Urban forests are recognized as a nature-based solution for stormwater management. This study assessed the underlying processes and extent of runoff reduction due to street trees with a paired-catchment experiment conducted in two sewersheds of Fond du Lac, Wisconsin. Computer models are flexible, fast, and low-cost options to generalize and assess the hydrologic processes determined in field studies. A state-of-the-art, public-domain model, which explicitly simulates urban tree hydrology, i-Tree Hydro, was used to simulate the paired-catchment experiment, and results from field observations and simulation predictions were compared to assess model validity and suitability as per conditions in the broader Great Lakes basin. Model parameters were aligned with observed conditions using automatic and manual calibration. Model performance metrics were used to quantify the weekly performance of calibration and to validate predictions. Those calibration metrics differed substantially between the two periods simulated, but most calibration metrics remained positive, indicating the model was not fitting only the period used for calibration. Predicted avoided runoff for a five-month leaf-on period was 64 L/m2 of canopy, 4 % lower than the field-estimated avoided runoff of 66 L/m2 of canopy. Interception was the most directly comparable process between the model and field observations. Based on 5 storms sampled, field estimation of precipitation intercepted and retained on trees averaged 63 % and ranged from 22 % to 81 %, while model estimation averaged 61 % and ranged from 36 % to 99 %. This model was able to fit predictions to observed catchment discharge but required extensive manual calibration to do so. The i-Tree Hydro model predicted avoided runoff comparable with the field study and earlier assessments. Additional field studies in similar settings are needed to confirm findings and improve transferability to other tree species and environmental settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ufug.2022.127649","usgsCitation":"Coville, R.C., Kruegler, J., Selbig, W.R., Hirabayashi, S., Loheid, S., Avery, W., Shuster, W., Haefner, R.J., Scharenbroch, B.C., Endreny, T.A., and Nowak, D., 2022, Loss of street trees predicted to cause 6000 L/tree increase in leaf-on stormwater runoff for Great Lakes urban sewershed: Urban Forestry & Urban Greening, v. 74, 127649, 11 p., https://doi.org/10.1016/j.ufug.2022.127649.","productDescription":"127649, 11 p.","ipdsId":"IP-133935","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447219,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ufug.2022.127649","text":"Publisher Index Page"},{"id":412277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.50874870835374,\n 43.82183481554111\n ],\n [\n -88.50874870835374,\n 43.736154677156634\n ],\n [\n -88.38456095868207,\n 43.736154677156634\n ],\n [\n -88.38456095868207,\n 43.82183481554111\n ],\n [\n -88.50874870835374,\n 43.82183481554111\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"74","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coville, Robert C. 0000-0002-6895-2564","orcid":"https://orcid.org/0000-0002-6895-2564","contributorId":269851,"corporation":false,"usgs":false,"family":"Coville","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":862263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kruegler, James 0000-0002-2671-0807","orcid":"https://orcid.org/0000-0002-2671-0807","contributorId":269853,"corporation":false,"usgs":false,"family":"Kruegler","given":"James","email":"","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":862264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hirabayashi, Satoshi","contributorId":301162,"corporation":false,"usgs":false,"family":"Hirabayashi","given":"Satoshi","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":862266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loheid, Stephen","contributorId":301163,"corporation":false,"usgs":false,"family":"Loheid","given":"Stephen","email":"","affiliations":[{"id":38319,"text":"UW Madison","active":true,"usgs":false}],"preferred":false,"id":862267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Avery, William 0000-0002-2651-9906","orcid":"https://orcid.org/0000-0002-2651-9906","contributorId":269858,"corporation":false,"usgs":false,"family":"Avery","given":"William","email":"","affiliations":[{"id":18002,"text":"University of Wisconsin - 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Stevens Point","active":true,"usgs":false}],"preferred":false,"id":862271,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Endreny, Theodore A.","contributorId":195489,"corporation":false,"usgs":false,"family":"Endreny","given":"Theodore","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":862272,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nowak, Dave","contributorId":301166,"corporation":false,"usgs":false,"family":"Nowak","given":"Dave","email":"","affiliations":[{"id":35159,"text":"USDS Forest Service","active":true,"usgs":false}],"preferred":false,"id":862273,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70262385,"text":"70262385 - 2022 - Choosing an optimal duck season: Integrating hunter values and duck abundance","interactions":[],"lastModifiedDate":"2025-01-21T16:02:10.870812","indexId":"70262385","displayToPublicDate":"2022-07-05T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16872,"text":"The Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Choosing an optimal duck season: Integrating hunter values and duck abundance","docAbstract":"State wildlife agencies have long struggled to identify optimal hunting season dates for migratory game bird species that meet the diverse and often competing interests of stakeholders. Many approaches have been used to ensure the regulated community participates in the decision-making process, including public hearings, hunter season-date preference surveys, and hunter task forces or committees. Although hearings, surveys, and task force approaches include portions of the regulated community (typically the most avid) they may not necessarily reflect the opinions and values of all stakeholders. Additionally, these approaches rely heavily on limited anecdotal observations that may be unduly influenced by hunter avidity (e.g., days spent afield), hunter density, species preferences, and property access. To address the challenges caused by engaging only portions of the stakeholder community, we used a structured decision-making framework that included a 2017 duck hunter survey to elucidate values of a representative sample of the regulated community in each waterfowl hunting zone in the state of New York, USA. Rather than asking duck hunters about their specific duck hunting season date preferences, we asked them to rank 6 objectives describing what they value in their hunting experience (e.g., maximizing the opportunity to see mallards [Anas platyrhynchos] and black ducks [Anas rubripes], maximizing the number of weekend days). Four of the 6 objectives described duck species availability (i.e., abundance or immigration) and the remaining 2 described considerations that affect an individual hunter's opportunity or limitation to going duck hunting (i.e., holidays or conflicting hunting seasons). We used spatiotemporal abundance models derived from eBird citizen science data to estimate abundance and immigration rates of ducks in each waterfowl zone. We evaluated up to 9 unique season date alternatives developed by duck hunter task forces to determine which season date alternative best satisfied the competing objectives of duck hunters in each zone. The approach we developed allowed for selection of optimal duck hunting season dates and successfully involved avid duck hunters in the regulation development stages, while ensuring that the values of a representative sample of all stakeholders were directly considered through a clear and transparent decision-making process.
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York\",\"nation\":\"USA \"}}]}","volume":"46","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Stiller, Joshua C.","contributorId":349098,"corporation":false,"usgs":false,"family":"Stiller","given":"Joshua C.","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":924010,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siemer, William F.","contributorId":349099,"corporation":false,"usgs":false,"family":"Siemer","given":"William F.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":924011,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Kelly A.","contributorId":349100,"corporation":false,"usgs":false,"family":"Perkins","given":"Kelly A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":924012,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924009,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256663,"text":"70256663 - 2022 - Increased landscape disturbance and streamflow variability threaten fish biodiversity in the Red River catchment, USA","interactions":[],"lastModifiedDate":"2024-08-29T16:01:13.724866","indexId":"70256663","displayToPublicDate":"2022-07-04T10:55:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Increased landscape disturbance and streamflow variability threaten fish biodiversity in the Red River catchment, USA","docAbstract":"Stream fish distributions are hypothesized to be strongly associated with landscape characteristics at multiple scales. Variation in flow regimes and intensity of landscape disturbance are associated with stream fish distributions; however, relationships are poorly understood in many high-diversity regions. Our objective was to identify occurrence relationships between fish distributions and streamflow and landscape characteristics in the south-central United States.
Our study area was the central Red River catchment in Oklahoma, Texas and Arkansas, USA.
We used existing fish surveys to model the occurrence of a diverse, warmwater assemblage among hydraulic response units (HRUs). We used multispecies occupancy modelling to identify variation in occurrence probability among 111 stream fishes in relation to landscape disturbance and flow regime characteristics.
We found occurrence relationships with landscape disturbance and 11 metrics comprising all flow-regime components. The relationships varied within both major species groups and some genera. Frequency and duration were the most common metrics underlying flow regime relationships. More common stream fishes tended to be positively associated with higher levels of landscape disturbance and flow regime metrics representing variability; conversely, narrow-ranged fishes tended to be negatively associated. Occurrence relationships with flow metrics representing high-flow events were predominately negative. As expected, many species were strongly associated with ecoregion with landscape disturbance and flow relationships held constant.
Our study informs land use and water management decisions and stream fish conservation at multiple spatial scales. Collectively, the findings suggest potential homogenization of the Red River fish assemblage with increased landscape disturbance and streamflow variability. A reduction in landscape disturbance and maintenance of natural flow patterns at coarser scales may benefit endemic and narrow-ranged fishes. Our findings also help guide finer-scale land use and water management decisions by identifying stream network areas with a high occurrence probability of less tolerant fishes.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13595","usgsCitation":"Mollenhauer, R., Mouser, J., Roland, V., and Brewer, S.K., 2022, Increased landscape disturbance and streamflow variability threaten fish biodiversity in the Red River catchment, USA: Diversity and Distributions, v. 28, no. 9, p. 1934-1950, https://doi.org/10.1111/ddi.13595.","productDescription":"17 p.","startPage":"1934","endPage":"1950","ipdsId":"IP-122736","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447225,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13595","text":"Publisher Index Page"},{"id":433318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma, Texas","otherGeospatial":"Red River catchment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.5,\n 35.5\n ],\n [\n -98.5,\n 33\n ],\n [\n -92.5,\n 33\n ],\n [\n -92.5,\n 35.5\n ],\n [\n -98.5,\n 35.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"28","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Mollenhauer, R.","contributorId":276144,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mouser, J.B.","contributorId":244447,"corporation":false,"usgs":false,"family":"Mouser","given":"J.B.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roland, Victor L. vroland@usgs.gov","contributorId":5879,"corporation":false,"usgs":true,"family":"Roland","given":"Victor L.","email":"vroland@usgs.gov","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":908551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908552,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232473,"text":"70232473 - 2022 - A model of the spatiotemporal dynamics of soil carbon following coastal wetland loss applied to a Louisiana salt marsh in the Mississippi River Deltaic Plain","interactions":[],"lastModifiedDate":"2023-06-09T13:37:02.064318","indexId":"70232473","displayToPublicDate":"2022-07-04T10:04:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"A model of the spatiotemporal dynamics of soil carbon following coastal wetland loss applied to a Louisiana salt marsh in the Mississippi River Deltaic Plain","docAbstract":"The potential for carbon sequestration in coastal wetlands is high due to protection of carbon (C) in flooded soils. However, excessive flooding can result in the conversion of the vegetated wetland to open water. This transition results in the loss of wetland habitat in addition to the potential loss of soil carbon. Thus, in areas experiencing rapid wetland submergence, such as the Mississippi River Delta, coastal wetlands could become a significant source of carbon emissions if land loss is not mitigated. To accurately assess the capacity of wetlands to store (or emit) carbon in dynamic environments, it is critical to understand the fate of soil carbon following the transition from vegetated wetland to open water. We developed a simple soil carbon model representing soil depths to 1 m using the data collected from a Louisiana coastal salt marsh in the Mississippi River Deltaic Plain to predict soil carbon density and stock following the transition from a vegetated salt marsh to an open water pond. While immediate effects of ponding on the distribution of carbon within the 1-m soil profile were apparent, there were no effects of ponding on the overall, integrated, carbon stocks 14 years, following wetland submergence. Rather, the model predicts that soil carbon losses in the first meter will be realized over long periods of time (∼200 years) due to changes in the source of carbon (biomass vs. mineral sediment) with minimal losses through mineralization.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG006807","usgsCitation":"Schoolmaster, D.R., Stagg, C., Creamer, C., Laurenzano, C., Ward, E., Waldrop, M., Baustian, M., Aw, T., Merino, S., Villani, R.K., and Scott, L., 2022, A model of the spatiotemporal dynamics of soil carbon following coastal wetland loss applied to a Louisiana salt marsh in the Mississippi River Deltaic Plain: Journal of Geophysical Research: Biogeosciences, v. 127, no. 6, e2022JG006807, 15 p.; Data Release, https://doi.org/10.1029/2022JG006807.","productDescription":"e2022JG006807, 15 p.; Data Release","ipdsId":"IP-133880","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":402921,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417836,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P916JH3L"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.0050048828125,\n 29.807284450222504\n ],\n [\n -91.263427734375,\n 29.176145182559758\n ],\n [\n -90.274658203125,\n 29.036960648558267\n ],\n [\n -89.17053222656249,\n 28.91682310329166\n ],\n [\n -89.000244140625,\n 29.10897615145302\n ],\n [\n -89.3023681640625,\n 29.869228848968312\n ],\n [\n -90.439453125,\n 30.130875412002318\n ],\n [\n -90.889892578125,\n 30.424992973925598\n ],\n [\n -91.351318359375,\n 31.01057105944174\n ],\n [\n -91.9171142578125,\n 30.99173704508671\n ],\n [\n -92.0050048828125,\n 29.807284450222504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schoolmaster, Donald R. Jr. 0000-0003-0910-4458 schoolmasterd@usgs.gov","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":4746,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","suffix":"Jr.","email":"schoolmasterd@usgs.gov","middleInitial":"R.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":845615,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":220330,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":845616,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Creamer, Courtney 0000-0001-8270-9387","orcid":"https://orcid.org/0000-0001-8270-9387","contributorId":201952,"corporation":false,"usgs":true,"family":"Creamer","given":"Courtney","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":845617,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laurenzano, Claudia 0000-0003-1406-8658","orcid":"https://orcid.org/0000-0003-1406-8658","contributorId":218316,"corporation":false,"usgs":false,"family":"Laurenzano","given":"Claudia","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":845618,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":217389,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":845619,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":845620,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Baustian, Melissa M.","contributorId":189569,"corporation":false,"usgs":false,"family":"Baustian","given":"Melissa M.","affiliations":[],"preferred":false,"id":845621,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aw, Tiong","contributorId":292731,"corporation":false,"usgs":false,"family":"Aw","given":"Tiong","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":845622,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Merino, Sergio 0000-0002-2834-2243 merinos@usgs.gov","orcid":"https://orcid.org/0000-0002-2834-2243","contributorId":3653,"corporation":false,"usgs":true,"family":"Merino","given":"Sergio","email":"merinos@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":845623,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Villani, Rachel Katherine 0000-0002-8494-8178","orcid":"https://orcid.org/0000-0002-8494-8178","contributorId":290308,"corporation":false,"usgs":true,"family":"Villani","given":"Rachel","email":"","middleInitial":"Katherine","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":845624,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Scott, Laura 0000-0003-0303-5340","orcid":"https://orcid.org/0000-0003-0303-5340","contributorId":292733,"corporation":false,"usgs":false,"family":"Scott","given":"Laura","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":845625,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70232468,"text":"70232468 - 2022 - Genome-wide genetic diversity may help identify fine-scale genetic structure among lake whitefish spawning groups in Lake Erie","interactions":[],"lastModifiedDate":"2022-09-27T16:52:01.341745","indexId":"70232468","displayToPublicDate":"2022-07-04T09:43:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Genome-wide genetic diversity may help identify fine-scale genetic structure among lake whitefish spawning groups in Lake Erie","docAbstract":"In Lake Erie, lake whitefish Coregonus clupeaformis supported lucrative fisheries before populations were decimated by overfishing and water quality degradation. In recent years, there has been a renewed interest in lake whitefish and management of the fishery they support. Lake whitefish spawn on several reefs throughout Lake Erie, but the relative recruitment dynamics and contributions of spawning groups to the fishery are not well understood. Modern high-throughput sequencing approaches offer new opportunities to census population diversity and to identify subtle differences among closely related populations. We used high-throughput sequencing data to evaluate the genetic structure and diversity of lake whitefish collected opportunistically across broad spatial scales in Lake Erie. Using RAD-capture (Rapture), we sequenced and genotyped individuals (N = 88) from the west, central, and east basin of Lake Erie at 120,268 single nucleotide polymorphisms (SNPs). Lake whitefish from Niagara and Crib Reefs (west basin) diverged from the three collections. Interestingly, these were the only lake whitefish collected during the act of spawning (late November), and all other fish were collected pre-spawn (August-early November). These results suggest that some lake whitefish spawning reefs may be reproductively isolated, though definition of these groups into stocks will require more intentional sampling during the act of spawning.
Increasing risk of pathogen spillover coupled with overall declines in wildlife population abundance in the Anthropocene make infectious disease a relevant concern for species conservation worldwide. While emerging molecular tools could improve our diagnostic capabilities and give insight into mechanisms underlying wildlife disease risk, they have rarely been applied in practice. Here, employing a previously reported gene transcription panel of common immune markers to track physiological changes, we present a detailed analysis over the course of both acute and chronic infection in one wildlife species where disease plays a critical role in conservation, bighorn sheep (Ovis canadensis). Differential gene transcription patterns distinguished between infection statuses over the course of acute infection and differential correlation (DC) analyses identified clear changes in gene co-transcription patterns over the early stages of infection, with transcription of four genes—TGFb, AHR, IL1b and MX1—continuing to increase even as transcription of other immune-associated genes waned. In a separate analysis, we considered the capacity of the same gene transcription panel to aid in differentiating between chronically infected animals and animals in other disease states outside of acute disease events (an immediate priority for wildlife management in this system). We found that this transcription panel was capable of accurately identifying chronically infected animals in the test dataset, though additional data will be required to determine how far this ability extends. Taken together, our results showcase the successful proof of concept and breadth of potential utilities that gene transcription might provide to wildlife disease management, from direct insight into mechanisms associated with differential disease response to improved diagnostic capacity in the field.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coac046","usgsCitation":"Bowen, L., Manlove, K.R., Roug, A., Waters-Dynes, S.C., LaHue, N., and Wolff, P., 2022, Using transcriptomics to predict and visualize disease status in bighorn sheep (Ovis canadensis): Conservation Physiology, v. 10, no. 1, 16 p., https://doi.org/10.1093/conphys/coac046.","productDescription":"16 p.","ipdsId":"IP-137827","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447240,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coac046","text":"Publisher Index Page"},{"id":404486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bowen, Lizabeth 0000-0001-9115-4336 lbowen@usgs.gov","orcid":"https://orcid.org/0000-0001-9115-4336","contributorId":4539,"corporation":false,"usgs":true,"family":"Bowen","given":"Lizabeth","email":"lbowen@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":847549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manlove, Kezia R.","contributorId":198305,"corporation":false,"usgs":false,"family":"Manlove","given":"Kezia","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":847550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roug, Annette","contributorId":181940,"corporation":false,"usgs":false,"family":"Roug","given":"Annette","email":"","affiliations":[],"preferred":false,"id":847551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waters-Dynes, Shannon C. 0000-0002-9707-4684 swaters@usgs.gov","orcid":"https://orcid.org/0000-0002-9707-4684","contributorId":5826,"corporation":false,"usgs":true,"family":"Waters-Dynes","given":"Shannon","email":"swaters@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":847552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LaHue, Nate","contributorId":293650,"corporation":false,"usgs":false,"family":"LaHue","given":"Nate","email":"","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":847553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wolff, Peregrine","contributorId":238063,"corporation":false,"usgs":false,"family":"Wolff","given":"Peregrine","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":847554,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232381,"text":"sir20225053 - 2022 - A structured decision-making framework for managing cyanobacterial harmful algal blooms in New York State parks","interactions":[],"lastModifiedDate":"2022-07-05T10:59:01.78767","indexId":"sir20225053","displayToPublicDate":"2022-07-01T13:25:00","publicationYear":"2022","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":"2022-5053","displayTitle":"A Structured Decision-Making Framework for Managing Cyanobacterial Harmful Algal Blooms in New York State Parks","title":"A structured decision-making framework for managing cyanobacterial harmful algal blooms in New York State parks","docAbstract":"Cyanobacteria are increasingly a global water-quality concern because of the potential for these organisms to develop into potentially harmful blooms that affect ecological, economic, and public health. Cyanobacterial harmful algal blooms (CyanoHABs) can lead to a decrease in water quality and affect many of the recreational and ecological benefits of parks that include lakes. The New York State Office of Parks, Recreation and Historic Preservation (OPRHP) is a State agency within the New York State Executive Department charged with the operation of State parks and historic sites. Many New York State parks include lakes or other freshwater bodies, which can be susceptible to CyanoHABs. The OPRHP faces difficult decisions regarding prevention of and response to CyanoHABs. The U.S. Geological Survey partnered with the OPRHP and the New York State Department of Environmental Conservation to develop a structured decision-making template for managing CyanoHABs in OPRHP parks. Two parks, Moreau Lake State Park and Rockland Lake State Park, served as case studies to motivate and test the template. This report describes how the principles of structured decision making can be used to navigate the challenges associated with managing CyanoHABs in OPRHP parks. Management objectives and strategies for CyanoHABs in parks are described, strategies to evaluate consequences and manage tradeoffs are discussed, and potential challenges to the implementation of preferred alternatives are considered. General guidance is provided so the OPRHP can undertake the structured decision-making process for CyanoHABs in any of its parks. In addition, this report represents the first effort to create a strategy for applying decision analysis tools to the complex natural resource challenge of CyanoHAB mitigation and management. The case studies and template are intended to serve as an example that natural resource managers faced with CyanoHABs challenges can use to inform their decision-making processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225053","collaboration":"Prepared in cooperation with the New York State Office of Parks, Recreation and Historic Preservation and the New York State Department of Environmental Conservation","usgsCitation":"Graham, J.L., Cebada Mora, G.M., Gorney, R.M., Ball, L.C., Mengelt, C., and Runge, M.C., 2022, A structured decision-making framework for managing cyanobacterial harmful algal blooms in New York State parks: U.S. Geological Survey Scientific Investigations Report 2022–5053, 22 p., 3 app., https://doi.org/10.3133/sir20225053.","productDescription":"Report: vi, 22 p.; 3 Appendixes; Template","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122386","costCenters":[{"id":474,"text":"New York Water Science 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Report Piece"},"url":"https://pubs.usgs.gov/sir/2022/5053/sir20225053_sdm_cyanohabs_template.docx","text":"Template","size":"27.9 KB","linkHelpText":"- Blank template for a structured decision-making report on cyanobacterial harmful algal blooms"},{"id":402736,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5053/images/"},{"id":402730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5053/coverthb2.jpg"},{"id":402731,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5053/sir20225053.pdf","text":"Report","size":"2.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5053"},{"id":402735,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5053/sir20225053.XML"}],"country":"United States","state":"New 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
No abstract available.
","language":"Portuguese","publisher":"Agência Nacional de Águas e Saneamento Básico","usgsCitation":"Hadler Troger, F., Ayrimoraes Soares, S.R., Leite Cavalcanti, D., de Souza, M.L., Restivo, D., and Miller, O.L., 2022, Modelagem de qualidade da agua: Aplicação do SPARROW, 44 p.","productDescription":"44 p.","ipdsId":"IP-134335","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":407964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407963,"rank":1,"type":{"id":15,"text":"Index 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Troger, Flavio","contributorId":297353,"corporation":false,"usgs":false,"family":"Hadler Troger","given":"Flavio","email":"","affiliations":[],"preferred":false,"id":853890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ayrimoraes Soares, Sergio Rodrigues","contributorId":297354,"corporation":false,"usgs":false,"family":"Ayrimoraes Soares","given":"Sergio","email":"","middleInitial":"Rodrigues","affiliations":[],"preferred":false,"id":853891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leite Cavalcanti, Diana","contributorId":297355,"corporation":false,"usgs":false,"family":"Leite Cavalcanti","given":"Diana","email":"","affiliations":[],"preferred":false,"id":853892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Souza, Marcelo Luiz","contributorId":297182,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"Luiz","affiliations":[{"id":64313,"text":"Brazilian National Water Agency","active":true,"usgs":false}],"preferred":false,"id":853614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Restivo, Daniel Edmund 0000-0002-4822-317X","orcid":"https://orcid.org/0000-0002-4822-317X","contributorId":297183,"corporation":false,"usgs":true,"family":"Restivo","given":"Daniel Edmund","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853616,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":216556,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853615,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238966,"text":"70238966 - 2022 - The importance of phenology and thermal exposure to early life history success of nonnative Smallmouth Bass in the Yellowstone River","interactions":[],"lastModifiedDate":"2022-12-19T15:04:42.505069","indexId":"70238966","displayToPublicDate":"2022-07-01T08:55:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"The importance of phenology and thermal exposure to early life history success of nonnative Smallmouth Bass in the Yellowstone River","docAbstract":"Knowledge of potential spread by introduced species is critical to effective management and conservation. The Smallmouth Bass Micropterus dolomieu is an example of a fish that has been introduced globally, often spreads after introduction, and has substantial predatory impacts on fish assemblages. Nonnative Smallmouth Bass in the free-flowing Yellowstone River, Montana, have expanded from warmer, downstream sections of river into colder, upstream sections containing socio-economically valuable trout fisheries. We sought insight into mechanisms controlling upstream spread by evaluating whether progressively colder upstream climates physiologically constrained successful recruitment by limiting age-0 growth and preventing overwinter survival (i.e., population establishment). We documented the phenology, growth, and overwinter survival of age-0 Smallmouth Bass across a temperature gradient leading to their upstream extent in the Yellowstone River. The upstream extent of population establishment did not appear limited by water temperature alone. Age-0 body size at the onset of winter did not differ significantly between colder, upstream reaches and warmer, downstream reaches. Instead, the earlier hatch timing exhibited by some age-0 individuals in upstream sections allowed them to experience longer growing seasons than many individuals in downstream sections. This counter-intuitive hatching phenology mediated much of the expected decreases in growth in colder, upstream climates. Furthermore, evidence of successful overwinter survival and simulations of age-0 starvation mortality indicated that age-0 individuals at the upstream extent of their distribution successfully recruited to the age-1 year-class during four consecutive years. However, age-0 individuals were rare or absent throughout the uppermost upstream distribution of adults, suggesting that something other than temperature limits or discourages reproduction farther upstream. Taken together, our results suggest that Smallmouth Bass have not yet reached the thermal limit of their upstream distribution in the Yellowstone River and that future spread may challenge fisheries managers tasked with management of coldwater trout fisheries in this river.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10364","usgsCitation":"Voss, N.S., Al-Chokhachy, R.K., Sepulveda, A., Verhille, C.E., Ruggles, M.P., and Zale, A.V., 2022, The importance of phenology and thermal exposure to early life history success of nonnative Smallmouth Bass in the Yellowstone River: Transactions of the American Fisheries Society, v. 151, no. 5, p. 527-542, https://doi.org/10.1002/tafs.10364.","productDescription":"16 p.","startPage":"527","endPage":"542","ipdsId":"IP-127000","costCenters":[{"id":398,"text":"Montana Cooperative Fishery Research Unit","active":false,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":447256,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10364","text":"Publisher Index Page"},{"id":435788,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KXZ295","text":"USGS data release","linkHelpText":"Juvenile Smallmouth Bass Sampling from the Yellowstone River, Montana 2016-2019"},{"id":410709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.33112682108907,\n 44.635286071837356\n ],\n [\n -107.16730846171409,\n 44.635286071837356\n ],\n [\n -107.16730846171409,\n 46.37521312420196\n ],\n [\n -111.33112682108907,\n 46.37521312420196\n ],\n [\n -111.33112682108907,\n 44.635286071837356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Voss, Nicholas S.","contributorId":300117,"corporation":false,"usgs":false,"family":"Voss","given":"Nicholas","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":859441,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Al-Chokhachy, Robert K. 0000-0002-2136-5098 ral-chokhachy@usgs.gov","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":300118,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","email":"ral-chokhachy@usgs.gov","middleInitial":"K.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":859442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":859443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verhille, Christine E.","contributorId":174642,"corporation":false,"usgs":false,"family":"Verhille","given":"Christine","email":"","middleInitial":"E.","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":859444,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ruggles, Michael P.","contributorId":300121,"corporation":false,"usgs":false,"family":"Ruggles","given":"Michael","email":"","middleInitial":"P.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":859445,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zale, Alexander V. 0000-0003-1703-885X","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":244099,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":859446,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232420,"text":"70232420 - 2022 - Continental shelves as detrital mixers: U-Pb and Lu-Hf detrital zircon provenance of the Pleistocene–Holocene Bering Sea and its margins","interactions":[],"lastModifiedDate":"2022-09-27T16:51:10.428879","indexId":"70232420","displayToPublicDate":"2022-07-01T08:20:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5781,"text":"The Depositional Record","active":true,"publicationSubtype":{"id":10}},"title":"Continental shelves as detrital mixers: U-Pb and Lu-Hf detrital zircon provenance of the Pleistocene–Holocene Bering Sea and its margins","docAbstract":"Continental shelves serve as critical transfer zones in sediment-routing systems, linking the terrestrial erosional and deep-water depositional domains. The degree to which clastic sediment is mixed and homogenized during transfer across broad shelves has important implications for understanding deep-sea detrital records. Wide continental shelves are thought to act as capacitors characterized by transient sediment storage during sea level rise and sediment remobilization during sea level fall. This study attempts to test the hypothesis that sea level lowstand yields more efficient and direct sediment transfer from fluvial sources to deep-sea sinks compared to highstand when sediment is sequestered and mixed on the shelf. We test this by evaluating U-Pb and Lu-Hf detrital zircon provenance trends along the vast Bering Sea shelf and deep-marine Beringian continental margin. We present 5884 U-Pb ages and 402 Lu-Hf analyses from 30 samples to characterize the provenance of modern to Pleistocene sediment across the Bering Sea region. We used both forward and inverse numerical mixture modeling to estimate the abundance of distinct fluvial sources in shelfal and deep-water deposits. These results demonstrate that sediment in the Bering Sea is derived from a mixture of regional fluvial sources, but that the Yukon River is the primary detrital source for sediment throughout the region. Although Yukon River signatures are abundant in all basin samples, the relative proportions of Yukon vs other sources vary spatially across the shelf. A comparison of Holocene and surficial sediment with Pleistocene deposits shows that sediment across the shelf and in the deep-sea remains well-mixed between climate states. Thus, detrital provenance signatures in deep-marine deposits outward of broad transfer zones are likely to represent mixtures of fluvial sources regardless of sea level.","language":"English","publisher":"Wiley","doi":"10.1002/dep2.203","usgsCitation":"Malkowski, M., Johnstone, S., Sharman, G.R., White, C.J., Scheirer, D.S., and Barth, G., 2022, Continental shelves as detrital mixers: U-Pb and Lu-Hf detrital zircon provenance of the Pleistocene–Holocene Bering Sea and its margins: The Depositional Record, v. 8, no. 3, p. 1008-1030, https://doi.org/10.1002/dep2.203.","productDescription":"23 p.","startPage":"1008","endPage":"1030","ipdsId":"IP-130161","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":447258,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/dep2.203","text":"Publisher Index Page"},{"id":435789,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FUXON3","text":"USGS data release","linkHelpText":"Detrital zircon geochronology and geochemistry data from the seafloor of the Bering Sea and adjacent river systems"},{"id":402821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"Bering Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.9,\n 50\n ],\n [\n -150,\n 50\n ],\n [\n -150,\n 68\n ],\n [\n -179.9,\n 68\n ],\n [\n -179.9,\n 50\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 160,\n 50\n ],\n [\n 179.9,\n 50\n ],\n [\n 179.9,\n 66\n ],\n [\n 160,\n 66\n ],\n [\n 160,\n 50\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Malkowski, Matthew A.","contributorId":221753,"corporation":false,"usgs":false,"family":"Malkowski","given":"Matthew A.","affiliations":[{"id":40415,"text":". Department of Geological Sciences, Stanford University, Stanford CA 94305","active":true,"usgs":false}],"preferred":false,"id":845488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnstone, Samuel 0000-0002-3945-2499","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":207545,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":845489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":845490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Colin J.","contributorId":292687,"corporation":false,"usgs":false,"family":"White","given":"Colin","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":845491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scheirer, Daniel S. 0000-0001-8015-7072 dscheirer@usgs.gov","orcid":"https://orcid.org/0000-0001-8015-7072","contributorId":214825,"corporation":false,"usgs":true,"family":"Scheirer","given":"Daniel","email":"dscheirer@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":845492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barth, Ginger 0000-0003-0867-7799 gbarth@usgs.gov","orcid":"https://orcid.org/0000-0003-0867-7799","contributorId":264955,"corporation":false,"usgs":true,"family":"Barth","given":"Ginger","email":"gbarth@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":845493,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233933,"text":"70233933 - 2022 - Functional composition of plant communities mediates biomass effects on ecosystem service recovery across an experimental dryland restoration network","interactions":[],"lastModifiedDate":"2022-09-15T14:18:48.828438","indexId":"70233933","displayToPublicDate":"2022-07-01T07:18:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Functional composition of plant communities mediates biomass effects on ecosystem service recovery across an experimental dryland restoration network","docAbstract":"There have been over 507 million cases of COVID-19, the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in 6 million deaths globally. Wastewater surveillance has emerged as a valuable tool in understanding SARS-CoV-2 burden in communities. The National Wastewater Surveillance System (NWSS) partnered with the United States Geological Survey (USGS) to implement a high-frequency sampling program. This report describes basic surveillance and sampling statistics as well as a comparison of SARS-CoV-2 trends between high-frequency sampling 3–5 times per week, referred to as USGS samples, and routine sampling 1–2 times per week, referred to as NWSS samples. USGS samples provided a more nuanced impression of the changes in wastewater trends, which could be important in emergency response situations. Despite the rapid implementation time frame, USGS samples had similar data quality and testing turnaround times as NWSS samples. Ensuring there is a reliable sample collection and testing plan before an emergency arises will aid in the rapid implementation of a high-frequency sampling approach. High-frequency sampling requires a constant flow of information and supplies throughout sample collection, testing, analysis, and data sharing. High-frequency sampling may be a useful approach for increased resolution of disease trends in emergency response.
The distribution of groundwater age is useful for evaluating the susceptibility and sustainability of groundwater resources. Here, we compute the aquifer-scale cumulative distribution function to characterize the age distribution for 21 Principal Aquifers that account for ~80% of public-supply pumping in the United States. The aquifer-scale cumulative distribution function for each Principal Aquifer was derived from an ensemble of modeled age distributions (~60 samples per aquifer) based on multiple tracers: tritium, tritiogenic helium-3, sulfur hexafluoride, chlorofluorocarbons, carbon-14, and radiogenic helium-4. Nationally, the groundwater is 38% Anthropocene (since 1953), 34% Holocene (75 – 11,800 years ago), and 28% Pleistocene (>11,800 years ago). The Anthropocene fraction ranges from <5 to 100%, indicating a wide range in susceptibility to land-surface contamination. The Pleistocene fraction of groundwater exceeds 50% in 7 eastern aquifers that are predominately confined. The Holocene fraction of groundwater exceeds 50% in 5 western aquifers that are predominately unconfined. The sustainability of pumping from these Principal Aquifers depends on rates of recharge and release of groundwater stored in fine-grained layers.
In 2018–20, the U.S. Geological Survey, in cooperation with Upper Black Squirrel Creek Ground Water Management District, sampled 48 wells for Phase III of a multiphase plan investigating groundwater quality in the alluvial aquifer of the Upper Black Squirrel Creek Basin (UBSB), El Paso County, Colorado. Results for samples collected from October to December each year were used to assess spatial and temporal changes in groundwater quality and to differentiate sources of nitrate. Groundwater was predominantly classified as mixed-cation and mixed-anion water type in the aquifer, with variable chemistry along the periphery. Concentrations of constituents in groundwater were generally less than regulatory standards, except for nitrate in four wells. Isotopes of nitrogen and oxygen in nitrate identified four different potential sources or processes affecting nitrate in the alluvial aquifer: naturally occurring nitrate from soils, nitrate from animal and (or) human waste, and an unknown source, along with evidence of denitrification. Pharmaceutical compounds and personal-care products were detected in seven wells, with three wells having multiple detections. Stable isotopes of water indicated variability in seasonality of recharge throughout the UBSB alluvial aquifer. Nitrate concentrations from the 1984 study and the 1996 study were compared to the more recent concentrations in the 2013 study and the 2018–20 study. The northern one-third of the UBSB alluvial aquifer had a statistically significant increase in nitrate concentration from the 2013 study to the 2018–20 study, but no change was shown from the 1984 study to the 1996 study. The opposite was found true for the southern two-thirds of the UBSB alluvial aquifer with no statistically significant difference in nitrate concentration from the 2013 study to the 2018–20 study. Analysis of temporal changes indicated an increase in median and maximum nitrate concentrations from the 2013 study to the 2018–20 study throughout the UBSB alluvial aquifer. Continued sampling of wells in the UBSB would be beneficial to better determine temporal changes in groundwater quality, characterize human effects on water quality, and understand characteristics of the alluvial aquifer pertaining to sustainability of the resource.
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U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
Many may recall “Take Me Home, Country Roads,” made famous by John Denver, leads with the lyric “almost heaven, West Virginia, Blue Ridge Mountains, Shenandoah River.” The descriptors are apt. Nicknamed the “Mountain State,” West Virginia inspires thoughts of coal mining or logging in the Appalachian Mountains and valleys, or the leaping trout in the winding waters of New River Gorge National Park & Reserve.
West Virginia is second only to Wyoming in coal production nationwide. Its mines produced more than 67 million tons of coal in 2020. Logging pumped about $3.4 billion into the State’s economy in 2019; tourist spending added another $4.6 billion.
Those industries are key for West Virginia, but agriculture and fisheries also play a role in the State's economic fortunes. Peaches and apples are major drivers of food production, as are beef and poultry. Trout, meanwhile, are caught and sold commercially, and are stocked throughout the State's rivers for local and visiting anglers.
Scientists, land managers, and others use imagery from the U.S. Geological Survey Landsat satellite program's deep historical archive to better understand and manage West Virginia’s storied forests, fields, mountains, and foothills.
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Virginia\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Cyanobacterial blooms are an issue drawing increasing concern in freshwater lakes and reservoirs in the United States due to the real and sometimes perceived harms they can cause through cyanotoxin production or other effects. These types of blooms are often referred to as cyanobacterial harmful algal blooms (cyanoHABs). Cyanotoxin exposure can potentially lead to human health effects through recreation and consumption of drinking water and may impact fisheries, wildlife, domestic pets, and livestock. Characterizing the societal impacts of cyanotoxin production, exposure, and effects and estimating the potential value of information of an early warning system can inform and support freshwater lake and reservoir management decisions and future research directions. A Bayesian decision tree analysis was utilized to identify uses, users, and benefits of the information provided by this research. Specifically, the potential value related to a cyanoHAB early warning system, based on potential toxicity, was analyzed that would provide information two additional days earlier relative to cyanoHAB toxicity. The evaluation considers the application of this information for freshwater lake management - whether or not to post an advisory or warning to avoid recreational water contact. The model was parameterized with data from the state of Kansas and the value of avoided foregone recreation and avoided health effects was derived. The estimated annual value of information ranges between \\$565 thousand to \\$2.3 million (2018 United States Dollars (USD)) for the state of Kansas alone based on provided assumptions. The results demonstrate a lower bound of the value of a cyanoHAB early warning system and suggest additional research to understand how the use and value of this information could support research prioritization and further illustrate the return on research investment. This analysis does not incorporate the full suite of potential societal costs that may be associated with a cyanoHAB event such as drinking water treatment, impacts to irrigation, or power generation.
No abstract available.
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221061","usgsCitation":"Reynolds, R.L., Goldstein, H.L., Kokaly, R.F., and Derry, J., 2022, Microplastic particles in dust-on-snow, Upper Colorado River Basin, Colorado Rocky Mountains, 2013–16: U.S. Geological Survey Open-File Report 2022–1061, 7 p., https://doi.org/10.3133/ofr20221061.","productDescription":"vi, 7 p.","onlineOnly":"Y","ipdsId":"IP-141503","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":501783,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113221.htm","linkFileType":{"id":5,"text":"html"}},{"id":402600,"rank":3,"type":{"id":34,"text":"Image 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U.S. Geological Survey
P.O. Box 25046, Mail Stop 980
Denver, CO 80225
Hurricane Maria struck the island of Puerto Rico on September 20, 2017, as a Category 4 storm. The hurricane traversed the island from southeast to northwest and produced recorded 48-hour rainfall totals of up to 30.01 inches. Estimates of the human death toll range from 2,975 to 4,645, possibly more.
The U.S. Geological Survey (USGS) hydrologic monitoring network sustained substantial wind and flood damage during the hurricane. Eighty-five of the 300 hydrologic monitoring stations operating in Puerto Rico and the U.S. Virgin Islands prior to the passage of Hurricane Maria were destroyed or damaged. During the weeks and months after the hurricane, USGS field crews in Puerto Rico prioritized repair of the hydrologic monitoring network and collected hydrologic information to characterize the magnitude of observed peak streamflows at 20 streamgage and to develop new theoretical stage-streamflow relations for 58 streamgages where stream channels were substantially altered; the theoretical stage-streamflow relations were used to estimate Hurricane Maria peak streamflows for 39 of those sites. As part of a pilot program, USGS field crews installed continuous slope-area monitoring equipment at two remote streamgages to automate the collection of high-streamflow stage data.
Hurricane Maria peak streamflows and rankings were determined for 73 USGS streamgages in Puerto Rico. New rank 1 period-of-record peak streamflows occurred at 28 sites, rank 2 period-of-record peak streamflows occurred at 17 sites, and rank 3 period-of-record peak streamflows occurred at 9 sites; period-of-record peak streamflows at the remaining 19 sites either ranked from 4th to 20th or were not ranked. Annual exceedance probabilities for 53 unregulated peak streamflows ranged from greater than 50.0 percent (recurrence interval of less than 2 years) to 0.3 percent (recurrence interval of 333 years), with the majority (28 of 53) in the range of 10.0–2.1 percent (recurrence intervals of 10–48 years).
A comparison of period-of-record ranks for the largest flood events that have occurred in Puerto Rico since the 1960s indicated that Hurricane Maria produced more record peak streamflows than either Hurricane Hortense in 1996 or Hurricane Georges in 1998. Limited pre-1960s hydrologic data preclude quantitative comparison with earlier storms.
As part of this study, a maximum peak-streamflow envelope curve for Puerto Rico was developed using historical peak-streamflow information available through 2017. Other post-Hurricane Maria USGS activities summarized in this report include (1) Global Navigation Satellite System surveys at all stations in the USGS hydrologic monitoring network, used to tie the network to the Puerto Rico Vertical Datum of 2002; and (2) telemetered monitoring of the Lago Guajataca Dam in northwestern Puerto Rico, which was damaged and at risk of failure from October to December 2017.
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As humans increasingly dominate the nitrogen cycle, deposition of reactive nitrogen (Nr) will continue to have adverse consequences for ecosystems. In the Rocky Mountains, Nr deposition remains elevated and has become increasingly dominated by ammonium, despite efforts to reduce emissions. Currently, spatial models of Nr deposition do not fully account for urban and agricultural emissions, sources that contribute to the observed high rates of ammonium deposition in adjacent ecosystems. To address this gap in the Colorado Front Range, we measured Nr deposition along a transect from urban and agricultural plains to subalpine forests. We found elevated values of wet Nr deposition at the urban and foothill sites (4.7 and 4.4 kg N ha−1 yr−1, respectively), and lower values at the montane and subalpine sites (2.5–2.8 kg N ha−1 yr−1). Ammonium dominated wet and bulk Nr deposition, accounting for approximately 69% of bulk Nr deposition. Seasonally, bulk Nr deposition was highest in the spring months, when air masses from the plains are transported west into the mountains. Previous work has demonstrated that high elevations of the Colorado Front Range are especially sensitive to Nr deposition due to thin soil and minimal vegetation. Our results indicate that despite lower precipitation, the fire-prone forested foothills receive even greater Nr deposition than higher elevations, due to proximity to urban and agricultural Nr sources. The interaction between elevated Nr deposition and wildfire in this region may pose a risk to water supplies and ecosystems, and is an important topic for future research.
The Lucerne Valley is in the southwestern part of the Mojave Desert and is about 75 miles northeast of Los Angeles, California. The Lucerne Valley groundwater basin encompasses about 230 square miles and is separated from the Upper Mojave Valley groundwater basin by splays of the Helendale Fault. Since its settlement, groundwater has been the primary source of water for agricultural, industrial, municipal, and domestic uses. Groundwater withdrawal from pumping has exceeded the amount of water recharged to the basin, causing groundwater declines of more than 100 feet between 1917 and 2016 in the center of the basin. The continued withdrawal has resulted in an increase in pumping costs, reduced well efficiency, and land subsidence near Lucerne Lake. Although the volume of pumping has declined in recent years, there is concern that new agricultural growth and limits on imported water will continue to strain the sustainability of the groundwater system.
To address these concerns, the U.S. Geological Survey entered into a cooperative agreement with the Mojave Water Agency to develop a better understanding of the Lucerne Valley hydrogeologic system and provide tools to help evaluate and manage the effects of future development in the Lucerne Valley. The objectives of this study were to (1) improve the understanding of the aquifer system, (2) improve the understanding of subsidence in the basin, and (3) incorporate the understanding into a groundwater-flow model that can be used to help manage the groundwater resources in the Lucerne Valley. The model developed for this study covers the period of 1942–2016 and can help evaluate various proposed water-management scenarios during different climatic and hydrologic conditions.
The aquifer system consists of a shallow aquifer, a confining unit, and middle and lower aquifers. These layered water-bearing units were identified based on geologic units of the mostly unconsolidated sediments and hydrologic properties. These alluvial deposits consist of clay, silt, sand, and gravel; some places also contain clay and silty clay lacustrine deposits. Several faults act, at least in part, as barriers to groundwater flow on the eastern, southern, and western edges of the basin. Present-day natural recharge is primarily from the infiltration of runoff from the San Bernardino Mountains to the south; however, stable and radioactive isotopes show that groundwater from the middle of the Lucerne Valley was older than about 10,000 years and probably was recharged as infiltration from streams draining the mountains in the Mojave Desert to the north, which probably does not occur under present-day climatic conditions. The annual average natural recharge for 1942–2016, estimated by a Basin Characterization Model, was about 635 acre-feet per year; the average amount of treated wastewater effluent transferred to the Lucerne Valley for artificial recharge annually ranged from about 1,500 to 4,000 acre-feet per year during 1980–2016. Pumpage estimates for 1942–2016 ranged from about 3,000 acre-feet in 1942 to about 18,300 acre-feet in 1984. The total cumulative amount of groundwater removed from the basin by pumping between 1942 and 2016 was estimated to be about 700,000 acre-feet, which was about 10 times greater than the cumulative amount of recharge to the entire Lucerne Valley groundwater basin. Before groundwater development, the direction of groundwater flow was from the southern part of the basin northward to discharge areas near Lucerne Lake, where it discharged through springs along the Helendale Fault and by evapotranspiration. Since the early 1900s, groundwater-level declines have mostly eliminated the areas where natural discharge occurred and exceeded 100 feet in the middle of the basin between the early 1950s and mid-1990s, and as much as 25 feet near the margins from about the mid-1950s to 2000s. A decrease in the rate of pumping after the mid-1990s lessened the hydraulic stress on the middle and lower aquifers and enabled hydraulic heads in the middle of the basin to recover slightly as groundwater near the margins of the basin moved toward the pumping depression. Although trends in groundwater levels in the center of the basin have reversed since the mid-1990s, levels at the basin margins continue to decline as the movement of groundwater from the margins fills the pumping depression and gradually flattens the groundwater table throughout the basin.
The long-term extraction of groundwater and associated dewatering of the fine-grained sediments present within the aquifer system has resulted in aquifer compaction and consequently land subsidence, primarily near Lucerne Lake. Analysis of interferometric synthetic aperture radar data shows that almost 11 inches of land subsidence has occurred south of Lucerne Lake between April 1992 and November 2009; less subsidence occurred elsewhere in the basin during this period. This differential land subsidence has caused fissures and cracks in the ground surface, which have buckled the pavement and undercut roads in several locations.
The Lucerne Valley Hydrologic Model was developed using the finite-difference groundwater modeling software One Water Hydrologic Model to represent the hydrologic conditions and stresses during 1942–2016. The model has a uniform grid of approximately 92 acres per cell (2,000 feet by 2,000 feet) and has four layers representing the water-bearing units. The results from the calibrated model simulations indicated that groundwater pumpage exceeded recharge, resulting in an estimated net cumulative depletion of groundwater storage (discharge minus recharge) of about 465,000 acre-feet from 1942 to 2016. The model simulated as much as 7.5 feet (90 inches; 2,286 millimeters) of aquifer compaction, which indicates the extensive fine-grained deposits and measured subsidence near Lucerne Lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225048","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Stamos, C.L., Larsen, J.D., Powell, R.E., Matti, J.C., and Martin, P., 2022, Hydrogeology and simulation of groundwater flow in the Lucerne Valley groundwater basin, California: U.S. Geological Survey Scientific Investigations Report 2022-5048, 120 p., https://doi.org/10.3133/sir20225048.","productDescription":"Report: xi, 120 p.; Appendix; Data Release","numberOfPages":"120","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095487","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":403187,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221063","text":"Open-File Report 2022-1063","description":"Fackrell, J.K., 2022, Groundwater quality of the Lucerne Valley groundwater basin, California: U.S. Geological Survey Open-File Report 2022-1063, 19 p., https://doi.org/10.3133/ofr20221063.","linkHelpText":"- Groundwater Quality of the Lucerne Valley Groundwater Basin, California"},{"id":402644,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94W41EL","text":"MODFLOW-OWHM model used to simulate groundwater flow and evaluate storage in the Lucerne Valley Groundwater Basin, California","description":"Larsen, J.D., 2022, MODFLOW-OWHM model used to simulate groundwater flow and evaluate storage in the Lucerne Valley Groundwater Basin, California: U.S. Geological Survey data release, https://doi.org/10.5066/P94W41EL."},{"id":402643,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5048/sir20225048_appendix1.txt","text":"Appendix 1","size":"27 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Sites with groundwater-level data available on the U. S. Geological Survey National Water Inventory System Web service (NWISWeb) from 1911-2016 within the Lucerne Valley, California"},{"id":402641,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5048/sir20225048.xml"},{"id":402640,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5048/sir20225048.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5048"},{"id":402639,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5048/covrthb.jpg"},{"id":402695,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225048/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5048"},{"id":402642,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5048/images"}],"country":"United States","state":"California","otherGeospatial":"Lucerne Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.666667,\n 34.266667\n ],\n [\n -117.083333,\n 34.266667\n ],\n [\n -117.083333,\n 34.666667\n ],\n [\n -116.666667,\n 34.666667\n ],\n [\n -116.666667,\n 34.266667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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White sturgeon (Acipenser transmontanus) are long-lived, late-maturing, benthic-feeding fish that are ideal candidates for assessing the bioaccumulation of persistent chemicals. In this study, composite tissue samples of brain, liver, gonad, and fillet were collected from white sturgeon in 2009 from five sites in the Hanford Reach of the Columbia River near Hanford, Washington. The composite tissue samples at each site were analyzed for the concentrations of individual chemicals as well as the total concentrations of four chemical classes: (1) organochlorine (OC) pesticides, (2) industrial or personal care products, (3) polybrominated diphenyl ether (PBDE) congeners, and (4) polychlorinated biphenyl (PCB) congeners. The results showed that chemicals from all four classes were present in the fish, and that OC pesticides and degradation products (such as oxychlordane, fipronil sulfide, and dichlorodiphenyltrichloroethane (DDT) degradates, PBDE congeners, and PCB congeners) often were present in all tissues and at all sites. Gonad tissues generally had the highest total concentration of each chemical class, followed by brains, livers, and fillets. The concentrations of several chemicals or chemical classes exceeded many of the human health benchmarks for two different populations (general/recreational consumers and subsistence/Tribal consumers), and this was especially true for the total concentrations of DDT degradation products and PCB congeners. These results suggest that continued monitoring of resident fish in the Hanford Reach, as well as assessments of the health impacts on consumers of those fish, are warranted.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225020","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Payne, S.E., Wise, D.R., Davis, J.W., and Nilsen, E.B., 2022, Assessment of persistent chemicals of concern in white sturgeon (Acipenser transmontanus) in the Hanford Reach of the Columbia River, southeastern Washington, 2009: U.S. Geological Survey Scientific Investigations Report 2022–5020, 25 p., https://doi.org/10.3133/sir20225020.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-113471","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":402577,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5020/coverthb.jpg"},{"id":402579,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5020/images"},{"id":402580,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5020/sir20225020.XML"},{"id":402578,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5020/sir20225020.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5020"},{"id":402624,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225020/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5020"},{"id":502374,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113220.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.05859375,\n 46.13417004624326\n ],\n [\n -118.89404296875,\n 46.13417004624326\n ],\n [\n -118.89404296875,\n 46.95776134668866\n ],\n [\n -120.05859375,\n 46.95776134668866\n ],\n [\n -120.05859375,\n 46.13417004624326\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2022.894571","usgsCitation":"Friedl, M.A., Woodcock, C.E., Olofsson, P., Zhu, Z., Loveland, T., Stanimirova, R., Arevalo, P., Bullock, E.L., Hu, K., Zhang, Y., Turlej, K., Tarrio, K., Kristina, M., Gorelick, N., Wang, J.A., Barber, C., and Souza Jr., C., 2022, Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat: Frontiers in Remote Sensing, v. 3, 894571, 15 p., https://doi.org/10.3389/frsen.2022.894571.","productDescription":"894571, 15 p.","ipdsId":"IP-142442","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447282,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2022.894571","text":"Publisher Index 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