Plant communities vary both abruptly and gradually over time but differentiating between types of change can be difficult with existing classification and ordination methods. Structural topic modeling (STRUTMO), a text mining analysis, offers a flexible methodology for analyzing both types of temporal trends.
Our objectives were to (1) identify post-fire dominant sagebrush steppe plant association types and ask how they vary with time at a landscape (multi-fire) scale and (2) ask how often major association changes are apparent at the plot-level scale.
We used STRUTMO and plant species cover collected between 2002–2022 across six large burn areas (1941 plots) in the Great Basin, USA to characterize landscape change in dominant plant association up to 14 years post-fire. In a case study, we assessed frequency of large annual changes (≥ 10% increase in one association and decrease in another) between associations at the plot-level scale.
STRUTMO revealed 10 association types dominated by either perennial bunchgrasses, mixed perennial or annual grasses and forbs, or exotic annual grasses. Across all study fires, associations dominated by large-statured perennial bunchgrasses increased then stabilized, replacing the Sandberg bluegrass (Poa secunda)-dominated association. The cheatgrass (Bromus tectorum)-dominant association decreased and then increased. At the plot-level, bidirectional changes among associations occurred in ~ 75% of observations, and transitions from annual invaded to perennial associations were more common than the reverse.
The analysis revealed that associations dominated by some species (i.e. crested wheatgrass, Agropyron cristatum, Siberian wheatgrass, Agropyron fridgida, or medusahead, Taeniatherum caput-medusae) were more stable than associations dominated by others (i.e. Sandberg bluegrass or cheatgrass). Strong threshold-like transitions were not observed at the multi-fire scale, despite frequent ephemeral plot-level changes.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10980-024-01824-0","usgsCitation":"Applestein, C., Anthony, C.R., and Germino, M., 2024, Analysis adapted from text mining quantitively reveals abrupt and gradual plant-community transitions after fire in sagebrush steppe: Landscape Ecology, v. 39, 64, 16 p., https://doi.org/10.1007/s10980-024-01824-0.","productDescription":"64, 16 p.","ipdsId":"IP-155006","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":440260,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s10980-024-01824-0","text":"Publisher Index Page"},{"id":426445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.0478683572567,\n 45.37400249120009\n ],\n [\n -122.0478683572567,\n 38.46464355773841\n ],\n [\n -113.71058234080544,\n 38.46464355773841\n ],\n [\n -113.71058234080544,\n 45.37400249120009\n ],\n [\n -122.0478683572567,\n 45.37400249120009\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2024-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":205748,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":896180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Christopher R. 0000-0003-0968-224X","orcid":"https://orcid.org/0000-0003-0968-224X","contributorId":296314,"corporation":false,"usgs":true,"family":"Anthony","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":896181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":896182,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252084,"text":"70252084 - 2024 - River control points for algal productivity revealed by transport analysis","interactions":[],"lastModifiedDate":"2024-03-13T11:45:21.970555","indexId":"70252084","displayToPublicDate":"2024-03-01T06:43:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"River control points for algal productivity revealed by transport analysis","docAbstract":"Measurement of planktonic chlorophyll-a—a proxy for algal biomass—in rivers may represent local production or algae transported from upstream, confounding understanding of algal bloom development in flowing waters. We modeled 3 years of chlorophyll-a transport through a 394-km portion of the Illinois River and found that although algal biomass is longitudinally widespread, most net production occurs at river control points in the upper reaches (up to 3.7 Mg chlorophyll-a y−1 km−1). Up to 69% of the algal biomass in the upper river was a result of within-reach production, with the remainder recruited from headwaters and tributaries. High chlorophyll-a measured farther downstream was largely because of transport from source-area control points, with substantial net losses of algal biomass occurring in the lower river. Modeling the often-overlooked river transport component is necessary to characterize where, when, and why planktonic algae grow and predict how far and fast they move downstream.
Snow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88–92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths.
As part of the periodic update of the geothermal energy assessments for the USA (e.g., last update by Williams and others, 2008), a new three-dimensional temperature map has been constructed for the Great Basin, USA. Williams and DeAngelo (2011) identified uncertainty in estimates of conductive heat flow near land surface as the largest contributor to uncertainty in previously published temperature maps. The new temperature maps incorporate new conductive heat flow estimates developed by DeAngelo and others (2023). Predicted temperatures at depth are compared with representative measurements (for conductively dominated conditions), showing good agreement under relatively simple uniform conditions. Inputs included radiogenic heat production for all layers of 1.89 μW/m3, effective bulk thermal conductivity of 2.7 W/m/°C for all rocks underlying sedimentary basins, and a previously published (Williams and DeAngelo, 2011) empirically driven estimate of increasing thermal conductivity with depth in sedimentary sequences. The resulting three-dimensional temperature model is published in a USGS data release associated with this manuscript (Burns and others, 2023).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 49th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"49th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 12-14, 2024","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Workshop","usgsCitation":"Burns, E.R., DeAngelo, J., and Williams, C.F., 2024, Updated three-dimensional temperature maps for the Great Basin, USA, in Proceedings of the 49th workshop on geothermal reservoir engineering, Stanford, CA, February 12-14, 2024, 12 p.","productDescription":"12 p.","ipdsId":"IP-158036","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":426556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426549,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=36304"}],"country":"United States","state":"Arizona, California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.1678252423948,\n 34.95930930933167\n ],\n [\n -112.13481296270123,\n 36.38011070446173\n ],\n [\n -110.41839315886372,\n 39.724144797392114\n ],\n [\n -111.71560833919142,\n 41.53235529739101\n ],\n [\n -111.42411871894447,\n 43.51066554918043\n ],\n [\n -114.37594318706135,\n 43.98121176013663\n ],\n [\n -116.31862299441079,\n 42.72836632941687\n ],\n [\n -119.85738414099404,\n 43.39318722988335\n ],\n [\n -121.46376336575122,\n 42.21714661857473\n ],\n [\n -121.06118635706744,\n 39.6313773486587\n ],\n [\n -118.96617577098581,\n 37.07912459245523\n ],\n [\n -115.1678252423948,\n 34.95930930933167\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":896488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":896489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":896490,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252064,"text":"70252064 - 2024 - Sea turtle density surface models along the United States Atlantic coast","interactions":[],"lastModifiedDate":"2024-03-12T14:58:09.654368","indexId":"70252064","displayToPublicDate":"2024-02-29T09:38:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Sea turtle density surface models along the United States Atlantic coast","docAbstract":"Spatially explicit estimates of marine species distribution and abundance are required to quantify potential impacts from human activities such as military training and testing, fisheries interactions, and offshore energy development. There are 4 protected species of sea turtle (loggerhead, green, Kemp’s ridley, and leatherback) commonly found along the east coast of the USA, our study area, and which require impact assessments. Data from 7 different survey organizations were used to create density surface models for the 4 sea turtle species utilizing 1.2 million km of line-transect surveys. A substantial portion (29.7%) of available sightings were not identified to the species level. Not including these sightings would underestimate density, so a conditional random forest model was used to assign unidentified sightings to species. Higher densities of loggerhead, green, and Kemp’s ridley sea turtles were predicted south of the Outer Banks in cool months, transitioning northwards in late spring to occupy seasonal neritic habitats. The highest leatherback densities were predicted off the coasts of Georgia and Florida. Leatherbacks were also predicted throughout offshore areas. The predicted distribution patterns generally matched satellite tracking and strandings data, indicating the models reproduced established seasonal movements. Surveys rarely detect sea turtles smaller than 40 cm, so these age classes are not represented. The models are the first for the study area to apply availability bias estimates developed in or near the study area and attempt to classify unidentified sightings to the species level, providing an updated, critical tool for conservation management along the eastern seaboard.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01298","usgsCitation":"DiMatteo, A., Roberts, J.J., Jones-Farrand, D.T., Garrison, L., Hart, K., Kenney, R.D., McLellan, W.A., Lomac-MacNair, K., Palka, D., Rickard, M.E., Roberts, K., Zoidis, A.M., and Sparks, L., 2024, Sea turtle density surface models along the United States Atlantic coast: Endangered Species Research, v. 53, p. 227-245, https://doi.org/10.3354/esr01298.","productDescription":"19 p.","startPage":"227","endPage":"245","ipdsId":"IP-154482","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":440271,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01298","text":"Publisher Index Page"},{"id":426553,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Florida, Georgia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Rhode Island, South Carolina, Virginia","otherGeospatial":"Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -57.93627018077767,\n 46.35190712057363\n ],\n [\n -63.06317767562888,\n 48.071961216623635\n ],\n [\n -71.30344277897166,\n 43.13946856546261\n ],\n [\n -71.80832999327407,\n 41.862600860703395\n ],\n [\n -74.50295610940375,\n 40.814804466010116\n ],\n [\n -76.87672546434797,\n 39.666220426927055\n ],\n [\n -77.10004487486839,\n 35.93970224582324\n ],\n [\n -81.71588138883396,\n 31.544829146262018\n ],\n [\n -80.60117913786426,\n 26.310207631307307\n ],\n [\n -81.06912455364952,\n 25.83857941124684\n ],\n [\n -82.70465928026937,\n 29.11226261565656\n ],\n [\n -83.63137912414963,\n 30.41405891714605\n ],\n [\n -87.16972564675274,\n 30.306755223785572\n ],\n [\n -83.87424700132446,\n 27.063066500883522\n ],\n [\n -82.83132577671518,\n 23.770525785543356\n ],\n [\n -80.574320123905,\n 24.06588592956416\n ],\n [\n -79.30071206924853,\n 26.25623333516093\n ],\n [\n -79.48694740536136,\n 31.343635783809773\n ],\n [\n -74.30210816483918,\n 34.877155333197436\n ],\n [\n -57.93627018077767,\n 46.35190712057363\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","noUsgsAuthors":false,"publicationDate":"2024-02-29","publicationStatus":"PW","contributors":{"authors":[{"text":"DiMatteo, Andrew","contributorId":334722,"corporation":false,"usgs":false,"family":"DiMatteo","given":"Andrew","email":"","affiliations":[{"id":80216,"text":"McLaughlin Research Corporation","active":true,"usgs":false}],"preferred":false,"id":896411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Jason J.","contributorId":334723,"corporation":false,"usgs":false,"family":"Roberts","given":"Jason","email":"","middleInitial":"J.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":896412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones-Farrand, D. 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Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":896413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garrison, Lance","contributorId":244391,"corporation":false,"usgs":false,"family":"Garrison","given":"Lance","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":896414,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":896415,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kenney, Robert D.","contributorId":334724,"corporation":false,"usgs":false,"family":"Kenney","given":"Robert","email":"","middleInitial":"D.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":896416,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLellan, William A.","contributorId":334725,"corporation":false,"usgs":false,"family":"McLellan","given":"William","email":"","middleInitial":"A.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":896417,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lomac-MacNair, Kate","contributorId":334726,"corporation":false,"usgs":false,"family":"Lomac-MacNair","given":"Kate","email":"","affiliations":[{"id":80218,"text":"Tetra Tech and Cetos Research Organization","active":true,"usgs":false}],"preferred":false,"id":896418,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Palka, Debra","contributorId":334727,"corporation":false,"usgs":false,"family":"Palka","given":"Debra","email":"","affiliations":[{"id":80220,"text":"National Marine Fisheries Service, Northeast Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":896419,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rickard, Meghan E.","contributorId":334728,"corporation":false,"usgs":false,"family":"Rickard","given":"Meghan","email":"","middleInitial":"E.","affiliations":[{"id":80221,"text":"New York Natural Heritage Program, College of Environmental Science and Forestry, State University of New York","active":true,"usgs":false}],"preferred":false,"id":896420,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Roberts, Kelsey E. 0000-0001-8422-632X","orcid":"https://orcid.org/0000-0001-8422-632X","contributorId":176734,"corporation":false,"usgs":false,"family":"Roberts","given":"Kelsey E.","affiliations":[],"preferred":false,"id":896421,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Zoidis, Ann M.","contributorId":334729,"corporation":false,"usgs":false,"family":"Zoidis","given":"Ann","email":"","middleInitial":"M.","affiliations":[{"id":80218,"text":"Tetra Tech and Cetos Research Organization","active":true,"usgs":false}],"preferred":false,"id":896422,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sparks, L.","contributorId":334730,"corporation":false,"usgs":false,"family":"Sparks","given":"L.","email":"","affiliations":[{"id":65980,"text":"Naval Undersea Warfare Center","active":true,"usgs":false}],"preferred":false,"id":896423,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70262167,"text":"70262167 - 2024 - Bird-habitat associations and local-scale vegetation structure in lowland brushlands","interactions":[],"lastModifiedDate":"2025-01-15T16:01:49.99293","indexId":"70262167","displayToPublicDate":"2024-02-29T08:50:21","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Bird-habitat associations and local-scale vegetation structure in lowland brushlands","docAbstract":"Brushlands support a diverse suite of bird species, including species of conservation concern in the western Great Lakes region of central North America. Information on how to effectively manage lowland brushlands for birds and associations between breeding birds and local-scale vegetation structure and composition is lacking. We surveyed lowland brushlands from 2016–2018 in Minnesota, USA, to assess bird-habitat associations using avian point-count surveys and fixed-radius vegetation plots. We used Poisson regression models to assess the associations between breeding bird species richness, total abundance, and abundance of frequently detected species (using counts as an index for abundance) to woody stem density and height, patchiness of woody stem density, variation of woody stem height, and number of woody plant species. Sedge wrens (Cistothorus stellaris), the most abundant species, were negatively associated with multiple woody plant metrics and positively associated with patchiness. Common yellowthroats (Geothlypis trichas) were the second-most abundant species and associated with low-stature woody plants (<1 m based on average heights in study sites). Bird species richness, alder flycatchers (Empidonax alnorum), chestnut-sided warblers (Setophaga pensylvanica), swamp sparrows (Melospiza georgiana), veeries (Catharus fuscescens), and yellow warblers (Setophaga petechia) increased with woody vegetation height. Chestnut-sided warbler and Nashville warbler (Leiothlypis ruficapilla) abundances also increased with woody stem density. We suggest that managing lowland brushlands to promote diverse woody plant structure, including tall shrubs and areas with patchy, open herbaceous cover, by implementing temporally and spatially variable disturbance regimes, may benefit bird species that rely on lowland brushlands with a range of vegetation structure requirements.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22568","usgsCitation":"Hawkinson, A., Montgomery, R.A., Roy, C.L., Shartell, L., Andersen, D.E., Stevens, T.K., Knosalla, L., and Frelich, L.E., 2024, Bird-habitat associations and local-scale vegetation structure in lowland brushlands: Journal of Wildlife Management, v. 88, no. 4, e22568, 24 p., https://doi.org/10.1002/jwmg.22568.","productDescription":"e22568, 24 p.","ipdsId":"IP-141848","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467027,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22568","text":"Publisher Index Page"},{"id":466421,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Aitkin County, Carlton County, St. Louis County","otherGeospatial":"east-central Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.55095209733292,\n 47.81578189067989\n ],\n [\n -93.55095209733292,\n 46.62374994560324\n ],\n [\n -92.13724027549586,\n 46.62374994560324\n ],\n [\n -92.13724027549586,\n 47.81578189067989\n ],\n [\n -93.55095209733292,\n 47.81578189067989\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-02-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Hawkinson, Annie J","contributorId":348300,"corporation":false,"usgs":false,"family":"Hawkinson","given":"Annie J","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Montgomery, Rebecca A.","contributorId":328437,"corporation":false,"usgs":false,"family":"Montgomery","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Charlotte L.","contributorId":274613,"corporation":false,"usgs":false,"family":"Roy","given":"Charlotte","email":"","middleInitial":"L.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923336,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shartell, Lindsey M.","contributorId":348301,"corporation":false,"usgs":false,"family":"Shartell","given":"Lindsey M.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923337,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923338,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stevens, Thomas K.","contributorId":333873,"corporation":false,"usgs":false,"family":"Stevens","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":923593,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Knosalla, Lori J.","contributorId":348574,"corporation":false,"usgs":false,"family":"Knosalla","given":"Lori J.","affiliations":[],"preferred":false,"id":923594,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frelich, Lee E.","contributorId":179338,"corporation":false,"usgs":false,"family":"Frelich","given":"Lee","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":923339,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70258807,"text":"70258807 - 2024 - Influence of inherited structure on flexural extension in foreland basin systems: Evidence from the northern Arkoma basin and southern Ozark dome, USA","interactions":[],"lastModifiedDate":"2024-09-26T13:54:59.26177","indexId":"70258807","displayToPublicDate":"2024-02-29T08:48:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14252,"text":"Earth Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Influence of inherited structure on flexural extension in foreland basin systems: Evidence from the northern Arkoma basin and southern Ozark dome, USA","docAbstract":"Extensional faults are key components of foreland basin systems. They form within the upper crust in response to flexure of the lithosphere and accommodate subsidence within the foredeep and forebulge depozones. Such faults are excellent proxies for orogenic system evolution and control the distribution of natural resources and hazards. However, the spatiotemporal evolution of flexural extension has not been documented previously at a regional scale, thereby limiting our understanding of underlying geodynamic controls. Here, we resolve late Paleozoic flexural extension in the northern Arkoma basin and southern Ozark dome, USA. We synthesize a large database of previous mapping, existing research, subsurface data, and geophysical data into 3D geologic and 2D kinematic models. Mesh surfaces representing several key horizons from the Carboniferous Period (ca. 335-306 Ma) were constructed. These surfaces were built from oil and gas well tops (n = ∼10,000) and surface geologic map contacts using an advanced kriging method. The mesh surfaces are offset by a complex 3D fault network, allowing detailed analysis of along-strike and down-dip variations in fault displacement. Analysis of the 3D model reveals a regular and repeated fault segmentation pattern wherein E-W striking, left- and foreland-stepping en échelon normal faults are segmented by inherited NE striking basement faults. Maximum vertical separation along the E-W normal faults is generally focused between the inherited NE-trending faults. This suggests that the inherited basement faults delocalized extensional strain during late Paleozoic normal faulting. Maximum vertical separation and fault localization may correlate to areas with high-amplitude positive magnetic anomalies interpreted as Mesoproterozoic granitic rocks. Speculative covariance of magnetic anomalies and fault displacements implies that the relatively strong basement granite concentrated stress, leading to localized faulting within the relatively thin sedimentary cover. Lastly, we show that flexural extension migrated southeast to northwest from the Chesterian-Morrowan (ca. 335-319 Ma) to the Desmoinesian (ca. 306 Ma). The migratory flexural extension may be explained by diachronous loading during Pangean assembly, or by synchronous loading but variable load compensation due to inherent factors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2024.104715","usgsCitation":"Lutz, B.M., Hudson, M.R., Smith, T.M., Dechesne, M., Spangler, L.R., McCafferty, A.E., Amaral, C.M., Griffis, N.P., and Hirtz, J.A., 2024, Influence of inherited structure on flexural extension in foreland basin systems: Evidence from the northern Arkoma basin and southern Ozark dome, USA: Earth Science Reviews, v. 251, 104715, 34 p., https://doi.org/10.1016/j.earscirev.2024.104715.","productDescription":"104715, 34 p.","ipdsId":"IP-158572","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":467028,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.earscirev.2024.104715","text":"Publisher Index Page"},{"id":462278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Arkoma basin-Ozark dome","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.86189715961346,\n 36.498606432744694\n ],\n [\n -94.88562385432049,\n 36.498606432744694\n ],\n [\n -94.88562385432049,\n 34.16941702666095\n ],\n [\n -91.86189715961346,\n 34.16941702666095\n ],\n [\n -91.86189715961346,\n 36.498606432744694\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"251","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lutz, Brandon Michael 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0000-0002-2223-7047","orcid":"https://orcid.org/0000-0002-2223-7047","contributorId":295310,"corporation":false,"usgs":true,"family":"Spangler","given":"Leland","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":914101,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":914102,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amaral, Chelsea Morgan 0000-0003-4632-4097","orcid":"https://orcid.org/0000-0003-4632-4097","contributorId":313539,"corporation":false,"usgs":true,"family":"Amaral","given":"Chelsea","email":"","middleInitial":"Morgan","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":914103,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Griffis, Neil Patrick 0000-0002-2506-7549","orcid":"https://orcid.org/0000-0002-2506-7549","contributorId":330218,"corporation":false,"usgs":true,"family":"Griffis","given":"Neil","email":"","middleInitial":"Patrick","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":914104,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hirtz, Jaime Ann Megumi 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characterization for the conterminous United States","docAbstract":"We update the ground-motion characterization for the 2023 National Seismic Hazard Model (NSHM) for the conterminous United States. The update includes the use of new ground-motion models (GMMs) in the Cascadia subduction zone; an adjustment to the central and eastern United States (CEUS) GMMs to reduce misfits with observed data; an updated boundary for the application of GMMs for shallow, crustal earthquakes in active tectonic regions (i.e. western United States (WUS)) and stable continental regions (i.e. CEUS); and the use of improved models for the site response of deep sedimentary basins in the WUS and CEUS. Site response updates include basin models for the California Great Valley and for the Portland and Tualatin basins, Oregon, as well as long-period basin effects from three-dimensional simulations in the Greater Los Angeles region and in the Seattle basin; in the CEUS, we introduce a broadband (0.01- to 10-s period) amplification model for the effects of the passive-margin basins of the Atlantic and Gulf Coastal Plains. In addition, we summarize progress on implementing rupture directivity models into seismic hazard models, although they are not incorporated in the 2023 NSHM. We implement the ground-motion characterization for the 2023 NSHM in the US Geological Survey’s code for probabilistic seismic hazard analysis, nshmp-haz-v2, and present the sensitivity of hazard to these changes. Hazard calculations indicate widespread effects from adjustments to the CEUS GMMs, from the incorporation of Coastal Plain amplification effects, and from the treatment of shallow-basin and out-of-basin sites in the San Francisco Bay Area and Los Angeles region, as well as locally important changes from subduction-zone GMMs, and from updated and new WUS basins.
","language":"English","publisher":"SAGE Publications","doi":"10.1177/87552930231223995","usgsCitation":"Moschetti, M.P., Aagaard, B.T., Ahdi, S.K., Altekruse, J.M., Boyd, O.S., Frankel, A.D., Herrick, J.A., Petersen, M.D., Powers, P.M., Rezaeian, S., Shumway, A., Smith, J.A., Stephenson, W.J., Thompson, E.M., and Withers, K., 2024, The 2023 US National Seismic Hazard Model: Ground-motion characterization for the conterminous United States: Earthquake Spectra, v. 40, no. 2, p. 1158-1190, https://doi.org/10.1177/87552930231223995.","productDescription":"33 p.","startPage":"1158","endPage":"1190","ipdsId":"IP-155703","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487645,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930231223995","text":"Publisher Index 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olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927387,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frankel, Arthur D. 0000-0001-9119-6106 afrankel@usgs.gov","orcid":"https://orcid.org/0000-0001-9119-6106","contributorId":146285,"corporation":false,"usgs":true,"family":"Frankel","given":"Arthur","email":"afrankel@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herrick, Julie A. 0000-0003-0682-760X","orcid":"https://orcid.org/0000-0003-0682-760X","contributorId":243649,"corporation":false,"usgs":true,"family":"Herrick","given":"Julie","middleInitial":"A.","affiliations":[],"preferred":true,"id":927389,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927390,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927391,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927392,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shumway, Allison 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927393,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smith, James Andrew 0000-0002-5565-9254 jimsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-5565-9254","contributorId":332933,"corporation":false,"usgs":true,"family":"Smith","given":"James","email":"jimsmith@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":927394,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927395,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"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":927396,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Withers, Kyle 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927397,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70251920,"text":"70251920 - 2024 - Allochthonous marsh subsidies enhances food web productivity in an estuary and its surrounding ecosystem mosaic","interactions":[],"lastModifiedDate":"2026-02-10T19:26:32.121354","indexId":"70251920","displayToPublicDate":"2024-02-29T06:51:13","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":"Allochthonous marsh subsidies enhances food web productivity in an estuary and its surrounding ecosystem mosaic","docAbstract":"Terrestrial organic matter is believed to play an important role in promoting resilient estuarine food webs, but the inherent interconnectivity of estuarine systems often obscures the origins and importance of these terrestrial inputs. To determine the relative contributions of terrestrial (allochthonous) and aquatic (autochthonous) organic matter to the estuarine food web, we analyzed carbon, nitrogen, and sulfur stable isotopes from multiple trophic levels, environmental strata, and habitats throughout the estuarine habitat mosaic. We used a Bayesian stable isotope mixing model (SIMM) to parse out relationships among primary producers, invertebrates, and a pelagic and demersal fish species (juvenile Chinook salmon and sculpin, respectively). The study was carried out in the Nisqually River Delta (NRD), Washington, USA, a recently-restored, macrotidal estuary with a diverse habitat mosaic. Plant groupings of macroalgae, eelgrass, and tidal marsh plants served as the primary base components of the NRD food web. About 90% of demersal sculpin diets were comprised of benthic and pelagic crustaceans that were fed by autochthonous organic matter contributions from aquatic vegetation. Juvenile salmon, on the other hand, derived their energy from a mix of terrestrial, pelagic, and benthic prey, including insects, dipterans, and crustaceans. Consequently, allochthonous terrestrial contributions of organic matter were much greater for salmon, ranging between 26 and 43%. These findings demonstrate how connectivity among estuarine habitat types and environmental strata facilitates organic matter subsidies. This suggests that management actions that improve or restore lateral habitat connectivity as well as terrestrial-aquatic linkages may enhance allochthonous subsidies, promoting increased prey resources and ecosystem benefits in estuaries.
The U.S. Geological Survey, in cooperation with the City of Crystal Lake, Illinois, started a study to increase understanding of groundwater and surface-water interaction between the glacial aquifer and the city’s namesake lake, Crystal Lake, and the effect of higher and lower precipitation conditions on groundwater and lake levels. The results from this study could be used by the city and others to aid in lake management strategies. This report describes the hydrologic lake budget and each of the budget components, which are then used in the construction, calibration, and application of a regional groundwater flow model. The flow model is used to simulate the shallow groundwater flow system and the lake responses to increased and decreased precipitation under the current weir elevation and the proposed lowered weir elevation.
Using the program groundwater flow analytic element model (GFLOW), a two-dimensional, steady-state model was constructed. The model was calibrated by matching target water levels and stream base flows by adjusting model input parameters. A sensitivity analysis was completed by adjusting the parameters within reasonable ranges and noting the magnitude of changes in model calibration targets. Potential effects of extended wet and dry periods (within historical ranges and published predicted ranges) were evaluated by adjusting precipitation, groundwater recharge, and discharge at Crystal Lake culvert outlet in the model and comparing the resulting simulated lake stage and water budgets to stages and water budgets from the calibrated model.
Model results under average, wet, and dry conditions with a lowered weir of 1 foot at the Crystal Lake culvert outlet indicate minor changes in the simulated lake-water budgets and associated lake levels and groundwater elevation contours; however, simulations with an increased outflow at the Crystal Lake culvert outlet decreased the lake water levels by as much as 1.87 feet and also decreased the groundwater levels surrounding the lake by about 1–2 feet during average and wet conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245007","collaboration":"City of Crystal Lake","usgsCitation":"Gahala, A.M., Bristow, E.L.D., Sharpe, J.B., Metcalf, B.G., and Matson, L.A., 2024, Simulation of groundwater and surface-water interaction and lake resiliency at Crystal Lake, City of Crystal Lake, Illinois: U.S. Geological Survey Scientific Investigations Report 2024–5007, 43 p., https://doi.org/10.3133/sir20245007.","productDescription":"Report: vii, 43 p.;3 Data Releases; Database","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137122","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":426065,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MVOLW","text":"USGS data release","linkHelpText":"Seepage Meter Data Collected at Crystal Lake, City of Crystal Lake, Illinois, 2020"},{"id":426064,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97BTQZO","text":"USGS data release","linkHelpText":"GFLOW groundwater flow model of Crystal Lake, City of Crystal Lake, Illinois"},{"id":426060,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5007/sir20245007.pdf","text":"Report","size":"3.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5007"},{"id":426070,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245007/full","text":"Report"},{"id":426059,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5007/coverthb.jpg"},{"id":426062,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5007/sir20245007.XML","text":"Report","description":"SIR 2024–5007"},{"id":426063,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5007/images"},{"id":426067,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS Water Data for the Nation"},{"id":499421,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116142.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois","otherGeospatial":"Crystal Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.40229797795483,\n 42.27567643715733\n ],\n [\n -88.40229797795483,\n 42.20603158225242\n ],\n [\n -88.31028234452793,\n 42.20603158225242\n ],\n [\n -88.31028234452793,\n 42.27567643715733\n ],\n [\n -88.40229797795483,\n 42.27567643715733\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 N Goodwin Ave
Urbana, IL 61801
Contact Pubs Warehouse
Wildfire is a major proximate cause of historical and ongoing losses of intact big sagebrush (Artemisia tridentata Nutt.) plant communities and declines in sagebrush obligate wildlife species. In recent decades, fire return intervals have shortened and area burned has increased in some areas, and habitat degradation is occurring where post-fire re-establishment of sagebrush is hindered by invasive annual grasses. In coming decades, the changing climate may accelerate these wildfire and invasive feedbacks, although projecting future wildfire dynamics requires a better understanding of long-term wildfire drivers across the big sagebrush region. Here, we integrated wildfire observations with climate and vegetation data to derive a statistical model for the entire big sagebrush region that represents how annual wildfire probability is influenced by climate and fine fuel characteristics.
Wildfire frequency varied significantly across the sagebrush region, and our statistical model represented much of that variation. Biomass of annual and perennial grasses and forbs, which we used as proxies for fine fuels, influenced wildfire probability. Wildfire probability was highest in areas with high annual forb and grass biomass, which is consistent with the well-documented phenomenon of increased wildfire following annual grass invasion. The effects of annuals on wildfire probability were strongest in places with dry summers. Wildfire probability varied with the biomass of perennial grasses and forbs and was highest at intermediate biomass levels. Climate, which varies substantially across the sagebrush region, was also predictive of wildfire probability, and predictions were highest in areas with a low proportion of precipitation received in summer, intermediate precipitation, and high temperature.
We developed a carefully validated model that contains relatively simple and biologically plausible relationships, with the goal of adequate performance under novel conditions so that useful projections of average annual wildfire probability can be made given general changes in conditions. Previous studies on the impacts of vegetation and climate on wildfire probability in sagebrush ecosystems have generally used more complex machine learning approaches and have usually been applicable to only portions of the sagebrush region. Therefore, our model complements existing work and forms an additional tool for understanding future wildfire and ecological dynamics across the sagebrush region.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-024-00252-4","usgsCitation":"Holdrege, M.C., Schlaepfer, D.R., Palmquist, K.A., Crist, M., Doherty, K., Lauenroth, W.K., Remington, T., Riley, K.L., Short, K.C., Tull, J.C., Wiechman, L.A., and Bradford, J., 2024, Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region: Fire Ecology, v. 20, 22, 20 p., https://doi.org/10.1186/s42408-024-00252-4.","productDescription":"22, 20 p.","ipdsId":"IP-153750","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":440277,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-024-00252-4","text":"Publisher Index Page"},{"id":435030,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFC6YC","text":"USGS data release","linkHelpText":"Observed wildfire frequency, modelled wildfire probability, climate, and fine fuels across the big sagebrush region in the western United States"},{"id":428128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -127.62177633718863,\n 50.05750578959115\n ],\n [\n -127.62177633718863,\n 35.59525050646282\n ],\n [\n -102.83662008718844,\n 35.59525050646282\n ],\n [\n -102.83662008718844,\n 50.05750578959115\n ],\n [\n -127.62177633718863,\n 50.05750578959115\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationDate":"2024-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Holdrege, Martin C.","contributorId":333140,"corporation":false,"usgs":false,"family":"Holdrege","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":79741,"text":"Department of Wildland Resource and the Ecology Center, Utah State University, Logan, UT 84322","active":true,"usgs":false}],"preferred":false,"id":899642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":899643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":899644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crist, Michele R.","contributorId":178453,"corporation":false,"usgs":false,"family":"Crist","given":"Michele R.","affiliations":[],"preferred":false,"id":899645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, Kevin E.","contributorId":177793,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin E.","affiliations":[],"preferred":false,"id":899646,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":899647,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Remington, Thomas E.","contributorId":296730,"corporation":false,"usgs":false,"family":"Remington","given":"Thomas E.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":899648,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Riley, Karin L.","contributorId":169453,"corporation":false,"usgs":false,"family":"Riley","given":"Karin","email":"","middleInitial":"L.","affiliations":[{"id":25512,"text":"US Forest Service Fire Science Lab","active":true,"usgs":false}],"preferred":false,"id":899649,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Short, Karen C.","contributorId":335894,"corporation":false,"usgs":false,"family":"Short","given":"Karen","email":"","middleInitial":"C.","affiliations":[{"id":80571,"text":"U.S. Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory, 5775 W Broadway Street, Missoula, Montana 59808, USA","active":true,"usgs":false}],"preferred":false,"id":899650,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tull, John C. 0000-0002-0680-008X","orcid":"https://orcid.org/0000-0002-0680-008X","contributorId":201650,"corporation":false,"usgs":false,"family":"Tull","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":899651,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wiechman, Lief A.","contributorId":335895,"corporation":false,"usgs":false,"family":"Wiechman","given":"Lief","email":"","middleInitial":"A.","affiliations":[{"id":80572,"text":"U.S Geological Survey, Ecosystems Mission Area, 12201 Sunrise Valley Drive Reston, Virginia 20192, USA","active":true,"usgs":false}],"preferred":false,"id":899652,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":899653,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70251824,"text":"70251824 - 2024 - Polyphase stratabound scheelite-ferberite mineralization at Mallnock, Eastern Alps, Austria","interactions":[],"lastModifiedDate":"2024-07-15T14:50:27.321887","indexId":"70251824","displayToPublicDate":"2024-02-28T06:46:21","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"Polyphase stratabound scheelite-ferberite mineralization at Mallnock, Eastern Alps, Austria","docAbstract":"A peculiar type of stratabound tungsten mineralization in metacarbonate rocks was discovered and explored at Mallnock (Austria) during the late 1980s. It is the only tungsten occurrence in the Eastern Alps in which scheelite is associated with wolframite (96 mol% ferberite). The tungsten prospect is located in the Austroalpine Drauzug-Gurktal Nappe System recording polyphase low-grade regional metamorphism. Raman spectroscopy of carbonaceous material yield maximum metamorphic temperatures of 296 ± 27 °C and 258 ± 27 °C, which are assigned to Variscan and Eoalpine metamorphism, respectively. Scheelite and ferberite occur as polyphase stockwork-like mineralization in Fe-rich magnesite in the northern ore zone (Mallnock North), whereas in the western ore zone (Mallnock West), scheelite-quartz veinlets are exclusively hosted in dolomitic marbles. LA-ICP-MS analyses of scheelite and ferberite yield low contents of Mo, Nb, Ta, and rare earth elements, but high contents of Na and Sr. Uranium is particularly high in scheelite (up to 200 µg/g) and makes this mineral a suitable target for U–Pb dating. In situ U–Pb dating of scheelite yielded an early Permian age (294 ± 8 Ma) for Mallnock West and a Middle Triassic age (239 ± 3 Ma) for Mallnock North. A monzodioritic dike close to Mallnock yielded a U–Pb apatite date of 282 ± 9 Ma and supports the polyphase formation of this mineralization. The U–Pb scheelite ages indicate that a model for tungsten metallogeny in the Eastern Alps must also consider remobilization of tungsten by metamorphic fluids. In the Alps, the Permian to Triassic period (ca. 290–225 Ma) is characterized by an overall extensional geodynamic setting related to the breakup of Pangea. Lithospheric thinning caused higher heat flow, low-P metamorphism, and anatexis in the lower crust, which led to enhanced crustal fluid flow in the upper crust. These processes were not only responsible for the formation of metasomatic hydrothermal magnesite and siderite deposits in the Eastern Alps but also for this unique magnesite-ferberite-scheelite mineralization at Mallnock.
Robust estimates of colonial waterbird (CWB) breeding population trends are deficient owing to a lack of range wide, standardized survey efforts. Evaluating conservation priorities and effectiveness of management requires reliable trend estimates across multiple spatial scales. One potential data source for CWB trend estimation is the Colonial Waterbird Database, created in 2003 by U.S. Geological Survey and the U.S. Fish and Wildlife Service and intermittently updated since then. The database combines state or provincial survey data, particularly from the United States Atlantic Flyway, with historical colony counts obtained from publications. We combined recently collected survey data from Atlantic Flyway states and provinces with data archived in the database to generate population size trend estimates for five species: Double-crested Cormorant (Phalacrocorax auritus), Laughing Gull (Leucophaeus atricilla), Least Tern (Sternula antillarum), Common Tern (Sterna hirundo), and Black Skimmer (Rynchops niger). These species represent two actively managed conflict species and three species of conservation concern, respectively. We used mixed effects models to fit an exponential growth model to determine yearly trends in populations at Atlantic Flyway- and state-scales with survey data collected between 1964 and 2019. Direction of within-state trend estimates varied. Trends for some species (Common Tern, Laughing Gull) were increasing in northern states and decreasing further south. At the Flyway scale, Double-crested Cormorant increased (2.08 ± 0.28 % year-1) and Least Tern (-1.40 ± 0.36 % year-1) and Black Skimmer (-1.13 ± 0.68 % year -1) decreased, while Flyway-scale trends in Common Tern and Laughing Gull were not significant. Our analysis provides cross-state trend estimates to inform CWB management actions along the Atlantic Flyway.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Loman, Z., Loftin, C., Spiegel, C., and Boettcher, R., 2024, Trends in colony sizes for five colonial waterbird species in the Atlantic Flyway: Cooperator Science Series 155-2024, Report: ii, 46 p.; Appendix.","productDescription":"Report: ii, 46 p.; Appendix","ipdsId":"IP-122759","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481434,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/trends-colony-sizes-five-colonial-waterbird-species-atlantic-flyway"},{"id":481463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Loman, Zachary G.","contributorId":145932,"corporation":false,"usgs":false,"family":"Loman","given":"Zachary G.","affiliations":[],"preferred":false,"id":925474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":925473,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spiegel, Caleb S.","contributorId":350196,"corporation":false,"usgs":false,"family":"Spiegel","given":"Caleb S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":925475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boettcher, Ruth","contributorId":350198,"corporation":false,"usgs":false,"family":"Boettcher","given":"Ruth","affiliations":[{"id":83696,"text":"Virginia Division of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":925476,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252136,"text":"70252136 - 2024 - Brief communication: Recent estimates of glacier mass loss for western North America from laser altimetry","interactions":[],"lastModifiedDate":"2025-01-16T21:12:54.08479","indexId":"70252136","displayToPublicDate":"2024-02-27T09:33:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Brief communication: Recent estimates of glacier mass loss for western North America from laser altimetry","docAbstract":"Glaciers in western North American outside of Alaska are often overlooked in global studies because their potential to contribute to changes in sea level is small. Nonetheless, these glaciers represent important sources of freshwater, especially during times of drought. Differencing recent ICESat-2 data from a digital elevation model derived from a combination of synthetic aperture radar data (TerraSAR-X/TanDEM-X), we find that over the period 2013–2020, glaciers in western North America lost mass at a rate of -12.3 ± 3.5 Gt yr−1. This rate is comparable to the rate of mass loss (-11.7 ± 1.0 Gt yr−1) for the period 2018–2022 calculated through trend analysis using ICESat-2 and Global Ecosystems Dynamics Investigation (GEDI) data.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-18-889-2024","usgsCitation":"Menounos, B., Gardner, A., Florentine, C., and Fountain, A., 2024, Brief communication: Recent estimates of glacier mass loss for western North America from laser altimetry: The Cryosphere, v. 18, no. 2, p. 889-894, https://doi.org/10.5194/tc-18-889-2024.","productDescription":"6 p.","startPage":"889","endPage":"894","ipdsId":"IP-158375","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":440285,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-18-889-2024","text":"Publisher Index Page"},{"id":426665,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"western North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.84375306887944,\n 35.77625603152046\n ],\n [\n -104.12455729393525,\n 37.79434503358385\n ],\n [\n -109.27603690198475,\n 50.31414964400753\n ],\n [\n -127.03183614912706,\n 65.04677641859246\n ],\n [\n -134.85471715128634,\n 65.87524390993823\n ],\n [\n -137.79246375218807,\n 59.017734626913665\n ],\n [\n -133.23078321077972,\n 52.789216057460294\n ],\n [\n -125.43702158084562,\n 47.89598278955353\n ],\n [\n -122.93229214268086,\n 41.15784268873253\n ],\n [\n -119.39681142809803,\n 35.854881720423364\n ],\n [\n -117.84375306887944,\n 35.77625603152046\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Menounos, Brian","contributorId":225514,"corporation":false,"usgs":false,"family":"Menounos","given":"Brian","email":"","affiliations":[{"id":41154,"text":"Geography Program and Natural Resources and Environmental Studies Institute, University of Northern British Columbia","active":true,"usgs":false}],"preferred":false,"id":896708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, Alex","contributorId":24274,"corporation":false,"usgs":true,"family":"Gardner","given":"Alex","email":"","affiliations":[],"preferred":false,"id":896709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":896710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fountain, Andrew","contributorId":334864,"corporation":false,"usgs":false,"family":"Fountain","given":"Andrew","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":896711,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251828,"text":"70251828 - 2024 - Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades","interactions":[],"lastModifiedDate":"2024-05-20T15:25:06.169475","indexId":"70251828","displayToPublicDate":"2024-02-27T07:02:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades","docAbstract":"9 earthquakes ruptured the full Cascadia subduction zone (CSZ) in the past 10 kyr, a hypothesis that relies on concurrent turbidite deposition generated from seismogenic strong ground motion along the ∼1100 km margin. Correlation of marine turbidite deposits is based on petrophysical characteristics and radiocarbon geochronology, the latter of which relies on a series of age corrections and calibrations for marine radiocarbon age and sedimentological parameters. In this work, I isolate several key variables in turbidite age assessment and systematically test how previous assumptions and new calibration curves affect estimated ages, and thus whether geochronologic analyses independently support coeval turbidite deposition. For radiocarbon age calibration, I test the impact of (1) updating global marine reservoir age corrections; (2) updating local marine reservoir age estimates; and (3) selectively applied marine reservoir age excursions. From the calibrated radiocarbon ages, I calculate turbidite age and uncertainty using a Monte Carlo approach with a broad range of sedimentation rates and substratal erosion. By simply updating the global marine radiocarbon calibration, individual radiocarbon ages differ from published estimates by several hundred years. Updates to the local reservoir age corrections are minimal because existing data remain limited yet have potential for great impact on turbidite ages. Of the sedimentological parameters tested, sedimentation rate has the largest impact on estimated turbidite age, with individual ages changing up to 500 yr from published estimates. For radiocarbon samples of turbidites previously inferred to correlate, the individual ages typically show increased scatter and overall uncertainty, even for models that only update the global marine reservoir calibration. These results highlight the major age uncertainty associated with current coseismic turbidite age analyses in Cascadia and how independent constraints on local reservoir corrections and sedimentation rate are critical for accurate turbidite age estimates in the Pacific Northwest.
For species of conservation concern, long-term monitoring is vital to properly characterize changes in population distribution and abundance over time. In addition, long-term monitoring guides management decisions by informing and evaluating the efficacy of management actions. A long-term monitoring initiative for the federally threatened Stephens' Kangaroo rat (Dipodomys stephensi, SKR) was established in 2005, across 628 hectares within Marine Corps Base Camp Pendleton (MCBCP), San Diego, California, USA. From 2005 to 2018, we tracked trends in area occupied by SKR, trends in relative SKR densities within occupied habitat, and modeled probabilities of SKR occupancy, colonization, extinction, with habitat, climate, and disturbance covariates. Area occupied by SKR increased almost 2-fold from 2005 to 2018 on MCBCP, while density in occupied habitat increased almost 3-fold. Increased area occupied was correlated with increases in estimated density among years, indicating SKR population growth occurs by expansion into suitable habitat patches, as well as increases in numbers within occupied habitat. SKR occupancy was positively associated with gentle slopes (<10%) and moderate open ground (40–80%) and forb cover (>40%). They were more likely to colonize previously unoccupied habitat when there were moderate levels of open ground (40–80%) and low shrub cover (<20%), while more likely to go locally extinct in areas with high slopes (>10%), less open ground (<20%), and increased non-native grass cover (>40%). Additionally, probabilities of SKR occupancy and colonization were higher in areas with moderate levels of disturbance, which was positively associated with open ground and forb cover. We conclude that long-term occupancy and density monitoring is effective in informing status and trends of spatially dynamic species and that moderate habitat-based disturbance is compatible with the management of SKR.
In support of a preliminary analysis performed by New York State Department of Environmental Conservation that found elevated nutrient levels along selected reaches of the Mohawk River, a one-dimensional, unsteady hydraulic and water-quality model (Hydrologic Engineering Center River Analysis System Nutrient Simulation Module 1 [HEC–RAS NSM I]) was developed by the U.S. Geological Survey for the 127-mile reach of the Mohawk River between Rome and Cohoes, New York. The model was designed to accurately simulate within-channel flow conditions for this highly regulated, control-structure dense river reach. The model was calibrated for the period of May through September 2016 using available streamflow, temperature, and water-quality data. Nitrogen, phosphorus, dissolved oxygen, and water column algae were balanced within the model; however, the nutrient model calibration was focused on phosphorus.
The HEC–RAS hydraulic model simulated streamflow adequately at the calibration locations with observed and simulated daily flows demonstrating coefficient of determination (r2) values ranging from 0.91 to 0.97, mean absolute error ranging from 15–20 percent, and bias ranging from −7 to 16 percent. The water temperature model within HEC–RAS NSM I demonstrated remarkable ability to simulate water temperature: typical water temperature errors were less than 1.0 degree Celsius (°C). Simulated water temperature results closely tracked observed continuous water temperature data at three locations on the Mohawk River, with mean absolute error for the 2016 study period ranging from 0.87 to 0.90 °C, and a root mean square error of 1.00 to 1.07 °C.
Performance criteria for the water-quality (nutrient) model were applied differently than the water temperature model because of the temporally coarse discrete samples collected for the project. The average difference between final simulated concentrations and observed concentrations of organic phosphorus for all sample locations was within 0.01 milligrams per liter (mg/L) and within 0.09 mg/L for orthophosphate using all locations except Rome, which was within 0.25 mg/L.
The calibrated model was used to implement nine phosphorus reduction scenarios by applying reductions to wastewater treatment plant effluent concentrations within the model. Monthly mean differences were computed for five comparison locations. Scenario results were generally linear and predictable; scenarios implementing the highest reductions showed correspondingly larger differences in Mohawk River concentrations downstream from the wastewater treatment plants associated with those reductions. The largest monthly mean differences were realized from reduction scenario nine and ranged from −0.018 to −0.076 mg/L for organic phosphorus and from 0.001 to −0.138 mg/L for orthophosphate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245005","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation","usgsCitation":"Suro, T.P., Niemoczynski, M.J., and Boetsma, A., 2024, Development and calibration of HEC–RAS hydraulic, temperature, and nutrient models for the Mohawk River, New York: U.S. Geological Survey Scientific Investigations Report 2024–5005, 90 p., https://doi.org/10.3133/sir20245005","productDescription":"Report: xii, 90 p.; Data Release","numberOfPages":"90","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127136","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":425874,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FRAYLT","text":"USGS data release","linkHelpText":"HEC–RAS hydraulic, temperature, and nutrient models for the Mohawk River between Rome and Cohoes, New York"},{"id":425872,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5005/images/"},{"id":425873,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5005/sir20245005.XML"},{"id":425869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5005/coverthb.jpg"},{"id":425870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5005/sir20245005.pdf","text":"Report","size":"20.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5005"},{"id":425871,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245005/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5005"},{"id":499420,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116141.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.4,\n 42.0\n ],\n [\n -73.2,\n 42.0\n ],\n [\n -73.2,\n 43.4\n ],\n [\n -75.4,\n 43.4\n ],\n [\n -75.4,\n 42.0\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike
Lawrenceville, NJ 08648
Disease outbreaks, skin lesions, fish kill events, and reproductive abnormalities have been observed in wild populations of Centrarchids in watersheds throughout the United States. Occurrence of synthetic and natural hormones from wastewater treatment plants and livestock operations, pesticides from agricultural land use, and phytoestrogens have been implicated as potential causes of these adverse effects. Our objective was to investigate possible immunomodulation in adult largemouth bass (Micropterus salmoides) in response to a seasonal exposure to environmentally relevant contaminants in outdoor experimental ponds. Exposures included 17α-ethinylestradiol (EE2; 3.6 ng/L) or a binary mixture of endocrine-active substances commonly detected in surface waters, estrone (E1; 85.6 ng/L) and atrazine (ATR; 5.4 µg/L). The 4-month exposure was conducted from July to November. Functional immune responses of anterior kidney-derived leukocytes were evaluated in December in the week following the end of the dosing period, and in the following April, four months after dosing ended and just prior to spawning. Concentrations of EE2 and E1 in the ponds fell below detectable levels in December, but detectable concentrations of ATR (2.9 µg/L) persisted at least through May. For each sampling time, anterior kidney leukocytes were isolated and grown in primary culture for the assessment of zymosan-stimulated respiratory burst and lectin-stimulated mitogenic responses. We observed seasonal differences in respiratory burst stimulation over time and treatment with a significantly greater response in April relative to December. Respiratory burst activity was also significantly greater in April for fish exposed to the E1+ATR relative to control. In April, prior to spawning, we observed a significantly dampened mitogenic response to PHAP (a T cell mitogen) and LPS (a B cell mitogen) in the EE2 treatment relative to control fish. There were no significant differences in mitogenic responses or respiratory burst between sexes. However, there was significantly higher alternative complement pathway hemolytic activity in males compared to females in both the control and E1+ATR treatment groups. Our results demonstrate that environmentally relevant concentrations of contaminants can alter immune function in a socioeconomically important fish species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cirep.2024.200140","usgsCitation":"Leet, J.K., Richter, C.A., Claunch, R., Gale, R., Tillitt, D.E., and Iwanowicz, L., 2024, Immunomodulation in adult largemouth bass (Micropterus salmoides) exposed to a model estrogen or mixture of endocrine disrupting contaminants during early gonadal recrudescence: Comparative Immunology Reports, v. 6, 200140, 7 p., https://doi.org/10.1016/j.cirep.2024.200140.","productDescription":"200140, 7 p.","ipdsId":"IP-157542","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":440303,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cirep.2024.200140","text":"Publisher Index Page"},{"id":435034,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91G7KMO","text":"USGS data release","linkHelpText":"Alternative complement pathway assay data for adult largemouth bass exposed in outdoor ponds to 17alpha-ethinylestradiol or an estrone-atrazine mixture"},{"id":430896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Leet, Jessica Kristin 0000-0001-8142-6043","orcid":"https://orcid.org/0000-0001-8142-6043","contributorId":225505,"corporation":false,"usgs":true,"family":"Leet","given":"Jessica","email":"","middleInitial":"Kristin","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":905906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":905907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Claunch, Rachel 0000-0003-1762-2175 rclaunch@usgs.gov","orcid":"https://orcid.org/0000-0003-1762-2175","contributorId":182424,"corporation":false,"usgs":true,"family":"Claunch","given":"Rachel","email":"rclaunch@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":905908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gale, Robert 0000-0002-8533-141X","orcid":"https://orcid.org/0000-0002-8533-141X","contributorId":299958,"corporation":false,"usgs":false,"family":"Gale","given":"Robert","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":905909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tillitt, Donald E. 0000-0002-8278-3955 dtillitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":1875,"corporation":false,"usgs":true,"family":"Tillitt","given":"Donald","email":"dtillitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":905910,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":905911,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254155,"text":"70254155 - 2024 - Evaluating ecosystem protection and fragmentation of the world's major mountain regions","interactions":[],"lastModifiedDate":"2024-06-03T15:07:51.193185","indexId":"70254155","displayToPublicDate":"2024-02-26T07:07:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating ecosystem protection and fragmentation of the world's major mountain regions","docAbstract":"Conserving mountains is important for protecting biodiversity because they have high beta diversity and endemicity, facilitate species movement, and provide numerous ecosystem benefits for people. Mountains are often thought to have lower levels of human modification and contain more protected area than surrounding lowlands. To examine this, we compared biogeographic attributes of the largest, contiguous, mountainous region on each continent. In each region, we generated detailed ecosystems based on Köppen−Geiger climate regions, ecoregions, and detailed landforms. We quantified anthropogenic fragmentation of these ecosystems based on human modification classes of large wild areas, shared lands, and cities and farms. Human modification for half the mountainous regions approached the global average, and fragmentation reduced the ecological integrity of mountain ecosystems up to 40%. Only one-third of the major mountainous regions currently meet the Kunming-Montreal Global Biodiversity Framework target of 30% coverage for all protected areas; furthermore, the vast majority of ecosystem types present in mountains were underrepresented in protected areas. By measuring ecological integrity and human-caused fragmentation with a detailed representation of mountain ecosystems, our approach facilitates tracking progress toward achieving conservation goals and better informs mountain conservation.
This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta’s Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16050797","usgsCitation":"Li, W., Hsu, C., Wang, S., Yang, Y., Lee, H., Liljedahl, A., Witharana, C., Yang, Y., Rogers, B.M., Arundel, S., Jones, M.B., McHenry, K., and Solis, P., 2024, Segment anything model can not segment anything: Assessing AI foundation model's generalizability in permafrost mapping: Remote Sensing, v. 16, no. 5, 797, 17 p., https://doi.org/10.3390/rs16050797.","productDescription":"797, 17 p.","ipdsId":"IP-154259","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":489031,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16050797","text":"Publisher Index Page"},{"id":464229,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Wenwen","contributorId":300739,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hsu, Chia-Yu","contributorId":302720,"corporation":false,"usgs":false,"family":"Hsu","given":"Chia-Yu","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Sizhe","contributorId":242975,"corporation":false,"usgs":false,"family":"Wang","given":"Sizhe","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Yezhou","contributorId":346316,"corporation":false,"usgs":false,"family":"Yang","given":"Yezhou","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918666,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lee, Hyunho","contributorId":346310,"corporation":false,"usgs":false,"family":"Lee","given":"Hyunho","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918667,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liljedahl, Anna","contributorId":70218,"corporation":false,"usgs":true,"family":"Liljedahl","given":"Anna","affiliations":[],"preferred":false,"id":918668,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Witharana, Chandi","contributorId":346320,"corporation":false,"usgs":false,"family":"Witharana","given":"Chandi","email":"","affiliations":[{"id":56085,"text":"Woodwell Climate Research Center","active":true,"usgs":false}],"preferred":false,"id":918669,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yang, Yili","contributorId":346322,"corporation":false,"usgs":false,"family":"Yang","given":"Yili","email":"","affiliations":[{"id":56085,"text":"Woodwell Climate Research Center","active":true,"usgs":false}],"preferred":false,"id":918670,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Brendan M.","contributorId":169247,"corporation":false,"usgs":false,"family":"Rogers","given":"Brendan","email":"","middleInitial":"M.","affiliations":[{"id":25456,"text":"Woods Hole Research Center, Falmouth, MA, United States","active":true,"usgs":false}],"preferred":false,"id":918671,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":918672,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jones, Matthew B.","contributorId":346334,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":918740,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"McHenry, Kenton","contributorId":346324,"corporation":false,"usgs":false,"family":"McHenry","given":"Kenton","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":918674,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Solis, Patricia","contributorId":346325,"corporation":false,"usgs":false,"family":"Solis","given":"Patricia","email":"","affiliations":[],"preferred":false,"id":918675,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70253575,"text":"70253575 - 2024 - Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts","interactions":[],"lastModifiedDate":"2024-05-02T13:27:05.700199","indexId":"70253575","displayToPublicDate":"2024-02-24T08:19:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts","docAbstract":"Wildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure.
We demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels.
Using Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding.
We document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city.
We demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-024-01811-5","usgsCitation":"Sankey, T.T., Tango, L., Tatum, J., and Sankey, J., 2024, Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts: Landscape Ecology, v. 39, 58, 16 p., https://doi.org/10.1007/s10980-024-01811-5.","productDescription":"58, 16 p.","ipdsId":"IP-139042","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":440320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-024-01811-5","text":"Publisher Index Page"},{"id":428319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","city":"Flagstaff","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.69345880006296,\n 35.25720796087404\n ],\n [\n -111.69345880006296,\n 35.189455530499785\n ],\n [\n -111.63079495096041,\n 35.189455530499785\n ],\n [\n -111.63079495096041,\n 35.25720796087404\n ],\n [\n -111.69345880006296,\n 35.25720796087404\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2024-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sankey, Temuulen Ts.","contributorId":332965,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"Ts.","affiliations":[{"id":79706,"text":"Northern Arizona University, School of Informatics, Computing and Cyber Systems, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":899931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tango, Lauren","contributorId":335948,"corporation":false,"usgs":false,"family":"Tango","given":"Lauren","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":899932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tatum, Julia","contributorId":335949,"corporation":false,"usgs":false,"family":"Tatum","given":"Julia","email":"","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":899933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":899934,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251883,"text":"70251883 - 2024 - The geochemistry of continental hydrothermal systems","interactions":[],"lastModifiedDate":"2024-03-05T13:25:53.096551","indexId":"70251883","displayToPublicDate":"2024-02-24T07:21:19","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The geochemistry of continental hydrothermal systems","docAbstract":"Hydrothermal systems on the continents are of great significance because they are primary sources of economically important metals and geothermal energy, they are tourist attractions, they support bathing and health resorts, and they host extreme life forms. Research on hot springs and their deposits provide clues to early life on Earth and possibly on Mars and have led to major breakthroughs in biotechnology. Aqueous and gas-rich hydrothermal fluids also contribute to a range of volcanic hazards including the destabilization of volcanic edifices, acting as propellant in steam-driven hydrothermal explosions, reducing effective stresses in mudflows (lahars), emitting toxic and potentially lethal gases, and transporting toxic metals to watersheds. The main goals of this review are to summarize the state of knowledge on the chemistry of continental hydrothermal systems and highlight the myriad processes that operate under a wide range of temperatures, pressures, chemical compositions, and oxidation states.