1. The occupancy modelling framework offers tremendous flexibility in estimating species abundance and distribution patterns while accounting for imperfect detection, and has seen rapid growth and adoption since its introduction at the beginning of the century.
2. At the same time, in an era of big data, there are increasing demands on developing quantitative skills and proficiency in young ecologists, many of whom lack the quantitative training needed to conduct research professionally.
3. We introduce occupancyTuts, an R package that features 28 learnr tutorials that teach the statistical underpinnings of several occupancy models. The tutorials include written content, instructional videos, R exercises, and quiz elements, covering a range of topics including statistical underpinnings, single- and dynamic-occupancy models, study design and several of the ‘spin-off’ models that extend the basic framework.
4. We plan for development of new tutorials that use RPresence as the analysis engine, and welcome new tutorial contributions that use other R packages as the analysis engine as well.
","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.14285","usgsCitation":"Donovan, T.M., Hines, J.E., and MacKenzie, D., 2024, occupancyTuts: Occupancy modelling tutorials with RPresence: Methods in Ecology and Evolution, v. 15, no. 3, p. 477-483, https://doi.org/10.1111/2041-210X.14285.","productDescription":"7 p.","startPage":"477","endPage":"483","ipdsId":"IP-156509","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440251,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14285","text":"Publisher Index Page"},{"id":433381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hines, James E. 0000-0002-3927-9411 jhines@usgs.gov","orcid":"https://orcid.org/0000-0002-3927-9411","contributorId":342662,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":910266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"MacKenzie, Darryl","contributorId":342664,"corporation":false,"usgs":false,"family":"MacKenzie","given":"Darryl","affiliations":[{"id":81910,"text":"Proteus Consulting","active":true,"usgs":false}],"preferred":false,"id":910267,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259731,"text":"70259731 - 2024 - Determining the distribution, status, and linkages of Agassiz's desert tortoise populations in the uplands surrounding the Coachella Valley","interactions":[],"lastModifiedDate":"2024-10-22T12:28:52.510945","indexId":"70259731","displayToPublicDate":"2024-03-01T07:27:37","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Determining the distribution, status, and linkages of Agassiz's desert tortoise populations in the uplands surrounding the Coachella Valley","docAbstract":"In support of the goals of the Coachella Valley Conservation Commission and the Bureau of Land Management, we performed surveys to determine the status, distribution, demographics, and possible genetic linkages of Agassiz’s desert tortoise (Gopherus agassizii) populations within the Coachella Valley Multiple Species Habitat Conservation Plan (CVMSHCP) area during a multi-decadal megadrought. Federal, state, and numerous parcels of privately owned conservation lands in the uplands around the periphery of the Coachella Valley were surveyed. Desert tortoise distribution data collected will be used to develop a species habitat model. These data will be overlaid with location data of flammable invasive plant species to inform fuels management and invasive plant control efforts in sensitive species habitat, as well as continue to identify high quality habitat and important desert tortoise linkage areas around the Coachella Valley. We also surveyed two established G. agassizii study plots – one at the Mesa Wind Energy Facility near Palm Springs, California and one at the Boyd Deep Canyon Desert Research Center. At those locations, we used resurveys and radio telemetry to assess habitat use, demography, and population status. We also used camera trapping at Deep Canyon to observe tortoise activities and behaviors in an unusual low elevation tortoise habitat.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 CVCC Annual Report","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Coachella Valley Conservation Commission","collaboration":"Coachella Valley Conservation Commission, Bureau of Land Management, UCR Boyd Deep Canyon Desert Research Center, Joshua Tree National Park, University of California Riverside, California State University at Fullerton, California Department of Fish and Wildlife","usgsCitation":"Puffer, M.R., Lovich, J.E., and Cummings, K.L., 2024, Determining the distribution, status, and linkages of Agassiz's desert tortoise populations in the uplands surrounding the Coachella Valley, 77 p.","productDescription":"77 p.","startPage":"177","endPage":"253","ipdsId":"IP-150045","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":463075,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cvmshcp.org/annual-reports/Annual-Report-2023.pdf"},{"id":463092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.83577301985537,\n 34.107460375171385\n ],\n [\n -116.83577301985537,\n 33.31867102335272\n ],\n [\n -115.76010558997707,\n 33.31867102335272\n ],\n [\n -115.76010558997707,\n 34.107460375171385\n ],\n [\n -116.83577301985537,\n 34.107460375171385\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Puffer, Michele R. 0000-0003-4957-0963","orcid":"https://orcid.org/0000-0003-4957-0963","contributorId":225575,"corporation":false,"usgs":true,"family":"Puffer","given":"Michele","email":"","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":916494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":916495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cummings, Kristy L. 0000-0002-8316-5059","orcid":"https://orcid.org/0000-0002-8316-5059","contributorId":202061,"corporation":false,"usgs":true,"family":"Cummings","given":"Kristy","email":"","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":916496,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251870,"text":"70251870 - 2024 - Implementation of the CREED approach for environmental assessments","interactions":[],"lastModifiedDate":"2024-07-01T14:21:23.001449","indexId":"70251870","displayToPublicDate":"2024-03-01T06:51:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Implementation of the CREED approach for environmental assessments","docAbstract":"Environmental exposure data are a key component of chemical and ecological assessments, supporting and guiding environmental management decisions and regulations. Measures taken to protect the environment based on exposure data can have social and economic implications. Flawed information may lead to measures being taken in the wrong place or to important action not being taken. Although the advantages of harmonizing evaluation methods have been demonstrated for hazard information, no comparable approach is established for exposure data evaluation. The goal of Criteria for Reporting and Evaluating Exposure Datasets (CREED) is to improve the transparency and consistency with which exposure data are evaluated regarding usability in environmental assessments. Here, we describe the synthesis of the CREED process, and propose methods and tools to summarize and interpret the outcomes of the data usability evaluation in support of decision-making and communication. The CREED outcome includes a summary that reports any key gaps or shortcomings in the reliability (data quality) and relevance (fitness for purpose) of the data being considered. The approach has been implemented in a workbook template (provided as Supporting Information), for assessors to readily follow the workflow and create a report card for any given dataset. The report card communicates the outcome of the CREED evaluation and summarizes important dataset attributes, providing a concise reference pertaining to the dataset usability for a specified purpose and documenting data limitations that may restrict data use or increase environmental assessment uncertainty. The application of CREED is demonstrated through three case studies, which also were used during beta testing of the methodology. As experience with the CREED approach application develops, further improvements may be identified and incorporated into the framework. Such development is to be encouraged in the interest of better science and decision-making, and to make environmental monitoring and assessment more cost-effective.
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":70251851,"text":"70251851 - 2024 - Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery","interactions":[],"lastModifiedDate":"2024-03-04T12:26:52.188745","indexId":"70251851","displayToPublicDate":"2024-03-01T06:18:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":899,"text":"Arctic, Antarctic, and Alpine Research","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery","docAbstract":"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.
Understanding age and growth are important for fisheries science and management; however, age data are not routinely collected for many populations. We propose and test a method of borrowing age–length data across increasingly broader spatiotemporal levels to create a hierarchical age–length key (HALK). We assessed this method by comparing growth and mortality metrics to those estimated from lake–year age–length keys ages using seven common freshwater fish species across the upper Midwestern United States. Levels used for data borrowing began most specifically by borrowing within lake across time and increased in breadth to include data within the Hydrologic Unit Code (HUC) 10 watershed, HUC8 watershed, Level III Ecoregion, and finally a species-wide data ALK using all available data with our study for a species. Median deviation in mean length of age-3 fish was within 1 cm for the most specific HALK levels, and median deviation in total annual mortality was close to 0 for most species when borrowing occurred within HUC10 and HUC8 watersheds. Percent error in growth curves increased with data borrowing, but plateaued—or even decreased—for some species when data borrowing expanded across spatial levels. We present the HALK as a method for gaining age information about a fishery when age data are unavailable.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.11019","usgsCitation":"Frater, P.N., Feiner, Z.S., Hansen, G., Isermann, D.A., Latzka, A.W., and Jensen, O., 2024, The incredible HALK: borrowing data for age assignment: Fisheries Magazine, v. 49, no. 3, p. 117-128, https://doi.org/10.1002/fsh.11019.","productDescription":"12 p.","startPage":"117","endPage":"128","ipdsId":"IP-152013","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440269,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fsh.11019","text":"Publisher Index Page"},{"id":433013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, South Dakota, Wisconsin","otherGeospatial":"upper Midwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.34241140551524,\n 49.05094743128561\n ],\n [\n -104.34241140551524,\n 38.11034629510851\n ],\n [\n -84.84599091399372,\n 38.11034629510851\n ],\n [\n -84.84599091399372,\n 49.05094743128561\n ],\n [\n -104.34241140551524,\n 49.05094743128561\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Frater, Paul N.","contributorId":342573,"corporation":false,"usgs":false,"family":"Frater","given":"Paul","email":"","middleInitial":"N.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feiner, Zachary S.","contributorId":342575,"corporation":false,"usgs":false,"family":"Feiner","given":"Zachary","email":"","middleInitial":"S.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Gretchen J.A.","contributorId":342577,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J.A.","affiliations":[{"id":37643,"text":"University of Minnesota-Twin Cities","active":true,"usgs":false}],"preferred":false,"id":910200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Latzka, Alexander W.","contributorId":342581,"corporation":false,"usgs":false,"family":"Latzka","given":"Alexander","email":"","middleInitial":"W.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910202,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jensen, Olaf P.","contributorId":342584,"corporation":false,"usgs":false,"family":"Jensen","given":"Olaf P.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910203,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252068,"text":"70252068 - 2024 - Updated three-dimensional temperature maps for the Great Basin, USA","interactions":[],"lastModifiedDate":"2024-03-12T15:18:38.070643","indexId":"70252068","displayToPublicDate":"2024-02-29T10:14:02","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Updated three-dimensional temperature maps for the Great Basin, USA","docAbstract":"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.
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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 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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.
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Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona","title":"Water resources inventory of the Las Cienegas National Conservation Area, southeastern Arizona","docAbstract":"The Las Cienegas National Conservation Area was established by the Las Cienegas National Conservation Area Establishment Act of 1999 (Public Law 106–538) and is managed by the Bureau of Land Management. Located in southeastern Arizona, the conservation area contains more than 45,000 acres of rolling grassland, wetlands, and woodlands surrounded by isolated mountain ranges that are part of the Madrean archipelago. This report describes the surface-water and groundwater resources within, and hydrologically connected to, the conservation area.
Two primary aquifers have been identified within the Las Cienegas National Conservation Area: a Quaternary alluvial aquifer and a Miocene to Pliocene basin-fill aquifer. The Quaternary alluvial aquifer consists of Quaternary saturated stream alluvium along Cienega Creek and its major tributaries. This aquifer provides the water necessary for base flow in the perennial stream reaches that support aquatic life and for wetland and riparian habitat along the stream courses. Wells and piezometers completed in the Quaternary alluvial aquifer show both seasonal and daily water-level fluctuation patterns, as well as responses to flood flows in Cienega Creek. The basin-fill aquifer, in contrast, consists chiefly of Miocene to Pliocene alluvium within a sedimentary basin that is at least 4,800 feet deep. This aquifer is developed for anthropogenic uses more often than the Quaternary alluvial aquifer is developed. Generally, water levels in wells completed in the basin-fill aquifer have gradually declined a few feet between 2011, when measurements began, and 2022, when this report was written. Most water-chemistry samples available from the basin-fill aquifer had either a sodium-bicarbonate or calcium-bicarbonate water type. Previous research has shown that most recharge to the basin-fill aquifer likely comes from mountain-front and mountain-block recharge. Research further shows that this aquifer likely provides most of the recharge to the Quaternary alluvial aquifer. Because no production wells completed in bedrock exist within the conservation area, little is known about the hydraulic properties of the bedrock therein, but usable quantities of water can likely be produced from places where the bedrock has highly developed joint or fracture systems.
During 2006–2021, the average combined length of measured perennial stream reaches within the main part of the Las Cienegas National Conservation Area was 6.35 miles. The average annual base flow of Cienega Creek during 2002–2021, estimated with the Standard Base-Flow Index method using data from a streamgage within the conservation area, was 0.62 cubic feet per second. Monthly mean streamflow measured at this streamgage for the same period ranged from a low of 0.29 cubic feet per second (in June) to a high of 9.8 cubic feet per second (in July). The July average is heavily influenced by a flood that occurred in July 2021; the median July streamflow for 2002–2021 is just 0.84 cubic feet per second. Periods with no daily flow are not uncommon at this gage during late May and June.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235131","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Mason, J.P., 2024, Water resources inventory of the Las Cienegas National Conservation Area, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2023–5131, 31 p., https://doi.org/10.3133/sir20235131.","productDescription":"vii, 31 p.","numberOfPages":"31","onlineOnly":"Y","ipdsId":"IP-144415","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":432298,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X94P5B","text":"USGS Data Release","description":"Mason, J.P., 2023, Supplemental groundwater level, spring flow, and streamflow data for the Water Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9X94P5B.","linkHelpText":"Supplemental groundwater level, spring flow, and streamflow data for the Water Resources Inventory of the Las Cienegas National Conservation Area, Southeastern Arizona"},{"id":423631,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5131/sir20235131.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5131"},{"id":499394,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116143.htm","linkFileType":{"id":5,"text":"html"}},{"id":425761,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5131/covrthb.jpg"},{"id":423634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5131/sir20235131.xml"},{"id":423633,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5131/images"},{"id":423632,"rank":2,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235131/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona","otherGeospatial":"Las Cienegas National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.778891765439,\n 31.89756957809155\n ],\n [\n -110.778891765439,\n 31.602822981414448\n ],\n [\n -110.36561821653889,\n 31.602822981414448\n ],\n [\n -110.36561821653889,\n 31.89756957809155\n ],\n [\n -110.778891765439,\n 31.89756957809155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
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":70251736,"text":"sir20245003 - 2024 - Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2023","interactions":[],"lastModifiedDate":"2024-03-22T14:44:40.924129","indexId":"sir20245003","displayToPublicDate":"2024-02-27T18:55:19","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5003","displayTitle":"Status of Water-Level Altitudes and Long-Term and Short-Term Water-Level Changes in the Chicot and Evangeline (Undifferentiated) and Jasper Aquifers, Greater Houston Area, Texas, 2023","title":"Status of water-level altitudes and long-term and short-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2023","docAbstract":"Since the early 1900s, groundwater withdrawn from the primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers—has been an important source of water in the greater Houston area, Texas. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of water-level altitudes and water-level changes in these aquifers in the greater Houston area.
In this report, the Chicot and Evangeline aquifers are treated as a single aquifer for the purposes of providing annual assessments of regional-scale water-level altitudes and water-level changes over time. In 2023, shaded depictions of water-level altitudes for the Chicot and Evangeline aquifers (undifferentiated) ranged from about 286 feet (ft) below the North American Vertical Datum of 1988 (NAVD 88) to about 169 ft above NAVD 88. The largest decline in water-level altitudes indicated by the 1977–2023 long-term water-level-change map was in south-central Montgomery County southeast of The Woodlands. In comparison, the 1990–2023 long-term water-level-change map depicts the largest declines in water-level altitudes in localized areas at or near certain wells in parts of northwestern Harris County and south-central Montgomery County. The largest rise in water-level altitudes for 1977–2023 is depicted in a relatively large area in southeastern Harris County, whereas the largest rise in water-level altitudes for 1990–2023 is depicted in a relatively large area in central Harris County. The 5-year short-term water-level-change map depicts the largest declines at three wells in northern Fort Bend County, one well in western Harris County, and three wells in south-central Montgomery County and the largest rise at one well in central Harris County. The 1-year short-term water-level-change map depicts the largest declines at one well in northern Fort Bend County and two wells in southwestern Harris County and the largest rises at one well in northern Brazoria County and one well in south-central Montgomery County.
In 2023, shaded depictions of water-level altitudes for the Jasper aquifer ranged from about 242 ft below NAVD 88 to about 218 ft above NAVD 88. The 2000–23 long-term water-level-change map depicts water-level declines throughout the study area where water-level-measurement data from the aquifer were collected, with the largest declines in north-central Harris County and south-central Montgomery County south of The Woodlands. The 5-year short-term water-level-change map depicts the largest declines at two wells in central Montgomery County near Conroe and two wells in south-central Montgomery County southeast of The Woodlands and the largest rise at one well in western Montgomery County. The 1-year short-term water-level-change map depicts the largest declines at four wells in south-central Montgomery County southeast of The Woodlands and one well in central Montgomery County near Conroe and the largest rises at two wells in western Montgomery County.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
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":70251812,"text":"70251812 - 2024 - Sensitivity testing of marine turbidite age estimates along the Cascadia subduction zone","interactions":[],"lastModifiedDate":"2024-06-03T14:56:43.631507","indexId":"70251812","displayToPublicDate":"2024-02-27T07:00:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity testing of marine turbidite age estimates along the Cascadia subduction zone","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.
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
We described source and phosphorus (P) retention potential of soft, fine-grained, streambed sediment and associated phosphorus (sed-P) during summer low-flow conditions. Combining in-channel, sed-P storage with relative age provided context on relevance to western Lake Erie Basin management goals.
In 2019, rapid geomorphic assessment (30 reaches) compared streambed-sediment storage (S) to streambank erosion (E), providing annual sediment budgets (S:E). Streambed sediment (13 reaches) was fingerprinted and analyzed for sed-P. The P saturation ratio (PSR; four reaches) quantified potential sorption/desorption of dissolved P (DP) between the water column and streambed sediment. Analyses were supplemented with data from 2017 and 2021. The ratio of two fallout radionuclides, beryllium-7 (54-day half-life) and excess lead-210 (22.3 years), apportioned “new” sediment based on time since rainfall contact.
Streambed sediment was mostly streambank (54–96%) for contributing areas > 2.7 km2; for upstream reaches, a larger percentage was apportioned as upland (cropland, pasture, forest, and road), with < 30% streambank. Streambank erosion correlated with contributing area; however, soil type (ecoregion), stream characteristics, and land use combined to drive streambed-sediment storage. Individual-reach S:E (accumulation of 0.01–35 years of streambank erosion) differentiated erosional and depositional in-channel environments. Most reaches indicated that 17–57% of sediment had recent contact with rainfall. Streambed-sediment PSR indicated a low potential for further sorption of DP from the water column; one reach was a P source when sampled.
Sed-P was higher in streambed sediment than in source samples, which varied by land use and ecoregion. This indicates homogenization resulting from in-stream sorption of DP during sediment transport that occurs over multiple events.
","language":"English","publisher":"Springer","doi":"10.1007/s11368-023-03713-6","usgsCitation":"Williamson, T.N., Fitzpatrick, F., Kreiling, R.M., Blount, J.D., and Karwan, D.L., 2024, Sediment budget of a Maumee River headwater tributary: How streambank erosion, streambed-sediment storage, and streambed-sediment source inform our understanding of legacy phosphorus: Journal of Soils and Sediments, v. 24, p. 1447-1463, https://doi.org/10.1007/s11368-023-03713-6.","productDescription":"17 p.","startPage":"1447","endPage":"1463","ipdsId":"IP-154572","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":440300,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11368-023-03713-6","text":"Publisher Index Page"},{"id":426142,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana, Ohio","otherGeospatial":"Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.25514832288071,\n 41.67558956302901\n ],\n [\n -85.14964718502311,\n 41.67558956302901\n ],\n [\n -85.14964718502311,\n 40.43443489714787\n ],\n [\n -83.25514832288071,\n 40.43443489714787\n ],\n [\n -83.25514832288071,\n 41.67558956302901\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2024-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209588,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":202193,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":893766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blount, James D. 0000-0002-0006-3947 jblount@usgs.gov","orcid":"https://orcid.org/0000-0002-0006-3947","contributorId":200231,"corporation":false,"usgs":true,"family":"Blount","given":"James","email":"jblount@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karwan, Diana L.","contributorId":207315,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":893768,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251904,"text":"70251904 - 2024 - Metabarcoding is (usually) more cost effective than seining or qPCR for detecting tidewater gobies and other estuarine fishes","interactions":[],"lastModifiedDate":"2024-03-06T12:50:28.823747","indexId":"70251904","displayToPublicDate":"2024-02-26T06:48:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Metabarcoding is (usually) more cost effective than seining or qPCR for detecting tidewater gobies and other estuarine fishes","docAbstract":"Many studies have shown that environmental DNA (eDNA) sampling can be more sensitive than traditional sampling. For instance, past studies found a specific qPCR probe of a water sample is better than a seine for detecting the endangered northern tidewater goby, Eucyclogobius newberryi. Furthermore, a metabarcoding sample often detects more fish species than a seine detects. Less consideration has been given to sampling costs. To help managers choose the best sampling method for their budget, I estimated detectability and costs per sample to compare the cost effectiveness of seining, qPCR and metabarcoding for detecting endangered tidewater gobies as well as the associated estuarine fish community in California. Five samples were enough for eDNA methods to confidently detect tidewater gobies, whereas seining took twice as many samples. Fixed program costs can be high for qPCR and seining, whereas metabarcoding had high per-sample costs, which led to changes in relative cost-effectiveness with the number of locations sampled. Under some circumstances (multiple locations visited or an already validated assay), qPCR was a bit more cost effective than metabarcoding for detecting tidewater gobies. Under all assumptions, seining was the least cost-effective method for detecting tidewater gobies or other fishes. Metabarcoding was the most cost-effective sampling method for multiple species detection. Despite its advantages, metabarcoding has gaps in sequence databases, can yield vague results for some species, and can lead novices to serious errors. Seining remains the only way to rapidly assess densities, size distributions, and fine-scale spatial distributions.
Increasingly, genomic data are being used to supplement field-based ecological studies to help evaluate recovery status and trends in endangered species. We collected genomic data to address two related questions regarding the Least Bell’s Vireo (Vireo bellii), an endangered migratory songbird restricted to southern California riparian habitat for breeding. First, we sought to delineate the range limits and potential overlap between Least Bell’s Vireo and its sister subspecies, the Arizona Bell’s Vireo, by analyzing samples from the deserts of eastern California, southwestern Nevada, Utah and Arizona. Second, we evaluated genetic structure among Least Bell’s Vireo populations in coastal California and estimated effective population size. Clustering analyses based on 10,571 single nucleotide polymorphisms (SNPs) from 317 samples supported two major groups that aligned closely to the previously defined subspecies ranges. The first cluster included birds in the Central Valley, all coastal drainages, and westernmost deserts of California, with no further sub-structuring among coastal drainages. Almost all birds from the Amargosa River in eastern California and eastward assigned to the second cluster; however, low levels of gene flow were detected across the subspecies groups, with greater rates of gene flow from Arizona Bell’s Vireo to Least Bell’s Vireo than the reverse. Admixed individuals occurred in the California deserts; and although smaller than coastal populations, desert populations may be important for maintaining and replenishing genetic diversity and facilitating the movement of potentially adaptive genes between subspecies. Within Least Bell’s Vireo, local populations in coastal drainages comprised a single genetic population, with some evidence of close relatives distributed across drainages, suggesting these could function as a well-connected metapopulation. These results are consistent with previous Least Bell’s Vireo banding studies that reported high rates of dispersal among drainages. Effective population size for both subspecies was high, suggesting that adaptive potential has been maintained despite previous declines.
","language":"English","publisher":"American Ornithological Society","doi":"10.1093/ornithapp/duae009","usgsCitation":"Vandergast, A.G., Kus, B., Wood, D.A., Mitelberg, A., Smith, J.G., and Milano, E., 2024, High inter-population connectivity and occasional gene flow between subspecies improves recovery potential for the endangered Least Bell’s Vireo: Ornithological Applications, v. 126, no. 3, duae009, 13 p., https://doi.org/10.1093/ornithapp/duae009.","productDescription":"duae009, 13 p.","ipdsId":"IP-156012","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":440316,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae009","text":"Publisher Index Page"},{"id":426117,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitelberg, Anna 0000-0002-3309-9946 amitelberg@usgs.gov","orcid":"https://orcid.org/0000-0002-3309-9946","contributorId":218945,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Julia G. 0000-0001-9841-1809","orcid":"https://orcid.org/0000-0001-9841-1809","contributorId":221086,"corporation":false,"usgs":true,"family":"Smith","given":"Julia","email":"","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":895594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Milano, Elizabeth R.","contributorId":334415,"corporation":false,"usgs":false,"family":"Milano","given":"Elizabeth R.","affiliations":[{"id":80134,"text":"former USGS employee; currently USFS","active":true,"usgs":false}],"preferred":false,"id":895595,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70260947,"text":"70260947 - 2024 - Segment anything model can not segment anything: Assessing AI foundation model's generalizability in permafrost mapping","interactions":[],"lastModifiedDate":"2025-01-30T16:07:20.584644","indexId":"70260947","displayToPublicDate":"2024-02-24T08:35:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Segment anything model can not segment anything: Assessing AI foundation model's generalizability in permafrost mapping","docAbstract":"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":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","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":70252580,"text":"70252580 - 2024 - Prioritizing water availability study settings to address geogenic contaminants and related societal factors","interactions":[],"lastModifiedDate":"2024-03-29T12:02:53.562013","indexId":"70252580","displayToPublicDate":"2024-02-24T06:59:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing water availability study settings to address geogenic contaminants and related societal factors","docAbstract":"Water availability for human and ecological uses depends on both water quantity and water quality. The U.S. Geological Survey (USGS) is developing strategies for prioritizing regional-scale and watershed basin-scale studies of water availability across the nation. Previous USGS ranking processes for basin-scale studies incorporated primarily water quantity factors but are now considering additional water quality factors. This study presents a ranking based on the potential impacts of geogenic constituents on water quality and consideration of societal factors related to water quality. High-concentration geogenic constituents, including trace elements and radionuclides, are among the most prevalent contaminants limiting water availability in the USA and globally. Geogenic constituents commonly occur in groundwater because of subsurface water–rock interactions, and their distributions are controlled by complex geochemical processes. Geogenic constituent mobility can also be affected by human activities (e.g., mining, energy production, irrigation, and pumping). Societal factors and relations to drinking water sources and water quality information are often overlooked when evaluating research priorities. Sociodemographic characteristics, data gaps resulting from historical data-collection disparities, and infrastructure condition/age are examples of factors to consider regarding environmental justice. This paper presents approaches for ranking and prioritizing potential basin-scale study areas across the contiguous USA by considering a suite of conventional physical and geochemical variables related to geogenic constituents, with and without considering variables related to societal factors. Simultaneous consideration of societal and conventional factors could provide decision makers with more diverse, interdisciplinary tools to increase equity and reduce bias in prioritizing focused research areas and future water availability studies.
Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land-use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022a) and incorporates population lek count data for 1960–2023. Included in this update are changes in terminology. Specifically, we now use the terms Period 1 (previously Long), Period 2 (previously Medium/Long), Period 3 (previously Medium), Period 4 (previously Short/Medium), Period 5 (previously Short), and Period 6 (previously Recent) to identify specific trends. State-space models estimated 2.8-percent average annual decline in sage-grouse populations between 1966 and 2021 (Period 1, six population oscillations) across their geographical range. Average annual decline among climate clusters for the same number of oscillations ranged between 2.1 and 3.1 percent. Cumulative declines were 41.1, 64.5, and 78.4 percent range-wide during Period 5 (19 years), Period 3 (35 years), and Period 1 (55 years), respectively. Population growth during 2022 and 2023 continue to point to 2021 as the most recent range-wide nadir.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1190","collaboration":"Prepared in cooperation with the Bureau of Land Management","programNote":"Ecosystems Mission Areas—Species Management Research Program and Land Management Research Program","usgsCitation":"Prochazka, B.G., Coates, P.S., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2024, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2023 (ver. 1.1, April 2024): U.S. Geological Survey Data Report 1190, 18 p., https://doi.org/10.3133/dr1190.","productDescription":"Report: viii, 18 p., and data release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-161357","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":427870,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"USGS Data Release","description":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 3.0, February 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9OQWGIV.","linkHelpText":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 3.0, February 2024)"},{"id":427874,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/dr/1190/versionHist.txt"},{"id":426097,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1190/covrthb.jpg"},{"id":427865,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1190/dr1190.xml"},{"id":427866,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1190/images"},{"id":427867,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1190/full"},{"id":427864,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1190/dr1190.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}}],"edition":"Version 1.0: Feb 2024; Version 1.1: Apr 2024","contact":"Western Ecological Research Center
U.S. Geological Survey
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The objective of the study is to assess when liquefaction is triggered in a suite of ground motions following simplified approaches and measure the remaining post-triggering energy content of those ground motions. For liquefaction-induced deformations, current simplified analysis procedures do not directly incorporate temporal effects and rely on peak transient intensity measurements. Liquefaction hazard from short-duration, small to moderate-magnitude (M) earthquakes (M4.5–7.5) is adequately expressed using transient intensity measurements. However, subduction-zone interface earthquakes can have magnitudes greater than 9.0, with ground-motion durations exceeding 300 s. Using 525 ground motions from the NGA-Subduction (NGA-Sub) database for subduction-zone earthquakes with M8.25–9.25, the timing of liquefaction was calculated using cyclic counting procedures by assuming a reference stress condition and incorporating cyclic strengths from laboratory element testing. A complementary analysis was completed using 514 crustal ground motion records from the NGA-West2 database for M6.75–7.75. Several trends were identified during this study. First, liquefaction will likely trigger during the first half of the ground motion duration, independent of the earthquake source type. However, subduction-zone motions have larger post-triggering energy content compared to crustal earthquakes. The findings from this work indicate that accurately predicting liquefaction-induced deformations from subduction-zone earthquakes may be substantially improved by using robust time-based liquefaction analysis procedures.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, Geo-Congress 2024","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Geo-Congress 2024","conferenceDate":"February 25–28, 2024","conferenceLocation":"Vancouver, British Columbia, Canada","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/9780784485316.026","usgsCitation":"Carey, T.J., Naik, A., Makdisi, A.J., and Mason, H., 2024, Liquefaction timing and post-triggering seismic energy: A comparison of crustal and subduction zone earthquakes, in Proceedings, Geo-Congress 2024, Vancouver, British Columbia, Canada, February 25–28, 2024, p. 240-249, https://doi.org/10.1061/9780784485316.026.","productDescription":"10 p.","startPage":"240","endPage":"249","ipdsId":"IP-157153","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":428324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carey, Trevor J.","contributorId":335970,"corporation":false,"usgs":false,"family":"Carey","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":899983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Naik, Atira","contributorId":335971,"corporation":false,"usgs":false,"family":"Naik","given":"Atira","email":"","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":899984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":899985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mason, Henry 0000-0003-4279-2854","orcid":"https://orcid.org/0000-0003-4279-2854","contributorId":293188,"corporation":false,"usgs":true,"family":"Mason","given":"Henry","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":899986,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}