To predict future status of the northwestern pond turtle (Actinemys marmorata) and southwestern pond turtle (Actinemys pallida) species, we developed a stochastic stage-based matrix population model to simulate future population conditions. We constructed a demographic population viability analysis for each species based on a post-breeding, single sex, stage-based life history diagram elicited from taxa experts and derived from relevant literature. Demographic parameters were based on estimates from published literature and data provided to the U.S. Fish and Wildlife Service (USFWS). Using the most recent observations of turtles, available habitat, local abundances, and current threat conditions, we calculated spatially explicit initial abundances to initialize our stochastic projection. In order to incorporate multiple types of uncertainty (ecological, parametric, temporal), we built three embedded simulation loops within the simulation model. Representing ecological uncertainty, species status was projected into the future using multiple plausible future scenarios based on two representative concentration pathways (RCP 4.5, 8.5) and two shared socioeconomic pathways (SSP 2, 5) to reflect plausible alternative future trajectories of relevant environmental conditions. Parametric uncertainty was
included for survival estimates of all life stages due the inconsistency of estimates across the species’ range. Temporal variability or environmental stochasticity was included in the form of randomized variation from the mean demographic parameter values in each year of the approximately 80-year simulation.
The model output included probability of extinction and estimated abundance through 2100 for each unique Analysis Unit (AU) and for the full geographic range of the species except populations in the state of Washington. The AUs in Washington are conservation dependent and sustained by a head-starting and reintroduction program. Thus, the population dynamics do not match our model for the rest of the range and therefore the Washington AUs were included in this projection modeling effort. There is already pre-existing, detailed PVA for these specific populations (Pramuk et al. 2012, p.41-60), and the Status assessment report can use those results for inference about future status. We discuss the results of Pramuk et al. (2012, p.41-61) alongside our own. Probability of extinction was overall higher for the southwestern pond turtle as compared to the northwestern species and population growth rates were strongly negative for both species (approximately -3% annually for all AUs for all scenarios). This appendix is organized into three primary sections: 1) a description of the life history, the core population dynamics model, and demographic parameters, 2) a description of methods for establishing initial abundances of the populations for the future viability modeling, and 3) a description of the methods for modeling effects of various threats on future demographic rates and the results of future conditions scenarios.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Species status assessment report for Northwestern Pond Turel (Actinemys marmorata) and Southwestern Pond Turtle (Actinemys pallida)","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Gregory, K.M., and McGowan, C.P., 2023, Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida ) (version 1.1), 43 p.","productDescription":"43 p.","ipdsId":"IP-146555","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494872,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/node/5110701"},{"id":501175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"version 1.1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gregory, Kaili M.","contributorId":360548,"corporation":false,"usgs":false,"family":"Gregory","given":"Kaili","middleInitial":"M.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":947202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":10145,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":947203,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257346,"text":"70257346 - 2023 - Landscape transcriptomics as a tool for addressing global change effects across diverse species","interactions":[],"lastModifiedDate":"2024-08-28T16:42:00.761546","indexId":"70257346","displayToPublicDate":"2023-04-01T09:29:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Landscape transcriptomics as a tool for addressing global change effects across diverse species","docAbstract":"Landscape transcriptomics is an emerging field studying how genome-wide expression patterns reflect dynamic landscape-scale environmental drivers, including habitat, weather, climate, and contaminants, and the subsequent effects on organismal function. This field is benefitting from advancing and increasingly accessible molecular technologies, which in turn are allowing the necessary characterization of transcriptomes from wild individuals distributed across natural landscapes. This research is especially important given the rapid pace of anthropogenic environmental change and potential impacts that span levels of biological organization. We discuss three major themes in landscape transcriptomic research: connecting transcriptome variation across landscapes to environmental variation, generating and testing hypotheses about the mechanisms and evolution of transcriptomic responses to the environment, and applying this knowledge to species conservation and management. We discuss challenges associated with this approach and suggest potential solutions. We conclude that landscape transcriptomics has great promise for addressing fundamental questions in organismal biology, ecology, and evolution, while providing tools needed for conservation and management of species.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13796","usgsCitation":"Keagy, J., Drummond, C.P., Gilbert, K.J., Christina Grozinger, Hamilton, J., Hines, H.M., Lasky, J., Logan, C.A., Sawers, R., and Wagner, T., 2023, Landscape transcriptomics as a tool for addressing global change effects across diverse species: Molecular Ecology Resources, 16 p., https://doi.org/10.1111/1755-0998.13796.","productDescription":"16 p.","ipdsId":"IP-147507","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.13796","text":"Publisher Index Page"},{"id":433254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Keagy, Jason","contributorId":342368,"corporation":false,"usgs":false,"family":"Keagy","given":"Jason","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drummond, Chloe P.","contributorId":342369,"corporation":false,"usgs":false,"family":"Drummond","given":"Chloe","email":"","middleInitial":"P.","affiliations":[{"id":25495,"text":"Mount Holyoke College","active":true,"usgs":false}],"preferred":false,"id":910028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilbert, Kadeem J.","contributorId":342370,"corporation":false,"usgs":false,"family":"Gilbert","given":"Kadeem","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christina Grozinger","contributorId":342371,"corporation":false,"usgs":false,"family":"Christina Grozinger","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hamilton, Jill","contributorId":342372,"corporation":false,"usgs":false,"family":"Hamilton","given":"Jill","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910031,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hines, Heather M.","contributorId":342373,"corporation":false,"usgs":false,"family":"Hines","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910032,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lasky, Jesse","contributorId":342374,"corporation":false,"usgs":false,"family":"Lasky","given":"Jesse","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910033,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Logan, Cheryl A.","contributorId":342375,"corporation":false,"usgs":false,"family":"Logan","given":"Cheryl","email":"","middleInitial":"A.","affiliations":[{"id":81516,"text":"California State University Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":910034,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sawers, Ruairidh","contributorId":342376,"corporation":false,"usgs":false,"family":"Sawers","given":"Ruairidh","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910035,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910036,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70266579,"text":"70266579 - 2023 - Automated soft pressure sensor array-based sea lamprey detection using machine learning","interactions":[],"lastModifiedDate":"2025-05-09T14:14:32.73205","indexId":"70266579","displayToPublicDate":"2023-04-01T09:10:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9956,"text":"IEEE Sensors Journal","active":true,"publicationSubtype":{"id":10}},"title":"Automated soft pressure sensor array-based sea lamprey detection using machine learning","docAbstract":"Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.
","language":"English","publisher":"IEEE","doi":"10.1109/JSEN.2023.3249625","usgsCitation":"Shi, H., Mei, Y., González-Afanador, I., Chen, C., Miehls, S.M., Holbrook, C., Sepulveda, N., and Tan, X., 2023, Automated soft pressure sensor array-based sea lamprey detection using machine learning: IEEE Sensors Journal, v. 23, p. 7546-7557, https://doi.org/10.1109/JSEN.2023.3249625.","productDescription":"12 p.","startPage":"7546","endPage":"7557","ipdsId":"IP-147136","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2023-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hongyang","contributorId":354871,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mei, Yu","contributorId":354872,"corporation":false,"usgs":false,"family":"Mei","given":"Yu","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"González-Afanador, Ian","contributorId":354873,"corporation":false,"usgs":false,"family":"González-Afanador","given":"Ian","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Claudia","contributorId":354874,"corporation":false,"usgs":false,"family":"Chen","given":"Claudia","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936605,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sepulveda, Nelson","contributorId":354866,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936606,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tan, Xiaobo","contributorId":354875,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936607,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248495,"text":"70248495 - 2023 - The Lower Cretaceous sequence of western Alaska – demise of the Koyukuk terrane?","interactions":[],"lastModifiedDate":"2023-09-15T13:40:34.465244","indexId":"70248495","displayToPublicDate":"2023-04-01T08:29:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1168,"text":"Canadian Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The Lower Cretaceous sequence of western Alaska – demise of the Koyukuk terrane?","docAbstract":"Lower Cretaceous marine sedimentary rocks, deposited in shallow shelf and basin settings and unconformity-bound, are well exposed in southwest Alaska. Collections of Early Cretaceous fossils from across western Alaska show that similar and coeval Lower Cretaceous clastic rocks are widely distributed though only locally exposed. Volcanic rocks become an important part of the Lower Cretaceous sequence in the Yukon-Koyukuk basin where they have been interpreted to represent a mobile intra-oceanic island arc, the Koyukuk terrane, that collided with Arctic Alaska to form the Brooks Range orogen. The volcanic rocks are chemically unlike Aleutian arc rocks but share compositional characteristics with spatially related, mid-Cretaceous alkaline intrusive rocks. The volcanic-bearing sequence was also deposited on an angular unconformity, includes both shallow shelf and basin depositional settings, and is unconformably overlain by mid-Cretaceous clastic rocks. The volcanic rocks are therefore considered part of the Lower Cretaceous sequence now identified across western Alaska. In this interpretation, the Lower Cretaceous volcanic rocks are an initial expression of the mid-Cretaceous tectonic regime that included extensional exhumation and subsidence, crustal and upper mantle melting, and high temperature metamorphism in the hinterland of the Brooks Range orogen. The Cretaceous heating that led to hinterland crust and upper mantle change may have been caused by deep mantle disturbances in a post-subduction setting. This interpretation has implications for the timing of contractional orogenesis, the location and nature of the related continental borderland, and the tectonic setting for development of the Anguyucham and related oceanic terranes.","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjes-2022-0041","usgsCitation":"Hudson, T.L., Blodgett, R., and Wilson, F.H., 2023, The Lower Cretaceous sequence of western Alaska – demise of the Koyukuk terrane?: Canadian Journal of Earth Sciences, v. 60, no. 4, p. 422-441, https://doi.org/10.1139/cjes-2022-0041.","productDescription":"20 p.","startPage":"422","endPage":"441","ipdsId":"IP-126283","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":420829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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Challenge","docAbstract":"The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al. 2018). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, “Challenge”) (Figure 1), an open platform for the ecological and data science communities to forecast NEON data before they are collected.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2616","usgsCitation":"Thomas, R.Q., Boettiger, C., Carey, C.C., Dietze, M., Johnson, L.R., Kenney, M.A., McLachlan, J.S., Peters, J.A., Sokol, E.R., Weltzin, J., Willson, A., and Woelmer, W., 2023, The NEON Ecological Forecasting Challenge: Frontiers in Ecology and the Environment, v. 21, no. 3, p. 112-113, https://doi.org/10.1002/fee.2616.","productDescription":"2 p.","startPage":"112","endPage":"113","ipdsId":"IP-145337","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":443983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.2616","text":"Publisher Index Page"},{"id":415161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, R. Quinn","contributorId":242825,"corporation":false,"usgs":false,"family":"Thomas","given":"R.","email":"","middleInitial":"Quinn","affiliations":[{"id":48537,"text":"Assistant Professor, Forest Resources & Environmental Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":868549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boettiger, Carl","contributorId":201833,"corporation":false,"usgs":false,"family":"Boettiger","given":"Carl","affiliations":[{"id":36267,"text":"Dept of Environmental Science, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":868550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":868551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dietze, Michael","contributorId":248349,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","affiliations":[],"preferred":false,"id":868552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Leah R.","contributorId":139035,"corporation":false,"usgs":false,"family":"Johnson","given":"Leah","email":"","middleInitial":"R.","affiliations":[{"id":12621,"text":"University of Chicago and University of South Florida","active":true,"usgs":false}],"preferred":false,"id":868553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kenney, Melissa A.","contributorId":202414,"corporation":false,"usgs":false,"family":"Kenney","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":868554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLachlan, Jason S.","contributorId":245535,"corporation":false,"usgs":false,"family":"McLachlan","given":"Jason","email":"","middleInitial":"S.","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peters, Jody A.","contributorId":303908,"corporation":false,"usgs":false,"family":"Peters","given":"Jody","email":"","middleInitial":"A.","affiliations":[{"id":65926,"text":"U Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868556,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sokol, Eric R.","contributorId":303909,"corporation":false,"usgs":false,"family":"Sokol","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":55597,"text":"National Ecological Observatory Network","active":true,"usgs":false}],"preferred":false,"id":868557,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weltzin, Jake 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":196323,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":true,"id":868558,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Willson, Alyssa","contributorId":303910,"corporation":false,"usgs":false,"family":"Willson","given":"Alyssa","email":"","affiliations":[{"id":65926,"text":"U Notre Dame","active":true,"usgs":false}],"preferred":false,"id":868559,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Woelmer, Whitney M.","contributorId":303911,"corporation":false,"usgs":false,"family":"Woelmer","given":"Whitney M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":868560,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70244297,"text":"70244297 - 2023 - Habitat use by breeding waterbirds in relation to tidal marsh restoration in the San Francisco Bay estuary","interactions":[],"lastModifiedDate":"2023-06-13T13:22:07.509614","indexId":"70244297","displayToPublicDate":"2023-04-01T08:14:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Habitat use by breeding waterbirds in relation to tidal marsh restoration in the San Francisco Bay estuary","docAbstract":"Near-Earth Objects (NEOs) are asteroids and comets that orbit the Sun, but have orbits that can bring them into Earth’s neighborhood—within 30 million miles of Earth’s orbit. Planetary defense is “applied planetary science” to address the NEO impact risks on Earth.
This National Preparedness Strategy and Action Plan for Near-Earth Objects and Planetary Defense (2023 Planetary Defense Strategy) updates the United States’ first comprehensive Near-Earth Object Preparedness Strategy and Action Plan, released in 2018. The 2023 Planetary Defense Strategy builds on existing efforts by Federal Departments and Agencies to address the hazard of near-Earth object impacts, includes evaluation of where progress has been made since 2018, and focuses future work on planetary defense across the U.S. government. The 2023 Planetary Defense Strategy maintains and updates the core five goals of the 2018 Strategy and Action Plan, while adding a sixth goal to improve governance and interagency coordination on planetary defense issues across Federal Departments and Agencies for the decade ahead.
The 2023 Planetary Defense Strategy focuses on six goals:
• Goal 1: Enhance NEO detection, tracking, and characterization capabilities.
• Goal 2: Improve NEO modeling, prediction, and information integration.
• Goal 3: Develop technologies for NEO deflection and disruption missions.
• Goal 4: Increase international cooperation on NEO preparation.
• Goal 5: Strengthen and routinely exercise NEO impact emergency procedures and action protocols.
• Goal 6: Improve U.S. governance of planetary defense through new interagency collaboration.
","language":"English","publisher":"White House Office of Science, Technology, and Policy (OSTP)","usgsCitation":"Daniels, M., Johnson, L., Kommel, R., Besha, P., Brody, P., Conole, K., Fast, K., Fernandez, A., Gaume, R., Greenaugh, K., Guglietta, R., Howard, D., Hu, G., Joseph, C., Keuker-Murphy, B.G., Lewis, L., Millard, L., Mozer, J., Poster, D., Titus, T.N., and Vanderley, A., 2023, National preparedness strategy & action plan for potentially hazardous near-Earth objects and planetary defense, iv, 34 p.","productDescription":"iv, 34 p.","ipdsId":"IP-150721","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":415223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415217,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.whitehouse.gov/wp-content/uploads/2023/04/2023-NSTC-National-Preparedness-Strategy-and-Action-Plan-for-Near-Earth-Object-Hazards-and-Planetary-Defense.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daniels, Matthew","contributorId":303927,"corporation":false,"usgs":false,"family":"Daniels","given":"Matthew","email":"","affiliations":[{"id":65936,"text":"Assistant Director, White House OSTP","active":true,"usgs":false}],"preferred":false,"id":868644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Lindley","contributorId":303928,"corporation":false,"usgs":false,"family":"Johnson","given":"Lindley","email":"","affiliations":[{"id":65937,"text":"Planetary Defense Officer, NASA","active":true,"usgs":false}],"preferred":false,"id":868645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kommel, Renata","contributorId":303929,"corporation":false,"usgs":false,"family":"Kommel","given":"Renata","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Besha, Patrick","contributorId":303930,"corporation":false,"usgs":false,"family":"Besha","given":"Patrick","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brody, Perry","contributorId":303931,"corporation":false,"usgs":false,"family":"Brody","given":"Perry","email":"","affiliations":[{"id":65939,"text":"DOC/NOAA","active":true,"usgs":false}],"preferred":false,"id":868648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conole, Kevin","contributorId":303932,"corporation":false,"usgs":false,"family":"Conole","given":"Kevin","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868649,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fast, Kelly","contributorId":303933,"corporation":false,"usgs":false,"family":"Fast","given":"Kelly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":868650,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fernandez, Angelo","contributorId":303934,"corporation":false,"usgs":false,"family":"Fernandez","given":"Angelo","email":"","affiliations":[{"id":65941,"text":"JCS","active":true,"usgs":false}],"preferred":false,"id":868651,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gaume, Ralph","contributorId":303935,"corporation":false,"usgs":false,"family":"Gaume","given":"Ralph","email":"","affiliations":[{"id":65942,"text":"NSF","active":true,"usgs":false}],"preferred":false,"id":868652,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Greenaugh, Kevin","contributorId":303936,"corporation":false,"usgs":false,"family":"Greenaugh","given":"Kevin","email":"","affiliations":[{"id":34199,"text":"DOE","active":true,"usgs":false}],"preferred":false,"id":868653,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Guglietta, Ryan","contributorId":303937,"corporation":false,"usgs":false,"family":"Guglietta","given":"Ryan","email":"","affiliations":[{"id":65943,"text":"State","active":true,"usgs":false}],"preferred":false,"id":868654,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Howard, Diane","contributorId":303938,"corporation":false,"usgs":false,"family":"Howard","given":"Diane","email":"","affiliations":[{"id":65944,"text":"NSpC","active":true,"usgs":false}],"preferred":false,"id":868655,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hu, Grace","contributorId":303939,"corporation":false,"usgs":false,"family":"Hu","given":"Grace","email":"","affiliations":[{"id":65945,"text":"OMB","active":true,"usgs":false}],"preferred":false,"id":868656,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Joseph, Christine","contributorId":303940,"corporation":false,"usgs":false,"family":"Joseph","given":"Christine","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":868657,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Keuker-Murphy, Brig Gen Traci","contributorId":303941,"corporation":false,"usgs":false,"family":"Keuker-Murphy","given":"Brig","email":"","middleInitial":"Gen Traci","affiliations":[{"id":65946,"text":"USSPACECOM","active":true,"usgs":false}],"preferred":false,"id":868658,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lewis, L.A.","contributorId":303942,"corporation":false,"usgs":false,"family":"Lewis","given":"L.A.","affiliations":[{"id":30786,"text":"FEMA","active":true,"usgs":false}],"preferred":false,"id":868659,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Millard, Lindsay","contributorId":303943,"corporation":false,"usgs":false,"family":"Millard","given":"Lindsay","email":"","affiliations":[{"id":65947,"text":"OSD(R&E)","active":true,"usgs":false}],"preferred":false,"id":868660,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Mozer, Joel","contributorId":303944,"corporation":false,"usgs":false,"family":"Mozer","given":"Joel","email":"","affiliations":[{"id":65948,"text":"DOD/USSF","active":true,"usgs":false}],"preferred":false,"id":868661,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Poster, Dianne","contributorId":303945,"corporation":false,"usgs":false,"family":"Poster","given":"Dianne","email":"","affiliations":[{"id":65949,"text":"DOC","active":true,"usgs":false}],"preferred":false,"id":868662,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":868663,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Vanderley, Ashley","contributorId":303946,"corporation":false,"usgs":false,"family":"Vanderley","given":"Ashley","email":"","affiliations":[{"id":65942,"text":"NSF","active":true,"usgs":false}],"preferred":false,"id":868664,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70242034,"text":"70242034 - 2023 - Low estradiol production of non-laying whooping cranes (Grus americana) is associated with the failure of small follicles to enter follicular hierarchy","interactions":[],"lastModifiedDate":"2023-04-12T14:42:48.623808","indexId":"70242034","displayToPublicDate":"2023-04-01T06:50:48","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1738,"text":"General and Comparative Endocrinology","active":true,"publicationSubtype":{"id":10}},"title":"Low estradiol production of non-laying whooping cranes (Grus americana) is associated with the failure of small follicles to enter follicular hierarchy","docAbstract":"For endangered species managed ex situ, production of offspring is a key factor to ensure healthy and self-sustaining populations. However, current breeding goals for the whooping crane (Grus americana) are impeded by poor reproduction. Our study sought to better understand mechanisms regulating ovarian function in ex situ managed whooping cranes and the regulatory function of the hypothalamic-pituitary-gonadal (HPG) axis in relation to follicle formation and egg laying. To characterize hormonal regulation of follicular development and ovulation, we collected weekly blood samples from six female whooping cranes during two breeding seasons, for a total of 11 reproductive cycles. The plasma samples were assessed for follicle stimulating hormone, luteinizing hormone, estradiol, and progesterone and the yolk precursors vitellogenin and very low-density lipoprotein. Ultrasonographic examination of the ovary was conducted at the time of blood collection. Preovulatory follicles (>12 mm) were present in laying cycles (n = 6) but absent in non-laying cycles (n = 5). The patterns of plasma hormone and yolk precursor concentrations corresponded to the stage of follicle development. Specifically, gonadotropin and yolk precursors concentrations increased as follicles transitioned from the non-yolky to yolky stage but did not increase further as the follicle advanced to preovulatory and ovulatory stages. Estrogen and progesterone concentrations increased as follicle size increased and reached peak concentrations (P < 0.05) when follicles developed to ovulatory and preovulatory stages, respectively. While overall mean circulating gonadotropin, progesterone, and yolk precursor concentrations did not differ for laying versus non-laying cycles, mean plasma estradiol in laying cycles was significantly higher than that in non-laying cycles. In summary, the findings suggested that disruption of mechanisms regulating follicle recruitment is likely responsible for the oviposition failure of the captive female whooping crane.
The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d−1, above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.
Fall bottom trawl (fall BT) and lakewide acoustic (AC) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The fall BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m at fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species [i.e., Alewife (Alosa pseudoharengus), Bloater (Coregonus hoyi), Rainbow Smelt (Osmerus mordax), Deepwater Sculpin (Myoxocephalus thompsonii), Slimy Sculpin (Cottus cognatus), Round Goby (Neogobius melanostomus), Ninespine Stickleback (Pungitius pungitius)] as well as age-0 Yellow Perch (Perca flavescens) and large (> 350 mm) Burbot (Lota lota). The AC survey has been conducted each late summer/early fall since 2004, and the 2022 survey consisted of 26 transects [570 km total (354 miles)] covering bottom depths ranging from 5 to 255 m and 37 midwater trawl tows above bottom depths ranging 5 to 232 m; this survey estimates densities of three prey fish species (i.e., Alewife, Bloater, and Rainbow Smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks. In spring of 2022, an additional spring bottom trawl survey (spring BT) was implemented across six of the transects sampled in the fall and sites ranged in depth from 9 to 236 m. The goal of the spring BT was to explore seasonal differences in biomass density and distributions of key prey species, mostly notably Alewife.
Total prey fish biomass density from the spring BT was 2.1 kg/ha. For the AC survey, total biomass density of prey fish equaled 6.2 kg/ha, 37% higher than the long-term average (2004-2021) of 4.5 kg/ha and 0.43 kg/ha higher than the 2021 estimate. For the fall BT, total biomass density of prey fish equaled 8.7 kg/ha, the highest value since 2013 and 21% higher than average value from 20042021 (6.8 kg/ha). The 2022 fall BT biomass density was still well below the average over the entirety of the time series (1973-2021; 34.3 kg/ha). Over the period both surveys have been conducted (2004-2021), total biomass density has trended downward in the fall BT (despite a high 2022 estimate) and remained relatively stable in the AC survey.
Bloater was the dominant species (by biomass) among prey fishes in both the spring and fall BT, while the AC survey reported co-dominance of Bloater and Alewife. Mean biomass of yearling and older (YAO) Alewife was 0.38 kg/ha in the spring BT, 3.0 kg/ha in the AC survey, and 0.10 in the fall BT. Alewife were aggregated in deepwater habitats in the spring of 2022 (> 110 m). Since 2014, catchability of YAO Alewives for the fall BT has been substantially lower than the AC survey. Results of the 2022 spring BT do not suggest that catchability is substantially higher in the spring than the fall.
Comparing the acoustic estimate to previous years, YAO Alewife biomass was 40% higher than the average from 2004-2021. An age-7 fish was recorded for the first time since 2009. Despite the rare catches of older fish, the Alewife age distribution still appears truncated, with age-1 fish as the most represented age class in all three surveys. Numeric density of age-0 Alewife from the AC survey was 7 fish/ha in 2022, which is the third lowest in the time series and well below the longterm mean of 452 fish/ha. Biomass density of large (≥120 mm) Bloater was 2.7 kg/ha in the AC survey and 4.4 kg/ha in the fall BT - each at least an order of magnitude lower than what was estimated by the fall BT between 1981 and 1998. Following a record high year in 2021 (1,037 fish/ha), the numeric density of small (<120 mm) Bloater was only 15 fish/ha in the AC survey.
Meanwhile, small Bloater density estimated in the fall BT was 261 fish/ha, the highest value since 1990 and likely partially reflective of a large 2021 year-class. Biomass density of large Rainbow Smelt (≥90 mm) was 0.29 kg/ha in the AC survey and 0.12 kg/ha in the fall BT survey, continuing the trend of low Rainbow Smelt biomass that has been observed since 2001. Numeric density of small (<90 mm) Rainbow Smelt was 21 fish/ha in the AC survey and 2.7 fish/ha in the fall BT, indicating a weak year-class. All four prey fish species sampled only by the fall BT indicated below average biomass densities. Deepwater Sculpin biomass density was estimated at 0.41 kg/ha, which makes 12 of the past 13 years when biomass was <1 kg/ha. Slimy Sculpin was estimated at 0.10 kg/ha, the highest estimate since 2016 but still only 25% of the long-term average. Round Goby was estimated at 1.3 kg/ha, above the average biomass of 0.82 kg/ha since 2008 but similar to intermittent high values observed throughout the dataset. Ninespine Stickleback density was 1.5 fish/ha. Burbot biomass remained near record low levels, and no age-0 Yellow Perch were caught, indicating a weak Yellow Perch year-class in 2022.
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Excessive levels of filamentous algae can compromise aesthetic quality, limit recreational activities, and have negative effects on aquatic life including strong fluctuations in dissolved oxygen levels and a reduction in overall biodiversity. To increase understanding of the biology of the upper White River Basin in Colorado, identify potential factors promoting or limiting nuisance algal abundance, and outline information to aid in the understanding and protection of water resources, the U.S. Geological Survey (USGS), in cooperation with the White River and Douglas Creek Conservation Districts and the White River Algae Technical Advisory Group, initiated a study to collect and analyze physical, chemical, and biological information for the upper White River Basin. The report describes long-term changes and spatial variations in streamflow and nutrient concentrations and loads in the upper White River Basin and identifies possible nutrient sources in the basin.
Long-term streamflow and nutrient data indicate that conditions in the upper White River Basin have become more favorable to benthic algae over varying timescales. Upward trends in total phosphorus concentrations and loads were found at three sites across the basin from 2000 to 2020. Total phosphorus loads increased around 50 percent, ranging from 18 to 48 pounds per year. Annual estimated concentrations of total phosphorus from 2005 to 2020 were above algal-specific nutrient criteria at the North Fork White River at Buford, Colo., indicating that phosphorus concentrations at this site likely promote algal growth. Discrete concentrations of total phosphorus exceeded algal-specific nutrient criteria on the South Fork and main stem White River during the summer season, though less frequently than samples collected from the North Fork White River. Nitrogen to phosphorus molar ratios collected from July to September indicate movement from colimitation (10–22) to nitrogen limited (less than 13) conditions at the North Fork White River at Buford, Colo. and the South Fork White River at Buford, Colo. starting in 2012. The magnitude of trends in phosphorus loads were generally greater than trends in concentrations across all sites, indicating that the largest changes in concentrations occurred during greater streamflow periods.
At White River above Coal Creek, near Meeker, Colo., significant downward trends in streamflow were found in August and September for mean streamflow (15 and 14 percent per decade, respectively) and 7-day minimum streamflows (23 and 22 percent per decade, respectively). Significant downward trends in annual 7-day minimum streamflows of 24 percent per decade, or 66 percent over the 40-year period of analysis, were also observed. Though not significant based on 90-percent confidence intervals, downward trends in 1-day maximum and mean streamflows in May and June and corresponding increases in April may indicate a shift toward earlier snowmelt runoff, as observed across western North America and the Colorado River Basin. Alteration of the annual hydrograph can influence factors that influence algae including nutrient input and dilution potential, water temperature, dissolved oxygen, light availability, and physical disturbance.
Results from a synoptic-style sampling identified the lower North Fork White River subbasin as a large source of phosphorus to the downstream system. Large increases in phosphorus loads were observed below Marvine Creek. Synoptic samples and samples collected during spring and summer of 2019 and 2020 also show large increases in total nitrogen, orthophosphate, and total phosphorus occurring at the furthest three downstream sites on the White River. To further evaluate sources of nitrogen in the upper White River Basin, the dual isotopic composition of nitrate was compared across four sites. The isotopic compositions of nitrate were all within the expected range of typical soil-derived nitrate, though the same values can also be derived from a mixture of agricultural fertilizer and manure or septic sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225112","collaboration":"Prepared in cooperation with the White River and Douglas Creek Conservation Districts","usgsCitation":"Day, N.K., 2023, Characterization of streamflow and nutrient occurrence in the upper White River Basin, Colorado, 1980–2020: U.S. Geological Survey Scientific Investigations Report 2022–5112, 37 p., https://doi.org/10.3133/sir20225112.","productDescription":"Report: vi, 37 p.; 2 Data Release","onlineOnly":"Y","ipdsId":"IP-133327","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":414992,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":415083,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5112/sir20225112.xml"},{"id":415082,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5112/images"},{"id":415567,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5112"},{"id":414990,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235009","text":"USGS Scientific Investigations Report 2023-5009—","linkHelpText":"Investigation of Potential Factors Controlling Benthic Algae in the Upper White River Basin, Colorado, 2018–21"},{"id":414991,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E82RMQ","text":"USGS data release","linkHelpText":"Channel Characteristics, benthic algae, and water quality model data for selected sites in the upper White River Basin, Colorado, 2018-21"},{"id":414989,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233005","text":"USGS Fact Sheet 2023-3005—","linkHelpText":"Potential Factors Controlling Benthic Algae in the Upper White River Basin, Colorado, 2018–21"},{"id":414988,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5112/sir20225112.pdf","text":"Report","size":"7.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5112"},{"id":414987,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5112/coverthb.jpg"},{"id":500459,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114622.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.20175418327835,\n 40.550962714804655\n ],\n [\n -108.20175418327835,\n 39.298023775605145\n ],\n [\n -105.58075670984697,\n 39.298023775605145\n ],\n [\n -105.58075670984697,\n 40.550962714804655\n ],\n [\n -108.20175418327835,\n 40.550962714804655\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Nuisance levels of benthic filamentous green algae are becoming increasingly common in surface waters of Colorado and the western United States. In 2018 the U.S. Geological Survey began a study in cooperation with the White River and Douglas Creek Conservation Districts, Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District to collect and analyze physical, chemical, and biological information for the upper White River Basin in Colorado and investigate causes of benthic algal blooms in the basin. This report (1) presents site-specific data including water temperature, riparian canopy cover, streambed particle size, and algal biomass and community composition; (2) describes the potential for streambed movement during spring runoff using physical channel characteristics and peak streamflow velocities; and (3) explains the results of a linear mixed-effects model used to test hypotheses about the influence of physical and chemical factors in explaining the occurrence of algal blooms across the basin.
Benthic algal biomass ranged from 0.7 to 309 milligrams per square meter during the summer (July–August) from 2018 through 2021 and exceeded the Colorado Department of Public Health and Environment criteria of 150 milligrams per square meter on four occasions, in 2018. Four genera of filamentous green algae were identified in the upper White River Basin, including Cladophora, Stigeoclonium, Ulothrix, and Spirogyra. Many genera of cyanobacteria were present, including some capable of producing toxins and taste and odor compounds. The nuisance diatom Didymosphenia geminata, commonly referred to as didymo, was found at two sites on the South Fork White River and along the main stem White River.
Hypotheses pertaining to the influence of measured variables on algal biomass were tested with a linear mixed-effects model. Median rock size and mean August water temperature had significant positive effects, meaning that greater bed stability and higher mean August water temperatures result in greater algal biomass. Total nitrogen to total phosphorus ratios had a significant negative effect on algal biomass, meaning that more nitrogen-limiting conditions, or greater phosphorus availability, corresponded to greater algal biomass.
Streamflow and water temperature data at White River above Coal Creek near Meeker, Colo., were used to assess possible causes of bloom conditions across years, including when algal blooms were first studied in the basin during 2016 and 2017. Early or low-magnitude peak streamflow conditions were not prerequisites for algal bloom occurrence. Conversely, relatively large, late, and long-lasting peak streamflows, such as those measured in 2019, may limit algal blooms during the same year and into subsequent years, as evidenced by extremely low algal biomass in 2019 and 2020. The broad spatial extent of bloom conditions indicates that the factors contributing to the occurrence of algal blooms are likely basinwide. Findings from this multiyear study indicate that the effects caused by larger peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of an algal bloom. The findings also indicate that in the absence of disturbance other resources, including substrate size, water temperature, and nutrient availability, moderate algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Nuisance levels of benthic algae are becoming increasingly common in surface waters of the western United States and can compromise aesthetic quality, limit recreational activities, block water infrastructure, and negatively affect aquatic life. In cooperation with the White River and Douglas Creek Conservation Districts, the Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District, the U.S. Geological Survey studied physical, chemical, and biological factors potentially controlling the occurrence of benthic algae in the upper White River Basin, Colorado, from 2018 through 2021. Multiple approaches were used to assess nutrients and physical conditions in the upper White River Basin. A linear mixed-effects model was used to evaluate the relative effect of different factors on algal biomass across water-quality sites.
The frequency and severity of algal blooms in the upper White River Basin may be affected by long-term changes in nutrient availability and streamflow, specifically changes in the timing and magnitude of high and low streamflow. The effects of large peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of algal blooms through years. Large, late, and long-lasting peak streamflow may limit algal blooms during the same year and into subsequent years. Without streambed disturbance, other factors such as nutrients and water temperature may have a larger effect on algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Bathymetric change analyses document historical patterns of sediment deposition and erosion, providing valuable insight into the sediment dynamics of coastal systems, including pathways of sediment and sediment-bound contaminants. In 2014 and 2015, the Office for Coastal Management, in partnership with the National Oceanic and Atmospheric Administration (NOAA) Office of Coastal Management, provided funding for new bathymetric surveys of large portions of San Francisco Bay. A total of 93 bathymetric surveys were conducted during this 2-year period, using a combination of interferometric sidescan and multibeam sonar systems. These data, along with recent NOAA, U.S. Geological Survey (USGS), U.S. Army Corps of Engineers, and private contractor surveys collected from 1999 to 2020 (hereinafter referred to as 2010s), were used to create the most comprehensive bathymetric digital elevation models (DEMs) of San Francisco Bay since the 1980s. Comparing DEMs created from these 2010s surveys with USGS DEMs created from NOAA’s 1971–1990 (hereinafter referred to as 1980s) surveys provides information on the quantities and patterns of erosion and deposition in San Francisco Bay during the 9 to 47 years between surveys. This analysis reveals that in the areas surveyed in both the 1980s and 2010s, the bay floor lost about 34 million cubic meters of sediment since the 1980s. Results from this study can be used to assess how San Francisco Bay has responded to changes in the system, such as sea-level rise and variation in sediment supply from the Sacramento-San Joaquin Delta and local tributaries, and supports the creation of a new, system-wide sediment budget. This report provides data on the quantities and patterns of sediment volume change in San Francisco Bay for ecosystem managers that are pertinent to various sediment-related issues, including restoration of tidal marshes, exposure of legacy contaminated sediment, and strategies for the beneficial use of dredged sediment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231031","collaboration":"Prepared in cooperation with the Regional Monitoring Program for Water Quality in San Francisco Bay","usgsCitation":"Fregoso, T.A., Foxgrover, A.C., and Jaffe, B.E., 2023, Sediment deposition, erosion, and bathymetric change in San Francisco Bay, California, 1971–1990 and 1999–2020 (ver. 1.1, June 2024): U.S. Geological Survey Open-File Report 2023–1031, 19 p., https://doi.org/ 10.3133/ ofr20231031.","productDescription":"Report: vi, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-135389","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":435389,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1332UUW","text":"USGS data release","linkHelpText":"Bathymetric change analysis in San Francisco Bay, California, from 1971 to 2020"},{"id":430608,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1031/versionHist.txt","size":"10.7 KB","linkFileType":{"id":2,"text":"txt"}},{"id":415025,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1031/images"},{"id":415024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1031/ofr20231031.pdf","text":"Report","size":"15.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1031/coverthb2.jpg"},{"id":499186,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114619.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.84417858005195,\n 38.240616044555935\n ],\n [\n -122.84417858005195,\n 37.276937922454465\n ],\n [\n -121.28479077260828,\n 37.276937922454465\n ],\n [\n -121.28479077260828,\n 38.240616044555935\n ],\n [\n -122.84417858005195,\n 38.240616044555935\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: March 31, 2023; Version 1.1: June 28, 2024","contact":"Pacific Coastal and Marine Science Center
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Development of Seismic Source Characterization (SSC) models, which is an essential part of Probabilistic Seismic Hazard Analyses (PSHA), can help forecast the temporal and spatial distribution of future damaging earthquakes (\uD835\uDC40w≥ 5) in seismically active regions. Because it is impossible to associate all earthquakes with known faults, seismic source models for PSHA often include sources of diffuse seismicity in which future earthquake scenarios are not localized on mapped faults. These sources of diffuse seismicity are referred to as area source zones, distributed seismicity zones, or just source zones. During the early years of PSHA studies, it was assumed that earthquakes in seismotectonic zones have (1) uniform spatial distribution, (2) Poisson temporal distribution, and (3) exponential magnitude distribution (NRC, 2012). In seismically active regions (e.g., the Western United States), where active faults are readily identified, models of the spatial distribution of earthquakes include both the fault source geometries and the distributed seismicity (background) source zones. Source characterization of active faults is complemented by paleoseismic studies with estimates of earthquake magnitudes, dates of occurrences, and slip rates, which provide important information for PSHA studies.
In the Central and Eastern United States (CEUS) very few Quaternary-active faults have the requisite information for use in PSHA (i.e., fault geometry and dimensions, event rates or slip rates, etc.), and we lack knowledge about the causative faults for most observed seismicity in the region. As a result, area source zones are frequently used in site-specific PSHA in the CEUS to represent diffuse seismicity that cannot be associated with faults. However, there are examples of active fault sources in the CEUS, such as the Meers fault, the Cheraw fault, and New Madrid region, where individual faults can be characterized.
The source characterization models for background seismicity are based, to a large extent, on an assumption that spatial distribution of historical and recorded seismicity will not change substantially for time periods of interest for PSHA (approximately the next 50-100 years for engineered structures). Furthermore, studies such as those by Kafka (2007, 2009) found a correlation between the locations of small- to moderate-magnitude earthquakes and the locations of large-magnitude earthquakes, indicating that we can, with some level of confidence, use the spatial pattern of smaller earthquakes to forecast the future pattern of damaging earthquakes.
Within background seismicity zones, the earthquake rate forecast is developed using spatial smoothing of the small to moderate magnitude events in earthquake catalogs. Different methodologies are used for this purpose and can predict varying distributions of seismicity rates. This in turn affects the results of a seismic hazard analysis. The U.S. Geological Survey (USGS) and Nuclear Regulatory Commission (NRC) use different methods for computing spatially smoothed seismicity rates in the CEUS; the USGS uses kernel-based spatial smoothing methods in developing the National Seismic Hazard Model (NSHM), and the method adopted in the Central and Eastern United States Seismic Source Characterization (CEUS-SSC) project is used when evaluating seismic hazard for nuclear power plant siting. These methods are described and the impact on seismic hazard are evaluated in this Research Information Letter (RIL).
Another important input to estimating the rate of distributed seismicity is event magnitudes listed in earthquake catalogs. A substantial source of uncertainty in catalogs is the magnitude assigned to a given earthquake. Numerous different magnitude types exist, with each magnitude type computed in a different way. Therefore, for the sake of consistency, both the CEUS-SSC and the USGS NSHM have attempted to assemble a complete catalog with a uniform magnitude determination. To this end, moment magnitude, \uD835\uDC40w, which is a physics-based measurement, has been adopted as the standard. However, \uD835\uDC40w was not computed routinely until the past few decades. To address this issue, the CEUS-SSC conducted extensive analyses to determine conversion equations from which to take a routinely computed network (e.g., \uD835\uDC40L or \uD835\uDC5AbLg ) and convert it into \uD835\uDC40w. Another issue with using \uD835\uDC40w is that it becomes increasingly difficult to compute for earthquakes with \uD835\uDC40 less than ~4.
This study investigates the effects of moment magnitude estimation and spatial smoothing methods on estimation of the earthquake rate forecast and on seismic hazard. We investigate the validity of the magnitude conversion equations and their associated uncertainties by applying them to a case study for induced earthquakes in southern Kansas and northern Oklahoma, and summarize the use of the decay of the seismic coda to estimate \uD835\uDC40w for small earthquakes (\uD835\uDC40w < 4. Furthermore, the study documents a comparison and assessment of background seismicity smoothing methods implemented by the USGS for the NSHM and used by the CEUS-SSC for siting nuclear facilities based on probabilistic seismic hazard estimates from multiple source zones in the CEUS and for multiple sites.
","language":"English","publisher":"Nuclear Regulatory Commission","usgsCitation":"Anooshehpoor, R., Weaver, T., Ake, J., Munson, C., Moschetti, M.P., Shelly, D.R., and Powers, P.M., 2023, Magnitude conversion and earthquake recurrence rate models for the central and eastern United States: Research Information Letter 2023-03, 81 p.","productDescription":"81 p.","ipdsId":"IP-148166","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":415346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415324,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://adamswebsearch2.nrc.gov/webSearch2/main.jsp?AccessionNumber=ML23073A370","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","otherGeospatial":"central and eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115,\n 50\n ],\n [\n -115,\n 25\n ],\n [\n -65,\n 25\n ],\n [\n -65,\n 50\n ],\n [\n -115,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anooshehpoor, Rasool","contributorId":303980,"corporation":false,"usgs":false,"family":"Anooshehpoor","given":"Rasool","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weaver, Thomas","contributorId":303981,"corporation":false,"usgs":false,"family":"Weaver","given":"Thomas","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ake, Jon","contributorId":303982,"corporation":false,"usgs":false,"family":"Ake","given":"Jon","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868792,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munson, Cliff","contributorId":303983,"corporation":false,"usgs":false,"family":"Munson","given":"Cliff","email":"","affiliations":[{"id":34771,"text":"Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":868793,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868794,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868795,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":868796,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255002,"text":"70255002 - 2023 - Using decision analysis to determine the feasibility of a conservation translocation","interactions":[],"lastModifiedDate":"2024-06-11T15:23:15.270351","indexId":"70255002","displayToPublicDate":"2023-03-31T10:19:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14243,"text":"Decision Analysis","active":true,"publicationSubtype":{"id":10}},"title":"Using decision analysis to determine the feasibility of a conservation translocation","docAbstract":"Conservation translocations, intentional movements of species to protect against extinction, have become widespread in recent decades and are projected to increase further as biodiversity loss continues worldwide. The literature abounds with analyses to inform translocations and assess whether they are successful, but the fundamental question of whether they should be initiated at all is rarely addressed formally. We used decision analysis to assess northern leopard frog reintroduction in northern Idaho, with success defined as a population that persists for at least 50 years. The Idaho Department of Fish and Game was the decision maker (i.e., the agency that will use this assessment to inform their decisions). Stakeholders from government, indigenous groups, academia, land management agencies, and conservation organizations also participated. We built an age-structured population model to predict how management alternatives would affect probability of success. In the model, we explicitly represented epistemic uncertainty around a success criterion (probability of persistence) characterized by aleatory uncertainty. For the leading alternative, the mean probability of persistence was 40%. The distribution of the modelling results was bimodal, with most parameter combinations resulting in either very low (<5%) or relatively high (>95%) probabilities of success. Along with other considerations, including cost, the Idaho Department of Fish and Game will use this assessment to inform a decision regarding reintroduction of northern leopard frogs. Conservation translocations may benefit greatly from more widespread use of decision analysis to counter the complexity and uncertainty inherent in these decisions.
","language":"English","publisher":"Informs","doi":"10.1287/deca.2023.0472","usgsCitation":"Keating, L., Randall, L., Stanton, R., McCormack, C., Lucid, M., Seaborn, T., Converse, S.J., Canessa, S., and Moehrenschlager, A., 2023, Using decision analysis to determine the feasibility of a conservation translocation: Decision Analysis, v. 20, no. 4, p. 295-310, https://doi.org/10.1287/deca.2023.0472.","productDescription":"16 p.","startPage":"295","endPage":"310","ipdsId":"IP-142737","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443992,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/2434/1040192","text":"External Repository"},{"id":429880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keating, Laura","contributorId":338249,"corporation":false,"usgs":false,"family":"Keating","given":"Laura","email":"","affiliations":[{"id":81105,"text":"Wilder Institute/Calgary Zoo","active":true,"usgs":false}],"preferred":false,"id":903054,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Randall, Lea","contributorId":338250,"corporation":false,"usgs":false,"family":"Randall","given":"Lea","email":"","affiliations":[{"id":81105,"text":"Wilder Institute/Calgary Zoo","active":true,"usgs":false}],"preferred":false,"id":903055,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Rebecca","contributorId":338251,"corporation":false,"usgs":false,"family":"Stanton","given":"Rebecca","email":"","affiliations":[{"id":81105,"text":"Wilder Institute/Calgary Zoo","active":true,"usgs":false}],"preferred":false,"id":903056,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCormack, Casey","contributorId":338252,"corporation":false,"usgs":false,"family":"McCormack","given":"Casey","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":903057,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lucid, Michael","contributorId":338253,"corporation":false,"usgs":false,"family":"Lucid","given":"Michael","email":"","affiliations":[{"id":81108,"text":"Selkirk Wildlife Science, LLC","active":true,"usgs":false}],"preferred":false,"id":903058,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Seaborn, Travis","contributorId":338254,"corporation":false,"usgs":false,"family":"Seaborn","given":"Travis","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":903059,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903060,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Canessa, Stefano","contributorId":149295,"corporation":false,"usgs":false,"family":"Canessa","given":"Stefano","email":"","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":903133,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moehrenschlager, Axel","contributorId":338100,"corporation":false,"usgs":false,"family":"Moehrenschlager","given":"Axel","affiliations":[{"id":56586,"text":"czs","active":true,"usgs":false}],"preferred":false,"id":903134,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263433,"text":"70263433 - 2023 - The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake","interactions":[],"lastModifiedDate":"2025-02-11T15:25:05.142851","indexId":"70263433","displayToPublicDate":"2023-03-31T09:20:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2467,"text":"Journal of Structural Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake","docAbstract":"Seismic behavior and performance of the new Self- Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System is studied using response data recorded during the October 14, 2019, \uD835\uDC40\uD835\uDC644.6 Pleasant Hill earthquake. The new bridge went into service within the last decade as a replacement for the older truss bridge that spanned between Yerba Buena Island and East Bay. During the October 19, 1989, M6.9 Loma Prieta earthquake, which occurred ∼100 km away from the Bay Bridge, a section of the upper deck of the old truss bridge fell onto the lower deck—thus closing this important lifeline between San Francisco and East Bay. The new SAS Bridge (as well as the rest of the Bay Bridge) is instrumented by the California Strong Motion Instrumentation Program (CSMIP). The unique SAS Bridge is suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the eastbound (E) and westbound (W) decks. At both the west and east ends of the SAS, there is a hinge system that connects the W and E decks to the skyways leading to highways. For the west side, the SAS is led to a tunnel at Yerba Buena Island. The response data analyses highlight the complex and yet identifiable coupled response of the deck, tower, and cable system. Using system identification methods including spectral analyses of both acceleration and displacement time history data, the fundamental frequencies (periods) and critical damping percentages are extracted for the main components (tower, deck, and cables) of the bridge where the sensors are deployed. Frequencies (periods) are then compared with the values computed during the design and analysis process of the bridge. The analyses in this paper showed that there is strong evidence of a beating effect attributed to low critical damping percentages and coupled modes. A possible correlation of fundamental periods of such suspension bridges with their span lengths is discussed. The beating effect and period versus span length can be significant topics for further research.
","language":"English","publisher":"American Society of Civil Engineering","doi":"10.1061/JSENDH.STENG-11725","usgsCitation":"Celebi, M., 2023, The new Self Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System: A preliminary study of its response and behavior during a small earthquake: Journal of Structural Engineering, v. 149, no. 6, 05023003, 12 p., https://doi.org/10.1061/JSENDH.STENG-11725.","productDescription":"05023003, 12 p.","ipdsId":"IP-138272","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jsendh.steng-11725","text":"Publisher Index Page"},{"id":481928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Bridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.30992911723865,\n 37.83469490117358\n ],\n [\n -122.36848380330268,\n 37.83469490117358\n ],\n [\n -122.36848380330268,\n 37.80847229984835\n ],\n [\n -122.30992911723865,\n 37.80847229984835\n ],\n [\n -122.30992911723865,\n 37.83469490117358\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"149","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":926975,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70263877,"text":"70263877 - 2023 - Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy","interactions":[],"lastModifiedDate":"2025-02-27T14:48:12.355469","indexId":"70263877","displayToPublicDate":"2023-03-31T08:41:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy","docAbstract":"Understanding the timing and drivers of migration can be beneficial for improving response efforts aimed at reducing invasive species densities. Efforts by management agencies to remove grass carp (Ctenopharyngodon idella), an invasive species to the Laurentian Great Lakes, have been ongoing in Lake Erie tributaries since 2018. To bolster efforts, deployment of a non-physical barrier has been proposed downstream of a known grass carp spawning location near Brady’s Island (BI) in the Sandusky River, OH, USA to limit recruitment. However, knowledge of grass carp migratory timing, the environmental variables that cue carp migration, and the potential effects the barrier might impose on native fish [e.g., walleye (Sander vitreus)] movements would help inform barrier deployment and scheduling. We used detection data from grass carp (n = 29) and walleye (n = 84) tagged with acoustic transmitters to address four objectives: (1) quantify interannual variation (years = 2015–2021) of grass carp migration timing to BI; (2) evaluate timing of different grass carp movement modalities (residents and migrants); (3) assess overlap in migration timing with native walleye, and (4) evaluate environmental cues of grass carp migration to BI. Median grass carp arrival at BI occurred within a three-week period (148–165 Julian days), suggesting that deploying a barrier immediately prior to this time frame may be effective for deterring grass carp spawning. Temperature, photoperiod, and discharge influenced grass carp migration timing given that most arrival events occurred at daylengths > 14.5 h, temperatures exceeding 18 °C, and low discharge events (< 3,000 cubic feet second−1 [CFS]). Minimal interannual variability in migration timing existed for grass carp and walleye over a six-year period. However, the median departure time of walleye was more than 45 days before the median arrival time of grass carp, suggesting a spawning barrier may minimally affect walleye spawning. No differences in arrival timing at BI were observed between grass carp migratory contingents, indicating that if a barrier were deployed in the spring, it would likely affect all grass carp spatial contingents. This work highlights management implications of barrier control efforts of aquatic invasive species and provides insight into the environmental cues that grass carp use for upstream migration.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-023-03049-9","usgsCitation":"Bopp, J., Brenden, T.O., Faust, M., Vandergoot, C., Kraus, R., Roberts, J., and Nathan, L., 2023, Drivers and timing of grass carp movement within the Sandusky River, Ohio: Implications to potential spawning barrier response strategy: Biological Invasions, v. 25, p. 2439-2459, https://doi.org/10.1007/s10530-023-03049-9.","productDescription":"21 p.","startPage":"2439","endPage":"2459","ipdsId":"IP-140268","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":482553,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.5,\n 41.6\n ],\n [\n -83.5,\n 41.6\n ],\n [\n -83.5,\n 41.3\n ],\n [\n -82.5,\n 41.3\n ],\n [\n -82.5,\n 41.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Bopp, Justin","contributorId":340933,"corporation":false,"usgs":false,"family":"Bopp","given":"Justin","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":928798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":928799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faust, Matthew D.","contributorId":348473,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew D.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":928800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher","contributorId":351529,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher","affiliations":[{"id":84005,"text":"Michigan State University/GLATOS","active":true,"usgs":false}],"preferred":false,"id":928801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":928803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":928802,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nathan, Lucas","contributorId":351530,"corporation":false,"usgs":false,"family":"Nathan","given":"Lucas","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":928804,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242013,"text":"70242013 - 2023 - The Everglades vulnerability analysis: Linking ecological models to support ecosystem restoration","interactions":[],"lastModifiedDate":"2023-06-08T14:48:49.884131","indexId":"70242013","displayToPublicDate":"2023-03-31T07:08:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"The Everglades vulnerability analysis: Linking ecological models to support ecosystem restoration","docAbstract":"Understanding of the Everglades’ ecological vulnerabilities and restoration needs has advanced over the past decade but has not been applied in an integrated manner. To address this need, we developed the Everglades Vulnerability Analysis (EVA), a decision support tool that uses modular Bayesian networks to predict the ecological outcomes of a subset of the ecosystem’s health indicators. This tool takes advantage of the extensive modeling work already done in the Everglades and synthesizes information across indicators of ecosystem health to forecast long-term, landscape-scale changes. In addition, the tool can predict indicator vulnerability through comparison to user-defined ideal system states that can vary in the level of certainty of outcomes. An integrated understanding of the Everglades system is essential for evaluation of trade-offs at local, regional, and system-wide scales. Through EVA, Everglades restoration decision makers can provide effective guidance during restoration planning and implementation processes to mitigate unintended consequences that could result in further damage to the Everglades system.
In the present study, we evaluate the temporal variability in runup and total water level for sandy beaches along Cape Cod (Massachusetts, USA), and their impact on dune and beach erosion. We use a 43-year hindcast of waves and water levels and calculate runup and total water level based on the Stockdon formulation using previously extracted beach slopes. The dominant components of the runup are identified and their temporal variability evaluated. The seasonal and interannual variability of total water level is evaluated. For most locations along the outer Cape Cod coast, the comparison between total water level and dune elevations suggested that the coastal response remained predominantly under swash regime. The results over these study locations could be extended to other similar areas at regional scales to provide better characterization of total water level and coastal change at long temporal scales.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, LA","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0024","usgsCitation":"Aretxabaleta, A., Sherwood, C.R., Blanton, B., Over, J.R., Traykovski, P.A., and Sogut, E., 2023, Temporal variability of runup and total water level on Cape Cod sandy beaches, in The proceedings of the coastal sediments 2023, New Orleans, LA, April 11-15, 2023, p. 267-281, https://doi.org/10.1142/9789811275135_0024.","productDescription":"15 p.","startPage":"267","endPage":"281","ipdsId":"IP-142382","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416230,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.6829885324346,\n 42.11339300926423\n ],\n [\n -70.6829885324346,\n 41.483021409405666\n ],\n [\n -69.82916879983807,\n 41.483021409405666\n ],\n [\n -69.82916879983807,\n 42.11339300926423\n ],\n [\n -70.6829885324346,\n 42.11339300926423\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blanton, B.O.","contributorId":304434,"corporation":false,"usgs":false,"family":"Blanton","given":"B.O.","email":"","affiliations":[{"id":66069,"text":"Renaissance Computing Institute","active":true,"usgs":false}],"preferred":false,"id":870464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Over, Jin-Si R. 0000-0001-6753-7185 jover@usgs.gov","orcid":"https://orcid.org/0000-0001-6753-7185","contributorId":260178,"corporation":false,"usgs":true,"family":"Over","given":"Jin-Si","email":"jover@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870465,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Traykovski, Peter A. 0000-0002-8163-6857","orcid":"https://orcid.org/0000-0002-8163-6857","contributorId":69487,"corporation":false,"usgs":false,"family":"Traykovski","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":6706,"text":"Woods Hole Oceanographic Institution,","active":true,"usgs":false}],"preferred":false,"id":870466,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sogut, Erdinc 0000-0002-8291-9429","orcid":"https://orcid.org/0000-0002-8291-9429","contributorId":304424,"corporation":false,"usgs":true,"family":"Sogut","given":"Erdinc","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870467,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70242804,"text":"70242804 - 2023 - Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact","interactions":[],"lastModifiedDate":"2023-06-08T14:49:26.09138","indexId":"70242804","displayToPublicDate":"2023-03-31T06:59:58","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact","docAbstract":"Overwash is an important process that enables a barrier island to migrate landward to adapt to rising sea levels but can also impact vegetated areas and create coastal hazards for populated barrier islands. Our overall objectives were to hindcast overwash events from September 2008 to November 2009 and assess whether overwash impacts could be detected using moderate-resolution imagery (30 m). Estimates of wave and still water levels can be benchmarked against morphological characteristics from elevation datasets to predict overwash events. These observations can be combined with optical remote sensing data used to monitor for changes in vegetation greenness over time to evaluate potential impacts from overwash. This study highlighted how physical-based overwash data can be paired with observations of greenness. The results from our study highlighted that a discernable drop in greenness can be detected for major hurricanes, such as Hurricane Gustav in 2008, with a weaker signal observed for smaller magnitude events in 2009 like Hurricane Ida. Tracking overwash impacts to vegetation can be helpful for observing impacts to vegetation associated with restoration efforts and advancing our understanding of general overwash impacts and recovery.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0203","collaboration":"The Water Institute of the Gulf, U.S. Army Corps of Engineers","usgsCitation":"Enwright, N., Dalyander, P., Jenkins, R.L., Godsey, E.S., and Stelly, S.J., 2023, Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact, in The proceedings of the coastal sediments 2023, p. 2206-2219, https://doi.org/10.1142/9789811275135_0203.","productDescription":"14 p.; Data Release","startPage":"2206","endPage":"2219","ipdsId":"IP-147117","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":415994,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417818,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A19Q8J"}],"noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217781,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":869824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":869825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenkins, Robert L 0000-0002-9163-7773 rljenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-9163-7773","contributorId":304231,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert","email":"rljenkins@usgs.gov","middleInitial":"L","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":869826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godsey, Elizabeth S.","contributorId":304232,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","email":"","middleInitial":"S.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":869827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stelly, Spencer J. 0000-0003-1050-1733","orcid":"https://orcid.org/0000-0003-1050-1733","contributorId":215852,"corporation":false,"usgs":false,"family":"Stelly","given":"Spencer","email":"","middleInitial":"J.","affiliations":[{"id":39319,"text":"Student Services Contractor at the U.S. Geological Survey Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":869828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70242005,"text":"70242005 - 2023 - Monkeypox virus in animals: Current knowledge of viral transmission and pathogenesis in wild animal reservoirs and captive animal models","interactions":[],"lastModifiedDate":"2023-09-29T16:15:43.39572","indexId":"70242005","displayToPublicDate":"2023-03-31T06:44:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3700,"text":"Viruses","active":true,"publicationSubtype":{"id":10}},"title":"Monkeypox virus in animals: Current knowledge of viral transmission and pathogenesis in wild animal reservoirs and captive animal models","docAbstract":"Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.
We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.
Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.15163","usgsCitation":"Aunins, A.W., Mueller, S.J., Fike, J., and Cornman, R.S., 2023, Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling: PeerJ, v. 11, e15163, 34 p., https://doi.org/10.7717/peerj.15163.","productDescription":"e15163, 34 p.","ipdsId":"IP-146615","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":444004,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.15163","text":"Publisher Index Page"},{"id":435391,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NNZNVH","text":"USGS data release","linkHelpText":"Metabarcode sequencing of aquatic environmental DNA from the Potomac River Watershed, 2015-2020"},{"id":415048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Sara J.","contributorId":303889,"corporation":false,"usgs":false,"family":"Mueller","given":"Sara","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":868370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}