Understanding the near-surface environment where atmospheric and solid earth processes interact, often termed the “Critical Zone,” is important for assessing resources and building resilient societies. Here, we examine a volcanic landscape in the Oregon Cascade Range, an understudied Critical Zone setting that is host to major regional water resources, pervasive silicate weathering, and significant geohazards. We leverage a bedrock age chronosequence to show that the volcanic Critical Zone undergoes a structural shift, from depth extents of >1 km to meters, over timescales of ~1 My. We map an active groundwater volume comparable to major continental lakes, stored at the Cascade Range crest. This state shift makes volcanic landscape evolution a unique probe of deep coupling between Earth systems.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2415155122","usgsCitation":"Karlstrom, L., Klema, N., Grant, G., Finn, C., Sullivan, P.L., Cooley, S., Simpson, A., Fasth, B., Cashman, K., Ferrier, K., Ball, L.B., and McKay, D., 2025, State shifts in the deep Critical Zone drive landscape evolution in volcanic terrains: PNAS, v. 122, no. 2, e2415155122, 9 p., https://doi.org/10.1073/pnas.2415155122.","productDescription":"e2415155122, 9 p.","ipdsId":"IP-169317","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":487573,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2415155122","text":"Publisher Index Page"},{"id":480753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Karlstrom, Leif","contributorId":265509,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Leif","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":924276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klema, Nathaniel","contributorId":349383,"corporation":false,"usgs":false,"family":"Klema","given":"Nathaniel","affiliations":[{"id":49196,"text":"Fort Lewis College","active":true,"usgs":false}],"preferred":false,"id":924277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grant, Gordon E.","contributorId":30881,"corporation":false,"usgs":false,"family":"Grant","given":"Gordon E.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":924278,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":229711,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":924279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sullivan, Pamela L.","contributorId":107605,"corporation":false,"usgs":true,"family":"Sullivan","given":"Pamela","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":924281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cooley, Sarah","contributorId":349565,"corporation":false,"usgs":false,"family":"Cooley","given":"Sarah","affiliations":[],"preferred":false,"id":924438,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Simpson, Alex","contributorId":349388,"corporation":false,"usgs":false,"family":"Simpson","given":"Alex","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":924282,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fasth, Becky","contributorId":349390,"corporation":false,"usgs":false,"family":"Fasth","given":"Becky","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":924283,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cashman, Katherine","contributorId":349391,"corporation":false,"usgs":false,"family":"Cashman","given":"Katherine","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":924284,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ferrier, Ken","contributorId":349393,"corporation":false,"usgs":false,"family":"Ferrier","given":"Ken","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":924285,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ball, Lyndsay B. 0000-0002-6356-4693 lbball@usgs.gov","orcid":"https://orcid.org/0000-0002-6356-4693","contributorId":1138,"corporation":false,"usgs":true,"family":"Ball","given":"Lyndsay","email":"lbball@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":924286,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"McKay, Daniele","contributorId":349395,"corporation":false,"usgs":false,"family":"McKay","given":"Daniele","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":924287,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70271401,"text":"70271401 - 2025 - The influence of pre-existing structures on geothermal springs: Inferences from potential field mapping in Surprise Valley, CA and other sites In the northwestern Great Basin","interactions":[],"lastModifiedDate":"2025-09-11T14:11:27.743233","indexId":"70271401","displayToPublicDate":"2024-12-01T09:07:45","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The influence of pre-existing structures on geothermal springs: Inferences from potential field mapping in Surprise Valley, CA and other sites In the northwestern Great Basin","docAbstract":"Surprise Valley, located in the northwestern Great Basin, is an asymmetric extensional basin that marks a major tectonic transition between the relatively un-extended volcanic Modoc Plateau to the west, and the Basin and Range to the east that has undergone 10-15% extension. In addition, it sits just north of the Walker Lane which accommodates up to 20% of dextral slip associated with Pacific-North American plate interactions.\nThermal springs issue from eight areas within Surprise Valley. Most of these occur within the basin and are not situated on the main basin forming range-front faults. As a result, efforts to resolve the structural setting of the valley’s hydrothermal system have relied on geophysics to characterize basin structure and geology. \nExtensive efforts to map the basin with ground and airborne magnetics have revealed a >35 km-long linear, intra-basin magnetic high, interpreted as a buried dike swarm. Geothermal springs on the eastern side of the valley, including Seifert hot springs, Leonards hot springs, and Surprise Valley hot springs (SVHS), are all situated along the magnetic high and occur at local breaks and bends in the anomaly, suggesting that fracture permeability is enhanced along the feature and particularly at these discontinuities. \nRecent studies, including drilling over the anomaly near SVHS that likely intersected dike material, as well as subsequent mapping and sampling of dikes outcropping along the anomaly on the playa surface south of SVHS, confirm (as previously inferred) that mafic intrusives are the principal source of the anomaly. Similar interpretations made in two other valleys (in southern Oregon and northwestern Nevada), where inferred intra-basin dikes appear to be spatially correlated with hot springs or prospective geothermal resource areas, suggest that the impact of pre-existing basement structure on hydrothermal activity may pertain more generally to other hydrothermal settings throughout the Great Basin. If so, efforts to map basement may enhance understanding structural controls on some geothermal systems.\nFurthermore, similarities across these disparate sites suggest that magmatism may play a much larger role in accommodating extension and influencing basin evolution across the western Great Basin than previously recognized.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Rising","usgsCitation":"Glen, J.M., and Earney, T.E., 2025, The influence of pre-existing structures on geothermal springs: Inferences from potential field mapping in Surprise Valley, CA and other sites In the northwestern Great Basin, in Using the Earth to save the Earth, v. 48, p. 1786-1800.","productDescription":"15 p.","startPage":"1786","endPage":"1800","ipdsId":"IP-168956","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":495307,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":495304,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1035058","linkFileType":{"id":5,"text":"html"}}],"volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":948388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":948389,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259608,"text":"70259608 - 2024 - Geophysical identification of potential groundwater – surface water interactions in the Bonita Peak Mining District, San Juan County, Colorado","interactions":[],"lastModifiedDate":"2026-04-21T16:37:51.078379","indexId":"70259608","displayToPublicDate":"2025-02-01T11:27:26","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesNumber":"EPA/600/R-24/007","title":"Geophysical identification of potential groundwater – surface water interactions in the Bonita Peak Mining District, San Juan County, Colorado","docAbstract":"This report includes the results and interpretation of geophysical investigations to aid in the location, identification, and conceptual site model (CSM) development of the near surface groundwater and surface water hydrology of the Upper Cement Creek and California Gulch of the Animas Rivers within the Bonita Peak Mining Districts (BPMD). Mine influenced water (MIW) from numerous mine adits within the BPMD has altered the geochemistry and ecology of these ground and surface waters. Many studies of varying scales have investigated the geology, hydrogeology, geochemistry, and aqueous chemistry of these adits and impacted areas. Field investigations using non-invasive geophysical methods were conducted in the summer of 2019 to aid in identifying potential groundwater inputs to these streams and further the CSM of the near surface within and along these streams.
Two field deployments were completed in August and September of 2019. In August 2019, Phase 1 was completed using multifrequency electromagnetic induction (EMI) and thermal infrared imaging (TIR) to measure the bulk electrical conductivity, magnetic susceptibility, and the temperature response at known and suspected groundwater seeps into the Upper Cement Creek and California Gulch stretches of these alpine streams. Results and interpretation from Phase 1 were used to co-locate stretches of these streams for further detailed fiber optic distributed temperature system (FODTS) monitoring of the temperature dynamics for submeter identification of potential groundwater seeps during Phase 2. Phase 2 included additional EMI measurements, FODTS measurements, and repeated some Phase 1 measurements for improved data quality. All these data were used for the combined qualitative interpretation identifying seep locations.
The data from both Phase 1 and Phase 2 are available in the USGS data release at: https://www.sciencebase.gov/catalog/item/5f18a30082cef313ed84879b. These data include detailed near-surface electrical conductivity and magnetic susceptibility mapping, discrete TIR imaging, and several kilometers of FODTS data. These data were georeferenced and used to identify potential groundwater seeps into these streams. The results indicate a complex near surface geophysical and hydrogeologic regime. Seeps are classified and spatially correlated to the EMI, TIR, and geochemical sampling results.
This report is a thorough compilation of the geophysical field efforts in the summer of 2019, the data processing, and interpretations of these data. These data alone should not be used for final interpretations of the complex hydrogeology within these alpine systems. Rather, these data should be combined with other known data from various investigations to further refine the CSM and guide decisions for future investigations, watershed protective measures, and other site decisions. Geographical information system (GIS) layers of these results are available to project managers for such integration and combined interpretations.
","language":"English","publisher":"U.S. Environmental Protection Agency","usgsCitation":"Werkema, D.D., Terry, N.C., Briggs, M.A., Rutila, E., and Dyment, S., 2024, Geophysical identification of potential groundwater – surface water interactions in the Bonita Peak Mining District, San Juan County, Colorado, iii, 54 p.","productDescription":"iii, 54 p.","ipdsId":"IP-166193","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":503283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":503282,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://assessments.epa.gov/risk/document/&deid%3D364773"}],"country":"United States","state":"Colorado","county":"San Juan County","otherGeospatial":"Bonita Peak MIning District","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-107.5857,37.9702],[-107.5786,37.9667],[-107.5721,37.9636],[-107.5632,37.9573],[-107.5584,37.9524],[-107.5549,37.9493],[-107.5502,37.9475],[-107.5361,37.9445],[-107.5319,37.9414],[-107.5324,37.9378],[-107.5347,37.9337],[-107.5352,37.9291],[-107.5351,37.9237],[-107.532,37.9178],[-107.5278,37.9088],[-107.5247,37.9039],[-107.5212,37.9007],[-107.5211,37.8967],[-107.5279,37.8875],[-107.5324,37.8806],[-107.5329,37.8748],[-107.5317,37.8734],[-107.5305,37.8716],[-107.5204,37.8618],[-107.5179,37.8554],[-107.5184,37.8486],[-107.5176,37.84],[-107.5146,37.8342],[-107.5127,37.8288],[-107.5121,37.8265],[-107.5109,37.8256],[-107.5068,37.8243],[-107.491,37.8236],[-107.4828,37.8223],[-107.4757,37.817],[-107.4705,37.8143],[-107.4669,37.8107],[-107.4627,37.8044],[-107.4578,37.7918],[-107.457,37.785],[-107.4581,37.7791],[-107.4666,37.7668],[-107.4677,37.7645],[-107.4695,37.7645],[-107.4777,37.768],[-107.4812,37.7684],[-107.4829,37.7675],[-107.484,37.7648],[-107.4824,37.7407],[-107.4832,37.6374],[-107.6698,37.6372],[-107.6849,37.6375],[-107.6867,37.6375],[-107.9686,37.6377],[-107.9628,37.6401],[-107.96,37.6415],[-107.9583,37.6429],[-107.9572,37.6456],[-107.9572,37.6479],[-107.9579,37.6524],[-107.9604,37.6592],[-107.9629,37.6646],[-107.966,37.6718],[-107.9685,37.6777],[-107.9698,37.6822],[-107.9699,37.6867],[-107.9688,37.6899],[-107.966,37.6936],[-107.9615,37.6977],[-107.9575,37.7005],[-107.9534,37.7024],[-107.9505,37.7029],[-107.9471,37.7029],[-107.9389,37.7017],[-107.936,37.7017],[-107.9331,37.7027],[-107.9274,37.706],[-107.9239,37.7074],[-107.9181,37.7079],[-107.9135,37.7098],[-107.9094,37.7112],[-107.9049,37.7154],[-107.9014,37.7168],[-107.8968,37.7173],[-107.8904,37.717],[-107.8817,37.7162],[-107.8764,37.7163],[-107.8747,37.7172],[-107.873,37.7213],[-107.8726,37.7259],[-107.8733,37.7317],[-107.8717,37.7368],[-107.8684,37.7431],[-107.8644,37.7477],[-107.8627,37.7509],[-107.8622,37.7537],[-107.8629,37.7559],[-107.8641,37.7582],[-107.8659,37.76],[-107.8677,37.7617],[-107.8683,37.7635],[-107.8672,37.7663],[-107.8615,37.7732],[-107.8592,37.7737],[-107.854,37.7742],[-107.8493,37.7734],[-107.8446,37.7721],[-107.8423,37.7721],[-107.84,37.7726],[-107.8354,37.7767],[-107.8275,37.7859],[-107.8224,37.7915],[-107.8213,37.7928],[-107.8225,37.7955],[-107.8268,37.8063],[-107.8263,37.8082],[-107.8258,37.81],[-107.8085,37.8207],[-107.8056,37.8212],[-107.8004,37.8212],[-107.7975,37.8213],[-107.7952,37.8222],[-107.7935,37.8236],[-107.7918,37.8277],[-107.7885,37.8332],[-107.7868,37.8355],[-107.7845,37.8378],[-107.7812,37.8451],[-107.7762,37.8556],[-107.7756,37.857],[-107.7768,37.8592],[-107.7781,37.8615],[-107.7741,37.8656],[-107.7655,37.8739],[-107.7553,37.8845],[-107.7479,37.8923],[-107.7422,37.8982],[-107.7359,37.9038],[-107.7188,37.8977],[-107.7077,37.8955],[-107.7024,37.892],[-107.6977,37.8912],[-107.6942,37.8917],[-107.6897,37.8967],[-107.6879,37.8976],[-107.6862,37.899],[-107.6839,37.9],[-107.681,37.9],[-107.6682,37.9011],[-107.6595,37.9039],[-107.6514,37.9081],[-107.6422,37.9146],[-107.6394,37.9187],[-107.6389,37.9237],[-107.6404,37.9368],[-107.6405,37.9404],[-107.6407,37.9491],[-107.6385,37.9545],[-107.635,37.9586],[-107.6263,37.9588],[-107.6216,37.9588],[-107.6077,37.9636],[-107.5961,37.9669],[-107.588,37.9688],[-107.5857,37.9702]]]},\"properties\":{\"name\":\"San Juan\",\"state\":\"CO\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Werkema, Dale D.","contributorId":190401,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":915932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":915933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rutila, Elizabeth 0000-0003-0288-9678","orcid":"https://orcid.org/0000-0003-0288-9678","contributorId":224637,"corporation":false,"usgs":false,"family":"Rutila","given":"Elizabeth","email":"","affiliations":[{"id":40900,"text":"Oakridge Institute for Science and Education","active":true,"usgs":false}],"preferred":false,"id":915935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dyment, Stephen","contributorId":224638,"corporation":false,"usgs":false,"family":"Dyment","given":"Stephen","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":915936,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256974,"text":"70256974 - 2024 - New developments at the Center for Engineering Strong-Motion Data (CESMD)","interactions":[],"lastModifiedDate":"2026-03-27T18:53:34.419926","indexId":"70256974","displayToPublicDate":"2024-12-01T13:50:33","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"New developments at the Center for Engineering Strong-Motion Data (CESMD)","docAbstract":"The Center for Engineering Strong-Motion Data (CESMD), an internationally utilized joint center of the U.S. Geological Survey (USGS) and the California Geological Survey (CGS), provides a single access point for earthquake strong-motion records and station metadata from the CGS California Strong-Motion Instrumentation Program (CSMIP), the USGS National Strong-Motion Project (NSMP), the USGS Advanced National Seismic System, and other affiliates. The CESMD has been continuously improving its webtools to facilitate the access of strong-motion data and metadata for use in post-earthquake response and for scientific and engineering research applications. The Center provides raw and processed strong-motion data via the Engineering Data Center (EDC) and the Virtual Data Center (VDC) web portals. This paper focuses on the strong-motion products provided by the EDC where more than 48,000 records with peak ground accelerations greater than 0.1% g from over 2400 earthquakes are currently hosted. and on the ongoing efforts to develop data access tools and applications. The new developments and ongoing efforts in the EDC include: 1) enhancements to the CESMD webservices to facilitate access to station metadata, earthquake information, and strong motion records 2) new features to the interactive map interface, improving the visualization and access to earthquake, station, and record information, 3) efforts to develop a new web application tool for data format conversion from a number of data formats, 4) efforts to unify varying waveform data formats into a consistent format, 5) ongoing efforts to compile seismic station site geology, measured or inferred Vs30 values, shear-wave profiles, NEHRP site class, and available structural instrument deployment schematics, and 6) a special studies pages for research topic-specific ground motion datasets that offer uniform processing of records from a variety of sources.","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30-July 5, 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association for Earthquake Engineering","usgsCitation":"Hagos, L., Haddadi, H., Schleicher, L.S., Steidl, J.H., Thompson, E.M., Crume, H., Dhar, M., and Leue, N., 2024, New developments at the Center for Engineering Strong-Motion Data (CESMD), 18th World Conference on Earthquake Engineering, Milan, Italy, June 30-July 5, 2024, 12 p.","productDescription":"12 p.","ipdsId":"IP-162026","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":501746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501745,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=23440&conference=18WCEE","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hagos, Lijam","contributorId":300811,"corporation":false,"usgs":false,"family":"Hagos","given":"Lijam","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":909039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haddadi, Hamid","contributorId":39891,"corporation":false,"usgs":true,"family":"Haddadi","given":"Hamid","email":"","affiliations":[],"preferred":false,"id":909040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schleicher, Lisa Sue 0000-0001-6528-1753","orcid":"https://orcid.org/0000-0001-6528-1753","contributorId":264892,"corporation":false,"usgs":true,"family":"Schleicher","given":"Lisa","email":"","middleInitial":"Sue","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":909041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steidl, Jamison Haase 0000-0003-0612-7654","orcid":"https://orcid.org/0000-0003-0612-7654","contributorId":239709,"corporation":false,"usgs":true,"family":"Steidl","given":"Jamison","email":"","middleInitial":"Haase","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":909042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":909043,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crume, Heather","contributorId":341854,"corporation":false,"usgs":false,"family":"Crume","given":"Heather","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":909044,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dhar, M.","contributorId":300865,"corporation":false,"usgs":false,"family":"Dhar","given":"M.","email":"","affiliations":[],"preferred":false,"id":909045,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leue, N.","contributorId":341856,"corporation":false,"usgs":false,"family":"Leue","given":"N.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":909046,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261182,"text":"70261182 - 2024 - Ticks without borders: Microbiome of immature neotropical tick species parasitizing migratory songbirds along northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2024-11-27T15:42:50.611708","indexId":"70261182","displayToPublicDate":"2024-11-17T09:17:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19837,"text":"Frontiers in Cellular and Infection Microbiology","active":true,"publicationSubtype":{"id":10}},"title":"Ticks without borders: Microbiome of immature neotropical tick species parasitizing migratory songbirds along northern Gulf of Mexico","docAbstract":"Introduction: The long-distance, seasonal migrations of birds make them an effective ecological bridge for the movement of ticks. The introduction of exotic tick species to new geographical regions can cause the emergence of novel tick-borne pathogens. This study examined the prevalence of exotic tick species parasitizing migratory songbirds at stopover sites along the northern Gulf of Mexico using the mitochondrial 12S rRNA gene.
Methods: Overall, 421 individual ticks in the genera Amblyomma, Haemaphysalis, and Ixodes were recorded from 28 songbird species, of which Amblyomma and Amblyomma longirostre were the most abundant tick genera and species, respectively. A high throughput 16S ribosomal RNA sequencing approach characterized the microbial communities and identified pathogenic microbes in all tick samples.
Results and discussion: Microbial profiles showed that Proteobacteria was the most abundant phylum. The most abundant pathogens were Rickettsia and endosymbiont Francisella, Candidatus Midichloria, and Spiroplasma. Permutation multivariate analysis of variance revealed that the relative abundance of Francisella and Rickettsia drives microbial patterns across the tick genera. We also noted a higher percentage of positive correlations in microbe-microbe interactions among members of the microbial communities. Network analysis suggested a negative correlation between a) Francisella and Rickettsia and, b) Francisella and Cutibacterium. Lastly, mapping the distributions of bird species parasitized during spring migrations highlighted geographic hotspots where migratory songbirds could disperse ticks and their pathogens at stopover sites or upon arrival to their breeding grounds, the latter showing mean dispersal distances from 421–5003 kilometers. These findings spotlight the potential role of migratory birds in the epidemiology of tick-borne pathogens.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcimb.2024.1472598","usgsCitation":"Karim, S., Zenzal, T.J., Beati, L., Sen, R., Adegoke, A., Kumar, D., Downs, L.P., Keko, M., Nussbaum, A., Becker, D.J., and Moore, F.R., 2024, Ticks without borders: Microbiome of immature neotropical tick species parasitizing migratory songbirds along northern Gulf of Mexico: Frontiers in Cellular and Infection Microbiology, v. 14, 1472598, 15 p., https://doi.org/10.3389/fcimb.2024.1472598.","productDescription":"1472598, 15 p.","ipdsId":"IP-155766","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":466758,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcimb.2024.1472598","text":"Publisher Index Page"},{"id":464571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.40131577203148,\n 31.254592735096182\n ],\n [\n -88.36729061913346,\n 30.10135972035171\n ],\n [\n -87.46455520517436,\n 30.10135972035171\n ],\n [\n -87.55501002782326,\n 31.31260199216662\n ],\n [\n -88.40131577203148,\n 31.254592735096182\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.1339319721782,\n 30.466724457964204\n ],\n [\n -93.1658440056944,\n 29.43813226845178\n ],\n [\n -90.77810437140585,\n 29.325063575125725\n ],\n [\n -90.7750284309227,\n 30.626660373854534\n ],\n [\n -93.1339319721782,\n 30.466724457964204\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Karim, Shahid","contributorId":346547,"corporation":false,"usgs":false,"family":"Karim","given":"Shahid","email":"","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":919539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zenzal, Theodore J. Jr. 0000-0001-7342-1373","orcid":"https://orcid.org/0000-0001-7342-1373","contributorId":224399,"corporation":false,"usgs":true,"family":"Zenzal","given":"Theodore","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":919540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beati, Lorenza","contributorId":148019,"corporation":false,"usgs":false,"family":"Beati","given":"Lorenza","email":"","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":919541,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sen, Raima","contributorId":346549,"corporation":false,"usgs":false,"family":"Sen","given":"Raima","email":"","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":919542,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adegoke, Abdulsalam","contributorId":346552,"corporation":false,"usgs":false,"family":"Adegoke","given":"Abdulsalam","email":"","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":919543,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Deepak","contributorId":346555,"corporation":false,"usgs":false,"family":"Kumar","given":"Deepak","email":"","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":919544,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Downs, Latoyia P.","contributorId":346558,"corporation":false,"usgs":false,"family":"Downs","given":"Latoyia","email":"","middleInitial":"P.","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":919545,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Keko, Mario","contributorId":346561,"corporation":false,"usgs":false,"family":"Keko","given":"Mario","email":"","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":919546,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nussbaum, Ashly","contributorId":346564,"corporation":false,"usgs":false,"family":"Nussbaum","given":"Ashly","email":"","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":919547,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Becker, Daniel J.","contributorId":334313,"corporation":false,"usgs":false,"family":"Becker","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":919548,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Moore, Frank R.","contributorId":54582,"corporation":false,"usgs":false,"family":"Moore","given":"Frank","email":"","middleInitial":"R.","affiliations":[{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":919549,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70262024,"text":"70262024 - 2024 - Penguin colony georegistration using camera pose estimation and phototourism","interactions":[],"lastModifiedDate":"2025-01-10T16:30:20.637582","indexId":"70262024","displayToPublicDate":"2024-10-30T10:26:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Penguin colony georegistration using camera pose estimation and phototourism","docAbstract":"Satellite-based remote sensing and uncrewed aerial imagery play increasingly important roles in the mapping of wildlife populations and wildlife habitat, but the availability of imagery has been limited in remote areas. At the same time, ecotourism is a rapidly growing industry and can yield a vast catalog of photographs that could be harnessed for monitoring purposes, but the inherently ad-hoc and unstructured nature of these images make them difficult to use. To help address this, a subfield of computer vision known as phototourism has been developed to leverage a diverse collection of unstructured photographs to reconstruct a georeferenced three-dimensional scene capturing the environment at that location. Here we demonstrate the use of phototourism in an application involving Antarctic penguins, sentinel species whose dynamics are closely tracked as a measure of ecosystem functioning, and introduce a semi-automated pipeline for aligning and registering ground photographs using a digital elevation model (DEM) and satellite imagery. We employ the Segment Anything Model (SAM) for the interactive identification and segmentation of penguin colonies in these photographs. By creating a textured 3D mesh from the DEM and satellite imagery, we estimate camera poses to align ground photographs with the mesh and register the segmented penguin colony area to the mesh, achieving a detailed representation of the colony. Our approach has demonstrated promising performance, though challenges persist due to variations in image quality and the dynamic nature of natural landscapes. Nevertheless, our method offers a straightforward and effective tool for the georegistration of ad-hoc photographs in natural landscapes, with additional applications such as monitoring glacial retreat.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0311038","usgsCitation":"Wu, H., Flynn, C., Hall, C., Che-Castaldo, C., Samaras, D., Schwaller, M., and Lynch, H., 2024, Penguin colony georegistration using camera pose estimation and phototourism: PLoS ONE, v. 19, no. 10, e0311038, 18 p., https://doi.org/10.1371/journal.pone.0311038.","productDescription":"e0311038, 18 p.","ipdsId":"IP-160209","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0311038","text":"Publisher Index Page"},{"id":465996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Wu, Haoyu","contributorId":347903,"corporation":false,"usgs":false,"family":"Wu","given":"Haoyu","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":922740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flynn, Clare","contributorId":347904,"corporation":false,"usgs":false,"family":"Flynn","given":"Clare","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":922741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, Carole","contributorId":347905,"corporation":false,"usgs":false,"family":"Hall","given":"Carole","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":922742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Che-Castaldo, Christian Joseph 0000-0002-7670-2178","orcid":"https://orcid.org/0000-0002-7670-2178","contributorId":347906,"corporation":false,"usgs":true,"family":"Che-Castaldo","given":"Christian Joseph","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":922743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Samaras, Dimitris","contributorId":347907,"corporation":false,"usgs":false,"family":"Samaras","given":"Dimitris","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":922744,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwaller, Mathew","contributorId":347909,"corporation":false,"usgs":false,"family":"Schwaller","given":"Mathew","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":922745,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lynch, Heather J.","contributorId":347911,"corporation":false,"usgs":false,"family":"Lynch","given":"Heather J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":922746,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268261,"text":"70268261 - 2024 - Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series","interactions":[],"lastModifiedDate":"2025-06-18T15:03:04.772207","indexId":"70268261","displayToPublicDate":"2024-10-30T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series","docAbstract":"Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5–10 ha) and ponds (<5 ha) due to limitations in satellite resolution and challenges extracting individual small waterbodies from low-albedo surfaces, vegetated water, and lotic water systems including rivers and streams. In this study, we developed generalizable pond mapping strategies based on their spatial-temporal-spectral characteristics to fully exploit accessible medium-resolution optical and synthetic aperture radar (SAR) time series to identify ponds. Our novel approach entails: (1) making full use of ponds' characteristics from an object-based perspective; (2) extracting pond objects using seeds of prominent water pixels defined by the SAR VH signal; (3) constructing training samples of ponds with high representativeness; and (4) improving inter-class discrimination by combining features from optical and SAR data. We designed a novel Optical-SAR Pond Object Mapper (OptiSAR-POM) to achieve an improved estimate of pond size distribution by incorporating mapping strategies into the object-based image analysis framework. We generated landscape objects through an elaborate water-focused segmentation approach, which adaptively aligned the segmentation parameters with the size and distribution patterns of ponds to identify small waterbodies and increase inter-class variability. We further introduced an interactive learning process to construct random forests for object-based classification, which incorporated adaptive empirical thresholds to identify potential pond objects and select representative training samples of varying sizes. We tested the OptiSAR-POM framework using Sentinel-1/2 time series at three county-level study sites and three supplementary watershed-level study sites in the United States and China. Our approach yielded high overall accuracy (>95 %) for all sites and highlighted the ability of Sentinel-1/2 imagery to accurately detect small ponds (0.1–1 ha) across diverse landscapes. The average producer's accuracy for small ponds at county-level sites improved by ∼45 % compared to that of all other products with a 10-m or higher spatial resolution, addressing the absence of such information in existing regional and global datasets. The generated county-level pond maps revealed the numerical dominance of ponds in lentic waters, their substantial area contribution in human-impacted regions, and the relevance of studying biogeochemical processes in smaller waterbodies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2024.114484","usgsCitation":"Liu, D., Zhu, X., Holgerson, M., Bansal, S., and Xu, X., 2024, Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series: Remote Sensing of Environment, v. 315, 114484, 21 p., https://doi.org/10.1016/j.rse.2024.114484.","productDescription":"114484, 21 p.","ipdsId":"IP-167730","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":490911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Denghong","contributorId":357052,"corporation":false,"usgs":false,"family":"Liu","given":"Denghong","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":940631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Xioalin","contributorId":357055,"corporation":false,"usgs":false,"family":"Zhu","given":"Xioalin","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":940632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holgerson, Meredith","contributorId":218790,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":940633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Xiangtao","contributorId":348758,"corporation":false,"usgs":false,"family":"Xu","given":"Xiangtao","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":940635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259202,"text":"sir20245062F - 2024 - Streams, springs, and volcanic lakes for volcano monitoring","interactions":[{"subject":{"id":70259202,"text":"sir20245062F - 2024 - Streams, springs, and volcanic lakes for volcano monitoring","indexId":"sir20245062F","publicationYear":"2024","noYear":false,"chapter":"F","displayTitle":"Streams, Springs, and Volcanic Lakes for Volcano Monitoring","title":"Streams, springs, and volcanic lakes for volcano monitoring"},"predicate":"IS_PART_OF","object":{"id":70259167,"text":"sir20245062 - 2024 - Recommended capabilities and instrumentation for volcano monitoring in the United States","indexId":"sir20245062","publicationYear":"2024","noYear":false,"title":"Recommended capabilities and instrumentation for volcano monitoring in the United States"},"id":1}],"isPartOf":{"id":70259167,"text":"sir20245062 - 2024 - Recommended capabilities and instrumentation for volcano monitoring in the United States","indexId":"sir20245062","publicationYear":"2024","noYear":false,"title":"Recommended capabilities and instrumentation for volcano monitoring in the United States"},"lastModifiedDate":"2024-10-17T20:36:12.820796","indexId":"sir20245062F","displayToPublicDate":"2024-10-04T10:25:00","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":"2024-5062","chapter":"F","displayTitle":"Streams, Springs, and Volcanic Lakes for Volcano Monitoring","title":"Streams, springs, and volcanic lakes for volcano monitoring","docAbstract":"Volcanic unrest can trigger appreciable change to surface waters such as streams, springs, and volcanic lakes. Magma degassing produces gases and soluble salts that are absorbed into groundwater that feeds streams and lakes. As magma ascends, the amount of heat and degassing will increase, and so will any related geochemical and thermal signal. Subsurface magma movement can cause pressurization that alters hydrostatic head and may induce groundwater discharge. Fluid-pressure changes have been linked to distal volcano-tectonic earthquakes (White and McCausland, 2016; Coulon and others, 2017) and phreatic eruptions (for example, Yamaoka and others, 2016). Clearly, changes in groundwater and surface waters are both indicators of unrest and clues to how and where magma is rising toward the surface. Where possible, it is prudent to incorporate real-time hydrologic data into multiparameter monitoring of restless volcanoes. Hydrologic dynamics can also be tracked by changes in groundwater levels that are commonly measured in shallow boreholes (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume, on boreholes; Hurwitz and Lowenstern, 2024).
Although inferred to be common, relatively few volcano-hydrology anomalies are well documented, and many are essentially anecdotal (Newhall and others, 2001), reflecting the fact that high-resolution time series remain rare. Extreme examples include the 2008 eruption of Nevado del Huila, Colombia, where relatively minor phreatomagmatic eruptions were accompanied by expulsion of as much as 300 million cubic meters of groundwater from fissures high on the volcano (Worni and others, 2011), generating large lahars. Substantial decreases in flow rate from springs about 8 kilometers from the summit of Mayon Volcano, Philippines, have been noted before most eruptions in the 20th century (Newhall and others, 2001). Stream monitoring at Redoubt Volcano in 2009 allowed Werner and others (2012) to recognize that groundwater was unable to absorb (or scrub) the high flux of volcanic gas and that a high CO2/SO2 precursor signal had been evident for 5 months prior to the eruption. A key to better interpreting hydrologic anomalies—or even identifying them—is therefore obtaining adequate baseline data.
Most hydrologic monitoring at U.S. volcanoes has been accomplished by intermittent sampling surveys with annual or less frequent sampling (for example, https://hotspringchem.wr.usgs.gov/index.php). More frequent sampling, however, generally is needed to establish reliable baselines. A recent hydrologic and hydrothermal monitoring experiment at 25 sites and 10 of the 12 level 4 (very high threat) volcanoes in the U.S. portion of the Cascade Range demonstrated that there is sufficient temporal variability in hydrothermal fluxes, even during quiescent periods, that one-time measurements will commonly have limited interpretive value (Crankshaw and others, 2018). Thus, surveys are best augmented with data from streamgages (for example, Evans and others, 2004; Bergfeld and others, 2008). Streamflow (water discharge) data allow measured temperature and specific conductance to be converted to heat and solute mass fluxes, which could be insightful parameters for detecting anomalous activity (McCleskey and others, 2012). At the Yellowstone Caldera, long-term monitoring of river solutes has allowed calculation of the chloride flux, a proxy for heat discharge (Hurwitz and others, 2007; McCleskey and others, 2016) from the subsurface magma. This is readily accomplished because data from streamgages are continuously recorded and archived by the U.S. Geological Survey (USGS) National Water Information System (NWIS) (USGS, 2024).
Similar studies on stratovolcanoes or shield volcanoes would be scientifically useful, and yet are logistically challenging, requiring streamgages on numerous radial drainages complemented by either frequent manual sampling or numerous deployments of equipment to measure water temperature and specific conductance as a proxy for water chemistry. Another challenge is that some volcanic areas, especially shield volcanoes, are characterized by near-surface porous rocks and soils, such that surface streams are rare and replaced by distant, dilute large-volume springs with only a trace of any original volcanically sourced water (Manga, 2001; Hurwitz and others, 2021).
Volcanic lakes are worthy of special attention for monitoring efforts, as their temperature and composition can provide evidence of increased flux of volatile-rich fluids from below. Quantifying changes in volatile and heat release from magma can be simpler in lakes than for volcanoes with radial drainages and no major lakes. Moreover, volcanic lakes pose a range of hazards themselves, including phreatomagmatic eruptions, debris flows, flank collapse, tsunamis, and toxic gas release (Mastin and Witter, 2000; Delmelle and others, 2015; Manville, 2015; Rouwet and others, 2015)—hazards that have historically been responsible for substantial loss of life at many volcanoes worldwide (Manville, 2015). Catastrophic CO2 release at Lake Nyos, Cameroon, in 1986 suffocated about 1,750 people and about 3,500 livestock and was probably triggered by a large landslide into the gas-saturated lake (Kling and others, 1987; Evans and others, 1993). Gas-charged springs in Soda Bay within Clear Lake (California) have caused almost a dozen deaths to bathers in the past hundred years (ABC News, 2000). A 2005 example of lake overturn and abundant gas release was documented at Mount Chiginagak in Alaska (Schaefer and others, 2008) but did not result in any human casualties. Although thermally stratified lakes, which promote trapping of exsolved magmatic gas, tend to develop in tropical regions, the phenomenon can also arise where salinity creates meromixis (a condition in which a lake does not mix completely), as occurs in Mono Lake, California (Jellison and Melack, 1993; Jellison and others, 1998).
If magma erupts or flows into a lake, the interaction between hot magma and cold water can be explosive (Mastin and others, 2004; Zimanowski and others, 2015) and substantially expand the area affected by the eruption. Another hazard is the breaching of crater rims by landslides triggered by volcanic and (or) seismic activity. Under some circumstances, substantial volumes of water can be displaced, leading to large floods and lahars. Late Holocene lake flooding from Aniakchak Crater in the Alaska Peninsula (Waythomas, 2022) and from Paulina Lake in Newberry Crater, Oregon (Chitwood and Jensen, 2000), caused by the failure of outlet sills, testify to the substantial hazards at lake-filled calderas.
Several volcanic systems in the United States host lakes known to receive heat and gas from underlying magma. These lakes vary widely in area, depth, and chemical composition. Lakes are present at level 4 volcanoes, including Crater Lake and Newberry Volcano in Oregon; Yellowstone Caldera in Wyoming; Long Valley Caldera, Clear Lake volcanic field, Medicine Lake, and Salton Buttes in California; and Aniakchak Crater, Mount Katmai, Fisher Caldera, Mount Okmok, and Kaguyak Crater, among others, in Alaska. A water lake was present in Halemaʻumaʻu, the crater of Kīlauea, Hawai‘i (fig. F1), from October 2019 to December 2020. Level 3 volcanoes with lakes include Mono Lake volcanic field (Calif.), Mount Bachelor (Ore.), Ukinrek Maars and Mount Chiginagak (Alaska), and Soda Lake (Nevada). In addition, there are lakes at many levels 1 and 2 volcanoes. In the United States, there are no strongly acidic lakes that receive abundant input of magmatic gas, such as those found at Mount Ruapehu (New Zealand), Ijen and Kelud (Indonesia), and Poás (Costa Rica). Nevertheless, many contain fluids that provide clues to magmatic processes below.
Since publication of a previous report on recommended instrumentation for volcano monitoring (Moran and others, 2008), continuous hydrologic monitoring has become increasingly feasible. However, changes in water pressure, temperature, and chemistry remain, in general, poorly studied phenomena at volcanoes (Sparks, 2003; National Academies of Sciences, Engineering, and Medicine, 2017). Recent efforts by the USGS have included the temporary study of Cascade Range volcanoes, which included frequent (15 minute to hourly) temporal sampling of temperature, depth, and conductivity (Crankshaw and others, 2018; Ingebritsen and Evans, 2019). At Yellowstone Caldera, many streamgages have now added thermistors and specific conductance sensors, allowing estimation of time-dependent chloride flux as a proxy for variations in subsurface heat flux (McCleskey and others, 2012, 2016). Efforts to better understand lakes have also accelerated, with bathymetric mapping and sampling carried out at several locations in the United States. Especially thorough work was done at Yellowstone Lake thanks to the Hydrothermal Dynamics of Yellowstone Lake (HD-YLAKE, https://hdylake.org) project, funded primarily by the National Science Foundation. In addition to geophysical surveys and recovery of cores and other samples, HD-YLAKE investigations included remotely operated vehicle (ROV) investigations of hydrothermal vents on the lake floor (fig. F2). Data collected by the ROV provided a better understanding of the thermal and chemical influx from lake-bottom hydrothermal systems (Sohn and others, 2017).
In this chapter, we focus on detecting changes in the chemistry, temperature, discharge, or water levels of streams, springs, and lakes that can be caused by seismicity, volumetric strains, or increases in gas flux associated with ascending magma. There is unavoidable overlap with other chapters of this report. Samples of water and gas can also be obtained in boreholes (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume; Hurwitz and Lowenstern, 2024), both shallow and deep. Gas monitoring (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062e.\">chapter E, this volume; Lewicki and others, 2024) relies in part on samples from springs and wells, particularly where measurable gas plumes are absent. Water acts as a trigger and lubricant for landslides and sediment-rich floods, and so hydrology has obvious relevance for lahar monitoring, as discussed in of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–H, 6 p., https://doi.org/10.3133/sir20245062h. \">chapter H (this volume; Thelen and others, 2024). Shared situational awareness among scientists engaged in geophysical, gas, and hydrologic monitoring will improve overall understanding of the volcanic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062F","usgsCitation":"Ingebritsen, S.E., and Hurwitz, S., 2024, Streams, springs, and volcanic lakes for volcano monitoring, chap. F of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–F, 9 p., https://doi.org/10.3133/sir20245062F.","productDescription":"iii, 9 p.","numberOfPages":"9","onlineOnly":"N","ipdsId":"IP-149695","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/covrthbf.jpg"},{"id":462450,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/sir20245062f.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
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The U.S. Geological Survey (USGS), in cooperation with the Washington State Department of Ecology (Ecology), conducted a study to describe the current understanding of the regional groundwater system of the lower Duwamish River valley and groundwater and surface-water interactions in the lower Duwamish Waterway. The lower Duwamish Waterway is the final 5-mile (mi) reach of the Duwamish River before it empties into Elliott Bay in Puget Sound near Seattle, Washington. A nearshore site (hereinafter referred to as “Nearshore Site” to distinguish the particular site from general discussions of nearshore areas) along the western shoreline of the Duwamish River, about 1.5 mi upstream from the river mouth, was selected for focused groundwater data collection by USGS. Data loggers were deployed in seven groundwater wells and one stilling well in the Duwamish River to measure specific conductance, temperature, and depth at 15-minute intervals for a period of about 2 years.
At the Nearshore Site during 2020–22, water levels in the shallow wells were 3–8 feet (ft) higher than water levels in the deep wells, providing evidence for a low-permeability layer between the shallow and deep aquifers in this area. The shallow wells had a pronounced seasonal variability, with high water levels in winter and low water levels in summer. Data from the deep wells showed far less seasonal variability, with slight increases in winter and a near-constant water level from spring to autumn. The deep wells had a strong hydraulic connection to the Duwamish River, as evidenced by the synchronous water-level variability during the tidal cycle, whereas the shallow wells had minimal to no tidal response. The potentiometric maps developed for the Nearshore Site and surrounding areas indicate large differences in groundwater-flow directions for the shallow and deep aquifers at low and high tides. For the shallow aquifer, flow is toward the lower Duwamish Waterway near the Nearshore Site, regardless of the tidal condition. For the deep aquifer, a potentiometric trough forms parallel to the shoreline during high tide, indicating that groundwater flow converges from the uplands to the west and the Duwamish River to the east. The geometry of the potentiometric surfaces between the nearshore-most well and the shoreline is complex and is further confounded by intermittent shoreline armoring and other buried infrastructure, which could serve as either a barrier or a conduit to flow.
Groundwater and surface-water interactions in the lower Duwamish Waterway are inherently complex as a result of three overarching factors. First, water levels in the lower reaches of the Duwamish River vary daily by 11–16 ft because of tides from Puget Sound, which create large swings in the hydraulic gradient in the nearshore groundwater system. Second, the density and chemical composition of water in the Duwamish River change daily with the tides and seasonally, which constrains how river water entering the nearshore sediments interacts with discharging groundwater. Third, the nearshore subsurface and shoreline conditions are heterogenous because of extensive shoreline armoring over the past century, which governs the flow of groundwater and infiltrating river water. These unique features of groundwater and surface-water interactions in the lower Duwamish Waterway thus govern the transport of terrestrial contaminants to the lower Duwamish Waterway. Furthermore, the heterogenous aquifer properties in the lower Duwamish Waterway contribute to spatially and temporally dynamic contaminant-transport processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245046","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Mitchell, J.N., and Conn, K.E., 2024, Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5046, 43 p., https://doi.org/10.3133/sir20245046.","productDescription":"Report: vi, 43 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-158073","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":497961,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117497.htm","linkFileType":{"id":5,"text":"html"}},{"id":434877,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.XML"},{"id":434876,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5046/images"},{"id":434875,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1F7QX42","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of groundwater- and surface-water-interactions report— Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington"},{"id":434874,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245046/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5046"},{"id":434873,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.pdf","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5046"},{"id":434872,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.jpg"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Lower Duwamish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.45687695827122,\n 47.691936104689205\n ],\n [\n -122.45687695827122,\n 47.28\n ],\n [\n -122,\n 47.28\n ],\n [\n -122,\n 47.691936104689205\n ],\n [\n -122.45687695827122,\n 47.691936104689205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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The Coastal Science Navigator is an online gateway to a wide variety of U.S. Geological Survey (USGS) coastal change hazards-related information, data, and tools relevant to stakeholders’ scientific and decision-making needs. The products within the Coastal Science Navigator provide data related to past, present, and future threats to our coastlines. The filter search allows users to see all available products and identify relevant options by time scale, geographic scope, coastal hazard theme, and other filters. The guided search suggests products based on users’ answers to a short series of questions. A comprehensive summary is available for each product.
The idea for the Coastal Science Navigator arose in 2020 in response to stakeholder feedback identifying the need for a central source for USGS coastal science information. It was published in July 2023 and initially included 55 products. Regular updates are planned to integrate other existing and new products.
This guide introduces some of the many coastal change hazards-related products available through the USGS. In it, we showcase the products included in the Coastal Science Navigator’s initial publication in July 2023. While it is not representative of all the information, tools, and data available, we hope it serves as a compelling snapshot of what the USGS has to offer and encourages you to explore the Coastal Science Navigator to discover more of the products you need.
To navigate this guide, the products have been organized by the time scale they are best suited for—past, present, or future—although many products cover multiple time scales. An additional section features software, one of the many product types available as filters within the Coastal Science Navigator. Other products include downloadable data, websites, and geonarratives (web pages that combine text, images, and interactive maps into narratives you can scroll through). Featured geographic scopes are also highlighted within this guide, detailing some of the many regions in which the USGS conducts research and illustrating another way to filter products within the Coastal Science Navigator.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1523","usgsCitation":"Anderberg, M., and Ernst, S., 2024, Coastal Science Navigator companion guide—Discover the U.S. Geological Survey coastal science products you need: U.S. Geological Survey Circular 1523, 31 p., https://doi.org/10.3133/cir1523.","productDescription":"iv, 31 p.","numberOfPages":"31","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-159018","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499076,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117116.htm","linkFileType":{"id":5,"text":"html"}},{"id":430649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1523/coverthb.jpg"},{"id":430650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1523/cir1523.pdf","text":"Report","size":"15.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1523"},{"id":430680,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/apps/coastalsciencenavigator/index.html","text":"Coastal Science Navigator"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
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384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
President Biden has been clear that the ocean is central to life on Earth. As he has proclaimed, “the ocean powers millions of jobs; feeds and sustains us; and is a rejuvenating source of inspiration, exploration, and recreation.” The Biden-Harris Administration has worked hard to fulfill the President’s goal to protect and conserve at least 30% of U.S. waters by 2030. The ocean faces increased threats from warming, overfishing, increased acidity, and loss of biodiversity. It is now more important than ever to sustain the many benefits that the ocean, coasts, and Great Lakes provide, including food, a favorable climate, recreation, physical and mental health, and for many, a sense of cultural identity. Ocean life represents an irreplaceable heritage, the foundation of a habitable planet, and a vast trove of resources. Keeping our ocean healthy requires reliable information on the changing status of these living organisms, the drivers of biodiversity change, and options for effectively addressing those drivers. Over 2 million species are estimated to live in the ocean, yet only about 240,000 species have been described by scientists. Most of the ocean’s benefits result from those diverse species interacting with one another and the environment they create. To protect and conserve the ocean, we as a nation need to make better use of existing knowledge and prioritize acquiring new biodiversity knowledge to enable better policy and management decisions. The ability to monitor ocean species and habitats has expanded dramatically over the past decade, with innovations in technology, genomics, taxonomy, big data management and sharing, artificial intelligence, and machine learning. Yet large fractions of the U.S. ocean remain almost unknown. The National Ocean Biodiversity Strategy (strategy) reflects the urgent need to leverage these advances. The goals of this strategy must be guided by the nation’s diverse voices and ways of knowing, in order to maximize effective and equitable stewardship of the ocean’s diverse life and its benefits to people. The strategy is intended as a guiding document for government to advance three overarching goals:
● Goal 1: Drive delivery of ocean biodiversity knowledge at the national scale. Objectives include developing an Implementation Plan for achieving the strategy’s three goals; establishing a coordination mechanism to manage the implementation; and documenting gaps in biodiversity knowledge and the benefits of ocean biodiversity to people and economies.
● Goal 2: Strengthen tools and institutions to deliver ocean biodiversity knowledge. Objectives include establishing a robust information pipeline to support indicators and dynamic maps of ocean biodiversity, from the coasts to the deep sea. This pipeline should include expanded observing systems and comprehensive data management; science and technology solutions to accelerate the availability of biodiversity information; and plans to leverage previous investments to rebuild and expand the nation’s human capital and infrastructure to sustain foundational taxonomy and biodiversity science.
● Goal 3: Protect, conserve, restore, and sustainably use ocean biodiversity. Objectives include expanding the collection, delivery, and use of biodiversity knowledge to inform actions that advance ocean protection, conservation, restoration, and sustainable development. Government should lead in establishing and incentivizing diverse partnerships across scales and sectors to implement those actions and should educate and involve the public to discover and value the nation’s diverse ocean life. Achieving these goals will require commitments across society: new federal and private investments, coordination across sectors to address climate and equity challenges, and engagement of Indigenous Knowledge holders and frontline communities as full partners throughout planning and implementation. The Subcommittee on Ocean Science and Technology (SOST) IWG-Biodiversity will begin developing an Implementation Plan to describe and direct specific actions to implement the strategy. Successful implementation of the strategy will harmonize and expand collection and delivery of timely knowledge on ocean life to all of society. The strategy will also enable evidence-based management and protection of the ocean. Advancing the strategy will build human and institutional capital and partnerships that support both existing mandates and new needs to rebuild and sustain biodiversity, achieve healthy ocean ecosystems, and manage living resources. Implementing the strategy will deliver knowledge for monitoring, modeling, forecasting, and assessments that support food security, public health, and cultural values, and that more effectively protect, conserve, and restore nature.
","language":"English","publisher":"White House Office of Science, Technology, and Policy (OSTP)","collaboration":"National Oceanic and Atmospheric Administration, Smithsonian, Bureau of Ocean Energy Management, University Corporation for Atmospheric Research, Environmental Protection Agency, Office of Naval Research, National Science Foundation, National Aeronautics and Space Administration","usgsCitation":"Canonico, G., Duffy, J., Edmonson, M., Fillingham, K., Benson, A., Bisson, K., Demopoulos, A., Hinchey, B., Matsumoto, K., Meyer, C., Price, J., Shen, E., Turner, W., Weise, M., Vander Woude, A., and Wenzel, L., 2024, The National Ocean Biodiversity Strategy, vi, 18 p.","productDescription":"vi, 18 p.","ipdsId":"IP-166759","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":430840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430823,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/06/NSTC_National-Ocean-Biodiversity-Strategy.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Canonico, Gabrielle","contributorId":217563,"corporation":false,"usgs":false,"family":"Canonico","given":"Gabrielle","email":"","affiliations":[{"id":39659,"text":"National Oceanographic and Atmospheric Administration, US Integrated Ocean Observing System, Silver Spring, MD, USA","active":true,"usgs":false}],"preferred":false,"id":905819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, J. 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2024 - Climate, food and humans predict communities of mammals in the United States","interactions":[],"lastModifiedDate":"2024-09-10T15:02:17.761832","indexId":"70257547","displayToPublicDate":"2024-06-27T10:18:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Climate, food and humans predict communities of mammals in the United States","docAbstract":"The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities.
Conterminous United States.
2010–2021.
Twenty-five species of mammals.
We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities.
Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west.
Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.
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Data describing flow patterns in river channels can be obtained efficiently via image-based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image-based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)-based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image-derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4333","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2024, An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA): River Research and Applications, v. 40, no. 8, p. 1602-1616, https://doi.org/10.1002/rra.4333.","productDescription":"15 p.","startPage":"1602","endPage":"1616","ipdsId":"IP-163908","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.4333","text":"Publisher Index Page"},{"id":462540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":914715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":914716,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257451,"text":"70257451 - 2024 - Predicting the odds of chronic wasting disease with Habitat Risk software","interactions":[],"lastModifiedDate":"2024-09-09T16:06:16.803506","indexId":"70257451","displayToPublicDate":"2024-06-03T08:55:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6475,"text":"Spatial and Spatio-temporal Epidemiology","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the odds of chronic wasting disease with Habitat Risk software","docAbstract":"Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (Odocoileus virginianus) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.
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Since 2011 (water year [WY] 2012), Fall Creek Lake has been temporarily drawn down each year to facilitate downstream passage of juvenile spring Chinook salmon (Oncorhynchus tshawytscha) through the 55-meter (m) high dam. This annual dam operation is temporary, typically lasting about 1–2 weeks from WY 2012 through 2020 (drawdown operations in WY 2022–24 have increased to more than 6 weeks). Drawdown of the reservoir results in lake levels being lowered to the elevation near the historical, pre-dam streambed. The annual streambed drawdowns of WY 2012–18 have improved fish passage and led the USACE to formally adopt streambed drawdowns as part of annual operations at Fall Creek Dam. However, temporarily lowering the lake to streambed creates free-flowing conditions in the reservoir that result in the erosion and episodic export of predominantly sand and finer-grained sediments (less than 2 millimeters [mm]) to the lower gravel-bed reaches of Fall Creek and the Middle Fork Willamette River. The introduction of large volumes of sand and finer-grain sediment into the dam-regulated reaches downstream from Fall Creek Dam prompted questions about the geomorphic responses to annual streambed drawdowns within Fall Creek Lake and downstream reaches along Fall Creek and the Middle Fork Willamette River. The U.S. Geological Survey (USGS) in partnership with USACE initiated a comprehensive geomorphic and sediment transport investigation to assess the coupled processes of reservoir erosion, sediment evacuation from Fall Creek Lake, and patterns of sediment transport and deposition in reaches downstream from the Fall Creek Dam that have resulted from annual streambed drawdowns.
The purpose of this report is to systematically describe the processes of sediment erosion, transport, and deposition at Fall Creek Lake and geomorphic interactions between reaches upstream and downstream from Fall Creek Dam that relate to dam operations. Specifically, this report focuses on evaluating geomorphic responses to streambed drawdowns from WY 2012 through 2018 and placing drawdown-induced geomorphic responses within the broader context of physiographic and historical conditions and dam operations of Fall Creek and Middle Fork Willamette Rivers. Key objectives for this study were to characterize changes in reservoir morphology and substrate at Fall Creek Lake, describe the character and temporal pattern of sediment transport downstream from Fall Creek Dam, characterize geomorphic changes in channel reaches downstream from the Fall Creek Dam, and relate these data to the annual streambed drawdowns of WY 2012–18. This study uses multiple independent monitoring and measurement approaches to assess site, reach, and river-scale geomorphic responses to drawdowns to inform dam and reservoir management. Patterns and processes of reservoir evolution were assessed with geomorphic mapping and volumetric analyses of topography through comparison of multiple digital surface models (DSMs). Just downstream from Fall Creek Dam, analyses of sediment export from the reservoir focused on suspended sediment but also incorporated bedload analyses to assess sediment sizes. Geomorphic assessments downstream from the dam used reach-scale and site-scale approaches to document changes in channel morphology and substrate, including site measurements of sand and finer-grained sediment deposition and in-channel bed-material, volumetric change analyses from comparison of digital elevation models (DEMs), and repeat geomorphic mapping. Findings from this study inform river management and dam operations by providing an understanding of (1) coupled upstream-downstream geomorphic responses to the Fall Creek Lake streambed drawdowns, (2) geomorphic responses of Fall Creek Lake streambed drawdowns in comparison to drawdowns at other large dams, (3) controls on reservoir erosion and downstream geomorphic responses, and (4) implications for future hydrogeomorphic changes that may result from continued drawdowns and monitoring activities to assess those changes.
To understand the volume and distribution of sediment accumulation in Fall Creek Lake since dam closure in 1965, decadal-scale sedimentation patterns (spanning approximately 1965–2016) are evaluated using a combination of storage curve analyses and geomorphic mapping. Short-term (drawdown event-scale) patterns of erosion, sedimentation, and sediment export downstream are evaluated using a combination of geomorphic mapping and change detection analyses that quantify the distribution and total volume of sediment erosion and deposition within Fall Creek Lake.
Geomorphic mapping of reservoir topography and analyses of historical datasets reveals four categories of landforms and sediment processes within Fall Creek Lake related to lake level operations:
Where the reservoir floor is mapped for this study as pelagic (deep water), deposition up to 3 meters (m) thick by lacustrine processes and burial of pre-dam topography with deposits thinning toward the edges of the valley floor and upstream areas of reservoir are observed. Despite over 50 years of sediment accumulation since dam construction, the main stream channels of Fall and Winberry Creeks (or reservoir thalwegs) through the reservoir are well defined, though their distinct morphology is likely influenced by a long history of recurring historical drawdowns to or near streambed since dam construction. Unregulated streamflow and sediment transport through the reservoir primarily are confined to these channels during the streambed drawdown periods. Erosional channel-like features created by drawdowns are carved through underlying, unconsolidated reservoir floor sediments and are most prominent in the lower reservoir below minimum conservation pool (the low pool elevation during winter flood season); sediment generated from the formation of these drawdown channels is more likely to be transported through and out of the reservoir than sediment deposits along the reservoir hillslopes at the valley margins that are separated from main channels by areas of low-gradient reservoir floor. Morphologic changes in the lower reservoir topography between January 2012 and November 2016 indicate overall net erosion of about 129,500 cubic meters (m3). The most prominent geomorphic changes occurred along the main channels of Fall and Winberry Creeks near the Fall Creek Dam where incision, lateral migration, and slumping banks resulted in vertical and lateral adjustments to channel position, whereas most changes fell below the detectable limit on higher-elevation reservoir floor surfaces except where erosion occurred along features mapped as drawdown channels.
USGS implemented a sediment monitoring program in WY 2013–18 to evaluate the quantity and character of reservoir sediment exported from Fall Creek Lake during streambed drawdowns. Turbidity and suspended sediments were monitored annually autumn through spring to span the WY 2013–18 streambed drawdowns; however, unequal monitoring timeframes each year reduced the ability to compare results and factors affecting sediment export from the reservoir difficult between years. These data were originally measured to develop regressions and compute suspended-sediment loads (SSL). Bedload sediment monitoring from a cableway at the Fall Creek streamgage was completed in the autumn-winter of WY 2013 and 2017. The limited number of samples and presumed variability in sediment supply from the reservoir precluded construction of streamflow and bedload discharge relations to compute more than instantaneous bedload.
Sand and finer-grained silts and clays were transported from the reservoir in suspension, though some coarser grains (up to 32 mm) were also mobilized and transported downstream from the dam as bedload. Observations of increased sediment transport downstream from Fall Creek Dam coincided with lake levels approaching about 3 m (10 feet [ft] or elevation 690 ft) above the streambed regulating outlets. Suspended-sediment loads computed for the full monitoring periods WY 2013–18 at the Fall Creek streamgage, located 1.4 kilometers (km) downstream from Fall Creek Dam, range from 54,700 metric tons (t) in WY 2013 to 13,900 t in WY 2018. Although the total annual SSL varied from year to year, the overall seasonal patterns of suspended sediment transport throughout each year were similar during monitoring in WY 2013-18. Suspended-sediment loads were low prior to the drawdown, then increased rapidly as lake levels lowered and approached the streambed. In the weeks following the drawdown period, as pool levels were increased, SSL remained slightly elevated above pre-drawdown levels but generally declined through the following winter and spring except during streamflow-driven pulses of suspended-sediment transport. WY 2013 had the greatest total computed SSL for each streambed drawdown and partial-year monitoring period. SSL computed for the partial-year period have generally decreased since WY 2013 and have varied by about 6,800 t with the exception of WY 2014. WY 2014 SSL reflects anomalously low sediment export due to low streamflows and freezing conditions that stabilized reservoir floor deposits. Bedload measurements in the short 1.4-km reach between Fall Creek Dam and the Fall Creek streamgage showed an inverse correlation between bedload transport rates and discharge, which probably reflects diminishing supply of coarse-sized sediment. Sand was more abundant (60–100 percent) than gravel in bedload samples confirming sand and finer-grained sediment dominated sediment evacuated from the reservoir during streambed drawdowns at Fall Creek Lake.
In the days, weeks, and months following streambed drawdown operations at Fall Creek Dam through WY 2018, sites downstream from the dam displayed a variety of geomorphic responses to reservoir sediment delivery within the main channel and overbank areas. Evaluation of streambed elevations at two streamgages located 1.4 km downstream from the dam on Fall Creek and 16.3 km downstream from the dam on the Middle Fork Willamette River indicated the effects of drawdown sediment on bed elevations were modest and transient. Repeat particle size measurements (October 2015 and September 2016) at five sites along Fall Creek and the Middle Fork Willamette River showed similar grain-sized distributions that do not reveal substantial deposition of fine-grained sediment related to the WY 2016 streambed drawdown. Altogether, these findings indicate that transport capacity in the main, low-flow channels of Fall Creek and Middle Fork Willamette River during WY 2012–18 was sufficient to mobilize and evacuate reservoir sediments from streambed drawdowns or other bank material and tributary sources. However, other monitoring for this study indicate low-velocity zones in off-channel areas are prime locations for sand and finer-grain sediment deposition. Patterns of overbank sediment accumulation indicate that the magnitude and timing of overbank deposition on bars and low-elevation floodplain varies with proximity to the dam, geomorphic setting, streamflows, and other factors. Sand and finer-grained reservoir sediments carried as suspended-sediment load in the reaches downstream from Fall Creek Dam were deposited in overbank areas as observed with clay-horizon markers during WY 2016–17. Overbank deposition quantified with Geomorphic Change Detection (GCD) software evaluated landform-scale patterns of erosion and deposition using repeat light detection and ranging (lidar) surveys at two sites in the Upper Fall Creek reach and one site in the Jasper reach for 3 years (2012–15) and one site in the Clearwater reach for 6 years (2009–15). Deposition thickness and spatial patterns from the GCD analysis were variable; some sites had dispersed but measurable deposition while at others, deposition was highly localized and exceeded 1 m in depth. Patterns of overbank deposition illustrate interactions among bar morphology, local hydraulics, and suspended-sediment transport dynamics that can create patches of highly localized deposition. The measured deposition at the two Fall Creek GCD sites likely resulted from reservoir sediments released from Fall Creek Lake during streambed drawdowns in WY 2016 and 2017 because the limited sediment inputs from bank material (geomorphically laterally stable reach) or tributaries (no significant tributaries) provided few other sediment sources. On the Middle Fork Willamette River, observed patterns of overbank deposition could reflect sediment sourced from upstream tributaries, bank erosion, or Fall Creek Lake streambed drawdown operations.
Despite the introduction of several thousand tons of reservoir sediment delivered from the Fall Creek Lake streambed drawdowns to below-dam river corridors, reach-scale mapping of channel features downstream from Fall Creek Dam shows minimal evidence of changes in channel planform or landforms that can be attributed to a drawdowns in WY 2012–16. On Upper Fall Creek reach, widespread increases in gravel bars or other in-channel sediment did not result from the five streambed drawdowns. The main changes attributable to sediment releases from Fall Creek Lake were localized increases in vegetated bar area, particularly on channel margin areas where sand and finer-grain sediment was deposited and rapidly colonized by vegetation. The area of mapped secondary water features decreased between 2005 and 2016, but that may be due to lower discharges depicted in the 2016 aerial photographs and less mapped area of inundation. Primary changes along the Lower Fall Creek reach include a 6.4 percent decrease in area of secondary water features between 2011 and 2016, and a nearly twofold increase in the area of unvegetated bars. Immediately downstream from the Fall Creek confluence, there were negligible changes in the location and areas of vegetated bars and the main wetted channel between 2005 and 2016, and local increases in bar area cannot be attributed solely to deposition of reservoir sediments from Fall Creek Lake because (1) areas along the Middle Fork Willamette River just upstream from the Fall Creek confluence display similar type and magnitude of changes and (2) some of the increases at the confluence area pre-date the drawdowns. The cumulative effect of sediment releases from Fall Creek Lake streambed drawdowns from WY 2012 to 2016 on downstream channel planform and landforms are modest compared to the river-scale transformations and planform changes that occurred in the decades following dam construction.
Multiple aspects of Fall Creek Dam infrastructure and operations exert first-order controls on the magnitudes of reservoir erosion that occur during the streambed drawdowns and ultimately determine the sediment delivery to downstream reaches. Key aspects of the dam and its operations that are most relevant to assessing geomorphic responses to streambed drawdowns include the (1) dam infrastructure, including configuration and size of regulating outlets and their proximity to the streambed which dictates the capacity and competence of the river to deliver sediment to downstream reaches and mode of sediment transport as suspended-sediment load or bedload; (2) frequency of historical drawdowns and long-term, year-round dam operations and lake level management, which partly dictate reservoir morphology and locations and magnitudes of readily erodible materials; (3) dam operations and hydroclimatic conditions during the streambed drawdown (including length of the drawdown and streamflows entering the reservoir), which directly control the timing, duration and magnitude of reservoir erosion and sediment evacuation; and (4) dam operations following the streambed drawdown operation that regulate streamflows (and thereby sediment transport conditions) downstream of Fall Creek Dam which primarily reflect interactions between hydroclimatic conditions and flood control operations.
Patterns of sediment erosion and evacuation observed in this study at Fall Creek Lake from WY 2012–18 suggest that reservoir erosion during annual streambed drawdowns may remain similar or decrease in future years assuming (1) annual streambed drawdown operations are implemented in similar manner as the WY 2012–18 drawdowns (in terms of duration, late autumn or early winter implementation, rate of pool-level lowering to reach streambed, and other factors), (2) streambed drawdowns coincide with similar conditions as were observed WY 2012–18 (similar sediment yield into reservoir, low reservoir inflows, limited precipitation, moderate air temperature), and (3) no major geomorphic changes in the main reservoir channels of Fall and Winberry Creeks occur (for example, channel avulsion). Under such conditions, it is hypothesized that the stream channel within the reservoir would achieve a quasi-equilibrium state with respect to annual influx and export of sediment and aided by the substantial amount of in-channel bedrock, will remain laterally stable without erosion across reservoir deposits.
Patterns of sediment transport measured at the Fall Creek streamgage downstream from Fall Creek Dam provide insight into the potential effects of future streambed drawdowns at Fall Creek Lake. Analyses of suspended sediment measured in WY 2013–18 show a major reduction in suspended-sediment loads between WY 2013 and later years, indicating streamflows transporting sediment through the reservoir to downstream reaches during streambed drawdowns have become supply limited. The 6-year suspended-sediment monitoring and sampling program is insufficient to make predictions about future sediment transport conditions because of uneven monitoring periods and varying controls on reservoir sediment erosion. It is likely that future suspended-sediment loads will be variable but similar to those observed in WY 2015–18 if operational, climatic, and geomorphological factors remain similar to those monitored WY 2015–18. Suspended-sediment loads downstream from Fall Creek Lake will likely remain highest when regulating outlets are fully open and Fall Creek is free flowing with the reservoir fully drained with little to no residual pool. Over time, it is possible that the suspended-sediment loads would reflect mobilization of reservoir sediment deposited in the previous year rather than erosion of sediment deposited years or decades earlier. Bedload is likely to remain a small fraction of the total sediment load evacuated from the reservoir and is relatively modest compared with pre-dam bedload transport rates because most coarse sediment remains trapped by the dam.
If sediment releases from Fall Creek Lake and ensuing streamflow conditions follow a similar pattern in the future as was assessed in this study spanning WY 2012–18, near-term geomorphic adjustments downstream of the dam are expected to be modest. Barring major operational, climatic, and geomorphological changes, local site-scale deposition on bars, overbank areas, or off-channel features that persists several months after the streambed drawdown will likely continue to be highly variable, ranging from negligible to several centimeters of deposition. At the landform-scale, low velocity areas nearest to Fall Creek Dam will likely continue to undergo rapid deposition immediately during and after a streambed drawdown event, similar to patterns observed for WY 2012–18. Some of the sediment entering these off-channel features and margin areas may be temporarily stored, then later remobilized and dispersed farther downstream. But if newly deposited sediment persists through the following spring, there is a greater likelihood that local vegetation will establish, reinforce deposited material, and trap sediment during later drawdowns. The reach-scale geomorphic changes may become more apparent if (1) streambed drawdowns continued for several decades, and geomorphic changes were measured at decadal scales or (2) the amount of sediment introduced to downstream reaches substantially increased and (or) sediment transport capacity decreased. The continued streamflow regulation of Fall Creek Dam after sediment releases provides an opportunity to strategically manage streamflows during and after the streambed drawdowns to minimize downstream sediment impacts and ensure other operational thresholds are satisfied.
This study provides a comprehensive foundation of datasets and geomorphic analyses to inform dam operations at Fall Creek Lake, monitor sediment transport downstream, and consider operational schemes for future drawdowns. The datasets from this study also provide baselines of sediment transport and geomorphic conditions to assess future changes in reservoir and downstream environments. Future monitoring could be tailored to address specific questions regarding the long-term geomorphic effects of streambed drawdowns on fluvial habitats, flood hazards, cultural resources, or downstream water quality. Future monitoring activities could focus on the relevant geomorphic processes and spatial domains within the three categories used for this study: (1) reservoir erosion and net sediment evacuation, (2) sediment delivery to downstream reaches, including magnitude and temporal pattern of sediment transport, and (3) geomorphic responses of downstream reaches to sediment delivery. Specifically, high priority future monitoring activities could include:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Climate is an important driver of ungulate life-histories, population dynamics, and migratory behaviors. Climate conditions can directly impact ungulates via changes in the costs of thermoregulation and locomotion, or indirectly, via changes in habitat and forage availability, predation, and species interactions. Many studies have documented the effects of climate variability and climate change on North America’s ungulates, recording impacts to population demographics, physiology, foraging behavior, migratory patterns, and more. However, ungulate responses are not uniform and vary by species and geography. Here, we present a systematic map describing the abundance and distribution of evidence on the effects of climate variability and climate change on native ungulates in North America.
We searched for all evidence documenting or projecting how climate variability and climate change affect the 15 ungulate species native to the U.S., Canada, Mexico, and Greenland. We searched Web of Science, Scopus, and the websites of 62 wildlife management agencies to identify relevant academic and grey literature. We screened English-language documents for inclusion at both the title and abstract and full-text levels. Data from all articles that passed full-text review were extracted and coded in a database. We identified knowledge clusters and gaps related to the species, locations, climate variables, and outcome variables measured in the literature.
We identified a total of 674 relevant articles published from 1947 until September 2020. Caribou (Rangifer tarandus), elk (Cervus canadensis), and white-tailed deer (Odocoileus virginianus) were the most frequently studied species. Geographically, more research has been conducted in the western U.S. and western Canada, though a notable concentration of research is also located in the Great Lakes region. Nearly 75% more articles examined the effects of precipitation on ungulates compared to temperature, with variables related to snow being the most commonly measured climate variables. Most studies examined the effects of climate on ungulate population demographics, habitat and forage, and physiology and condition, with far fewer examining the effects on disturbances, migratory behavior, and seasonal range and corridor habitat.
The effects of climate change, and its interactions with stressors such as land-use change, predation, and disease, is of increasing concern to wildlife managers. With its broad scope, this systematic map can help ungulate managers identify relevant climate impacts and prepare for future changes to the populations they manage. Decisions regarding population control measures, supplemental feeding, translocation, and the application of habitat treatments are just some of the management decisions that can be informed by an improved understanding of climate impacts. This systematic map also identified several gaps in the literature that would benefit from additional research, including climate effects on ungulate migratory patterns, on species that are relatively understudied yet known to be sensitive to changes in climate, such as pronghorn (Antilocapra americana) and mountain goats (Oreamnos americanus), and on ungulates in the eastern U.S. and Mexico.
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Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":221497,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Ciara G.","contributorId":271273,"corporation":false,"usgs":false,"family":"Johnson","given":"Ciara","email":"","middleInitial":"G.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":898183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kurth, Katherine Anne 0000-0002-6883-8307","orcid":"https://orcid.org/0000-0002-6883-8307","contributorId":334177,"corporation":false,"usgs":true,"family":"Kurth","given":"Katherine","email":"","middleInitial":"Anne","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":898184,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlin, Maxfield A.","contributorId":335367,"corporation":false,"usgs":false,"family":"Carlin","given":"Maxfield","email":"","middleInitial":"A.","affiliations":[{"id":37275,"text":"none","active":true,"usgs":false}],"preferred":false,"id":898185,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70252171,"text":"sir20235060 - 2024 - Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2018 high resolution mapping surveys","interactions":[],"lastModifiedDate":"2026-01-29T22:56:17.468327","indexId":"sir20235060","displayToPublicDate":"2024-04-01T11:21:42","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":"2023-5060","displayTitle":"Assessing Spatial Variability of Nutrients, Phytoplankton, and Related Water-Quality Constituents in the California Sacramento–San Joaquin Delta at the Landscape Scale: 2018 High Resolution Mapping Surveys","title":"Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2018 high resolution mapping surveys","docAbstract":"This study examined the abundance and distribution of nutrients and phytoplankton in the tidal aquatic environments of the Sacramento–San Joaquin Delta (Delta) and Suisun Bay, comprising three spatial surveys conducted in May, July, and October of 2018 that used continuous underway high frequency sampling and measurements onboard a high-speed boat to characterize spatial variation across the extent of the Delta. The method used involves simultaneously collecting information about the concentration and spatial distribution of all major nutrient forms with analogous information about the major classes of phytoplankton and associated water-quality conditions. The results showed substantial variation across space and time, providing an unprecedented snapshot of the dynamic environmental processes that shape the ways nutrients interact with and affect aquatic habitats in the Delta.
The purposes of this study were to improve our understanding of how hydrodynamics, landscape features, and aquatic primary productivity interact to drive nutrient cycling and transport in the Delta and to provide insights into the underlying processes most directly responsible for the conditions at the time of this study, and thus into the range of conditions that may be expected following the wide array of prospective future changes to the Delta. One major anticipated change at the time of this study was the planned upgrade to the Sacramento Regional Wastewater Treatment Plant, but the study also informs our understanding of potential effects from other changes to the Delta, such as those caused by other nutrient-management actions, flow actions, large-scale wetland restoration, drought, flood, levee failure, and changes to water management.
Nutrient loading is the primary driver of nutrient concentrations in the Delta, but several other major drivers interact to shape their distribution and effects: geomorphology, hydrodynamics, landscape features, and aquatic productivity. Hydrodynamics affect timescales of transport and dilution of nutrient loads in the Delta. During transit through the system, channel geometry, tidal mixing, and water exports affect hydrodynamics in diverse ways that influence water-residence and transport times, thereby markedly affecting the range of times during which natural internal cycling can alter nutrient concentrations and forms. Channel geometry and location shape tidal energy and river currents into these observed dynamics. Interactions with Delta aquatic landscapes such as herbaceous tidal marsh, submerged aquatic vegetation, and large expanses of intertidal or subtidal sediments (all highly productive landscapes) exert demand on available nutrient supplies but can also simultaneously transform and generate nutrients. Finally, while phytoplankton require nutrients to sustain production and thus are a potential nutrient sink, the amount and form of nutrients also can influence the occurrence of harmful algal blooms (HABs) that adversely affect aquatic organisms as well as affect the occurrence of beneficial algal blooms that result in production of algae that are favorable for imperiled Delta pelagic aquatic food webs.
The surveys revealed a complex mosaic of spatial variation, with nutrient concentrations varying from near zero to well above concentrations considered eutrophic; nutrient concentrations were more often related to the extent of hydrologic transport and mixing than to specific geographic locations or to specific landscape features. Similarly, the surveys identified phytoplankton abundance ranging from near detection to the level of large phytoplankton blooms, with large variation in phytoplankton community composition. Although the study occurred during a period of low bloom activity, phytoplankton productivity appeared to be the strongest potential sink for inorganic nutrients in the Delta, indicating that it is a larger control on nutrient concentrations and distribution than previously understood. Cycling and transformation within the water column only appeared to substantially lower total nutrient concentrations at the longest estimated transport timescales. Contrary to expectations, we did not observe substantial nutrient depletion near landscape-scale features such as open-water habitats, submerged aquatic vegetation beds, extensive wetlands, or exposed sediments, indicating that these habitat types did not act as major sinks for nutrients in the Delta during these surveys. These results indicated that nutrient reduction efforts may have the greatest effect on pelagic phytoplankton productivity in the more productive reaches of the Delta and estuary, but these effects are unlikely to be magnified by changes to nutrient loss within the Delta over conceivable changes in flow conditions, Delta water management actions, or large-scale wetland restoration activities. Nevertheless, local processes were shown to cause substantial loss, and thus integrating of nutrient effects with other indicators of aquatic habitat conditions will help inform planning future actions at specific sites.
Finally, we note that the primary contribution of this study was intended to be the survey data themselves. Aside from the results highlighted in this report, the surveys are a benchmark against which future environmental change may be evaluated, including changes to nutrient management or water exports, drought, large-scale wetland restoration, and climate change. Further, although we highlight some of the main findings from the surveys in this report, the necessarily limited scope precludes examination of many topics for which these surveys may be highly informative. To facilitate the utility of these data to stakeholders, managers, and researchers, we have released the data online (Bergamaschi and others, 2020) and created an online data exploration portal (https://ca.water.usgs.gov/bay-delta/2018-delta-wide-mapping-surveys.html) where users may query the surveys in a variety of ways to test hypotheses, examine relationships, assess spatial trends, and download data. The data exploration portal is intended to be an immersive experience that allows users to gain greater understanding of the complex interactions that shape Delta aquatic environments. This report is intended as a companion to the portal, allowing the reader to challenge and further explore the highlighted findings.
This study was a collaboration between the U.S. Geological Survey and the Delta Regional Monitoring Program, with additional funding provided from U.S. Geological Survey Cooperative Matching Funds Program.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
StreamStats is a U.S. Geological Survey (USGS) web application that provides streamflow statistics, such as the 1-percent annual exceedance probability peak flow, the mean flow, and the 7-day, 10-year low flow, to the public through a map-based user interface. These statistics are used in many ways, such as in the design of roads, bridges, and other structures; in delineation of floodplains for land-use zoning and setting of insurance rates; for regulatory purposes, such as the permitting of wastewater discharges; and for hydrologic and climate change studies. StreamStats was first developed for Massachusetts and released in 2001. The application provided users with the ability to obtain streamflow statistics computed from data collected at USGS streamgages and to obtain estimates of streamflow statistics for user-selected ungaged sites. Massachusetts StreamStats used geographic information system software and digital mapping to compute drainage-basin characteristics, which were then used in statistical models to estimate streamflow statistics for the user-selected sites. The statistical models were in the form of equations that were developed through a process known as regression analysis. StreamStats was the first known web application with the ability to do interactive geoprocessing.
The utility of Massachusetts StreamStats was instantly apparent, leading the USGS to develop a version of StreamStats that could be implemented nationally. USGS State offices normally were required to develop custom regression equations and prepare local digital mapping data needed for implementing StreamStats for their States. Funding needed to complete this work usually was provided through cooperative agreements between the USGS and State agencies. In 2004, Idaho became the first to be released in the national version of StreamStats. By 2023, 44 States were fully implemented and six were undergoing implementation.
StreamStats has undergone many modifications over the years to keep up with changes to the underlying software and to add functionality. Customized functionality and separate linked StreamStats applications were developed for several States. Meeting the high demand for additions and improvements to StreamStats while also adhering to budgetary constraints has, at times, been challenging. The StreamStats development team has identified numerous additional improvements that could be made to provide better performance and more functionality. The lessons learned from the experience of building and operating StreamStats for nearly 25 years could be relevant to others interested in pursuing efforts of a similar scale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1514","usgsCitation":"Ries, K.G., III, Steeves, P.A., and McCarthy, P., 2024, StreamStats—A quarter century of delivering web-based geospatial and hydrologic information to the public, and lessons learned: U.S. Geological Survey Circular 1514, 40 p., https://doi.org/10.3133/cir1514.","productDescription":"viii, 40 p.","numberOfPages":"40","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-102663","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":499073,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116170.htm","linkFileType":{"id":5,"text":"html"}},{"id":426017,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.XML","description":"CIR 1514 XML"},{"id":426018,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1514/images/"},{"id":426016,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1514/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"CIR 1514 HTML"},{"id":426015,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.pdf","text":"Report","size":"7.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1514 PDF"},{"id":426014,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1514/coverthb.jpg"}],"contact":"National Coordinator, StreamStats
U.S. Geological Survey
1728 Lampman Drive, Suite D
Billings, MT 59102
Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16060947","usgsCitation":"Kumar, A., Roy, A.H., Andreadis, K., He, X., and Butler, C., 2024, A multi-sensor approach to characterize winter water-level drawdown patterns in lakes: Remote Sensing, v. 16, no. 6, 947, 23 p., https://doi.org/10.3390/rs16060947.","productDescription":"947, 23 p.","ipdsId":"IP-159744","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16060947","text":"Publisher Index Page"},{"id":433622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-03-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Abhishek","contributorId":342795,"corporation":false,"usgs":false,"family":"Kumar","given":"Abhishek","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andreadis, Konstantinos","contributorId":258831,"corporation":false,"usgs":false,"family":"Andreadis","given":"Konstantinos","affiliations":[{"id":52307,"text":"Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":912754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"He, Xinchen","contributorId":316775,"corporation":false,"usgs":false,"family":"He","given":"Xinchen","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Caitlyn","contributorId":316779,"corporation":false,"usgs":false,"family":"Butler","given":"Caitlyn","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912756,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251901,"text":"70251901 - 2024 - Potential use of poultry farms by wild waterfowl in California's Central Valley varies across space, times of day, and species: implications for influenza transmission risk","interactions":[],"lastModifiedDate":"2024-10-23T15:51:57.505662","indexId":"70251901","displayToPublicDate":"2024-03-05T06:42:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Potential use of poultry farms by wild waterfowl in California's Central Valley varies across space, times of day, and species: implications for influenza transmission risk","docAbstract":"Interactions between wildlife and livestock can lead to cross-species disease transmission, which incurs economic costs and threatens wildlife conservation. Wild waterfowl are natural hosts of avian influenza viruses (AIVs), are often abundant near poultry farms, and have been linked to outbreaks of AIVs in poultry. Interspecific and seasonal variation in waterfowl movement and habitat use means that the risk of disease transmission between wild birds and poultry inevitably varies across species, space, and time. Here, we used GPS telemetry data from 10 waterfowl species in and near California's Central Valley, a region where both wild waterfowl and domestic poultry are abundant, to study selection of poultry farms by waterfowl across diel, seasonal, and annual cycles. We found that waterfowl selected for wetlands, open water, protected areas, and croplands, which meant that they generally avoided habitats that were likely to be used for poultry farming. These selection patterns were linked to species' ecology and diel behavioral patterns, such that avoidance of poultry habitats was stronger for local or partial migrants than for long-distance migrants, and stronger during daytime than at night. We then combined these habitat selection results with data on poultry farm locations to map risk of waterfowl–poultry contact across the Central Valley. Average selection strength at poultry farms was low, suggesting that current placement of poultry farms is generally effective for limiting risk of contact with wild birds. When we combined these habitat selection results with data on species' abundances and AIV infection prevalence, we found dramatic variation in potential AIV transmission risk among species. These results could be used to prioritize surveillance and biosecurity efforts for regions and times of relatively high risk. More generally, these results highlight that fine-scale movement data can help identify interspecific, seasonal, and diel patterns in animal behaviors that affect wildlife and poultry health.
The recent destruction of thousands of homes by lava flows from La Palma volcano, Canary Islands, and Nyiragongo volcano, Democratic Republic of Congo, serves as a reminder of the devastating impact that lava flows can have on communities living in volcanically active regions. Damage to buildings and infrastructure can have widespread and long-lasting effects on rehabilitation and livelihoods. Our understanding of how lava flows interact with buildings is limited and based upon sparse empirical data. Often a binary impact is assumed (destroyed when in contact with the flow and intact when not in contact with the flow), although previous events have shown this to be an oversimplification. Empirical damage data collected after past events provide an evidence base from which to better understand lava flow impacts across a range of building types, environments, and eruption styles, as well as to explore the temporal and spatial trends in these impacts. However, information on lava flow impacts is scattered across literature, reports, and maps; no comprehensive dataset of lava flow impacts exists. In this study, we compile and standardise lava flow impact information from previously compiled data, eruption records, and published literature to create the first comprehensive global dataset of impacts on the built environment from lava flows. We found that since the first recorded event between 5494 yr B.P. and 5387 yr B.P., lava flows from at least 155 events have impacted buildings or infrastructure (e.g., roads, electricity pylons, ski-lifts), with most (47%, n = 73) recorded as located in Europe. Over the last century, there have been approximately seven lava flow impact events per decade (n = 71 total). This greatly expands on the past compilations of lava flow impact events. Since ca. 1800 CE, impacts have been consistently documented for less than 14% of recorded eruptions with lava flows globally; prior to 1800 CE, impacts were recorded much more variably (between 0 and 70% of lava flows in any 10-year time bin). The most destructive recorded events were the 1669 CE lava flows at Etna volcano, Italy, which destroyed up to 12 villages and part of the city of Catania, and the 2002 CE lava flows at Nyiragongo volcano, Democratic Republic of Congo, which destroyed up to 14,000 buildings. We found that few studies in the dataset report building typology, damage severity, or hazard intensity at the building-level scale, limiting our ability to assess past building-lava interactions. Future collection of building-level hazard and impact data, supplemented with non-English language records, can be used to inform models that forecast future impacts, support lava flow risk assessments, and develop potential mitigation measures.
Mangrove is one of the most productive and sensitive ecosystems in the world. Due to the complexity and specificity of mangrove habitat, the development of mangrove is regulated by several factors. Species distribution models (SDMs) are effective tools to identify the potential habitats for establishing and regenerating the ecosystem. Such models usually include exclusively environmental factors. Nevertheless, recent studies have challenged this notion and highlight the importance of including biotic interactions. Both factors are necessary for a mechanistic understanding of the mangrove distribution in order to promote the protection and restoration of mangroves. Thus, we present a novel approach of combining environmental factors and interactions with salt marsh for projecting mangrove distributions at the global level and within latitudinal zones. To test the salt marsh interaction, we fit the MaxEnt model with two predicting sets: (1) environments only and (2) environments + salt marsh interaction index (SII). We found that both sets of models had good predictive ability, although the SII improved model performance slightly. Potential distribution areas of mangrove decrease with latitudes, and are controlled by biotic and abiotic factors. Temperature, precipitation and wind speed are generally critical at both global scale and ecotones along latitudes. SII is important on global scale, with a contribution of 5.9%, ranking 6th, and is particularly critical in the 10–30°S and 20–30°N zone. Interactions with salt marsh, including facilitation and competition, are shown to affect the distribution of mangroves at the zone of coastal ecotone, especially in the latitudinal range from 10° - 30°. The contribution of SII to mangrove distribution increases with latitudes due to the difference in the adaptive capacity of salt marsh plants and mangroves to environments. Totally, this study identified and quantified the effects of salt marsh on mangrove distribution by establishing the SII. The results not only facilitate to establish a more accurate mangrove distribution map, but also improve the efficiency of mangrove restoration by considering the salt marsh interaction in the mangrove management projects. In addition, the method of incorporating biotic interaction into SDMs through establish the biotic interaction index has contributed to the development of SDMs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.119892","usgsCitation":"Cui, L., DeAngelis, D., Berger, U., Cao, M., Zhang, Y., Zhang, X., and Jiang, J., 2024, Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients: Journal of Environmental Management, v. 351, 119892, 13 p., https://doi.org/10.1016/j.jenvman.2023.119892.","productDescription":"119892, 13 p.","ipdsId":"IP-142484","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":424944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"351","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cui, Lina","contributorId":333612,"corporation":false,"usgs":false,"family":"Cui","given":"Lina","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":893339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berger, Uta","contributorId":224016,"corporation":false,"usgs":false,"family":"Berger","given":"Uta","affiliations":[{"id":40811,"text":"TU Dresden, Institute of Forest Growth and Computer Science, Germany","active":true,"usgs":false}],"preferred":false,"id":893340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cao, Minmin","contributorId":333613,"corporation":false,"usgs":false,"family":"Cao","given":"Minmin","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Yaqi","contributorId":333614,"corporation":false,"usgs":false,"family":"Zhang","given":"Yaqi","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893342,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Xiaomin","contributorId":333615,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaomin","email":"","affiliations":[{"id":79948,"text":"Zhejiang Academy of Forestry","active":true,"usgs":false}],"preferred":false,"id":893343,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Jiang","contributorId":191968,"corporation":false,"usgs":false,"family":"Jiang","given":"Jiang","email":"","affiliations":[],"preferred":false,"id":893344,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70252117,"text":"70252117 - 2024 - Arizona Groundwater Explorer: Interactive maps for evaluating the historical and current groundwater conditions in wells in Arizona, USA","interactions":[],"lastModifiedDate":"2024-03-15T14:33:00.87352","indexId":"70252117","displayToPublicDate":"2023-11-22T09:30:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Arizona Groundwater Explorer: Interactive maps for evaluating the historical and current groundwater conditions in wells in Arizona, USA","docAbstract":"Groundwater is an important water source in Arizona, accounting for about 41% of water use in this mostly arid-to-semiarid state in the southwestern United States, and the availability of groundwater resources in the state is a concern. To provide accessible information from depth-to-groundwater data, a series of web-based interactive maps were developed, called the Arizona Groundwater Explorer (AGEx). Scripts were written to harmonize and synthesize groundwater datasets from the two largest publicly available sources, subset these data to address different groundwater availability questions, and display the results in online, interactive maps. The combined dataset contained 1,820,122 depth-to-groundwater measurements from 1891 through 2022 from 41,918 wells in Arizona. Data views are provided for 20 topics, including recent (2020 or later) depth to groundwater (4,569 wells), historical (pre-1950) depth to groundwater (4,287 wells), wells with long-term (≥50 years) records (1,183 wells), wells with recent groundwater level decline (277 wells), wells with recent groundwater level rise (120 wells), and linear trends in groundwater levels over ten 10-year periods (number of wells ranging from 341 in 1978–1987 to 1,208 in 2003–2012), among others. With ongoing drought in the region resulting in declining surface-water supplies in Arizona, groundwater may play an even larger role in satisfying water needs in the state. The AGEx series of maps provides a nonspecialist audience with an improved understanding of historical, current, and changes in groundwater levels in Arizona.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-023-02748-w","usgsCitation":"Tillman, F.D., and Flynn, M.E., 2024, Arizona Groundwater Explorer: Interactive maps for evaluating the historical and current groundwater conditions in wells in Arizona, USA: Hydrogeology Journal, v. 32, p. 645-661, https://doi.org/10.1007/s10040-023-02748-w.","productDescription":"17 p.","startPage":"645","endPage":"661","ipdsId":"IP-151669","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":441014,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-023-02748-w","text":"Publisher Index Page"},{"id":426664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"32","noUsgsAuthors":false,"publicationDate":"2023-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896671,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flynn, Marilyn E. 0009-0009-5192-4389 meflynn@usgs.gov","orcid":"https://orcid.org/0009-0009-5192-4389","contributorId":305562,"corporation":false,"usgs":true,"family":"Flynn","given":"Marilyn","email":"meflynn@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896672,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70247343,"text":"70247343 - 2024 - High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature","interactions":[],"lastModifiedDate":"2024-03-11T14:23:49.518723","indexId":"70247343","displayToPublicDate":"2023-07-21T11:05:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature","docAbstract":"Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf-level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf-level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies-cytotype relate to leaf-level temperature. When considered across the whole season, stomatal conductance was negatively, non-linearly correlated with crown-level temperature derived from UAS. Subspecies identity best explained crown-level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf-level temperature. These results show how fine-scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate-change-induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene-by-environment interactions.
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