Submarine mass wasting events have damaged underwater structures and propagated waves that have inundated towns and affected human populations in nearby coastal areas. Susceptibility to submarine landslides can be pronounced in degrading cryospheric environments, where existing glaciers can provide high volumes of sediment, while cycles of glaciation and ice-loss can damage and destabilize slopes. Despite their contribution to potential tsunami hazard, submarine landslides can be difficult to study because of limited access and data collection in underwater environments. Here we present a method to quantify and map the submarine landslide susceptibility of sediment-covered slopes in Glacier Bay, Glacier Bay National Park and Preserve, Alaska, using multibeam-sonar bathymetric digital elevation models (DEMs) and historical maps of glacial extents over the last ∼250 years. After mapping an inventory of >7,000 landslide scarps in submarine sediments, we filtered the inventory by size to account for limitations in DEM resolution and spatial scales relevant to tsunami hazards. We then assessed landslide concentration, accounting for the age of the initial exposure of submarine slopes by deglaciation. We found a positive correlation between landslide concentration and deglaciation age, which we interpreted as a mean landslide accumulation rate over the period of record. Local deviations from this rate indicated differences in susceptibility. Additionally, we accounted for some of the effect of material and morphometric properties by estimating the submarine bedrock-sediment distribution using a morphometric model and assessing the relationship between slope angle and landslide incidence. Finally, we supplemented our susceptibility assessment with a geomorphic component based on the propensity of active submarine fans and deltas to produce landslides. Thus, our map of submarine landslide susceptibility incorporates three components: age-adjusted landslide concentration, slope angle, and geomorphology. We find that areas of mapped high susceptibility correlate broadly with areas of high sediment input and availability, locations of fans and deltas, and steep sediment-covered glacially carved fjords and troughs. Areas of high submarine landslide susceptibility in Glacier Bay moderately correspond with locations of known high-hazard subaerial slopes, but more research on submarine and subaerial landslides in degrading cryospheric environments would be beneficial to better understand landslide and tsunami hazards.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.821188","usgsCitation":"Avdievitch, N.N., and Coe, J.A., 2022, Submarine landslide susceptibility mapping in recently deglaciated terrain, Glacier Bay, Alaska: Frontiers in Earth Science, v. 10, 821188, 10 p., https://doi.org/10.3389/feart.2022.821188.","productDescription":"821188, 10 p.","ipdsId":"IP-129361","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":448383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.821188","text":"Publisher Index Page"},{"id":397597,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -137.120361328125,\n 58.07787626787517\n ],\n [\n -135.75,\n 58.07787626787517\n ],\n [\n -135.75,\n 59.226555635719215\n ],\n [\n -137.120361328125,\n 59.226555635719215\n ],\n [\n -137.120361328125,\n 58.07787626787517\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Avdievitch, Nikita N. 0000-0002-2507-2962","orcid":"https://orcid.org/0000-0002-2507-2962","contributorId":225492,"corporation":false,"usgs":true,"family":"Avdievitch","given":"Nikita","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":838822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":838823,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70233225,"text":"70233225 - 2022 - Nanoscale isotopic evidence resolves origins of giant Carlin-type ore deposits","interactions":[],"lastModifiedDate":"2022-07-19T12:24:08.784005","indexId":"70233225","displayToPublicDate":"2022-03-24T07:21:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Nanoscale isotopic evidence resolves origins of giant Carlin-type ore deposits","docAbstract":"The western North American Great Basin's Carlin-type deposits represent the largest accumulation of gold in the Northern Hemisphere. The controversy over their origins echoes the debate between Neptunists and Plutonists at the birth of modern geology: were the causative processes meteoric or magmatic? Sulfur isotopes have long been considered key to decoding metal cycling in the Earth's crust, but previous studies of Carlin-type pyrite lacked the spatial resolution to quantify differences among the numerous generations of sulfide mineralization. We developed a new dual-method, nanoscale approach to examine the fine-grained ore pyrite. The δ34S of the ore pyrite varies systematically with Au concentration at the nanoscale, indicating that both magmatic and meteoric fluids contributed during mineralization, but the magmas brought the gold. Repeated oscillations in fluid ratios upgraded the metal content, resulting in high gold endowment. Our results demonstrate that high-spatial-resolution studies are key to elucidate the spatiotemporal evolution of complex hydrothermal systems.
Despite several secretive marsh bird (SMB) species being listed as critically imperiled throughout the mid-continent of North America, limited information on SMB distribution and habitat use within primary migratory corridors results in uncertainty on contributions of wetlands in mid-latitude states toward their annual cycle needs. Our objectives were to quantify temporal patterns of SMB wetland occupancy during spring migration at a mid-latitude state and evaluate the relationships between SMB colonization probability and water-level management practices, and the resulting habitat conditions during spring migration. We conducted a 2-year, dynamic occupancy study (2013–2014) that included 6 rounds of repeated call-back surveys to detect the presence of 5 SMB species (i.e., Virginia rail [Rallus limicola], sora [Porzana carolina], king rail [R. elegans], least bittern [Ixobrychus exilis], and American bittern [Botaurus lentiginosus]) during spring (Apr–Jun) on 107 wetlands across 8 conservation areas and 4 national wildlife refuges throughout Missouri, USA. We detected sora most frequently, followed by least bittern, American bittern, Virginia rail, and king rail. Coefficient estimates indicated colonization probability for all species was positively associated with emergent vegetation cover and negatively associated with amount of open water. Open water was the only variable in the best supported model explaining American bittern site colonization, to which they were negatively associated. Virginia rail colonization had a strong positive association with vegetation height, whereas least bittern and sora site colonization were influenced positively by water depth and agriculture, respectively. Based on the habitat associations within and among SMB species identified in this study, wetland managers can tailor management strategies to optimize spring migration habitat for single- or multi-species objectives.
On August 8, 2020 northwest North Carolina experienced a Mw 5.1 earthquake that caused damage to buildings and roads in the city of Sparta. A regional centroid moment tensor solution shows the earthquake was the result of slip on a reverse fault with a minor strike-slip component. InSAR data, from the Japan Aerospace Exploration Agency’s ALOS2 satellite, reveal a deformation field that is more complex than expected from a single reverse fault earthquake. The data also reveal an apparent fault rupture at the Earth’s surface that caused damage to local roads. Modeling of the InSAR deformation field indicates the fault rupture is associated with a very shallow normal faulting event with an equivalent Mw of about 5.1, that overprinted the reverse fault deformation field and possibly occurred aseismically.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320210044","usgsCitation":"Wicks, C., and Chiu, J., 2022, Surface rupture on a secondary fault associated with the August 8, 2020, Mw 5.1 Sparta North Carolina Earthquake: The Seismic Record, v. 2, no. 1, p. 59-67, https://doi.org/10.1785/0320210044.","productDescription":"9 p.","startPage":"59","endPage":"67","ipdsId":"IP-135349","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320210044","text":"Publisher Index Page"},{"id":482384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","city":"Sparta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.17200973627948,\n 36.532296644809335\n ],\n [\n -81.17200973627948,\n 36.456479302893044\n ],\n [\n -81.0462093796463,\n 36.456479302893044\n ],\n [\n -81.0462093796463,\n 36.532296644809335\n ],\n [\n -81.17200973627948,\n 36.532296644809335\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Wicks, Charles 0000-0002-0809-1328","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":9023,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiu, Jer-Ming","contributorId":351278,"corporation":false,"usgs":false,"family":"Chiu","given":"Jer-Ming","affiliations":[{"id":83945,"text":"Univ. of Memphis, CERI","active":true,"usgs":false}],"preferred":false,"id":928304,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230199,"text":"70230199 - 2022 - Remote sensing of visible dye concentrations during a tracer experiment on a large, turbid river","interactions":[],"lastModifiedDate":"2022-04-04T16:39:43.401734","indexId":"70230199","displayToPublicDate":"2022-03-23T11:30:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of visible dye concentrations during a tracer experiment on a large, turbid river","docAbstract":"Understanding dispersion in rivers is critical for numerous applications, such as characterizing larval drift for endangered fish species and responding to spills of hazardous materials. Injecting a visible dye into the river can yield insight on dispersion processes, but conventional field instrumentation yields limited data on variations in dye concentration over time at a few, fixed points. Remote sensing can provide more detailed, spatially distributed information on the dye's motion, but this approach has only been tested in clear-flowing streams. The purpose of this study was to assess the potential of remote sensing to facilitate tracer studies in more turbid rivers. To pursue this objective, we injected Rhodamine WT dye into the Missouri River and collected field spectra from a boat, videos from a small unoccupied aircraft system (sUAS), and orthophotos from an airplane. Applying an optimal band ratio analysis (OBRA) algorithm to the field spectra revealed strong correlations (R2 = 0.936) between a spectrally based quantity and in situ concentration measurements. OBRA also performed well for broadband RGB (red, green, blue) images extracted from the sUAS-based videos; the resulting concentration maps were used to produce animations that captured movement of the dye pulse. Spectral mixture analysis of repeat orthophoto coverage yielded relative concentration estimates that provided a synoptic perspective on dispersion of the dye throughout the entire 13.8 km reach over the full 2.5-hr duration of the experiment. The results of this study demonstrate the potential to remotely sense tracer dye concentrations in large, highly turbid rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR031396","usgsCitation":"Legleiter, C.J., Sansom, B.J., and Jacobson, R., 2022, Remote sensing of visible dye concentrations during a tracer experiment on a large, turbid river: Water Resources Research, v. 58, no. 4, e2021WR031396, 23 p., https://doi.org/10.1029/2021WR031396.","productDescription":"e2021WR031396, 23 p.","ipdsId":"IP-133418","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448396,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr031396","text":"Publisher Index Page"},{"id":435912,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JDISO3","text":"USGS data release","linkHelpText":"Remotely sensed data and field measurements for mapping visible dye concentrations during a tracer experiment on the Missouri River near Columbia, MO, May 5, 2021"},{"id":398020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Columbia","otherGeospatial":"Missouri River, Searcy's Bend","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.50162124633789,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.856552783257754\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":839523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sansom, Brandon James 0000-0001-7999-9547","orcid":"https://orcid.org/0000-0001-7999-9547","contributorId":289636,"corporation":false,"usgs":true,"family":"Sansom","given":"Brandon","email":"","middleInitial":"James","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":839524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacobson, R. B. 0000-0002-8368-2064","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":92614,"corporation":false,"usgs":true,"family":"Jacobson","given":"R. B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":839525,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230024,"text":"70230024 - 2022 - Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome","interactions":[],"lastModifiedDate":"2023-03-24T16:54:57.706489","indexId":"70230024","displayToPublicDate":"2022-03-23T11:26:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome","docAbstract":"Invasions of native plant communities by non-native species present major challenges for ecosystem management and conservation. Invasive annual grasses such as cheatgrass, medusahead, and ventenata are pervasive and continue to expand their distributions across imperiled sagebrush-steppe communities of the western United States. These invasive grasses alter native plant communities, ecosystem function, and fire regimes, threatening sagebrush ecosystem persistence. Spatial data describing the distribution and abundance of invasive species are often used by resource managers to identify, target, and determine needed interventions. However, there are challenges associated with translating these datasets into management actions. We conducted a review of available spatial products to assess advances in, and barriers to, applying contemporary model-based maps to support rangeland management. We found dozens of regional data products describing cheatgrass or annual herbaceous cover and few maps describing ventenata or medusahead. Over the past decade, IAG spatial data increased in spatial and temporal resolution and increasingly used response variables that indicate the severity of infestation such as percent cover. Despite improvements, use of such data is limited by the time required to find, compare, understand, and translate model-based maps into management strategy. There is also a need for products with higher spatial resolution and accuracy. In collaboration with a multipartner stakeholder group, we identified key considerations that guide selection of IAG spatial data products for use by land managers and other users. On the basis of these considerations, we discuss issues that contribute to a research-implementation gap between users and product developers and suggest future directions for improved development of management-ready spatial products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2022.01.006","usgsCitation":"Tarbox, B.C., Van Schmidt, N.D., Shyvers, J.E., Saher, D., Heinrichs, J., and Aldridge, C.L., 2022, Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome: Rangeland Ecology & Management, v. 82, p. 104-115, https://doi.org/10.1016/j.rama.2022.01.006.","productDescription":"12 p.","startPage":"104","endPage":"115","ipdsId":"IP-129019","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":435913,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VW97AO","text":"USGS data release","linkHelpText":"Database of invasive annual grass spatial products for the western United States January 2010 to February 2021"},{"id":397530,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tarbox, Bryan C. 0000-0001-5040-3949","orcid":"https://orcid.org/0000-0001-5040-3949","contributorId":288930,"corporation":false,"usgs":true,"family":"Tarbox","given":"Bryan","email":"","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Schmidt, Nathan D. 0000-0002-5973-7934","orcid":"https://orcid.org/0000-0002-5973-7934","contributorId":288931,"corporation":false,"usgs":true,"family":"Van Schmidt","given":"Nathan","email":"","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shyvers, Jessica E. 0000-0002-4307-0004","orcid":"https://orcid.org/0000-0002-4307-0004","contributorId":288929,"corporation":false,"usgs":true,"family":"Shyvers","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saher, D. Joanne 0000-0002-2452-2570","orcid":"https://orcid.org/0000-0002-2452-2570","contributorId":288928,"corporation":false,"usgs":false,"family":"Saher","given":"D. Joanne","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":838723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":838724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":838725,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229992,"text":"70229992 - 2022 - Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny","interactions":[],"lastModifiedDate":"2022-03-24T15:18:53.662638","indexId":"70229992","displayToPublicDate":"2022-03-23T09:13:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny","title":"Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny","docAbstract":"Background
Bats of the genus Lasiurus occur throughout the Americas and have diversified into at least 20 species among three subgenera. The hoary bat (Lasiurus cinereus) is highly migratory and ranges farther across North America than any other wild mammal. Despite the ecological importance of this species as a major insect predator, and the particular susceptibility of lasiurine bats to wind turbine strikes, our understanding of hoary bat ecology, physiology, and behavior remains poor.
Methods
To better understand adaptive evolution in this lineage, we used whole-genome sequencing to identify protein-coding sequence and explore signatures of positive selection. Gene models were predicted with Maker and compared to seven well-annotated and phylogenetically representative species. Evolutionary rate analysis was performed with PAML.
Results
Of 9,447 single-copy orthologous groups that met evaluation criteria, 150 genes had a significant excess of nonsynonymous substitutions along the L. cinereus branch (P < 0.001 after manual review of alignments). Selected genes as a group had biased expression, most strongly in thymus tissue. We identified 23 selected genes with reported immune functions as well as a divergent paralog of Steep1 within suborder Yangochiroptera. Seventeen genes had roles in lipid and glucose metabolic pathways, partially overlapping with 15 mitochondrion-associated genes; these adaptations may reflect the metabolic challenges of hibernation, long-distance migration, and seasonal variation in prey abundance. The genomic distribution of positively selected genes differed significantly from background expectation by discrete Kolmogorov–Smirnov test (P < 0.001). Remarkably, the top three physical clusters all coincided with islands of conserved synteny predating Mammalia, the largest of which shares synteny with the human cat-eye critical region (CECR) on 22q11. This observation coupled with the expansion of a novel Tbx1-like gene family may indicate evolutionary innovation during pharyngeal arch development: both the CECR and Tbx1 cause dosage-dependent congenital abnormalities in thymus, heart, and head, and craniodysmorphy is associated with human orthologs of other positively selected genes as well.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.13130","usgsCitation":"Cornman, R.S., and Cryan, P.M., 2022, Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: Prominence of thymus expression, immune and metabolic function, and regions of ancient synteny: PeerJ, v. 10, e13130, 39 p., https://doi.org/10.7717/peerj.13130.","productDescription":"e13130, 39 p.","ipdsId":"IP-132867","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448401,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.13130","text":"Publisher Index Page"},{"id":435914,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OZAGYU","text":"USGS data release","linkHelpText":"Gene annotations for the hoary bat (Lasiurus [Aeorestes] cinereus) and alignments with other bat gene sets for evolutionary analysis"},{"id":397455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"editors":[{"text":"Meegaskumbura, Madhava","contributorId":289186,"corporation":false,"usgs":false,"family":"Meegaskumbura","given":"Madhava","email":"","affiliations":[],"preferred":false,"id":838650,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838601,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229989,"text":"70229989 - 2022 - Mass balance of two perennial snowfields: Niwot Ridge, Colorado and the Ulaan Taiga, Mongolia.","interactions":[],"lastModifiedDate":"2022-03-23T14:11:47.712745","indexId":"70229989","displayToPublicDate":"2022-03-23T08:59:12","publicationYear":"2022","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":"Mass balance of two perennial snowfields: Niwot Ridge, Colorado and the Ulaan Taiga, Mongolia.","docAbstract":"Perennial snowfields are generally receding worldwide, though the precise mechanisms causing recessions are not always well understood. Here we apply a numerical snowpack model to identify the leading factors controlling the mass balance of two perennial snowfields that have significant human interest: Arapaho glacier, located at Niwot Ridge in the Colorado Rocky Mountains (United States), and a snowfield located in the Ulaan Taiga (Mongolia). The two locations were chosen because they differ in elevation, slope and aspect. However, both have sub-arctic climates and are located within semi-arid regions. We show that for these two locations the snowfield mass balance is primarily sensitive to air temperature and wind speed, followed by precipitation and dust deposition amounts. We find that the sensitivities are similar for the center of the snowfield as well as the margins.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230430.2022.2027591","usgsCitation":"Williams, K.E., McKay, C.P., Toon, O.B., and Jennings, K.S., 2022, Mass balance of two perennial snowfields: Niwot Ridge, Colorado and the Ulaan Taiga, Mongolia.: Arctic, Antarctic, and Alpine Research, v. 54, no. 1, p. 41-61, https://doi.org/10.1080/15230430.2022.2027591.","productDescription":"21 p.","startPage":"41","endPage":"61","ipdsId":"IP-125219","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":448403,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15230430.2022.2027591","text":"Publisher Index Page"},{"id":397454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mongolia, United States","state":"Colorado","otherGeospatial":"Arapaho Glacier, Rocky Mountains, Ulaan Taiga Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.65105438232422,\n 40.02022014033094\n ],\n [\n -105.65028190612793,\n 40.019825755305554\n ],\n [\n -105.64877986907958,\n 40.02097603859142\n ],\n [\n -105.64865112304688,\n 40.022126302485596\n ],\n [\n -105.64830780029297,\n 40.02183052219348\n ],\n [\n -105.648136138916,\n 40.020680253312854\n ],\n [\n -105.64774990081787,\n 40.02074598348557\n ],\n [\n -105.64714908599852,\n 40.0214032817301\n ],\n [\n -105.64556121826172,\n 40.02107463339935\n ],\n [\n -105.64478874206543,\n 40.021731928477855\n ],\n [\n -105.64435958862305,\n 40.02206057364261\n ],\n [\n -105.64418792724608,\n 40.02166619925493\n ],\n [\n -105.64324378967285,\n 40.02192911576667\n ],\n [\n -105.64332962036133,\n 40.02258640261354\n ],\n [\n -105.64298629760742,\n 40.023046499638816\n ],\n [\n -105.64375877380371,\n 40.02330941083162\n ],\n [\n -105.64405918121338,\n 40.023802366587425\n ],\n [\n -105.64491748809814,\n 40.023703775721195\n ],\n [\n -105.64521789550781,\n 40.02327654698791\n ],\n [\n -105.64607620239258,\n 40.02350659356135\n ],\n [\n -105.64641952514648,\n 40.02373663935909\n ],\n [\n -105.64641952514648,\n 40.023966684381186\n ],\n [\n -105.64581871032713,\n 40.02390095731116\n ],\n [\n -105.64423084259033,\n 40.02524834959007\n ],\n [\n -105.64410209655762,\n 40.02567556597571\n ],\n [\n -105.64427375793457,\n 40.02620136708539\n ],\n [\n -105.64538955688477,\n 40.026431403796515\n ],\n [\n -105.64603328704834,\n 40.02616850463476\n ],\n [\n -105.6471061706543,\n 40.02606991718791\n ],\n [\n -105.64796447753906,\n 40.02603705467397\n ],\n [\n -105.65015316009521,\n 40.024361045471494\n ],\n [\n -105.65096855163574,\n 40.022652130949986\n ],\n [\n -105.65105438232422,\n 40.02022014033094\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 98.7945556640625,\n 50.40851753069726\n ],\n [\n 99.8602294921875,\n 50.40851753069726\n ],\n [\n 99.8602294921875,\n 51.68958500811337\n ],\n [\n 98.7945556640625,\n 51.68958500811337\n ],\n [\n 98.7945556640625,\n 50.40851753069726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Kaj E. 0000-0003-1755-1872 kewilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-1755-1872","contributorId":196988,"corporation":false,"usgs":true,"family":"Williams","given":"Kaj","email":"kewilliams@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":838596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKay, Christopher P.","contributorId":197097,"corporation":false,"usgs":false,"family":"McKay","given":"Christopher","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":838597,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Toon, Owen B. 0000-0002-1394-3062","orcid":"https://orcid.org/0000-0002-1394-3062","contributorId":289134,"corporation":false,"usgs":false,"family":"Toon","given":"Owen","email":"","middleInitial":"B.","affiliations":[{"id":12502,"text":"University of Colorado - Boulder","active":true,"usgs":false}],"preferred":false,"id":838598,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennings, Keith S. 0000-0002-4660-1472","orcid":"https://orcid.org/0000-0002-4660-1472","contributorId":289136,"corporation":false,"usgs":false,"family":"Jennings","given":"Keith","email":"","middleInitial":"S.","affiliations":[{"id":36969,"text":"Lynker Technologies","active":true,"usgs":false}],"preferred":false,"id":838599,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230158,"text":"70230158 - 2022 - Geophysical imaging of the Yellowstone hydrothermal plumbing system","interactions":[],"lastModifiedDate":"2022-04-11T11:01:05.08063","indexId":"70230158","displayToPublicDate":"2022-03-23T08:53:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical imaging of the Yellowstone hydrothermal plumbing system","docAbstract":"The nature of Yellowstone National Park’s plumbing system linking deep thermal fluids to its legendary thermal features is virtually unknown. The prevailing concepts of Yellowstone hydrology and chemistry are that fluids reside in reservoirs with unknown geometries, flow laterally from distal sources and emerge at the edges of lava flows. Here we present a high-resolution synoptic view of pathways of the Yellowstone hydrothermal system derived from electrical resistivity and magnetic susceptibility models of airborne geophysical data. Groundwater and thermal fluids containing appreciable total dissolved solids significantly reduce resistivities of porous volcanic rocks and are differentiated by their resistivity signatures. Clay sequences mapped in thermal areas and boreholes typically form at depths of less than 1,000 metres over fault-controlled thermal fluid and/or gas conduits. We show that most thermal features are located above high-flux conduits along buried faults capped with clay that has low resistivity and low susceptibility. Shallow subhorizontal pathways feed groundwater into basins that mixes with thermal fluids from vertical conduits. These mixed fluids emerge at the surface, controlled by surficial permeability, and flow outwards along deeper brecciated layers. These outflows, continuing between the geyser basins, mix with local groundwater and thermal fluids to produce the observed geochemical signatures. Our high-fidelity images inform geochemical and groundwater models for hydrothermal systems worldwide.
","language":"English","publisher":"Nature","doi":"10.1038/s41586-021-04379-1","usgsCitation":"Finn, C., Bedrosian, P.A., Holbrook, W.S., Auken, E., Bloss, B.R., and Crosbie, K.J., 2022, Geophysical imaging of the Yellowstone hydrothermal plumbing system: Nature, v. 603, p. 643-647, https://doi.org/10.1038/s41586-021-04379-1.","productDescription":"5 p.","startPage":"643","endPage":"647","ipdsId":"IP-126049","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":448406,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1038/s41586-021-04379-1","text":"External Repository"},{"id":435916,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LVAU7W","text":"USGS data release","linkHelpText":"Airborne Electromagnetic Survey Processed Data and Models Data Release, Yellowstone National Park, Wyoming, 2016"},{"id":435915,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MCJ9B6","text":"USGS data release","linkHelpText":"Airborne Electromagnetic and Magnetic Survey, Yellowstone National Park, 2016 - Minimally Processed Data"},{"id":397933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.1,\n 44.25\n ],\n [\n -110.25,\n 44.25\n ],\n [\n -110.25,\n 45\n ],\n [\n -111.1,\n 45\n ],\n [\n -111.1,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"603","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":229711,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, W. Steven","contributorId":175481,"corporation":false,"usgs":false,"family":"Holbrook","given":"W.","email":"","middleInitial":"Steven","affiliations":[],"preferred":false,"id":839333,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auken, Esben","contributorId":193991,"corporation":false,"usgs":false,"family":"Auken","given":"Esben","email":"","affiliations":[],"preferred":false,"id":839334,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bloss, Benjamin R. 0000-0002-1678-8571 bbloss@usgs.gov","orcid":"https://orcid.org/0000-0002-1678-8571","contributorId":139981,"corporation":false,"usgs":true,"family":"Bloss","given":"Benjamin","email":"bbloss@usgs.gov","middleInitial":"R.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839335,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crosbie, Kayla J 0000-0002-2724-1264","orcid":"https://orcid.org/0000-0002-2724-1264","contributorId":289565,"corporation":false,"usgs":true,"family":"Crosbie","given":"Kayla","email":"","middleInitial":"J","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":839336,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238338,"text":"70238338 - 2022 - Spatially integrating microbiology and geochemistry to reveal complex environmental health issues: Anthrax in the contiguous United States","interactions":[],"lastModifiedDate":"2022-11-17T12:39:57.630159","indexId":"70238338","displayToPublicDate":"2022-03-22T06:38:24","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Spatially integrating microbiology and geochemistry to reveal complex environmental health issues: Anthrax in the contiguous United States","docAbstract":"Maxent models were run using the B. anthracis presence data and/or the animal outbreak presence data. Models run using the animal outbreak data alone utilized two scales: the Outbreak State scale which included only states reporting animal anthrax outbreaks from 2001 to 2013 and the National scale which included all states in the contiguous United States. Three iterations of the environmental data were used and included the Sample Location dataset which utilized the environmental variable data with assigned latitude and longitude locations from the USGS NASGLP project; the Normalized dataset which scaled the environmental variables so that the values fell between 0 and 1; and the Interpolated dataset which provided an interpolation of the environmental variables averaged for each county and assigned to a point for that county at the centroid (rather than using the NASGLP latitude and longitude location). Two metrics were used to measure model performance including the widely used area under the curve (AUC) and an alternative method, the True Skill Statistic (TSS). The AUC gives the probability that a randomly chosen presence location has been correctly ranked higher than the absence/background site. AUC values at 0.5 or lower mean the ranking is no better than random, while the AUC values nearer to 1 mean the model is a better predictor. The TSS provides a comparison of how well the background predictions made by the model match the model results at the test dataset (presence) locations. TSS values near +1 means the model approaches perfect agreement, while values near −1 indicate the model is no better than random.
Maxent models to determine the influence of environmental factors on the B. anthracis distribution using the PCR data yielded a low TSS, which suggested the model might be underfitting the data. This was not surprising due to the difficulty in recovering B. anthracis in soil samples as well as the samples themselves being discrete in nature and only capturing a snapshot in time. Therefore, the distribution of B. anthracis and its niche in the contiguous United States could not be determined in this study. However, efforts to investigate environmental factors that would have a higher potential of supporting an anthrax outbreak in wildlife and livestock yielded better results. Results showed that most of the Maxent models in this study performed best when using the Outbreak State scale. When the models were scaled up to the National scale, model performance declined, except for the Normalized variable dataset. At the Outbreak State scale, a large proportion of the area was predicted to be of higher probability for wildlife/livestock anthrax outbreaks, and the statistical measures assumed the model was underfitting the data. The model with the highest AUC and TSS scores for this study was the Outbreak State scale using Sample Location dataset (AUC = 0.918 and TSS = 0.82). Some of the variables found to be closely related to the occurrence of B. anthracis in this study included pH, drainage potential, and concentration of elements including Na, Ca, Sr, and Mg, which have also been found to be related to animal outbreaks or to the occurrence of B. anthracis in previous studies.
The models in the current study indicated possible regions that have not had recent wildlife/livestock anthrax outbreaks but contained environmental conditions that could potentially support an outbreak if one were to occur (Michigan and Maine). This work provides an extension to the use of ecological niche modeling to outbreak potential in livestock/wildlife in the United States because it utilizes additional soil geochemistry data and has shown that further validation techniques, such as the TSS, should be considered in addition to AUC. Results from this study could be used by animal and public health officials to identify areas with a higher potential for anthrax outbreak in wildlife and livestock due to naturally occurring soil and environmental conditions.
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,{"id":70229831,"text":"ofr20221020 - 2022 - Chandeleur Islands to Breton Island bathymetric and topographic datasets and operational sediment budget development: Methodology and analysis report","interactions":[],"lastModifiedDate":"2026-03-27T20:00:27.171886","indexId":"ofr20221020","displayToPublicDate":"2022-03-21T15:25:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1020","displayTitle":"Chandeleur Islands to Breton Island Bathymetric and Topographic Datasets and Operational Sediment Budget Development: Methodology and Analysis Report","title":"Chandeleur Islands to Breton Island bathymetric and topographic datasets and operational sediment budget development: Methodology and analysis report","docAbstract":"This study is part of the Coastal Protection and Restoration Authority (CPRA) Louisiana Barrier Island Comprehensive Monitoring (BICM) program. The goal of the BICM program is to provide long-term data on the barrier islands of Louisiana for monitoring change and assisting in coastal management. The BICM program uses historical data and acquires new data to map and monitor shoreline position, sediment properties, topography, bathymetry, and habitat. Since 2006, the U.S. Geological Survey (USGS) has collected geophysical and sedimentologic data across the Breton National Wildlife Refuge (BNWR) through the BICM program and collaborative USGS projects such as the Barrier Island Evolution Research project (under CPRA contract number 2000339324, BICM2–Chandeleurs TopoBathy DEM), which builds upon the previous BICM physical assessment of the BNWR outlined in a separate report. This project uses topographic and bathymetric data from three periods (1917–1922, 2006–2007, and 2013–2015) to develop digital elevation models (DEMs), measure elevation change, and calculate sediment budgets for the barrier island system. The sediment budget analysis, derived from the volumetric change between the three periods, is necessary for understanding sediment transport dynamics along barrier islands and providing information for effective coastal management. This report describes the methods used to acquire, process, and produce these products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221020","collaboration":"Prepared in cooperation with the Coastal Protection and Restoration Authority of Louisiana","programNote":"Louisiana Barrier Island Comprehensive Monitoring Program 2015–2020","usgsCitation":"Flocks, J.G., Forde, A.S., and Bernier, J.C., 2022, Chandeleur Islands to Breton Island bathymetric and topographic datasets and operational sediment budget development—Methodology and analysis report: U.S. Geological Survey Open-File Report 2022–1020, 48 p., https://doi.org/10.3133/ofr20221020.","productDescription":"ix, 48 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122915","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":397352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231020/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397307,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1020/coverthb.jpg"},{"id":397308,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1020/ofr20221020.pdf","text":"Report","size":"47.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1020"},{"id":397309,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1020/images/"},{"id":397310,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1020/ofr20221020.XML"},{"id":501765,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112713.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Island, Breton National Wildlife Refuge, Chandeleur Islands, Curlew Shoals, Grand Gosier Shoals, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.22477722167967,\n 29.351656186711196\n ],\n [\n -88.83064270019531,\n 29.438999582891338\n ],\n [\n -88.61228942871094,\n 29.685070141332993\n ],\n [\n -88.59992980957031,\n 29.956124387148986\n ],\n [\n -88.72833251953125,\n 30.19439868711761\n ],\n [\n -89.09431457519531,\n 30.064934211006477\n ],\n [\n -89.00230407714844,\n 29.854341876042557\n ],\n [\n -89.14306640625,\n 29.664189403696138\n ],\n [\n -89.36073303222656,\n 29.467101009006807\n ],\n [\n -89.22477722167967,\n 29.351656186711196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates). These relations and rates provide insight into populations and inputs for other models. For example, growth curves may vary across lakes showing fish populations differ due to management actions or underlying environmental conditions. A fisheries manager could use this information to set lake-specific harvest limits or an ecologist could use this information to test scientific hypotheses about fish populations. The above example also demonstrates how populations exist within hierarchical structures where sub-populations may be nested within a meta-population. More generally, these hierarchical structures may be both biological (e.g., different lakes or river pools) and statistical (e.g., correlated error structures). Currently, limited options exist for fitting these hierarchical models and people seeking to use them often must program their own implementations. Furthermore, many fisheries managers and researchers may not have Bayesian programming skills, but many can use interactive languages such as R. Additionally, programs such as JAGS often require long run times (e.g., hours if not days) to fit hierarchical models and programs such as Stan can be more difficult to program because it is a compiled language. We created fishStan to share hierarchical models for fisheries and ecology in an easy-to-use R package.
","language":"English","publisher":"Open Journals","doi":"10.21105/joss.03444","usgsCitation":"Erickson, R.A., Stich, D.S., and Hebert, J.L., 2022, FishStan: Hierarchical Bayesian models for fisheries: Journal of Open Source Software, v. 7, no. 71, 3444, 2 p., https://doi.org/10.21105/joss.03444.","productDescription":"3444, 2 p.","ipdsId":"IP-125667","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":448415,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/joss.03444","text":"Publisher Index Page"},{"id":397534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"71","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":838685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":838686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hebert, Jillian Lee 0000-0003-4893-8287","orcid":"https://orcid.org/0000-0003-4893-8287","contributorId":289197,"corporation":false,"usgs":true,"family":"Hebert","given":"Jillian","email":"","middleInitial":"Lee","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":838687,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229832,"text":"ofr20221010 - 2022 - Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","interactions":[],"lastModifiedDate":"2026-03-27T19:46:42.747184","indexId":"ofr20221010","displayToPublicDate":"2022-03-21T10:33:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1010","displayTitle":"Documentation of Models Describing Relations Between Continuous Real-Time and Discrete Water-Quality Constituents in the Little Arkansas River, South-Central Kansas, 1998–2019","title":"Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019","docAbstract":"Data were collected at two monitoring sites along the Little Arkansas River in south-central Kansas that bracket most of the easternmost part of the Equus Beds aquifer. The data were used as part of the city of Wichita’s aquifer storage and recovery project to evaluate source water quality. The U.S. Geological Survey, in cooperation with the City of Wichita, has continued to monitor the water quality of these sites through 2019 to update previously published regression-based models using continuously measured physicochemical properties and discretely sampled water-quality constituents of interest. The purpose of this report is to provide an update of the previously published linear regression models that have been used to continuously compute estimates of water-quality constituent concentrations or densities at these two sites. Water-quality constituent model updates include those for dissolved and suspended solids, suspended-sediment concentration, hardness, alkalinity, primary ions (bicarbonate, calcium, sodium, chloride, and sulfate), nutrients (total Kjeldahl nitrogen and total phosphorus), total organic carbon, indicator bacteria (Escherichia coli and fecal coliform bacteria), a trace element (arsenic), and a pesticide (atrazine).
Regression analyses were used to develop surrogate models that related continuously measured physicochemical properties, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Specific conductance was an explanatory variable for dissolved solids, primary ions, and atrazine. Turbidity was an explanatory variable for total suspended solids and sediment, nutrients, total organic carbon, and indicator bacteria. Streamflow and water temperature were explanatory variables for dissolved arsenic. Seasonal components were included as explanatory variables for atrazine models. The amount of variance explained by most of the updated models was within 5 percent of previously published models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221010","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2022, Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019: U.S. Geological Survey Open-File Report 2022–1010, 34 p., https://doi.org/10.3133/ofr20221010.","productDescription":"Report: vii, 34 p.; 2 Appendixes; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-126572","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":397345,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221010/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397333,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397331,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River at Highway 50 near Halstead, Kansas (Halstead Site; U.S. Geological Survey Station Number 07143672)"},{"id":501756,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112715.htm","linkFileType":{"id":5,"text":"html"}},{"id":397330,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1010/images"},{"id":397332,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River near Sedgwick, Kansas (Sedgwick Site; U.S. Geological Survey Station Number 07144100)"},{"id":397329,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.XML"},{"id":397328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1010"},{"id":397327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1010/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 38.533333\n ],\n [\n -98.1667,\n 38.533333\n ],\n [\n -98.1667,\n 37.714244967649265\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Habitat loss is the most prevalent threat to biodiversity in North America. One of the most threatened landscapes in the United States is the sagebrush (Artemisia spp.) ecosystem, much of which has been fragmented or converted to non-native grasslands via the cheatgrass-fire cycle. Like many sagebrush obligates, greater sage-grouse (Centrocercus urophasianus) depend upon sagebrush for food and cover and are affected by changes to this ecosystem. We investigated habitat selection by 28 male greater sage-grouse during each of 3 years after a 113,000-ha wildfire in a sagebrush steppe ecosystem in Idaho and Oregon. During the study period, seeding and herbicide treatments were applied for habitat restoration. We evaluated sage-grouse responses to vegetation and post-fire restoration treatments. Throughout the 3 years post-fire, sage-grouse avoided areas with high exotic annual grass cover but selected strongly for recovering sagebrush and moderately strongly for perennial grasses. By the third year post-fire, they preferred high-density sagebrush, especially in winter when sagebrush is the primary component of the sage-grouse diet. Sage-grouse preferred forb habitat immediately post-fire, especially in summer, but this selection preference was less strong in later years. They also selected areas that were intensively treated with herbicide and seeded with sagebrush, grasses, and forbs, although these responses varied with time since treatment. Wildfire can have severe consequences for sagebrush-obligate species due to loss of large sagebrush plants used for food and for protection from predators and thermal extremes. Our results show that management efforts, including herbicide application and seeding of plants, directed at controlling exotic annual grasses after a wildfire can positively affect habitat selection by sage-grouse.
Coregonine fishes are important to Laurentian Great Lakes food webs and fisheries and are central to basin-wide conservation initiatives. In Lake Ontario, binational management objectives include conserving and restoring spawning stocks of cisco (Coregonus artedi) and lake whitefish (C. clupeaformis), but the spatial extent of contemporary coregonine spawning habitat and the environmental factors regulating early life success are not well characterized. In Spring 2018, we conducted a binational ichthyoplankton assessment to describe the spatial extent of coregonine spawning habitat across Lake Ontario. We then quantified the relative importance of a suite of biophysical variables hypothesized to influence coregonine early life success using generalized additive mixed models and multimodel inference. Between April 10 and May 14, we conducted 1,092 ichthyoplankton tows and captured 2,350+ coregonine larvae across 17 sampling areas, predominantly within embayments. Although 95% of catches were in the eastern basin, coregonine larvae were also found in historical south shore spawning areas. Most coregonine larvae were cisco; <6% were lake whitefish. Observed catches of both species across sampling areas were strongly and similarly associated with ice cover duration, but the importance of site-specific characteristics varied, such as distance to shore and site depth for cisco and lake whitefish, respectively. These results suggest that regional-scale climatic drivers and local environmental habitat characteristics interact to regulate early life stage success. Furthermore, strong regional and cross-species variation in larval distributions emphasize the importance of lake-wide assessments for monitoring both the current eastern basin populations and potential expansions into western Lake Ontario habitats.
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Evans, Cooper, A., Reinhart, D., Cameron David, and Weidel, B., 2022, Contemporary spatial extent and environmental drivers of larval coregonine distributions across Lake Ontario: Journal of Great Lakes Research, v. 48, no. 2, p. 359-370, https://doi.org/10.1016/j.jglr.2021.07.009.","productDescription":"12 p.","startPage":"359","endPage":"370","ipdsId":"IP-126583","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.925537109375,\n 43.265206318396025\n ],\n [\n -79.8101806640625,\n 43.281204464332745\n ],\n [\n -79.5904541015625,\n 43.18515250937298\n ],\n [\n -79.3597412109375,\n 43.16512263158296\n ],\n [\n -79.1839599609375,\n 43.193162620926074\n ],\n [\n -78.9532470703125,\n 43.27320591705845\n ],\n [\n -78.6236572265625,\n 43.329173667843904\n ],\n [\n -78.4259033203125,\n 43.369119087738554\n ],\n [\n -78.189697265625,\n 43.35713822211053\n ],\n [\n -78.123779296875,\n 43.35713822211053\n ],\n [\n -77.95898437499999,\n 43.35314407444698\n ],\n [\n -77.750244140625,\n 43.32517767999296\n ],\n [\n -77.596435546875,\n 43.23719944365308\n ],\n [\n -77.51953125,\n 43.197167282501276\n ],\n [\n -77.5140380859375,\n 43.241201214257885\n ],\n [\n -77.3602294921875,\n 43.26920624914964\n ],\n [\n -77.14050292968749,\n 43.27720532212024\n ],\n [\n -77.01416015625,\n 43.26120612479979\n ],\n [\n -76.89880371093749,\n 43.213183300738876\n ],\n [\n -76.904296875,\n 43.26920624914964\n ],\n [\n -76.7340087890625,\n 43.32517767999296\n ],\n [\n -76.6461181640625,\n 43.37311218382002\n ],\n [\n -76.5966796875,\n 43.42898792344155\n ],\n [\n -76.48681640625,\n 43.48082639482503\n ],\n [\n -76.4208984375,\n 43.50872101129684\n ],\n [\n -76.365966796875,\n 43.52465500687185\n ],\n [\n -76.31103515625,\n 43.51270490464819\n ],\n [\n -76.2396240234375,\n 43.51270490464819\n ],\n [\n -76.17919921875,\n 43.624147145668076\n ],\n [\n -76.1407470703125,\n 43.6599240747891\n ],\n [\n -76.1846923828125,\n 43.691707903073805\n ],\n [\n -76.1956787109375,\n 43.78299262890581\n ],\n [\n -76.201171875,\n 43.8503744993026\n ],\n [\n -76.0968017578125,\n 43.91768033000405\n ],\n [\n -75.9979248046875,\n 44.008620115415354\n ],\n [\n -76.1297607421875,\n 44.08363928284644\n ],\n [\n -76.300048828125,\n 44.14279782818058\n ],\n [\n -76.3934326171875,\n 44.17826452922573\n ],\n [\n -76.453857421875,\n 44.25700308645885\n ],\n [\n -76.629638671875,\n 44.25700308645885\n ],\n [\n -76.8109130859375,\n 44.17038488259618\n ],\n [\n -76.97021484375,\n 44.08758502824516\n ],\n [\n -77.069091796875,\n 44.071800467511565\n ],\n [\n -77.069091796875,\n 43.96514454266273\n ],\n [\n -77.080078125,\n 43.88997537383687\n ],\n [\n -77.3162841796875,\n 43.96119063892024\n ],\n [\n -77.4151611328125,\n 43.96909818325171\n ],\n [\n -77.574462890625,\n 44.06390660801779\n ],\n [\n -77.7337646484375,\n 44.040218713142146\n ],\n [\n -78.01391601562499,\n 44.004669106432225\n ],\n [\n -78.33251953125,\n 43.95328204198018\n ],\n [\n -78.5137939453125,\n 43.90185050527358\n ],\n [\n -78.7664794921875,\n 43.88997537383687\n ],\n [\n -78.9862060546875,\n 43.862257524417934\n ],\n [\n -79.1015625,\n 43.81471121600004\n ],\n [\n -79.2279052734375,\n 43.723474896114794\n ],\n [\n -79.3377685546875,\n 43.65197548731187\n ],\n [\n -79.4805908203125,\n 43.644025847699496\n ],\n [\n -79.5684814453125,\n 43.56845179881218\n ],\n [\n -79.617919921875,\n 43.52465500687185\n ],\n [\n -79.6343994140625,\n 43.464880828929545\n ],\n [\n -79.7113037109375,\n 43.37710501700073\n ],\n [\n -79.82666015625,\n 43.329173667843904\n ],\n [\n -79.925537109375,\n 43.265206318396025\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor A. Brown","contributorId":348419,"corporation":false,"usgs":false,"family":"Taylor A. Brown","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lars G. Rudstam","contributorId":348420,"corporation":false,"usgs":false,"family":"Lars G. Rudstam","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jeremy P. 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Evans","affiliations":[{"id":83364,"text":"St. Mary's College","active":true,"usgs":false}],"preferred":false,"id":923453,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cooper, Amanda","contributorId":348575,"corporation":false,"usgs":false,"family":"Cooper","given":"Amanda","affiliations":[],"preferred":false,"id":923606,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Reinhart, Daren J.","contributorId":348576,"corporation":false,"usgs":false,"family":"Reinhart","given":"Daren J.","affiliations":[],"preferred":false,"id":923607,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Cameron David","contributorId":348436,"corporation":false,"usgs":false,"family":"Cameron David","affiliations":[{"id":62863,"text":"Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":923455,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923454,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70229973,"text":"70229973 - 2022 - Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan","interactions":[],"lastModifiedDate":"2022-03-21T15:08:43.6858","indexId":"70229973","displayToPublicDate":"2022-03-20T09:43:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan","docAbstract":"Attempts to mitigate shoreline microbial contamination require a thorough understanding of pollutant sources, which often requires multiple years of data collection (e.g., point/nonpoint) and the interacting factors that influence water quality. Because restoration efforts can alter shoreline or beach morphology, revisiting source inputs is often necessary. Microbial source tracking (MST) using source-specific molecular markers, genomic community analyses, and physical modeling was used to identify contamination sources along three Lake Michigan beaches of the Laurentian Great Lakes with historically high fecal indicator bacteria (FIB, E. coli) concentrations. Genetic markers for human (Bacteroides HF183) and mixed gull species (Catellicoccus marimammalium) fecal sources were tested from water and sediment. Gene sequencing (16S rRNA) was used to identify similarities in bacterial communities in nearshore water, river inputs, sand, sediment, and groundwater. Synoptic surveys of water exchange were conducted to determine nearshore-offshore interactions of FIB. In addition to these MST studies, best management practices to mitigate FIB, including gull deterrence, slope grading, wetland establishment, and shoreline plantings, were reviewed for their effectiveness at reducing FIB concentrations over time. Using multiple tools for MST helped identify primary and secondary sources of FIB (gulls, stormwater inputs) and the physical processes that exacerbate FIB concentrations (onshore currents, limited circulation). Management actions were successful in the short-term at reducing FIB, but scope of success was temporally limited, with FIB concentrations often rebounding. Results highlight the usefulness of MST to inform best management practices and the need for a sustained adaptive approach that adjusts for changes in the coastal system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.01.009","usgsCitation":"Nevers, M., Buszka, P.M., Byappanahalli, M., Cole, T., Corsi, S., Jackson, P.R., Kinzelman, J.L., Nakatsu, C.H., and Phanikumar, M.S., 2022, Microbial source tracking and evaluation of best management practices for restoring degraded beaches of Lake Michigan: Journal of Great Lakes Research, v. 48, no. 2, p. 441-454, https://doi.org/10.1016/j.jglr.2022.01.009.","productDescription":"14 p.","startPage":"441","endPage":"454","ipdsId":"IP-127246","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2022.01.009","text":"Publisher Index Page"},{"id":397344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Wisconsin","city":"Chicago, Racine","otherGeospatial":"Jeorse Park Beach, Lake Michigan, North Beach, 63rd Street Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.9345703125,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 41.60928183876483\n ],\n [\n -86.7095947265625,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 42.84777884235988\n ],\n [\n -87.9345703125,\n 41.60928183876483\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buszka, Paul M. 0000-0001-8218-826X pmbuszka@usgs.gov","orcid":"https://orcid.org/0000-0001-8218-826X","contributorId":1786,"corporation":false,"usgs":true,"family":"Buszka","given":"Paul","email":"pmbuszka@usgs.gov","middleInitial":"M.","affiliations":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":838535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Travis 0000-0002-0935-381X","orcid":"https://orcid.org/0000-0002-0935-381X","contributorId":289099,"corporation":false,"usgs":false,"family":"Cole","given":"Travis","affiliations":[{"id":62047,"text":"Crane Environmental Services, LLC","active":true,"usgs":false}],"preferred":false,"id":838536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838538,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kinzelman, Julie L.","contributorId":236944,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","email":"","middleInitial":"L.","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":838539,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nakatsu, Cindy H 0000-0003-0663-180X","orcid":"https://orcid.org/0000-0003-0663-180X","contributorId":215593,"corporation":false,"usgs":false,"family":"Nakatsu","given":"Cindy","email":"","middleInitial":"H","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":838540,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Phanikumar, Mantha S.","contributorId":147924,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":838541,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249395,"text":"70249395 - 2022 - Anthropogenic stressors compound climate impacts on inland lake dynamics: The case of Hamun Lakes","interactions":[],"lastModifiedDate":"2023-10-05T12:28:37.214715","indexId":"70249395","displayToPublicDate":"2022-03-19T07:26:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17043,"text":"Science of the Total Envionrment","active":true,"publicationSubtype":{"id":10}},"title":"Anthropogenic stressors compound climate impacts on inland lake dynamics: The case of Hamun Lakes","docAbstract":"Inland lakes face unprecedented pressures from climatic and anthropogenic stresses, causing their recession and desiccation globally. Climate change is increasingly blamed for such environmental degradation, but in many regions, direct anthropogenic pressures compound, and sometimes supersede, climatic factors. This study examined a human-environmental system – the terminal Hamun Lakes on the Iran-Afghanistan border – that embodies amplified challenges of inland waters. Satellite and climatic data from 1984 to 2019 were fused, which documented that the Hamun Lakes lost 89% of their surface area between 1999 and 2001 (3809 km2 versus 410 km2), coincident with a basin-wide, multi-year meteorological drought. The lakes continued to shrink afterwards and desiccated in 2012, despite the above-average precipitation in the upstream basin. Rapid growth in irrigated agricultural lands occurred in upstream Afghanistan in the recent decade, consuming water that otherwise would have fed the Hamun Lakes. Compounding upstream anthropogenic stressors, Iran began storing flood water that would have otherwise drained to the lakes, for urban and agricultural consumption in 2009. Results from a deep Learning model of Hamun Lakes' dynamics indicate that the average lakes' surface area from 2010 to 2019 would have been 2.5 times larger without increasing anthropogenic stresses across the basin. The Hamun Lakes' desiccation had major socio-environmental consequences, including loss of livelihood, out-migration, dust-storms, and loss of important species in the region.
Secretive marsh birds (SMBs) are important indicator species of coastal wetlands but are difficult to detect and monitor. In coastal Louisiana, an important stronghold for these species, climate and hydrological models predict that freshwater and intermediate marshes will expand in the next 50 years, while brackish marshes will shrink. We used a multi-species Bayesian hierarchical occupancy model to estimate detection and occupancy probabilities for 11 SMB species in low and high saline marshes using data from automated recording units at 33 sites in southwestern Louisiana from February–June 2012. A quadratic effect of Julian date, but not minimum daily temperature nor precipitation affected detection of SMB species. King Rail (Rallus elegans), American Bittern (Botaurus lentiginosus), Common Gallinule (Gallinula galeata), and Pied-billed Grebe (Podilymbus podiceps) occupied mainly freshwater and intermediate marshes. Clapper Rail (Rallus crepitans), Seaside Sparrow (Ammospiza maritima), and Sora (Porzana carolina) predominantly occupied brackish and salt marshes. American Coot (Fulica americana), Purple Gallinule (Porphyrio martinica), Least Bittern (Ixobrychus exilis), and Marsh Wren (Cistothorus palustris) occupied both low and high saline marshes, showing flexibility that could maintain populations of these species as marsh salinities change in the future. If the current distribution of SMB species persists as marsh availability changes under future conditions, populations of the 4 species we found in low saline marshes may increase, whereas populations of at least 2 species found primarily in high saline marshes may decrease. Our modeling indicates that automatic recording units can produce comparable detection probabilities to other studies using traditional SMB sampling methods.
Climate change is thawing and potentially mobilizing vast quantities of organic carbon (OC) previously stored for millennia in permafrost soils of northern circumpolar landscapes. Climate-driven increases in fire and thermokarst may play a key role in OC mobilization by thawing permafrost and promoting transport of OC. Yet, the extent of OC mobilization and mechanisms controlling terrestrial-aquatic transfer are unclear. We demonstrate that hydrologic transport of soil dissolved OC (DOC) from the active layer and thawing permafrost to headwater streams is extremely heterogeneous and regulated by the interactions of soils, seasonal thaw, fire, and thermokarst. Repeated sampling of streams in eight headwater catchments of interior Alaska showed that the mean age of DOC for each stream ranges widely from modern to ∼2,000 years B.P. Together, an endmember mixing model and nonlinear, generalized additive models demonstrated that Δ14C-DOC signature (and mean age) increased from spring to fall, and was proportional to hydrologic contributions from a solute-rich water source, related to presumed deeper flow paths found predominantly in silty catchments. This relationship was correlated with and mediated by catchment properties. Mean DOC ages were older in catchments with >50% burned area, indicating that fire is also an important explanatory variable. These observations underscore the high heterogeneity in aged C export and difficulty of extrapolating estimates of permafrost-derived DOC export from watersheds to larger scales. Our results provide the foundation for developing a conceptual model of permafrost DOC export necessary for advancing understanding and prediction of land-water C exchange in changing boreal landscapes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GB007242","usgsCitation":"Koch, J.C., Bogard, M., Butman, D., Finlay, K., Ebel, B., James, J., Johnston, S.E., Jorgenson, M., Pastick, N., Spencer, R., Striegl, R., Walvoord, M.A., and Wickland, K., 2022, Heterogeneous patterns of aged organic carbon export driven by hydrologic flow paths, soil texture, fire, and thaw in discontinuous permafrost headwaters: Global Biogeochemical Cycles, v. 36, no. 4, e2021GB007242, 16 p., https://doi.org/10.1029/2021GB007242.","productDescription":"e2021GB007242, 16 p.","ipdsId":"IP-134558","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448441,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gb007242","text":"Publisher Index Page"},{"id":398213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151,\n 65\n ],\n [\n -147,\n 65\n ],\n [\n -147,\n 67\n ],\n [\n -151,\n 67\n ],\n [\n -151,\n 65\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":839768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bogard, Matthew","contributorId":272635,"corporation":false,"usgs":false,"family":"Bogard","given":"Matthew","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":839769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butman, David","contributorId":224754,"corporation":false,"usgs":false,"family":"Butman","given":"David","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":839770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Finlay, Kerri","contributorId":289777,"corporation":false,"usgs":false,"family":"Finlay","given":"Kerri","email":"","affiliations":[{"id":27547,"text":"University of Regina","active":true,"usgs":false}],"preferred":false,"id":839771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":839772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"James, Jason","contributorId":289778,"corporation":false,"usgs":false,"family":"James","given":"Jason","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":839773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnston, Sarah Ellen","contributorId":213256,"corporation":false,"usgs":false,"family":"Johnston","given":"Sarah","email":"","middleInitial":"Ellen","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":839774,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jorgenson, M. Torre","contributorId":202940,"corporation":false,"usgs":false,"family":"Jorgenson","given":"M. Torre","affiliations":[{"id":36554,"text":"Ecoscience","active":true,"usgs":false}],"preferred":false,"id":839775,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pastick, Neal 0000-0002-4321-6739","orcid":"https://orcid.org/0000-0002-4321-6739","contributorId":222683,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":839776,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Spencer, Rob 0000-0003-0860-4717","orcid":"https://orcid.org/0000-0003-0860-4717","contributorId":241050,"corporation":false,"usgs":false,"family":"Spencer","given":"Rob","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":839777,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Striegl, Rob 0000-0002-8251-4659","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":264605,"corporation":false,"usgs":false,"family":"Striegl","given":"Rob","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":839778,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":839779,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":206313,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":839780,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70229833,"text":"70229833 - 2022 - A comparison of eDNA and visual survey methods for detection of longnose darter Percina nasuta in Missouri","interactions":[],"lastModifiedDate":"2022-03-21T13:49:21.154022","indexId":"70229833","displayToPublicDate":"2022-03-18T08:46:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6476,"text":"Fishes","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A comparison of eDNA and visual survey methods for detection of longnose darter Percina nasuta in Missouri","title":"A comparison of eDNA and visual survey methods for detection of longnose darter Percina nasuta in Missouri","docAbstract":"The longnose darter Percina nasuta is a rare and cryptic fish that recently disappeared from much of its historic range. We developed and used an environmental DNA (eDNA) assay for longnose darter paired with visual surveys to better determine the species’ range and compare detection probability between sampling approaches in an occupancy modeling framework. We detected longnose darter eDNA further upstream in the mainstem St. Francis River than previously reported and in a tributary for the first time. Our multi-scale occupancy approach compared models where detection was constant against a model that allowed detection to vary by survey method. The constant model received the most support indicating survey method was not a strong predictor and detection was estimated at 0.70 (0.45–0.86; 95% CI) across both methods. Our study produced effective longnose darter eDNA primers and demonstrated the application of eDNA for sampling small-bodied, cryptic fish. We detected longnose darter eDNA 27 km upstream of their known range and determined that snorkel surveys are the most efficient sampling method if water clarity allows. We recommend target sample sizes to achieve various detection goals for both sample methods and our results inform future design of distributional and monitoring efforts.
","language":"English","publisher":"MPDI","doi":"10.3390/fishes7020070","usgsCitation":"Westhoff, J.T., Berkman, L.K., Klymus, K.E., Thompson, N., and Richter, C.A., 2022, A comparison of eDNA and visual survey methods for detection of longnose darter Percina nasuta in Missouri: Fishes, v. 7, no. 2, 70, 16 p., https://doi.org/10.3390/fishes7020070.","productDescription":"70, 16 p.","ipdsId":"IP-121698","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":448443,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes7020070","text":"Publisher Index Page"},{"id":435919,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z597LN","text":"USGS data release","linkHelpText":"Longnose darter (Percina nasuta) eDNA survey results from the St. Francis River, Missouri 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\"}}]}","volume":"7","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Westhoff, Jacob Thomas 0000-0002-2347-5098","orcid":"https://orcid.org/0000-0002-2347-5098","contributorId":288958,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"Thomas","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkman, Leah K.","contributorId":275349,"corporation":false,"usgs":false,"family":"Berkman","given":"Leah","email":"","middleInitial":"K.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":838494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838496,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838497,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229838,"text":"70229838 - 2022 - Errors in aerial survey count data: Identifying pitfalls and solutions","interactions":[],"lastModifiedDate":"2022-03-21T13:41:26.57231","indexId":"70229838","displayToPublicDate":"2022-03-18T08:34:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Errors in aerial survey count data: Identifying pitfalls and solutions","docAbstract":"Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.
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The virus was initially described in the 1960s after outbreaks occurred in Europe, but a global expansion of SVCV has been ongoing since the late 1990s. Genetic typing of SVCV isolates separates them into 4 genotypes that are correlated with geographic origin: Ia (Asia), Ib and Ic (Eastern Europe), and Id (Central Europe). We compared infectivity and virulence of 8 SVCV strains, including 4 uncharacterized Chinese Ia isolates and representatives of genotypes Ia-d in 2 morphologically distinct varieties of koi: long-fin semi-scaled Beni Kikokuryu koi and short-fin fully scaled Sanke koi. Mortality ranged from 4 to 82% in the Beni Kikokuryu koi and 0 to 94% in the Sanke koi following immersion challenge. Genotype Ia isolates of Asian origin had a wide range in virulence (0-94%). Single isolates representing the European genotypes Ib and Ic were moderately virulent (38-56%). Each virus strain produced similar levels of mortality in both koi breeds, with the exception of the SVCV Id strain that appeared to have both moderate and high virulence phenotypes (60% in Beni Kikokuryu koi vs. 87% in Sanke koi). Overall SVCV strain virulence appeared to be a dominant factor in determining disease outcomes, whereas intraspecies variation, based on koi variety, had less of an impact. This study is the first side-by-side comparison of Chinese SVCV isolates and genotype Ia-d strain virulence in a highly susceptible host.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03650","usgsCitation":"Emmenegger, E.J., Bueren, E.K., Jia, P., Hendrix, N., and Liu, H., 2022, Comparative virulence of spring viremia of carp virus (SVCV) genotypes in two koi varieties: Disease of Aquatic Organisms, v. 148, p. 95-112, https://doi.org/10.3354/dao03650.","productDescription":"18 p.","startPage":"95","endPage":"112","ipdsId":"IP-129753","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":448448,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.3354/dao03650","text":"External Repository"},{"id":435920,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WS6Q0M","text":"USGS data release","linkHelpText":"Comparative Virulence of Spring Viremia of Carp Virus (SVCV) Genotypes in Two Koi Varieties"},{"id":398121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Emmenegger, Eveline J. 0000-0001-5217-6030 eemmenegger@usgs.gov","orcid":"https://orcid.org/0000-0001-5217-6030","contributorId":2434,"corporation":false,"usgs":true,"family":"Emmenegger","given":"Eveline","email":"eemmenegger@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":839551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bueren, Emma K. 0000-0002-5738-3917","orcid":"https://orcid.org/0000-0002-5738-3917","contributorId":289657,"corporation":false,"usgs":false,"family":"Bueren","given":"Emma","email":"","middleInitial":"K.","affiliations":[{"id":62212,"text":"Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061","active":true,"usgs":false}],"preferred":false,"id":839552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jia, Peng","contributorId":191750,"corporation":false,"usgs":false,"family":"Jia","given":"Peng","email":"","affiliations":[],"preferred":false,"id":839553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hendrix, Noble","contributorId":289658,"corporation":false,"usgs":false,"family":"Hendrix","given":"Noble","email":"","affiliations":[{"id":62214,"text":"QEDA Consulting, 4007 Densmore Ave N, Seattle, WA 98103, USA","active":true,"usgs":false}],"preferred":false,"id":839554,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Hong","contributorId":191763,"corporation":false,"usgs":false,"family":"Liu","given":"Hong","email":"","affiliations":[],"preferred":false,"id":839555,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229808,"text":"70229808 - 2022 - Temporal greenness trends in stable natural land cover and relationships with climatic variability across the conterminous United States","interactions":[],"lastModifiedDate":"2022-03-17T13:30:53.219328","indexId":"70229808","displayToPublicDate":"2022-03-17T08:23:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Temporal greenness trends in stable natural land cover and relationships with climatic variability across the conterminous United States","docAbstract":"Assessment of temporal trends in vegetation greenness and related influences aids understanding of recent change in terrestrial ecosystems and feedbacks from weather, climate, and environment. We analyzed 1-km normalized difference vegetation index (NDVI) timeseries data (1989–2016) derived from the Advanced Very High Resolution Radiometer (AVHRR) and developed growing season time-integrated NDVI (GS-TIN) for estimating seasonal vegetation activity across stable natural land cover in the conterminous United States (CONUS). After removing areas from analysis that had experienced land cover conversion or modification, we conducted a monotonic trend analysis on the GS-TIN timeseries and found that significant positive temporal trends occurred over 35% of the area, while significant negative trends were observed over only 3.5%. Positive trends were prevalent in the forested lands of the eastern third of CONUS and far northwest, as well as in grasslands in the north central plains. We observed negative and nonsignificant trends mainly in the shrublands and grasslands across the northwest, southwest, and west central plains. To understand the relationship of climate variability with these temporal trends, we conducted partial and multiple correlation analyses on GS-TIN, growing season temperature, and water-year precipitation timeseries. The GS-TIN trends in northern forests were positively correlated with temperature. The GS-TIN trends in the central and western shrublands and grasslands were negatively correlated with temperature and positively correlated with precipitation. 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lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":838423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":838424,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236084,"text":"70236084 - 2022 - Regional-scale liquefaction analyses","interactions":[],"lastModifiedDate":"2022-08-30T10:53:02.18042","indexId":"70236084","displayToPublicDate":"2022-03-17T08:19:53","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Regional-scale liquefaction analyses","docAbstract":"Regional-scale liquefaction hazard analyses are necessary for resilience planning and prioritization of seismic upgrades for critical distributed infrastructure such as levees, pipelines, roadways, and electrical transmission facilities. Two approaches are often considered for liquefaction hazard analysis of distributed infrastructure: (1) conventional, site-specific probe or borehole-based analyses, which do not quantify the uncertainty between investigation locations; or (2) surface geology-based analyses, which often neglect localized geotechnical properties and include a great amount of uncertainty. We describe an analytical method to unify the disparate site-specific and deposit-scale approaches using Gaussian processes. We use borehole data to produce spatial fields of random variables for liquefaction triggering analyses, such as groundwater elevation, soil texture classification, penetration resistance, and cyclic resistance ratio that converge to the site-specific uncertainty at sampling locations but also quantify the uncertainty in-between sampling locations. We demonstrate the effectiveness of Gaussian process models for regional-scale liquefaction hazard analyses in two example studies in Washington state and California, US.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geo-Congress 2022: Geophysical and earthquake engineering and soil dynamics","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Geo-Congress 2022","conferenceDate":"March 20-23, 2022","conferenceLocation":"Charlotte, NC","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/9780784484043.039","usgsCitation":"Greenfield, M.W., and Grant, A.R., 2022, Regional-scale liquefaction analyses, in Geo-Congress 2022: Geophysical and earthquake engineering and soil dynamics, Charlotte, NC, March 20-23, 2022, p. 401-410, https://doi.org/10.1061/9780784484043.039.","productDescription":"10 p.","startPage":"401","endPage":"410","ipdsId":"IP-130480","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":405788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Greenfield, Michael W.","contributorId":267916,"corporation":false,"usgs":false,"family":"Greenfield","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":40903,"text":"Greenfield Geotechnical, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":849956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":849957,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70239283,"text":"70239283 - 2022 - Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions","interactions":[],"lastModifiedDate":"2023-01-06T12:40:06.995804","indexId":"70239283","displayToPublicDate":"2022-03-17T06:34:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions","docAbstract":"Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high-frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady-state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub-daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation-based approach to estimate the sub-daily flux under highly dynamic flow conditions using multi-depth temperature observations at a 5-min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi-depth temperature data at 1- or 2-hr time intervals assuming one-dimensional flow and heat transport governing equation. By comparing estimated fluxes with model-generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation-based flux estimation method was able to capture the vertical sub-daily fluxes using multi-depth high-resolution temperature data alone, even in the presence of multi-dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1-D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.