Wildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t-distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t-test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-20-079","usgsCitation":"Sauer, J.R., Link, W., Seamans, M.E., and Rau, R.D., 2021, American Woodcock singing-ground survey: Comparison of four models for trend in population size: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 83-97, https://doi.org/10.3996/JFWM-20-079.","productDescription":"15 p.","startPage":"83","endPage":"97","onlineOnly":"N","ipdsId":"IP-127453","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":453075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-079","text":"Publisher Index Page"},{"id":384754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Eastern and Central United States and Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.412109375,\n 50.17689812200107\n ],\n [\n -95.537109375,\n 51.069016659603896\n ],\n [\n -95.888671875,\n 48.922499263758255\n ],\n [\n -95.00976562499999,\n 43.58039085560784\n ],\n [\n -93.955078125,\n 39.027718840211605\n ],\n [\n -93.515625,\n 30.826780904779774\n ],\n [\n -86.748046875,\n 32.10118973232094\n ],\n [\n -82.6171875,\n 29.99300228455108\n ],\n [\n -77.783203125,\n 34.161818161230386\n ],\n [\n -75.76171875,\n 35.88905007936091\n ],\n [\n -60.29296874999999,\n 45.706179285330855\n ],\n [\n -60.29296874999999,\n 47.100044694025215\n ],\n [\n -67.412109375,\n 50.17689812200107\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William 0000-0002-9913-0256","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":221718,"corporation":false,"usgs":true,"family":"Link","given":"William","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seamans, Mark E","contributorId":256724,"corporation":false,"usgs":false,"family":"Seamans","given":"Mark","email":"","middleInitial":"E","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rau, Rebecca D.","contributorId":256726,"corporation":false,"usgs":false,"family":"Rau","given":"Rebecca","email":"","middleInitial":"D.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813145,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218843,"text":"70218843 - 2021 - A systematic review of potential habitat suitability for the jaguar Panthera onca in central Arizona and New Mexico, USA","interactions":[],"lastModifiedDate":"2021-03-17T12:09:21.458718","indexId":"70218843","displayToPublicDate":"2021-03-16T07:00:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2968,"text":"Oryx","active":true,"publicationSubtype":{"id":10}},"title":"A systematic review of potential habitat suitability for the jaguar Panthera onca in central Arizona and New Mexico, USA","docAbstract":"In April 2019, the U.S. Fish and Wildlife Service (USFWS) released its recovery plan for the jaguar Panthera onca after several decades of discussion, litigation and controversy about the status of the species in the USA. The USFWS estimated that potential habitat, south of the Interstate-10 highway in Arizona and New Mexico, had a carrying capacity of c. six jaguars, and so focused its recovery programme on areas south of the USA–Mexico border. Here we present a systematic review of the modelling and assessment efforts over the last 25 years, with a focus on areas north of Interstate-10 in Arizona and New Mexico, outside the recovery unit considered by the USFWS. Despite differences in data inputs, methods, and analytical extent, the nine previous studies found support for potential suitable jaguar habitat in the central mountain ranges of Arizona and New Mexico. Applying slightly modified versions of the USFWS model and recalculating an Arizona-focused model over both states provided additional confirmation. Extending the area of consideration also substantially raised the carrying capacity of habitats in Arizona and New Mexico, from six to 90 or 151 adult jaguars, using the modified USFWS models. This review demonstrates the crucial ways in which choosing the extent of analysis influences the conclusions of a conservation plan. More importantly, it opens a new opportunity for jaguar conservation in North America that could help address threats from habitat losses, climate change and border infrastructure.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0030605320000459","usgsCitation":"Sanderson, E.W., Fisher, K., Peters, R., Beckmann, J.P., Bird, B., Bradley, C., Bravo, J., Grigione, M.M., Hatten, J., Gonzalez, C., Menke, K., Miller, J., Miller, P., Mormorunni, C., Robinson, M., Thomas, R.E., and Wilcox, S., 2021, A systematic review of potential habitat suitability for the jaguar Panthera onca in central Arizona and New Mexico, USA: Oryx, p. 1-12, https://doi.org/10.1017/S0030605320000459.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-114595","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":453076,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0030605320000459","text":"Publisher Index 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Cristina","contributorId":255473,"corporation":false,"usgs":false,"family":"Mormorunni","given":"Cristina","email":"","affiliations":[{"id":51544,"text":"Wildlife Conservation Society, Rocky Mountain West Program","active":true,"usgs":false}],"preferred":false,"id":812407,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Robinson, Michael","contributorId":255474,"corporation":false,"usgs":false,"family":"Robinson","given":"Michael","affiliations":[{"id":51536,"text":"Center for Biological Diversity, Geographic Information Systems","active":true,"usgs":false}],"preferred":false,"id":812408,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Thomas, Robert E","contributorId":255475,"corporation":false,"usgs":false,"family":"Thomas","given":"Robert","email":"","middleInitial":"E","affiliations":[{"id":51545,"text":"Bordercats Working Group, Lakewood, 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Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health","docAbstract":"Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2021.620715","usgsCitation":"Sepulveda, A., Hoegh, A.B., Gage, J.A., Caldwell Eldridge, S.L., Birch, J.M., Stratton, C., Hutchins, P.R., and Barnhart, E.P., 2021, Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health: Frontiers in Ecology and Evolution, v. 9, 13 p., https://doi.org/10.3389/fevo.2021.620715.","productDescription":"13 p.","ipdsId":"IP-123750","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":453078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.620715","text":"Publisher Index Page"},{"id":384620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming, Montana","otherGeospatial":"Yellowstone River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.400390625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 43.89789239125797\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoegh, Andrew B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":812862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gage, Joshua A.","contributorId":255726,"corporation":false,"usgs":false,"family":"Gage","given":"Joshua","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":812863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell Eldridge, Sara L. 0000-0001-8838-8940 seldridge@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-8940","contributorId":4981,"corporation":false,"usgs":true,"family":"Caldwell Eldridge","given":"Sara","email":"seldridge@usgs.gov","middleInitial":"L.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":812864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birch, James M.","contributorId":255728,"corporation":false,"usgs":false,"family":"Birch","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":812865,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stratton, Christian","contributorId":217711,"corporation":false,"usgs":false,"family":"Stratton","given":"Christian","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":812866,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hutchins, Patrick R. 0000-0001-5232-0821 phutchins@usgs.gov","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":198337,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick","email":"phutchins@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812867,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812868,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219207,"text":"70219207 - 2021 - Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","interactions":[],"lastModifiedDate":"2021-05-18T14:07:03.389177","indexId":"70219207","displayToPublicDate":"2021-03-15T13:32:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3042,"text":"Petroleum Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","docAbstract":"Geological carbon sequestration (GCS) is necessary to help meet emissions reduction goals, but groundwater contamination may occur if CO2 and/or brine were to leak out of deep storage formations into the shallow subsurface. For this study, a natural analogue was investigated: in the Virgin River Basin of southwest Utah, water with moderate salinity and high CO2 concentrations is leaking upward into shallow aquifers that contain heavy metal-bearing concretions. The aquifer system is comprised of the Navajo and Kayenta formations, which are pervasive across southern Utah and have been considered as a potential GCS injection unit where they are sufficiently deep. Numerical models of the site were constructed based on measured water chemistry and head distributions from previous studies. Simulations were used to improve understanding of the rate and distribution of the upwelling flow into the aquifers, and to assess the reactive transport processes that may occur if the upwelling fluids were to interact with a zone of iron oxide and other heavy metals, representing the concretions that are common in the area. Various mineralogies were tested, including one in which Pb+2 was adsorbed onto ferrihydrite, and another in which it was bound within a solid mixture of litharge (PbO) and hematite (Fe2O3). Results indicate that metal mobilization depends strongly on the source zone composition and that Pb+2 transport can be naturally attenuated by gas phase formation and carbonate mineral precipitation. These findings could be used to improve risk assessment and mitigation strategies at geological carbon sequestration sites.
","language":"English","publisher":"The Geological Society of London","doi":"10.1144/petgeo2020-109","usgsCitation":"Plampin, M.R., Blondes, M., Sonnenthal, E., and Craddock, W.H., 2021, Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah: Petroleum Geoscience, v. 27, no. 3, petgeo2020-109, 15 p., https://doi.org/10.1144/petgeo2020-109.","productDescription":"petgeo2020-109, 15 p.","ipdsId":"IP-120620","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":384769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":\"47\",\"properties\":{\"name\":\"Utah\",\"nation\":\"USA 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Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":813215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":813217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813218,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228877,"text":"70228877 - 2021 - Migration phenology and patterns of American woodcock in central North America derived using satellite telemetry","interactions":[],"lastModifiedDate":"2022-02-23T16:11:58.626898","indexId":"70228877","displayToPublicDate":"2021-03-15T10:04:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Migration phenology and patterns of American woodcock in central North America derived using satellite telemetry","docAbstract":"American woodcock Scolopax minor (hereafter woodcock) migration ecology is poorly understood, but has implications for population ecology and management, especially related to harvest. To describe woodcock migration patterns and phenology, we captured and equipped 73 woodcock with satellite tracking devices in the Central Management Region (analogous to the Mississippi Flyway) of North America and documented migration paths of 60 individual woodcock and 87 autumn or spring woodcock migrations during 2014–2016. Woodcock migration at the scale of the Central Management Region was more synchronous in spring than in autumn, but unlike most other migratory birds, average duration of autumn migration (31 days) was shorter than duration of spring migration (53 days). This difference in migration duration resulted from woodcock making more close-together migratory stopovers during spring migration, not because woodcock had individual stopovers of longer duration. During autumn migration, the number of days, the number of stopovers, migration end date and net migration displacement were negatively related to initiation date and rate of migration, and the number of stopovers and the net migration displacement were negatively related with migration end date. Spring migration duration, end date, the number of stopovers and net migration displacement were negatively related to migration rate and initiation date was positively related to migration rate, suggesting that woodcock that initiated spring migration later had faster migration rates. Juvenile female woodcock began spring migration later than adult female woodcock. Our results provide a basis for comparing current harvest seasons with presence of migrating woodcock during autumn and provide insight into differential harvest of migratory versus local woodcock on breeding areas.
","language":"English","publisher":"Nordic Council of Wildlife Research","doi":"10.2981/wlb.00816","usgsCitation":"Moore, J.D., Andersen, D.E., Cooper, T., Duguay, J.P., Oldenburger, S.L., Stewart, C.A., and Krementz, D.G., 2021, Migration phenology and patterns of American woodcock in central North America derived using satellite telemetry: Wildlife Biology, v. 2021, no. 1, wlb.00816, 12 p., https://doi.org/10.2981/wlb.00816.","productDescription":"wlb.00816, 12 p.","ipdsId":"IP-118838","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":453080,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00816","text":"Publisher Index Page"},{"id":396349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"central North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.0966796875,\n 27.293689224852407\n ],\n [\n -83.408203125,\n 27.293689224852407\n ],\n [\n -83.408203125,\n 52.429222277955134\n ],\n [\n -99.0966796875,\n 52.429222277955134\n ],\n [\n -99.0966796875,\n 27.293689224852407\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2021","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Joseph D.","contributorId":199996,"corporation":false,"usgs":false,"family":"Moore","given":"Joseph","email":"","middleInitial":"D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":835759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":835758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, Tom","contributorId":279952,"corporation":false,"usgs":false,"family":"Cooper","given":"Tom","affiliations":[{"id":57391,"text":"U S Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835760,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duguay, Jeffrey P.","contributorId":279953,"corporation":false,"usgs":false,"family":"Duguay","given":"Jeffrey","email":"","middleInitial":"P.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":835761,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oldenburger, Shaun L.","contributorId":177598,"corporation":false,"usgs":false,"family":"Oldenburger","given":"Shaun","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":835762,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, C. Al","contributorId":279955,"corporation":false,"usgs":false,"family":"Stewart","given":"C.","email":"","middleInitial":"Al","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":835763,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835764,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70218647,"text":"sir20215004 - 2021 - Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","interactions":[],"lastModifiedDate":"2021-03-16T11:43:44.760872","indexId":"sir20215004","displayToPublicDate":"2021-03-15T08:46:16","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5004","displayTitle":"Numerical Simulation of the Effects of Groundwater Withdrawal and Injection of High-Salinity Water on Salinity and Groundwater Discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","title":"Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","docAbstract":"Kaloko-Honokōhau National Historical Park (KAHO) is located on the west coast of the island of Hawaiʻi and contains water resources exposed in fishponds, anchialine pools, and marine waters that are cultural resources and that provide habitat for threatened, endangered, and other culturally important native species. KAHO’s water resources are sustained by and dependent on groundwater discharge. In 1978, the year of KAHO authorization, the lands immediately surrounding KAHO were undeveloped and zoned for conservation purposes; at present, most surrounding lands are either developed or zoned for industrial, commercial, or residential use. Urbanization of the North Kona District has increased the need for additional drinking and nonpotable (irrigation) water. Because KAHO’s water resources may be affected by existing and proposed groundwater withdrawals and injections of high-salinity water in the surrounding area, the U.S. Geological Survey, in cooperation with the National Park Service, undertook this study to refine the understanding of how groundwater withdrawals and injection of high-salinity water may affect KAHO’s water resources.
Changes in KAHO water resources, in terms of changes in salinity and groundwater discharge, were modeled for selected scenarios of groundwater withdrawal and high-salinity water injection in the aquifer. The numerical model was developed using the model code SUTRA, which accounts for density-dependent flow and salinity transport, and included the coastal-confined groundwater system beneath the coastal freshwater-lens system. Model results indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the salinity of KAHO’s anchialine pools, which provide habitat for the endangered orangeblack Hawaiian damselfly (Megalagrion xanthomelas). The magnitude of the effect is dependent on the amount and location of the withdrawal. Greater withdrawal rates cause greater increases in salinity in KAHO, other factors being equal. For a given withdrawal rate, the greatest increase in salinity in KAHO is associated with wells withdrawing groundwater in an area inland of KAHO and the least increase in salinity is associated with wells near the coast. Model results also indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the groundwater discharge, in terms of the freshwater component (water with zero salinity) of the discharge, through KAHO. Greater withdrawal rates cause greater reductions in freshwater discharge through KAHO. For a given withdrawal rate, the greatest reduction in freshwater discharge through KAHO is associated with wells near the north boundary of KAHO and the least reduction is associated with wells near the coast to the north and south of KAHO.
Injection of high-salinity water that is denser than ocean water can affect the salinity of damselfly habitat in KAHO, with the magnitude of the effect dependent on the location of the injection. Model results indicate that salinity may either increase or decrease in the anchialine pools that provide damselfly habitat in KAHO, depending on the site of injection. Injection inland of KAHO and at sites immediately north and south of KAHO causes a simulated decrease in salinity at the damselfly habitat, whereas injection farther north and south of KAHO causes an increase in salinity. Injection of high-salinity water also causes a reduction in freshwater discharge through KAHO, with the greatest reduction associated with distant injection wells to the north and south of KAHO and the least reduction associated with wells located near and immediately inland from KAHO.
The numerical groundwater models developed for this study have a number of limitations. Lack of understanding of the subsurface geology constrains the ability to accurately model the groundwater-flow system. The models developed for this study are nonunique, cannot account for local-scale heterogeneities in the aquifer, and contain uncertainties related to recharge, boundary conditions, assigned parameter values in the model, and representations of the different hydrogeological features. Confidence in model results can be improved by addressing these and other limitations. In spite of these limitations, the three-dimensional numerical model developed for this study provides a useful conceptual understanding of the potential effects of additional withdrawals and injections on groundwater resources in KAHO. Further evaluation of the ecologic effects of the simulated changes in groundwater quality and quantity in KAHO is needed but is beyond the scope of this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215004","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Oki, D.S., 2021, Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2021–5004, 59 p., https://doi.org/10.3133/sir20215004.","productDescription":"Report: viii, 59 p.; Data Release","numberOfPages":"59","ipdsId":"IP-119308","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZ3EVJ","linkHelpText":"SUTRA Model Used to Evaluate the Effects of Groundwater Withdrawal and Injection, Kaloko-Honokōhau National Historical Park, Hawaiʻi"},{"id":383762,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5004/sir20215004.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383761,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5004/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaloko-Honokōhau National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.03761672973633,\n 19.66829132832601\n ],\n [\n -156.01186752319336,\n 19.66829132832601\n ],\n [\n -156.01186752319336,\n 19.69350614042769\n ],\n [\n -156.03761672973633,\n 19.69350614042769\n ],\n [\n -156.03761672973633,\n 19.66829132832601\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, conducts research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2019, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2019 included deploying a portable seismic array near Steamboat Geyser in Norris Geyser Basin that recorded signals from seven major water eruptions; deploying a semipermanent Global Positioning System array; surveying soil carbon dioxide flux and temperature and operating an eddy covariance system to make continuous measurements; collecting and analyzing water samples from Shoshone Geyser Basin, the outlets of Shoshone and Lewis Lakes, Cinder Pool in Norris Geyser Basin, and several locations along Obsidian Creek; exploring and documenting a new thermal area near Tern Lake that was discovered in 2018; measuring specific conductance along major rivers to determine the chloride flux and total heat output of the Yellowstone hydrothermal system; conducting an inventory of hydrothermal features in Norris Geyser Basin and Upper Geyser Basin as part of a park-wide project that began in 2018; and sampling of tree rings and silica sinter deposits in the Upper Geyser Basin to better understand hydrothermal activity over time.
Continuing the pattern that started in 2018, Steamboat Geyser, in Norris Geyser Basin, erupted 48 times in 2019—a new record for a calendar year! Overall, however, noteworthy geyser activity in Yellowstone National Park was much reduced relative to the previous year. Thermal features on Geyser Hill in the Upper Geyser Basin had returned to their normal activity styles after Ear Spring’s September 2018 eruption and did not show any significant changes in 2019. Giant Geyser, also in the Upper Geyser Basin, did not experience any eruptions after March 2019. Seismicity was reduced relative to previous years, and deformation of Norris Geyser Basin, which started as uplift in 2015 and paused in late 2018, shifted to subsidence in late 2019. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1473","usgsCitation":"Yellowstone Volcano Observatory, 2021, Yellowstone Volcano Observatory 2019 annual report: U.S. Geological Survey Circular 1473, 35 p., https://doi.org/10.3133/cir1473.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"N","ipdsId":"IP-119028","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":384319,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1473/cir1473.pdf","text":"Report","size":"62 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384318,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1473/covrthb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"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 -111.060791015625,\n 43.731414013769\n ],\n [\n -109.281005859375,\n 43.731414013769\n ],\n [\n -109.281005859375,\n 45.00365115687186\n ],\n [\n -111.060791015625,\n 45.00365115687186\n ],\n [\n -111.060791015625,\n 43.731414013769\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"Crex Meadows Wildlife Area (Crex) is a 30,000-acre property in Burnett County, Wisconsin. Crex is managed by the Wisconsin Department of Natural Resources (WDNR) with the goal of providing public recreation opportunities while also protecting the quality of native ecological communities and species on the property. The WDNR’s management strategy includes controlling water levels at flowages in Crex using a system of dikes, water control structures, ditches, and a diversion pump. For the past several decades there has been concern among nearby landowners that the water manage-ment strategy at Crex may be contributing to groundwater flooding in adjacent, privately held properties. This issue has been particularly contentious during periods when regional groundwater elevations are already high. This study was conducted in response to those concerns. For the study, a network of 12 monitoring wells was installed in and to the west of Crex. Groundwater elevations were recorded in the wells before, during, and after water-level changes in the western Crex flowages to assess if groundwater elevations to the west of Crex are detectably affected by the flowage water levels.
This study successfully collected groundwater elevations in 11 study wells during a 3-month period in 2019 when water elevations in the Dike 6 flowage and Erickson flowage were lowered and then raised. The data logger at a 12th location failed and no data were recorded. The groundwater elevation trends in these study wells were compared with groundwater elevation trends at a regional U.S. Geological Survey well to provide information for determining if changing the flowage elevations had a noticeable response in the study wells west of Crex Meadows. This analysis was done by (1) evaluating study well groundwater elevation trends compared to the regional well, (2) using a scatter plot of study well and regional well data during raising and lowering periods,
(3) assessing horizontal hydraulic gradient data during the study period, and (4) assessing the cumulative departure from the mean groundwater elevation for each well.
Overall, regional groundwater elevations had a down-ward trend before and during the flowage lowering period and then had an upward trend during the flowage raising period. This pattern was observed in the regional well and in all the study wells adjacent to and several miles from the flowages. The similarity in patterns indicates that precipitation and regional groundwater flow conditions were the dominant drivers of the system during the study period. The scatter plot and cumulative departure from the mean analysis showed that in addition to regional trends, wells 1, 6, and 7 were likely affected by the changes in the flowage water levels. Overall, at least on the timescale of this study, water management at Crex likely did not have detectable effects on wells outside the Crex property. Wells installed on the Crex property including the wells in the lakebeds of the flowages (wells 1 and 7) and possibly well 6 east of the flowages showed what seems to be minor affects due to water management at Crex.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205149","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources","usgsCitation":"Haserodt, M.J., and Fienen, M.N., 2020, Assessment of groundwater trends near Crex Meadows, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5149, 32 p., https://doi.org/10.3133/sir20205149.","productDescription":"vi, 36 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-117629","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385958,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5149/versionHist.txt","text":"Version History","size":"1.69 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5149 Version History"},{"id":385957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5149/sir20205149.pdf","text":"Report","size":"13.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5149"},{"id":384275,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5149/coverthb3.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Crex Meadows","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.51930236816406,\n 45.81540082150529\n ],\n [\n -92.51861572265625,\n 45.829756159282766\n ],\n [\n -92.51861572265625,\n 45.84506443975059\n ],\n [\n -92.52616882324219,\n 45.84506443975059\n ],\n [\n -92.52754211425781,\n 45.87853662114514\n ],\n [\n -92.55226135253906,\n 45.882360730184025\n ],\n [\n -92.55088806152344,\n 45.90768880475299\n ],\n [\n -92.60856628417967,\n 45.90386643939614\n ],\n [\n -92.67105102539061,\n 45.897654534346906\n ],\n [\n -92.68272399902344,\n 45.88618457602257\n ],\n [\n -92.68135070800781,\n 45.867062714815475\n ],\n [\n -92.69096374511719,\n 45.817315080406246\n ],\n [\n -92.691650390625,\n 45.80008438131991\n ],\n [\n -92.68753051757812,\n 45.79338211440398\n ],\n [\n -92.64770507812499,\n 45.79338211440398\n ],\n [\n -92.61543273925781,\n 45.813965084145295\n ],\n [\n -92.57972717285156,\n 45.817315080406246\n ],\n [\n -92.57492065429688,\n 45.82688538784564\n ],\n [\n -92.54539489746094,\n 45.82640691154487\n ],\n [\n -92.54539489746094,\n 45.81109349837976\n ],\n [\n -92.51930236816406,\n 45.81540082150529\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 15, 2021; Version 1.1: May 26, 2021","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.
A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.
The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.
In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.
The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.
The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.
A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.
Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.
The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.
Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The 1973 Oklahoma Water Law (82 OK Stat § 82-1020.5) requires that the Oklahoma Water Resources Board (OWRB) conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin (hereinafter referred to as an “aquifer”). The maximum annual yield allocated per acre of land is known as the equal-proportionate-share (EPS) pumping rate. At present (2021), the OWRB has not yet established a maximum annual yield and EPS pumping rate for the Salt Fork Red River aquifer. To provide updated information to the OWRB that could support evaluation and determination of an appropriate maximum annual yield, the U.S. Geological Survey (USGS), in cooperation with the OWRB, conducted a hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater availability in the Salt Fork Red River aquifer.
The Salt Fork Red River aquifer in Greer, Harmon, and Jackson Counties of southwestern Oklahoma is composed of about 274.5 square miles of alluvium and terrace deposits associated with the Salt Fork Red River. The mean annual recharge rate to the Salt Fork Red River aquifer for the period 1980–2015 was estimated to be about 2.94 inches per year, or 10.0 percent of the mean annual precipitation for the same period (29.4 inches per year). This 1980–2015 mean annual recharge rate is equivalent to a mean annual recharge rate of about 38,000 acre-feet per year (acre-ft/yr) for the Salt Fork Red River aquifer excluding about 19,764 acres comprising the Mulberry Creek and Horse Creek terraces. The mean annual recharge rates upgradient and downgradient from USGS streamgage 07300500 Salt Fork Red River at Mangum, Okla. (hereinafter referred to as the “Mangum gage”), apportioned by aquifer area (41.5 and 58.5 percent, respectively), were about 16,000 and 22,000 acre-ft/yr, respectively. Mean annual groundwater use for the study period (1980–2015) was 3,532.7 acre-ft/yr; about 77 percent of that groundwater use was for irrigation, and about 23 percent was for public supply. Most groundwater use for irrigation was associated with wells in the Martha terrace.
A hydrogeologic framework was developed for the Salt Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of the numerical groundwater-flow model of the Salt Fork Red River aquifer described in this report. A conceptual model for the Salt Fork Red River aquifer that reasonably represents the groundwater-flow system was developed to constrain the construction and calibration of the numerical model. The conceptual-model water budget estimated mean annual inflows to, and outflows from, the Salt Fork Red River aquifer for the period 1980–2015 and included a subaccounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from the Mangum gage.
The numerical groundwater-flow model of the Salt Fork Red River aquifer was constructed by using MODFLOW-2005 with the Newton formulation solver. The model of the Salt Fork Red River aquifer was spatially discretized into 1,050 rows, 1,125 columns, about 170,000 active cells measuring 200 by 200 feet (ft), and a single convertible layer. The model was temporally discretized into 432 monthly transient stress periods (each with two time steps to improve model stability). An initial steady-state stress period represented mean annual inflows to, and outflows from, the aquifer and produced a solution that was used as the initial condition for subsequent transient stress periods as well as some groundwater-availability scenarios. The model was calibrated to water-table-altitude observations at selected wells and base-flow observations at selected streamgages.
The simulated saturated thickness of the Salt Fork Red River aquifer was determined by subtracting the altitude of the aquifer base from the simulated water-table altitude at the end of the numerical-model period (2015). The simulated saturated thickness was more than 75 ft in a paleochannel in the Dodson terrace near the Texas border. The mean aquifer thickness (sum of saturated and unsaturated) was 49.62 ft, and the mean saturated thickness was 28.55 ft. A simulated mean transmissivity of 1,024 feet squared per day was computed from the calibrated hydraulic conductivity and saturated thickness of each cell. The simulated available water in storage at the end of the numerical-model period (2015) was 526,117 acre-feet (acre-ft); about 42 percent of that total was available upgradient from the Mangum gage, and about 58 percent of that total was available downgradient from the Mangum gage (including the Mangum terrace).
Three types of groundwater-availability scenarios were run using the calibrated numerical model. These scenarios were used to (1) estimate the EPS pumping rate that ensures a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought on base flow and groundwater storage. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were about 0.51, 0.48, and 0.48 acre-foot per acre per year, respectively. Given the 155,929-acre modeled aquifer area, these rates correspond to annual yields of about 78,800, 74,900, and 74,700 acre-ft/yr, respectively. For the 20-year EPS scenario, decreasing and increasing recharge by 10 percent resulted in a 6-percent change in the EPS pumping rate in both cases; for the 40- and 50-year EPS scenarios, decreasing and increasing recharge by 10 percent resulted in a 7-percent change in the EPS pumping rate in both cases.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the Salt Fork Red River aquifer and base flows in the Salt Fork Red River. The effects of well withdrawals were evaluated by quantifying differences in groundwater storage and base flow in four 50-year scenarios, which applied (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2015), (3) 2015 pumping rates, and (4) increasing demand pumping rates at simulated wells. The increasing demand pumping rates assumed a cumulative 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwestern Oklahoma. Groundwater storage after 50 years with no pumping was 535,000 acre-ft, or 8,900 acre-ft (1.7 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-table-altitude increase of 0.48 ft. Groundwater storage after 50 years of pumping at the mean rate for the study period (1980–2015) was 519,900 acre-ft, or 6,200 acre-ft (1.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.34 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 513,100 acre-ft, or 13,000 acre-ft (2.5 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.71 ft. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was 509,700 acre-ft, or 16,500 acre-ft (3.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.89 ft.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage. The period January 1983–December 1992 was chosen as the simulated drought period. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought) at the end of the simulated drought period (1992). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1983–92). Upstream inflows from the Salt Fork Red River, Turkey Creek, and Bitter Creek were reduced by 75 percent. Groundwater storage at the end of the drought period (1992) was 479,200 acre-ft, or 53,200 acre-ft (10.0 percent) less than the groundwater storage of the calibrated numerical model at the end of the drought period. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 2.9 ft. At the end of the 10-year hypothetical drought period, simulated base flows at the Mangum gage and USGS streamgage 07301110 Salt Fork Red River near Elmer, Okla., had decreased by about 80 and 70 percent, respectively.
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U.S. Geological Survey
1505 Ferguson Lane
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","tableOfContents":"Earthquake clustering (grouping in space and time) is a widely observed mode of strain release in the upper crust, although this behavior on individual faults is a departure from classic elastic rebound theory. In this study, we consider factors responsible for a cluster of earthquakes on the Bear River fault zone (BRF), a recently activated, 44-km-long normal fault on the eastern margin of Basin and Range extension in the Rocky Mountains. The entire surface-rupturing history of the BRF, as gleaned from paleoseismic and geomorphic observations, began only 4500 years ago and consists of at least three large events. Rupture of the BRF is spatially complex and is clearly conditioned by preexisting structure. In particular, where the south end of the fault intersects older thrust faults and upturned strata along the south-dipping flank of the Precambrian basement-cored Uinta arch, the main trace ends abruptly in a set of orthogonal splays that accommodate down-dropping of a large hanging-wall graben against the arch. We hypothesize that the geomechanically strong Uinta arch crustal block impeded the development of the BRF and, over time, enabled a significant accumulation of elastic strain energy, eventually giving rise to a pulse of strain release in the mid- to late Holocene. We surmise that variations in fault strength, both in space and time, is a cause of earthquake clustering on the BRF and on other faults that are structurally and tectonically immature. The first two earthquakes on the BRF occurred during the same period of time as a regional cluster of earthquakes in the Middle Rocky Mountains, suggesting that isolated faults in this slowly extending region interact through widespread changes in stress conditions.
Geophysical investigations documenting enhanced magnetic susceptibility (MS) within the water table fluctuation zone at hydrocarbon contaminated sites suggest that MS can be used as a proxy for investigating microbial mediated iron reduction during intrinsic bioremediation. Here, we investigated the microbial community composition over a 5-year period at a hydrocarbon-contaminated site that exhibited transient elevated MS responses. Our objective was to determine the key microbial populations in zones of elevated MS. We retrieved sediment cores from the petroleum-contaminated site near Bemidji, MN, United States, and performed MS measurements on these cores. We also characterized the microbial community composition by high-throughput 16S rRNA gene amplicon sequencing from samples collected along the complete core length. Our spatial and temporal analysis revealed that the microbial community composition was generally stable throughout the period of investigation. In addition, we observed distinct vertical redox zonations extending from the upper vadose zone into the saturated zone. These distinct redox zonations were concomitant with the dominant microbial metabolic processes as follows: (1) the upper vadose zone was dominated by aerobic microbial populations; (2) the lower vadose zone was dominated by methanotrophic populations, iron reducers and iron oxidizers; (3) the smear zone was dominated by iron reducers; and (4) the free product zone was dominated by syntrophic and methanogenic populations. Although the common notion is that high MS values are caused by high magnetite concentrations that can be biotically formed through the activities of iron-reducing bacteria, here we show that the highest magnetic susceptibilities were measured in the free-phase petroleum zone, where a methanogenic community was predominant. This field study may contribute to the emerging knowledge that methanogens can switch their metabolism from methanogenesis to iron reduction with associated magnetite precipitation in hydrocarbon contaminated sediments. Thus, geophysical methods such as MS may help to identify zones where iron cycling/reduction by methanogens is occurring.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2021.598172","usgsCitation":"Beaver, C.L., Atekwana, E.A., Bekins, B.A., Ntarlagiannis, D., Slater, L., and Rossbach, S., 2021, Methanogens and their syntrophic partners dominate zones of enhanced magnetic susceptibility at a petroleum contaminated site: Frontiers in Earth Science, v. 9, 598172, 18 p., https://doi.org/10.3389/feart.2021.598172.","productDescription":"598172, 18 p.","ipdsId":"IP-125198","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2021.598172","text":"Publisher Index Page"},{"id":384449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaver, Carol L.","contributorId":255451,"corporation":false,"usgs":false,"family":"Beaver","given":"Carol","email":"","middleInitial":"L.","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":812376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atekwana, Estella A.","contributorId":255452,"corporation":false,"usgs":false,"family":"Atekwana","given":"Estella","email":"","middleInitial":"A.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":812377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":812378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ntarlagiannis, Dimitrios","contributorId":255453,"corporation":false,"usgs":false,"family":"Ntarlagiannis","given":"Dimitrios","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":812379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Slater, Lee D.","contributorId":255454,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":812380,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rossbach, Silvia","contributorId":255455,"corporation":false,"usgs":false,"family":"Rossbach","given":"Silvia","email":"","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":812381,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219910,"text":"70219910 - 2021 - The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed","interactions":[],"lastModifiedDate":"2021-04-19T11:51:47.992809","indexId":"70219910","displayToPublicDate":"2021-03-15T06:56:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed","docAbstract":"The Panola Mountain Research Watershed (PMRW) is a 41‐hectare forested catchment within the Piedmont Province of the Southeastern United States. Observations, experimentation, and numerical modelling have been conducted at Panola over the past 35 years. But to date, these studies have not been fully incorporated into a more comprehensive synthesis. Here we describe the evolving perceptual understanding of streamflow generation mechanisms at the PMRW. We show how the long‐term study has enabled insights that were initially unforeseen but are also unachievable in short‐term studies. In particular, we discuss how the accumulation of field evidence, detailed site characterization, and modelling enabled a priori hypotheses to be formed, later rejected, and then further refined through repeated field campaigns. The extensive characterization of the soil and bedrock provided robust process insights not otherwise achievable from hydrometric measurements and numerical modelling alone. We focus on two major aspects of streamflow generation: the role of hillslopes (and their connection to the riparian zone) and the role of catchment storage in controlling fluxes and transit times of water in the catchment. Finally, we present location‐independent hypotheses based on our findings at PMRW and suggest ways to assess the representativeness of PMRW in the broader context of headwater watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14127","usgsCitation":"Aulenbach, B.T., Hooper, R.P., van Meerveld, H.J., Burns, D., Freer, J.E., Shanley, J.B., Huntington, T., McDonnell, J.J., and Norman E. Peters, 2021, The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed: Hydrological Processes, v. 35, no. 4, e14127, 14 p., https://doi.org/10.1002/hyp.14127.","productDescription":"e14127, 14 p.","ipdsId":"IP-125152","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":385149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.78149414062499,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 33.25706340236547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Richard P 0000-0002-3329-9622","orcid":"https://orcid.org/0000-0002-3329-9622","contributorId":257488,"corporation":false,"usgs":false,"family":"Hooper","given":"Richard","email":"","middleInitial":"P","affiliations":[{"id":52045,"text":"Tufts University, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":814372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Meerveld, H. J. 0000-0002-7547-3270","orcid":"https://orcid.org/0000-0002-7547-3270","contributorId":257489,"corporation":false,"usgs":false,"family":"van Meerveld","given":"H.","email":"","middleInitial":"J.","affiliations":[{"id":52048,"text":"University of Zurich, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":814373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freer, James E. 0000-0001-6388-7890","orcid":"https://orcid.org/0000-0001-6388-7890","contributorId":188139,"corporation":false,"usgs":false,"family":"Freer","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":814375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McDonnell, Jeffery J. 0000-0002-3880-3162","orcid":"https://orcid.org/0000-0002-3880-3162","contributorId":62723,"corporation":false,"usgs":false,"family":"McDonnell","given":"Jeffery","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":814378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Norman E. Peters 0000-0002-0637-9424","orcid":"https://orcid.org/0000-0002-0637-9424","contributorId":207130,"corporation":false,"usgs":false,"family":"Norman E. Peters","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":814379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219224,"text":"70219224 - 2021 - Sea turtles across the North Pacific are exposed to perfluoroalkyl substances","interactions":[],"lastModifiedDate":"2021-04-01T11:54:08.775984","indexId":"70219224","displayToPublicDate":"2021-03-15T06:48:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Sea turtles across the North Pacific are exposed to perfluoroalkyl substances","docAbstract":"Perfluorinated alkyl substances (PFASs) are global, persistent, and toxic contaminants. We assessed PFAS concentrations in green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) turtles from the North Pacific. Fifteen compounds were quantified via liquid chromatography tandem mass spectrometry from 62 green turtle and 6 hawksbill plasma samples from Hawai’i, Palmyra Atoll, and the Northern Marianas Islands. Plasma from 14 green turtles severely afflicted with fibropapillomatosis, and eggs from 12 Hawaiian hawksbill nests from 7 females were analyzed. Perfluorooctane sulfonate (PFOS) predominated in green turtle plasma; perfluorononanoic acid (PFNA) predominated in hawksbill tissues. Concentrations were greater in hawksbill than green turtle plasma (p < 0.05), related to trophic differences. Green turtle plasma PFOS concentrations were related to human populations from highest to lowest: Hawai’i, Marianas, Palmyra. Influence on fibropapillomatosis was not evident. PFASs were maternally transferred to hawksbill eggs, with decreasing concentrations with distance from airports and with clutch order from one female. A risk assessment of PFOS showed concern for immunosuppression in Kailua green turtles and alarming concern for hawksbill developmental toxicity. Perfluoroundecanoic (PFUnA) and perfluorotridecanoic (PFTriA) acid levels were correlated with reduced emergence success (p < 0.05). Studies to further examine PFAS effects on sea turtle development would be beneficial.
Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.
Industrial chemical contamination within coastal regions of the Great Lakes can pose serious risks to wetland habitat and offshore fisheries, often resulting in fish consumption advisories that directly affect human and wildlife health. Mercury (Hg) is a contaminant of concern in many of these highly urbanized and industrialized coastal regions, one of which is the Saint Louis River estuary (SLRE), the second largest tributary to Lake Superior. The SLRE has legacy Hg contamination that drives high Hg concentrations within sediments, but it is unclear whether legacy-derived Hg actively cycles within the food web. To understand the relative contributions of legacy versus contemporary Hg sources in coastal zones, Hg, carbon, and nitrogen stable isotope ratios were measured in sediments and food webs of SLRE and the Bad River, an estuarine reference site. Hg stable isotope values revealed that legacy contamination of Hg was widespread and heterogeneously distributed in sediments of SLRE, even in areas lacking industrial Hg sources. Similar isotope values were found in benthic invertebrates, riparian spiders, and prey fish from SLRE, confirming legacy Hg reaches the SLRE food web. Direct comparison of prey fish from SLRE and the Bad River confirmed that Hg isotope differences between the sites were not attributable to fractionation associated with rapid Hg bioaccumulation at estuarine mouths, but due to the presence of industrial Hg within SLRE. The Hg stable isotope values of game fish in both estuaries were dependent on fish migration and diet within the estuaries and extending into Lake Superior. These results indicate that Hg from legacy contamination is actively cycling within the SLRE food web and, through migration, this Hg also extends into Lake Superior via game fish. Understanding sources and the movement of Hg within the estuarine food web better informs restoration strategies for other impaired Great Lakes coastal zones.
Watershed degradation due to invasion threatens downstream water flows and associated ecosystem services. While this topic has been studied across landscapes that have undergone invasive-driven state changes (e.g., native forest to invaded grassland), it is less well understood in ecosystems experiencing within-system invasion (e.g. native forest to invaded forest). To address this subject, we conducted an integrated ecological and ecohydrological study in tropical forests impacted by invasive plants and animals. We measured soil infiltration capacity in multiple fenced (i.e., ungulate-free)/unfenced and native/invaded forest site pairs along moisture and substrate age gradients across Hawaii to explore the effects of invasion on hydrological processes within tropical forests. We also characterized forest composition, structure and soil characteristics at these sites to assess the direct and vegetation-mediated impacts of invasive species on infiltration capacity. Our models show that invasive ungulates negatively affect soil infiltration capacity consistently across the wide moisture and substrate age gradients considered. Additionally, several soil characteristics known to be affected by invasive ungulates were associated with local infiltration rates, indicating that the long-term secondary effects of high ungulate densities in tropical forests may be stronger than effects observed in this study. The effect of invasive plants on infiltration was complex and likely to depend on their physiognomy within existing forest community structure. These results provide clear evidence for managers that invasive ungulate control efforts can improve ecohydrological function of mesic and wet forest systems critical to protecting downstream and nearshore resources and maintaining groundwater recharge.
Eurasian collared doves (Streptopelia decaocto) were introduced into Florida in the 1980s and have since established populations throughout the continental United States. Pigeon paramyxovirus-1 (PPMV-1), a species-adapted genotype VI Avian orthoavulavirus 1, has caused periodic outbreaks among collared doves in the U.S. since 2001 with outbreaks occasionally involving native doves. In California, PPMV-1 mortality events were first documented in Riverside County in 2014 with subsequent outbreaks in 23 additional counties from southern to northern California between 2015 and 2019. Affected collared doves exhibited torticollis and partial paralysis. Pale kidneys were frequently visible on gross necropsy (65.4%; 51/78) while lymphoplasmacytic interstitial nephritis often with acute tubular necrosis (96.0%; 24/25) and pancreatic necrosis (80.0%; 20/25) were common findings on histopathology. In total, PPMV-1 was confirmed by rRT-PCR and sequence analysis from oropharyngeal and/or cloacal swabs in 93.0% (40/43) of the collared doves tested from 16 California counties. In 2017, Avian orthoavulavirus 1 was confirmed in a native mourning dove (Zenaida macroura) found dead during a PPMV-1 outbreak in collared doves by rRT-PCR from formalin-fixed paraffin-embedded (FFPE) tissues, after the initial rRT-PCR from swabs failed to detect the virus. Molecular sequencing of the fusion protein of isolates collected from collared doves during outbreaks in 2014, 2016, and 2017 identified two distinct subgenotypes, VIa and VIn. Subgenotype VIn has been primarily isolated from collared doves in the southern U.S., while VIa has been isolated from mixed avian species in the northeastern U.S., indicating two independent introductions into California. While populations of collared doves are not expected to be substantially impacted by this disease, PPMV-1 may pose a threat to already declining populations of native columbids. This threat could be assessed by monitoring native and non-native columbids for PPMV-1. Based on our study, swab samples may not be sufficient to detect infection in native columbids and may require the use of non-traditional diagnostic approaches, such as FFPE tissues, to ensure virus detection.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.meegid.2021.104809","usgsCitation":"Rogers, K., Mete, A., Ip, H., Torchetti, M.K., Killian, M.L., and Crossley, B., 2021, Emergence and molecular characterization of pigeon Paramyxovirus-1 in non-native Eurasian collared doves (Streptopelia decaocto) in California, USA: Infection, Genetics and Evolution, v. 91, 104809, 8 p., https://doi.org/10.1016/j.meegid.2021.104809.","productDescription":"104809, 8 p.","ipdsId":"IP-123317","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":453094,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.meegid.2021.104809","text":"Publisher Index Page"},{"id":384623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.1142578125,\n 32.65787573695528\n ],\n [\n -114.47753906249999,\n 32.80574473290688\n ],\n [\n -114.169921875,\n 34.27083595165\n ],\n [\n -120.05859375,\n 39.13006024213511\n ],\n [\n -120.05859375,\n 42.09822241118974\n ],\n [\n -124.3212890625,\n 42.032974332441405\n ],\n [\n -124.365234375,\n 40.34654412118006\n ],\n [\n -123.74999999999999,\n 38.85682013474361\n ],\n [\n -121.904296875,\n 36.20882309283712\n ],\n [\n -120.62988281249999,\n 34.45221847282654\n ],\n [\n -117.1142578125,\n 32.65787573695528\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rogers, Krysta","contributorId":255719,"corporation":false,"usgs":false,"family":"Rogers","given":"Krysta","email":"","affiliations":[{"id":51652,"text":"Wildlife Investigations Laboratory, California Department of Fish and Wildlife, 1701 Nimbus Road Suite D, Rancho Cordova, CA 95670, U.S.A.;","active":true,"usgs":false}],"preferred":false,"id":812821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mete, Ash","contributorId":255720,"corporation":false,"usgs":false,"family":"Mete","given":"Ash","email":"","affiliations":[{"id":51653,"text":"California Animal Health and Food Safety Laboratory, University of California, Davis, West Health Sciences Drive, Davis, CA 95616, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":812822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ip, Hon S. 0000-0003-4844-7533","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":126815,"corporation":false,"usgs":true,"family":"Ip","given":"Hon S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":812823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torchetti, Mia K.","contributorId":252830,"corporation":false,"usgs":false,"family":"Torchetti","given":"Mia","email":"","middleInitial":"K.","affiliations":[{"id":50437,"text":"US Department of Agriculture – Veterinary Services, Ames, Iowa, USA","active":true,"usgs":false}],"preferred":false,"id":812824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Killian, Mary L.","contributorId":29685,"corporation":false,"usgs":false,"family":"Killian","given":"Mary","email":"","middleInitial":"L.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":812825,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crossley, Beate","contributorId":225109,"corporation":false,"usgs":false,"family":"Crossley","given":"Beate","email":"","affiliations":[{"id":41039,"text":"and California Animal Health and Food Safety Laboratory, University of California Davis, USA","active":true,"usgs":false}],"preferred":false,"id":812826,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218824,"text":"70218824 - 2021 - Comparative morphology of freshwater sculpin inhabiting different environmental conditions in the Chesapeake Bay headwaters","interactions":[],"lastModifiedDate":"2021-04-22T17:43:56.262386","indexId":"70218824","displayToPublicDate":"2021-03-13T06:46:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Comparative morphology of freshwater sculpin inhabiting different environmental conditions in the Chesapeake Bay headwaters","docAbstract":"We compared body morphology of two freshwater sculpin taxa that inhabit distinct environmental conditions in the Chesapeake Bay watershed of eastern North America: Potomac sculpin (C. girardi, Robins; PS) and checkered sculpin (C. sp. cf. girardi; CS). Both taxa are endemic to the study area, but PS are more broadly distributed than CS which are limited to karst groundwater-dominated streams in the central Potomac River basin. We examined preserved specimens from sites encompassing their geographic range (six sites per taxon) to evaluate taxonomic differences and environmental effects. Pelvic fin ray counts and body shape distinguished the study taxa. Morphological variation exhibited stronger relationships to environmental covariates (site elevation and basin size) in PS than CS as expected. In addition, the frequency of specimens with a united median chin pore increased with site elevation in PS (but not CS), suggesting thermal effects on preoperculomanibular canal development. However, contrary to expectation, PS did not exhibit greater among-population variation in body shape than CS, and this indicates the potential importance of unmeasured environmental differences among karst groundwater-dominated streams in the study area. Our study also indicated the utility of stream-level management units for CS, an undescribed species recognized as “critically imperiled” by state agencies.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-021-01078-8","usgsCitation":"Hitt, N.P., Kessler, K.G., Macmillan, H.E., Rogers, K.M., and Raesly, R.L., 2021, Comparative morphology of freshwater sculpin inhabiting different environmental conditions in the Chesapeake Bay headwaters: Environmental Biology of Fishes, v. 104, p. 309-324, https://doi.org/10.1007/s10641-021-01078-8.","productDescription":"16 p.","startPage":"309","endPage":"324","ipdsId":"IP-118788","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":384404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.76123046875,\n 37.19533058280065\n ],\n [\n -74.99267578125,\n 37.19533058280065\n ],\n [\n -74.99267578125,\n 39.791654835253425\n ],\n [\n -77.76123046875,\n 39.791654835253425\n ],\n [\n -77.76123046875,\n 37.19533058280065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"104","noUsgsAuthors":false,"publicationDate":"2021-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812292,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kessler, Karmann G. 0000-0001-5681-4909","orcid":"https://orcid.org/0000-0001-5681-4909","contributorId":242765,"corporation":false,"usgs":true,"family":"Kessler","given":"Karmann","email":"","middleInitial":"G.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Macmillan, Hannah Eisemann 0000-0002-5221-4989","orcid":"https://orcid.org/0000-0002-5221-4989","contributorId":255404,"corporation":false,"usgs":true,"family":"Macmillan","given":"Hannah","email":"","middleInitial":"Eisemann","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812294,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Karli M. 0000-0002-6188-7405","orcid":"https://orcid.org/0000-0002-6188-7405","contributorId":237955,"corporation":false,"usgs":true,"family":"Rogers","given":"Karli","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raesly, Richard L.","contributorId":172208,"corporation":false,"usgs":false,"family":"Raesly","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":13481,"text":"Department of Biology, Frostburg State University, 101 Braddock Road, Frostburg, MD","active":true,"usgs":false}],"preferred":false,"id":812296,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231651,"text":"70231651 - 2021 - Linking altered flow regimes to biological condition: An example using benthic macroinvertebrates in small streams of the Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2022-05-18T15:38:14.741057","indexId":"70231651","displayToPublicDate":"2021-03-12T10:34:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Linking altered flow regimes to biological condition: An example using benthic macroinvertebrates in small streams of the Chesapeake Bay watershed","docAbstract":"Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8–4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7–10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
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timing is a key life-history characteristic that influences fitness and population performance. For migratory animals, however, appropriately timing birth on one seasonal range may be constrained by events occurring during other parts of the migratory cycle. We investigated how the use of capital and income resources may facilitate flexibility in reproductive phenology of migratory mule deer in western Wyoming, USA, over a 5-yr period (2015–2019). Specifically, we examined how seasonal interactions affected three interrelated life-history characteristics: fetal development, birth mass, and birth timing. Females in good nutritional condition at the onset of winter and those that migrated short distances had more developed fetuses (measured as fetal eye diameter in March). Variation in parturition date was explained largely by fetal development; however, there were up to 16 d of plasticity in expected birth date. Plasticity in expected birth date was shaped by income resources in the form of exposure to spring green-up. Although individuals that experienced greater exposure to spring green-up were able to advance expected birth date, being born early or late with respect to fetal development had no effect on birth mass of offspring. Furthermore, we investigated the trade-offs migrating mule deer face by evaluating support for existing theory that predicts that births should be matched to local peaks in resource availability at the birth site. In contrast to this prediction, only long-distance migrants that paced migration with the flush of spring green-up, giving birth shortly after ending migration, were able to match birth with spring green-up. Shorter-distance migrants completed migration sooner and gave birth earlier, seemingly trading off more time for offspring to grow and develop over greater access to resources. Thus, movement tactic had profound downstream effects on birth timing. These findings highlight a need to reconsider classical theory on optimal birth timing, which has focused solely on conditions at the birth site.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3334","usgsCitation":"Aikens, E., Dwinnell, S., LaSharr, T., Jakopak, R., Fralick, G., Randall, J., Kaiser, R., Thonhoff, M., Kauffman, M., and Monteith, K., 2021, Migration distance and maternal resource allocation determine timing of birth in a large herbivore: Ecology, v. 102, no. 6, e03334, 12 p., https://doi.org/10.1002/ecy.3334.","productDescription":"e03334, 12 p.","ipdsId":"IP-125404","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3334","text":"Publisher Index Page"},{"id":396917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.98388671874999,\n 41.91045347666418\n ],\n [\n -109.6875,\n 41.91045347666418\n ],\n [\n -109.6875,\n 43.628123412124616\n ],\n [\n -110.98388671874999,\n 43.628123412124616\n ],\n [\n -110.98388671874999,\n 41.91045347666418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Aikens, Ellen O.","contributorId":288165,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen O.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwinnell, Samantha P.H.","contributorId":288166,"corporation":false,"usgs":false,"family":"Dwinnell","given":"Samantha P.H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaSharr, Tayler N.","contributorId":288167,"corporation":false,"usgs":false,"family":"LaSharr","given":"Tayler N.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jakopak, Rhiannon P.","contributorId":288168,"corporation":false,"usgs":false,"family":"Jakopak","given":"Rhiannon P.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fralick, Gary L.","contributorId":288169,"corporation":false,"usgs":false,"family":"Fralick","given":"Gary L.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":837545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Randall, Jill","contributorId":288170,"corporation":false,"usgs":false,"family":"Randall","given":"Jill","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":837546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaiser, Rusty","contributorId":288171,"corporation":false,"usgs":false,"family":"Kaiser","given":"Rusty","affiliations":[{"id":56194,"text":"fs","active":true,"usgs":false}],"preferred":false,"id":837547,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thonhoff, Mark","contributorId":288174,"corporation":false,"usgs":false,"family":"Thonhoff","given":"Mark","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":837548,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837540,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Monteith, Kevin L.","contributorId":288177,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837549,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70218491,"text":"70218491 - 2021 - Expanding the repertoire of electron acceptors for the anaerobic oxidation of methane in carbonates in the Atlantic and Pacific Ocean","interactions":[],"lastModifiedDate":"2021-09-15T13:30:53.980925","indexId":"70218491","displayToPublicDate":"2021-03-12T08:32:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1956,"text":"ISME Journal","active":true,"publicationSubtype":{"id":10}},"title":"Expanding the repertoire of electron acceptors for the anaerobic oxidation of methane in carbonates in the Atlantic and Pacific Ocean","docAbstract":"Authigenic carbonates represent a significant microbial sink for methane, yet little is known about the microbiome responsible for the methane removal. We identify carbonate microbiomes distributed over 21 locations hosted by seven different cold seeps in the Pacific and Atlantic Oceans by carrying out a gene-based survey using 16S rRNA- and mcrA gene sequencing coupled with metagenomic analyses. Based on 16S rRNA gene amplicon analyses, these sites were dominated by bacteria affiliated to the Firmicutes, Alpha- and Gammaproteobacteria. ANME-1 and -2 archaeal clades were abundant in the carbonates yet their typical syntrophic partners, sulfate-reducing bacteria, were not significantly present. Based on mcrA amplicon analyses, the Candidatus Methanoperedens clades were also highly abundant. Our metagenome analysis indicated that methane oxidizers affiliated to the ANME-1 and -2, may be capable of performing complete methane- and potentially short-chain alkane oxidation independently using oxidized sulfur and nitrogen compounds as terminal electron acceptors. Gammaproteobacteria are hypothetically capable of utilizing oxidized nitrogen compounds and may be involved in syntrophy with methane-oxidizing archaea. Carbonate structures represent a window for a more diverse utilization of electron acceptors for anaerobic methane oxidation along the Atlantic and Pacific Margin.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41396-021-00918-w","usgsCitation":"Beckmann, S., Farag, I.F., Zhao, R., Christman, G., Prouty, N.G., and Biddle, J.F., 2021, Expanding the repertoire of electron acceptors for the anaerobic oxidation of methane in carbonates in the Atlantic and Pacific Ocean: ISME Journal, v. 15, p. 2523-2536, https://doi.org/10.1038/s41396-021-00918-w.","productDescription":"14 p.","startPage":"2523","endPage":"2536","ipdsId":"IP-119545","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41396-021-00918-w","text":"Publisher Index Page"},{"id":385081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2021-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Beckmann, Sabrina","contributorId":224434,"corporation":false,"usgs":false,"family":"Beckmann","given":"Sabrina","email":"","affiliations":[],"preferred":false,"id":811203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farag, Ibrahim F.","contributorId":252951,"corporation":false,"usgs":false,"family":"Farag","given":"Ibrahim","email":"","middleInitial":"F.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":811204,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhao, Rui","contributorId":252952,"corporation":false,"usgs":false,"family":"Zhao","given":"Rui","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":811205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christman, Glenn","contributorId":252954,"corporation":false,"usgs":false,"family":"Christman","given":"Glenn","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":811206,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811207,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Biddle, Jennifer F.","contributorId":224433,"corporation":false,"usgs":false,"family":"Biddle","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":811208,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220543,"text":"70220543 - 2021 - Multiple-scale relationships between vegetation, the wildland–urban interface, and structure loss to wildfire in California","interactions":[],"lastModifiedDate":"2021-05-20T12:08:44.207613","indexId":"70220543","displayToPublicDate":"2021-03-12T08:13:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Multiple-scale relationships between vegetation, the wildland–urban interface, and structure loss to wildfire in California","docAbstract":"Recent increases in destructive wildfires are driving a need for empirical research documenting factors that contribute to structure loss. Existing studies show that fire risk is complex and varies geographically, and the role of vegetation has been especially difficult to quantify. Here, we evaluated the relative importance of vegetation cover at local (measured through the Normalized Difference Vegetation Index) and landscape (as measured through the Wildland–Urban Interface) scales in explaining structure loss from 2013 to 2018 in California—statewide and divided across three regions. Generally, the pattern of housing relative to vegetation better explained structure loss than local-scale vegetation amount, but the results varied regionally. This is likely because exposure to fire is a necessary first condition for structure survival, and sensitivity is only relevant once the fire reaches there. The relative importance of other factors such as long-term climatic variability, distance to powerlines, and elevation also varied among regions. These suggest that effective fire risk reduction strategies may need to account for multiple factors at multiple scales. The geographical variability in results also reinforces the notion that “one size does not fit all”. Local-scale empirical research on specific vegetation characteristics relative to structure loss is needed to inform the most effective customized plan.
","language":"English","publisher":"MDPI","doi":"10.3390/fire4010012","usgsCitation":"Syphard, A.D., Rustigian-Romsos, H., and Keeley, J., 2021, Multiple-scale relationships between vegetation, the wildland–urban interface, and structure loss to wildfire in California: Fire, v. 4, no. 1, 12, 15 p., https://doi.org/10.3390/fire4010012.","productDescription":"12, 15 p.","ipdsId":"IP-127664","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4010012","text":"Publisher Index Page"},{"id":385764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.21142578125,\n 42.01665183556825\n ],\n [\n -124.47509765625,\n 40.49709237269567\n ],\n [\n -123.79394531249999,\n 38.92522904714054\n ],\n [\n -122.49755859375,\n 37.10776507118514\n ],\n [\n -120.4541015625,\n 34.470335121217474\n ],\n [\n -118.32275390624999,\n 33.779147331286474\n ],\n [\n -117.24609374999999,\n 32.58384932565662\n ],\n [\n -114.6533203125,\n 32.76880048488168\n ],\n [\n -114.5654296875,\n 32.93492866908233\n ],\n [\n -114.697265625,\n 33.15594830078649\n ],\n [\n -114.521484375,\n 33.97980872872457\n ],\n [\n -114.08203125,\n 34.252676117101515\n ],\n [\n -114.43359375,\n 34.813803317113155\n ],\n [\n -120.05859375,\n 39.07890809706475\n ],\n [\n -119.99267578124999,\n 42.00032514831621\n ],\n [\n -124.21142578125,\n 42.01665183556825\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Syphard, Alexandra D.","contributorId":8977,"corporation":false,"usgs":false,"family":"Syphard","given":"Alexandra","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":815956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rustigian-Romsos, Heather","contributorId":258207,"corporation":false,"usgs":false,"family":"Rustigian-Romsos","given":"Heather","email":"","affiliations":[{"id":52235,"text":"Conservation Biology Institute, Corvallis, OR 97333, USA","active":true,"usgs":false}],"preferred":false,"id":815957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815958,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218782,"text":"sir20215014 - 2021 - Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020","interactions":[],"lastModifiedDate":"2021-03-15T11:42:02.813757","indexId":"sir20215014","displayToPublicDate":"2021-03-12T08:09:47","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5014","displayTitle":"Extending Seasonal Discharge Records for Streamgage Sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through Water Year 2020","title":"Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020","docAbstract":"Daily mean discharge records are needed for management of selected streams in the Fortymile River Basin. The U.S. Geological Survey, in cooperation with the U.S. Bureau of Land Management, updated a technique for estimating seasonal (partial year) discharge at two short-record streamgage sites in the basin and evaluated the accuracy of the estimates. Daily mean discharge values were estimated for May 15–September 30, 1976–82 and 2006–18, for U.S. Geological Survey streamgage sites 15330000 (North Fork Fortymile River above Middle Fork near Franklin, Alaska) and 15331000 (Middle Fork Fortymile River near mouth near Chicken, Alaska). Relations between discharge for each study streamgage and an index streamgage on the main-stem Fortymile River (15348000, Fortymile River near Steele Creek, Alaska) for concurrent seasonal periods in 2019 and 2020 were developed using the maintenance of variance extension type 3 (MOVE.3) record extension technique. The MOVE.3 regressions were used to estimate daily mean discharges at the study streamgage sites for the selected season for the longer period of record of the index streamgage. Additionally, estimated records were generated from the regressions for the concurrent seasonal periods to evaluate the accuracy of the record extension techniques. The modified Nash-Sutcliffe efficiency coefficients for the estimated records were 0.53 for the North Fork Fortymile River (15330000) and 0.70 for the Middle Fork Fortymile River (15331000) streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215014","collaboration":"Prepared in cooperation with the U.S. Bureau of Land Management","usgsCitation":"Curran, J.H., 2021, Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020: U.S. Geological Survey Scientific Investigations Report 2021–5014, 11 p., https://doi.org/10.3133/sir20215014.","productDescription":"Report: v, 11 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-125266","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":384327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5014/sir20215014.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5014"},{"id":384361,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VCAOEZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Extended seasonal discharge records for selected streamgage sites in the Fortymile River Basin, Alaska, 1976-2020"},{"id":384326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5014/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"North Fork Fortymile River, Middle Fork Fortymile River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -146.46972656249997,\n 62.91523303947614\n ],\n [\n -141.0205078125,\n 62.91523303947614\n ],\n [\n -141.0205078125,\n 65.60387765860433\n ],\n [\n -146.46972656249997,\n 65.60387765860433\n ],\n [\n -146.46972656249997,\n 62.91523303947614\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508