Study Region: Rocky Mountains, United States
Study Focus: Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer.
New hydrological insights for region: In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100949","usgsCitation":"Newman, C.P., Kisfalusi, Z.D., and Holmberg, M.J., 2021, Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA: Journal of Hydrology: Regional Studies, v. 38, p. 1-20, https://doi.org/10.1016/j.ejrh.2021.100949.","productDescription":"100949, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":450390,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100949","text":"Publisher Index Page"},{"id":436141,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7DHLY","text":"USGS data release","linkHelpText":"Water-level and well-discharge data related to aquifer testing in Wet Mountain Valley, Colorado, 2019"},{"id":390678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains, Wet Mountain Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.85052490234375,\n 38.31149091244452\n ],\n [\n -105.50033569335938,\n 37.79676317682161\n ],\n [\n -105.08010864257812,\n 37.95394377350263\n ],\n [\n -105.47012329101562,\n 38.449286817153556\n ],\n [\n -105.85052490234375,\n 38.31149091244452\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kisfalusi, Zachary D. 0000-0001-6016-3213","orcid":"https://orcid.org/0000-0001-6016-3213","contributorId":222422,"corporation":false,"usgs":true,"family":"Kisfalusi","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmberg, Michael J. 0000-0002-1316-0412 mholmber@usgs.gov","orcid":"https://orcid.org/0000-0002-1316-0412","contributorId":190084,"corporation":false,"usgs":true,"family":"Holmberg","given":"Michael","email":"mholmber@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224031,"text":"ofr20211089 - 2021 - Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","interactions":[],"lastModifiedDate":"2021-10-20T14:18:57.158711","indexId":"ofr20211089","displayToPublicDate":"2021-10-20T10:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1089","displayTitle":"Managed Aquifer Recharge Suitability—Regional Screening and Case Studies in Jordan and Lebanon","title":"Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","docAbstract":"The U.S. Geological Survey, at the request of the U.S. Agency for International Development, led a 5-year regional project to develop and apply methods for water availability and suitability mapping for managed aquifer recharge (MAR) in the Middle East and North Africa region. A regional model of surface runoff for the period from 1984 to 2015 was developed to characterize water availability using remote sensing data on climate, vegetation, and topography in Jordan, Lebanon, and surrounding areas. Surface runoff was accumulated to characterize potential streamflow available for MAR and these data were combined with land surface slope to prepare a regional screening map of MAR suitability, illustrating suitability mapping concepts and methods. The application of the methods is demonstrated by the evaluation of water availability and suitability for potential MAR in study areas in Jordan and Lebanon. Locations suitable for MAR are present in both Jordan and Lebanon, but limitations exist in both countries, related primarily to water availability in Jordan and land areas of suitable terrain in Lebanon. An additional feasibility study including field investigations would likely provide decision makers with essential information for further development of the use of MAR in Jordan, Lebanon, and the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211089","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Goode, D.J., ed., 2021, Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon: U.S. Geological Survey Open-File Report 2021–1089, 87 p., https://doi.org/10.3133/ofr20211089.","productDescription":"Report: xi, 87 p.; 2 Data Releases","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124064","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436143,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":390660,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"- Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":389216,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971ZVHF","text":"USGS data release","linkHelpText":"Assembly of satellite-based rainfall datasets in situ data and rainfall climatology contours for the MENA region"},{"id":389217,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TXLT1X","text":"USGS data release","linkHelpText":"Modeling accumulated surface runoff and water availability for aquifer storage and recovery in the MENA region from 1984–2015"},{"id":389215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1089/ofr20211089.pdf","text":"Report","size":"22.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1089"},{"id":389214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1089/coverthb.jpg"}],"country":"Jordan, Lebanon","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[35.54567,32.39399],[35.71992,32.70919],[36.83406,32.31294],[38.79234,33.37869],[39.19547,32.16101],[39.00489,32.01022],[37.00217,31.50841],[37.99885,30.5085],[37.66812,30.33867],[37.50358,30.00378],[36.74053,29.86528],[36.50121,29.50525],[36.06894,29.19749],[34.95604,29.35655],[34.9226,29.50133],[35.42092,31.10007],[35.39756,31.48909],[35.54525,31.7825],[35.54567,32.39399]]],[[[35.8211,33.27743],[35.5528,33.26427],[35.46071,33.08904],[35.12605,33.0909],[35.48221,33.90545],[35.97959,34.61006],[35.9984,34.64491],[36.44819,34.59394],[36.61175,34.20179],[36.06646,33.82491],[35.8211,33.27743]]]]},\"properties\":{\"name\":\"Jordan\"}}]}","contact":"U.S. Geological Survey
Office of International Programs
917 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
directoroip@usgs.gov
Habitat loss from land-use change is one of the top causes of declines in wildlife species of concern. As such, it is critical to assess and reassess habitat suitability as land cover and anthropogenic features change for both monitoring and developing current information to inform management decisions. However, there are obstacles that must be overcome to develop consistent assessments through time. A range-wide lek habitat suitability model for the lesser prairie-chicken (Tympanuchus pallidicinctus), currently under review by the U. S. Fish and Wildlife Service for potential listing under the Endangered Species Act) was published in 2016. This model was based on lek data from 2002 to 2012, land cover data ranging from 2001 to 2013, and anthropogenic features from circa 2011, and has been used to help guide lesser prairie-chicken management and anthropogenic development actions. We created a second iteration model based on new lek surveys (2015 to 2019) and updated predictor layers (2016 land cover and cleaned/ updated anthropogenic data) to evaluate changes in lek suitability and to quantify current range-wide habitat suitability. Only three of 11 predictor variables were directly comparable between the iterations, making it difficult to directly assess what predicted changes resulted from changes in model inputs versus actual landscape change. The second iteration model showed a similar positive relationship with land cover and negative with anthropogenic features to the first iteration, but exhibited more variation among candidate models. Range-wide, more suitable habitat was predicted in the second iteration. The Shinnery Oak Ecoregion, however, exhibited a loss in predicted suitable habitat which could be due to predictor source changes. Iterated models such as this are important to ensure current information is being used in conservation and development decisions.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0256633","usgsCitation":"Jarnevich, C.S., Belamaric, P.N., Fricke, K., Houts, M., Rossi, L., Beauprez, G.M., Cooper, B., and Martin, R., 2021, Challenges in updating habitat suitability models: An example with the lesser prairie-chicken: PLoSOne, v. 16, no. 9, e0256633, 19 p., https://doi.org/10.1371/journal.pone.0256633.","productDescription":"e0256633, 19 p.","ipdsId":"IP-120427","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450394,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0256633","text":"Publisher Index Page"},{"id":436145,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MS0QR0","text":"USGS data release","linkHelpText":"Second Iteration of Range Wide Lesser Prairie Chicken Lek Habitat Suitability in 2019, Predicted in Southern Great Plains"},{"id":390673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.64453124999999,\n 31.98944183792288\n ],\n [\n -101.2060546875,\n 31.98944183792288\n ],\n [\n -101.22802734375,\n 34.34343606848294\n ],\n [\n -99.97558593749999,\n 34.361576287484176\n ],\n [\n -96.328125,\n 36.98500309285596\n ],\n [\n -96.328125,\n 40.01078714046552\n ],\n [\n -102.10693359375,\n 40.027614437486655\n ],\n [\n -102.12890625,\n 39.62261494094297\n ],\n [\n -104.0625,\n 39.67337039176558\n ],\n [\n -103.11767578124999,\n 37.03763967977139\n ],\n [\n -103.07373046875,\n 36.4566360115962\n ],\n [\n -105.62255859375,\n 34.65128519895413\n ],\n [\n -105.64453124999999,\n 31.98944183792288\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":825400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belamaric, Pairsa Nicole 0000-0001-7529-0370","orcid":"https://orcid.org/0000-0001-7529-0370","contributorId":267846,"corporation":false,"usgs":true,"family":"Belamaric","given":"Pairsa","email":"","middleInitial":"Nicole","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":47756,"text":"Student contractor to the U.S. Geological Survey Fort Collins Science Center","active":true,"usgs":false}],"preferred":true,"id":825401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fricke, Kent","contributorId":267847,"corporation":false,"usgs":false,"family":"Fricke","given":"Kent","affiliations":[{"id":40289,"text":"Kansas Department of Wildlife, Parks, and Tourism","active":true,"usgs":false}],"preferred":false,"id":825402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houts, Mike","contributorId":267848,"corporation":false,"usgs":false,"family":"Houts","given":"Mike","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false},{"id":33109,"text":"Kansas Biological Survey, Lawrence, KS","active":true,"usgs":false}],"preferred":false,"id":825403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rossi, Liza","contributorId":267849,"corporation":false,"usgs":false,"family":"Rossi","given":"Liza","email":"","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":825404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beauprez, Grant M.","contributorId":172889,"corporation":false,"usgs":false,"family":"Beauprez","given":"Grant","email":"","middleInitial":"M.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":825405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cooper, Brett","contributorId":267850,"corporation":false,"usgs":false,"family":"Cooper","given":"Brett","email":"","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":825406,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Russell","contributorId":267876,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":825471,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229154,"text":"70229154 - 2021 - Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas","interactions":[],"lastModifiedDate":"2022-03-01T15:14:30.323759","indexId":"70229154","displayToPublicDate":"2021-10-20T09:14:18","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas","docAbstract":"A concentrated animal feeding operation (CAFO) established in Newton County, Arkansas, near Big Creek, a tributary of the Buffalo National River, raised concern about potential degradation of water quality in the karst watershed. In this study, isotopic tools were combined with standard geochemical approaches to characterize nutrient sources and dynamics in the Big Creek watershed. An isotopic and geochemical reference database of potential nutrient sources in the Big Creek watershed was constructed based on samples collected from representative potential sources. Nutrient sources and stream samples were analyzed for delta (δ)15N-NO3, δ18O NO3, and a suite of selected dissolved ions. Data provide evidence of modification of potential local nutrient source signatures by nitrification, atmospheric deposition, evaporation, and denitrification. Samples taken from the CAFO waste pond, a septic system, field and parking lot runoff, fertilizer, and hog manure exhibited different δ15N-NO3 and δ18O-NO3 values as compared to stream samples. Stream δ15N-NO3 and δ18O-NO3 values cannot be explained by direct input of any one of these potential sources without modification of the isotopic composition by mixing or fractionation. Big Creek nitrate isotope values (-3.4 per mil [‰] to 6.7‰ δ15N-NO3 and -7.6 to 9.1‰ δ18O-NO3) were similar to values expected from nitrification of nitrogen stored in soils sampled in the watershed (2.8 to 7.6‰ δ15N-NO3 and 3.4 to 4.8‰ δ18O-NO3).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"conferenceTitle":"2020 KIG workshop","conferenceDate":"October 19-20, 2021","conferenceLocation":"Online","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Sokolosky, K., and Hays, P.D., 2021, Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas, in U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021, v. 8, Online, October 19-20, 2021, p. 54-63.","productDescription":"10 p.","startPage":"54","endPage":"63","ipdsId":"IP-117025","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":396600,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/publication/sir20205019"},{"id":396602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","county":"Newton County","otherGeospatial":"Big Creek, Buffalo National River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6307373046875,\n 36.50522086338427\n ],\n [\n -94.4549560546875,\n 35.40696093270201\n ],\n [\n -91.7578125,\n 35.420391545750746\n ],\n [\n -91.768798828125,\n 36.50522086338427\n ],\n [\n -94.6307373046875,\n 36.50522086338427\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Kuniansky, Eve L. 0000-0002-5581-0225 elkunian@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":932,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"elkunian@usgs.gov","middleInitial":"L.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":836807,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Spangler, Lawrence E. 0000-0003-3928-8809 spangler@usgs.gov","orcid":"https://orcid.org/0000-0003-3928-8809","contributorId":973,"corporation":false,"usgs":true,"family":"Spangler","given":"Lawrence","email":"spangler@usgs.gov","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836808,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Sokolosky, Kelly","contributorId":287479,"corporation":false,"usgs":false,"family":"Sokolosky","given":"Kelly","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":836794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hays, Phillip D. 0000-0001-5491-9272 pdhays@usgs.gov","orcid":"https://orcid.org/0000-0001-5491-9272","contributorId":4145,"corporation":false,"usgs":true,"family":"Hays","given":"Phillip","email":"pdhays@usgs.gov","middleInitial":"D.","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836795,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225524,"text":"70225524 - 2021 - Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","interactions":[],"lastModifiedDate":"2023-11-08T16:34:39.150126","indexId":"70225524","displayToPublicDate":"2021-10-20T08:25:50","publicationYear":"2021","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":"Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","docAbstract":"Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.
Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and other explanatory variables that describe the groundwater flow system and surface characteristics. Methods to address the imbalanced occurrence of elevated and low Mn concentrations are compared and used to more accurately predict concentrations of interest for human health and drinking water quality.
New hydrological insights for the region: Elevated Mn concentrations were more likely in shallow groundwater, close to recharge areas and in topographically low areas where soil or unsaturated processes influence groundwater quality. Predicted concentrations greater than the health threshold of 300 micrograms per liter extended across 17 % of the surficial aquifer area, but across <1% of the areas of underlying aquifers. pH and variables related to flow-system position and near-surface processes were more important predictors than the probability of low dissolved oxygen (DO). Mapped variable influence (SHAP values) showed that both pH and DO variables were related to hydrogeologic conditions. Class weights, which improved the predictive ability for elevated Mn without altering the data, was the preferred method to address class imbalance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100925","usgsCitation":"DeSimone, L.A., and Ransom, K.M., 2021, Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning: Journal of Hydrology: Regional Studies, v. 37, 100925, 20 p., https://doi.org/10.1016/j.ejrh.2021.100925.","productDescription":"100925, 20 p.","ipdsId":"IP-126500","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450397,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100925","text":"Publisher Index Page"},{"id":436146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M64CD1","text":"USGS data release","linkHelpText":"Data used to model and map manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA"},{"id":390662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, New York, North Carolina, Pennsylvania, Virginia","city":"Baltimore, New York, Philadelphia, Richmond, Washington D.C.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.1142578125,\n 41.22824901518529\n ],\n [\n -73.63037109375,\n 40.94671366508002\n ],\n [\n -75.816650390625,\n 39.78321267821705\n ],\n [\n -78.321533203125,\n 38.30718056188316\n ],\n [\n -78.079833984375,\n 35.200744801724014\n ],\n [\n -77.069091796875,\n 35.06597313798418\n ],\n [\n -76.80541992187499,\n 34.94899072578227\n ],\n [\n -76.475830078125,\n 35.092945313732635\n ],\n [\n -76.387939453125,\n 35.34425514918409\n ],\n [\n -76.036376953125,\n 35.29943548054545\n ],\n [\n -75.509033203125,\n 35.84453450421662\n ],\n [\n -75.882568359375,\n 36.43012234551576\n ],\n [\n -75.904541015625,\n 37.055177106660814\n ],\n [\n -75.860595703125,\n 37.28279464911045\n ],\n [\n -75.201416015625,\n 38.07404145941957\n ],\n [\n -74.893798828125,\n 38.496593518947584\n ],\n [\n -75.289306640625,\n 39.16414104768742\n ],\n [\n -74.94873046875,\n 39.138581990583525\n ],\n [\n -75.069580078125,\n 38.976492485539396\n ],\n [\n -74.87182617187499,\n 38.865374851611634\n ],\n [\n -74.124755859375,\n 39.715638134796336\n ],\n [\n -73.883056640625,\n 40.38839687388361\n ],\n [\n -74.058837890625,\n 40.50544628405211\n ],\n [\n -71.74072265625,\n 40.98819156349393\n ],\n [\n -72.1142578125,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeSimone, Leslie A. 0000-0003-0774-9607 ldesimon@usgs.gov","orcid":"https://orcid.org/0000-0003-0774-9607","contributorId":195635,"corporation":false,"usgs":true,"family":"DeSimone","given":"Leslie","email":"ldesimon@usgs.gov","middleInitial":"A.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825413,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225698,"text":"70225698 - 2021 - Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","interactions":[],"lastModifiedDate":"2021-11-03T12:50:00.117476","indexId":"70225698","displayToPublicDate":"2021-10-20T07:46:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","docAbstract":"Understanding the rates and patterns of tidal wetland elevation changes relative to sea-level is essential for understanding the extent of potential wetland loss over the coming years. Using an enhanced and more flexible modeling framework of an ecosystem model (WARMER-2), we explored sea-level rise (SLR) impacts on wetland elevations and carbon sequestration rates through 2100 by considering plant community transitions, salinity effects on productivity, and changes in sediment availability. We incorporated local experimental results for plant productivity relative to inundation and salinity into a species transition model, as well as site-level estimates of organic matter decomposition. The revised modeling framework includes an improved calibration scheme that more accurately reconstructs soil profiles and incorporates parameter uncertainty through Monte Carlo simulations. Using WARMER-2, we evaluated elevation change in three tidal wetlands in the San Francisco Bay Estuary, CA, USA along an estuarine tidal and salinity gradient with varying scenarios of SLR, salinization, and changes in sediment availability. We also tested the sensitivity of marsh elevation and carbon accumulation rates to different plant productivity functions. Wetland elevation at all three sites was sensitive to changes in sediment availability, but sites with greater initial elevations or space for upland transgression persisted longer under higher SLR rates than sites at lower elevations. Using a multi-species wetland vegetation transition model for organic matter contribution to accretion, WARMER-2 projected increased elevations relative to sea levels (resilience) and higher rates of carbon accumulation when compared with projections assuming no future change in vegetation with SLR. A threshold analysis revealed that all three wetland sites were likely to eventually transition to an unvegetated state with SLR rates above 7 mm/yr. Our results show the utility in incorporating additional estuary-specific parameters to bolster confidence in model projections. The new WARMER-2 modeling framework is widely applicable to other tidal wetland ecosystems and can assist in teasing apart important drivers of wetland elevation change under SLR.
Historically, Cisco Coregonus artedi and Lake Whitefish Coregonus clupeaformis were abundant throughout the Laurentian Great Lakes, but overharvest, habitat degradation, and interactions with exotic species caused most populations to collapse by the mid-1900s. Strict commercial fishery regulations and improved environmental and ecological conditions allowed Cisco to partially recover only in Lake Superior, whereas Lake Whitefish recovered in all the upper Great Lakes (Superior, Michigan, and Huron). The differential responses of Cisco and Lake Whitefish to improved environmental and ecological conditions in lakes Michigan and Huron have led to questions about potential negative interactions between these species. To provide context for fishery managers, we tested for positive and negative correlations between historical (1929–1970) Cisco and Lake Whitefish commercial gill net catch per effort (CPE; kg/km of net) at a variety of spatial scales in Michigan waters of the upper Great Lakes. The three best-fit spatial models—LAKEWIDE, REGIONAL 10, and SIMPLE—all had similar levels of support (scaled second-order Akaike Information Criterion < 3.0), and we used these models to determine whether there was a significant correlation between Cisco and Lake Whitefish CPE (positive and negative). There was either no correlation between Cisco and Lake Whitefish CPE or a positive correlation for most (12 of 13) pairwise (Cisco–Lake Whitefish) comparisons. We identified no strong positive or negative correlations in the lakewide (LAKEWIDE) or reduced (SIMPLE) models. In the regional model (REGIONAL 10), we identified strong and positive correlations between Cisco and Lake Whitefish CPE in two regions (ρ = 0.59–0.71) and a weak negative correlation in one region (ρ = −0.45). Collectively, our findings suggest that Cisco and Lake Whitefish CPE were largely independent of each other; thus, these species likely did not interact to the detriment of one another in Michigan waters of the upper Great Lakes during 1929–1970.
Shorebird use of horseshoe crab (Limulus polyphemus) eggs as food items has been well documented along the Atlantic coast of the United States at northeastern stopover sites such as the Delaware Bay. However, the relationship between migratory shorebirds and horseshoe crab eggs has not been well studied in the South Atlantic Bight. The objective of our study was to assess the spatial and temporal overlap between the density of horseshoe crab eggs and the relative abundance of foraging shorebirds during spring migration in the Cape Romain-Santee Delta Region (CRSD), South Carolina, USA. The CRSD is a site of international importance for shorebirds that supports ∼100,000 shorebirds annually. We also sought to determine if horseshoe crab eggs were present in the diets of shorebirds at these sites. We monitored study plots between March and June 2015–2016 at predicted horseshoe crab spawning sites on beaches throughout Cape Romain National Wildlife Refuge. We conducted weekly shorebird surveys and collected core samples of beach substrate twice per month to measure densities of horseshoe crab eggs. We found a positive correlation between number of foraging shorebirds and horseshoe crab eggs for both years. In a molecular analysis of shorebird fecal samples, 95% of the samples tested contained DNA from horseshoe crab eggs. The spatial and temporal overlap between shorebirds and horseshoe crab eggs, and the dietary analysis of fecal samples, suggest that there are areas of localized horseshoe crab spawning that shorebirds can utilize as a food source during spring in Cape Romain National Wildlife Refuge.
","language":"English","publisher":"The Wilson Ornithological Society","doi":"10.1676/21-00009","usgsCitation":"Takahashi, F., Sanders, F., and Jodice, P.G., 2021, Spatial and temporal overlap between foraging shorebirds and spawning horseshoe crabs (Limulus polyphemus) in the Cape Romain-Santee Delta Region of the U.S. Atlantic coast: Wilson Journal of Ornithology, v. 133, no. 1, p. 58-72, https://doi.org/10.1676/21-00009.","productDescription":"15 p.","startPage":"58","endPage":"72","ipdsId":"IP-124095","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1676/21-00009","text":"Publisher Index Page"},{"id":396758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Cape Romain National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.63027954101562,\n 32.88881315761995\n ],\n [\n -79.5794677734375,\n 32.90466807419695\n ],\n [\n -79.57191467285156,\n 32.931182680502246\n ],\n [\n -79.48986053466797,\n 33.00866349457558\n ],\n [\n -79.47235107421875,\n 33.005208549965474\n ],\n [\n -79.46102142333984,\n 33.00981511270531\n ],\n [\n -79.42531585693358,\n 33.01499720819903\n ],\n [\n -79.4036865234375,\n 33.01643662512605\n ],\n [\n -79.40059661865234,\n 33.00779977111022\n ],\n [\n -79.37828063964842,\n 33.00808767987181\n ],\n [\n -79.36248779296874,\n 33.00779977111022\n ],\n [\n -79.35081481933594,\n 33.03370779524476\n ],\n [\n -79.34154510498047,\n 33.04752229444311\n ],\n [\n -79.33502197265625,\n 33.066801409996835\n ],\n [\n -79.34154510498047,\n 33.071117051436254\n ],\n [\n -79.35047149658203,\n 33.05183888113547\n ],\n [\n -79.3597412109375,\n 33.05529199813852\n ],\n [\n -79.36866760253906,\n 33.05040004241502\n ],\n [\n -79.37210083007812,\n 33.06565053653404\n ],\n [\n -79.38514709472656,\n 33.067952268409094\n ],\n [\n -79.39098358154297,\n 33.073130945006625\n ],\n [\n -79.39544677734375,\n 33.07284324874736\n ],\n [\n -79.3927001953125,\n 33.07830931680089\n ],\n [\n -79.3985366821289,\n 33.08952807659452\n ],\n [\n -79.3978500366211,\n 33.09240445113745\n ],\n [\n -79.39132690429688,\n 33.09326734515043\n ],\n [\n -79.3927001953125,\n 33.099307366092084\n ],\n [\n -79.38995361328125,\n 33.109660722085074\n ],\n [\n -79.4036865234375,\n 33.099307366092084\n ],\n [\n -79.44557189941406,\n 33.07974769931395\n ],\n [\n -79.46136474609375,\n 33.07773395720986\n ],\n [\n -79.49432373046875,\n 33.06248555690915\n ],\n [\n -79.54032897949219,\n 33.03975190521167\n ],\n [\n -79.55509185791016,\n 33.042629907076225\n ],\n [\n -79.58702087402344,\n 33.016724505691684\n ],\n [\n -79.6343994140625,\n 32.967771310880366\n ],\n [\n -79.65019226074219,\n 32.94501323438982\n ],\n [\n -79.64229583740234,\n 32.936657533381286\n ],\n [\n -79.6395492553711,\n 32.92167292013293\n ],\n [\n -79.63027954101562,\n 32.91129747043409\n ],\n [\n -79.6343994140625,\n 32.90178557318628\n ],\n [\n -79.63027954101562,\n 32.88881315761995\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Takahashi, F.","contributorId":287942,"corporation":false,"usgs":false,"family":"Takahashi","given":"F.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":837222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders, F.","contributorId":287943,"corporation":false,"usgs":false,"family":"Sanders","given":"F.","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":837223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":837224,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229677,"text":"70229677 - 2021 - Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distribution","interactions":[],"lastModifiedDate":"2022-09-02T16:41:05.254001","indexId":"70229677","displayToPublicDate":"2021-10-19T06:26:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distribution","docAbstract":"Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.
Karst hydrogeologic systems represent challenging and unique conditions to scientists attempting to study groundwater flow and contaminant transport. Karst terrains are characterized by distinct and beautiful landscapes, caverns, and springs, and many of the exceptional karst areas are designated as national or state parks. The range and complexity of landforms and groundwater flow systems associated with karst terrains are enormous, perhaps more than any other type of aquifer.
The U.S. Geological Survey (USGS) Karst Interest Group (KIG), formed in 2000, is a loosely knit, grassroots organization of USGS and non-USGS scientists and researchers devoted to fostering better communication among scientists working on, or interested in, karst aquifers. The primary mission of the KIG is to encourage and support interdisciplinary collaboration and technology transfer among scientists working in karst areas. To accomplish its mission, the KIG has organized a series of workshops. To date (2021), eight KIG workshops, including the workshop documented in this report, have been held. This workshop is the first virtual workshop. The abstracts and extended abstracts provide a snapshot in time of past and current karst-related studies. The field trip guide is included in the proceedings volume even though the field trip will not occur in person.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205019","usgsCitation":"Kuniansky, E.L., and Spangler, L.E., eds., 2021, U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021: U.S. Geological Survey Scientific Investigations Report 2020–5019, 147 p., https://doi.org/10.3133/sir20205019.","productDescription":"iv, 147 p.","numberOfPages":"147","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114317","costCenters":[{"id":448,"text":"National Water Availability and Use Program","active":false,"usgs":true}],"links":[{"id":390521,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5019/coverthb.jpg"},{"id":390522,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5019/sir20205019.pdf","text":"Report","size":"9.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5019"}],"contact":"Water Mission Area
U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
https://www.usgs.gov/mission-areas/water-resources/science/karst-aquifers
No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Hartpence, A., 2021, Landsat Update October 2021: Landsat Update, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-133501","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":399093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399092,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/news/landsat-update-october-2021"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hartpence, Anya 0000-0002-4510-3236","orcid":"https://orcid.org/0000-0002-4510-3236","contributorId":247379,"corporation":false,"usgs":false,"family":"Hartpence","given":"Anya","email":"","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":825263,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70225510,"text":"sir20215111 - 2021 - Sediment transport in the Yankee Fork of the Salmon River near Stanley, Idaho, water years 2012–19","interactions":[],"lastModifiedDate":"2022-09-27T13:57:31.794232","indexId":"sir20215111","displayToPublicDate":"2021-10-18T09:44:49","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5111","displayTitle":"Sediment Transport in the Yankee Fork of the Salmon River near Stanley, Idaho, Water Years 2012–19","title":"Sediment transport in the Yankee Fork of the Salmon River near Stanley, Idaho, water years 2012–19","docAbstract":"Placer and dredging operations in the Yankee Fork Basin, Idaho, have left more than 5 miles of the lower Yankee Fork of the Salmon River (Yankee Fork) in a highly altered fluvial condition, resulting in poor habitat quantity and quality for native fish species. Since 2011, the Bureau of Reclamation and other stakeholders have implemented a series of restoration efforts to improve the connectivity of the river with its floodplain and to improve aquatic and terrestrial habitat in the Yankee Fork. In conjunction with these rehabilitation efforts, the U.S. Geological Survey monitored streamflow and suspended-sediment and bedload transport during water years 2012–19 at four sites in the affected lower reach of the Yankee Fork. The objectives of the monitoring were to (1) identify source areas of sediment, (2) quantify sediment transport in the lower Yankee Fork, and (3) provide a benchmark to evaluate the effects of rehabilitation efforts in the basin.
During the 8 years of sampling, the annual flow-weighted suspended-sediment concentrations (SSCs) were largest at the most downstream Clayton site, ranging from a low of 11 milligrams per liter (mg/L) in 2015 to 145 mg/L in 2017. The Clayton site also had the largest flow-weighted concentrations of suspended sand and suspended fines. At relatively low streamflow, the fine-grained fraction of the suspended sediment was the dominant component of the SSC at all sites, with an increase in the sand-size fraction as streamflow increased during snowmelt runoff. Each of the three main-stem Yankee Fork sites indicated a large amount of hysteresis in SSCs during snowmelt runoff, with concentrations on the rising limb of the hydrograph larger than concentrations on the falling limb at similar streamflow. Hysteresis was particularly evident in the fine-grained fraction of suspended sediment, indicating that sediment transport in the lower Yankee Fork is more limited by the supply of fine-grained sediment as compared to coarser-grained sediment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215111","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Clark, G.M., and Ducar, S.D., 2021, Sediment transport in the Yankee Fork of the Salmon River near Stanley, Idaho, water years 2012–19: U.S. Geological Survey Scientific Investigations Report 2021–5111, 36 p., https://doi.org/10.3133/sir20215111.","productDescription":"Report: vi, 36 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-126315","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":397358,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5111/sir20215111.XML"},{"id":397357,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5111/images"},{"id":390610,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T1F6SW","text":"USGS data release","description":"USGS data release","linkHelpText":"Synthetic streamflow regressions and daily mean streamflow estimates at three sites on the Yankee Fork Salmon River near Clayton, ID, Water Years 2012-2019"},{"id":403445,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215111/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5111"},{"id":390609,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5111/sir20215111.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5111"},{"id":390608,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5111/coverthb.jpg"}],"country":"United States","state":"Idaho","city":"Stanley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.0213623046875,\n 44.05601169578525\n ],\n [\n -113.84033203125,\n 44.05601169578525\n ],\n [\n -113.84033203125,\n 44.89479576469787\n ],\n [\n -115.0213623046875,\n 44.89479576469787\n ],\n [\n -115.0213623046875,\n 44.05601169578525\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Satellite-derived bathymetry (SDB) based upon an empirical band ratio method is a cost-effective means for mapping nearshore bathymetry in coastal areas vulnerable to natural hazards. This is particularly important for the low-lying coastal community of Unalakleet, Alaska, that has been negatively affected not only by flooding, storm surge, and historically strong storms but also by high erosion rates stemming from the Unalakleet River and Norton Sound. The purpose of this study was to assess the viability of different satellite imagery, including Landsat 8 (L8) Operational Land Imager, Sentinel–2B, WorldView–3, and L8 Provisional Aquatic Reflectance science product, for deriving SDB for Unalakleet, Alaska. Correlations were performed between satellite imagery band ratios and topobathymetric (topobathy) light detection and ranging (lidar) and in situ single-beam sound navigation and ranging (sonar). The satellite imagery correlations with topobathy lidar did not yield as high of a linear relation with water depths as the satellite imagery correlations with the single-beam sonar. An extinction depth, where light no longer attenuates through the water column, was not identified because of the shallow depths within the topobathy lidar and single-beam sonar datasets. Although some single-beam soundings measured at 7 meters deep, the correlations with the SDB band ratios did not yield a strong linear relation. Satellite imagery band ratio correlations with Electronic Navigational Chart soundings did not yield a strong linear relation because of older source data. Less than optimal linear regressions were most likely due to the geography of Unalakleet, Alaska, a low-lying coastal community subject to high erosion rates from surrounding waters. This study is one of the first attempts to compare different satellite imagery band ratio correlations with topobathy lidar and in situ sonar to assess the viability for nearshore SDB for coastal Unalakleet, Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215097","usgsCitation":"Poppenga, S.K., and Danielson, J.J., 2021, A comparison of Landsat 8 Operational Land Imager and Provisional Aquatic Reflectance science product, Sentinel–2B, and WorldView–3 imagery for empirical satellite-derived bathymetry, Unalakleet, Alaska: U.S. Geological Survey Scientific Investigations Report 2021–5097, 15 p., https://doi.org/10.3133/sir20215097.","productDescription":"Report: vii, 15 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-132009","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":390537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5097/coverthb.jpg"},{"id":390538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5097/sir20215097.pdf","text":"Report","size":"1.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5097"},{"id":390539,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9238F8K","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Nearshore bathymetry data from the Unalakleet River mouth, Alaska, 2019"}],"country":"United States","state":"Alaska","city":"Unalakleet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -164.4873046875,\n 63.16675579239305\n ],\n [\n -159.6038818359375,\n 63.16675579239305\n ],\n [\n -159.6038818359375,\n 64.58146958015028\n ],\n [\n -164.4873046875,\n 64.58146958015028\n ],\n [\n -164.4873046875,\n 63.16675579239305\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
We examined a study plot sampled in the Conecuh National Forest of southern Alabama in 1999 and again in 2016 after stand thinning and persistent prescribed fire were used to improve habitat quality. These management activities were designed, in part, to enhance habitat quality for the gopher tortoise (Gopherus polyphemus), a species considered for protection under the Endangered Species Act because of range-wide population declines. Comparisons of vegetation structure were made at burrows that were active in 1999 and remained active in 2016, and at burrows that were active in 1999 but were classified as abandoned in 2016. Burrows that remained active across this span of time were distinctive in retaining a greater reduction of canopy cover and lacking hardwoods while having a high percentage of longleaf pine in neighboring canopy trees. Active burrows that were abandoned in 2016 were distinctive in possessing hardwoods among neighboring canopy trees, lacking pine seedlings in the midstory, and having increased frequency of legumes and decreased bare ground in the understory. All burrows experienced a decrease in canopy cover and an increase in distance to, and dbh of, neighboring canopy trees; all burrows also had a decrease in litter and non-legume forbs as well as an increase in shrub stems in the understory and midstory. These results indicate that management activities were successful in opening canopy cover but were largely unsuccessful at reducing shrub stems and increasing understory grasses and forbs. Nevertheless, burrows of gopher tortoises increased in abundance during this time period. The size distribution of these burrows changed from a unimodal distribution dominated by adults to a bimodal distribution indicative of increased juvenile recruitment. Thus, the gopher tortoise population increased in association with vegetation changes that suggest tortoises are more sensitive to shading caused by canopy closure than they are to available forage.
","language":"English","publisher":"Natural Areas Association","doi":"10.3375/21-3","usgsCitation":"Pudner, R.C., Waddle, H., Mersmann, S.P., Kush, J.S., Guyer, C., and Hermann, S.M., 2021, Changes in vegetation structure and gopher tortoise population structure after 17 years of restoration management: Natural Areas Journal, v. 41, no. 4, p. 273-282, https://doi.org/10.3375/21-3.","productDescription":"10 p.","startPage":"273","endPage":"282","ipdsId":"IP-125278","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","county":"Covington County","otherGeospatial":"Conecuh National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.7139720916748,\n 31.016235250004833\n ],\n [\n -86.65655136108397,\n 31.016235250004833\n ],\n [\n -86.65655136108397,\n 31.061095508655708\n ],\n [\n -86.7139720916748,\n 31.061095508655708\n ],\n [\n -86.7139720916748,\n 31.016235250004833\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pudner, Rebecca C.","contributorId":289770,"corporation":false,"usgs":false,"family":"Pudner","given":"Rebecca","email":"","middleInitial":"C.","affiliations":[{"id":62236,"text":"Directorate of Public Works, Natural Resource Management Branch, IWE-PWE-N, Fort Benning, GA","active":true,"usgs":false}],"preferred":false,"id":839744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":206866,"corporation":false,"usgs":true,"family":"Waddle","given":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mersmann, Suzi P.","contributorId":289771,"corporation":false,"usgs":false,"family":"Mersmann","given":"Suzi","email":"","middleInitial":"P.","affiliations":[{"id":62238,"text":"SP Mersmann Environmental Services","active":true,"usgs":false}],"preferred":false,"id":839746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kush, John S.","contributorId":289772,"corporation":false,"usgs":false,"family":"Kush","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":62240,"text":"School of Forestry and Wildlife Science, Auburn University","active":true,"usgs":false}],"preferred":false,"id":839747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guyer, Craig","contributorId":104800,"corporation":false,"usgs":false,"family":"Guyer","given":"Craig","email":"","affiliations":[],"preferred":false,"id":839748,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hermann, Sharon M.","contributorId":175058,"corporation":false,"usgs":false,"family":"Hermann","given":"Sharon","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":839749,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241512,"text":"70241512 - 2021 - Testing a generalizable machine learning workflow for aquatic invasive species on Rainbow Trout (Oncorhynchus mykiss) in northwest Montana","interactions":[],"lastModifiedDate":"2023-03-22T13:39:36.559013","indexId":"70241512","displayToPublicDate":"2021-10-18T08:27:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13624,"text":"Frontiers in Big Data","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Testing a generalizable machine learning workflow for aquatic invasive species on Rainbow Trout (Oncorhynchus mykiss) in northwest Montana","title":"Testing a generalizable machine learning workflow for aquatic invasive species on Rainbow Trout (Oncorhynchus mykiss) in northwest Montana","docAbstract":"Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fdata.2021.734990","usgsCitation":"Carter, S.C., van Rees, C.B., Hand, B., Muhlfeld, C.C., Luikart, G., and Kimball, J., 2021, Testing a generalizable machine learning workflow for aquatic invasive species on Rainbow Trout (Oncorhynchus mykiss) in northwest Montana: Frontiers in Big Data, v. October 2021, no. 4, 734990, 16 p., https://doi.org/10.3389/fdata.2021.734990.","productDescription":"734990, 16 p.","ipdsId":"IP-131069","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":450414,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fdata.2021.734990","text":"Publisher Index Page"},{"id":414546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, British Columbia, Montana","otherGeospatial":"Upper Flathead River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.23345756511358,\n 47.33781313835249\n ],\n [\n -112.23345756511358,\n 49.73936740861234\n ],\n [\n -116.91479840469856,\n 49.73936740861234\n ],\n [\n -116.91479840469856,\n 47.33781313835249\n ],\n [\n -112.23345756511358,\n 47.33781313835249\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"October 2021","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Sean C.","contributorId":292837,"corporation":false,"usgs":false,"family":"Carter","given":"Sean","email":"","middleInitial":"C.","affiliations":[{"id":63038,"text":"Numerical Terradynamic Simulation Group, University of Montana","active":true,"usgs":false}],"preferred":false,"id":867066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Rees, Charles B.","contributorId":198604,"corporation":false,"usgs":false,"family":"van Rees","given":"Charles","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":867067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hand, Brian K.","contributorId":139248,"corporation":false,"usgs":false,"family":"Hand","given":"Brian K.","affiliations":[{"id":12707,"text":"Flathead Lake Biological Station, Fish and Wildlife Genomics Group, University of Montana, Polson, MT 59860","active":true,"usgs":false}],"preferred":false,"id":867068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":867069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":867070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kimball, John S","contributorId":167148,"corporation":false,"usgs":false,"family":"Kimball","given":"John S","affiliations":[{"id":5091,"text":"Flathead Lake Biological Station, Fish and Wildlife Genomics Group, Division of Biological Sciences, University of Montana, Polson, MT 59860, USA","active":true,"usgs":false}],"preferred":false,"id":867071,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70225670,"text":"70225670 - 2021 - Acute mortality in California tiger salamander (Ambystoma californiense) and Santa Cruz long-toed salamander (Ambystoma macrodactylum croceum) caused by Ribeiroia ondatrae (Class: Trematoda)","interactions":[],"lastModifiedDate":"2021-11-16T16:03:03.350757","indexId":"70225670","displayToPublicDate":"2021-10-18T08:20:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Acute mortality in California tiger salamander (Ambystoma californiense) and Santa Cruz long-toed salamander (Ambystoma macrodactylum croceum) caused by Ribeiroia ondatrae (Class: Trematoda)","title":"Acute mortality in California tiger salamander (Ambystoma californiense) and Santa Cruz long-toed salamander (Ambystoma macrodactylum croceum) caused by Ribeiroia ondatrae (Class: Trematoda)","docAbstract":"In early September 2019, a morbidity and mortality event affecting California tiger salamanders (Ambystoma californiense) and Santa Cruz long-toed salamanders (Ambystoma macrodactylum croceum) in late stages of metamorphosis was reported at a National Wildlife Refuge in Santa Cruz County, California, U.S.A. During the postmortem disease investigation, severe integumentary metacercarial (Class: Trematoda) infection, associated with widespread skin lesions, was observed. Planorbid snails collected from the ponds of the refuge within seven days of the mortality event were infected with Ribeiroia ondatrae, a digenetic trematode that can cause malformation and death in some amphibians. We suggest sustained seasonal high-water levels due to active habitat management along with several years of increased rainfall led to increased bird visitation, increased over-wintering of snails, and prolonged salamander metamorphosis, resulting in a confluence of conditions and cascading of host-parasite dynamics to create a hyper-parasitized state.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijppaw.2021.10.008","usgsCitation":"Keller, S., Roderick, C., Caris, C., Grear, D.A., and Cole, R.A., 2021, Acute mortality in California tiger salamander (Ambystoma californiense) and Santa Cruz long-toed salamander (Ambystoma macrodactylum croceum) caused by Ribeiroia ondatrae (Class: Trematoda): International Journal for Parasitology: Parasites and Wildlife, v. 16, p. 255-261, https://doi.org/10.1016/j.ijppaw.2021.10.008.","productDescription":"7 p.","startPage":"255","endPage":"261","ipdsId":"IP-130453","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":450418,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijppaw.2021.10.008","text":"Publisher Index Page"},{"id":436151,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CD92ZZ","text":"USGS data release","linkHelpText":"Carcass weights, 28S rRNA alignment file and parasite sample vouchers collected from California tiger salamanders (Ambystoma californiense) CTS and Santa Cruz long-toed salamander (Ambystoma macrodactylum croceum) SCLT from Prospect or Ellicott Pond, on Ellicott Slough National Wildlife Refuge, California U.S.A. recorded September 11, 2019"},{"id":391267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Cruz County","otherGeospatial":"Ellicott Slough National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.80971145629881,\n 36.9106467256463\n ],\n [\n -121.80035591125488,\n 36.9106467256463\n ],\n [\n -121.80035591125488,\n 36.92423384099305\n ],\n [\n -121.80971145629881,\n 36.92423384099305\n ],\n [\n -121.80971145629881,\n 36.9106467256463\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keller, Saskia","contributorId":255627,"corporation":false,"usgs":false,"family":"Keller","given":"Saskia","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":826146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roderick, Constance 0000-0001-8330-8024","orcid":"https://orcid.org/0000-0001-8330-8024","contributorId":215346,"corporation":false,"usgs":true,"family":"Roderick","given":"Constance","email":"","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":826147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caris, Christopher","contributorId":268197,"corporation":false,"usgs":false,"family":"Caris","given":"Christopher","email":"","affiliations":[{"id":55590,"text":"U.S. Fish and Wildlife Service, San Francisco Bay National Wildlife Refuge Complex, Fremont, CA, USA","active":true,"usgs":false}],"preferred":false,"id":826148,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":826149,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, Rebecca A. 0000-0003-2923-1622 rcole@usgs.gov","orcid":"https://orcid.org/0000-0003-2923-1622","contributorId":2873,"corporation":false,"usgs":true,"family":"Cole","given":"Rebecca","email":"rcole@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":826150,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226179,"text":"70226179 - 2021 - The effects of ENSO and the North American monsoon on mast seeding in two Rocky Mountain conifer species","interactions":[],"lastModifiedDate":"2021-11-16T12:59:50.389487","indexId":"70226179","displayToPublicDate":"2021-10-18T06:58:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The effects of ENSO and the North American monsoon on mast seeding in two Rocky Mountain conifer species","docAbstract":"We aimed to disentangle the patterns of synchronous and variable cone production (i.e. masting) and its relationship to climate in two conifer species native to dry forests of western North America. We used cone abscission scars to reconstruct ca 15 years of recent cone production in Pinus edulis and Pinus ponderosa, and used redundancy analysis to relate time series of annual cone production to climate indices describing the North American monsoon and the El Niño Southern Oscillation (ENSO). We show that the sensitivity to climate and resulting synchrony in cone production varies substantially between species. Cone production among populations of P. edulis was much more spatially synchronous and more closely related to large-scale modes of climate variability than among populations of P. ponderosa. Large-scale synchrony in P. edulis cone production was associated with the North American monsoon and we identified a dipole pattern of regional cone production associated with ENSO phase. In P. ponderosa, these climate indices were not strongly associated with cone production, resulting in asynchronous masting patterns among populations. This study helps frame our understanding of mast seeding as a life-history strategy and has implications for our ability to forecast mast years in these species.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2020.0378","usgsCitation":"Wion, A., Pearse, I., Rodman, K., Veblen, T.T., and Redmond, M., 2021, The effects of ENSO and the North American monsoon on mast seeding in two Rocky Mountain conifer species: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 376, no. 1839, https://doi.org/10.1098/rstb.2020.0378.","ipdsId":"IP-126312","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450421,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":391740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","issue":"1839","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wion, Andreas","contributorId":225092,"corporation":false,"usgs":false,"family":"Wion","given":"Andreas","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodman, Kyle C.","contributorId":238090,"corporation":false,"usgs":false,"family":"Rodman","given":"Kyle C.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":826733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Veblen, Thomas T.","contributorId":192285,"corporation":false,"usgs":false,"family":"Veblen","given":"Thomas","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":826734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Redmond, Miranda D.","contributorId":225094,"corporation":false,"usgs":false,"family":"Redmond","given":"Miranda","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826735,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226183,"text":"70226183 - 2021 - The ecology and evolution of synchronized reproduction in long-lived plants","interactions":[],"lastModifiedDate":"2021-11-16T12:56:10.688362","indexId":"70226183","displayToPublicDate":"2021-10-18T06:54:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The ecology and evolution of synchronized reproduction in long-lived plants","docAbstract":"Populations of many long-lived plants exhibit spatially synchronized seed production that varies extensively over time, so that seed production in some years is much higher than on average, while in others, it is much lower or absent. This phenomenon termed masting or mast seeding has important consequences for plant reproductive success, ecosystem dynamics and plant–human interactions. Inspired by recent advances in the field, this special issue presents a series of articles that advance the current understanding of the ecology and evolution of masting. To provide a broad overview, we reflect on the state-of-the-art of masting research in terms of underlying proximate mechanisms, ontogeny, adaptations, phylogeny and applications to conservation. While the mechanistic drivers and fitness consequences of masting have received most attention, the evolutionary history, ontogenetic trajectory and applications to plant–human interactions are poorly understood. With increased availability of long-term datasets across broader geographical and taxonomic scales, as well as advances in molecular approaches, we expect that many mysteries of masting will be solved soon. The increased understanding of this global phenomenon will provide the foundation for predictive modelling of seed crops, which will improve our ability to manage forests and agricultural fruit and nut crops in the Anthropocene.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2020.0369","usgsCitation":"Pesendorfer, M.B., Ascoli, D., Bogdziewicz, M., Hacket-Pain, A., Pearse, I., and Vacchiano, G., 2021, The ecology and evolution of synchronized reproduction in long-lived plants: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 376, no. 1839, https://doi.org/10.1098/rstb.2020.0369.","ipdsId":"IP-130378","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450424,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2020.0369","text":"Publisher Index Page"},{"id":391738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","issue":"1839","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Pesendorfer, Mario B.","contributorId":201187,"corporation":false,"usgs":false,"family":"Pesendorfer","given":"Mario","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":826744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ascoli, Davide","contributorId":224289,"corporation":false,"usgs":false,"family":"Ascoli","given":"Davide","email":"","affiliations":[{"id":40848,"text":"University of Torino","active":true,"usgs":false}],"preferred":false,"id":826745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bogdziewicz, Michal","contributorId":256849,"corporation":false,"usgs":false,"family":"Bogdziewicz","given":"Michal","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":826746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hacket-Pain, Andrew","contributorId":224290,"corporation":false,"usgs":false,"family":"Hacket-Pain","given":"Andrew","affiliations":[{"id":16977,"text":"University of Liverpool","active":true,"usgs":false}],"preferred":false,"id":826747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826743,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vacchiano, Giorgio","contributorId":224295,"corporation":false,"usgs":false,"family":"Vacchiano","given":"Giorgio","email":"","affiliations":[{"id":40851,"text":"University of Milan","active":true,"usgs":false}],"preferred":false,"id":826748,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70225673,"text":"70225673 - 2021 - Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States","interactions":[],"lastModifiedDate":"2021-11-02T11:54:43.920548","indexId":"70225673","displayToPublicDate":"2021-10-18T06:51:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States","docAbstract":"Groundwater is an important source of drinking water supplies in the conterminous United State (CONUS), and presence of high nitrate concentrations may limit usability of groundwater in some areas because of the potential negative health effects. Prediction of locations of high nitrate groundwater is needed to focus mitigation and relief efforts. A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate. Nitrate was predicted at a 1 km resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. The model provided accurate estimates at national and regional scales: the training (R2 of 0.83) and hold-out (R2 of 0.49) data fits compared favorably to previous studies. Predicted nitrate concentrations were less than 1 mg/L across most of the CONUS. Nationally, well depth, soil and climate characteristics, and the absence of developed land use were among the most influential explanatory factors. Only 1% of the area in either water supply zone had predicted nitrate concentrations greater than 10 mg/L; however, about 1.4 M people depend on groundwater for their drinking supplies in those areas. Predicted high concentrations of nitrate were most prevalent in the central CONUS. In areas of predicted high nitrate concentration, applied manure, farm fertilizer, and agricultural land use were influential predictor variables. This work represents the first application of XGB to a three-dimensional national-scale groundwater quality model and provides a significant milestone in the efforts to document nitrate in groundwater across the CONUS.
Variable eruptive style and explosivity is common in basaltic to basaltic andesite volcanoes but can have uncertain origins. Veniaminof volcano in the Alaska-Aleutian arc is a frequently active open-vent center, regularly producing Strombolian eruptions and small lava flows from an intracaldera cone within an intracaldera ice cap. The September–December 2018 eruption of Veniaminof evolved in explosivity over time. The eruption was documented with frequent satellite observations, syn- and post-eruption structure-from-motion photo surveys, and post-eruption sampling of lava flows and tephra preserved in the syn-eruption snowpack. Lava flows with a total volume of ~ 6 × 106 m3 flowed down the cone flanks into the ice cap, overthickening at ice marginal flow fronts. Smaller tephra deposits were estimated at ~ 1–2 × 106 m3 dense rock equivalent, with almost half of this volume deposited directly on the eruptive cone itself. Erupted products were basaltic andesite, and composition (54 ± 0.7 wt% bulk rock, 58 ± 0.7 wt% glass SiO2), sideromelane microlite crystallinity (20–30%), and microlite number density (plagioclase 6.4 ± 2.6 × 105 n/mm3) did not change significantly over the eruption suggesting a similar magma source and ascent rate. We defined tephra componentry with groundmass microcrystalline textures using backscatter electron images. The componentry of tephra groundmass showed significant increases in tachylite grains, defined here by the presence of dendritic interstitial nanolites, corresponding to increasing seismic tremor and periods of increased ash emissions. We suggest that these componentry changes reflect increasing undercooled zones on the conduit margins that increased brittle shearing, fragmentation, and ultimately ash emissions.
There is evidence that variable and synchronous reproduction in seed plants (masting) correlates to modes of climate variability, e.g. El Niño Southern Oscillation and North Atlantic Oscillation. In this perspective, we explore the breadth of knowledge on how climate modes control reproduction in major masting species throughout Earth's biomes. We posit that intrinsic properties of climate modes (periodicity, persistence and trends) drive interannual and decadal variability of plant reproduction, as well as the spatial extent of its synchrony, aligning multiple proximate causes of masting through space and time. Moreover, climate modes force lagged but in-phase ecological processes that interact synergistically with multiple stages of plant reproductive cycles. This sets up adaptive benefits by increasing offspring fitness through either economies of scale or environmental prediction. Community-wide links between climate modes and masting across plant taxa suggest an evolutionary role of climate variability. We argue that climate modes may ‘bridge’ proximate and ultimate causes of masting selecting for variable and synchronous reproduction. The future of such interaction is uncertain: processes that improve reproductive fitness may remain coupled with climate modes even under changing climates, but chances are that abrupt global warming will affect Earth's climate modes so rapidly as to alter ecological and evolutionary links.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2020.0380","usgsCitation":"Ascoli, D., Hacket-Pain, A., Pearse, I.S., Vacchiano, G., Corti, S., and Davini, P., 2021, Modes of climate variability bridge proximate and evolutionary mechanisms of masting: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 376, no. 1839, https://doi.org/10.1098/rstb.2020.0380.","ipdsId":"IP-127671","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450429,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2020.0380","text":"External Repository"},{"id":391736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","issue":"1839","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Ascoli, Davide","contributorId":224289,"corporation":false,"usgs":false,"family":"Ascoli","given":"Davide","email":"","affiliations":[{"id":40848,"text":"University of Torino","active":true,"usgs":false}],"preferred":false,"id":826814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hacket-Pain, Andrew","contributorId":224290,"corporation":false,"usgs":false,"family":"Hacket-Pain","given":"Andrew","affiliations":[{"id":16977,"text":"University of Liverpool","active":true,"usgs":false}],"preferred":false,"id":826815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vacchiano, Giorgio","contributorId":224295,"corporation":false,"usgs":false,"family":"Vacchiano","given":"Giorgio","email":"","affiliations":[{"id":40851,"text":"University of Milan","active":true,"usgs":false}],"preferred":false,"id":826817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corti, Susanna","contributorId":268854,"corporation":false,"usgs":false,"family":"Corti","given":"Susanna","email":"","affiliations":[{"id":55694,"text":"Istituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale delle Ricerche","active":true,"usgs":false}],"preferred":false,"id":826818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davini, Paolo","contributorId":268855,"corporation":false,"usgs":false,"family":"Davini","given":"Paolo","email":"","affiliations":[{"id":55694,"text":"Istituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale delle Ricerche","active":true,"usgs":false}],"preferred":false,"id":826819,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259595,"text":"70259595 - 2021 - Active virus-host interactions at sub-freezing temperatures in Arctic peat soil","interactions":[],"lastModifiedDate":"2024-10-16T11:52:44.042031","indexId":"70259595","displayToPublicDate":"2021-10-18T06:48:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5838,"text":"Microbiome","onlineIssn":"2049-2618","active":true,"publicationSubtype":{"id":10}},"title":"Active virus-host interactions at sub-freezing temperatures in Arctic peat soil","docAbstract":"Winter carbon loss in northern ecosystems is estimated to be greater than the average growing season carbon uptake and is primarily driven by microbial decomposers. Viruses modulate microbial carbon cycling via induced mortality and metabolic controls, but it is unknown whether viruses are active under winter conditions (anoxic and sub-freezing temperatures).
We used stable isotope probing (SIP) targeted metagenomics to reveal the genomic potential of active soil microbial populations under simulated winter conditions, with an emphasis on viruses and virus-host dynamics. Arctic peat soils from the Bonanza Creek Long-Term Ecological Research site in Alaska were incubated under sub-freezing anoxic conditions with H218O or natural abundance water for 184 and 370 days. We sequenced 23 SIP-metagenomes and measured carbon dioxide (CO2) efflux throughout the experiment. We identified 46 bacterial populations (spanning 9 phyla) and 243 viral populations that actively took up 18O in soil and respired CO2 throughout the incubation. Active bacterial populations represented only a small portion of the detected microbial community and were capable of fermentation and organic matter degradation. In contrast, active viral populations represented a large portion of the detected viral community and one third were linked to active bacterial populations. We identified 86 auxiliary metabolic genes and other environmentally relevant genes. The majority of these genes were carried by active viral populations and had diverse functions such as carbon utilization and scavenging that could provide their host with a fitness advantage for utilizing much-needed carbon sources or acquiring essential nutrients.
Overall, there was a stark difference in the identity and function of the active bacterial and viral community compared to the unlabeled community that would have been overlooked with a non-targeted standard metagenomic analysis. Our results illustrate that substantial active virus-host interactions occur in sub-freezing anoxic conditions and highlight viruses as a major community-structuring agent that likely modulates carbon loss in peat soils during winter, which may be pivotal for understanding the future fate of arctic soils' vast carbon stocks.
","language":"English","publisher":"Springer","doi":"10.1186/s40168-021-01154-2","usgsCitation":"Trubl, G., Kimbrel, J.A., Liquet-Gonzalez, J., Nuccio, E.E., Weber, P.K., Pett-Ridge, J., Jansson, J.K., Waldrop, M., and Blazewicz, S., 2021, Active virus-host interactions at sub-freezing temperatures in Arctic peat soil: Microbiome, v. 9, 208, 15 p., https://doi.org/10.1186/s40168-021-01154-2.","productDescription":"208, 15 p.","ipdsId":"IP-128011","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467223,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40168-021-01154-2","text":"Publisher Index Page"},{"id":462901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Trubl, Gareth","contributorId":345156,"corporation":false,"usgs":false,"family":"Trubl","given":"Gareth","email":"","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kimbrel, Jeffrey A","contributorId":345157,"corporation":false,"usgs":false,"family":"Kimbrel","given":"Jeffrey","email":"","middleInitial":"A","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liquet-Gonzalez, Jose","contributorId":345158,"corporation":false,"usgs":false,"family":"Liquet-Gonzalez","given":"Jose","email":"","affiliations":[{"id":82502,"text":"Lawrence Livermore National Labs","active":true,"usgs":false}],"preferred":false,"id":915864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nuccio, Erin E.","contributorId":345159,"corporation":false,"usgs":false,"family":"Nuccio","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":915865,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Peter K.","contributorId":345160,"corporation":false,"usgs":false,"family":"Weber","given":"Peter","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":915866,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pett-Ridge, Jennifer","contributorId":254974,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Jennifer","affiliations":[{"id":51376,"text":"Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore CA 94551","active":true,"usgs":false}],"preferred":false,"id":915867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jansson, Janet K.","contributorId":345161,"corporation":false,"usgs":false,"family":"Jansson","given":"Janet","email":"","middleInitial":"K.","affiliations":[{"id":82503,"text":"Pacific Northwest National Labs","active":true,"usgs":false}],"preferred":false,"id":915868,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":915869,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blazewicz, Steve 0000-0001-7517-1750","orcid":"https://orcid.org/0000-0001-7517-1750","contributorId":272100,"corporation":false,"usgs":false,"family":"Blazewicz","given":"Steve","email":"","affiliations":[{"id":13621,"text":"Lawrence Livermore National Laboratory","active":true,"usgs":false}],"preferred":false,"id":915870,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70226188,"text":"70226188 - 2021 - Understanding mast seeding for conservation and land management","interactions":[],"lastModifiedDate":"2021-11-16T12:49:09.966096","indexId":"70226188","displayToPublicDate":"2021-10-18T06:47:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Understanding mast seeding for conservation and land management","docAbstract":"Masting, the intermittent and synchronous production of large seed crops, can have profound consequences for plant populations and the food webs that are built on their seeds. For centuries, people have recorded mast crops because of their importance in managing wildlife populations. In the past 30 years, we have begun to recognize the importance of masting in conserving and managing many other aspects of the environment: promoting the regeneration of forests following fire or other disturbance, conserving rare plants, conscientiously developing the use of edible seeds as non-timber forest products, coping with the consequences of extinctions on seed dispersal, reducing the impacts of plant invasions with biological control, suppressing zoonotic diseases and preventing depredation of endemic fauna. We summarize current instances and future possibilities of a broad set of applications of masting. By exploring in detail several case studies, we develop new perspectives on how solutions to pressing conservation and land management problems may benefit by better understanding the dynamics of seed production. A lesson common to these examples is that masting can be used to time management, and often, to do this effectively, we need models that explicitly forecast masting and the dynamics of seed-eating animals into the near-term future.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2020.0383","usgsCitation":"Pearse, I.S., Wion, A., Gonzalez, A., and Pesendorfer, M.B., 2021, Understanding mast seeding for conservation and land management: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 376, no. 1839, https://doi.org/10.1098/rstb.2020.0383.","ipdsId":"IP-126922","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450431,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8520776","text":"External Repository"},{"id":391735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"376","issue":"1839","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wion, Andreas","contributorId":225092,"corporation":false,"usgs":false,"family":"Wion","given":"Andreas","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gonzalez, Angela","contributorId":268856,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Angela","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":826822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pesendorfer, Mario B.","contributorId":201187,"corporation":false,"usgs":false,"family":"Pesendorfer","given":"Mario","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":826823,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}