Relations between stage (water level) and discharge of streamflow through a natural channel are the result of time-varying processes, which are commonly described by time-varying stage-discharge ratings. Hydrographers with the U.S. Geological Survey successfully maintain the accuracy of streamflow data by manually applying time-tested approaches to adapt ratings to temporal changes in hydraulic conditions. The difficulty with the manual approach is that it is a subjective, time-consuming process that requires considerable skill and experience to implement. In addition, manual adjustments of ratings make quantification of resulting streamflow data uncertainties problematic. A computer-aided adaptive stage-discharge estimation approach is proposed to track sequential changes in the relation between stage and discharge at continuous-record streamgages. In this report, adaptations are based strictly on discrete measurement data that are then used to compute the magnitudes and uncertainties of streamflow. The approach entails the parameterization of a cubic regression spline (CRS) for the stage-discharge relation based on an existing rating or on a set of discrete measurements. A state-space model is then parameterized to track temporal changes in stage-discharge relations beginning with the initial CRS parameterization using discrete measurements. Finally, Kalman estimation is used with the state-space model to estimate the magnitude and uncertainty of flows. In a case study using data from streamgage U.S. Geological Survey 04122500 Marquette River at Scottville, Michigan, a five-parameter CRS model was estimated from data in an existing stage-discharge rating to provide an initial CRS parameter set for a state-space model. The initial CRS parameters were updated sequentially in a state-space model based on periodic discrete measurements of stage and discharge that spanned a 30-year period for this analysis. Additional analysis is needed to determine the timing of rapidly varying shifts more precisely in stage-discharge relations than the relatively infrequent discrete measurements currently enabled. Unit streamflow estimates based on flow in a local streamgaging network may provide a basis for adapting a stage-discharge rating at unit time intervals by augmenting discrete measurement data within the state-space model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225083","usgsCitation":"Holtschlag, D.J., 2022, A computer-aided approach for adapting stage-discharge ratings and characterizing uncertainties of streamflow data with discrete measurements: U.S. Geological Survey Scientific Investigations Report 2022–5083, 36 p., https://doi.org/10.3133/sir20225083.","productDescription":"viii, 36 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-130821","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407483,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5083/images"},{"id":407482,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5083/sir20225083.XML"},{"id":407480,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5083/coverthb.jpg"},{"id":407481,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5083/sir20225083.pdf","text":"Report","size":"47.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5083"}],"country":"United States","state":"Michigan","otherGeospatial":"Pere Marquette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.20147705078125,\n 43.56845179881218\n ],\n [\n -85.49011230468749,\n 43.56845179881218\n ],\n [\n -85.49011230468749,\n 44.03429525903966\n ],\n [\n -86.20147705078125,\n 44.03429525903966\n ],\n [\n -86.20147705078125,\n 43.56845179881218\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The characteristic pattern of variation in flow magnitude, frequency, duration, timing, and rate of change defines the flow regime of rivers and streams and is a key driver of ecosystem processes in fluvial ecosystems. Understanding how freshwater biotic assemblages change across gradients of hydrology and anthropogenic-source disturbance in different streamflow regimes is crucial to managing for sustainable environmental flows and watershed conservation. We compiled long-term (1916–2016) occurrence records for fishes collected in the Ouachita-Ozark Interior Highlands and West Gulf Coastal Plain streams, together with hydrologic metrics calculated from daily streamflow data measured at USGS stream gauging stations (n = 111), to examine important drivers and thresholds for fish assemblage turnover in groundwater (GW), runoff (RO), and intermittent (INT) flow regimes. We also examined the importance of spatial gradients (latitude, longitude, elevation, drainage area) and anthropogenic-source stressors (Hydrologic Disturbance Index; HDI) for fish assemblage turnover using a gradient forest modeling approach. Watershed fragmentation was of high importance for fish assemblage turnover in RO and INT streams, while changes in dam storage were more important for fishes in GW streams. Hydrologic metrics describing seasonal and stochastic properties of daily streamflow (Mag6) were most important for fish assemblage turnover in INT streams. Timing of high flow events had significantly higher importance compared to flow magnitude, duration, and frequency metrics, especially for fish assemblages in GW and INT streams. The frequency and timing of low flow events had high importance for fish assemblage turnover across all stream flow classes, while the magnitude of low flows and the magnitude and rate of change of average flows was most important for INT stream fish assemblages. In addition to benefiting multi-species conservation and management actions through identification of local and regional flow-ecology relationships generalized across different flow regimes, the results of this study provide a better understanding of complex nonlinear threshold effects, which is critical to anticipating changes in aquatic ecosystems and communities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.109500","usgsCitation":"Fox, J.T., and Magoulick, D.D., 2022, Hydrologic and environmental thresholds in stream fish assemblage structure across flow regimes: Ecological Indicators, v. 144, 109500, 12 p., https://doi.org/10.1016/j.ecolind.2022.109500.","productDescription":"109500, 12 p.","ipdsId":"IP-141446","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.109500","text":"Publisher Index Page"},{"id":433199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.0072909145776,\n 32.996331346931214\n ],\n [\n -91.86367305364405,\n 32.95089330614236\n ],\n [\n -91.50751795508893,\n 33.152929696557536\n ],\n [\n -91.43542283894192,\n 33.983279606945345\n ],\n [\n -91.88620071539745,\n 34.66359118761602\n ],\n [\n -90.24911433667984,\n 36.41777106986588\n ],\n [\n -89.39173105840047,\n 37.127658750904004\n ],\n [\n -90.27870353794154,\n 38.050842204957206\n ],\n [\n -92.57312445398881,\n 38.27074078230794\n ],\n [\n -94.07764032833012,\n 38.04668122401986\n ],\n [\n -94.69043619691729,\n 36.83802911087706\n ],\n [\n -96.4642878616655,\n 35.22823697686589\n ],\n [\n -96.5088412530875,\n 33.78407715154462\n ],\n [\n -95.25898895253292,\n 33.910484415403275\n ],\n [\n -94.09522136802627,\n 33.61028174407495\n ],\n [\n -94.0072909145776,\n 32.996331346931214\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"144","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fox, John Tyler","contributorId":341398,"corporation":false,"usgs":false,"family":"Fox","given":"John","email":"","middleInitial":"Tyler","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908352,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236979,"text":"ofr20221077 - 2022 - Field investigation of sub-isokinetic sampling by the US D-96-type suspended-sediment sampler and its effect on suspended-sediment measurements","interactions":[],"lastModifiedDate":"2026-03-30T20:32:00.626998","indexId":"ofr20221077","displayToPublicDate":"2022-09-27T09:04:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1077","displayTitle":"Field Investigation of Sub-Isokinetic Sampling by the US D-96-Type Suspended-Sediment Sampler and its Effect on Suspended-Sediment Measurements","title":"Field investigation of sub-isokinetic sampling by the US D-96-type suspended-sediment sampler and its effect on suspended-sediment measurements","docAbstract":"Collection of accurate suspended-sediment data using depth-integrating samplers requires that they operate isokinetically, that is, that they sample at the local stream velocity unaffected by the presence of the suspended-sediment sampler. Sub-isokinetic suspended-sediment sampling causes grain-size dependent positive biases in the suspended-sediment concentration measured by the suspended-sediment sampler. Collapsible bag suspended-sediment samplers like the US D-96 and the lighter US D-96-A1 depth-integrating samplers have shown a tendency to sample sub-isokinetically under low stream velocities (below ~3.5 feet per second), colder water temperatures, and longer sampling durations. Previous work concluded that the time-dependent decrease in the intake efficiency of the US D-96-type sampler could be partially overcome by increasing the venting of water from the sampler cavity by shortening the sampler tray. The standard-length sampler tray partially blocks the rear vent hole; shortening the sampler tray effectively increases the area of the sampler-cavity rear vent hole. This previous work showed that removing the partial blockage of the rear vent hole caused by the sampler tray resulted in both an increase in intake efficiency and a decrease in the positive bias in measured suspended-sand concentration.
Herein, a series of tests were conducted on the Colorado River in Arizona using different modifications to a US D-96-A1 sampler to see if physical enlargement of the rear vent hole would produce further improvements in intake efficiency. Results from these tests show that physical enlargement of the rear vent hole, beyond that already effectively achieved by shortening the sampler tray, did not result in any further improvement in intake efficiency. However, these tests also indicated that physically increasing the area of the rear vent hole did not affect the suspended-sediment data collected by the US D-96-A1 sampler. Furthermore, comparisons of suspended-sediment data collected using the US D-96-A1 sampler and the isokinetic US P-61-A1 point-integrating sampler show that the suspended-sediment data collected by the US D-96-type sampler can be accurate in certain circumstances despite the tendency of this sampler to sample sub-isokinetically over the entire depth of a sampling vertical. We surmise that this result could arise from the US D-96-A1 sampler collecting sample isokinetically when the water-sediment mixture enters the nozzle, but that the water-sediment mixture only enters the nozzle intermittently while the sampler transits a sampling vertical.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221077","usgsCitation":"Sabol, T.A., Topping, D.J., Griffiths, R.E., and Dramais, G., 2022, Field investigation of sub-isokinetic sampling by the US D-96-type suspended-sediment sampler and its effect on suspended-sediment measurements: U.S. Geological Survey Open-File Report 2022-1077, 14 p., https://doi.org/10.3133/ofr20221077.","productDescription":"v, 14 p.","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-127691","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501827,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113586.htm","linkFileType":{"id":5,"text":"html"}},{"id":407352,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1077/ofr20221077.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1077"},{"id":407351,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1077/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.58950805664062,\n 36.830722025409784\n ],\n [\n -111.54144287109375,\n 36.830722025409784\n ],\n [\n -111.54144287109375,\n 36.88236678807325\n ],\n [\n -111.58950805664062,\n 36.88236678807325\n ],\n [\n -111.58950805664062,\n 36.830722025409784\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.38714599609375,\n 35.755149755962755\n ],\n [\n -113.33358764648438,\n 35.755149755962755\n ],\n [\n -113.33358764648438,\n 35.777435736805614\n ],\n [\n -113.38714599609375,\n 35.777435736805614\n ],\n [\n -113.38714599609375,\n 35.755149755962755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Passive membrane samplers—semipermeable membrane devices and polar organic chemical integrative samplers—were deployed for 22 continuous days at 7 sites along the West Maui, Hawaiʻi, coastline in February and March 2017 to assess organic contaminants at shallow coral reef ecosystems from diverse upstream inputs. The distribution of organic compounds observed at these coastal sites showed considerable variability; high concentrations of microbially sourced organic compounds observed at all sites, with pentadecane as the predominant normal alkane, showed the relative importance of marine and microbial organic matter to the coastal carbon pool. Pharmaceuticals and personal care products, as well as flame retardants, were also detected at all sites. Of the seven sites sampled, the Kahekili Beach Park site had the highest number of unique contaminants and the Honokōwai Stream site had the highest concentrations of compounds. Two individual compounds, a flame retardant and a fragrance, were ubiquitous across the studied West Maui reefs, including at the least-developed site. A direct correlation to upstream land-use practices or legacy agricultural inputs was not readily observed since polychlorinated biphenyls, pesticides, herbicides, or insecticides were not detected. Results provide a snapshot of relative contaminant abundances as well as inputs to select nearshore environments along the West Maui coastline captured during the 2017 wet season, which was drier than expected. These data can be useful for understanding the range of stressors potentially affecting nearshore ecosystems, such as groundwater inputs and watershed runoff.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221065","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the State of Hawai‘i Department of Health","usgsCitation":"Campbell, P.L., Prouty, N.G., and Storlazzi, C.D., 2022, Pharmaceuticals and personal care products in passive samplers at seven coastal sites off West Maui, Hawaiʻi: U.S. Geological Survey Open-File Report 2022–1065, 14 p., https://doi.org/10.3133/ofr20221065.","productDescription":"Report: vii, 12 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-132890","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501819,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113585.htm","linkFileType":{"id":5,"text":"html"}},{"id":435674,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFE0OP","text":"USGS data release","linkHelpText":"Pharmaceuticals and personal care products measured in passive samplers at seven coastal sites off West Maui during February and March 2017"},{"id":407356,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1065/ofr20221065.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1065"},{"id":407355,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1065/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.708984375,\n 20.78693059257028\n ],\n [\n -156.5826416015625,\n 20.78693059257028\n ],\n [\n -156.5826416015625,\n 21.04349121680354\n ],\n [\n -156.708984375,\n 21.04349121680354\n ],\n [\n -156.708984375,\n 20.78693059257028\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
The semi-arid Walla Walla River Basin (WWRB) spans 1777 square miles in the states of Washington and Oregon and supports a diverse agricultural region as well as cities and rural communities that are partially reliant on groundwater. Historically, surface water and groundwater data have been collected in the WWRB by several entities including federal, state, local, and tribal governments; irrigation districts; universities; and non-profits. This report describes the surface and groundwater data collection by the U.S. Geological Survey from February 2018 to April 2022 to provide the Washington State Department of Ecology and other stakeholders basic knowledge of existing water resources in the WWRB, Washington. Additionally, the data were collected to build a long-term groundwater dataset, with the intent to provide data for better understanding to assist in informed decisions about groundwater use, management, and conservation throughout the basin, and for future inclusion in a conceptual model of the groundwater-flow system (conceptual model). Data were collected and compiled for 237 sites—191 wells and 46 surface-water discharge sites. A small annual network of deep basalt wells was established in February 2018 to commence the data collection. In March 2020 and April 2021, additional field inventories were performed by locating and measuring groundwater wells in the WWRB. A subset of the inventoried wells were selected for an annual or a quarterly water level network to be measured until July 2024. In August 2020, field reconnaissance identified 46 surface-water sites to be measured for discharge, estimating gaining and losing reaches in streams for groundwater influences.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1163","collaboration":"Prepared in cooperation with Washington Department of Ecology","usgsCitation":"Fasser, E.T., and Dunn, S.B., 2022, Groundwater and surface-water data collection for the Walla Walla River Basin, Washington, 2018–22: Data Report 1163, 8 p., https://doi.org/10.3133/dr1163.","productDescription":"Report: vi, 8 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-140692","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":407252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1163/coverthb.jpg"},{"id":407253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1163/dr1163.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1163"},{"id":407254,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1163/images"},{"id":407255,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1163/dr1163.XML"},{"id":407256,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D2C2DK","text":"USGS data release","description":"USGS data release","linkHelpText":"Dataset of groundwater and surface water data collection for the Walla Walla Basin in Washington, 2018–2022"},{"id":407257,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1163/full","text":"Report"},{"id":501271,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113584.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Walla Walla River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.21264648437499,\n 45.62172169252446\n ],\n [\n -117.191162109375,\n 45.62172169252446\n ],\n [\n -117.191162109375,\n 47.092565552235705\n ],\n [\n -119.21264648437499,\n 47.092565552235705\n ],\n [\n -119.21264648437499,\n 45.62172169252446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
In the United States (US), private-supply tapwater (TW) is rarely monitored. This data gap undermines individual/community risk-management decision-making, leading to an increased probability of unrecognized contaminant exposures in rural and remote locations that rely on private wells. We assessed point-of-use (POU) TW in three northern plains Tribal Nations, where ongoing TW arsenic (As) interventions include expansion of small community water systems and POU adsorptive-media treatment for Strong Heart Water Study participants. Samples from 34 private-well and 22 public-supply sites were analyzed for 476 organics, 34 inorganics, and 3 in vitro bioactivities. 63 organics and 30 inorganics were detected. Arsenic, uranium (U), and lead (Pb) were detected in 54%, 43%, and 20% of samples, respectively. Concentrations equivalent to public-supply maximum contaminant level(s) (MCL) were exceeded only in untreated private-well samples (As 47%, U 3%). Precautionary health-based screening levels were exceeded frequently, due to inorganics in private supplies and chlorine-based disinfection byproducts in public supplies. The results indicate that simultaneous exposures to co-occurring TW contaminants are common, warranting consideration of expanded source, point-of-entry, or POU treatment(s). This study illustrates the importance of increased monitoring of private-well TW, employing a broad, environmentally informative analytical scope, to reduce the risks of unrecognized contaminant exposures.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.2c00293","usgsCitation":"Bradley, P., Romanok, K., Smalling, K., Focazio, M.J., Charboneau, R., George, C.M., Navas-Acien, A., O’Leary, M., Red Cloud, R., Zacher, T., Breitmeyer, S.E., Cardon, M.C., Cuny, C.K., Ducheneaux, G., Enright, K., Evans, N., Gray, J., Harvey, D.E., Hladik, M.L., Kanagy, L.K., Loftin, K.A., McCleskey, R., Medlock-Kakaley, E., Meppelink, S.M., Valder, J., and Weis, C.P., 2022, Tapwater exposures, effects potential, and residential risk management in Northern Plains Nations: Environmental Science and Technology Water, v. 2, no. 10, p. 1772-1788, https://doi.org/10.1021/acsestwater.2c00293.","productDescription":"17 p.","startPage":"1772","endPage":"1788","ipdsId":"IP-117867","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":446328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
In spite of overall improvements in air and water quality, biological stress from low pH and high concentrations of inorganic aluminum continue to impact fish and fish habitat in northeastern North America, with independent and interactive effects on individuals, populations and communities. Integrative indicators can therefore be useful in monitoring both impact and recovery across multiple scales. Using coupled water chemistry (pH, conductivity, and base cation and inorganic aluminum concentration), geographic (site elevation and watershed area) and biological (fish diversity, fish abundance, gill aluminum concentration and gill physiology) data, we developed an integrated indicator of acid aluminum stress across the White and Green mountains in central New England, USA. As has been established in a number of previous studies, preliminary analysis clearly indicated that across all sites, inorganic aluminum concentration was consistently greatest during the spring season. Structural Equation modelling (SEM) revealed that toxic conditions (concurrent low pH and high concentrations of inorganic aluminum) were well summarized with an integrated toxicity score, related to both base cation concentrations and elevation, with sites at higher elevations more likely to experience toxic conditions as well as low base cation concentrations. As hypothesized, fish diversity and abundance were negatively related to toxicity score. In spite of considerable variation among individuals, gill aluminum was positively related to toxicity score for both Atlantic salmon and brook trout. Observed elevated gill aluminum levels associated with reduced gill metabolic activity in Atlantic salmon smolts from impacted systems likely result in impaired osmoregulatory function and seawater tolerance. Overall, our results suggest that the integrated toxicity score metric is strongly associated with a syndrome of physiological stress, reduced abundance, and low species diversity for stream fishes in New England and can likely serve as a reliable indicator of continued impairment or recovery of acid-aluminum vulnerable systems in this ecoregion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.109480","usgsCitation":"Zdasiuk, B.J., Chen, C.Y., McCormick, S.D., Nislow, K., Singley, J.G., and Kelly, J.T., 2022, Evaluating acid-aluminum stress in streams of the Northeastern U.S. at watershed, fish community and physiological scales: Ecological Indicators, v. 144, 109480, 12 p., https://doi.org/10.1016/j.ecolind.2022.109480.","productDescription":"109480, 12 p.","ipdsId":"IP-138154","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446353,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.109480","text":"Publisher Index Page"},{"id":408282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, Vermont","otherGeospatial":"Ammonoosuc basin, Merrimack basin, Saco River basin, West River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.80502319335938,\n 42.837709559849614\n ],\n [\n -72.54684448242188,\n 42.837709559849614\n ],\n [\n -72.54684448242188,\n 43.03577208929465\n ],\n [\n -72.80502319335938,\n 43.03577208929465\n ],\n [\n -72.80502319335938,\n 42.837709559849614\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.9879150390625,\n 43.757208878849376\n ],\n [\n -71.08978271484375,\n 43.757208878849376\n ],\n [\n -71.08978271484375,\n 44.5063000997406\n ],\n [\n -71.9879150390625,\n 44.5063000997406\n ],\n [\n -71.9879150390625,\n 43.757208878849376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"144","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zdasiuk, Benjamin J","contributorId":297871,"corporation":false,"usgs":false,"family":"Zdasiuk","given":"Benjamin","email":"","middleInitial":"J","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":854525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Celia Y.","contributorId":145630,"corporation":false,"usgs":false,"family":"Chen","given":"Celia","email":"","middleInitial":"Y.","affiliations":[{"id":16179,"text":"Dartmouth College, Hanover NH","active":true,"usgs":false}],"preferred":false,"id":854526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nislow, Keith H.","contributorId":276357,"corporation":false,"usgs":false,"family":"Nislow","given":"Keith H.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":854528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singley, Joel G","contributorId":297873,"corporation":false,"usgs":false,"family":"Singley","given":"Joel","email":"","middleInitial":"G","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":854529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, John T.","contributorId":212827,"corporation":false,"usgs":false,"family":"Kelly","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":38688,"text":"Department of Biology & Environmental Science, University of New Haven","active":true,"usgs":false}],"preferred":false,"id":854530,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236126,"text":"ofr20211034 - 2022 - Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012:","interactions":[],"lastModifiedDate":"2022-09-26T15:57:24.085486","indexId":"ofr20211034","displayToPublicDate":"2022-09-23T13:33:51","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1034","displayTitle":"Inventory of Eelgrass (Zostera marina) and Seaweeds at the End of the Alaska Peninsula, August–September 2012","title":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012:","docAbstract":"Coastal communities in Alaska are undergoing rapid environmental change from increasing temperatures and baseline data are needed to monitor potential impacts. We conducted the first surveys of the abundance and distribution of eelgrass (Zostera marina) and seaweeds in the western part of Izembek National Wildlife Refuge at the end of the Alaska Peninsula. Six embayments and two offshore islands were surveyed in August–September of 2012. Biotic (percent cover of eelgrass/seaweeds, presence/absences of five sessile invertebrates), and abiotic (water temperature, salinity, and depth) data were recorded at 257 survey points (range =9–74 points per site) across all sites. Twenty-two genera/species of seaweeds were identified at the six embayments. New seaweed species for the offshore islands of Sanak and Caton were added to an existing seaweed collection accessioned at the University of British Columbia Herbarium. We also collected samples of eelgrass to be accessioned at U.S. Geological Survey, Alaska Science Center-Molecular Ecology Laboratory, for future genetic analyses. Fifty-three species of birds and 13 species of mammals were observed and recorded during the survey period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211034","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., Dau, N.C., Lind, O., Payne, K.J., and Lindstrom, S.C., 2022, Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012: U.S. Geological Survey Open-File Report 2021–1034, 14 p., https://doi.org/10.3133/ofr20211034.","productDescription":"Report: iv, 14 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-118597","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405872,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K1ZOMY","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data from eelgrass (Zostera marina), seaweeds and selected invertebrates at six embayments and two islands at the end of the Alaska Peninsula"},{"id":405873,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405874,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"},{"id":405870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1034/coverthb.jpg"},{"id":405871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1034/ofr20211034.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1034"},{"id":405875,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":405876,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":405877,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165.10253906249997,\n 53.98193516209167\n ],\n [\n -161.0595703125,\n 53.98193516209167\n ],\n [\n -161.0595703125,\n 56.19448087726972\n ],\n [\n -165.10253906249997,\n 56.19448087726972\n ],\n [\n -165.10253906249997,\n 53.98193516209167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Eelgrass (<em>Zostera marina</em>) is a highly productive seagrass that plays an essential role in the health of the estuarine and coastal ecosystems; however, information about its abundance and distribution is insufficient in the Bering Sea along the Yukon Delta National Wildlife Refuge. We inventoried the spatial extent and abundance of eelgrass and seaweed in Duchikthluk and Shoal bays on Nunivak Island in July 2010. Using Landsat Thematic Mapper imagery, we estimated the spatial extent of eelgrass to be 1,232 hectares in Duchikthluk Bay and 40 hectares in Shoal Bay. The overall accuracy of the assessments was high (86–87 percent) based on ground truthing using field reference points. We used point-sampling methodology to assess eelgrass abundance relative to the presence of associated seaweeds and selected macro-invertebrates within each of bays. Eelgrass was found at water depths ranging from 0.1 to 2.9 meters across both bays, but the greatest density (>75 percent cover) occurred primarily in moderate to deep water (0.7–1.4 meters) in Duchikthluk Bay and deeper water (>2 meters) in Shoal Bay. The mean aboveground biomass was 39.4±4.0 grams per meter squared in Duchikthluk Bay. The eelgrass biomass was greater (67.6±11.0 grams per meter squared) in Shoal Bay, but this estimate was based on a small sample size (n=3). Seaweeds, representing six species, occurred in low abundance across both bays and were primarily associated with eelgrass. Gastropods were the most common macro-invertebrate, occurring at 45 percent of field points in Duchikthluk Bay.
Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
We conducted a point-sampling survey to determine eelgrass (Zostera marina) and seaweed abundance in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska, in July 2008–10. Eelgrass was known to be abundant in protected embayments of the southeastern Bering Sea and near the Togiak National Wildlife Refuge, but prior to this study, no systematic ground surveys had been conducted in these areas. We determined mean aboveground biomass of eelgrass to be highly variable among years observed, ranging from 32–72 grams dry weight per square meter (g/m2) during successive years in Nanvak Bay and among the studied embayments in 2010: 47±4 g/m2 in Nanvak Bay, 69±7 g/m2 in Chagvan Bay, and 74±15 g/m2 in Goodnews Bay. Seaweed density, abundance, and frequency scores were also highly variable among years and among embayments and were lower for seaweeds than for eelgrass in Nanvak and Chagvan bays, but not in Goodnews Bay. For all bays, mussels (Mytilus spp.) and gastropods were the most common macro-invertebrates detected during surveys, whereas sea stars, crabs, and sponges were not observed in the embayments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201114","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H, Hogrefe, K.R, Swaim, M.A., Donnelly, T.F., and Fairchild, L.L., 2022, Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10: U.S. Geological Survey Open-File Report 2020–1114, 14 p., https://doi.org/10.3133/ofr20201114.","productDescription":"Report: v, 14 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-117779","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405817,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BMFTH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data for eelgrass (Zostera marina) and seaweed distribution and abundance in bays adjacent to the Togiak National Wildlife Refuge, Alaska"},{"id":405818,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":405822,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405815,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1114/coverthb.jpg"},{"id":405816,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1114/ofr20201114.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1114"},{"id":405819,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405820,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"},{"id":405821,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":405823,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"}],"country":"United States","state":"Alaska","otherGeospatial":"Togiak National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.25,\n 58.5\n ],\n [\n -160,\n 58.5\n ],\n [\n -160,\n 59.25\n ],\n [\n -162.25,\n 59.25\n ],\n [\n -162.25,\n 58.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Surveyr
4210 University Driver
Anchorage, Alaska 99508
Declines in the distribution and abundance of seagrasses worldwide have prompted a need for baseline distribution maps of eelgrass (Zostera marina) in Alaska. We used high-resolution digital-color aerial photography and multi-spectral satellite imagery to map the distribution and spatial extent of eelgrass at 21 sites in coastal waters adjacent to Togiak National Wildlife Refuge (TNWR) in northwestern Bristol Bay and southern Kuskokwim Bay. The total spatial extent of eelgrass meadows was estimated to be 6,489 hectare (ha) almost equally divided between Bristol Bay (3,001 ha) and Kuskokwim Bay (3,488 ha). The four largest eelgrass beds occurred in Chagvan Bay (1,933 ha), the north side of Hagemeister Island (1,168 ha), Goodnews Bay (874 ha), and Nanvak Bay (599 ha). This report provides key baseline data useful for establishing a monitoring plan to assess trends in eelgrass along the coast of TNWR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., and Swaim, M.A., 2022, Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska: U.S. Geological Survey Open-File Report 2020–1080, 21 p., https://doi.org/10.3133/ofr20201080.","productDescription":"Report: v, 21 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-114072","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384513,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BMFTH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data for eelgrass (Zostera marina) abundance adjacent to the Togiak National Wildlife Refuge, Alaska"},{"id":384512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1080/ofr20201080.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1080"},{"id":384511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1080/coverthb1.jpg"},{"id":405745,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405747,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":405748,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405746,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384514,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":405749,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"}],"country":"United States","state":"Alaska","otherGeospatial":"Togiak National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.25,\n 58.5\n ],\n [\n -159.75,\n 58.5\n ],\n [\n -159.75,\n 59.25\n ],\n [\n -162.25,\n 59.25\n ],\n [\n -162.25,\n 58.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Eelgrass (Zostera marina) meadows are expansive along the lower Alaska Peninsula, supporting a rich diversity of marine life, yet little is known about their status and trends in the region. We tested techniques to inventory and monitor trends in the spatial extent and abundance of eelgrass in lagoons of the Izembek National Wildlife Refuge. We determined if Landsat imagery could be used to assess eelgrass spatial extent in shallow (less than 4 meter water depth) coastal waters of the refuge. We determined that this seagrass could be differentiated using Landsat imagery from other cover types (that is, channels and unvegetated tidal flats) with a high degree of accuracy (greater than 80 percent) in Izembek and Kinzarof Lagoons. Eelgrass meadows represented the largest cover type in Izembek (about 16,000 hectares) and Kinzarof (about 900 hectares) Lagoons, comprising between 45 and 50 percent of the spatial extent of these lagoons, respectively. When compared to estimates of spatial extent of eelgrass from previous studies, our results suggest little change in the spatial extent of eelgrass in Izembek Lagoon during the 28-year period 1978 through 2006. Preliminary mapping of eelgrass in other embayments indicated that this seagrass was also expansive in Big Lagoon (about 900 hectares; or 34 percent of the lagoon area) and Hook Bay (about 900 hectares; or 36 percent of the bay area) but not in Cold Bay (about 100 hectares; less than 5 percent of the bay area). We conducted an embayment-wide point sampling technique to assess aboveground biomass and distribution of eelgrass and seaweeds and presence of six macro-invertebrates during a 4-year period (2007–10). We determined that, when present, mean aboveground biomass of eelgrass was greater in Kinzarof Lagoon (182.5 plus or minus 12.1 grams dry weight per square meter) than in Izembek Lagoon (152.1 plus or minus 7.1 grams dry weight per square meter) in 2008–10, possibly reflecting the warmer sea temperatures and higher salinities found on the Gulf of Alaska side of the Alaska Peninsula. Seaweeds were more abundant in Kinzarof Lagoon than in Izembek Lagoon, surpassing aboveground biomass of eelgrass in both lagoons in 2008. Gastropods (4 percent of all points) and Caprella shrimp (25 percent) were the most common of the six macro-invertebrates surveyed in Izembek Lagoon, and Telmessus crab was the most common macro-invertebrate in Kinzarof Lagoon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201035","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly,T.F., Fairchild, L.L., Sowl, K.M., and Lindstrom, S.C., 2022, Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10: U.S. Geological Survey Open-File Report 2020–1035, 30 p., https://doi.org/10.3133/ofr20201035.","productDescription":"Report: vi, 30 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-112900","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZUDIOH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling for eelgrass (Zostera marina) and seaweeds in embayments adjacent to the Izembek National Wildlife Refuge, Alaska"},{"id":384515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":435682,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XNSWES","text":"USGS data release","linkHelpText":"Sampling Data for Eelgrass (Zostera marina) in Norma Bay, Izembek Lagoon, Alaska, 1987"},{"id":405752,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405751,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":379325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1035/ofr20201035.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1035"},{"id":405754,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"},{"id":405753,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1035/coverthb.jpg"},{"id":405750,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.37081909179688,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.00755132274014\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Seasonal precipitation affects carbon turnover and methane accumulation in karst subterranean estuaries, the region of coastal carbonate aquifers where hydrologic and biogeochemical processes regulate material exchange between the land and ocean. However, the impact that tropical cyclones exert on subsurface carbon cycling within karst landscapes is poorly understood. Here, we present 5-month-long hydrologic and chemical records from 1 and 2 km inland from the coastline within the Ox Bel Ha Cave System in the northeastern Yucatan Peninsula. The record encompasses wet and dry seasons and includes the impact of rainfall during the development of Tropical Storm Hanna in October 2014. Methane accumulated in highest concentrations at the inland site, especially during the wet season preceding the storm. Intense rainfall led to episodic increases in water level and salinity shifts at both sites, indicating a spatially widespread hydrologic response. The most profound storm effect was a ~ 0.8 mg L−1 pulse of dissolved oxygen that declined to zero within 2 weeks and corresponded with a reduction of methane. A positive shift in methane's stable carbon isotope content from −62.6‰ ± 0.6‰ before the storm to −44.0‰ ± 2.4‰ after the storm indicates microbial methane oxidation was a mechanism for the loss of groundwater methane. Post-storm methane concentrations did not recover to pre-storm levels during the observation period, suggesting tropical cyclones have long-lasting (months) effects on the carbon cycle. Compared to seasonal effects, mixing and oxygen inputs during storm-induced hydrologic forcing have an outsized biogeochemical influence within stratified coastal aquifers.
Animals that regularly traverse habitat extremes between the subtropics and subarctic are expected to exhibit foraging behaviors that respond to changes in dynamic ocean habitats, and these behaviors may facilitate adaptations to novel and changing climates. During the chick-provisioning stage, Laysan albatross Phoebastria immutabilis parents regularly undertake short- and long-distance foraging trips throughout the vast central North Pacific Ocean. We examined GPS tracking data among chick-provisioning albatrosses in Hawai‘i to characterize habitats during short- and long-distance trips. The study period encompassed a marine heatwave (2014) and the cooling period after an extreme El Niño event (2016), enabling us to examine foraging habitats under novel and changing climates. First passage time and generalized additive mixed models indicated that during 183 short and 110 long trips (n = 32 birds), wind-assisted flight efficiency, proximity to productive areas, and moonlit-searching were important in both subtropical and subarctic habitats. Laysan albatross took foraging trips that had similar lengths and durations in 2014 and 2016 and visited similar areas, indicating that their foraging range did not expand in response to climatic variability. A strategy that uses similar foraging areas across years combined with reliance on environmental processes that enhance flight efficiency (wind) and that enable searching behaviors (moonlight) indicate that Laysan albatross exhibit complex behavioral plasticity that allows them to utilize subtropical and subarctic habitats affected by dynamic climate variability. This strategy may benefit their ability to respond to oceanographic and climatic change, including expanding warm water regions and changing atmospheric conditions influenced by global warming.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps14148","usgsCitation":"Gilmour, M.E., Felis, J.J., Hester, M.M., Young, L.C., and Adams, J., 2022, Laysan albatross exhibit complex behavioral plasticity in the subtropical and subarctic North Pacific Ocean: Marine Ecology Progress Series, v. 697, p. 125-147, https://doi.org/10.3354/meps14148.","productDescription":"23 p.","startPage":"125","endPage":"147","ipdsId":"IP-137932","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14148","text":"Publisher Index Page"},{"id":409685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Hawaii","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 35.095301697524135\n ],\n [\n -177.86550682042343,\n 28.860574564487507\n ],\n [\n -155.53062317940885,\n 20.809565675106214\n ],\n [\n -147.22958348721264,\n 19.95935891124141\n ],\n [\n -134.00475959018274,\n 35.360858467785576\n ],\n [\n -136.90695297728425,\n 50.99805127330757\n ],\n [\n -142.66136789996617,\n 59.0913410074362\n ],\n [\n -147.22441346291703,\n 60.36962329343831\n ],\n [\n -154.7675208704121,\n 56.641133852727194\n ],\n [\n -176.45903611138576,\n 54.11395665374428\n ],\n [\n -178,\n 52.825004330008824\n ],\n [\n -179.9,\n 35.095301697524135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"697","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilmour, Morgan Elizabeth 0000-0002-2618-1095","orcid":"https://orcid.org/0000-0002-2618-1095","contributorId":289509,"corporation":false,"usgs":true,"family":"Gilmour","given":"Morgan","email":"","middleInitial":"Elizabeth","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hester, Michelle M. 0000-0002-0769-5904","orcid":"https://orcid.org/0000-0002-0769-5904","contributorId":197785,"corporation":false,"usgs":false,"family":"Hester","given":"Michelle","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":857664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Lindsay C.","contributorId":149044,"corporation":false,"usgs":false,"family":"Young","given":"Lindsay","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":857665,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857666,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256675,"text":"70256675 - 2022 - Foraging ecology of Red-billed Tropicbird Phaethon aethereus in the Caribbean during early chick rearing revealed by GPS tracking","interactions":[],"lastModifiedDate":"2024-08-13T11:18:30.441826","indexId":"70256675","displayToPublicDate":"2022-09-22T06:14:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2675,"text":"Marine Ornithology: Journal of Seabird Research and Conservation","onlineIssn":"2074-1235","printIssn":"1018-3337","active":true,"publicationSubtype":{"id":10}},"title":"Foraging ecology of Red-billed Tropicbird Phaethon aethereus in the Caribbean during early chick rearing revealed by GPS tracking","docAbstract":"Urban forests have largely been overlooked for the role they play in reducing stormwater runoff volume by using hydrologic processes such as interception (rainfall intercepted by tree canopy), evapotranspiration (the transfer of water from vegetation into the atmosphere) and infiltration (percolation of rainwater into the Earth’s soil). Early research into the effects of trees on urban stormwater runoff used simple estimates based on assumptions of canopy coverage and design storm criteria. In a review of available literature on how capable urban trees are at reducing runoff, the Center for Watershed Protection (2017) found only six studies; three of them used measured data from a single plot, and the other three used models. When identifying gaps in research on the role of trees in stormwater management, Kuehler and others (2017) highlighted the need for studies that scale the local effects of urban trees to the larger sewershed catchment area, allowing a more holistic understanding of the urban tree canopy effects on hydrology.
For these reasons, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, U.S. Forest Service, and the University of Wisconsin, quantified the effect of removing urban street trees and their canopy on stormwater generation in a medium-density residential area. Using a paired-catchment experimental design, rainfall-runoff relations were characterized in two medium-density residential catchments in Fond du Lac, Wisconsin, during May through September in 2018–20. Results of the study are detailed in Selbig and others (2022).
During the calibration phase, hydrograph metrics from paired runoff events were used to develop the relation between the control and test catchments with street trees in place. The ability to measure changes to the rainfall-runoff response after removal of tree canopy was made possible by an aggressive tree removal program by the city as a response to rapid infestation from the Agrilus planipennis (emerald ash borer). In March 2020, a total of 31 street trees were removed at the onset of the treatment period, resulting in a loss of 2,990 square meters of canopy over streets, driveways, sidewalks, and grassed areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223074","usgsCitation":"Selbig, W.R., Loheide, S.P., II, Shuster, W., Scharenbroch, B.C., Coville, R.C., Kruegler, J., Avery, W., Haefner, R., and Nowak, D., 2022, Loss of street tree canopy increases stormwater runoff: U.S. Geological Survey Fact Sheet 2022–3074, 4 p., https://doi.org/10.3133/fs20223074.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141242","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407081,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.XML"},{"id":407082,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3074/images"},{"id":407157,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20223074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":407079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3074/coverthb.jpg"},{"id":407080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.pdf","text":"Report","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3074"},{"id":501510,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113526.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.494873046875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.72148995228582\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The wing molt is an important annual life-history event that occurs in waterfowl and molt site selection can play an important role in determining survival. We tracked postbreeding movements of gadwall (Mareca strepera) and mallard (Anas platyrhynchos) females that bred in the Suisun Marsh (Suisun) of California, USA, to determine molt site selection and wing molt chronology. We attached backpack transmitters with global positioning system and global system for mobile communications (GPS-GSM) technology to female gadwalls and mallards within Suisun and tracked the birds following the breeding season during 2015–2018. We determined molt locations for 52 female gadwalls and 112 female mallards. Thirty of the marked gadwall females selected 2 regions within southern Oregon-northeastern California (SONEC) to undergo molt; 16 molted in the Upper Klamath Basin (southern OR) and 14 in the Lower Klamath Basin (northeastern CA). A large portion of female mallards molted in Suisun (n = 34) and the Sacramento Valley in California (n = 31) but also used the Upper Klamath Basin (n = 13), Lower Klamath Basin (n = 12), and the Yolo–Delta region in California (n = 12). On average, gadwalls departed Suisun on 30 July (±17.82 days [SD]), and mallards departed on 24 July (±22.69 days). Mean start date of molt for each species was similar: 27 August (±16.09 days) for gadwalls and 26 August (±21.03 days) for mallards. Molt end date was analogous for each species as well. Molt ended on average 1 October (±15.52 days) for gadwalls and on 5 October (±18.34 days) for mallards. Gadwalls and mallards showed intraspecific differences in average molt start and end date within the 3 main geographical zones: Suisun, Central Valley of California (Central Valley), and SONEC. Mean duration of wing molt for gadwalls was 34.72± 8.62 days and 41.09 ± 12.54 days for mallards. Both species primarily selected permanent marsh to undergo wing molt (gadwalls = 90.4%, mallards = 63.4%). Conservation and active management of these high-use molting areas used by California's primary breeding waterfowl species could enhance postbreeding survival, leading to increased breeding waterfowl populations.
A pipeline carrying unconventional oil and gas (OG) wastewater spilled approximately 11 million liters of wastewater into Blacktail Creek, North Dakota, USA. Flow of the mix of stream water and wastewater down the channel resulted in storage of contaminants in the hyporheic zone and along the banks, providing a long-term source of wastewater constituents to the stream. A multi-level investigation was used to assess the potential effects of oil and brine spills on aquatic life. In this study, we used a combination of experiments using a native fish species, Fathead Minnow (Pimephales promelas), field sampling of the microbial community structure, and measures of estrogenicity. The fish investigation included in situ experiments and experiments with collected site water. Estrogenicity was measured in collected site water samples, and microbial community analyses were conducted on collected sediments. During the initial post-spill investigation, February 2015, performing in situ fish bioassays was impossible because of ice conditions. However, microbial community (e.g., the presence of members of the Halomonadaceae, a family that is indicative of elevated salinity) and estrogenicity differences were compared to reference sites and point to early biological effects of the spill. We noted water column effects on in situ fish survival 6 months post-spill during June 2015. At that time, total dissolved ammonium (sum of ammonium and ammonia, TAN) was 4.41 mg NH4/L with an associated NH3 of 1.09 mg/L, a concentration greater than the water quality criteria established to protect aquatic life. Biological measurements in the sediment defined early and long-lasting effects of the spill on aquatic resources. The microbial community structure was affected during all sampling events. Therefore, sediment may act as a sink for constituents spilled and as such provide an indication of continued and cumulative effects post-spill. However, lack of later water column effects may reflect pulse hyporheic flow of ammonia from shallow ground water. Combining fish toxicological, microbial community structure and estrogenicity information provides a complete ecological investigation that defines potential influences of contaminants at organismal, population, and community levels. In general, in situ bioassays have implications for the individual survival and changes at the population level, microbial community structure defines potential changes at the community level, and estrogenicity measurements define changes at the individual and molecular level. By understanding effects at these various levels of biological organization, natural resource managers can interpret how a course of action, especially for remediation/restoration, might affect a larger group of organisms in the system. The current work also reviews potential effects of additional constituents defined during chemistry investigations on aquatic resources.
Aggregation processes control both the residence time and dispersal of volcanic ash during eruptions yet remain incompletely understood. The products of aggregation vary from simple ash clusters to large, complexly layered accretionary lapilli. Here we detail the micro-stratigraphy of a single population of accretionary lapilli that grew during the ∼431 CE Tierra Blanca Joven eruption from Ilopango Caldera, El Salvador. The accretionary lapilli were sampled 10 km from the caldera source within a sequence of ash-rich pyroclastic density current deposits and intercalated fall material, known as unit D, which is traceable >40 km from Ilopango. Scanning electron microscopy and image analysis reveal common facies that form distinct layers within the accretionary lapilli. Each facies is distinguished by quantitative and qualitative variations in particle size distribution, porosity, and particle fabric. We infer that these textures resulted from aggregation conditions that differed in terms of liquid water availability, particle concentration and grain size distributions. In our proposed model, a characteristic sequence of facies accreted from core to rim in the accretionary lapilli during passage through ash clouds generated by vent-derived plumes and pyroclastic density currents. The accretionary lapilli are mostly composed of smaller aggregates (ash clusters, ash pellets) and grew predominantly by accretion of already-formed aggregates, rather than by grain-by-grain accretion of individual particles. This finding is consistent with observations of rapid aggregate growth in volcanic plumes, suggesting a common evolutionary pathway for accretionary lapilli formation across diverse eruptions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107670","usgsCitation":"Hoult, H., Brown, R., Van Eaton, A.R., Hernandez, W., Dobson, K., and Woodward, B., 2022, Growth of complex volcanic ash aggregates in the Tierra Blanca Joven eruption of Ilopango Caldera, El Salvador: Journal of Volcanology and Geothermal Research, v. 431, 107670, 14 p., https://doi.org/10.1016/j.jvolgeores.2022.107670.","productDescription":"107670, 14 p.","ipdsId":"IP-130466","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467162,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dro.dur.ac.uk/37183/","text":"Publisher Index Page"},{"id":466640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"El Salvador","otherGeospatial":"Ilopango caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.5,\n 13.9\n ],\n [\n -89.5,\n 13.5\n ],\n [\n -88.9,\n 13.5\n ],\n [\n -88.9,\n 13.9\n ],\n [\n -89.5,\n 13.9\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"431","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoult, Henry","contributorId":349109,"corporation":false,"usgs":false,"family":"Hoult","given":"Henry","affiliations":[{"id":68342,"text":"Durham University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":924017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Richard J.","contributorId":191216,"corporation":false,"usgs":false,"family":"Brown","given":"Richard J.","affiliations":[],"preferred":false,"id":924018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hernandez, Walter","contributorId":218214,"corporation":false,"usgs":false,"family":"Hernandez","given":"Walter","email":"","affiliations":[{"id":39782,"text":"Ministerio de Medio Ambiente y Recursos Naturales, San Salvador, El Salvador","active":true,"usgs":false}],"preferred":false,"id":924020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobson, Katherine J","contributorId":349112,"corporation":false,"usgs":false,"family":"Dobson","given":"Katherine J","affiliations":[{"id":68342,"text":"Durham University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":924021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Woodward, Bryan","contributorId":349113,"corporation":false,"usgs":false,"family":"Woodward","given":"Bryan","affiliations":[{"id":68342,"text":"Durham University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":924022,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236825,"text":"dr1162 - 2022 - Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2021","interactions":[],"lastModifiedDate":"2026-03-18T19:31:00.697785","indexId":"dr1162","displayToPublicDate":"2022-09-20T10:17:13","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1162","displayTitle":"Water-Level Data for the Albuquerque Basin and Adjacent Areas, Central New Mexico, Period of Record Through September 30, 2021","title":"Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2021","docAbstract":"The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained primarily from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of water year 2021, the network consisted of 120 wells and piezometers at 54 locations. The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels at the wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at the sites through water year 2021 (October 1, 2020, through September 30, 2021). Water-level data that were collected in previous water years from wells that were later discontinued were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1162","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Bell, M.T., and Montero, N.Y., 2022, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2021: U.S. Geological Survey Data Report 1162, 43 p., https://doi.org/10.3133/dr1162.","productDescription":"Report: iv, 43 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-138357","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":406961,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1162/coverthb.jpg"},{"id":501270,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113524.htm","linkFileType":{"id":5,"text":"html"}},{"id":407061,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1162/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406967,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":406966,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1162/images"},{"id":406965,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1162/dr1162.XML"},{"id":406963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1162/dr1162.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1162"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin and adjacent areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.2540283203125,\n 33.95247360616282\n ],\n [\n -106.248779296875,\n 33.95247360616282\n ],\n [\n -106.248779296875,\n 35.51434313431818\n ],\n [\n -107.2540283203125,\n 35.51434313431818\n ],\n [\n -107.2540283203125,\n 33.95247360616282\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Nest predation is the main cause of nest failure for ducks. Understanding how habitat features influence predator movements may facilitate management of upland and wetland breeding habitats that reduces predator encounter rates with duck nests and increases nest survival rates. For 1618 duck nests, nest survival increased with distance to phragmites (Phragmites australis), shrubs, telephone poles, human structures, and canals, but not for four other habitat features. Using GPS collars, we tracked 25 raccoons (Procyon lotor) and 16 striped skunks (Mephitis mephitis) over 4 years during waterfowl breeding and found marked differences in how these predators were located relative to specific habitat features; moreover, the probability of duck nests being encountered by predators differed by species. Specifically, proximity to canals, wetlands, trees, levees/roads, human structures, shrubs, and telephone poles increased the likelihood of a nest being encountered by collared raccoons. For collared skunks, nests were more likely to be encountered if they were closer to canals, trees, and shrubs, and farther from wetlands and human structures. Most predator encounters with duck nests were attributable to a few individuals; 29.2% of raccoons and 38.5% of skunks were responsible for 95.6% of total nest encounters. During the central span of duck nesting (April 17–June 14: 58 nights), these seven raccoons and five skunks encountered >1 nest on 50.8 ± 29.2% (mean ± SD) and 41.5 ± 28.3% of nights, respectively, and of those nights individual raccoons and skunks averaged 2.60 ± 1.28 and 2.50 ± 1.09 nest encounters/night, respectively. For collared predators that encountered >1 nest, a higher proportion of nests encountered by skunks had evidence of predation (51.9 ± 26.6%) compared to nests encountered by raccoons (22.3 ± 17.1%). Because duck eggs were most likely consumed as raccoons and skunks opportunistically discovered nests, managing the habitat features those predators most strongly associated with could potentially reduce rates of egg predation.