No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Shriver, L.C., 2024, Spring 2024 edition: RAMPS Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-166294","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":430572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430571,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cms.usgs.gov/centers/southwest-biological-science-center/news/ramps-newsletter-spring-2024","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Laura Cecilia 0009-0008-5567-0868","orcid":"https://orcid.org/0009-0008-5567-0868","contributorId":334175,"corporation":false,"usgs":true,"family":"Shriver","given":"Laura","email":"","middleInitial":"Cecilia","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":905032,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254662,"text":"70254662 - 2024 - Best practices for genetic and genomic data archiving","interactions":[],"lastModifiedDate":"2024-07-15T15:11:44.799023","indexId":"70254662","displayToPublicDate":"2024-05-24T09:33:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17794,"text":"Nature, Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Best practices for genetic and genomic data archiving","docAbstract":"Genetic and genomic data are collected for a vast array of scientific and applied purposes. Despite mandates for public archiving, data are typically used only by the generating authors. The reuse of genetic and genomic datasets remains uncommon because it is difficult, if not impossible, due to non-standard archiving practices and lack of contextual metadata. But as the new field of macrogenetics is demonstrating, if genetic data and their metadata were more accessible and FAIR (findable, accessible, interoperable and reusable) compliant, they could be reused for many additional purposes. We discuss the main challenges with existing genetic and genomic data archives, and suggest best practices for archiving genetic and genomic data. Recognizing that this is a longstanding issue due to little formal data management training within the fields of ecology and evolution, we highlight steps that research institutions and publishers could take to improve data archiving.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-024-02423-7","usgsCitation":"Leigh, D.M., Vandergast, A.G., Hunter, M., Crandall, E.D., Funk, W., Garroway, C., Hoban, S.M., Oyler-McCance, S.J., Rellstab, C., Segelbacher, G., Schmidt, C., Vazquez-Dominguez, E., and Paz-Vinas, I., 2024, Best practices for genetic and genomic data archiving: Nature, Ecology and Evolution, v. 8, p. 1224-1232, https://doi.org/10.1038/s41559-024-02423-7.","productDescription":"9 p.","startPage":"1224","endPage":"1232","ipdsId":"IP-157740","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467004,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-04593895","text":"External Repository"},{"id":429571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Leigh, Deborah M.","contributorId":291307,"corporation":false,"usgs":false,"family":"Leigh","given":"Deborah","email":"","middleInitial":"M.","affiliations":[{"id":62679,"text":"WSL Swiss Federal Research Institute","active":true,"usgs":false}],"preferred":false,"id":902175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":902176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214958,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":902177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crandall, Eric D. 0000-0001-8580-3651","orcid":"https://orcid.org/0000-0001-8580-3651","contributorId":337181,"corporation":false,"usgs":false,"family":"Crandall","given":"Eric","email":"","middleInitial":"D.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":902178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, W. Chris 0000-0002-9254-6718","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":189580,"corporation":false,"usgs":false,"family":"Funk","given":"W. Chris","affiliations":[],"preferred":false,"id":902179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garroway, Colin J","contributorId":302145,"corporation":false,"usgs":false,"family":"Garroway","given":"Colin J","affiliations":[{"id":16603,"text":"University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":902180,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":902181,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":902182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rellstab, Christian 0000-0002-0221-5975","orcid":"https://orcid.org/0000-0002-0221-5975","contributorId":337184,"corporation":false,"usgs":false,"family":"Rellstab","given":"Christian","email":"","affiliations":[{"id":80990,"text":"Swiss Federal Research Institute, Switzerland","active":true,"usgs":false}],"preferred":false,"id":902183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":902184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schmidt, Chloe 0000-0003-2572-4200","orcid":"https://orcid.org/0000-0003-2572-4200","contributorId":337185,"corporation":false,"usgs":false,"family":"Schmidt","given":"Chloe","email":"","affiliations":[{"id":13699,"text":"German Centre for Integrative Biodiversity Research, Germany","active":true,"usgs":false}],"preferred":false,"id":902185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vazquez-Dominguez, Ella 0000-0001-6131-2014","orcid":"https://orcid.org/0000-0001-6131-2014","contributorId":337186,"corporation":false,"usgs":false,"family":"Vazquez-Dominguez","given":"Ella","email":"","affiliations":[{"id":80993,"text":"Universidad Nacional Autónoma de México, México","active":true,"usgs":false}],"preferred":false,"id":902186,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":902187,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70254539,"text":"70254539 - 2024 - Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands","interactions":[],"lastModifiedDate":"2024-05-31T14:34:42.026925","indexId":"70254539","displayToPublicDate":"2024-05-24T09:27:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands","docAbstract":"Dynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging.
We identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment.
Using high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance.
We found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R2 = 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales.
Our results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10980-024-01901-4","usgsCitation":"Zaiats, A., Cattau, M.E., Pilliod, D.S., Liu, R., Dumandan, P.K., Hojatimalekshah, A., Delparte, D.M., and Caughlin, T., 2024, Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands: Landscape Ecology, v. 39, 108, 16 p., https://doi.org/10.1007/s10980-024-01901-4.","productDescription":"108, 16 p.","ipdsId":"IP-157067","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":439502,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-024-01901-4","text":"Publisher Index Page"},{"id":429403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.01648249125195,\n 44.15423652632305\n ],\n [\n -117.01648249125195,\n 42.15843115518953\n ],\n [\n -115.43777530905156,\n 42.15843115518953\n ],\n [\n -115.43777530905156,\n 44.15423652632305\n ],\n [\n -117.01648249125195,\n 44.15423652632305\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii 0000-0001-8978-4152","orcid":"https://orcid.org/0000-0001-8978-4152","contributorId":257072,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cattau, Megan E 0000-0003-2164-3809","orcid":"https://orcid.org/0000-0003-2164-3809","contributorId":295715,"corporation":false,"usgs":false,"family":"Cattau","given":"Megan","email":"","middleInitial":"E","affiliations":[{"id":63922,"text":"Department of Human-Environment Systems, Boise State University","active":true,"usgs":false}],"preferred":false,"id":901795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":210334,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":901796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Rongsong","contributorId":43480,"corporation":false,"usgs":false,"family":"Liu","given":"Rongsong","email":"","affiliations":[],"preferred":false,"id":901797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dumandan, Patricia Kaye T. 0000-0003-3360-1534","orcid":"https://orcid.org/0000-0003-3360-1534","contributorId":257070,"corporation":false,"usgs":false,"family":"Dumandan","given":"Patricia","email":"","middleInitial":"Kaye T.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901798,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hojatimalekshah, Ahmad","contributorId":337022,"corporation":false,"usgs":false,"family":"Hojatimalekshah","given":"Ahmad","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901799,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delparte, Donna M. 0000-0002-9107-5117","orcid":"https://orcid.org/0000-0002-9107-5117","contributorId":317762,"corporation":false,"usgs":false,"family":"Delparte","given":"Donna","email":"","middleInitial":"M.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":901800,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":901801,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70257294,"text":"70257294 - 2024 - Reframing wildlife disease management problems with decision analysis","interactions":[],"lastModifiedDate":"2024-08-15T12:10:34.601257","indexId":"70257294","displayToPublicDate":"2024-05-24T07:09:15","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Reframing wildlife disease management problems with decision analysis","docAbstract":"Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade-offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision-making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision-making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision-making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white-nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision-making, including competing objectives, risks, prediction uncertainty, and limited resources.
The 26 January 1700 CE Cascadia subduction zone earthquake ruptured much of the plate boundary and generated a tsunami that deposited sand in coastal marshes from northern California to Vancouver Island. Although the depositional record of tsunami inundation is extensive in some of these marshes, few sites have been investigated in enough detail to map the inland extent of sand deposition and depict variability in tsunami deposit thickness and grain size. We collected 129 cores in marshes of the Salmon River estuary in Oregon and reanalyzed 114 core logs from a 1987–88 study that mapped the inland extent of circa 1700 CE sandy tsunami deposits. The ca. 1700 CE tsunami deposit in the Salmon River estuary is easily recognized in cores ≤1 m deep in which a buried marsh peat is overlain by a well sorted sand bed with a sharp lower contact that thins and fines inland. We use tsunami deposit data and models of sandy tsunami sediment transport (using Delft3D-FLOW) to test 15 rupture models that could represent a ca. 1700 CE earthquake. At least 12–16 m of slip offshore of the Salmon River, which results in 0.8–1.0 m of coastal coseismic subsidence, is required to match the ca. 1700 CE sand deposit's inland extent, which is consistent with models of heterogeneous megathrust slip in ca. 1700 CE. Our methods of detailed tsunami deposit mapping, combined with sediment transport modeling, can be used to test models of megathrust ruptures and their tsunamis to potentially improve earthquake and tsunami hazard assessments.
Population in western Sarpy County, Nebraska, has steadily increased over the last several decades and has led to increased groundwater use for domestic purposes. To meet the increase in demand, the Papio-Missouri River Natural Resources District is seeking to use all available sources of groundwater in western Sarpy County. Additionally, elevated groundwater nitrate plus nitrite as nitrogen concentrations were detected, indicating the need to better understand the groundwater quality of the area. Although the general geology of the area is understood, the area does not have detailed information on the extent of the various aquifers, particularly the Dakota aquifer. To characterize these aquifers, the Papio-Missouri River Natural Resources District invested in airborne electromagnetic surveys of the area to better understand the subsurface geology. Although these surveys improved understanding of the groundwater systems in the area, the Papio-Missouri River Natural Resources District wanted to integrate the subsurface information with available water-quality and groundwater-level data.
In response, the U.S. Geological Survey, in cooperation with the Papio-Missouri River Natural Resources District, the Nebraska Natural Resources Commission, and the Nebraska Department of Natural Resources, assembled geologic, hydrogeologic and nitrate plus nitrite as nitrogen information for the selected area into a three-dimensional visualization computer software package called GeoScene3D. The completed GeoScene3D project was assembled to provide a visualization of the groundwater systems and associated water-quality results in Sarpy County and to provide the Papio-Missouri River Natural Resources District managers with information that can be used to make more informed groundwater resource-planning decisions in the future. This report details the development of a three-dimensional model created within GeoScene3D to visualize the subsurface, particularly the Dakota Sandstone in western Sarpy County.
Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The global decline of pollinators, particularly insects, underscores the importance of enhanced monitoring of their populations and habitats. However, monitoring some pollinator habitat is challenging due to widespread species distributions and shifts in habitat requirements through seasons and life stages. The monarch butterfly (Danaus plexippus), a migratory insect pollinator that breeds widely throughout North America, presents a unique case study for testing a sampling framework to overcome these challenges. Monarchs exhibit discrete resource needs across life stages (e.g., larval requirement for milkweed, adult requirement for floral nectar), utilizing many land use types across their extensive geographic range during breeding and migration seasons. The Integrated Monarch Monitoring Program (IMMP) uses a standardized protocol with a generalized random tessellation stratified (GRTS) sampling design to gather spatially balanced and ecologically representative information on monarch habitats within the United States. The IMMP is applicable to various land use types and habitats used by breeding monarchs and may be extended to sites outside of the GRTS design to collect data on non-random sites of interest, such as legacy or conservation sites. Additionally, the IMMP’s modular design and publicly available training allows for broad participation, including involvement from community scientists. Here, we summarize habitat metrics (milkweed and floral resources) across 1,233 sites covering much of the monarch’s breeding range. We examine variation in milkweed density and floral resource availability on probabilistic (random) and non-probabilistic (convenience) samples and among land use types (site types). Additionally, we assess resource availability within core geographies for monarch breeding and migration, specifically within the U.S. Fish and Wildlife Service’s Monarch Conservation Units (western, northern, and southern United States). Milkweed density, floral frequency, and floral richness were higher on non-random sites and in the North region. Among site types, milkweed density was highest on Rights-of-Way and Unclassified Grassland, while floral frequency was lowest on Rights-of-Way. The IMMP represents the first field-based habitat monitoring program of this scale for monarchs, yielding a robust dataset on monarchs and their habitats across their breeding range and offering a framework for surveying the habitat of insect species with diverse habitat requirements or widespread distributions.
The U.S. Geological Survey (USGS) Northern Rocky Mountain Science Center is based in Bozeman, Montana, and has field offices in Glacier National Park, Mont.; Missoula, Mont.; and Knoxville, Tennessee. Our scientists respond to the natural resource management needs of Federal, Tribal, and State partners—directly engaging in the coproduction and application of integrated, interdisciplinary science—and perform place-based research throughout the northern Rocky Mountains, including Yellowstone and Glacier National Parks and the northern Great Plains. However, the scope and implications of our research extend across the Nation. Our research themes are as follows: (1) climate change and drought, (2) species at risk, (3) habitat in changing landscapes, and (4) invasive species and wildlife disease. This Fact Sheet highlights examples of dynamic partnerships and key advances in our themes in fiscal year 2023 (October 2022–September 2023).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20243012","programNote":"Species Management Research Program, Land Management Research Program, Biological Threats and Invasive Species Research Program, Land Change Science Program, Climate Adaptation Science Centers, and Environmental Health Program","usgsCitation":"Wojtowicz, T., 2024, U.S. Geological Survey Northern Rocky Mountain Science Center science highlights for fiscal year 2023: U.S. Geological Survey Fact Sheet 2024–3012, 4 p., https://doi.org/10.3133/fs20243012.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-159158","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":429149,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3012/fs20243012.pdf","text":"Report","size":"1.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3012"},{"id":429148,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3012/coverthb.jpg"},{"id":429207,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3012/fs20243012.xml","linkHelpText":"https://pubs.usgs.gov/fs/2024/3013/images"},{"id":429209,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3012/images"},{"id":429384,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243012/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3012"}],"contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Early detection of biological threats, such as invasive species, increases the likelihood that control efforts will be successful and cost-effective. Environmental deoxyribonucleic acid (eDNA) sampling is an established method for the efficient and sensitive early detection of new biological threats. The Rapid eDNA Assessment and Deployment Initiative & Network (READI-Net) is a project designed with partners to provide a full suite of tools to maximize the power of eDNA sampling for detecting invasive species. The READI-Net suite of tools will include the availability of autonomous eDNA samplers, multispecies molecular DNA detection tools, strategic sample design, standardized and repeatable lab analysis, and a communication framework to deliver eDNA detection results to inform invasive species science, policy, and management. The READI-Net project is part of a national strategy to implement eDNA sampling for the early detection of and rapid response to biological threats.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243013","programNote":"Biological Threats and Invasive Species Research Program","usgsCitation":"McKeon, L., and Wojtowicz, T., 2024, READI-Net—Providing tools for the early detection and management of aquatic invasive species: U.S. Geological Survey Fact Sheet 2024–3013, 2 p., https://doi.org/10.3133/fs20243013.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-159164","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":429178,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3013/coverthb.jpg"},{"id":429179,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3013/fs20243013.pdf","text":"Report","size":"960 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3013"},{"id":429210,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3013/images"},{"id":429211,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3013/fs20243013.xml"},{"id":429383,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243013/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3013"}],"contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Mauna Loa, the largest active volcano on Earth, has erupted 34 times since written descriptions became available in A.D. 1832. The most recent eruption of Mauna Loa occurred on November 27, 2022, after a 38 year hiatus; it lasted for 12 days. Some eruptions began with only brief seismic unrest, whereas others followed several months to a year of increased seismicity. Once underway, Mauna Loa’s eruptions can produce lava flows that may reach the sea in less than 24 hours, severing roads and utilities. For example, lava flows that erupted from the Southwest Rift Zone in 1950 advanced at an average rate of 9.3 kilometers per hour (5.8 miles per hour); all three lobes reached the ocean within ~24 hours. Near the eruptive vents, the flows likely traveled even faster. In terms of eruption frequency, pre-eruption warning, and rapid flow emplacement, Mauna Loa has great volcanic-hazard potential for the Island of Hawai‘i. Volcanic hazards on Mauna Loa can be anticipated, and risk substantially mitigated, by documenting its past activity to refine our knowledge of the hazards, and by alerting the public and local government officials of our findings and their implications for hazards assessments and risk.
The map of the north and west flanks of Mauna Loa shows the distribution and relation of volcanic and surficial sedimentary deposits. It incorporates previously reported work published as generalized small-scale maps and a more detailed map.
Within the mapped area, lava has flowed from three different source regions: the Northeast Rift Zone (22 percent), the summit (64 percent), and radial vents (14 percent). All three have different points of origin which, in turn, affect the flow characteristics and periodicity of activity.
The map area includes the uppermost part of the NERZ and extends from the highest elevation––13,040 feet at the south end of the Kokoolau quadrangle, just below the summit caldera––to the sea northwest and west of the summit. Lava that erupts from the north and west flanks typically flows to the west, northwest, or north, depending on the vent location. Both morphologic lava flow types—‘a‘ā and pāhoehoe—are present. Pāhoehoe units tend to spread out or widen in low-slope regions, such as in the saddle regions between Mauna Loa and Mauna Kea or between Mauna Loa and Hualālai. In comparison, ʻaʻā flows generally produce narrower flow lobes that have higher relief.
This map is the fifth in a series of five maps that will cover Mauna Loa volcano.
NOTE: Map sheet 1 contains lines and type with overprint. This feature may be turned on or off in the Adobe Acrobat page display preferences.
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Algal blooms in water, soils, dusts, and the environment have captured national attention because of concerns associated with exposure to algal toxins for humans and animals. Algal blooms naturally occur in all surface-water types and are important primary producers for aquatic ecosystems. However, excessive algae growth can be associated with many harmful effects ranging from aesthetic to toxicity concerns, so this excessive growth is commonly called a harmful algal bloom (HAB).
Ecological imbalances that can lead to excessive algal growth, such as increased nutrient availability to waterbodies from natural and anthropogenic sources, are well documented in scientific literature. On the other hand, fundamental scientific understandings of environmental causes and controls leading to algal toxin production, environmental exposures, and adverse health outcomes for humans and animals could benefit from more attention by U.S. Geological Survey (USGS) scientists. Understanding when, why, and how the toxin is produced by individual algal cells or communities and why the toxin is released to the surrounding waterbody requires fundamental research to determine a toxin’s role, whether it provides competitive advantage or if other potential reasons exist for toxin production and release, such as secretions from otherwise benign biological processes. This research will require groundbreaking scientific discovery about underlying biologic and abiotic (non-living) processes commonly complicated by local variation in land use, microbial species composition, and ecosystem structure of the surrounding watershed.
Although underlying processes by which HABs form may be similar from one waterbody to another, individual waterbodies may be controlled by local factors for HAB development and toxin production that are unique to the watershed. Consequently, many fundamental science gaps exist that prevent informed mitigation and prevention of toxic HAB events. There are also gaps in understanding local conditions that control algal growth unique to specific watersheds. Addressing these science gaps is needed to inform evidence-based decisions that protect human and animal health and that reduce recreational and socioeconomic losses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1520","usgsCitation":"Christensen, V.G., Crawford, C.J., Dusek, R.J., Focazio, M.J., Fogarty, L.R., Graham, J.L., Journey, C.A., Lee, M.E., Larson, J.H., Stackpoole, S.M., Mazzei, V., Pindilli, E.J., Rattner, B.A., Slonecker, T., McSwain, K.B., Reilly, T.J., and Lopez, A.E., 2024, Interdisciplinary science approach for harmful algal blooms (HABs) and algal toxins—A strategic science vision for the U.S. Geological Survey: U.S. Geological Survey Circular 1520, 39 p., https://doi.org/10.3133/cir1520.","productDescription":"Report: vi; 39 p.; Appendix","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-144573","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":429145,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1520/cir1520.XML","text":"XML","description":"CIR 1520"},{"id":429144,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1520/cir1520.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1520","linkHelpText":"–Interdisciplinary Science Approach for Harmful Algal Blooms (HABs) and Algal Toxins—A Strategic Science Vision for the U.S. Geological Survey"},{"id":429143,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1520/images"},{"id":429142,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1520/coverthb.jpg"},{"id":429203,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/circ/1520/cir1520_app1.pdf","text":"Appendix 1","size":"332 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":429146,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1520/full"}],"contact":"Director, Water Resources Mission Area
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Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86%. The models were deployed on an Arduino microcontroller platform and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65 and 75% were observed during validation. Models demonstrated good capture rates for lamprey attachments (63–85%), and average detection delays ranging from 9 to 36 s. A video demonstrating the operation of the system during a real-time validation test is also included in this work. While there is room for improving the accuracy of the system, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity enhancing control and conservation efforts across its entire range.
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","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102373","usgsCitation":"Acre, M.R., Hessler, T.M., Bonjour, S.M., Roberts, J., Colborne, S.F., Brenden, T., Nathan, L.R., Broaddus, D., Vandergoot, C.S., Mayer, C.M., Qian, S.S., Hunter, R., Brown, R.E., and Calfee, R.D., 2024, Capturing potential: Leveraging grass carp behavior Ctenopharyngodon idella for enhanced removal: Journal of Great Lakes Research, v. 50, 102373, 14 p., https://doi.org/10.1016/j.jglr.2024.102373.","productDescription":"102373, 14 p.","ipdsId":"IP-163490","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":439507,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102373","text":"Publisher Index Page"},{"id":430894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Wisconsin"},"lastModifiedDate":"2024-06-14T12:16:27.670601","indexId":"sir20235064D","displayToPublicDate":"2024-05-23T09:30:14","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5064","chapter":"D","displayTitle":"Peak Streamflow Trends in Michigan and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Michigan and their relation to changes in climate, water years 1921–2020","docAbstract":"This study characterizes hydroclimatic variability and change in peak streamflow and daily streamflow in Michigan from water years 1921 through 2020. Four analysis periods were examined: the 100-year period from water year 1921 through 2020, the 75-year period from water year 1946 through 2020, the 50-year period from water year 1971 through 2020, and the 30-year period from water year 1991 through 2020. Peak streamflow and climate data were available at 4, 29, 50, and 30 streamgages in the 100-, 75-, 50-, and 30-year periods, respectively. Daily streamflow was available for 4, 29, 74, and 79 streamgages in the 100-, 75-, 50-, and 30-year periods, respectively.
Peak streamflow for each streamgage and analysis period was assessed for monotonic trends and change points. Trends in peak streamflow were predominantly upward, with some isolated downward trends throughout the southern half of Michigan for all four analysis periods. Trends in the Upper Peninsula were downward in 75- and 50-year analysis periods and upward or neutral in the 30-year period. Upward trends in peak flows were largely driven by increases in precipitation, which occurred at nearly every streamgage in all analysis periods, with the greatest magnitude trends in winter and spring in the 50- and 30-year periods.
Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Groundwater interactions with mountain streams are often simplified in model projections, potentially leading to inaccurate estimates of streamflow response to climate change. Here, using a high-resolution, integrated hydrological model extending 400 m into the subsurface, we find groundwater an important and stable source of historical streamflow in a mountainous watershed of the Colorado River. In a warmer climate, increased forest water use is predicted to reduce groundwater recharge resulting in groundwater storage loss. Losses are expected to be most severe during dry years and cannot recover to historical levels even during simulated wet periods. Groundwater depletion substantially reduces annual streamflow with intermittent conditions predicted when precipitation is low. Expanding results across the region suggests groundwater declines will be highest in the Colorado Headwater and Gunnison basins. Our research highlights the tight coupling of vegetation and groundwater dynamics and that excluding explicit groundwater response to warming may underestimate future reductions in mountain streamflow.
We hypothesize that aquatic ecosystem services are likely to be inequitably accessible and addressing this hypothesis requires systematic assessment at regional and national scales. We used existing data from large-scale aquatic monitoring programs (National Coastal Condition Assessment, National Lakes Assessment) to examine relationships between ecosystem condition, approximating a subset of cultural and provisioning services, and inequality (population below poverty level, minority population). We also assessed whether monitoring sites equitably represented the gradient of socioeconomic backgrounds. Several water quality indicators were associated with significantly different minority and low-income percentages; however, the effect size was generally small, with the exception of nitrogen condition status. Minority communities were somewhat under-represented when comparing the distribution of all census blocks to those in proximity to monitoring sites. Analyses were sensitive to the skewed distribution of monitoring sites with a low frequency of observations at the more socially vulnerable part of the gradient. We discuss implications of these findings for improving the representation of vulnerable communities in large-scale monitoring programs.
Northern high-latitude glaciers impact nearshore marine ecosystems through the discharge of cold and fresh waters, including nutrients and organic matter. Fishes are important integrators of ecosystem processes and hold key positions in the transfer of energy to higher trophic positions in such systems. This study used a natural gradient in space and time, including watershed glacial cover (0–60%) of five adjacent estuaries and three sequential discharge periods (pre-peak, peak, post-peak) in the northern Gulf of Alaska (Kachemak Bay) to test whether differences in glacial cover of watersheds upstream of estuaries affect dietary resource use of nearshore fishes. Dietary resource use was assessed using stomach content and stable carbon and nitrogen isotope analyses to determine fish diet composition and trophic niche width. Crescent gunnel (Pholis laeta), a mostly sedentary species, was our focal species for comparisons across estuaries and discharge periods. Discharge period had a greater influence on diet composition and trophic niche width of crescent gunnels than watershed glacial coverage. Niche width of crescent gunnel was larger during the post-peak discharge period compared to pre-peak and peak periods, coincident with a shift in prey spectrum. However, watershed glacial cover was not a suitable predictor of niche width of crescent gunnel. Trophic resource use was also considered along this glacial cover gradient for two other fish species, Pacific staghorn sculpin (Leptocottus armatus) and starry flounder (Platichthys stellatus), but within the post-peak discharge period only. These species exploited a larger prey base compared to crescent gunnel, likely due to their greater mobility. Similar to crescent gunnel, there were no relationships in trophic niche width associated with watershed glacial coverage for these other species during the post-peak discharge period. Instead, trophic resource use of these three nearshore fish species was influenced by a more complex set of dynamic environmental variables (salinity, temperature, turbidity, and discharge), as well as static watershed characteristics, especially vegetation cover. Such drivers can act through changes in metabolic rates, modulating foraging strategies and trophic connectivity, as well as terrestrial nutrient delivery to support estuarine production. The environmental conditions associated with the glacially influenced estuaries during our study period (2020−2021) seemed within a range that allowed nearshore fishes to maintain energy pathways and prey bases across these estuaries, but it is unknown how these estuarine food webs may be influenced in years of extreme conditions such as during heat waves, droughts, or floods.
In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information-theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well-suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of “maximizing information and general utility” extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data.
Delineating wildlife population boundaries is important for effective population monitoring and management. The bobcat (Lynx rufus) is a highly mobile generalist carnivore that is ecologically and economically important. We sampled 1225 bobcats harvested in South Dakota, USA (2014–2019), of which 878 were retained to assess genetic diversity and infer population genetic structure using 17 microsatellite loci. We assigned individuals to genetic clusters (K) using spatial and nonspatial Bayesian clustering algorithms and quantified differentiation (FST and GST″) among clusters. We found support for population genetic structure at K = 2 and K = 4, with pairwise FST and GST″ values indicating weak to moderate differentiation, respectively, among clusters. For K = 2, eastern and western clusters aligned closely with historical bobcat management units and were consistent with a longitudinal suture zone for bobcats previously identified in the Great Plains. We did not observe patterns of population genetic structure aligning with major rivers or highways. Genetic divergence observed at K = 4 aligned roughly with ecoregion breaks and may be associated with environmental gradients, but additional sampling with more precise locational data may be necessary to validate these patterns. Our findings reveal that cryptic population structure may occur in highly mobile and broadly distributed generalist carnivores, highlighting the importance of considering population structure when establishing population monitoring programs or harvest regulations. Our study further demonstrates that for elusive furbearers, harvest can provide an efficient, broad-scale sampling approach for genetic population assessments.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11411","usgsCitation":"Fetherston, S.C., Lonsinger, R.C., Perkins, L.B., Lehman, C.P., Adams, J., and Waits, L., 2024, Genetic analysis of harvest samples reveals population structure in a highly mobile generalist carnivore: Ecology and Evolution, v. 14, no. 5, e11411, 13 p., https://doi.org/10.1002/ece3.11411.","productDescription":"e11411, 13 p.","ipdsId":"IP-157245","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11411","text":"Publisher Index Page"},{"id":433052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.76565071228751,\n 46.8024638543848\n ],\n [\n -104.76565071228751,\n 42.035583213643264\n ],\n [\n -95.53713508728745,\n 42.035583213643264\n ],\n [\n -95.53713508728745,\n 46.8024638543848\n ],\n [\n -104.76565071228751,\n 46.8024638543848\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Fetherston, Stuart C.","contributorId":341222,"corporation":false,"usgs":false,"family":"Fetherston","given":"Stuart","email":"","middleInitial":"C.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Lora B.","contributorId":341223,"corporation":false,"usgs":false,"family":"Perkins","given":"Lora","email":"","middleInitial":"B.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908107,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lehman, Chadwick P.","contributorId":341224,"corporation":false,"usgs":false,"family":"Lehman","given":"Chadwick","email":"","middleInitial":"P.","affiliations":[{"id":37104,"text":"South Dakota Department of Game, Fish and Parks","active":true,"usgs":false}],"preferred":false,"id":908108,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Jennifer R.","contributorId":341225,"corporation":false,"usgs":false,"family":"Adams","given":"Jennifer R.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":908109,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waits, Lisette P.","contributorId":341226,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":908110,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254581,"text":"70254581 - 2024 - Genome-wide association analysis of the resistance to infectious hematopoietic necrosis virus in two rainbow trout aquaculture lines confirms oligogenic architecture with several moderate effect quantitative trait loci","interactions":[],"lastModifiedDate":"2024-06-03T11:25:29.57809","indexId":"70254581","displayToPublicDate":"2024-05-23T06:21:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5062,"text":"Frontiers in Genetics","onlineIssn":"1664-8021","active":true,"publicationSubtype":{"id":10}},"title":"Genome-wide association analysis of the resistance to infectious hematopoietic necrosis virus in two rainbow trout aquaculture lines confirms oligogenic architecture with several moderate effect quantitative trait loci","docAbstract":"Infectious hematopoietic necrosis (IHN) is a disease of salmonid fish that is caused by the IHN virus (IHNV), which can cause substantial mortality and economic losses in rainbow trout aquaculture and fisheries enhancement hatchery programs. In a previous study on a commercial rainbow trout breeding line that has undergone selection, we found that genetic resistance to IHNV is controlled by the oligogenic inheritance of several moderate and many small effect quantitative trait loci (QTL). Here we used genome wide association analyses in two different commercial aquaculture lines that were naïve to previous exposure to IHNV to determine whether QTL were shared across lines, and to investigate whether there were major effect loci that were still segregating in the naïve lines. A total of 1,859 and 1,768 offspring from two commercial aquaculture strains were phenotyped for resistance to IHNV and genotyped with the rainbow trout Axiom 57K SNP array. Moderate heritability values (0.15–0.25) were estimated. Two statistical methods were used for genome wide association analyses in the two populations. No major QTL were detected despite the naïve status of the two lines. Further, our analyses confirmed an oligogenic architecture for genetic resistance to IHNV in rainbow trout. Overall, 17 QTL with notable effect (≥1.9% of the additive genetic variance) were detected in at least one of the two rainbow trout lines with at least one of the two statistical methods. Five of those QTL were mapped to overlapping or adjacent chromosomal regions in both lines, suggesting that some loci may be shared across commercial lines. Although some of the loci detected in this GWAS merit further investigation to better understand the biological basis of IHNV disease resistance across populations, the overall genetic architecture of IHNV resistance in the two rainbow trout lines suggests that genomic selection may be a more effective strategy for genetic improvement in this trait.
To improve flood-frequency estimates for rural streams in the Coastal Plain region of Louisiana, generalized least-squares regression techniques were used to relate corresponding annual exceedance probability streamflows for 211 streamgages in the region to a suite of explanatory variables that include physical, climatic, pedologic, and land-use characteristics of the streamgage drainage area. The resulting generalized least-squares models can be used to estimate selected annual exceedance probability streamflows for rural ungaged locations in the Coastal Plain region of Louisiana. For the 211 streamgages in the Coastal Plain region of Louisiana and surrounding States, annual peak-streamflow data available through the 2016 water year were used in this study. Two unique flood regions, the Mississippi Alluvial Plain and Coastal Plain, were identified as separate hydrologic regions based on statistical evaluation and significance of categorical variables representing the regions regressed against the 1-percent annual exceedance probability streamflow (the 100-year flood). Regional regression equations for estimating annual exceedance probability streamflow for the Mississippi Alluvial Plain region have been previously published; therefore, the purpose of this study was to generate updated regional regression equations for the Coastal Plain region of Louisiana. The final regression models used drainage area and channel slope as explanatory variables based on performance metrics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245031","issn":"2328-0328","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Ensminger, P.A., Wagner, D.M., and Whaling, A., 2024, Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016: U.S. Geological Survey Scientific Investigations Report 2024–5031, 18 p., https://doi.org/10.3133/sir20245031.","productDescription":"Report: viii, 18 p.; 2 Data Releases","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-091992","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":428761,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5031/coverthb.jpg"},{"id":428762,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5031/images"},{"id":428766,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":499458,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117008.htm","linkFileType":{"id":5,"text":"html"}},{"id":428767,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B48P2W","text":"USGS data release","linkHelpText":"Flood-frequency of rural, non-tidal streams in Louisiana and part of Arkansas, Mississippi, and Texas, 1877–2016"},{"id":428765,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245031/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5031 HTML"},{"id":428764,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5031/sir20245031.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5031 XML"},{"id":428763,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5031/sir20245031.pdf","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5031"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.00967112876856,\n 29.444945586186265\n ],\n [\n -88.68056894510033,\n 29.126157783971323\n ],\n [\n -88.60834825216723,\n 33.49872733992268\n ],\n [\n -89.99512218551115,\n 34.151214622331636\n ],\n [\n -90.94796783046621,\n 34.514518506267706\n ],\n [\n -93.48068301952253,\n 34.58384293733218\n ],\n [\n -94.52066293605651,\n 33.519758483995176\n ],\n [\n -94.62647823892294,\n 29.923162538843826\n ],\n [\n -94.00967112876856,\n 29.444945586186265\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Natural ecosystems store large amounts of carbon globally, as organisms absorb carbon from the atmosphere to build large, long-lasting, or slow-decaying structures such as tree bark or root systems. An ecosystem’s carbon sequestration potential is tightly linked to its biological diversity. Yet when considering future projections, many carbon sequestration models fail to account for the role biodiversity plays in carbon storage. Here, we assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. We link a macroecological model projecting changes in vascular plant richness under different scenarios with empirical data on relationships between biodiversity and biomass. We find that biodiversity declines from climate and land use change could lead to a global loss of between 7.44-103.14 PgC (global sustainability scenario) and 10.87-145.95 PgC (fossil-fueled development scenario). This indicates a self-reinforcing feedback loop, where higher levels of climate change lead to greater biodiversity loss, which in turn leads to greater carbon emissions and ultimately more climate change. Conversely, biodiversity conservation and restoration can help achieve climate change mitigation goals.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-024-47872-7","usgsCitation":"Weiskopf, S.R., Isbell, F., Arce-Plata, M.I., Di Marco, M., Harfoot, M., Johnson, J., Lerman, S.B., Miller, B.W., Morelli, T.L., Mori, A.S., Weng, E., and Ferrier, S., 2024, Biodiversity loss reduces global terrestrial carbon storage: Nature Communications, v. 15, 4354, 12 p., https://doi.org/10.1038/s41467-024-47872-7.","productDescription":"4354, 12 p.","ipdsId":"IP-152335","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439525,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-024-47872-7","text":"Publisher Index Page"},{"id":434954,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WUFMU","text":"USGS data release","linkHelpText":"Model outputs highlighting how biodiversity loss reduces global terrestrial carbon storage based on climate and land-use changes projected for 2050"},{"id":429349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2024-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":901599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Isbell, Forest","contributorId":271280,"corporation":false,"usgs":false,"family":"Isbell","given":"Forest","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":901600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arce-Plata, Maria Isabel","contributorId":271276,"corporation":false,"usgs":false,"family":"Arce-Plata","given":"Maria","email":"","middleInitial":"Isabel","affiliations":[{"id":54487,"text":"University of Montreal","active":true,"usgs":false}],"preferred":false,"id":901601,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Di Marco, Moreno","contributorId":336960,"corporation":false,"usgs":false,"family":"Di Marco","given":"Moreno","email":"","affiliations":[{"id":35391,"text":"Sapienza University of Rome","active":true,"usgs":false}],"preferred":false,"id":901602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harfoot, Mike","contributorId":271279,"corporation":false,"usgs":false,"family":"Harfoot","given":"Mike","email":"","affiliations":[{"id":56332,"text":"UNEP WCMC","active":true,"usgs":false}],"preferred":false,"id":901603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Justin A.","contributorId":211868,"corporation":false,"usgs":false,"family":"Johnson","given":"Justin A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":901604,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lerman, Susannah B.","contributorId":171615,"corporation":false,"usgs":false,"family":"Lerman","given":"Susannah","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":901605,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":901606,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":901607,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mori, Akira S.","contributorId":271281,"corporation":false,"usgs":false,"family":"Mori","given":"Akira","email":"","middleInitial":"S.","affiliations":[{"id":49222,"text":"Yokohama National University","active":true,"usgs":false}],"preferred":false,"id":901608,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Weng, Ensheng 0000-0002-1858-4847","orcid":"https://orcid.org/0000-0002-1858-4847","contributorId":267936,"corporation":false,"usgs":false,"family":"Weng","given":"Ensheng","email":"","affiliations":[{"id":49221,"text":"NASA Goddard Institute for Space Studies","active":true,"usgs":false}],"preferred":false,"id":901609,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ferrier, Simon 0000-0001-7884-2388","orcid":"https://orcid.org/0000-0001-7884-2388","contributorId":245542,"corporation":false,"usgs":false,"family":"Ferrier","given":"Simon","email":"","affiliations":[{"id":49219,"text":"Commonwealth Scientific and Industrial Research Organisation","active":true,"usgs":false}],"preferred":false,"id":901610,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ]}