Passive sampler devices were deployed in six northern California streams five times between November 2018 and December 2019 to measure the presence or absence of current-use pesticides in surface water. In the targeted areas, there are reported pesticide uses for agriculture, commercial forestry, and rights of way maintenance along with unreported pesticide use at private residences and cannabis grow sites. The sites sampled in this study were not previously monitored for current-use pesticides. Streams in the Sierra Nevada foothills of northern California are important habitats for many sensitive species including salmonids, but the logistics of sampling these areas can be difficult using traditional water-quality sampling techniques, especially when sampling watersheds where contaminant transport is episodic. Chemcatcher passive sampling devices and silicone bands were deployed in these areas to concentrate pesticides for days to weeks at a time. The U.S. Geological Survey, in cooperation with the Central Valley Regional Water Quality Control Board, was responsible for developing passive sampler field deployment and laboratory analytical methods for current-use pesticides, providing pesticide measurements from streams in the study region, and determining how well passive samplers detect pesticides in these environments. Six sites were monitored during the study, and passive sampler extracts were analyzed for a total 155 current-use pesticides in this study. A total of 19 out of the 155 pesticides including 9 insecticides, 5 fungicides, and 5 herbicides were detected in extracts from passive samplers. The most frequently detected pesticides were the herbicides hexazinone and dithiopyr, the insecticides bifenthrin and methoxyfenozide, and the fungicide azoxystrobin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225129","collaboration":"Prepared in cooperation with Central Valley Regional Water Quality Control Board","usgsCitation":"De Parsia, M.D., Orlando, J.L., and Hladik, M.L., 2023, Assessing the presence of current-use pesticides in mid-elevation Sierra Nevada streams using passive samplers, California, 2018–19: U.S. Geological Survey Scientific Investigations Report 2022–5129, 31 p., https://doi.org/10.3133/sir20225129.","productDescription":"Report: vi, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-126203","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":412877,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T0CSCT","text":"USGS data release","description":"USGS data release","linkHelpText":"Pesticide detections in streams throughout the foothills of the Sierra Nevada range using passive samplers from 2017 to 2019"},{"id":412879,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5129/sir20225129.XML"},{"id":412878,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5129/Images"},{"id":412876,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225129/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5129"},{"id":412875,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5129/sir20225129.pdf","text":"Report","size":"4.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5129"},{"id":412874,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5129/coverthb.jpg"},{"id":500483,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114338.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.30362079688356,\n 40.16751419593339\n ],\n [\n -122.30362079688356,\n 37.95075778589002\n ],\n [\n -118.92126815286719,\n 37.95075778589002\n ],\n [\n -118.92126815286719,\n 40.16751419593339\n ],\n [\n -122.30362079688356,\n 40.16751419593339\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Since Lost River suckers (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) hatched in the early 1990s, almost none of the fish have survived to adulthood. When full grown, Lost River suckers are the largest of the Klamath suckers, averaging about two and a half feet long, whereas shortnose suckers are at around twenty-one inches. Rather than an inability to spawn, these species are limited by very high mortality within the first year or two of life. There are many hypothesized causes of high juvenile sucker mortality, including poor water quality, diseases aggravated by warming water temperatures, and the reduction in wetland habitat that provides food and cover.
The number of adult endangered Lost River and shortnose suckers in Upper Klamath Lake, the primary remaining habitat for these species, declined by 65 to 85 percent between 2001 and 2020. Extinction is increasingly likely for these species unless their population trajectories can be changed. The Klamath Tribes, the U.S. government, the State of Oregon, and several nonprofits are working together to prevent sucker extinction in the Klamath Basin.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oregon Encyclopedia","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Oregon Historical Society","usgsCitation":"Burdick, S.M., 2023, Endangered Klamath suckers, chap. of Oregon Encyclopedia, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-139525","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":413132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":413123,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.oregonencyclopedia.org/articles/klamath-sucker/#.Y-1o3S_MK71"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.0607867403346,\n 42.48583055292471\n ],\n [\n -122.0607867403346,\n 42.2894405748352\n ],\n [\n -121.7919858325015,\n 42.2894405748352\n ],\n [\n -121.7919858325015,\n 42.48583055292471\n ],\n [\n -122.0607867403346,\n 42.48583055292471\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":864440,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70240976,"text":"70240976 - 2023 - Using mercury stable isotope fractionation to identify the contribution of historical mercury mining sources present in downstream water, sediment and fish","interactions":[],"lastModifiedDate":"2023-03-03T16:09:08.618256","indexId":"70240976","displayToPublicDate":"2023-02-09T10:05:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13442,"text":"Frontiers in Environmental Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Using mercury stable isotope fractionation to identify the contribution of historical mercury mining sources present in downstream water, sediment and fish","docAbstract":"Ecosystems downstream of mercury (Hg) contaminated sites can be impacted by both localized releases as well as Hg deposited to the watershed from atmospheric transport. Identifying the source of Hg in water, sediment, and fish downstream of contaminated sites is important for determining the effectiveness of source-control remediation actions. This study uses measurements of Hg stable isotopes in soil, sediment, water, and fish to differentiate between Hg from an abandoned Hg mine from non-mine-related sources. The study site is located within the Willamette River watershed (Oregon, United States), which includes free-flowing river segments and a reservoir downstream of the mine. The concentrations of total-Hg (THg) in the reservoir fish were 4-fold higher than those further downstream (>90 km) from the mine site in free-flowing sections of the river. Mercury stable isotope fractionation analysis showed that the mine tailings (δ202Hg: −0.36‰ ± 0.03‰) had a distinctive isotopic composition compared to background soils (δ202Hg: −2.30‰ ± 0.25‰). Similar differences in isotopic composition were observed between stream water that flowed through the tailings (particulate bound δ202Hg: −0.58‰; dissolved: −0.91‰) versus a background stream (particle-bound δ202Hg: −2.36‰; dissolved: −2.09‰). Within the reservoir sediment, the Hg isotopic composition indicated that the proportion of the Hg related to mine-release increased with THg concentrations. However, in the fish samples the opposite trend was observed—the degree of mine-related Hg was lower in fish with the higher THg concentrations. While sediment concentrations clearly show the influence of the mine, the relationship in fish is more complicated due to differences in methylmercury (MeHg) formation and the foraging behavior of different fish species. The fish tissue δ13C and Δ199Hg values indicate that there is a higher influence of mine-sourced Hg in fish feeding in a more sediment-based food web and less so in planktonic and littoral-based food webs. Identifying the relative proportion of Hg from local contaminated site can help inform remediation decisions, especially when the relationship between total Hg concentrations and sources do not show similar covariation between abiotic and biotic media.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvc.2023.1096199","usgsCitation":"Eckley, C.S., Eagles-Smith, C., Luxton, T., Hoffman, J.C., and Janssen, S., 2023, Using mercury stable isotope fractionation to identify the contribution of historical mercury mining sources present in downstream water, sediment and fish: Frontiers in Environmental Chemistry, v. 4, 1096199, 11 p., https://doi.org/10.3389/fenvc.2023.1096199.","productDescription":"1096199, 11 p.","ipdsId":"IP-146689","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":444517,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvc.2023.1096199","text":"Publisher Index Page"},{"id":413666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.37949100050008,\n 45.453180681982445\n ],\n [\n -123.61622713639957,\n 45.453180681982445\n ],\n [\n -123.61622713639957,\n 43.717858367382746\n ],\n [\n -122.37949100050008,\n 43.717858367382746\n ],\n [\n -122.37949100050008,\n 45.453180681982445\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2023-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Eckley, Chris S. 0000-0002-6986-4451","orcid":"https://orcid.org/0000-0002-6986-4451","contributorId":246031,"corporation":false,"usgs":false,"family":"Eckley","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":865585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":865586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luxton, Todd P","contributorId":221509,"corporation":false,"usgs":false,"family":"Luxton","given":"Todd P","affiliations":[{"id":40396,"text":"US Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":865587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hoffman, Joel C.","contributorId":84244,"corporation":false,"usgs":false,"family":"Hoffman","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":865588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865589,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258158,"text":"70258158 - 2023 - Integration of distributed streamflow measurement metadata for improved water resource decision-making","interactions":[],"lastModifiedDate":"2024-09-05T14:38:03.477734","indexId":"70258158","displayToPublicDate":"2023-02-09T09:35:35","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Integration of distributed streamflow measurement metadata for improved water resource decision-making","docAbstract":"Streamflow data are critical for monitoring and managing water resources, yet there are significant spatial gaps in our federal monitoring networks with biases toward large perennial rivers. In some cases, streamflow monitoring exists in these spatial gaps, but information about these monitoring locations is challenging to obtain. Here, we present a streamflow catalog for the United States Pacific Northwest that includes current and historical streamflow monitoring location information obtained from 32 organizations (other than the U.S. Geological Survey), which includes 2661 continuous streamflow gaging locations (22% are currently active) and 30,557 discrete streamflow measurements. A stakeholder advisory board with representatives from organizations that operate streamflow monitoring networks identified metadata requirements and provided feedback on the Streamflow Data Catalog user interface. Engagement with the water resources community through this effort highlighted challenges that water professionals face in collecting and managing streamflow data so that data are findable, accessible, interoperable, and reusable (FAIR). Over 60% of the streamflow monitoring locations in the Streamflow Data Catalog are not available online and are thus not findable through web search engines. Providing organizations technical assistance with standard measurement procedures, metadata collection, and web accessibility could substantially increase the availability and utility of streamflow information to water resources communities.
","language":"English","publisher":"MDPI","doi":"10.3390/w15040679","usgsCitation":"Kaiser, K.E., Blasch, K.W., and Schmitz, S., 2023, Integration of distributed streamflow measurement metadata for improved water resource decision-making: Water, v. 15, no. 4, 679, 11 p., https://doi.org/10.3390/w15040679.","productDescription":"679, 11 p.","ipdsId":"IP-148240","costCenters":[{"id":65563,"text":"Northwest Pacific Islands Regional Director's Office","active":true,"usgs":true}],"links":[{"id":444520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w15040679","text":"Publisher Index Page"},{"id":433499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaiser, Kendra E. 0000-0003-1773-6236","orcid":"https://orcid.org/0000-0003-1773-6236","contributorId":211475,"corporation":false,"usgs":false,"family":"Kaiser","given":"Kendra","email":"","middleInitial":"E.","affiliations":[{"id":38255,"text":"Boise State Unviersity","active":true,"usgs":false}],"preferred":false,"id":912398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blasch, Kyle W. 0000-0002-0590-0724","orcid":"https://orcid.org/0000-0002-0590-0724","contributorId":203415,"corporation":false,"usgs":true,"family":"Blasch","given":"Kyle","email":"","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmitz, Steven","contributorId":343922,"corporation":false,"usgs":false,"family":"Schmitz","given":"Steven","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":912400,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240677,"text":"70240677 - 2023 - Earth science looks to outer space","interactions":[],"lastModifiedDate":"2023-02-14T12:55:41.761432","indexId":"70240677","displayToPublicDate":"2023-02-09T06:52:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Earth science looks to outer space","docAbstract":"Satellite data are revolutionizing coastal science. A study revealing how the El Niño/Southern Oscillation impacts coastal erosion around the Pacific Rim shows what is possible.
","language":"English","publisher":"Nature","doi":"10.1038/s41561-023-01123-4","usgsCitation":"Barnard, P.L., and Vitousek, S., 2023, Earth science looks to outer space: Nature Geoscience, v. 16, p. 108-109, https://doi.org/10.1038/s41561-023-01123-4.","productDescription":"2 p.","startPage":"108","endPage":"109","ipdsId":"IP-146434","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":413041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2023-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864256,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241863,"text":"70241863 - 2023 - Pressurized upflow reactor system for the bioconversion of coal to methane: Investigation of the coal/sand interface effect","interactions":[],"lastModifiedDate":"2023-03-29T11:49:22.107524","indexId":"70241863","displayToPublicDate":"2023-02-09T06:47:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13781,"text":"Cleaner Chemical Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Pressurized upflow reactor system for the bioconversion of coal to methane: Investigation of the coal/sand interface effect","docAbstract":"Microbial generation of coal bed methane (CBM) represents a significant source of natural gas on Earth. While biostimulation has been demonstrated in batch cultures, environmental parameters such as overburden pressure and formation water flow need to be tested at the laboratory scale to understand in situ potential. We designed and constructed a high-pressure (HP) flow-through reactor system that simulates in situ conditions of underground coal seams. Two stainless-steel columns contained coal from the Powder River Basin (PRB), USA, or a coal/sand mixture to represent the interface of coal seams with sandstone layers, which are hypothesized to exhibit higher methanogenesis rates in situ. The system was filled with CBM formation water, inoculated with a methanogenic enrichment from PRB coal beds, and stimulated with algal biomass as a nutrient. The reactors were incubated under pressure (5.4 atm) and flow of CBM water (0.01 mL/min), and control batch cultures were incubated at ambient pressure and without flow (± amendment). Dissolved and headspace methane concentrations were analyzed over time by gas chromatography for 75 days. The pressurized reactors exhibited longer latency periods than ambient pressure controls, but methane production did not reach a plateau phase, which might reflect the impact of scale on the inoculum. The coal/sand reactor exhibited higher methane production than the coal-only reactor, a pattern also observed in the corresponding controls, suggesting an interface effect on methanogenesis. This study indicates that the HP flow test system we designed is well suited for the study of methanogenesis and provides a successful demonstration of CBM generation from the PRB in field-relevant laboratory conditions as a precursor to meso‑scale demonstrations.
Remotely sensed surface temperature (ST) has been widely used to monitor and assess landscape thermal conditions, hydrologic modeling, and surface energy balance. Landsat thermal sensors have continuously measured the Earth surface thermal radiance since August 1982. The thermal radiance measurements are atmospherically compensated and converted to Landsat STs and delivered as part of the U.S. Geological Survey Landsat Collection 1 U.S. Analysis Ready Data; however, the low satellite revisit cycles combined with the presence of clouds and cloud shadows reduce the number of valid retrievals. This reduction can limit the ability to monitor annual or seasonal variations in the surface thermal budget. These factors reduce the ability to use the temperature data to fit time series for historical trend analysis to match background climate variations. In this study, we implemented an approach that uses linear harmonic least absolute shrinkage and selection operator regression models to fill gaps because of clouds, shadows, and coarse temporal resolution. The gap-filled data provide increased temporal density of Landsat ST records. The gap-filled Landsat ST, therefore, can allow for an improved monitoring of annual, seasonal, or even monthly landscape thermal conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231006","usgsCitation":"Xian, G., Shi, H., Arab, S., Mueller, C., Hussain, R., Sayler, K., and Howard, D., 2023, Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm: U.S. Geological Survey Open-File Report 2023–1006, 15 p., https://doi.org/10.3133/ofr20231006.","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-144337","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412873,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1006/images"},{"id":412872,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.pdf","text":"Report","size":"41.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1006"},{"id":412880,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1006/coverthb.jpg"},{"id":499732,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114340.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.9318883744094,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 34.338976979151155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Introduction: To sustain black bear (Ursus americanus) populations, wildlife managers should understand the coupled socio-ecological systems that influence acceptance capacity for bears.
Method: In a study area encompassing a portion of New York State, we spatially matched datasets from three sources: human-bear conflict reports between 2006 and 2018, estimates of local bear density in 2017–2018, and responses to a 2018 property owner survey (n=1,772). We used structural equation modeling to test hypothesized relationships between local human-bear conflict, local bear density, and psychological variables.
Results: The final model explained 57% of the variance in acceptance. The effect of bear population density on acceptance capacity for bears was relatively small and was mediated by a third variable: perception of proximity to the effects of human-bear interactions. The variables that exerted a direct effect on acceptance were perception of bear-related benefits, perception of bear-related risks, perceived proximity to effects of human-bear interactions, and being a hunter. Perception of bear-related benefits had a greater effect on acceptance than perception of bear-related risks. Perceived proximity to effects of human-bear interactions was affected by local bear density, but also was affected by social trust. Increased social trust had nearly the same effect on perceived proximity as decreased bear density. Social trust had the greatest indirect effect on acceptance of any variable in the model.
Discussion: Findings suggest wildlife agencies could maintain public acceptance for bears through an integrated approach that combines actions to address bear-related perceptions and social trust along with active management of bear populations.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2023.1041393","usgsCitation":"Siemer, W., Lauber, T., Stedman, R., Hurst, J., Sun, C., Fuller, A.K., Hollingshead, N., Belant, J., and Kellner, K., 2023, Perception and trust influence acceptance for black bears more than bear density or conflicts: Frontiers in Conservation Science, v. 4, 1041393, 13 p., https://doi.org/10.3389/fcosc.2023.1041393.","productDescription":"1041393, 13 p.","ipdsId":"IP-147449","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2023.1041393","text":"Publisher Index Page"},{"id":466002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.92499168267652,\n 40.760363749644455\n ],\n [\n -73.65063699090229,\n 40.98264101294393\n ],\n [\n -73.6996393039949,\n 41.10089280193728\n ],\n [\n -73.49391688239375,\n 41.2484207881225\n ],\n [\n -73.56250536378525,\n 41.3073292248944\n ],\n [\n -73.48423008439727,\n 42.05372989136586\n ],\n [\n -73.5233960109858,\n 42.126434934459525\n ],\n [\n -73.41562767810787,\n 42.34405165012723\n ],\n [\n -73.83681626701608,\n 42.54652252735795\n ],\n [\n -74.44841929328119,\n 42.6495653802757\n ],\n [\n -75.7909273246029,\n 43.03044245093176\n ],\n [\n -76.43773682001827,\n 43.50144007856014\n ],\n [\n -77.04532365530125,\n 43.25213523900416\n ],\n [\n -78.07429641881241,\n 43.3804649394846\n ],\n [\n -79.06407311309604,\n 43.2521232638133\n ],\n [\n -79.02485730522619,\n 42.98027715144707\n ],\n [\n -78.90726018672747,\n 42.90136300879922\n ],\n [\n -79.08365221595503,\n 42.69283803617958\n ],\n [\n -79.75001378472541,\n 42.331662242956355\n ],\n [\n -79.76953018691533,\n 42.01940417603805\n ],\n [\n -75.3404244202763,\n 41.98299557666223\n ],\n [\n -75.07586228888928,\n 41.75679559693819\n ],\n [\n -75.0464736918644,\n 41.515116258185316\n ],\n [\n -74.78190029599845,\n 41.44169802010788\n ],\n [\n -74.66431063355593,\n 41.3681952398467\n ],\n [\n -73.93913471760371,\n 41.02160483993919\n ],\n [\n -73.92499168267652,\n 40.760363749644455\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2023-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Siemer, William F.","contributorId":348063,"corporation":false,"usgs":false,"family":"Siemer","given":"William F.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lauber, T. 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Bruce","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stedman, Richard C.","contributorId":348065,"corporation":false,"usgs":false,"family":"Stedman","given":"Richard C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hurst, Jeremy E.","contributorId":348066,"corporation":false,"usgs":false,"family":"Hurst","given":"Jeremy E.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":922916,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, Catherine C.","contributorId":348067,"corporation":false,"usgs":false,"family":"Sun","given":"Catherine C.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":922917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hollingshead, Nicholas A.","contributorId":348068,"corporation":false,"usgs":false,"family":"Hollingshead","given":"Nicholas A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Belant, Jerrold L.","contributorId":348069,"corporation":false,"usgs":false,"family":"Belant","given":"Jerrold L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922920,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kellner, Kenneth III","contributorId":348070,"corporation":false,"usgs":false,"family":"Kellner","given":"Kenneth","suffix":"III","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922921,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241144,"text":"70241144 - 2023 - Decoupling of species and plant communities of the U.S. Southwest: A CCSM4 climate scenario example","interactions":[],"lastModifiedDate":"2023-03-13T12:14:35.489863","indexId":"70241144","displayToPublicDate":"2023-02-08T07:10:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Decoupling of species and plant communities of the U.S. Southwest: A CCSM4 climate scenario example","docAbstract":"Climate change is predicted to alter the current climate suitability under which plant species and communities occur. Predictions of change have focused on individual species or entire communities, but theory indicates plants will not respond uniformly to climate change within or between communities. We developed models of the current climate suitability (the baseline) of 66 plant species characteristic of 29 plant communities of the arid Southwest, made predictions of climate suitability for the species under two climate change scenarios for the years 2041–2060 (Community Climate System Model version 1.4 [CCSM4] global climate model [GCM], Representative Concentration Pathway [RCP] 4.5 and 8.5 scenarios), and calculated changes in suitability between the future scenarios and baseline for each species. Climate change exposure for the entire community was then evaluated as the composite change of the predicted future climate suitability of the communities' characteristic species. Loss of 25% or more of favorable climate suitability was predicted for 39 (RCP4.5) and 51 (RCP8.5) species within their communities. The proportion of the study area with all species in a community having unfavorable suitability was 17.9% (RCP4.5) and 21.3% (RCP8.5) compared to 6.2% for baseline. We show that suitable climates for species within a plant community are not expected to be a single community-wide trajectory, but rather changes in climate suitability will be unique to the species and not experienced uniformly across the extant communities. This decoupling of plant species within their traditional plant communities may lead to a cascade of unanticipated ecological responses and unprecedented challenges to resource management. Our study results can inform hypotheses of the future successional track of plant communities, characteristic species, and the decisions resource managers must make for management.
Population estimates derived from monitoring efforts can be sensitive to the survey method selected, potentially leading to biased estimates and low precision relative to true population size. While small unmanned aerial systems (UAS) present a unique opportunity to survey avian populations while limiting disturbance, relatively little is known about how this method compares with more traditional approaches. In this study we compared population estimates of Snowy (Egretta thula) and Cattle Egrets (Bubulcus ibis) in a mixed-species colony in the Chesapeake Bay (Maryland, USA) derived from UAS photo counts, flush counts, flight-line surveys, and in-colony nest counts along with the time required to derive an estimate via each approach. We found that UAS counts and flush counts produced lower pair estimates than nest counts and flight-line surveys (P < 0.05), and required dramatically less time (x̄ = 6, 8, 84 and 90 min, respectively). These results suggest that while UAS have the potential to collect valuable survey data from breeding colonies that are hard to reach or are especially sensitive to the disturbance inherent in other methods, inherent biases should be considered and caution should be used when comparing results between survey types.
The International Union for Conservation of Nature (IUCN) Red List is an important and widely used tool for conservation assessment. The IUCN uses information about a species’ range, population size, habitat quality and fragmentation levels, and trends in abundance to assess extinction risk. Genetic diversity is not considered, although it affects extinction risk. Declining populations are more strongly affected by genetic drift and higher rates of inbreeding, which can reduce the efficiency of selection, lead to fitness declines, and hinder species’ capacities to adapt to environmental change. Given the importance of conserving genetic diversity, attempts have been made to find relationships between red-list status and genetic diversity. Yet, there is still no consensus on whether genetic diversity is captured by the current IUCN Red List categories in a way that is informative for conservation. To assess the predictive power of correlations between genetic diversity and IUCN Red List status in vertebrates, we synthesized previous work and reanalyzed data sets based on 3 types of genetic data: mitochondrial DNA, microsatellites, and whole genomes. Consistent with previous work, species with higher extinction risk status tended to have lower genetic diversity for all marker types, but these relationships were weak and varied across taxa. Regardless of marker type, genetic diversity did not accurately identify threatened species for any taxonomic group. Our results indicate that red-list status is not a useful metric for informing species-specific decisions about the protection of genetic diversity and that genetic data cannot be used to identify threat status in the absence of demographic data. Thus, there is a need to develop and assess metrics specifically designed to assess genetic diversity and inform conservation policy, including policies recently adopted by the UN's Convention on Biological Diversity Kunming-Montreal Global Biodiversity Framework.
Environmental DNA is a burgeoning tool used to address wide-ranging scientific questions, including determining diets of difficult-to-sample predators. Loons are large piscivorous diving birds that capture and consume prey underwater, making it nearly impossible to visually determine their diet via observation alone. Identifying species' diets is important for understanding basic life history traits, and revealing key prey species can clarify species' roles in complex trophic webs, aid in understanding population and community dynamics, and help identify critical habitat for protection. Current information about loon diet is largely anecdotal, and traditional non-observational methods for quantifying loon diet have limitations. Analysis of eDNA from loon feces may provide biologists with a non-invasive technique for determining diet without negative sampling effects, and with increased resolution as compared to other techniques. We surveyed lakes in two areas of northern Alaska for Yellow-billed Loons (Gavia adamsii). Loon fecal samples were collected opportunistically from latrine sites without disturbing any animals and analyzed using novel marker sets to determine loon species and diet. Fish species were detected in all fecal samples, the most common being Alaska blackfish (Dallia pectoralis), and ninespine stickleback (Pungitius pungitius). This research demonstrates that eDNA metabarcoding analyses of loon fecal samples can determine the specific loon species that deposited the feces and characterize the piscine portion of their diet with limited disturbance to the animals.
Elucidating the molecular structure of sedimentary organic matter (SOM) is key to understanding petroleum generation processes, as well as ancient sedimentary environments. SOM structure is primarily controlled by biogenic source material (e.g., marine vs. terrigenous), depositional conditions, and subsurface thermal history. Additional factors, e.g., strain, may also impact the molecular structure of SOM. Multiple spatially resolved approaches exist for in situ evaluation of SOM, including Raman and infrared spectroscopies, as well as mass spectrometric methods. While these methods have enabled increased understanding of the occurrence and distribution of SOM functional groups, they suffer from disadvantages including low spatial resolution (infrared spectroscopy), limited molecular information (Raman spectroscopy), and sample destruction (mass spectrometric methods). Recent technological advances have resulted in infrared spectrometers capable of breaking the Abbe diffraction limit, greatly increasing the spatial resolutions accessible for an infrared measurement.
Here we utilize optical photothermal infrared spectroscopy (O-PTIR) to record maps of functional group distributions at 500 nm spatial resolution in Tasmanites (algal microfossils) from the Upper Devonian Ohio Shale. These data allow for discrimination between Tasmanites, adjacent SOM, and fine-grained minerals. Additionally, functional group distributions within Tasmanites were found to be generally homogenous, although slight variations exist between the body and fold apices (zones of greatest deformation) which may indicate strain-induced chemical reactions. The data presented here represent the first application of O-PTIR to study SOM, highlighting the promise of this analytical approach for future studies evaluating the molecular composition of geologic materials at sub-micron scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2023.104569","usgsCitation":"Jubb, A., Stokes, M., McAleer, R.J., Hackley, P.C., Dillion, E., and Qu, J., 2023, Mapping ancient sedimentary organic matter molecular structure at nanoscales using optical photothermal infrared spectroscopy: Organic Geochemistry, v. 177, 104569, 9 p., https://doi.org/10.1016/j.orggeochem.2023.104569.","productDescription":"104569, 9 p.","ipdsId":"IP-140733","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":444534,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2023.104569","text":"Publisher Index Page"},{"id":412936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"177","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokes, Martha 0000-0002-2838-8380","orcid":"https://orcid.org/0000-0002-2838-8380","contributorId":269608,"corporation":false,"usgs":true,"family":"Stokes","given":"Martha","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":864040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":864041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dillion, Eoghan","contributorId":302334,"corporation":false,"usgs":false,"family":"Dillion","given":"Eoghan","email":"","affiliations":[{"id":65459,"text":"Photothermal Spectroscopy Corporation","active":true,"usgs":false}],"preferred":false,"id":864043,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Qu, Jing","contributorId":242671,"corporation":false,"usgs":false,"family":"Qu","given":"Jing","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":864042,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240482,"text":"70240482 - 2023 - Peak Cenozoic warmth enabled deep-sea sand deposition","interactions":[],"lastModifiedDate":"2023-02-09T12:45:45.865725","indexId":"70240482","displayToPublicDate":"2023-02-08T06:43:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Peak Cenozoic warmth enabled deep-sea sand deposition","docAbstract":"The early Eocene (~ 56–48 million years ago) was marked by peak Cenozoic warmth and sea levels, high CO2, and largely ice-free conditions. This time has been described as a period of increased continental erosion and silicate weathering. However, these conclusions are based largely on geochemical investigation of marine mudstones and carbonates or study of intermontane Laramide basin settings. Here, we evaluate the marine coarse siliciclastic response to early Paleogene hothouse climatic and oceanographic conditions. We compile an inventory of documented sand-rich (turbidite) deep-marine depositional systems, recording 59 instances of early Eocene turbidite systems along nearly all continental margins despite globally-elevated sea levels. Sand-rich systems were widespread on active margins (42 instances), but also on passive margins (17 instances). Along passive margins, 13 of 17 early Eocene systems are associated with known Eocene-age fluvial systems, consistent with a fluvial clastic response to Paleogene warming. We suggest that deep-marine sedimentary basins preserve clastic records of early Eocene climatic extremes. We also suggest that in addition to control by eustasy and tectonism, climate-driven increases in sediment supply (e.g., drainage integration, global rainfall, denudation) may significantly contribute to the global distribution and volume of coarse-grained deep-marine deposition despite high sea level.
In this work we present the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI) Commission on Volcanic Hazards and Risk (CVHR) Volcanic Hazard Maps Database and the accompanying volcanichazardmaps.org website. Using input from a series of IAVCEI CVHR Working Group on Hazard Mapping workshops, we developed a classification scheme and terminology framework for categorizing, discussing, naming, and searching for hazard maps. ≥ The database and website aim to serve as a resource for the volcanology community to explore how different aspects of hazard map development and design have been addressed in different countries, for different hazard processes, and for different intended purposes and audiences. Additionally, they act as a tool for presenting hazard map options to stakeholder groups and serve as a learning resource that can be incorporated into educational materials and training courses. In this work, we present the database and website, discuss the classification scheme, explore the enormous diversity of hazard maps, and suggest ways that the database and website can be used by the volcanic hazard mapping community.
In this study, we explore whether the Parker and Baltay (2022) site response models for the Los Angeles (LA) basin region can improve ground‐motion forecasts in the U.S. Geological Survey ShakeAlert earthquake early warning system (hereafter ShakeAlert). We implement the peak ground acceleration and peak ground velocity site response models of Parker and Baltay (2022) in ShakeAlert via the earthquake information to ground‐motion (hereafter eqinfo2GM) module, which predicts ground motions from the estimated earthquake parameters of magnitude, rupture length, and location. The nonergodic site response models for the greater LA area were developed using ground motions from 414 M 3–7.3 earthquakes in southern California. We test nonergodic ground‐motion forecasts for five earthquakes in the LA area: the 1994 M 6.7 Northridge earthquake, the 2008 M 5.4 Chino Hills earthquake, the 2019 M 7.1 Ridgecrest earthquake, the 2020 M 4.5 South El Monte earthquake, and a synthetic M 7.8 earthquake on the southern San Andreas fault from the ShakeOut scenario, which was the basis of a statewide emergency response exercise. From the test results, we find that with the nonergodic site response applied, ShakeAlert not only alerts larger areas but can also result in longer warning times in LA region. In addition, the modified Mercalli intensity (MMI) ground‐motion predictions generated by the ShakeAlert eqinfo2GM module are improved in accuracy when compared with the corresponding ShakeMap ground‐truth MMI when the nonergodic site response model is applied.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220145","usgsCitation":"Lin, R., Parker, G.A., McGuire, J., and Baltay Sundstrom, A.S., 2023, Applications of nonergodic site response models to ShakeAlert case studies in the Los Angeles area: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1324-1343, https://doi.org/10.1785/0120220145.","productDescription":"20 p.","startPage":"1324","endPage":"1343","ipdsId":"IP-142361","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":417403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.38078299074152,\n 34.43090673360663\n ],\n [\n -119.38078299074152,\n 33.385811767084306\n ],\n [\n -116.79288709511383,\n 33.385811767084306\n ],\n [\n -116.79288709511383,\n 34.43090673360663\n ],\n [\n -119.38078299074152,\n 34.43090673360663\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Rongrong 0000-0002-6234-2183","orcid":"https://orcid.org/0000-0002-6234-2183","contributorId":305696,"corporation":false,"usgs":true,"family":"Lin","given":"Rongrong","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":873573,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240218,"text":"sim3501 - 2023 - Colored shaded-relief bathymetric map and orthomosaic from structure-from-motion quantitative underwater imaging device with five cameras of the Lake Tahoe floor, California","interactions":[],"lastModifiedDate":"2026-02-19T17:50:10.38879","indexId":"sim3501","displayToPublicDate":"2023-02-07T12:49:09","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3501","displayTitle":"Colored Shaded-Relief Bathymetric Map and Orthomosaic from Structure-from-Motion Quantitative Underwater Imaging Device with Five Cameras of the Lake Tahoe Floor, California","title":"Colored shaded-relief bathymetric map and orthomosaic from structure-from-motion quantitative underwater imaging device with five cameras of the Lake Tahoe floor, California","docAbstract":"This two-sheet publication displays a high-resolution colored shaded-relief bathymetric map (sheet 1) and orthomosaic (sheet 2) of part of the Lake Tahoe floor in California generated from a U.S. Geological Survey towed surface vehicle with multiple downward-looking underwater cameras. The system is named the Structure-from-Motion Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). The cameras were synchronized with each other and with a survey-grade Global Navigation Satellite System. A total of 42,939 photographs were collected with nearly complete overlapping coverage of an area approximately 250 meters by 250 meters. A digital terrain model and an orthomosaic were generated from the overlapping photographs using Structure-from-Motion and photogrammetry techniques. Gaps are present in the bathymetry data owing to data-collection or -processing artifacts. These two sheets display the very fine details of the lake floor mapped using SQUID-5.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3501","usgsCitation":"Hatcher, G.A., Warrick, J.A., and Dartnell, P., 2022, Colored shaded-relief bathymetric map and orthomosaic from structure-from-motion quantitative underwater imaging device with five cameras of the Lake Tahoe floor, California: U.S. Geological Survey Scientific Investigations Map 3501, 2 sheets, scale 1:700, https://doi.org/10.3133/sim3501.","productDescription":"2 Sheets: 35.00 × 34.00 inches; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-139653","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":412576,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9934I6U","text":"USGS data release","description":"USGS data release","linkHelpText":"Point clouds, bathymetric maps, and orthoimagery generated from overlapping lakebed images acquired with the SQUID-5 system near Dollar Point, Lake Tahoe, CA, March 2021"},{"id":412577,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V44ZYS","text":"USGS data release","description":"USGS data release","linkHelpText":"Overlapping lakebed images and associated GNSS locations acquired near Dollar Point, Lake Tahoe, CA, March 2021"},{"id":412573,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3501/coverthb.jpg"},{"id":412574,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3501/sim3501_sheet1.pdf","text":"Sheet 1","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3501 Sheet 1"},{"id":412575,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3501/sim3501_sheet2.pdf","text":"Sheet 2","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3501 Sheet 2"},{"id":500207,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114342.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.23690229714447,\n 39.300192154621016\n ],\n [\n -120.23690229714447,\n 38.87400239989947\n ],\n [\n -119.8635257065711,\n 38.87400239989947\n ],\n [\n -119.8635257065711,\n 39.300192154621016\n ],\n [\n -120.23690229714447,\n 39.300192154621016\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
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Research on Bluegills, Lepomis macrochirus R., is abundant but typically focuses on water bodies with similar environmental conditions. We assessed Bluegill density, relative abundance (catch per unit effort [CPUE] by electrofishing), growth, and size structure in 60 lakes with wide-ranging surface areas (2–12,412 ha), trophic states (oligotrophic–hypereutrophic), and macrophyte abundances (0.3–100 percent of lake volume inhabited [PVI]) across Florida, USA. Bluegill density and CPUE increased with lake productivity and decreased with macrophyte abundance. Bluegill growth increased with lake productivity and CPUE of stock-length Florida Bass, Micropterus floridanus L., a Bluegill predator. Bluegill size structure increased with lake productivity and decreased with Bluegill density. Results indicate that Bluegill fisheries with abundant individuals of quality size (≥150 mm) require productive (>25 μg/L chlorophyll-a concentration) lakes with moderate to high macrophyte coverage (PVI 50–100), abundant stock-length Florida Bass (>40 fish/h of electrofishing), and Bluegill densities <300 fish/ha. This study provides an approach to predict Bluegill population demographics based on abiotic and biotic factors, establish fisheries management expectations, and develop regional and lake-specific management tools.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes8020100","usgsCitation":"Carlson, A.K., and Hoyer, M.V., 2023, Bluegill population demographics as related to abiotic and biotic factors in Florida lakes: Fishes, v. 8, no. 2, 100, 19 p., https://doi.org/10.3390/fishes8020100.","productDescription":"100, 19 p.","ipdsId":"IP-139048","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444546,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes8020100","text":"Publisher Index Page"},{"id":432951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":10,\"properties\":{\"name\":\"Florida\",\"nation\":\"USA 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trends","interactions":[],"lastModifiedDate":"2024-06-11T16:40:06.367815","indexId":"70254881","displayToPublicDate":"2023-02-07T11:37:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of multistate occupancy models for estimating relative abundance and population trends","docAbstract":"Detecting spatiotemporal changes in the abundances of organisms is key to effectively conserving species. While indices of abundance have long been used, there has been a shift toward model-based estimators that account for the detection process. Popular approaches including traditional occupancy models and N-mixture models entail tradeoffs. The traditional occupancy approach requires the researcher coarsen the characterization of abundance to the probability that a site is occupied or unoccupied. Conversely, N-mixture models make use of variation in counts, but perform poorly when individuals have low detectability or move into or out of sites between visits. Multistate occupancy models that differentiate relatively abundant from non-abundant states have the potential to fill this gap but have been underexplored. We conducted a simulation study to test whether multistate occupancy models could capture spatial abundance patterns and detect population declines in the face of low individual detection probability (p ≤ 0.3) and unmodeled heterogeneity (e.g., that arising from individual movement). We considered 10,773 scenarios to examine the effects of differing amounts of heterogeneity as well as alternative study designs, population parameters, and modeling choices. We tracked bias in the proportion of sites estimated to be in the abundant state for single-season models, and power to detect a declining trend across multiple years. We also evaluated data diagnostic metrics to provide guidance to users. Multistate occupancy models were able to differentiate sites with higher abundances from sites with lower abundances when there were at least medium levels of spatial heterogeneity in true abundances. If different sites were randomly selected each year, power to detect even large population declines (65%) was poor (power < 0.8). However, if the same sites were surveyed each year, and a dynamic multistate occupancy was used, multistate occupancy models could detect (power ≥ 0.8) relatively small declines (5-40%) in 20% of scenarios, and frequently detect large declines of 45-60% (mean power = 0.92). Conservation decisions rely on detecting change reliably, rarely needing absolute abundance information. Multistate occupancy models can improve our ability to detect changing abundance while accommodating low individual detection probability and heterogeneity in count monitoring data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2023.110303","usgsCitation":"Steen, V., Duarte, A., and Peterson, J., 2023, An evaluation of multistate occupancy models for estimating relative abundance and population trends: Ecological Modelling, v. 478, 110303, 9 p., https://doi.org/10.1016/j.ecolmodel.2023.110303.","productDescription":"110303, 9 p.","ipdsId":"IP-144901","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2023.110303","text":"Publisher Index Page"},{"id":429891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"478","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Steen, Valerie A. 0000-0002-1417-8139","orcid":"https://orcid.org/0000-0002-1417-8139","contributorId":205994,"corporation":false,"usgs":false,"family":"Steen","given":"Valerie A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":902764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":902765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256625,"text":"70256625 - 2023 - Support for the fasting endurance hypothesis of partial migration in a nearshore seabird","interactions":[],"lastModifiedDate":"2024-08-27T15:38:26.036411","indexId":"70256625","displayToPublicDate":"2023-02-07T10:30:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Support for the fasting endurance hypothesis of partial migration in a nearshore seabird","docAbstract":"Partial migration occurs when only a fraction of a population migrates instead of all individuals. Considered an evolutionary precursor to full migration, understanding why some individuals choose to undertake migration while others do not may serve to inform general migratory theory. While several hypotheses currently exist for explaining the maintenance of partial migration, empirical support for many is limited. To address this gap, we analyzed GPS data acquired from brown pelicans (Pelecanus occidentalis; n = 74), a partially migratory seabird, nesting on six colonies in the South Atlantic Bight over the course of four autumn migrations. We estimated that approximately 74% of pelicans nesting within the study area may be migratory on an annual basis, with the remainder staying within the surrounding marine ecoregion year-round. Mean date of migration initiation was 9 November, although movements occurred from September to December. Results from Cox's proportional hazards modeling indicated significant positive and negative effects of sea surface temperatures and body condition on migration rate, respectively. We suggest that the ontogenetic migration of the primary forage species of brown pelicans from estuarine to pelagic environments causes a seasonal reduction in prey and that pelicans in poor body condition are unable to meet the energetic demands potentially associated with this decrease in prey availability (i.e., the fasting endurance hypothesis of partial migration). Although we did not find evidence for a density-dependent migratory response, the effects of intraspecific competition on migration in pelicans also appear to warrant consideration.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4365","usgsCitation":"Wilkinson, B., and Jodice, P.G., 2023, Support for the fasting endurance hypothesis of partial migration in a nearshore seabird: Ecosphere, v. 14, no. 2, e4365, 14 p., https://doi.org/10.1002/ecs2.4365.","productDescription":"e4365, 14 p.","ipdsId":"IP-138118","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":489176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4365","text":"Publisher Index Page"},{"id":433202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, South Carolina","otherGeospatial":"Southern Atlantic Bight","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.05297920929696,\n 34.03073611807265\n ],\n [\n -81.51071034327317,\n 31.670634225271414\n ],\n [\n -81.44326290770108,\n 30.745131881151735\n ],\n [\n -78.41498319817921,\n 33.68781001381117\n ],\n [\n -79.05297920929696,\n 34.03073611807265\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkinson, Bradley P.","contributorId":341414,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Bradley P.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908374,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255215,"text":"70255215 - 2023 - Anthropogenic subsidies influence resource use during a mange epizootic in a desert coyote population","interactions":[],"lastModifiedDate":"2024-06-14T13:48:23.751212","indexId":"70255215","displayToPublicDate":"2023-02-07T08:43:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Anthropogenic subsidies influence resource use during a mange epizootic in a desert coyote population","docAbstract":"Colonization of urban areas by synanthropic wildlife introduces novel and complex alterations to established ecological processes, including the emergence and spread of infectious diseases. Aggregation at urban resources can increase disease transfer, with wide-ranging species potentially infecting outlying populations. The garrison at the National Training Center, Fort Irwin, California, USA, was recently colonized by mange-infected coyotes (Canis latrans) that also use the surrounding Mojave Desert. This situation provided an ideal opportunity to examine the effects of urban resources on disease dynamics. We evaluated seasonal space use and determined the influence of anthropogenic subsidies, water sources, and prey density on urban resource selection. We found no difference in home range size between healthy and infected individuals, but infected residents had considerably more spatial overlap with one another than healthy residents. All coyotes selected for anthropogenic subsidies during all seasons, while infected coyotes seasonally selected for urban water sources, and healthy coyotes seasonally selected for urban areas with greater densities of natural prey. These results suggest that while all coyotes were selecting for anthropogenic subsidies, infected resident coyotes demonstrated a greater tolerance for other conspecifics, which could be facilitating the horizontal transfer of sarcoptic mange to non-resident coyotes. Conversely, healthy coyotes also selected for natural prey and healthy residents exhibited a lack of spatial overlap with other coyotes suggesting they were not reliant on anthropogenic subsidies and were maintaining territories. Understanding the association between urban wildlife, zoonotic diseases, and urban resources can be critical in determining effective responses for mitigating future epizootics.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-023-05328-7","usgsCitation":"Reddell, C.D., Roemer, G.W., Delaney, D., Karish, T., and Cain, J.W., 2023, Anthropogenic subsidies influence resource use during a mange epizootic in a desert coyote population: Oecologia, v. 201, p. 435-447, https://doi.org/10.1007/s00442-023-05328-7.","productDescription":"13 p.","startPage":"435","endPage":"447","ipdsId":"IP-137129","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Fort Irwin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.2032931867908,\n 35.658455114584214\n ],\n [\n -117.2032931867908,\n 34.96919321548317\n ],\n [\n -116.06067559317455,\n 34.96919321548317\n ],\n [\n -116.06067559317455,\n 35.658455114584214\n ],\n [\n -117.2032931867908,\n 35.658455114584214\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"201","noUsgsAuthors":false,"publicationDate":"2023-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Reddell, Craig D.","contributorId":276276,"corporation":false,"usgs":false,"family":"Reddell","given":"Craig","email":"","middleInitial":"D.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":903747,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roemer, Gary W.","contributorId":273109,"corporation":false,"usgs":false,"family":"Roemer","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":903748,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Delaney, David K.","contributorId":276280,"corporation":false,"usgs":false,"family":"Delaney","given":"David K.","affiliations":[],"preferred":false,"id":903749,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karish, Talesha","contributorId":337900,"corporation":false,"usgs":false,"family":"Karish","given":"Talesha","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":903750,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903751,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240397,"text":"70240397 - 2023 - A review of common natural disasters as analogs for asteroid impact effects and cascading hazards","interactions":[],"lastModifiedDate":"2023-04-12T13:40:53.119991","indexId":"70240397","displayToPublicDate":"2023-02-07T08:30:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"A review of common natural disasters as analogs for asteroid impact effects and cascading hazards","docAbstract":"Modern civilization has no collective experience with possible wide-ranging effects from a medium-sized asteroid impactor. Currently, modeling efforts that predict initial effects from a meteor impact or airburst provide needed information for initial preparation and evacuation plans, but longer-term cascading hazards are not typically considered. However, more common natural disasters, such as volcanic eruptions, earthquakes, wildfires, dust storms, and hurricanes, are likely analogs that can provide the scope and scale of these potential effects. These events, especially the larger events with cascading effects, are key for understanding the scope and complexity of mitigation, relief, and recovery efforts for a medium-sized asteroid impact event. This paper reviews the initial and cascading effects of these natural hazards, describes the state of the art for modeling these hazards, and discusses the relevance of these hazards to expected long-term effects of an asteroid impact. Emergency managers, resource managers and planners, and research scientists involved in mitigation and recovery efforts would likely derive significant benefit from a framework linking multiple hazard models to provide a seamless sequence of related forecasts.
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