The northern Gulf of Mexico supports a substantial level of oil and gas extraction in marine waters and experiences acute and chronic exposure to marine pollution events. The region also supports a diverse array of breeding and migratory seabirds that are exposed to these pollutants during foraging and other activities. Among the pollutants of highest concern within the region are polycyclic aromatic hydrocarbons (PAHs) which tend to be toxic, carcinogenic, mutagenic, or teratogenic. We assessed PAH loads in blood from adult brown pelicans and from feathers of adults and chicks of brown pelicans in relation to individual (e.g., body condition, sex) and spatial (e.g., breeding location within the Gulf, home range size, migration distance) factors. Of the 24 PAHs assessed, 17 occurred at least once among all samples. There were no PAHs found in chicks that were not also found in adults. Alkylated PAHs occurred more commonly and were measured at higher summed concentrations compared to parent PAHs in all samples, indicating that exposure to oil and/or byproducts of oil may have been a substantial source of PAH contamination for brown pelicans during this study. Within adults, PAHs were more likely to occur, and to increase in concentration, in blood samples of females compared to males, although no difference was found in feather samples. We also found that occurrence of and concentration of PAHs increased in adults that migrated longer distances. In adults and chicks, the background levels of oil and gas development within the region of the colony was not a consistent predictor of the presence of or concentration of PAHs. We also found correlations of PAHs with hematological and biochemical biomarkers that suggested compromised health. Our results indicate that both short- and long-term exposure (i.e., blood and feathers, respectively) are occurring for this species and that even nest-bound chicks can accumulate high levels of PAHs. Long-term tracking of PAHs, as well as an assessment of sublethal effects of PAHs on pelicans, could enhance our understanding of the persistence and effects of this contaminant in the northern Gulf as could increasing the breadth of species studied.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1185659","usgsCitation":"Jodice, P.G., Lamb, J., Satgé, Y., and Perkins, C., 2023, Spatial and individual factors mediate the tissue burden of polycyclic aromatic hydrocarbons in adult and chick brown pelicans in the northern Gulf of Mexico: Frontiers in Ecology and Conservation, v. 11, 1185659, 18 p., https://doi.org/10.3389/fevo.2023.1185659.","productDescription":"1185659, 18 p.","ipdsId":"IP-151107","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":443231,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1185659","text":"Publisher Index Page"},{"id":432212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98,\n 30.4\n ],\n [\n -98,\n 25\n ],\n [\n -82,\n 25\n ],\n [\n -82,\n 30.4\n ],\n [\n -98,\n 30.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Juliet S.","contributorId":340736,"corporation":false,"usgs":false,"family":"Lamb","given":"Juliet S.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":907496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Satgé, Yvan G.","contributorId":340737,"corporation":false,"usgs":false,"family":"Satgé","given":"Yvan G.","affiliations":[{"id":81653,"text":"South Carolina Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":907497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Christopher","contributorId":340739,"corporation":false,"usgs":false,"family":"Perkins","given":"Christopher","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":907498,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241067,"text":"sir20215142 - 2023 - Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models","interactions":[],"lastModifiedDate":"2026-02-23T18:29:12.962569","indexId":"sir20215142","displayToPublicDate":"2023-06-01T13:55:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5142","displayTitle":"Groundwater Residence Times in Glacial Aquifers—A New General Simulation-Model Approach Compared to Conventional Inset Models","title":"Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models","docAbstract":"Groundwater is important as a drinking-water source and for maintaining base flow in rivers, streams, and lakes. Groundwater quality can be predicted, in part, by its residence time in the subsurface, but the residence-time distribution cannot be measured directly and must be inferred from models. This report compares residence-time distributions from four areas where groundwater flow and travel time were simulated with conventional simulation-inset models (IMs) and with a new automated model-construction method called general simulation models (GSMs). The comparison provides an opportunity to explore controls on travel time and improve the methods used in the creation of GSMs. These models can be useful for three main-use cases: (1) rapid testing of relationships that govern groundwater flow and age, (2) generation of consistent examples for training a machine-learning metamodel, and (3) serving as a starting point for more detailed models.
Comparison of the GSMs to IMs indicated a qualified pattern of agreement for residence-time distributions as indicated by the Nash-Sutcliffe efficiency and Spearman’s correlation coefficient. The agreement was best for the median values of the simulated residence times in young fractions of groundwater (defined as the fractions of groundwater in samples less than 65 years old) at the scale of the eight-digit hydrologic-unit code. Generally, the median values of the young fractions in the IMs were correlated with the median values from the GSMs. The relative trends across the four areas also were similar for the other residence-time metrics. The medians of residence-time metrics at finer scales show a fair degree of scatter. The GSM results compared most poorly for median travel times in the older fraction of groundwater (older than 65 years).
The GSM approach is intended as a flexible framework for developing models that can be useful individually as screening tools or collectively to support projects in statistical learning. Although one set of GSM algorithms was presented here, the approach can accommodate many types of data and also different categories of prior information. Comparison of GSMs and IMs suggests ways in which the GSMs, while remaining easy to construct and calibrate, can be improved for estimating groundwater travel times. IMs do not yield exact travel times, and matching GSMs to IMs does not guarantee an improvement; however, IMs provide a convenient benchmark against which to explore relations between physical characteristics of watersheds and the distribution of travel times within them.
This effort was undertaken as part of the National Water Quality Program of the U.S. Geological Survey to assist in determining the susceptibility of groundwater in glacial aquifers to a variety of natural and anthropogenic contaminants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215142","programNote":"National Water Quality Program","usgsCitation":"Starn, J.J., Kauffman, L.J., and Feinstein, D.T., 2023, Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models: U.S. Geological Survey Scientific Investigations Report 2021–5142, 37 p., https://doi.org/10.3133/sir20215142.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112499","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":500447,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114759.htm","linkFileType":{"id":5,"text":"html"}},{"id":413862,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5142/images/"},{"id":413858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5142/coverthb.jpg"},{"id":413859,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.pdf","text":"Report","size":"6.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5142"},{"id":413861,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.XML"},{"id":413863,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HS83JL","text":"USGS data release","linkHelpText":"MODPATH-NWT and MODPATH6 models used to compare a new general simulation model approach with a conventional inset model approach for groundwater residence time in glacial aquifers"},{"id":413860,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215142/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5142"}],"country":"United States","state":"Illinois, Indiana, Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.5,\n 46\n ],\n [\n -89,\n 46\n ],\n [\n -89,\n 41.5\n ],\n [\n -84.5,\n 41.5\n ],\n [\n -84.5,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
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Digital flood-inundation maps for an 8-mile reach of Papillion Creek near Offutt Air Force Base, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Air Force, Offutt Air Force Base. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages Papillion Creek at Fort Crook, Nebr. (station 06610795), and Papillion Creek at Harlan Lewis Road near La Platte, Nebr. (station 06610798). Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System database at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/.
Flood profiles were computed for the 8-mile stream reach by means of a one-dimensional step-backwater model. The model was calibrated by adjusting roughness coefficients to best represent the current (2022) stage-streamflow relation at the Papillion Creek at Fort Crook (station 06610795) streamgage.
The hydraulic model then was used to compute water-surface profiles for 157 scenarios using a combination of stage values in 1-foot (ft) stage intervals that ranged from 27 to 39 ft at the Papillion Creek at Fort Crook (station 06610795) streamgage and from 13.9 to 30.9 ft at the Papillion Creek at Harlan Lewis Road near La Platte (station 06610798) streamgage, as referenced to the local datums. The simulated water-surface profiles then were combined by a geographic information system with a digital elevation model, which had a 3.281-ft grid to delineate the area flooded and water depths at each stage. The availability of these flood-inundation maps, along with information regarding current stage from the USGS streamgages, can provide emergency management personnel and residents with information that is critical for flood response activities and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235054","collaboration":"Prepared in cooperation with the U.S. Air Force, Offutt Air Force Base","usgsCitation":"Strauch, K.R., and Hobza, C.M., 2023, Flood-inundation maps for an 8-mile reach of Papillion Creek near Offutt Air Force Base, Nebraska, 2022: U.S. Geological Survey Scientific Investigations Report 2023–5054, 12 p., https://doi.org/10.3133/sir20235054.","productDescription":"Report: vi, 12 p.; Data Release; Dataset","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-135851","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":417490,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5054/images"},{"id":417652,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235054/full"},{"id":417492,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQIXMN","text":"USGS data release","linkHelpText":"Flood inundation geospatial datasets for Papillion Creek near Offutt Air Force Base, Nebraska"},{"id":417489,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5054/sir20235054.XML"},{"id":417491,"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":417486,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5054/sir20235054.pdf","text":"Report","size":"3.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5054"},{"id":417485,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5054/coverthb.jpg"},{"id":500933,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114763.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nebraska","otherGeospatial":"Offutt Air Force Base, Papillion Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.966667,\n 41.15\n ],\n [\n -95.966667,\n 41.05\n ],\n [\n -95.8667,\n 41.05\n ],\n [\n -95.8667,\n 41.15\n ],\n [\n -95.966667,\n 41.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Impoundments can drastically change the physical and biological characteristics of fluvial systems. Changes in the physical characteristics, such as reductions in flow, increased sediment deposition, and increased surface area, often influence the system’s biological components, including plant, macroinvertebrate, and fish assemblages. In addition to having direct effects on impounded waterbodies, impoundments can also have wide-ranging effects at the watershed scale, particularly on upstream tributary streams. The purpose of this study was to assess the magnitude of these effects. We analyzed historical data from 26 streams distributed across five sub-basins in the Bluff Hills region of the Yazoo Basin, MS, USA. All five major tributary rivers in this region are impounded by large (11,240–26,143 hectares) reservoirs for flood control. We compared fish assemblages in streams located upstream and downstream of the four reservoirs using PERMANOVA, and contrary to expectations, we found no significant differences between the upstream and downstream assemblages. We explore several possible explanations for this discrepancy and suggest that stream assemblage response to impoundment may be nuanced by the regional species pool, the history of stream conditions in the watershed, and the resistance of the streams to periodic disturbances.
","language":"English","publisher":"MDPI","doi":"10.3390/d15060728","usgsCitation":"Faucheux, N.M., Miranda, L.E., Taylor, J.M., and Farris, J.L., 2023, Impact of dams on stream fish diversity: A different result: Diversity, v. 15, no. 6, 728, 14 p., https://doi.org/10.3390/d15060728.","productDescription":"728, 14 p.","ipdsId":"IP-152946","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":443234,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d15060728","text":"Publisher Index Page"},{"id":432156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"eastern Yazoo River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.8,\n 35\n ],\n [\n -91,\n 35\n ],\n [\n -91,\n 33\n ],\n [\n -88.8,\n 33\n ],\n [\n -88.8,\n 35\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-06-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Faucheux, Nicky M.","contributorId":271194,"corporation":false,"usgs":false,"family":"Faucheux","given":"Nicky","email":"","middleInitial":"M.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907448,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907449,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Jason M.","contributorId":212809,"corporation":false,"usgs":false,"family":"Taylor","given":"Jason","email":"","middleInitial":"M.","affiliations":[{"id":38685,"text":"USDA, ARS Sedimentation Lab","active":true,"usgs":false}],"preferred":false,"id":907450,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farris, Jerry L.","contributorId":340681,"corporation":false,"usgs":false,"family":"Farris","given":"Jerry","email":"","middleInitial":"L.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907451,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248413,"text":"70248413 - 2023 - Coastal acidification trends and controls in a subtropical estuary, Tampa Bay, Florida USA","interactions":[],"lastModifiedDate":"2023-09-13T13:17:10.1477","indexId":"70248413","displayToPublicDate":"2023-06-01T09:32:35","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1672,"text":"Florida Scientist","active":true,"publicationSubtype":{"id":10}},"title":"Coastal acidification trends and controls in a subtropical estuary, Tampa Bay, Florida USA","docAbstract":"Many coastal estuaries have experienced declines in pH over the past few decades due to coastal acidification. However, mean monthly water column pH values (collected during daylight hours) have increased in Tampa Bay, Florida over recent decades concurrent with seagrass recovery. We measured changes in carbonate system and water quality variables in Tampa Bay and the near-coastal Gulf of Mexico environment to quantify diurnal to seasonal trends, drivers, and controls of carbonate chemistry; identify exposure periods to low pH conditions; and to examine the potential for seagrasses to buffer acidification in Tampa Bay. Autonomous sensor packages deployed in Tampa Bay and the Gulf of Mexico from December 2017 to June 2020 recorded hourly measurements of seawater temperature, salinity, pressure, pHT (total scale), carbon dioxide (pCO2), dissolved oxygen (DO), and photosynthetically active radiation. Results indicated strong temperature and biological influence on DO, pHT, and pCO2 in Tampa Bay during the dry season, and only weak to moderate correlation of these variables with temperature and salinity during the wet season. Strong influence from biological processes during the wet season was coincident with spring-to-summer periods of maximum seagrass growth rates. Gulf of Mexico results indicated higher pHT and DO, and lower pCO2 than in Tampa Bay, with similar but attenuated seasonal variation. Results suggest potential benefits from seagrass photosynthesis increasing pHT, DO, and decreasing pCO2 in Tampa Bay, and delivery of high pHT, low pCO2 Gulf of Mexico water to Tampa Bay during flood tides. Approximately 30% of pHT and pCO2 data records collected in Tampa Bay were below pHT 7.900 and above pCO2 of 600 μatm, primarily during the wet season, indicating potential for dissolution of carbonate sediments that may also help buffer acidification conditions in Tampa.
","language":"English","publisher":"Florida Academy of Sciences","usgsCitation":"Yates, K.K., Moore, C., Lemon, M.K., Moyer, R.P., Tomasko, D.A., Masserini, R., and Sherwood, E.T., 2023, Coastal acidification trends and controls in a subtropical estuary, Tampa Bay, Florida USA: Florida Scientist, v. 86, no. 2, p. 214-228.","productDescription":"15 p.","startPage":"214","endPage":"228","ipdsId":"IP-122626","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":420720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Gulf of Mexico, Tampa Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.34603301937987,\n 28.090560342743913\n ],\n [\n -83.31697141493213,\n 28.090560342743913\n ],\n [\n -83.31697141493213,\n 27.425867242036304\n ],\n [\n -82.34603301937987,\n 27.425867242036304\n ],\n [\n -82.34603301937987,\n 28.090560342743913\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yates, Kimberly K. 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":214349,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Christopher 0000-0003-3210-4878 csmoore@usgs.gov","orcid":"https://orcid.org/0000-0003-3210-4878","contributorId":149727,"corporation":false,"usgs":true,"family":"Moore","given":"Christopher","email":"csmoore@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lemon, Mitchell K","contributorId":329645,"corporation":false,"usgs":false,"family":"Lemon","given":"Mitchell","email":"","middleInitial":"K","affiliations":[{"id":33877,"text":"CNTS","active":true,"usgs":false}],"preferred":false,"id":882885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moyer, Ryan P.","contributorId":198993,"corporation":false,"usgs":false,"family":"Moyer","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":13560,"text":"Florida Fish and Wildlife Conservation Commission, Eustis, FL","active":true,"usgs":false}],"preferred":false,"id":882817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tomasko, David A.","contributorId":172728,"corporation":false,"usgs":false,"family":"Tomasko","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":882818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Masserini, R. 0000-0002-2841-1819","orcid":"https://orcid.org/0000-0002-2841-1819","contributorId":329644,"corporation":false,"usgs":false,"family":"Masserini","given":"R.","email":"","affiliations":[{"id":78677,"text":"University of Tampa","active":true,"usgs":false}],"preferred":false,"id":882819,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sherwood, Edward T. 0000-0001-5330-302X","orcid":"https://orcid.org/0000-0001-5330-302X","contributorId":150472,"corporation":false,"usgs":false,"family":"Sherwood","given":"Edward","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":882820,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70260395,"text":"70260395 - 2023 - Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska","interactions":[],"lastModifiedDate":"2024-10-31T13:49:21.623893","indexId":"70260395","displayToPublicDate":"2023-06-01T08:39:58","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska","docAbstract":"Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska.
This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm.
We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems.
Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish (Carassius auratus) and the New Zealand mudsnail (Potamopyrgus antipodarum). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (Salvelinus fontinalis), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions.
Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).
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Anjanette","contributorId":345798,"corporation":false,"usgs":false,"family":"Steer","given":"Anjanette","email":"","affiliations":[{"id":82717,"text":"UAA","active":true,"usgs":false}],"preferred":false,"id":917526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osnas, Jeanne","contributorId":345799,"corporation":false,"usgs":false,"family":"Osnas","given":"Jeanne","email":"","affiliations":[{"id":82717,"text":"UAA","active":true,"usgs":false}],"preferred":false,"id":917527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":917528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, Aaron C.","contributorId":210583,"corporation":false,"usgs":false,"family":"Martin","given":"Aaron C.","affiliations":[{"id":38096,"text":"U.S. Fish and Wildlife Service, Alaska Regional Office","active":true,"usgs":false}],"preferred":false,"id":917529,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davis, Tammy","contributorId":345800,"corporation":false,"usgs":false,"family":"Davis","given":"Tammy","email":"","affiliations":[{"id":56688,"text":"adfg","active":true,"usgs":false}],"preferred":false,"id":917530,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kelty, Rachel","contributorId":345801,"corporation":false,"usgs":false,"family":"Kelty","given":"Rachel","email":"","affiliations":[{"id":82717,"text":"UAA","active":true,"usgs":false}],"preferred":false,"id":917531,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243901,"text":"70243901 - 2023 - John Wesley Powell Center for Analysis and Synthesis Newsletter, volume 7, issue 1","interactions":[],"lastModifiedDate":"2023-06-14T14:49:17.688585","indexId":"70243901","displayToPublicDate":"2023-06-01T08:19:23","publicationYear":"2023","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":14459,"text":"John Wesley Powell Center for Analysis and Synthesis Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"John Wesley Powell Center for Analysis and Synthesis Newsletter, volume 7, issue 1","docAbstract":"The John Wesley Powell Center for Synthesis & Analysis is a USGS initiative that aims to foster innovative thinking in Earth system science through collaborative analysis and synthesis of existing data and information. The Powell Center supports working groups that address some of the most pressing and complex questions facing society, such as climate change, biodiversity loss, water scarcity, natural hazards, and human-environment interactions.\n\nIn this newsletter, we highlight some of the recent activities of the Powell Center and its working groups.","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Baron, J., and Bingham, D.J., 2023, John Wesley Powell Center for Analysis and Synthesis Newsletter, volume 7, issue 1: John Wesley Powell Center for Analysis and Synthesis Newsletter, 2 p.","productDescription":"2 p.","ipdsId":"IP-149901","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":417908,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417907,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/powell-center-newsletter-v-7-no-1"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Liford, Amanda N. 0000-0002-6992-2543","orcid":"https://orcid.org/0000-0002-6992-2543","contributorId":257671,"corporation":false,"usgs":true,"family":"Liford","given":"Amanda","email":"","middleInitial":"N.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":873671,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Baron, Jill 0000-0002-5902-6251 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":194124,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":873669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bingham, Demi Jasmine 0009-0001-9597-8882","orcid":"https://orcid.org/0009-0001-9597-8882","contributorId":305721,"corporation":false,"usgs":true,"family":"Bingham","given":"Demi","email":"","middleInitial":"Jasmine","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":873670,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244082,"text":"70244082 - 2023 - Impacts and uncertainties of climate-induced changes in watershed inputs on estuarine hypoxia","interactions":[],"lastModifiedDate":"2023-06-01T13:06:59.504336","indexId":"70244082","displayToPublicDate":"2023-06-01T07:57:12","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Impacts and uncertainties of climate-induced changes in watershed inputs on estuarine hypoxia","docAbstract":"Multiple climate-driven stressors, including warming and increased nutrient delivery, are exacerbating hypoxia in coastal marine environments. Within coastal watersheds, environmental managers are particularly interested in climate impacts on terrestrial processes, which may undermine the efficacy of management actions designed to reduce eutrophication and consequent low-oxygen conditions in receiving coastal waters. However, substantial uncertainty accompanies the application of Earth system model (ESM) projections to a regional modeling framework when quantifying future changes to estuarine hypoxia due to climate change. In this study, two downscaling methods are applied to multiple ESMs and used to force two independent watershed models for Chesapeake Bay, a large coastal-plain estuary of the eastern United States. The projected watershed changes are then used to force a coupled 3-D hydrodynamic–biogeochemical estuarine model to project climate impacts on hypoxia, with particular emphasis on projection uncertainties. Results indicate that all three factors (ESM, downscaling method, and watershed model) are found to contribute substantially to the uncertainty associated with future hypoxia, with the choice of ESM being the largest contributor. Overall, in the absence of management actions, there is a high likelihood that climate change impacts on the watershed will expand low-oxygen conditions by 2050 relative to a 1990s baseline period; however, the projected increase in hypoxia is quite small (4 %) because only climate-induced changes in watershed inputs are considered and not those on the estuary itself. Results also demonstrate that the attainment of established nutrient reduction targets will reduce annual hypoxia by about 50 % compared to the 1990s. Given these estimates, it is virtually certain that fully implemented management actions reducing excess nutrient loadings will outweigh hypoxia increases driven by climate-induced changes in terrestrial runoff.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-20-1937-2023","usgsCitation":"Hinson, K.E., Friedrichs, M.A., Najjar, R.G., Herrmann, M., Bian, Z., Bhatt, G., St-Laurent, P., Tian, H., and Shenk, G.W., 2023, Impacts and uncertainties of climate-induced changes in watershed inputs on estuarine hypoxia: Biogeosciences, v. 20, no. 10, p. 1937-1961, https://doi.org/10.5194/bg-20-1937-2023.","productDescription":"25 p.","startPage":"1937","endPage":"1961","ipdsId":"IP-151711","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":443237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-20-1937-2023","text":"Publisher Index Page"},{"id":417643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay, James River, Potomac River, Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.02714093628992,\n 36.82353526720044\n ],\n [\n -75.9679722985874,\n 37.107188810688925\n ],\n [\n -75.9613980055088,\n 37.27478056969166\n ],\n [\n -75.81018926471236,\n 37.51504137048339\n ],\n [\n -75.52749466235369,\n 37.90511682250509\n ],\n [\n -75.67870340315011,\n 38.4632102094632\n ],\n [\n -75.79704067855599,\n 39.71333987986998\n ],\n [\n -76.07316098783689,\n 39.69310779262992\n ],\n [\n -77.52607975810103,\n 39.01191023782434\n ],\n [\n -77.6707142058197,\n 38.2931354196956\n ],\n [\n -77.756180015835,\n 37.50982650176154\n ],\n [\n -77.41431677577297,\n 37.42111800113112\n ],\n [\n -77.37487101730382,\n 37.31662036563128\n ],\n [\n -77.02643348416402,\n 37.211977253147836\n ],\n [\n -76.81605610566426,\n 37.143881287547615\n ],\n [\n -76.71086741641476,\n 37.09670197901805\n ],\n [\n -76.6779959510242,\n 36.95498773192651\n ],\n [\n -76.5793815548517,\n 36.86562468091324\n ],\n [\n -76.67142165794561,\n 36.74982306374538\n ],\n [\n -76.40187564174332,\n 36.71821041760633\n ],\n [\n -76.02714093628992,\n 36.82353526720044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hinson, Kyle E. 0000-0002-2737-2379","orcid":"https://orcid.org/0000-0002-2737-2379","contributorId":306024,"corporation":false,"usgs":false,"family":"Hinson","given":"Kyle","email":"","middleInitial":"E.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":874433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedrichs, Marjorie A. M. 0000-0003-2828-7595","orcid":"https://orcid.org/0000-0003-2828-7595","contributorId":222588,"corporation":false,"usgs":false,"family":"Friedrichs","given":"Marjorie","email":"","middleInitial":"A. M.","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":874434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Najjar, Raymond G. 0000-0002-3770-2300","orcid":"https://orcid.org/0000-0002-3770-2300","contributorId":261280,"corporation":false,"usgs":false,"family":"Najjar","given":"Raymond","email":"","middleInitial":"G.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":874435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herrmann, Maria","contributorId":198519,"corporation":false,"usgs":false,"family":"Herrmann","given":"Maria","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":874436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bian, Zihao","contributorId":306026,"corporation":false,"usgs":false,"family":"Bian","given":"Zihao","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":874437,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bhatt, Gopal 0000-0002-6627-793X","orcid":"https://orcid.org/0000-0002-6627-793X","contributorId":252963,"corporation":false,"usgs":false,"family":"Bhatt","given":"Gopal","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":874438,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"St-Laurent, Pierre 0000-0002-1700-9509","orcid":"https://orcid.org/0000-0002-1700-9509","contributorId":261288,"corporation":false,"usgs":false,"family":"St-Laurent","given":"Pierre","email":"","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":874439,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tian, Hanqin","contributorId":296449,"corporation":false,"usgs":false,"family":"Tian","given":"Hanqin","affiliations":[{"id":64042,"text":"Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, United States","active":true,"usgs":false}],"preferred":false,"id":874440,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874441,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70244124,"text":"70244124 - 2023 - Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection","interactions":[],"lastModifiedDate":"2023-06-09T15:27:22.153327","indexId":"70244124","displayToPublicDate":"2023-06-01T07:01:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection","docAbstract":"Many data collection efforts and modeling studies have focused on providing accurate estimates of streamflow while fewer efforts have sought to identify when and where surface water is present and the duration of surface water presence in stream channels, hereafter referred to as streamflow permanence. While physically-based hydrological models are frequently used to explore how water quantity may be influenced by various climatic and basin characteristics at local, regional, national, and global extents they are less often used to explore streamflow permanence. Herein, the Watershed Erosion Prediction Project (WEPP) hydrological model is applied to watersheds in the humid H. J. Andrews Experimental Forest (HJA) and watersheds of the arid Willow and Whitehorse creeks (WW), both in Oregon, to simulate daily (WW) and annual (HJA and WW) streamflow permanence. One thousand parameter combinations were tested to calibrate WEPP to observed streamflow in the HJA watersheds and one hundred parameter combinations were tested to calibrate WEPP to observed surface water presence time series data in WW watersheds. When calibrated to observed streamflow, WEPP correctly classified annual streamflow permanence for 83% of HJA stream reaches. In the WW, WEPP simulations correctly classified 63–93% of daily streamflow permanence observations and 59-87% of annual streamflow permanence classifications. Inclusion of a dry-day threshold (the maximum number of days a stream reach could be modeled ‘dry’ but still classified as permanent for the year) improved annual accuracy in three WW watersheds from 2-10%. Parameter sets that produced the best daily accuracies in WW resulted in poor annual accuracies. Results highlight the importance of evaluating physically-based streamflow permanence models on both permanent and nonpermanent streams at daily and annual time scales to ensure evaluation metrics are appropriate for interpretation purposes. Additionally, results suggest that strategic collection of surface water presence observations and streamflow observations may support robust calibration of physically based models to simulate streamflow permanence moving forward.
Two decades of drought in the southwestern USA are spurring concerns about increases in wind erosion, dust emissions, and associated impacts on ecosystems, agriculture, human health, and water supply. Different avenues of investigation into primary drivers of wind erosion and dust have yielded mixed results depending on the spatial and temporal sensitivity of the evidence. We monitored passive aeolian sediment traps from 2017 to 2020 across eighty-one sites near Moab UT to understand patterns of sediment flux. At measurement sites we collated climate, soil, topography and vegetation spatial layers to better understand the context of wind erosion and then combine these data with field observations of land use in models to characterize the influence of cattle grazing, oil and gas well pads, and vehicle/heavy equipment disturbance that potentially drive both exposure of bare soil and increases in erodible sediment supply that increase vulnerability to erosion. Disturbed areas with low soil calcium carbonate content yielded high sediment transport in dry years, but notably areas with little disturbance and low bare soil exposure had much less activity. Cattle grazing had the largest land use association with erosional activity with analyses suggesting that both herbivory and trampling from cattle could be drivers. The amount and distribution of bare soil exposure from new sub-annual fractional cover remote sensing products proved very helpful in mapping erosion, and new predictive maps informed by field data are presented to help depict spatial patterns of wind erosion activity. Our results suggest that despite the magnitude of current droughts, minimizing surface disturbance in vulnerable soils can mitigate a large portion of dust emissions. Results can help managers identify eroding areas where disturbance reduction and soil surface protection measures can be prioritized.
The Taurus porphyry Cu-Mo district contains four mineralized porphyry centers in the eastern interior of Alaska. All four centers were emplaced during a magmatic episode that spanned from ca. 72 to 67 Ma, with seven distinct igneous suites. Each igneous suite resulted in hydrothermal alteration and mineralization, with younger pulses overprinting older pulses. Each magmatic-hydrothermal system is not present at all four mineralized centers. Apart from the Dennison occurrence, each mineralized center records pulses of repeated intermediate-silicic magmatism and associated alteration and mineralization.
Laser ablation-inductively coupled plasma-mass spectrometry U-Pb zircon crystallization ages indicate that an early quartz porphyry dike swarm ranges in age from ca. 71 to 70 Ma and is associated with potassic, sericitic, and propylitic alteration. Quartz latite intrusions were emplaced at ca. 69 Ma and exhibit early sodiccalcic alteration overprinted by potassic, sericitic, and propylitic alteration. The Taurus monzonite suite is cut by quartz latite but yielded an ca. 70 Ma emplacement age and exhibits the largest footprint of potassic and sericitic alteration. Feldspar porphyry dikes were emplaced ca. 69 Ma and have significant tourmaline-bearing potassic and sericitic alteration. This suite was followed by development of an igneous breccia with a monzonitic igneous matrix. Sodic-calcic alteration was associated with the igneous brecciation. A small stock of monzonite was emplaced at ca. 68 Ma causing locally pervasive sericite-tourmaline-pyrite alteration. The youngest suite of magmatism dated in the district is a series of granodiorite porphyry dikes with weak sodic-calcic and propylitic alteration that truncates earlier alteration assemblages.
Mineralization in the district consists of chalcopyrite and molybdenite associated with sugary quartz veins with potassium feldspar and biotite alteration envelopes (A veins). Less common banded quartz-molybdenite veins (B veins) occur with potassium feldspar envelopes. Gold occurs throughout the district and is strongly correlated with copper grade. Sericitic alteration contains lower copper contents and is predominantly associated with quartz-pyrite veins with sericite envelopes (D veins). Pyrrhotite and local arsenopyrite are present in sericitic assemblages. Pyrrhotite also occurs as inclusions in pyrite within D veins.
Magmas across the district exhibit oxidized characteristics, evidenced by the presence of abundant magnetite, rare titanite, and elevated Eu/Eu* and Ce/Ce* in zircon. Zircon Th/U and Yb/Gd compositions suggest a fractionation path controlled by apatite, titanite, and hornblende. Zircon rare earth element ratios and trace element data indicate two distinct batches of magma evolved from mafic parental compositions to monzonite and granodioritic compositions via fractional crystallization. In the early pulse of magma (ca. 72–69 Ma), fractional crystallization was key to ore formation. Earlier, better mineralized suites evolve to less negative Eu anomalies (Eu/Eu* > 0.7), indicating more oxidized and higher-water-pressure conditions evidenced by the suppression of plagioclase crystallization, compared to later, more poorly mineralized suites.
The temporal and spatial evolution of the district was determined from mapping and U-Pb and Re-Os geochronology. Mapping of igneous and hydrothermal assemblages indicates that the locus of the intrusive suites and hydrothermal systems shifted spatially over time, based on the presence of high-temperature (K-silicate–dominant) alteration, which is coincident with the highest Cu and Au grades. The earliest hydrothermal system was centered at Bluff and East Taurus and transitioned to West Taurus during emplacement of the second magmatic suite. Emplacement of the third magmatic suite was centered back at East Taurus, and the fourth and fifth suites were centered at West Taurus. The latest suites were widespread without a core of high-temperature alteration marking a central locus. East Taurus contains the overlap of six of the seven magmatic and hydrothermal suites and has the highest intersected grades and tonnages in the district. The Bluff and Dennison occurrences exhibit fewer igneous suites and hydrothermal assemblages with weak mineralization. Sodic-calcic alteration, common on the deep and distal flanks of porphyry systems, is only present at West Taurus and is indicative of a localized source of high-salinity nonmagmatic fluids.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.4999","usgsCitation":"Kreiner, D.C., Holm-Denoma, C., Pianowski, L., Flood, Z., Stevenson, D.J., Graham, G.E., Vazquez, J.A., and Creaser, R.A., 2023, Geochronology and mapping constraints on the time-space evolution of the igneous and hydrothermal systems in the Taurus Cu-Mo district, eastern Alaska: Economic Geology, v. 118, no. 4, p. 745-778, https://doi.org/10.5382/econgeo.4999.","productDescription":"34 p.","startPage":"745","endPage":"778","ipdsId":"IP-136126","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":435302,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RANVXY","text":"USGS data release","linkHelpText":"U-Pb zircon data for igneous units related to mineralization in the eastern Yukon-Tanana upland, eastern Alaska"},{"id":435301,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P972SF7V","text":"USGS data release","linkHelpText":"Re-Os Geochronologic Data for Porphyry Deposits in the Yukon-Tanana Upland, Eastern Alaska"},{"id":435300,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q6GYMH","text":"USGS data release","linkHelpText":"Zircon Trace Element Data for Igneous Units Related to Mineralization in the Eastern Yukon-Tanana Upland and nearby areas, Eastern Alaska"},{"id":416484,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Yukon Territory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.83742916901255,\n 65.07747914096919\n ],\n [\n -148.81156301688165,\n 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jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":870930,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Creaser, Robert A 0000-0002-7672-035X","orcid":"https://orcid.org/0000-0002-7672-035X","contributorId":304556,"corporation":false,"usgs":false,"family":"Creaser","given":"Robert","email":"","middleInitial":"A","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":870931,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70244145,"text":"70244145 - 2023 - HyWaves: Hybrid downscaling of multimodal wave spectra to nearshore areas","interactions":[],"lastModifiedDate":"2023-06-05T11:25:46.025191","indexId":"70244145","displayToPublicDate":"2023-06-01T06:21:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5979,"text":"Ocean Modeling","active":true,"publicationSubtype":{"id":10}},"title":"HyWaves: Hybrid downscaling of multimodal wave spectra to nearshore areas","docAbstract":"Long-term and accurate wave hindcast databases are often required in different coastal engineering projects. The assessment of the nearshore wave climate is often accomplished by using downscaling techniques to translate offshore waves to coastal areas. However, dynamical downscaling approaches may incur huge computational cost. Additionally, the common use of bulk parameterizations are often not accurate for multidimensional waves. To overcome these limitations, we present a hybrid downscaling approach that combines mathematical algorithms (statistical downscaling) and numerical modeling (dynamical downscaling) over the individual spectral partitions. Every wave partition is downscaled and aggregated afterward by using principles of wave linear theory. By assuming linearity in the propagation of the wave celerity, the application of the method is limited from offshore to intermediate water depths. In addition, the method proposed uses a technique to simplify the spectral boundary conditions in complex domains. The methodology has been applied and validated in the island states of Samoa, American Samoa, Majuro, and Kwajalein, showing good skill at reproducing the spectral hourly time series of significant wave height, peak period, and peak direction. Moreover, an accurate representation of the observed energy spectrum was achieved. This study provides insight into the numerical approximation of the combined sea-swell states while improving the quality of fast spectral forecasting and early warning systems.
Selenium in aquatic ecosystems of the lower Gunnison River Basin in Colorado is affecting the recovery of populations of endangered, native fish species. Dietary exposure is the primary pathway for bioaccumulation of selenium in fish, and particulate selenium can be consumed directly by fish or by the invertebrates on which fish feed. Although selenium can be incorporated into particulate matter via biogeochemical processes, particulate selenium can also enter aquatic ecosystems of the lower Gunnison River Basin from sediments derived from the selenium-rich Mancos Shale. The U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board, conducted this study during 2018–19 to identify sources of selenium-rich suspended sediments from two watersheds underlain by Mancos Shale: Loutsenhizer Arroyo and Sunflower Drain, which is a locally known agricultural drainage near the municipality of Delta, Colorado.
A multipronged approach (fieldwork, laboratory work, and computer modeling) referred to as “sediment fingerprinting” was used to evaluate sources of suspended sediments in the streams flowing out of the two studied watersheds. Four potential source types for suspended sediments were identified and sampled (using soil plugs) within the watersheds: rangelands, agricultural fields, arroyo walls, and streambanks. The sediment fingerprinting approach used elemental concentrations and naturally occurring fallout radionuclides as tracers to apportion percent contributions from the four source types of suspended sediments found in streamflow from both watersheds.
To determine the dominant sources of suspended sediment in streamflow from both watersheds, a mathematical “unmixing” model was used. Unmixing models apportion source percentages to samples of material in which those sources are mixed. These models used elemental and isotopic data in the suspended sediments to unmix them into proportional contributions from source types. The results indicated that arroyo walls and streambanks generally dominated as sources of the suspended sediment. Arroyo walls and streambanks were channel-adjacent sources, with sediments mobilized by water flowing within the stream channel. These sources accounted for greater than 50 percent of suspended sediment in all but one sample and accounted for 100 percent of suspended sediment in 5 of the 11 samples collected. Rangeland and agricultural field sources were located in uplands outside of stream channels and were detected more often during the non-irrigation season. Rangeland and agricultural field sources each were found in 5 of the 11 samples collected. Concentrations of selenium in sediment-source samples were comparatively greater in streambanks and lower in rangelands, with agricultural fields and arroyo walls being intermediate. As a result, source apportionments for particulate selenium skewed towards sources adjacent to stream channels more than for suspended sediments. Water imports for irrigation have changed the hydrology of the watersheds, and a notable fraction of imported water passes through the watersheds rapidly. The rapid flowthrough water during the irrigation season likely contributes heavily to sediment erosion and transport in Loutsenhizer Arroyo and Sunflower Drain, particularly from channel-adjacent sources of sediment. Decreases in irrigation season streamflow, at least in Loutsenhizer Arroyo, may have decreased sediment erosion and transport during the 2018–20 irrigation seasons compared to the 2015–17 seasons.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235056","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board","usgsCitation":"Bern, C.R., Williams, C.A., and Smith, C.G., 2023, Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5056, 32 p., https://doi.org/10.3133/sir20235056.","productDescription":"Report: vii, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132717","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":485917,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114745.htm","linkFileType":{"id":5,"text":"html"}},{"id":417883,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5056"},{"id":417619,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.xml"},{"id":417618,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5056/images"},{"id":417612,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99EZZJK","text":"USGS data release","linkHelpText":"Geochemical and fallout radionuclide data for sediment source fingerprinting studies of the Loutsenhizer Arroyo and Sunflower Drain watersheds in western Colorado"},{"id":417611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5056"},{"id":417610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5056/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Loutsenhizer Arroyo Watershed, Sunflower Drain Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.2032088457371,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.871468271392075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
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Bathymetric data were collected at 10 water-supply lakes in north-central and west-central Missouri by the U.S. Geological Survey (USGS) in cooperation with the Missouri Department of Natural Resources and in collaboration with various local agencies, as part of a multiyear effort to establish or update the surface area and capacity tables for the surveyed lakes. The lakes were surveyed in June and July 2020. Seven of the lakes had been surveyed by the USGS between 2002 and 2007, and the recent surveys were compared to the earlier surveys to document changes in the bathymetric surface and capacity of the lake and produce a bathymetric change map.
Bathymetric data were collected using a high-resolution multibeam mapping system mounted on a boat. Supplemental depth data at two of the lakes were collected in shallow areas with an acoustic Doppler current profiler on a remote-controlled boat. Data points from the various sources were exported at a gridded data resolution appropriate to each lake, either 0.82 foot or 1.64 feet. Data outside the multibeam survey extent and greater than the surveyed water-surface elevation generally were obtained from data collected using aerial light detection and ranging (lidar) point cloud data, except at Holden City Lake. A linear enforcement technique was used to add points to the dataset in areas of sparse data (the upper ends of coves where the water was shallow or aquatic vegetation precluded data acquisition) based on surrounding multibeam and upland data values. The various point datasets were used to produce a three-dimensional triangulated irregular network surface of lake-bottom elevations for each lake. A surface area and capacity table was produced from the three-dimensional surface for each lake showing surface area and capacity at specified lake water-surface elevations. Various quality-assurance tests were conducted to ensure quality data were collected with the multibeam, including beam angle checks and patch tests. Additional quality-assurance tests were conducted on the gridded bathymetric data from the survey, the bathymetric surface created from the gridded data, and the contours created from the bathymetric survey.
If data from a previous bathymetric survey existed at a given lake, a bathymetric change map was generated from the elevation difference between the previous survey and the 2020 bathymetric survey data points. After reconciling any vertical datum disagreement between the previous survey and the 2020 survey, coincident points between the surveys were identified, and a bathymetric change map was generated using the coincident point data.
A decrease in capacity was observed at nearly all the lakes for which a previous survey existed, and the mean bathymetric change between the surveys was positive at all the lakes. The decrease in capacity at the primary spillway elevation ranged from –0.4 percent at Edwin A Pape Lake to 9.4 percent at upper Higginsville Reservoir. The mean bathymetric change ranged from 0.03 foot at Garden City New Lake to 1.75 feet at Harrisonville City Lake, which corresponds to a time-averaged mean bathymetric change ranging from 0.002 foot per year at Garden City New Lake to 0.132 foot per year at Harrisonville City Lake. The computed volumetric sedimentation rate generally ranged from 0.04 to 4.91 acre-feet per year at Garden City New Lake and Holden City Lake, respectively; however, Harrisonville City Lake had a substantially larger volumetric sedimentation rate of 42.7 acre-feet per year, corresponding to the substantial mean bathymetric change of 1.75 feet and combined with the relatively shorter interval between surveys. Harrisonville City Lake also had the second-largest decrease in capacity at the spillway elevation of 5.9 percent. As with the 2019 surveys, some changes observed in the bathymetric change maps likely result from the difference in data collection equipment and techniques between the surveys. Certain apparent erosional features around the perimeter of certain lakes may be the result of wave action or compaction of sediments exposed to air during low-water years, or may indicate an unidentified but systemic error in the older singlebeam echosounder survey data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235046","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Huizinga, R.J., Rivers, B.C., Richards, J.M., and Waite, G.J., 2023, Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in north-central and west-central Missouri, 2020: U.S. Geological Survey Scientific Investigations Report 2023–5046, 52 p., https://doi.org/10.3133/sir20235046.","productDescription":"Report: vii, 52 p.; 9 Plates: 24.00 x 24.00 inches; 2 Data Releases; Dataset","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-137680","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":417113,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BV1H0S","text":"USGS data release","linkHelpText":"Bathymetric and supporting data for various water supply lakes in north-central and west-central Missouri, 2020"},{"id":417114,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5f7784b182ce1d74e7d6c99a","text":"USGS data release","linkHelpText":"USGS 13 arc-second n39w095 1 x 1 degree"},{"id":417109,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5046/sir20235046.pdf","text":"Report","size":"9.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5046"},{"id":417108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5046/coverthb.jpg"},{"id":417590,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235046/full"},{"id":417110,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5046/sir20235046.XML"},{"id":500923,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114744.htm","linkFileType":{"id":5,"text":"html"}},{"id":417116,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://www.sciencebase.gov/catalog/item/4f70ab64e4b058caae3f8def","text":"USGS dataset","linkHelpText":"—Lidar Point Cloud—USGS National Map 3DEP downloadable data collection"},{"id":417112,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2023/5046/downloads","text":"Plates 1–9"},{"id":417111,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5046/images"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.5,\n 40.5\n ],\n [\n -94.5,\n 38.6\n ],\n [\n -93,\n 38.6\n ],\n [\n -93,\n 40.5\n ],\n [\n -94.5,\n 40.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
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Rolla, MO 65401
On December 24-25, 2020, 7.3 to 14.6 cm of rain fell on a large snowpack in the upper Esopus Creek (UEC) watershed in the Catskill Mountains of New York. The resulting flood had an annual exceedance probability (AEP) of 4 to 20% (recurrence intervals of 25 to 5 years) in streams across the watershed, resulted in substantial geomorphic adjustments in some stream channels, and transported the highest sediment concentrations observed since stream restoration projects in the UEC began in 2012. The largest flooding occurred in the Stony Clove Creek subbasin of the UEC which contains 8 sediment and turbidity reduction projects.
The UEC is the primary water source for the Ashokan Reservoir, part of New York City’s unfiltered water-supply system. A network of 16 turbidity-only and 13 suspended sediment and turbidity monitoring stations has been in operation within the UEC since October 2016. One of the primary purposes of this monitoring network is to investigate changes in suspended-sediment concentrations (SSC) and turbidity resulting from sediment and turbidity reduction projects (STRPs) implemented in tributaries to the UEC between 2012 and 2018. During the 2 to 8 years following the installation of the projects and prior to the 2020 flooding, declines in SSC and turbidity were measured at all monitoring sites although there were no flows that exceeded a 50% AEP flood. The flood of December 2020 had a 4-percent AEP at the subbasin outlet (Stony Clove Creek below Ox Clove at Chichester NY, USGS station number 01362370) and provided an opportunity to assess the effectiveness of the STRP following a large flood.
An order of magnitude increase in suspended-sediment concentration per unit discharge was measured at the outlet of the Stony Clove Creek subbasin following the flood. Increased SSC persisted for 3 months throughout the range in discharge and for at least 1 year at high discharges following the flood. The concentration-discharge relation returned to near pre-flood levels at low discharges but continued to remain above pre-flood levels at high discharges for more than 1 year. Mapped bank erosion increased in all Stony Clove subbasins following the flood and increases in stream contact with clay-rich glacial till and lacustrine sediments were greater relative to increases in contact with alluvium. Large increases in sediment concentration were observed where contact with glacial lacustrine material also increased. Minor increases in sediment concentration per unit discharge were measured from stream reaches where STRP were constructed and substantially less erosion was noted within those reaches relative to non-STRP reaches, though some breaches in revetments were noted.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","collaboration":"New York City Department of Environmental Protection","usgsCitation":"Siemion, J., Davis, W., and Bonville, D.B., 2023, Effects of a large flood on sediment and turbidity reduction projects in the Esopus Creek watershed, NY, in Proceedings of SEDHYD2023, St. Louis, MO, May 8-12, 2023, 16 p.","productDescription":"16 p.","ipdsId":"IP-151422","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":418614,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/past/2023Proceedings/100.pdf"},{"id":418621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Esopus Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.25100517394345,\n 42.21443069112155\n ],\n [\n -74.25100517394345,\n 42.052331211522414\n ],\n [\n -74.0569800004709,\n 42.052331211522414\n ],\n [\n -74.0569800004709,\n 42.21443069112155\n ],\n [\n -74.25100517394345,\n 42.21443069112155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Siemion, Jason 0000-0001-5635-6469 jsiemion@usgs.gov","orcid":"https://orcid.org/0000-0001-5635-6469","contributorId":127562,"corporation":false,"usgs":true,"family":"Siemion","given":"Jason","email":"jsiemion@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Wae D.","contributorId":315430,"corporation":false,"usgs":false,"family":"Davis","given":"Wae D.","affiliations":[{"id":68316,"text":"New York City Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":876540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonville, Donald B. 0000-0003-4480-9381","orcid":"https://orcid.org/0000-0003-4480-9381","contributorId":248849,"corporation":false,"usgs":true,"family":"Bonville","given":"Donald","email":"","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":876541,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247803,"text":"70247803 - 2023 - Knowledge gaps, uncertainties, and opportunities regarding the response of the Chesapeake Bay estuary to restoration efforts","interactions":[],"lastModifiedDate":"2023-08-18T12:18:04.306608","indexId":"70247803","displayToPublicDate":"2023-05-31T07:16:15","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Knowledge gaps, uncertainties, and opportunities regarding the response of the Chesapeake Bay estuary to restoration efforts","docAbstract":"As part of the Chesapeake Bay Program's (CBP's) Science and Technical Advisory Committee (STAC) initiative \"Achieving Water Quality Goals in the Chesapeake Bay: An Evaluation of System Response\", an Estuary Working Group was formed to generate an assessment of scientific knowledge gaps, uncertainties, and recent ecosystem changes to consider in light of CBP's impending goal of full implementation of management measures (Total Maximum Daily Load [TMDL] agreements and associated necessary stakeholder actions) that are being finalized and designed to achieve living resource-based nutrient targets by 2025. \n\nThis document summarizes aspects of the knowledge gaps, uncertainties, and associated needs and opportunities relating to our understanding of how the Chesapeake Bay estuary has responded to previously implemented management plans, and what features of the ecosystem may slow or enhance its response to future management actions in the face of expected continuations of climatic change.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Scientific and Technical Advisory Committee Report","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Chesapeake Bay Science and Technical Advisory Committee","collaboration":"University of Maryland Center for Environmental Science; Johns Hopkins University; University of Maryland Eastern Shore; U.S. Environmental Protection Agency; Foundation for Food and Agriculture Research","usgsCitation":"Testa, J.M., Dennison, W., Ball, W.P., Boomer, K., Gibson, D.M., Linker, L.C., Runge, M.C., and Sanford, L., 2023, Knowledge gaps, uncertainties, and opportunities regarding the response of the Chesapeake Bay estuary to restoration efforts, 61 p.","productDescription":"61 p.","ipdsId":"IP-146985","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419926,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419915,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeake.org/stac/wp-content/uploads/2023/05/23-004_Estuary-updated.pdf"}],"country":"United States","otherGeospatial":"Chesapeake Bay estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.63835995141072,\n 40.258031063896965\n ],\n [\n -77.63835995141072,\n 36.5239575992841\n ],\n [\n -75.22240433642538,\n 36.5239575992841\n ],\n [\n -75.22240433642538,\n 40.258031063896965\n ],\n [\n -77.63835995141072,\n 40.258031063896965\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Testa, Jeremy M.","contributorId":244524,"corporation":false,"usgs":false,"family":"Testa","given":"Jeremy","email":"","middleInitial":"M.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":880515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dennison, William C.","contributorId":248356,"corporation":false,"usgs":false,"family":"Dennison","given":"William C.","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":880516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ball, William P.","contributorId":174394,"corporation":false,"usgs":false,"family":"Ball","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":27446,"text":"Johns Hopkins University, Department of Geography and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":880517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boomer, Kathleen","contributorId":328534,"corporation":false,"usgs":false,"family":"Boomer","given":"Kathleen","email":"","affiliations":[{"id":78388,"text":"Foundation for Food and Agriculture Research","active":true,"usgs":false}],"preferred":false,"id":880518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gibson, Deirdre M","contributorId":328535,"corporation":false,"usgs":false,"family":"Gibson","given":"Deirdre","email":"","middleInitial":"M","affiliations":[{"id":78389,"text":"University of Maryland Eastern Shore","active":true,"usgs":false}],"preferred":false,"id":880519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Linker, Lewis C. 0000-0002-3456-3659","orcid":"https://orcid.org/0000-0002-3456-3659","contributorId":252964,"corporation":false,"usgs":false,"family":"Linker","given":"Lewis","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":880520,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":880521,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sanford, Lawrence","contributorId":328536,"corporation":false,"usgs":false,"family":"Sanford","given":"Lawrence","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":880522,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70245770,"text":"70245770 - 2023 - Viewing river corridors through the lens of critical zone science","interactions":[],"lastModifiedDate":"2023-06-27T12:04:16.335511","indexId":"70245770","displayToPublicDate":"2023-05-31T07:01:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Viewing river corridors through the lens of critical zone science","docAbstract":"River corridors integrate the active channels, geomorphic floodplain and riparian areas, and hyporheic zone while receiving inputs from the uplands and groundwater and exchanging mass and energy with the atmosphere. Here, we trace the development of the contemporary understanding of river corridors from the perspectives of geomorphology, hydrology, ecology, and biogeochemistry. We then summarize contemporary models of the river corridor along multiple axes including dimensions of space and time, disturbance regimes, connectivity, hydrochemical exchange flows, and legacy effects of humans. We explore how river corridor science can be advanced with a critical zone framework by moving beyond a primary focus on discharge-based controls toward multi-factor models that identify dominant processes and thresholds that make predictions that serve society. We then identify opportunities to investigate relationships between large-scale spatial gradients and local-scale processes, embrace that riverine processes are temporally variable and interacting, acknowledge that river corridor processes and services do not respect disciplinary boundaries and increasingly need integrated multidisciplinary investigations, and explicitly integrate humans and their management actions as part of the river corridor. We intend our review to stimulate cross-disciplinary research while recognizing that river corridors occupy a unique position on the Earth's surface.
No abstract available.
","conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","usgsCitation":"Keith, M.K., Wallick, J., Stratton Garvin, L.E., and Gordon, G., 2023, Coupled upstream-downstream geomorphic responses to deep reservoir drawdowns at Fall Creek Dam, Oregon, SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference, St. Louis, MO, May 8-12, 2023, 14 p.","productDescription":"14 p.","ipdsId":"IP-147088","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":418000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417999,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.sedhyd.org/2023Program/1/248.pdf"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.4250067091849,\n 46.136623316749194\n ],\n [\n -123.4250067091849,\n 43.631784058558026\n ],\n [\n -121.36046282001558,\n 43.631784058558026\n ],\n [\n -121.36046282001558,\n 46.136623316749194\n ],\n [\n -123.4250067091849,\n 46.136623316749194\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keith, Mackenzie K. 0000-0002-7239-0576 mkeith@usgs.gov","orcid":"https://orcid.org/0000-0002-7239-0576","contributorId":196963,"corporation":false,"usgs":true,"family":"Keith","given":"Mackenzie","email":"mkeith@usgs.gov","middleInitial":"K.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wallick, J. Rose 0000-0002-9392-272X rosewall@usgs.gov","orcid":"https://orcid.org/0000-0002-9392-272X","contributorId":3583,"corporation":false,"usgs":true,"family":"Wallick","given":"J. Rose","email":"rosewall@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875130,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stratton Garvin, Laurel E. 0000-0001-8567-8619 lstratton@usgs.gov","orcid":"https://orcid.org/0000-0001-8567-8619","contributorId":270182,"corporation":false,"usgs":true,"family":"Stratton Garvin","given":"Laurel","email":"lstratton@usgs.gov","middleInitial":"E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gordon, Gabriel W. 0000-0001-6866-0302 ggordon@usgs.gov","orcid":"https://orcid.org/0000-0001-6866-0302","contributorId":269773,"corporation":false,"usgs":true,"family":"Gordon","given":"Gabriel W.","email":"ggordon@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875132,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70245186,"text":"70245186 - 2023 - Water quality at Chaco Culture National Historical Park and the potential effects of hydrocarbon extraction","interactions":[],"lastModifiedDate":"2023-06-21T11:42:56.490152","indexId":"70245186","displayToPublicDate":"2023-05-31T06:37:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Water quality at Chaco Culture National Historical Park and the potential effects of hydrocarbon extraction","docAbstract":"Chaco Culture National Historical Park (CCNHP) is in the San Juan Basin of northwestern New Mexico, U.S.A. Its only water supply is in Gallup Sandstone aquifer, stratigraphically surrounded by layers long targeted for oil and natural gas extraction.
To assess groundwater flow direction, age, mixing between aquifers, and whether hydrocarbons extraction may affect water quality, we completed a geochemical groundwater sampling campaign. Groundwater at 11 sites was analyzed for major ions, hydrocarbon associated volatile organic carbon (VOC) compounds, noble gases, and the isotope systems δ2H, δ18O, 87Sr/86Sr, δ13C, and 14C.
Results demonstrate that all sampled groundwaters are exceedingly old and geochemically evolved, with a median 14C age of ∼41,000 years before present and a north flowing path. Three lines of evidence suggest mixing between aquifers through relatively impermeable shale units and mixing with hydrocarbons: 1) noble gases are fractionated likely through mixing with connate water expelled during hydrocarbon genesis; 2) several wells—including the park’s main supply well—contained trace amounts of hydrocarbon related VOC compounds; and 3) major ion analysis shows mixing trends between aquifers. We hypothesize that cross-aquifer mixing may be facilitated through the region’s numerous hydrocarbon related boreholes. Whether our findings are the result of oil and gas extraction or represent the natural state of the aquifers will require more research.
As scientific understanding of barrier morphodynamics has improved, so has the ability to reproduce observed phenomena and predict future barrier states using mathematical models. To use existing models effectively and improve them, it is important to understand the current state of morphodynamic modeling and the progress that has been made in the field. This manuscript offers a review of the literature regarding advancements in morphodynamic modeling of coastal barrier systems and summarizes current modeling abilities and limitations. Broadly, this review covers both event-scale and long-term morphodynamics. Each of these sections begins with an overview of commonly modeled phenomena and processes, followed by a review of modeling developments. After summarizing the advancements toward the stated modeling goals, we identify research gaps and suggestions for future research under the broad categories of improving our abilities to acquire and access data, furthering our scientific understanding of relevant processes, and advancing our modeling frameworks and approaches.
Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP)−1, TN had a MAE of 0.39 mg (L-TN)−1, and water velocity had a MAE of 0.10 m s−1. The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.
Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, “littoral-cell”-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water-level-corrected, satellite-derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in situ surveyed shorelines (e.g., mean sea-level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20-year hindcast period (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) compared to model calibration with monthly in situ observations at Ocean Beach. When comparing model-predicted shoreline positions to satellite-derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi-decadal simulations of shoreline change due to projected wave and sea-level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. The satellite-data-assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006936","usgsCitation":"Vitousek, S., Vos, K., Splinter, K., Erikson, L.H., and Barnard, P.L., 2023, A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions: JGR Earth Surface, v. 128, no. 7, e2022JF006936, 47 p., https://doi.org/10.1029/2022JF006936.","productDescription":"e2022JF006936, 47 p.","ipdsId":"IP-146968","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443298,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006936","text":"Publisher Index Page"},{"id":435306,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95T9188","text":"USGS data release","linkHelpText":"CoSMoS-COAST: The Coastal, One-line, Assimilated, Simulation Tool of the Coastal Storm Modeling System"},{"id":435305,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CJMB2H","text":"USGS data release","linkHelpText":"Projections of shoreline change for California due to 21st century sea-level rise"},{"id":419397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vos, Kilian","contributorId":317755,"corporation":false,"usgs":false,"family":"Vos","given":"Kilian","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":879266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Splinter, Kristen D.","contributorId":317757,"corporation":false,"usgs":false,"family":"Splinter","given":"Kristen D.","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":879267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879269,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70244016,"text":"70244016 - 2023 - Larval cisco and lake whitefish exhibit high distributional overlap within nursery areas","interactions":[],"lastModifiedDate":"2023-10-11T15:26:46.445116","indexId":"70244016","displayToPublicDate":"2023-05-29T08:22:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Larval cisco and lake whitefish exhibit high distributional overlap within nursery areas","docAbstract":"Coregonine fishes, including lake whitefish (Coregonus clupeaformis) and cisco (C. artedi), are socioecologically important in the Laurentian Great Lakes and of conservation concern, but the processes driving recruitment variability are unclear. In Lake Ontario, cisco and lake whitefish exhibit similar spawning behaviours and early life histories, but population trajectories are diverging. One hypothesis is that sympatric cisco and lake whitefish larvae occupy distinct habitats and experience dissimilar local environmental conditions, despite co-occurrence within nursery areas. We described the spatiotemporal distributions of larval cisco and lake whitefish among multiple Lake Ontario embayment nursery areas, characterised physical habitat features associated with their distributions, determined the degree of spatial habitat partitioning between species and evaluated how habitat niche divergence occurred along an ontogenetic progression. Both species were widely distributed across larval nursery areas, though lake whitefish were less abundant and more narrowly distributed than cisco. Within the yolk sac stage, lake whitefish occupied more nearshore, shallower and colder waters than cisco, indicating potential habitat niche partitioning between congeners. However, distributional differences were subtle and likely driven by differential hatch timing and staggered ontogenetic habitat shifts. Combined, our results illustrate similar habitat use between cisco and lake whitefish through the larval stage and demonstrate that ontogeny and species-specific phenology influence habitat use for these species. This study provides additional evidence that the early life histories of cisco and lake whitefish are highly similar and does not support the hypothesis that larval habitat use differences are a major driver of differential recruitment success for these species.
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