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","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Bulletin of the American Meteorological Society, State of the Climate, 2023 report","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-24-0101.1","usgsCitation":"Burgess, D., Wolken, G., Wouters, B., Andreassen, L., Florentine, C., Kohler, J., Luks, B., Palsson, F., Sass, L., Thomson, L., and Thorsteinsson, T., 2024, Glaciers and ice caps outside Greenland, v. 105, no. 8, 4 p., https://doi.org/10.1175/BAMS-D-24-0101.1.","productDescription":"4 p.","startPage":"S307","endPage":"S310","ipdsId":"IP-162903","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466959,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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Washington","indexId":"sir20245026v2","publicationYear":"2024","noYear":false,"chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington"},"predicate":"IS_PART_OF","object":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"id":1}],"isPartOf":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"lastModifiedDate":"2026-02-03T18:14:38.644212","indexId":"sir20245026v2","displayToPublicDate":"2024-08-21T14:09:17","publicationYear":"2024","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":"2024-5026","chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","docAbstract":"Groundwater flow in the active model area (AMA) was simulated using a groundwater-flow model. A steady-state model version of the model simulates equilibrium conditions, and a transient model version simulates monthly variability. The model corresponds to the physical and temporal dimensions of the conceptual model and groundwater budget. The steady-state model version represents average conditions for an 11-year period (January 1, 2005–December 31, 2015), and the transient model represents monthly hydrologic variability within that period. The 13-layer model was constructed using MODFLOW-NWT with a uniformly spaced grid consisting of 416 rows, 433 columns, and cells with a horizontal dimension of 500 feet (ft) on a side.
The model was calibrated to measured values of water levels in wells and lakes and estimated base flow for selected streamflow measurement stations, commonly referred to as streamgages. Model calibration was accomplished using a combination of manual and automatic methods, including the Model-Independent Parameter Estimation (PEST) program that adjusted model input parameters with the aim of minimizing the difference between estimated and model-simulated values of hydraulic head and base flow.
Model boundary conditions consist of all simulated groundwater inflow to and outflow from the AMA. For example, a stream reach that simulates a gain from or loss to groundwater is a boundary condition that allows water to exit or enter, respectively, the groundwater system. Other boundary conditions include springs, seeps, precipitation recharge, groundwater exchange with lakes and Puget Sound, and groundwater pumping. A comparison of the estimated groundwater budget to that simulated by the steady-state model version indicates that the relative percentages of total inflow or total outflow for six major categories of boundary conditions are similar for the two budgets.
The model was used to simulate three suites of scenarios of potential drought and water-use changes. Scenario 1 suite consisted of the steady-state model version that was run with 0, 15, 20, and 25 percent reduction of precipitation recharge to assess the corresponding reductions in base flow with decreasing recharge. The last simulation for the scenario 1 suite consisted of the transient model version simulating 3 years of consecutive seasonal drought, defined by the months of May through September, to assess the corresponding base-flow reductions. Scenario 2 suite consisted of the steady-state model version with all simulated groundwater use removed, compared with a simulation that includes current groundwater use to evaluate changes to potentiometric surfaces and base flows. Scenario 3 suite consisted of a transient model version of the model that simulated pumping increases for four different categories of water-supply wells (compared to no pumping increases) to evaluate resulting reductions in base flow. Although, these scenarios provide examples of model applications and useful insights, many other scenarios could be simulated. A description of how to download the model is described in the body of this report.
Uncertainty is associated with most model inputs. Groundwater levels, lake levels, and land-surface altitudes are relatively certain; other model inputs are far less certain, including precipitation recharge, base flow, hydraulic properties, water use, and the three-dimensional structure of subsurface hydrogeologic units. Models are useful not because of high levels of accuracy of all model inputs, but because they combine the best information and estimates available, thereby providing the best predictions available related to physical processes.
The model described in this report simulates groundwater flow on a regional scale, which has inherent limitations for simulating hydrologic scenarios at local scales. Model structures and inputs were generalized to be consistent with this regional scale. For example, the actual groundwater system has much greater heterogeneity of hydraulic conductivity than is possible within the model’s degrees of freedom. Variations in hydraulic gradients over distances less than 500 ft cannot be simulated. The distances between model features, such as a pumping well and a stream, must be placed at 500-ft intervals and are co-located if both features are within the same model cell.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v2","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Long, A.J., Wright, E.E., Fuhrig, L.T., and Bright, V.A.L., 2024, Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington, v. 2 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 2 chap. 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Washington"},{"id":432079,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.11.csv","text":"Table 1.11","size":"8 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.11","linkHelpText":"- Supplemental hydraulic-head targets for the steady-state model version set equal to the land surface to prevent groundwater flooding and corresponding simulated values, near the southeastern part of Puget Sound, Washington"},{"id":432074,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.6.csv","text":"Table 1.6","size":"637 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.6","linkHelpText":"- Time-series records of measured and simulated hydraulic-head values (transient model version) for selected wells used, near the southeastern part of Puget Sound, Washington, 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More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This report presents information used to characterize the groundwater-flow system and the development of a numerical model in the AMA. Included are descriptions of the geology and conceptual hydrogeologic framework, groundwater levels and flow directions, groundwater recharge and discharge, numerical groundwater-flow model construction and results, and model limitations. The study area encompasses the western part of Pierce County and the southwestern part of King County, Washington. The study area extends south to the Nisqually River, southwest to Tanwax Creek, northeast to the Green River, and north through the valley near Auburn and adjacent uplands. It is bounded on the east by foothills of the Cascade Range, and on the northwest by Puget Sound.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v1","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Welch, W.B., Bright, V.A.L., Gendaszek, A.S., Dunn, S.B., Headman, A.O., and Fasser, E.T., 2024, Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington, v. 1 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 3 chap. (A–C): U.S. Geological Survey Scientific Investigations Report 2024–5026–A–C, [variously paged; 71 p.], 1 pl., https://doi.org/10.3133/sir20245026v1.","productDescription":"Report: 71 p.; Data Release: 1 Plate: 50.67 × 37.33 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135626","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499451,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117216.htm","linkFileType":{"id":5,"text":"html"}},{"id":432059,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.jpg"},{"id":432065,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_plate.pdf","text":"Plate 1","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5026 Vol 1 Plate 1"},{"id":432064,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.XML"},{"id":432063,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/images"},{"id":433528,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_versionHist.txt","description":"SIR 2024-5026 Vol 1 Version History"},{"id":432062,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JFKLMG","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial data in support of the characterization of water resources near the southeastern part of Puget Sound, Washington"},{"id":432060,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.pdf","text":"Report","size":"57.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5026 Vol 1"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.8989970759464,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 47.52614250846048\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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The partners of the Chesapeake Bay are investing billions of dollars in the restoration of critical habitats to improve conditions for people and living resources throughout the Bay and its watershed. However, the recent proliferation of invasive Ictalurus furcatus (blue catfish) in the Chesapeake Bay’s major rivers has the potential to disrupt these restoration efforts and limit the full potential improvement of the ecosystem. The U.S. Geological Survey can help respond to this management challenge in the Nation’s largest estuary by leveraging its leadership and technical capabilities to work with resource managers, academics, and other stakeholders.
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U.S. Geological Survey
12100 Beech Forest Road
Laurel, Maryland 20708
Submerged macrophytes have complex effects on spatiotemporal characteristics of river ecosystems, including trout habitat. We investigated the impact of submerged macrophyte coverage on trout habitat in the Henrys Fork of the Snake River, Idaho, USA. We hypothesized that higher submerged macrophyte coverage would create new habitat types beneficial for trout growth. We assessed river physical and biotic attributes, trout habitat preferences, and estimated trout growth potential with bioenergetics models across a gradient of submerged macrophyte coverage (32–94%). We identified four distinct habitat types within the riverscape shaped by submerged macrophyte coverage. Increased submerged macrophyte coverage increased the frequency of habitat types with higher trout growth potential but reduced the occurrence of preferred habitat types. We observed no relationship between reach-scale trout growth potential and submerged macrophyte coverage. However, an outlier of very high trout growth potential at 94% submerged macrophyte coverage suggests a potential threshold effect. More study is required but our observations suggest macrophyte growth homogenized physical habitat characteristics, reduced flow velocities, and increased invertebrate drift, thereby enhancing trout growth potential. Our findings underscore the complex interplay between submerged macrophytes and trout habitat dynamics across scales, emphasizing the importance of considering both physical and biological effects on trout habitat.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-024-05671-7","usgsCitation":"McLaren, J.S., Van Kirk, R.W., Budy, P., and Brothers, S., 2024, The reach-scale biogeomorphic effect of submerged macrophytes on trout habitat suitability: Hydrobiologia, v. 851, p. 5167-5180, https://doi.org/10.1007/s10750-024-05671-7.","productDescription":"14 p.","startPage":"5167","endPage":"5180","ipdsId":"IP-157131","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485716,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Henrys Fork, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.18272502007463,\n 44.565942435149395\n ],\n [\n -111.69541678783352,\n 44.565942435149395\n ],\n [\n -111.69541678783352,\n 44.271529079239684\n ],\n [\n -111.18272502007463,\n 44.271529079239684\n ],\n [\n -111.18272502007463,\n 44.565942435149395\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"851","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"McLaren, John S.","contributorId":337322,"corporation":false,"usgs":false,"family":"McLaren","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":936626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Kirk, Robert W.","contributorId":337326,"corporation":false,"usgs":false,"family":"Van Kirk","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":81016,"text":"Henrys Fork Foundation","active":true,"usgs":false}],"preferred":false,"id":936627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Soren","contributorId":339019,"corporation":false,"usgs":false,"family":"Brothers","given":"Soren","email":"","affiliations":[{"id":81013,"text":"Department of Natural History","active":true,"usgs":false}],"preferred":false,"id":936629,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","interactions":[{"subject":{"id":70256697,"text":"sir20245026v1 - 2024 - Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington","indexId":"sir20245026v1","publicationYear":"2024","noYear":false,"chapter":"A-C","displayTitle":"Conceptual Hydrogeologic Framework and Groundwater Budget Near the Southeastern Part of Puget Sound, Washington","title":"Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington"},"predicate":"IS_PART_OF","object":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"id":1},{"subject":{"id":70256698,"text":"sir20245026v2 - 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The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This multichapter volume documents the development of the conceptual and numerical groundwater-flow models of groundwater flow. Chapters A, B, and C provide an overall introduction to the multichapter volume (Chapter A), the conceptual hydrogeologic framework (Chapter B), and the groundwater budget (Chapter C). Chapters D and E describe numerical groundwater-flow model construction and calibration (Chapter D) and the numerical groundwater-flow model results (Chapter E). Collectively, these reports present a characterization and simulation tool for groundwater resources near the southeastern part of Puget Sound, Washington.
","doi":"10.3133/sir20245026","usgsCitation":"Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5026, https://doi.org/10.3133/sir20245026.","onlineOnly":"Y","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":433028,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.74462161441157,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 49.512370286103504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Genetic diversity is essential for maintaining healthy populations and ecosystems. Several approaches have recently been developed to evaluate population genetic trends without necessarily collecting new genetic data. Such “genetic diversity indicators” enable rapid, large-scale evaluation across dozens to thousands of species. Empirical genetic studies, when available, provide detailed information that is important for management, such as estimates of gene flow, inbreeding, genetic erosion and adaptation. In this article, we argue that the development and advancement of genetic diversity indicators is a complementary approach to genetic studies in conservation biology, but not a substitute. Genetic diversity indicators and empirical genetic data can provide different information for conserving genetic diversity. Genetic diversity indicators enable affordable tracking, reporting, prioritization and communication, although, being proxies, do not provide comprehensive evaluation of the genetic status of a species. Conversely, genetic methods offer detailed analysis of the genetic status of a given species or population, although they remain challenging to implement for most species globally, given current capacity and resourcing. We conclude that indicators and genetic studies are both important for genetic conservation actions and recommend they be used in combination for conserving and monitoring genetic diversity.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-024-01632-8","usgsCitation":"Hoban, S.M., Paz-Vinas, I., Shaw, R.E., Castillo-Reina, L., da Silva, J.M., DeWoody, J., Ekblom, R., Fedorca, A., Forester, B.R., Funk, W., Geue, J.C., Heuertz, M., Hollingsworth, P.M., Hughes, A.C., Hunter, M., Hvilsom, C., Ishihama, F., Jordan, R., Kalamujic Stroil, B., Kershaw, F., Khoury, C.K., Koppa, V., Laikre, L., MacDonald, A.J., Mastretta-Yanes, A., Meek, M.H., Mergeay, J., Millette, K.L., O'Brien, D., Rincon-Parra, V.J., Rodriguez-Morales, M., Schuman, M.C., Segelbacher, G., Sunnucks, P., Taylor, R., Thurfjell, H., Vernesi, C., and Grueber, C.E., 2024, DNA-based studies and genetic diversity indicator assessments are complementary approaches to conserving evolutionary potential: Conservation Genetics, v. 25, p. 1147-1153, https://doi.org/10.1007/s10592-024-01632-8.","productDescription":"7 p.","startPage":"1147","endPage":"1153","ipdsId":"IP-160562","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439203,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-024-01632-8","text":"Publisher Index Page"},{"id":433496,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":912310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":912311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaw, Robyn E.","contributorId":260838,"corporation":false,"usgs":false,"family":"Shaw","given":"Robyn","email":"","middleInitial":"E.","affiliations":[{"id":52690,"text":"Environmental and Conservation Sciences, Murdoch University, Perth, Australia","active":true,"usgs":false}],"preferred":false,"id":912312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Castillo-Reina, Luis","contributorId":340272,"corporation":false,"usgs":false,"family":"Castillo-Reina","given":"Luis","email":"","affiliations":[{"id":81533,"text":"Department of Biology, Faculty of Science, KU Leuven","active":true,"usgs":false}],"preferred":false,"id":912313,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"da Silva, Jessica M.","contributorId":290139,"corporation":false,"usgs":false,"family":"da Silva","given":"Jessica","email":"","middleInitial":"M.","affiliations":[{"id":62352,"text":"South African National Biodiversity Institute, Kirstenbosch Research Centre, Rhodes Drive, Private Bag X7, 7735 Cape Town, South Africa","active":true,"usgs":false}],"preferred":false,"id":912314,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWoody, J. 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Alejandra","affiliations":[{"id":82257,"text":"Department of Biology, Faculty of Science, Pontificia Universidad Javeriana, Bogotá D.C","active":true,"usgs":false}],"preferred":false,"id":912340,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Schuman, Meredith C. 0000-0003-3159-3534","orcid":"https://orcid.org/0000-0003-3159-3534","contributorId":343905,"corporation":false,"usgs":false,"family":"Schuman","given":"Meredith","email":"","middleInitial":"C.","affiliations":[{"id":82258,"text":"Spatial Genetics, Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":912341,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":912342,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Sunnucks, Paul 0000-0002-8139-7059","orcid":"https://orcid.org/0000-0002-8139-7059","contributorId":333555,"corporation":false,"usgs":false,"family":"Sunnucks","given":"Paul","email":"","affiliations":[{"id":27950,"text":"School of Biological Sciences, Monash University","active":true,"usgs":false}],"preferred":false,"id":912343,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Taylor, Rebecca S. 0000-0002-8916-4858","orcid":"https://orcid.org/0000-0002-8916-4858","contributorId":343906,"corporation":false,"usgs":false,"family":"Taylor","given":"Rebecca S.","affiliations":[{"id":82259,"text":"Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, Canada","active":true,"usgs":false}],"preferred":false,"id":912344,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Thurfjell, Henrik","contributorId":305348,"corporation":false,"usgs":false,"family":"Thurfjell","given":"Henrik","email":"","affiliations":[{"id":66219,"text":"Swedish Species Information Centre, Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":912345,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Vernesi, Cristiano","contributorId":239922,"corporation":false,"usgs":false,"family":"Vernesi","given":"Cristiano","email":"","affiliations":[{"id":48051,"text":"Dept. of Sustainable Agroecosystems and Bioresources, Research and Innovation Centre - Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":912346,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Grueber, Catherine E.","contributorId":239927,"corporation":false,"usgs":false,"family":"Grueber","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":48055,"text":"School of Life and Environmental Sciences, Faculty of Science, The University of Sydney","active":true,"usgs":false}],"preferred":false,"id":912347,"contributorType":{"id":1,"text":"Authors"},"rank":38}]}} ,{"id":70257760,"text":"70257760 - 2024 - Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover","interactions":[],"lastModifiedDate":"2024-12-26T16:34:01.74251","indexId":"70257760","displayToPublicDate":"2024-08-21T07:21:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover","docAbstract":"Traditionally, local-scale habitat-relationship models are developed over small spatial extents, limiting model transferability and inference outside the study area. Thus, habitat managers frequently lack fine-scale information regarding the influence of vegetation composition and structure on site suitability or species abundance. Gymnorhinus cyanocephalus (Pinyon Jay) represents one declining species for which managers have limited information regarding the influence that vegetation composition and structure have on abundance at broad scales. To address this need, we developed a hierarchical Bayesian abundance model using summertime bird and vegetation data collected under the Integrated Monitoring in Bird Conservation Regions program to explain jay abundance as a function of local conditions. Our G. cyanocephalus abundance model allowed abundance relationships with pinyon pine (Pinus edulis and P. monophylla) and juniper (Juniperus spp.) to vary by ecoregion, thereby accounting for potential regional differences in habitat associations. We found G. cyanocephalus abundance was generally positively associated with pinyon pine and juniper cover; however, habitat relationships varied by ecoregion. Additionally, we found positive associations between jay abundance and grass cover, sagebrush cover, and percent bare ground. Our results agree with prior research suggesting mechanical removal of pinyon pine and juniper trees for sagebrush restoration or fuel treatments may negatively affect G. cyanocephalus. Managers wishing to reduce pinyon and juniper tree cover without negatively affecting G. cyanocephalus may benefit from targeting sites where both large-scale distribution models and our local habitat relationships suggest G. cyanocephalus are likely to occur in low numbers. Additionally, our modeled relationships indicate restoration that increases grass cover, sagebrush cover, and bare ground, while maintaining pinyon and (or) juniper cover, may lead to increased local densities of G. cyanocephalus.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/ornithapp/duae036","usgsCitation":"Van Lanen, N.J., Monroe, A., and Aldridge, C.L., 2024, Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover: Ornithological Applications, v. 126, no. 4, duae036, https://doi.org/10.1093/ornithapp/duae036.","productDescription":"duae036","ipdsId":"IP-158852","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439204,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae036","text":"Publisher Index Page"},{"id":433154,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":911621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":911622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":911623,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257808,"text":"70257808 - 2024 - On the uncertain intensity estimate of the 1859 Carrington storm","interactions":[],"lastModifiedDate":"2024-08-28T11:58:46.249223","indexId":"70257808","displayToPublicDate":"2024-08-21T06:57:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18352,"text":"Journal of Space Weather and Space Climate","active":true,"publicationSubtype":{"id":10}},"title":"On the uncertain intensity estimate of the 1859 Carrington storm","docAbstract":"A study is made of the intensity of the Carrington magnetic storm of September 1859 as inferred from visual measurements of horizontal-component geomagnetic disturbance made at the Colaba observatory in India. Using data from modern observatories, a lognormal statistical model of storm intensity is developed, to characterize the maximum-negative value of the storm-time disturbance index (maximum –Dst) versus geomagnetic disturbance recorded at low-latitude observatories during magnetic storms. With this model and a recently published presentation of the Colaba data, the most likely maximum –Dst of the Carrington storm and its credibility interval are estimated. A related model is used to examine individual Colaba disturbance values reported for the Carrington storm. Results indicate that only about one in a million storms with maximum –Dst like the Carrington storm would result in local disturbance greater than that reported from Colaba. This indicates that either the Colaba data were affected by magnetospheric-ionospheric current systems in addition to the ring current, or there might be something wrong with the Colaba data. If the most extreme Colaba disturbance value is included in the analysis, then, of all hypothetical storms generating the hourly average disturbance recorded at Colaba during the Carrington storm, the median maximum –Dst = 964 nT, with a 68% credibility interval of [855,1087] nT. If the most extreme Colaba disturbance value is excluded from the analysis, then the median maximum –Dst = 866 nT, with a 68% credibility interval of [768,977] nT. The widths of these intervals indicate that estimates of the occurrence frequency of Carrington-class storms are very uncertain, as are related estimates of risk for modern technological systems.
","language":"English","publisher":"EcoSciences","doi":"10.1051/swsc/2024015","usgsCitation":"Love, J.J., Rigler, E.J., Hayakawa, H., and Mursula, K., 2024, On the uncertain intensity estimate of the 1859 Carrington storm: Journal of Space Weather and Space Climate, v. 14, https://doi.org/10.1051/swsc/2024015.","productDescription":"21, 16 p.","startPage":"21","ipdsId":"IP-153188","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":439205,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1051/swsc/2024015","text":"External Repository"},{"id":433242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":911733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":911734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayakawa, H. 0000-0001-5370-3365","orcid":"https://orcid.org/0000-0001-5370-3365","contributorId":261775,"corporation":false,"usgs":false,"family":"Hayakawa","given":"H.","email":"","affiliations":[{"id":53009,"text":"Nagoya University, Rutherford Appleton Laboratory, Nishina Center","active":true,"usgs":false}],"preferred":false,"id":911735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mursula, Kalevi 0000-0003-4892-5056","orcid":"https://orcid.org/0000-0003-4892-5056","contributorId":343695,"corporation":false,"usgs":false,"family":"Mursula","given":"Kalevi","email":"","affiliations":[{"id":82163,"text":"Oulu University","active":true,"usgs":false}],"preferred":false,"id":911736,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257626,"text":"ofr20241036 - 2024 - Airborne lidar accuracy analysis for dual photogrammetric and lidar sensor pilot project in Colorado, 2019","interactions":[],"lastModifiedDate":"2026-01-29T19:50:12.601231","indexId":"ofr20241036","displayToPublicDate":"2024-08-20T16:19:54","publicationYear":"2024","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":"2024-1036","displayTitle":"Airborne Lidar Accuracy Analysis for Dual Photogrammetric and Lidar Sensor Pilot Project in Colorado, 2019","title":"Airborne lidar accuracy analysis for dual photogrammetric and lidar sensor pilot project in Colorado, 2019","docAbstract":"This report presents accuracy assessment results of the light detection and ranging (lidar) data collected in Colorado during a pilot project in fall 2019. The purpose of the pilot project was to assess the accuracy of lidar and imagery data collected simultaneously for the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program and the U.S. Geological Survey National Geospatial Program 3D Elevation Program (3DEP). A multiagency group consisting of U.S. Department of the Interior agencies and USDA agencies participated in the effort. Department of the Interior agencies included Bureau of Land Management, National Park Service, and U.S. Geological Survey; USDA agencies included the Farm Services Agency, the Natural Resource Conservation Service, and U.S. Forest Service. This pilot project was designed to help determine if a lidar sensor system has the potential to meet future 3DEP topographic lidar collection requirements, ideally at the same altitudes and leaf-on times that National Agriculture Imagery Program is flown.
The airborne sensor system from Leica Geosystems (part of Hexagon) (hereafter referred to as dual sensor system) was used in the pilot project and can collect imagery and three-dimensional point cloud data concurrently. This report examines the characteristics of lidar data from a geometric accuracy perspective. Field surveys were performed to evaluate the three-dimensional absolute and relative accuracy of the airborne lidar data and to determine if the data met 3DEP specifications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241036","usgsCitation":"Sampath, A., Irwin, J., and Kim, M., 2024, Airborne lidar accuracy analysis for dual photogrammetric and lidar sensor pilot project in Colorado, 2019: U.S. Geological Survey Open-File Report 2024–1036, 22 p., https://doi.org/10.3133/ofr20241036.","productDescription":"Report: v, 22 p.; Data Release","ipdsId":"IP-145014","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499258,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117220.htm","linkFileType":{"id":5,"text":"html"}},{"id":432948,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241036/full"},{"id":432946,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1036/ofr20241036.XML"},{"id":432945,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1036/images/"},{"id":432944,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1036/ofr20241036.pdf","text":"Report","size":"4.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1036"},{"id":432943,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1036/coverthb.jpg"},{"id":432947,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPDWUU","text":"USGS data release","linkHelpText":"Hybrid lidar/imagery sensor validation survey data, 2019"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.41673062084341,\n 40.689427754445916\n ],\n [\n -105.41673062084341,\n 40.45990268267724\n ],\n [\n -104.91983118255482,\n 40.45990268267724\n ],\n [\n -104.91983118255482,\n 40.689427754445916\n ],\n [\n -105.41673062084341,\n 40.689427754445916\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.24129047491809,\n 40.22901702069382\n ],\n [\n -106.24129047491809,\n 39.9645466499631\n ],\n [\n -105.60981370231355,\n 39.9645466499631\n ],\n [\n -105.60981370231355,\n 40.22901702069382\n ],\n [\n -106.24129047491809,\n 40.22901702069382\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Surveys for the endangered Southwestern Willow Flycatcher (Empidonax traillii extimus) were done at Marine Corps Base Camp Pendleton (MCBCP or “Base”), California, between May 9 and July 20, 2022. All of MCBCP’s historically occupied riparian habitat (core survey area) was surveyed for flycatchers in 2022. None of the non-core survey area was surveyed in 2022.
Eight transient Willow Flycatchers of unknown subspecies were observed on three of the five drainages surveyed in 2022. Willow Flycatchers were not detected at Fallbrook or Pilgrim Creeks. Transients occurred in a range of habitat types, including mixed willow (Salix spp.) riparian, riparian scrub, and upland scrub habitat. Exotic vegetation, primarily poison hemlock (Conium maculatum), was present in most of the flycatcher locations.
In 2022, for the second time since monitoring began in 2000, resident Southwestern Willow Flycatchers were not detected on Base. The decline was not isolated to MCBCP; similar declines have been documented across California in recent years.
The one uniquely banded adult female flycatcher present during the 2021 breeding season did not return to MCBCP in 2022. None of the transients observed during surveys were seen to carry bands.
From 2000 to 2022, adult annual survival of Southwestern Willow Flycatchers on MCBCP was 60±3 percent, whereas first-year survival was 20±3 percent.
A conspecific attraction study was initiated on Base in 2018 and repeated annually through 2022; flycatchers were not observed near automated playback units in 2022.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231080","collaboration":"Prepared in cooperation with Assistant Chief of Staff, Environmental Security, U.S. Marine Corps Base Camp Pendleton","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2024, Distribution, abundance, and breeding activities of the Southwestern Willow Flycatcher at Marine Corps Base Camp Pendleton, California—2022 annual report: U.S. Geological Survey Open-File Report 2023–1080, 27 p., https://doi.org/10.3133/ofr20231080.","productDescription":"vi, 27 p.","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-147621","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":423448,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231080/full"},{"id":423447,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1080/images"},{"id":423446,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1080/ofr20231080.xml","size":"200 KB","linkFileType":{"id":8,"text":"xml"}},{"id":423445,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1080/ofr20231080.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":423444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1080/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Marine Corps Base Camp Pendleton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.97155578495742,\n 33.68843828437876\n ],\n [\n -117.97155578495742,\n 33.07385423406275\n ],\n [\n -116.90578319960974,\n 33.07385423406275\n ],\n [\n -116.90578319960974,\n 33.68843828437876\n ],\n [\n -117.97155578495742,\n 33.68843828437876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The purpose of this report is to provide the Marine Corps with an annual summary of abundance, breeding activity, demography, and habitat use of the endangered Southwestern Willow Flycatcher (Empidonax traillii extimus) at Marine Corps Base Camp Pendleton (MCBCP). Surveys for the flycatcher were completed at MCBCP between May 5 and July 31, 2021. All of MCBCP’s historically occupied riparian habitat (core survey area) was surveyed for flycatchers in 2021. Additionally, one-fifth of the non-historically occupied riparian habitat (non-core survey area C) was surveyed for flycatchers. Twenty-four transient Willow Flycatchers of unknown subspecies were observed on five of the seven drainages surveyed in 2021. No Willow Flycatchers were detected at French or Las Flores Creeks. Transients occurred in a range of habitat types, including mixed willow (Salix spp.) riparian, riparian scrub, willow-sycamore (Platanus sp.) dominated or oak (Quercus spp.) sycamore-dominated riparian, and non-native-dominated riparian habitat. Exotic vegetation, primarily poison hemlock (Conium maculatum), was present in most flycatcher locations.
The resident population of Southwestern Willow Flycatchers on MCBCP declined 50 percent, from two individuals in 2020 to one individual in 2021. In 2021, the resident Southwestern Willow Flycatcher population on MCBCP consisted of one unpaired female occupying one territory. No males were observed in 2021. The resident flycatcher population was restricted to the Santa Margarita River, and distribution was limited to the Pueblitos breeding area. The resident flycatcher territory was located in mixed willow riparian habitat.
Nesting was initiated in late May and continued into late July. Two nesting attempts were documented, neither of which were successful. Infertile eggs likely accounted for both nest failures. No instances of Brown-headed Cowbird (Molothrus ater) parasitism were observed. The female flycatcher placed her nests in native sandbar willow (Salix exigua) and used the same nest location for both nesting attempts.
Of resident birds that were present at MCBCP in 2021, 100 percent were banded in previous years; no unbanded birds were detected. Of the two uniquely banded adult flycatchers (one male, one female) present during the 2020 breeding season, 50 percent (one female) returned to MCBCP in 2021. The banded female returned to the same breeding area and territory she occupied in 2020. Neither of the two nestlings banded in 2020 returned to MCBCP in 2021, and neither were detected off Base. From 2000 to 2021, adult over-winter survival of Southwestern Willow Flycatchers on MCBCP was 60±3 percent (mean±standard error [SE]), and first-year survival was 20±3 percent.
A conspecific attraction study that used automatic playback units to broadcast flycatcher vocalizations in order to encourage flycatchers to settle on MCBCP was initiated in 2018 and repeated annually through 2021. The single resident flycatcher (female) detected in 2021 settled close to an automated playback unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241039","collaboration":"Prepared in cooperation with Assistant Chief of Staff, Environmental Security, U.S. Marine Corps Base Camp Pendleton","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2024, Distribution, abundance, and breeding activities of the Southwestern Willow Flycatcher at Marine Corps Base Camp Pendleton, California—2021 annual report: U.S. Geological Survey Open-File Report 2024–1039, 35 p., https://doi.org/10.3133/ofr20241039.","productDescription":"viii, 35 p.","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-156551","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":432891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1039/covrthb.jpg"},{"id":432892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1039/ofr20241039.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432893,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1039/ofr20241039.xml"},{"id":432894,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1039/images"},{"id":432895,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241039/full"}],"country":"United States","state":"California","otherGeospatial":"Marine Corps Base Camp Pendleton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.79672069647273,\n 33.56385799493502\n ],\n [\n -117.79672069647273,\n 33.155239670288594\n ],\n [\n -117.12032821171901,\n 33.155239670288594\n ],\n [\n -117.12032821171901,\n 33.56385799493502\n ],\n [\n -117.79672069647273,\n 33.56385799493502\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
In 2016, the U.S. Geological Survey began a multiyear cooperative study with the New York State Department of Environmental Conservation to evaluate the sustainability of Long Island’s sole-source aquifer system through hydrogeologic mapping, compilation of groundwater chloride concentrations, and groundwater flow modeling. In the initial phase of the islandwide study, the hydrogeologic framework and extent of saltwater intrusion in aquifers in Kings, Queens, and Nassau Counties on western Long Island, N.Y., were investigated. The aquifer system underlying western Long Island has been under stress from pumping of public, irrigation (golf course), and industrial supply wells. Saltwater intrusion has occurred from surrounding embayments (East River, Long Island Sound, Jamaica Bay, and the Atlantic Ocean) due to pumping.
Eighteen boreholes were drilled and cores taken during 2019–21 to collect hydrogeologic, geochemical, and geophysical data to delineate the complex subsurface hydrogeology and extent of saltwater intrusion within the study area. Evaluation of the new cores, reexamination of legacy core descriptions, and analysis of borehole geophysical logs was used to refine the previously published hydrogeologic framework of Pleistocene and Cretaceous unconsolidated sediments in the area, including delineation of a previously undefined hydrogeologic unit between the Magothy aquifer and the Raritan confining unit, herein named the “upper Raritan aquifer.” The upper Raritan aquifer was first recognized in southeastern Nassau County from an analysis of about 50 closely spaced boreholes with high-resolution core descriptions and gamma-ray (gamma) logs. Further analysis of borehole logs across the study area indicated that the upper Raritan aquifer was also present in Kings and Queens Counties.
Nuclear magnetic resonance (NMR) logging was used for the first time on Long Island to provide estimates of the hydraulic properties of the major aquifer and confining units. Unlike other geophysical logs that record responses to the rock matrix and fluid properties and are strongly dependent on mineralogy, NMR logs record responses to the presence of hydrogen protons in the formation fluid to determine water fraction and pore-size distribution. NMR log analysis provided estimates of the clay-bound, capillary-bound, and mobile water fractions and hydraulic conductivity of aquifers and confining units penetrated by five wells in Nassau County.
Pumpage for public-supply and industrial wells on Long Island began in the 1870s with small, localized suppliers of populated areas in Kings and Queens Counties. By 1904–16, pumpage for public water supply in Kings County averaged 21 million gallons per day, and averaged 37 million gallons per day in Queens County, mostly from the upper glacial aquifer. Saltwater intrusion was reported as early as the beginning of the 20th century and included the upper glacial-Jameco-Magothy and Lloyd-North Shore aquifer systems. By 1936, pumping in central Kings County created a major cone of depression in the water table extending to the south shore of much of Kings County and into southwestern Queens County. Saltwater intrusion has caused the shutdown of public-supply wells in Kings, Queens, and Nassau Counties. A large saltwater intrusion wedge in the Lloyd aquifer was indicated in southern Queens County in the early part of the 20th century, and the saltwater interface may have been onshore predevelopment. Most of Kings and Queens Counties are intruded with saltwater in both the upper glacial-Jameco-Magothy and Lloyd-North Shore aquifers systems. Saltwater increased during the 20th century and continues to increase to the present (2023) in the Lloyd-North Shore aquifer system in Great Neck and Manhasset Neck in northern Nassau County. A major wedge of saltwater intrusion in the upper glacial-Jameco-Magothy aquifer in southwestern Nassau County appears to be increasing.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey has developed a transient, groundwater-flow model that simulates hydrologic conditions in the Long Island aquifer system as part of an ongoing (since 2016) multiyear, cooperative investigation with the New York State Department of Environmental Conservation. The goals of this investigation are to assist stakeholders and resource managers to evaluate the response of the hydrologic system to changes in future hydraulic stresses. Responses in the hydrologic system include changes in water levels in the hydrogeologic units; discharge to streams, coastal waters, and subsurface infrastructure; and the extent of saline groundwater in the aquifers. Hydraulic stresses include future water-supply management and changes in land use and infrastructure.
The numerical model synthesizes a diverse set of physiographic, geologic, climatic, land-use, and historical population, water use, and infrastructure data to physically represent the Long Island aquifer system from land surface to bedrock and to simulate annual hydrologic conditions between 1900 and 2019. A three-dimensional hydrogeologic framework was developed from existing and recently collected borehole geologic and geophysical data collected as part of a companion drilling program. Water-transmitting properties of the principal aquifer sediments were defined in three dimensions from new and existing lithologic logs. The distribution of recharge from precipitation was estimated from landscape characteristics and climate data. Anthropogenic recharge from wastewater, leaky infrastructure, and storm runoff were estimated from population, infrastructure, and pumping data.
Water-use data, including well locations, depths, and pumping rates, were obtained from historical sources and records and used to estimate pumping stresses continuously in time and space, at an annual average time scale. The data were incorporated into a three-dimensional numerical model using the U.S. Geological Survey finite difference modeling code MODFLOW 6; the model encompassed all of Long Island and surrounding surface waters and simulated historical hydrologic conditions from 1900 to 2019.
The calibration process involved trial and error adjustments using prior knowledge to improve general fit to observations followed by an inverse calibration to update and optimize input parameters, using an iterative ensemble smoother algorithm implemented in PEST++ version 5.0. This resulted in a model that generally was in good agreement with observed, dynamically varying hydrologic conditions from 1900 to 2019. The calibrated model was used to develop two base-case models for scenario testing of future, hypothetical conditions where one represented average-annual conditions, and one represented average-seasonal conditions from 2010 to 2019. The model representing average-annual conditions was modified further to represent an alternate sea-level position of 6 feet above the North American Vertical Datum of 1988, and the model representing average-seasonal conditions was modified to represent the average seasonal effects of a 5-year drought imposed upon current hydrologic conditions.
Recharge is the sole source of water to the aquifer system; groundwater discharges to coastal water and streams and is withdrawn by pumped wells. Model-estimated annual recharge ranged from about 11 inches in 1965 to 41 inches in 1983. On average, from 2010 to 2019, about 23 percent of water was pumped from wells, and about 47 and 27 percent discharged to coastal waters and streams, respectively; the remaining 4 percent was water that moved into storage in the aquifer matrix.
Water levels on Long Island vary naturally during time in response to changes in recharge; the amount of variation is largest in the interior of the island, in areas with highest water table altitudes near groundwater divides and lowest near streams and the coastal waters. The total range of water table altitudes on Long Island between 1900 and 2019 ranged from near 0 to more than 70 feet in western parts of Long Island. The largest range in altitudes is in New York City and is associated with areas of large historical withdrawals between the 1920s and the late 1980s. Water table altitudes generally varied by less than 10 feet in eastern Suffolk County, where the aquifer is under more natural conditions.
Saltwater intrusion is of great concern on Long Island, particularly in western Long Island where both the unconfined and confined parts of the aquifer system have been intruded in response to large-scale groundwater withdrawals; however, the volume of freshwater in the islandwide aquifer system only has changed by about 5 percent between 1900 and 2019. The decadal change in the freshwater volume was largest during the early and mid-20th century, corresponding to the largest historical pumping, but that volume change did not exceed 1 percent.
The negligible change in freshwater volume suggests that saltwater intrusion as of 2019 was limited at an islandwide scale but continues to occur in local areas of Queens and Nassau Counties, adversely affecting current water supplies and limiting future water supplies for affected communities. The regional groundwater model developed for this investigation is a tool that can be used to help determine the viability of current and future water supplies at a regional scale and can be used to support development of additional models at finer scale to support more focused assessments of groundwater sustainability.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
DNA metabarcoding is a rapidly advancing tool for diet assessment in wildlife ecology. Studies have used a variety of field collection methods to evaluate diet; however, there is a pressing need to understand the differences among sampling methods and the downstream inferential consequences they may have on our ability to document diet accurately and efficiently. We evaluated seven DNA metabarcoding sampling methods to assess the diet of a large avian predator: Buteo lagopus (rough-legged hawk). We collected beak swabs, talon swabs, cheek (buccal) swabs, cloacal swabs, and cloacal loops from captured birds, and collected fecal samples from both captured and uncaptured birds. We described and compared variation in prey recovery within and among the seven sampling methods and identified appropriate analytical methods to compare diet among individuals sampled via different methods. Beak and talon swabs produced the highest prey detection rates, yielded the greatest prey richness per sample, and contributed the most to an individual's total prey richness per sampling occasion compared to other sampling methods. Within individuals sampled using five methods during a single capture occasion, cloacal swabs and cheek swabs positively predicted prey richness and average prey mass, respectively, from fecal samples. While all methods identified similar dominant prey taxa that were consistent with prior diet studies, beak and talon swabs detected greater prey richness at both the individual and population levels. We propose a food residue duration hypothesis whereby methods which sample areas containing food DNA consumed from longer and more continuous pre-sampling time intervals explain variation among sampling methods in observed prey richness. Choice of sampling method can influence predator diet characterization and is particularly important if researchers wish to quantify uncommon diet items or compare diet metrics using samples collected via different methods.
","language":"English","publisher":"Wiley","doi":"10.1002/edn3.70000","usgsCitation":"Paprocki, N., Blair, S., Conway, C.J., Adams, J., Nerkowski, S.A., Kidd, J., and Waits, L., 2024, Comparison of seven DNA metabarcoding sampling methods to assess diet in a large avian predator: Environmental DNA, v. 6, no. 4, e70000, 12 p., https://doi.org/10.1002/edn3.70000.","productDescription":"e70000, 12 p.","ipdsId":"IP-160257","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490118,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.70000","text":"Publisher Index Page"},{"id":485837,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Paprocki, Neil","contributorId":354983,"corporation":false,"usgs":false,"family":"Paprocki","given":"Neil","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":936745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blair, Shannon","contributorId":354984,"corporation":false,"usgs":false,"family":"Blair","given":"Shannon","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":936746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936747,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Jennifer R.","contributorId":341225,"corporation":false,"usgs":false,"family":"Adams","given":"Jennifer R.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":936748,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nerkowski, Stacey A.","contributorId":338441,"corporation":false,"usgs":false,"family":"Nerkowski","given":"Stacey","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":936749,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kidd, Jeff W","contributorId":243473,"corporation":false,"usgs":false,"family":"Kidd","given":"Jeff W","affiliations":[],"preferred":false,"id":936750,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waits, Lisette","contributorId":189210,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette","affiliations":[],"preferred":false,"id":936751,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70267317,"text":"70267317 - 2024 - Landscape-scale modeling to forecast fluvial-aeolian sediment connectivity in river valleys","interactions":[],"lastModifiedDate":"2025-05-20T15:16:10.250817","indexId":"70267317","displayToPublicDate":"2024-08-20T10:08:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale modeling to forecast fluvial-aeolian sediment connectivity in river valleys","docAbstract":"Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial-aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river-sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar-aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL110106","usgsCitation":"Kasprak, A., Sankey, J., and Caster, J., 2024, Landscape-scale modeling to forecast fluvial-aeolian sediment connectivity in river valleys: Geophysical Research Letters, v. 51, no. 6, e2024GL110106, 10 p., https://doi.org/10.1029/2024GL110106.","productDescription":"e2024GL110106, 10 p.","ipdsId":"IP-165471","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":490135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl110106","text":"Publisher Index Page"},{"id":486217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.41686436104216,\n 36.95682017793375\n ],\n [\n -112.32831221274077,\n 36.95682017793375\n ],\n [\n -112.32831221274077,\n 36.00005971677052\n ],\n [\n -111.41686436104216,\n 36.00005971677052\n ],\n [\n -111.41686436104216,\n 36.95682017793375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":937704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937706,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257671,"text":"70257671 - 2024 - Wildlife health capacity enhancement in Thailand through the World Organisation for Animal Health Twinning Program","interactions":[],"lastModifiedDate":"2024-08-26T13:49:09.747597","indexId":"70257671","displayToPublicDate":"2024-08-20T08:34:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5720,"text":"Frontiers in Veterinary Science","onlineIssn":"2297-1769","active":true,"publicationSubtype":{"id":10}},"title":"Wildlife health capacity enhancement in Thailand through the World Organisation for Animal Health Twinning Program","docAbstract":"There is an increasing need for robust wildlife health programs that provide surveillance and management for diseases in wildlife and wild aquatic populations to manage associated risks. This paper illustrates the value of a systematic method to enhancing wildlife health programs. The U.S. Geological Survey and Mahidol University, Faculty of Veterinary Science, Thailand National Wildlife Health Center formally twinned under the auspices of the World Organisation for Animal Health to enhance wildlife health capacity in Thailand and the Southeast Asia Region. We used a system-wide approach to holistically and interdependently enhance capacity. The project commenced with a wildlife health program needs assessment, and capacity enhancement focused on strengthening the general wildlife health surveillance network and improving wildlife health information management. Activities included partner surveys, interactive and didactic workshops, and individual personnel training. Topics included development of wildlife health information management systems, analysis of the current surveillance network, development of a Theory of Change for a strengthened surveillance network, planning workshops to create a wildlife health network, training on wildlife disease outbreak investigation and field sample collection, leading networks, and individual training on bioinformatics and laboratory techniques. Engagement of stakeholders at all levels, continuous communication throughout the project, use of both strategic planning tools and pedagogical methods, and using iterative and adaptive approaches, were key factors to the success of this project.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fvets.2024.1462280","usgsCitation":"Suwanpakdee, S., Sangkachai, N., Wiratsudakul, A., Wiriyarat, W., Sakcamduang, W., Wongluechai, P., Pabutta, C., Sariya, L., Korkijthamkul, W., Blehert, D.S., White, C.L., Walsh, D.P., Stephen, C., Ratanakorn, P., and Sleeman, J.M., 2024, Wildlife health capacity enhancement in Thailand through the World Organisation for Animal Health Twinning Program: Frontiers in Veterinary Science, v. 11, 1462280, 11 p., https://doi.org/10.3389/fvets.2024.1462280.","productDescription":"1462280, 11 p.","ipdsId":"IP-164114","costCenters":[{"id":399,"text":"Montana Cooperative Wildlife Research Unit","active":false,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"links":[{"id":439206,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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Sarin","contributorId":317786,"corporation":false,"usgs":false,"family":"Suwanpakdee","given":"Sarin","email":"","affiliations":[{"id":69155,"text":"Thailand National Wildlife Health Center, Faculty of Veterinary Science, The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals & Department of Clinical Sciences and Public Health, Faculty of Veterinary Science","active":true,"usgs":false}],"preferred":false,"id":911370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sangkachai, Nareerat","contributorId":317783,"corporation":false,"usgs":false,"family":"Sangkachai","given":"Nareerat","email":"","affiliations":[{"id":69154,"text":"Thailand National Wildlife Health Center, Faculty of Veterinary Science & The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals","active":true,"usgs":false}],"preferred":false,"id":911371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiratsudakul, 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Science & Department of Clinical Sciences and Public Health, Faculty of Veterinary Science","active":true,"usgs":false}],"preferred":false,"id":911374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wongluechai, Peerawat","contributorId":343540,"corporation":false,"usgs":false,"family":"Wongluechai","given":"Peerawat","email":"","affiliations":[],"preferred":false,"id":911375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pabutta, Choenkwan","contributorId":343541,"corporation":false,"usgs":false,"family":"Pabutta","given":"Choenkwan","email":"","affiliations":[],"preferred":false,"id":911376,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sariya, Ladawan","contributorId":343542,"corporation":false,"usgs":false,"family":"Sariya","given":"Ladawan","email":"","affiliations":[],"preferred":false,"id":911377,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Korkijthamkul, Waruja","contributorId":343543,"corporation":false,"usgs":false,"family":"Korkijthamkul","given":"Waruja","email":"","affiliations":[],"preferred":false,"id":911378,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":140397,"corporation":false,"usgs":true,"family":"Blehert","given":"David","email":"dblehert@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":911400,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"White, C. LeAnn 0000-0002-5004-5165 clwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-5004-5165","contributorId":4315,"corporation":false,"usgs":true,"family":"White","given":"C.","email":"clwhite@usgs.gov","middleInitial":"LeAnn","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":911401,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":911402,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stephen, Craig","contributorId":168939,"corporation":false,"usgs":false,"family":"Stephen","given":"Craig","email":"","affiliations":[],"preferred":false,"id":911379,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ratanakorn, Parntep","contributorId":191779,"corporation":false,"usgs":false,"family":"Ratanakorn","given":"Parntep","email":"","affiliations":[],"preferred":false,"id":911380,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":911403,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70259729,"text":"70259729 - 2024 - A conterminous United States–Wide validation of relative tidal elevation products","interactions":[],"lastModifiedDate":"2024-10-22T12:15:37.822664","indexId":"70259729","displayToPublicDate":"2024-08-20T07:14:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"A conterminous United States–Wide validation of relative tidal elevation products","docAbstract":"Recent large-scale spatial products have been developed to assess wetland position in the tidal frame, but nationwide comparisons and validations are missing for these products. Wetland position within the tidal frame is a commonly used characteristic to compare wetlands across biogeomorphic gradients and factors heavily into wetland vulnerability models. We utilize a dataset of 365 surface elevation table stations across the conterminous USA containing ground-surveyed tidal datum and elevation data to validate two gridded, conterminous USA–wide relative tidal elevation products. We identified substantial differences between our ground-surveyed dataset and the gridded products, with the Gulf coast exhibiting the greatest error (p < 0.0001, n = 140). Error in relative tidal elevation products varied by coast, tidal range, and latitude. These differences in errors indicate that gridded relative tidal elevation products may be more accurate in coastal wetlands with larger tidal ranges (> 30 cm) and are less accurate in freshwater wetlands near the coast. This paper makes advances in understanding why relative tidal elevation differences occur among national datasets and identifies areas of future work that could support more robust vulnerability models.
Salinity, or total dissolved solids (TDS), of the Colorado River affects agricultural, municipal, and industrial water users and is an important concern in the Western United States. In the Paradox Valley of southwestern Colorado, natural discharge of sodium-chloride brine to the Dolores River from the underlying core of a salt-valley anticline accounts for about 6 percent of the salinity load to the Colorado River. Formation of the Paradox Valley began during the Miocene, and subsequent erosion exposed the Pennsylvania Paradox Formation in the core of the anticline where a cap rock, collapse features, breccia, and sodium-chloride saturated brine developed at the top of the exposed salt diapir. The discharge of brine to the Dolores River is affected by these dissolution features, along with seasonal hydrologic conditions and density-dependent flow between older dense brine and the younger fresh groundwater in the overlying alluvial aquifer. To reduce TDS concentrations in the Dolores River through the Paradox Valley, the Bureau of Reclamation has pumped brine from a series of shallow wells adjacent to the river since July 1996. The pumped brine is collected and piped to a deep disposal well where it is injected into the Mississippian Leadville Limestone at a depth of about 4,570-meters below land surface. The pumping and injection operation is collectively known as the Paradox Valley Unit (PVU), and by 2015, the PVU had substantially reduced TDS concentrations in the Dolores River by about 70 percent. Since 2019, injection-pressure limits and related seismic activity have constrained deep-well injection and thus brine pumping at the PVU.
In cooperation with the Bureau of Reclamation, the U.S. Geological Survey developed a MODFLOW-6 three-dimensional, variable-density groundwater flow and TDS transport model of the Paradox Valley to evaluate the effects of PVU pumping operations on brine discharge to the Dolores River and to guide additional research. The finite-difference model grid consists of 76 rows and 48 columns oriented from northwest to southeast in alignment with valley topography and groundwater-flow directions in the near-surface freshwater alluvial aquifer. A 7-layer hydrogeologic framework was developed from existing datasets to represent the alluvial aquifer, cap rock, collapse breccia, and groundwater flow and TDS transport from the underlying Paradox Formation salt to the Dolores River. The model represents a 33-year transient calibration period from 1987 through 2020 that includes pre-PVU conditions from 1987 through June 1996 and post-PVU conditions from July 1996 through 2020. A 1,000-year simulation of groundwater flow and coupled TDS transport computed the initial conditions for the subsequent 33-year transient simulation. Observations of precipitation, streamflow, evaporation, agricultural land use, and PVU brine pumping rates were used to specify appropriate boundary conditions to the model representing time-varying recharge, tributary streamflow, groundwater underflow, evapotranspiration (ET), and PVU pumping. Values for average monthly streamflow and TDS concentration at the upstream streamgage, the Dolores River at Bedrock (USGS streamgage 09169500), were specified as model input where the Dolores River enters Paradox Valley. Observed pumping from the PVU, water levels and TDS concentrations in groundwater, and streamflow and estimated TDS concentrations at the downstream streamgage, the Dolores River near Bedrock (USGS streamgage 09171100), were calibration targets that constrained the manual calibration of model parameters representing aquifer hydraulic conductivity, storage, streambed conductance, recharge, and (ET).
Two primary model-calibration targets were the match between observed and simulated TDS mass flux from PVU pumping wells and the match between estimated and simulated TDS mass flux to the Dolores River. The simulated TDS mass withdrawn by pumping wells is calculated by the model as the product of the assigned pumping rate and simulated groundwater TDS concentrations. Because actual pumping rates were assigned as simulated values, the total simulated PVU pumping for the 33-year calibration is within 0.5 percent of the observed values. However, simulated concentrations and thus mass flux of TDS withdrawn by the PVU pumping wells were consistently about 26 percent less than observed values for all the simulated time periods (33-year simulation, pre-PVU, and post-PVU). The representation of brine inflow was explored through additional modeling to evaluate the effect of the simulated brine source on groundwater TDS concentrations. Results indicated that a saturated-salt constant-flux brine source best replicated the magnitude and transient pattern observed for TDS mass flux from PVU pumping wells.
The simulated TDS mass flux to the Dolores River is compared to estimates based on observed streamflow and specific conductance (SC) data for the downstream streamgage. The calibrated model provided a close fit of simulated to measured streamflow at the downstream streamgage, and the calibrated model fit to estimated TDS concentrations at the downstream streamgage was reasonable. The greatest differences between simulated and estimated values occurred during drought periods from June 2000 to March 2003, May 2012 to June 2013, and October 2013 to October 2014, when simulated TDS concentrations in the river were greater than estimated concentrations. In general, simulated TDS mass flux to the river for the pre-PVU period is in good agreement with estimated values (2-percent difference), but the model overestimated TDS mass flux to the river by about 41 percent during the post-PVU period. The model uncertainty with respect to TDS mass flux to the river indicates other processes or model parameters not well represented by the model are affecting the system, especially during drought. During model calibration, the most sensitive parameters were identified as vertical hydraulic conductivity of the alluvial aquifer, conductance of the Dolores River streambed, ET extinction depth and rate, and recharge rate.
Five 5-year scenarios of conditions for 2021–25 were simulated to assist evaluation of alternative strategies to manage the discharge of brine into the Dolores River. The first scenario simulates no PVU pumping and serves as a base case for comparison to the other scenarios. Two scenarios simulate the effects of varying withdrawal timing at an annual rate about one-third less than during 2010 through 2018. During high-flow spring snowmelt runoff periods when brine discharge is naturally minimized, PVU pumping does not substantially affect salinity in the Dolores River, and comparison of these two scenarios indicates that scheduling brine withdrawals during times of low river stage is nearly as effective at reducing TDS mass flux to the river as pumping brine year-round. Cessation of pumping during periods of high river stage may be advantageous for system maintenance, brine injection, and seismic-risk reduction. The fourth scenario tested the effect of reducing irrigation-return flow on brine discharge and predicted a slight reduction of TDS mass flux to the Dolores River, but not as great a reduction as that of using the PVU to remove brine. The fifth scenario simulated 5 years of drought conditions without PVU pumping and indicates brine discharge during drought about 15 percent greater than during average hydrologic conditions. Results from scenario 5 are consistent with the calibrated model results and indicate that aquifer properties and ET processes and parameters may be affecting simulation results during drought.
The Paradox Valley groundwater model provides a reasonable overall match to observed conditions in the Dolores River. The model is useful for evaluating relative differences between brine management scenarios to inform PVU operational decisions and to identify gaps in data and process understanding. Representation of the brine source, hydraulic-conductivity parameters, and recharge and ET processes were identified as potential areas for additional field and modeling research. Additional research in the Paradox Valley might include field-data collection that provides additional information on the hydrogeologic framework, groundwater levels, groundwater TDS concentrations, stream characteristics, and aquifer properties. Additional modeling efforts could benefit from applying advanced tools for model development, calibration, and visualization including parameter-estimation and sensitivity analysis. Statistical evaluation of known model uncertainties such as hydraulic conductivity, streambed conductance, representations of the brine source, recharge, and ET could improve the match between simulated and estimated TDS mass flux from PVU pumping wells and to the Dolores River further informing model predictions and system understanding for the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245038","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Heywood, C.E., Paschke, S.S., Mast, M.A., and Watts, K.R., 2024, Simulation of groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2024–5038, 47 p., https://doi.org/10.3133/sir20245038.","productDescription":"Report: viii, 47 p.; Data Release; 3 Databases","onlineOnly":"Y","ipdsId":"IP-130109","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":432768,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09171100/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09171100 Dolores River near Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432764,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5038/coverthb.jpg"},{"id":432763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZW0FH5","text":"USGS data release","linkHelpText":"MODFLOW-6 model of variable-density groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Colorado"},{"id":432766,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09169500/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09169500 Dolores River at Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5038/sir20245038.pdf","text":"Report","size":"9.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5038"},{"id":499462,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117219.htm","linkFileType":{"id":5,"text":"html"}},{"id":432769,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235094","text":"Hydrogeologic Conceptual Model of Groundwater Occurrence and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.10372373203981,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.51805273423872\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
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Salinity, or total dissolved solids (TDS), of the Colorado River is a major concern in the southwestern United States where the river provides water to about 40 million people for municipal and industrial use and is used to irrigate about 5.5 million acres of land. Much of the salinity in the Colorado River Basin is derived from natural interactions of surface water and groundwater with various geologic materials (rocks, soils, and alluvial deposits). The Dolores River in southwest Colorado is a major tributary of the Colorado River that historically accounts for about 6 percent of the salinity load to the Upper Colorado River Basin with the Paradox Valley being the primary source of salinity to the Dolores River. The Paradox Valley, one of several salt-anticline valleys in the region, is a fault-bounded topographic basin aligned with and exposing an underlying salt-anticline core. Salt deposits in the Pennsylvanian Paradox Formation of the Hermosa Group form an elongated salt diapir oriented northwest to southeast that is up to 12,000 feet (ft) thick beneath the present valley floor. Surface erosion, groundwater circulation, and weathering during Tertiary and Quaternary valley formation contributed to development of a cap rock, collapse features, breccia, and brine at the top of the exposed salt diapir. Today (2023), brine occurring in the brecciated cap rock and underlying salt deposits is in hydraulic connection with an overlying freshwater alluvial aquifer, and depending on seasonal river stage and hydrologic conditions, the brine discharges to the Dolores River causing the observed increase in salinity as the river crosses the Paradox Valley.
To reduce salinity concentrations in the Dolores River, the Bureau of Reclamation (Reclamation) operates the Paradox Valley Unit (PVU). The PVU project consists of nine shallow brine pumping wells near the Dolores River and one deep disposal well where the brine is injected for disposal. When operational, the PVU pumping wells extract brine from the base of the alluvial aquifer that is piped and injected into a deep disposal well about 3 miles southwest of the PVU. The PVU became fully operational July 1, 1996, and by 2015, operation of the PVU had reduced salinity concentrations in the Dolores River by as much as 70 percent compared to pre-PVU conditions. In response to a 4.5 magnitude earthquake, injection operations, and thus PVU pumping, were ceased from March 2019 to June 2022. A trial period of PVU operation began in June 2022 with a reduced injection rate, and thus PVU pumping rate, of about two-thirds capacity to gather additional information and guide future operational decisions.
In cooperation with Reclamation, the U.S. Geological Survey (USGS) developed this report to present the current (2023) understanding of groundwater and brine occurrence and discharge to the Dolores River in the Paradox Valley. Results from the compilation of spatial datasets, groundwater sampling and age dating, and aquifer tests are presented to provide improved understanding of the Paradox Valley hydrogeology, to supply datasets for a numerical groundwater-flow and brine-transport model, and to support future operations of the PVU. The hydrogeologic data provided herein, along with the most recent loading analysis for the Dolores River in the Paradox Valley, and a previous conceptual model for brine discharge to the river are used to present a conceptual understanding of groundwater occurrence in the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235094","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Paschke, S.S., Mast, M.A., Gardner, P.M., Newman, C.P., and Watts, K.R., 2024, Hydrogeologic conceptual model of groundwater occurrence and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5094, 58 p., https://doi.org/10.3133/sir20235094.","productDescription":"Report: x, 54 p.; 2 Data Releases; Database","onlineOnly":"Y","ipdsId":"IP-125569","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":432678,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245038","text":"Simulation of Groundwater Flow and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"},{"id":432677,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the nation: U.S. Geological Survey National Water Information System database"},{"id":432676,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CJQDDU","text":"USGS data release","linkHelpText":"Geospatial datasets developed for a hydrogeologic conceptual model of brine discharge to the Dolores River, Paradox Valley, Colorado"},{"id":432675,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NV5U6F","text":"USGS data release","linkHelpText":"Water-level and pumping data, water-level models, and estimated hydraulic properties for the Paradox Valley alluvial aquifer in Montrose County, Colorado, 2013"},{"id":432674,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FMWX2J","text":"USGS data release","linkHelpText":"Recharge temperatures and groundwater-age models for the Paradox Valley alluvial aquifer, 2011, Colorado"},{"id":432669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5094/sir20235094.pdf","text":"Report","size":"9.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5094"},{"id":499380,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117218.htm","linkFileType":{"id":5,"text":"html"}},{"id":432668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5094/coverthb.jpg"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.23469392755278,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.62004006715256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
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Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2024.e03133","usgsCitation":"Gould, M.J., Clapp, J., Haroldson, M.A., Costello, C., Nowak, J.J., Martin, H., Ebinger, M., Bjornlie, D., Thompson, D., Dellinger, J.A., Mumma, M., Lukacs, P., and van Manen, F.T., 2024, A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem: Global Ecology and Conservation, v. 54, e03133, 16 p., https://doi.org/10.1016/j.gecco.2024.e03133.","productDescription":"e03133, 16 p.","ipdsId":"IP-166249","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2024.e03133","text":"Publisher Index Page"},{"id":432938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.19553511360566,\n 45.48126054576713\n ],\n [\n -112.23427309687703,\n 44.53619573717927\n ],\n [\n -110.92847041406486,\n 43.38122138835186\n ],\n [\n -109.24643599937531,\n 43.18040812667567\n ],\n [\n -109.13267230664917,\n 45.518110186630736\n ],\n [\n -112.19553511360566,\n 45.48126054576713\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gould, Matthew J.","contributorId":201504,"corporation":false,"usgs":false,"family":"Gould","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":911000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clapp, Justin","contributorId":256932,"corporation":false,"usgs":false,"family":"Clapp","given":"Justin","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":911001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":911002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Costello, Cecily M.","contributorId":145510,"corporation":false,"usgs":false,"family":"Costello","given":"Cecily M.","affiliations":[{"id":5117,"text":"University of Montana, College of Forestry and Conservation, University Hall, Room 309, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":911003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowak, J. Joshua","contributorId":171707,"corporation":false,"usgs":false,"family":"Nowak","given":"J.","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":911004,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, Hans","contributorId":331216,"corporation":false,"usgs":false,"family":"Martin","given":"Hans","email":"","affiliations":[{"id":79153,"text":"Univ. of Minnesota, St. Paul, MN","active":true,"usgs":false}],"preferred":false,"id":911005,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebinger, Michael","contributorId":300973,"corporation":false,"usgs":false,"family":"Ebinger","given":"Michael","affiliations":[{"id":35211,"text":"Montana Department of Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":911006,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bjornlie, Daniel D.","contributorId":145512,"corporation":false,"usgs":false,"family":"Bjornlie","given":"Daniel D.","affiliations":[{"id":16140,"text":"Wyoming Game & Fish Department, Large Carnivore Section, Lander, Wyoming 82520, USA","active":true,"usgs":false}],"preferred":false,"id":911007,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thompson, Daniel","contributorId":225736,"corporation":false,"usgs":false,"family":"Thompson","given":"Daniel","affiliations":[{"id":13584,"text":"Natural Resources Canada, Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":911008,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dellinger, Justin A.","contributorId":190532,"corporation":false,"usgs":false,"family":"Dellinger","given":"Justin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":911009,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mumma, Matthew A.","contributorId":202351,"corporation":false,"usgs":false,"family":"Mumma","given":"Matthew","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":911010,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lukacs, Paul","contributorId":189208,"corporation":false,"usgs":false,"family":"Lukacs","given":"Paul","affiliations":[],"preferred":false,"id":911011,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":911012,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70259350,"text":"70259350 - 2024 - Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane","interactions":[],"lastModifiedDate":"2024-10-04T14:11:40.777116","indexId":"70259350","displayToPublicDate":"2024-08-19T09:07:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane","docAbstract":"The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) provides coastal communities with 6-day notice of potential elevated water levels and coastal change (i.e., dune erosion, overwash, or inundation) on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines. The objective of this work is to assess the skill of forecasts during a period of elevated water levels along the coasts of North Carolina (NC) and South Carolina, USA caused by Hurricane Isaias in August 2020, using a combination of observations and model hindcasts. Water levels and waves were observed throughout the storm at three locations near Wrightsville Beach, NC, which provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra derived from a coupled Delft3D-SWAN simulation during the peak of Isaias, to complement observations by computing nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = −0.05 m; gain = 0.26; r2 = 0.99), suggesting that observations at the USGS sensor location were a useful proxy for total water level (TWL; sum of tide, surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach was consistent with observations from the USGS sensor (bias = −0.38 m and −0.74 m, scatter index = 0.22 and 0.28 for the two forecast model grids considered, respectively; weighted regression considering model uncertainty explained 95 percent of variability in observed TWL). Observed TWL was within the confidence interval of the TWL&CC Forecast for the 5 h at the storm peak. Forecast mean water levels (MWL; sum of tide, surge and wave setup) and tide gage observations were also consistent (bias = 0.07 m and 0.02 m for the forecast model grids; scatter index = 0.46; r2 = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m; scatter index = 0.18). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. The results demonstrate that wave-driven water levels contributed a significant portion of the forecast TWL during Isaias (52 percent during the three peak hours of the storm), and that TWL were represented using the forecast model. Mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient method indicates that the forecasting system can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2024.104590","usgsCitation":"Birchler, J.J., Palmsten, M.L., Doran, K., Karwandyar, S., Pardun, J.M., Oades, E.M., Mulligan, R.P., and Whitehead-Zimmers, E.S., 2024, Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane: Coastal Engineering, v. 193, 104590, 19 p., https://doi.org/10.1016/j.coastaleng.2024.104590.","productDescription":"104590, 19 p.","ipdsId":"IP-154794","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2024.104590","text":"Publisher Index Page"},{"id":462596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"193","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Kara S. 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":292448,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karwandyar, Sharifa 0000-0002-1531-2360","orcid":"https://orcid.org/0000-0002-1531-2360","contributorId":343816,"corporation":false,"usgs":false,"family":"Karwandyar","given":"Sharifa","email":"","affiliations":[{"id":82201,"text":"Department of Earth Sciences, Binghamton University","active":true,"usgs":false}],"preferred":false,"id":915010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pardun, Joshua Michael 0000-0003-4633-3970","orcid":"https://orcid.org/0000-0003-4633-3970","contributorId":335148,"corporation":false,"usgs":true,"family":"Pardun","given":"Joshua","email":"","middleInitial":"Michael","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oades, Elora M.","contributorId":343817,"corporation":false,"usgs":false,"family":"Oades","given":"Elora","email":"","middleInitial":"M.","affiliations":[{"id":82202,"text":"Department of Civil Engineering, Queen’s University","active":true,"usgs":false}],"preferred":false,"id":915012,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mulligan, Ryan P.","contributorId":194423,"corporation":false,"usgs":false,"family":"Mulligan","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":35723,"text":"Queen's University - Kingston, Ontario","active":true,"usgs":false}],"preferred":false,"id":915013,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Whitehead-Zimmers, Eli Sawyer 0000-0002-8925-3498","orcid":"https://orcid.org/0000-0002-8925-3498","contributorId":339930,"corporation":false,"usgs":true,"family":"Whitehead-Zimmers","given":"Eli","email":"","middleInitial":"Sawyer","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915014,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70259302,"text":"70259302 - 2024 - Crystal resorption as a driver for mush maturation: An experimental investigation","interactions":[],"lastModifiedDate":"2024-10-03T13:53:03.53727","indexId":"70259302","displayToPublicDate":"2024-08-19T08:47:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Crystal resorption as a driver for mush maturation: An experimental investigation","docAbstract":"The thermal state of a magma reservoir controls its physical and rheological properties: at storage temperatures close to the liquidus, magmas are dominated by melt and therefore mobile, while at lower temperatures, magmas are stored as a rheologically locked crystal network with interstitial melt (crystal mush). Throughout the lifetime of a magmatic system, temperature fluctuations drive transitions between mush-dominated and melt-dominated conditions. For example, magma underplating or magma recharge into a crystal mush supplies heat, leading to mush disaggregation and an increase in melt fraction via crystal resorption, before subsequent cooling reinstates a crystal mush via crystal accumulation and recrystallisation. Here, we examine the textural effects of such temperature-driven mush reprocessing cycles on the crystal cargo. We conducted high-P-T resorption experiments during which we nucleated, grew, resorbed, and recrystallised plagioclase crystals in a rhyolitic melt, imposing temperature fluctuations typical for plumbing systems in intermediate arc volcanoes (20–40 °C). The experiments reproduce common resorption textures and show that plagioclase dissolution irreversibly reduces 3D crystal aspect ratios, leading to more equant shapes. Comparison of our experimental results with morphologies of resorbed and unresorbed plagioclase crystals from Mount St. Helens (MSH) (USA) reveals a consistent trend in natural rocks: unresorbed plagioclase crystals (found in MSH dacite, basalt and quenched magmatic inclusions [QMIs]) have tabular shapes, while plagioclase crystals with one or more resorption horizons (found in MSH dacite, QMIs, and mush inclusions) show more equant shapes. Plagioclase crystals showing pervasive resorption (found in the dacite and mush inclusions) have even lower aspect ratios. We therefore suggest that crystal mush maturation results in progressively more equant crystal shapes: the shapes of plagioclase crystals in a magma reservoir will become less tabular every time they are remobilised and resorbed. This has implications for magma rheology and, ultimately, eruptibility, as crystal shape controls the maximum packing fraction and permeability of a crystal mush. We hypothesise that a mature mush with more equant crystals due to multiple resorption–recrystallisation events will be more readily remobilised than an immature mush comprising unresorbed, tabular crystals. This implies that volcanic behaviour and pre-eruptive magmatic timescales may vary systematically during thermal maturation of a crustal magmatic system, with large eruptions due to rapid wholesale remobilisation of mushy reservoirs being more likely in thermally mature systems.
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