associated with neonicotinoids and nontarget taxa, including aquatic invertebrates, pollinating insect species, and insectivorous birds. This study evaluated factors associated with clothianidin (CTN) degradation and sorption in Missouri wetland soils to assess the potential for wetland soils to mitigate potential environmental risks associated with neonicotinoids. Solid-to-solution partition coefficients (Kd) for CTN sorption to eight wetland soils were determined via single-point sorption experiments, and sorption isotherm experiments were conducted using the two most contrasting soils. Clothianidin degradation was determined under oxic and anoxic conditions over 60 d. Degradation data were fit to zero- and first-order kinetic decay models to determine CTN half-life (t0.5). Sorption results indicated CTN sorption to wetland soil was relatively weak (average Kd, 3.58 L kg–1); thus, CTN has the potential to be mobile and bioavailable within wetland soils. However, incubation results showed anoxic conditions significantly increased CTN degradation rates in wetland soils (anoxic average t0.5, 27.2 d; oxic average t0.5, 149.1 d). A significant negative correlation was observed between anoxic half-life values and soil organic C content (r2 = .782; p = .046). Greater CTN degradation rates in wetland soils under anoxic conditions suggest that managing wetlands to facilitate anoxic conditions could mitigate CTN presence in the environment and reduce exposure to nontarget organisms.
","language":"English","publisher":"ACSESS","doi":"10.1002/jeq2.20175","usgsCitation":"Beringer, C.J., Goyne, K., Lerch, R.N., Webb, E.B., and Mengel, D., 2021, Clothianidin decomposition in Missouri wetland soils: Journal of Environmental Quality, v. 50, no. 1, p. 241-251, https://doi.org/10.1002/jeq2.20175.","productDescription":"11 p.","startPage":"241","endPage":"251","ipdsId":"IP-118570","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454287,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/jeq2.20175","text":"External Repository"},{"id":395903,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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J.","contributorId":276297,"corporation":false,"usgs":false,"family":"Beringer","given":"C.","email":"","middleInitial":"J.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":834719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goyne, K. W.","contributorId":273205,"corporation":false,"usgs":false,"family":"Goyne","given":"K. W.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":834720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lerch, R. N.","contributorId":276298,"corporation":false,"usgs":false,"family":"Lerch","given":"R.","email":"","middleInitial":"N.","affiliations":[{"id":56949,"text":"USDA-Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":834721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mengel, D.","contributorId":244519,"corporation":false,"usgs":false,"family":"Mengel","given":"D.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":834723,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218686,"text":"70218686 - 2021 - Uncertainty in critical source area predictions from watershed-scale hydrologic models","interactions":[],"lastModifiedDate":"2021-03-05T13:28:39.226167","indexId":"70218686","displayToPublicDate":"2020-11-07T07:25:17","publicationYear":"2021","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":"Uncertainty in critical source area predictions from watershed-scale hydrologic models","docAbstract":"Watershed-scale hydrologic models are frequently used to inform conservation and restoration efforts by identifying critical source areas (CSAs; alternatively 'hotspots'), defined as areas that export relatively greater quantities of nutrients and sediment. The CSAs can then be prioritized or ‘targeted’ for conservation and restoration to ensure efficient use of limited resources. However, CSA simulations from watershed-scale hydrologic models may be uncertain and it is critical that the extent and implications of this uncertainty be conveyed to stakeholders and decision makers. We used an ensemble of four independently developed Soil and Water Assessment Tool (SWAT) models and a SPAtially Referenced Regression On Watershed attributes (SPARROW) model to simulate CSA locations for flow, phosphorus, nitrogen, and sediment within the ~17,000-km2 Maumee River watershed at the HUC-12 scale. We then assessed uncertainty in CSA simulations determined as the variation in CSA locations across the models. Our application of an ensemble of models - differing with respect to inputs, structure, and parameterization - facilitated an improved accounting of CSA prediction uncertainty. We found that the models agreed on the location of a subset of CSAs, and that these locations may be targeted with relative confidence. However, models more often disagreed on CSA locations. On average, only 16%–46% of HUC-12 subwatersheds simulated as a CSA by one model were also simulated as a CSA by a different model. Our work shows that simulated CSA locations are highly uncertain and may vary substantially across models. Hence, while models may be useful in informing conservation and restoration planning, their application to identify CSA locations would benefit from comprehensive uncertainty analyses to avoid inefficient use of limited resources.