Managers invest substantial resources to promote recovery of declining anadromous fish stocks. Recovery strategies are manifold and often include management actions intended to stimulate somatic growth, increase in-river survival, and motivate juvenile outmigration during favorable environmental conditions. Evaluating the efficacy of these management actions is difficult, however, because monitoring data that explicitly track individuals from egg deposition to juvenile outmigration are typically lacking. We developed an integrated population model that links two different and often collected types of anadromous fish monitoring data: spawning ground surveys and rotary screw trap juvenile catch data. The integrated model accounts for incomplete detection and uses the two sources of data to estimate juvenile demographic parameters in a multistate framework. We evaluated the model's performance using simulated data under a range of conditions typically encountered in similar surveys. Simulation results indicated that the model estimated juvenile survival, growth, and movement with no-to-minimal bias (i.e., ≥ 50 % of simulations ± 0–0.05). As an example case study, we fit the model to empirical fall-run Chinook Salmon (Oncorhynchus tshawytscha) monitoring data collected in California's Central Valley, U.S.A. In doing so, we evaluated the influence of environmental conditions (e.g., discharge, water temperature) and habitat availability on juvenile demographic rates. We demonstrated that through our integrated approach we could estimate state transition probabilities that are typically inestimable for naturally produced, unmarked juvenile fish when using traditional statistical approaches to analyze these types of monitoring data. Furthermore, the structure of our model can serve as a useful foundation for decision-support models within adaptive management programs by directly linking management actions, decision-support-model predictions, and monitoring.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110780","usgsCitation":"Wohner, P.J., Duarte, A., and Peterson, J., 2024, An integrated analysis for estimation of survival, growth, and movement of unmarked juvenile anadromous fish: Ecological Modelling, v. 495, 110780, 9 p., https://doi.org/10.1016/j.ecolmodel.2024.110780.","productDescription":"110780, 9 p.","ipdsId":"IP-165440","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488126,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2024.110780","text":"Publisher Index Page"},{"id":485452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5906056592707,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.63419131375724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"495","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wohner, Patti J.","contributorId":338233,"corporation":false,"usgs":false,"family":"Wohner","given":"Patti","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":339254,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":935786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935787,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269686,"text":"70269686 - 2024 - A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","interactions":[],"lastModifiedDate":"2025-07-30T14:53:21.858355","indexId":"70269686","displayToPublicDate":"2024-07-03T09:49:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","docAbstract":"The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2024.110146","usgsCitation":"Knipper, K., Anderson, M., Bambach, N., Melton, F., Ellis, Z., Yang, Y., Volk, J.M., McElrone, A., Kustas, W.P., Roby, M., Carrara, W., Castro, S., Kilic, A., Fisher, J.B., Ruhoff, A., Senay, G.B., Morton, C., Saa, S., and Allen, R., 2024, A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards: Agricultural and Forest Meteorology, v. 355, 110146, 18 p., https://doi.org/10.1016/j.agrformet.2024.110146.","productDescription":"110146, 18 p.","ipdsId":"IP-167142","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493302,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2024.110146","text":"Publisher Index Page"},{"id":493185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.24346411280976,\n 35.36581404018972\n ],\n [\n -119.04389955946334,\n 35.98229970980546\n ],\n [\n -119.09665048217278,\n 36.489548538637436\n ],\n [\n -120.48964025685427,\n 37.8956809895726\n ],\n [\n -121.30837256179902,\n 39.0131951853744\n ],\n [\n -121.62152001465728,\n 39.13926422978071\n ],\n [\n -122.21612821856195,\n 38.932254764781106\n ],\n [\n -121.71286410491697,\n 37.92123720196997\n ],\n [\n -120.92135482117862,\n 37.048426115873994\n ],\n [\n -120.01788903644467,\n 36.065623999918515\n ],\n [\n -119.49460792137992,\n 35.23527037396478\n ],\n [\n -119.24346411280976,\n 35.36581404018972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"355","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Knipper, Kyle","contributorId":333373,"corporation":false,"usgs":false,"family":"Knipper","given":"Kyle","email":"","affiliations":[{"id":79855,"text":"USDA Agriculture Research Service","active":true,"usgs":false}],"preferred":false,"id":944430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":269899,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":944431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bambach, Nicolas","contributorId":358904,"corporation":false,"usgs":false,"family":"Bambach","given":"Nicolas","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":944432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melton, Forrest","contributorId":223919,"corporation":false,"usgs":false,"family":"Melton","given":"Forrest","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":944433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellis, Zac","contributorId":358905,"corporation":false,"usgs":false,"family":"Ellis","given":"Zac","affiliations":[{"id":85705,"text":"Olan Food Ingredients","active":true,"usgs":false}],"preferred":false,"id":944434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Yun","contributorId":333379,"corporation":false,"usgs":false,"family":"Yang","given":"Yun","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":944435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Volk, J. M.","contributorId":269921,"corporation":false,"usgs":false,"family":"Volk","given":"J.","middleInitial":"M.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":944436,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McElrone, Andrew J.","contributorId":358906,"corporation":false,"usgs":false,"family":"McElrone","given":"Andrew J.","affiliations":[{"id":85706,"text":"University of California Davis, USDA ARS","active":true,"usgs":false}],"preferred":false,"id":944437,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kustas, William P.","contributorId":29962,"corporation":false,"usgs":false,"family":"Kustas","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":944438,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Roby, Matthew","contributorId":358907,"corporation":false,"usgs":false,"family":"Roby","given":"Matthew","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":944439,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carrara, Will","contributorId":269951,"corporation":false,"usgs":false,"family":"Carrara","given":"Will","email":"","affiliations":[],"preferred":false,"id":944440,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Castro, Sebastian","contributorId":358908,"corporation":false,"usgs":false,"family":"Castro","given":"Sebastian","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":944441,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kilic, Ayse","contributorId":269913,"corporation":false,"usgs":false,"family":"Kilic","given":"Ayse","email":"","affiliations":[{"id":16587,"text":"University of Nebraska Lincoln","active":true,"usgs":false}],"preferred":false,"id":944442,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fisher, Joshua B.","contributorId":211503,"corporation":false,"usgs":false,"family":"Fisher","given":"Joshua","email":"","middleInitial":"B.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":944443,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Ruhoff, Anderson","contributorId":269919,"corporation":false,"usgs":false,"family":"Ruhoff","given":"Anderson","email":"","affiliations":[{"id":56044,"text":"Universidade Federal do Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":944444,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944445,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Morton, Charles","contributorId":178787,"corporation":false,"usgs":false,"family":"Morton","given":"Charles","affiliations":[],"preferred":false,"id":944446,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Saa, Sebastian","contributorId":358909,"corporation":false,"usgs":false,"family":"Saa","given":"Sebastian","affiliations":[{"id":85707,"text":"Almond Board of California","active":true,"usgs":false}],"preferred":false,"id":944447,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Allen, Richard G.","contributorId":358910,"corporation":false,"usgs":false,"family":"Allen","given":"Richard G.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":944448,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70255996,"text":"70255996 - 2024 - Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","interactions":[],"lastModifiedDate":"2024-07-30T14:55:12.863126","indexId":"70255996","displayToPublicDate":"2024-07-03T06:58:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","docAbstract":"The Southern California Borderland hosts a variety of geologic and oceanographic features that allow for diverse habitats to occur in a restricted region with a strong oxygen minimum zone (OMZ) and hard substrates. These include ferromanganese (FeMn) crusts and phosphorites targeted for deep-seabed mining in other regions. Baseline studies regarding hardground macro- (> 0.3 mm) and megafaunal (> 2 cm) invertebrates are lacking, although they contribute to understanding nutrient cycling and resilience of deep-sea communities to ocean deoxygenation, fishing, or mineral extraction. With the goal of understanding how substrate type, depth, and dissolved oxygen concentration influence invertebrate trophic structure, we surveyed δ13C and δ15N values of invertebrates on hard substrates on the Southern California Borderland margin along a depth gradient (120–2400 m) through the OMZ at inshore (< 100 km from shore) and offshore (100–250 km from shore) sites, using generalized additive models and community-level metrics. Macrofaunal isotopic values correlate with substrate type, exhibiting higher trophic diversity on FeMn crusts and specialized communities on phosphorites. Megafaunal isotopic values correlate with proximity to shore; animals offshore seem to depend more on phytoplanktonic production than animals inshore. In general, δ15N increased with decreasing dissolved oxygen and increasing depth, possibly due to remineralization processes within the OMZ and with depth. We discuss how feeding modes and community composition might influence the observed patterns. This study elucidates the importance of the environmental context in shaping invertebrate trophic structure on continental margins and provides baseline knowledge that may be useful in regions where these minerals are targeted for extraction.
Introduction: Habitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years.
Methods: We used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats.
Results: The simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon.
Discussion: We hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2024.1428697","usgsCitation":"Peterson, J., and Duarte, A., 2024, An evaluation of tradeoffs in restoring ephemeral vs. perennial habitats to conserve animal populations: Frontiers in Conservation Science, v. 5, 1428697, 12 p., https://doi.org/10.3389/fcosc.2024.1428697.","productDescription":"1428697, 12 p.","ipdsId":"IP-165963","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488153,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2024.1428697","text":"Publisher Index Page"},{"id":485520,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.15324571467127,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 38.645272222200845\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256509,"text":"70256509 - 2024 - Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","interactions":[],"lastModifiedDate":"2024-08-12T16:08:21.662133","indexId":"70256509","displayToPublicDate":"2024-07-02T10:55:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","docAbstract":"Once thought to be extirpated from the Chesapeake Bay, fall spawning runs of Atlantic Sturgeon Acipenser oxyrinchus have been rediscovered in the Marshyhope Creek (MC)–Nanticoke River (NR) system of Maryland, United States. High recapture rates in past telemetry surveys suggested a small population in the two connected tributaries. This study aims to generate estimates of abundance and understand within system connectivity for spawning runs in 2020 and 2021.
Data from mobile side-scan sonar surveys and detections of acoustically tagged adults on stationary telemetry receivers were analyzed in an integrated model to estimate spawning season abundance and examine run timing and system connectivity for this population. An array of acoustic receivers was deployed throughout the MC–NR system to monitor the movement of tagged fish during the spawning run period from mid-August to late October. Side-scan sonar surveys were conducted weekly in September in an area of high spawner aggregation to generate count data on spawning run abundance.
In 2020 and 2021, 32 (95% credible interval [CRI] = 23–47) and 70 (95% CRI = 49–105) Atlantic Sturgeon, respectively, used the MC–NR system. The lower estimate for 2020 coincided with an earlier end to the spawning run related to cooler September temperatures in that year.
In both years, high spawning run connectivity between MC and the upper NR was observed. Overall, run estimates supported previous hypotheses that the MC–NR system supports a very small population and that both MC and the upper NR serve as important areas for spawning activity.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10292","usgsCitation":"Coleman, N., Fox, D., Horne, A., Hostetter, N.J., Madsen, J., O’Brien, M., Park, I., Stence, C., and Secor, D., 2024, Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 16, no. 3, e10292, 16 p., https://doi.org/10.1002/mcf2.10292.","productDescription":"e10292, 16 p.","ipdsId":"IP-152527","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10292","text":"Publisher Index Page"},{"id":432489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay, Marshyhope Creek–Nanticoke River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.89424670173172,\n 38.22898121612636\n ],\n [\n -75.80356837182107,\n 38.3806027695843\n ],\n [\n -75.65092235600899,\n 38.5420516415999\n ],\n [\n -75.51423724108903,\n 38.538485023124736\n ],\n [\n -75.51310206970021,\n 38.701340540846786\n ],\n [\n -75.85480590117533,\n 38.71288976149938\n ],\n [\n -75.86506053032485,\n 38.57767393758033\n ],\n [\n -75.88558745668757,\n 38.40381670546191\n ],\n [\n -75.94442936459689,\n 38.33482586063296\n ],\n [\n -75.96657879053116,\n 38.253499920059284\n ],\n [\n -75.89424670173172,\n 38.22898121612636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Coleman, Nicholas","contributorId":340953,"corporation":false,"usgs":false,"family":"Coleman","given":"Nicholas","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, Dewayne","contributorId":340954,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","affiliations":[{"id":37219,"text":"Delaware State University","active":true,"usgs":false}],"preferred":false,"id":907729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horne, Ashlee","contributorId":340955,"corporation":false,"usgs":false,"family":"Horne","given":"Ashlee","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":907731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madsen, John","contributorId":340956,"corporation":false,"usgs":false,"family":"Madsen","given":"John","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Brien, Michael","contributorId":340957,"corporation":false,"usgs":false,"family":"O’Brien","given":"Michael","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Ian","contributorId":340958,"corporation":false,"usgs":false,"family":"Park","given":"Ian","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stence, Chuck","contributorId":340959,"corporation":false,"usgs":false,"family":"Stence","given":"Chuck","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907735,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Secor, David","contributorId":340960,"corporation":false,"usgs":false,"family":"Secor","given":"David","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907736,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257549,"text":"70257549 - 2024 - Population density and zooplankton biomass influence anadromous juvenile river herring growth in freshwater lakes","interactions":[],"lastModifiedDate":"2024-09-06T16:53:43.602215","indexId":"70257549","displayToPublicDate":"2024-07-02T09:46:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Population density and zooplankton biomass influence anadromous juvenile river herring growth in freshwater lakes","docAbstract":"Anadromous river herring populations, collectively alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis), have experienced a multi-century decline in abundance and distribution. These declines have been attributed in part to anthropogenic threats in freshwater ecosystems (e.g., habitat fragmentation, overharvest, water pollution, watershed development). An understanding of variability in juvenile productivity and growth is critical to developing restoration approaches. We characterized variability in juvenile river herring growth among 11 freshwater lakes in the northeastern USA. We used age estimates from otoliths and length measurements to calculate growth rates of juvenile river herring (n = 1452). We tested the effects of juvenile river herring densities, zooplankton (biomass and size), habitat area (based on thermocline depth), and water quality (temperature, nutrients, chlorophyll a) on juvenile growth. Mean monthly growth rates ranged from 0.56 to 1.41 mm/d and typically increased throughout the summer. Increased juvenile growth was best predicted by lower juvenile density (β = − 0.104, P < 0.001) and higher zooplankton biomass (β = 0.032, P < 0.05). Combined with information about juvenile densities and mortality, these results broaden the understanding of anadromous juvenile river herring productivity, provide information that can contribute to refining stock assessment and life cycle models, and help to better understand the potential impacts of habitat conservation and restoration decisions.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10641-024-01565-8","usgsCitation":"Devine, M., Bittner, S., Roy, A.H., Gahagan, B.I., Armstrong, M., and Jordaan, A., 2024, Population density and zooplankton biomass influence anadromous juvenile river herring growth in freshwater lakes: Environmental Biology of Fishes, v. 107, p. 755-770, https://doi.org/10.1007/s10641-024-01565-8.","productDescription":"16 p.","startPage":"755","endPage":"770","ipdsId":"IP-162984","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, 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Fisheries","active":true,"usgs":false}],"preferred":false,"id":910803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jordaan, Adrian","contributorId":343340,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910804,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257461,"text":"70257461 - 2024 - Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","interactions":[],"lastModifiedDate":"2024-09-06T16:40:27.763678","indexId":"70257461","displayToPublicDate":"2024-07-02T09:29:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5082,"text":"Ticks and Tick-borne Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","docAbstract":"Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.
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0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910479,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255685,"text":"sir20245050 - 2024 - Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","interactions":[],"lastModifiedDate":"2026-02-03T19:34:03.41537","indexId":"sir20245050","displayToPublicDate":"2024-07-02T07:51:23","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5050","displayTitle":"Water-Quality Trends in the Kansas River, Kansas, since Enactment of the Clean Water Act, 1972–2020","title":"Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","docAbstract":"The Clean Water Act was passed by Congress in 1972 to regulate pollution within the waters of the United States. The U.S. Geological Survey (USGS), in cooperation with the Kansas Department of Health and Environment (KDHE), the Kansas Water Office, the Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, compiled and analyzed historical streamflow and water-quality data collected by USGS and KDHE to characterize trends in water-quality constituents of interest because of their relation to water supply, drinking-water treatment, and sediment and nutrient transport, among others (total dissolved solids, chloride, ammonia, dissolved inorganic nitrogen [ammonia and nitrate plus nitrite], total nitrogen, orthophosphate, total phosphorus, total suspended solids, and fecal coliform bacteria) during mean- and low-flow conditions in the Kansas River since the passage of the Clean Water Act in 1972 through 2020. Trends in water-quality concentrations, or densities, and loads were analyzed using the Exploration and Graphics for RivER Trends R package and Weighted Regressions on Time, Discharge, and Season (WRTDS) model at upstream (Kansas River at Wamego, Kansas; USGS station 06887500) and downstream (Kansas River at De Soto, Kansas; USGS station 06892350) locations along the Kansas River using streamflow and water-quality data collected by the USGS and KDHE during 1972 through 2020. The Exploration and Graphics for RivER Trends Confidence Intervals R package and WRTDS bootstrap test estimated direction, uncertainty, and likelihood of trends in concentration and loads for each water-quality constituent of interest.
Downward trends in concentration and load were observed for 5 of the 9 water-quality constituents at both sites during mean-flow conditions during the study period. During low-flow conditions, 7 of the 9 constituents exhibited downward trends, possibly reflecting reductions in point-source contributions to the Kansas River. Downward trends in ammonia, dissolved inorganic nitrogen, and total nitrogen during mean- and low-flow conditions were observed at both Kansas River sites, which were similar to patterns observed nationally. Upward trends were generally observed for orthophosphate and total phosphorus, which were similar to patterns observed at sites in the Mississippi River Basin. Downward trends, or no trend, were observed for chloride. Upward and downward trends were observed for total dissolved solids. Downward trends in total suspended solids and fecal coliform bacteria were observed at both sites, which were also similar to patterns observed nationally. The long-term trend analyses in this report are an essential step to understanding how water-quality conditions have changed in the Kansas River since the passage of the Clean Water Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245050","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., Klager, B.J., and Stiles, T.C., 2024, Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020: U.S. Geological Survey Scientific Investigations Report 2024–5050, 29 p., https://doi.org/10.3133/sir20245050.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158483","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":430605,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WVZ8X1","text":"USGS data release","linkHelpText":"Water-quality data and computed flow-normalized and low-flow concentrations and loads in the Kansas River, Kansas, 1972–2020"},{"id":430602,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5050/images/"},{"id":430601,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.XML"},{"id":430599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5050/coverthb.jpg"},{"id":430600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5050"},{"id":430731,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245050/full"},{"id":499470,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117100.htm","linkFileType":{"id":5,"text":"html"}},{"id":430604,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.5,\n 40\n ],\n [\n -97.5,\n 38.75\n ],\n [\n -94.5,\n 38.75\n ],\n [\n -94.5,\n 40\n ],\n [\n -97.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March-May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large-scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High-resolution Weather Research and Forecasting coupled with Chemistry model (WRF-Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo-based drag-partition was used to explore land surface-atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub-tropical jet stream interaction was a common upper-level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast-directed pressure gradient upstream and over the source areas resulted in strong near-surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid-tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF-Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust-on-snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo-based drag partition provides a basis for additional dust-on-snow simulations to assess the hydrologic impact in the Southwest US.
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The earthquake, which occurred in the early days of both broadband seismic networks and the Internet, spurred advances in seismic monitoring, real-time systems, and development of data products. Motivated by the 30th anniversary of the earthquake, we present a brief retrospective of the earthquake and its impact, and reconsider both ground motions and the aftershock distribution using modern tools and best-available data. With improvements in instrumentation and analysis methodology, recent earthquakes continue to reveal increasing complexity of ground motions, fault systems, and earthquake ruptures. Even in the absence of data from state-of-the art instrumentation, a retrospective consideration of ground motion data from the Northridge earthquake reveals complexities beyond what could be characterized (and modeled) thirty years ago. Aftershock relocations for both the 1971 Sylmar and 1994 Northridge earthquakes also reveal an updated view of fault complexity. Our study does provide a cautionary tale regarding legacy data sets and research results that are not easily accessible, which can result in discrepancies between catalog data and products from best-available science. We also briefly describe outreach products produced as part of the anniversary commemoration.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320240012","usgsCitation":"Hough, S.E., Graves, R., Cochran, E.S., Yoon, C., Blair, J.L., Haefner, S., Wald, D.J., and Quitoriano, V., 2024, The 17 January 1994 Northridge, California, earthquake: A retrospective analysis: The Seismic Record, v. 4, no. 3, p. 151-160, https://doi.org/10.1785/0320240012.","productDescription":"10 p.","startPage":"151","endPage":"160","ipdsId":"IP-164904","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489848,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320240012","text":"Publisher Index Page"},{"id":482225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Northridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.60523974052802,\n 34.27362638562232\n ],\n [\n -118.60523974052802,\n 34.17466422238154\n ],\n [\n -118.47245060179975,\n 34.17466422238154\n ],\n [\n -118.47245060179975,\n 34.27362638562232\n ],\n [\n -118.60523974052802,\n 34.27362638562232\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yoon, Clara 0000-0003-4521-3889","orcid":"https://orcid.org/0000-0003-4521-3889","contributorId":222019,"corporation":false,"usgs":true,"family":"Yoon","given":"Clara","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blair, James Luke 0000-0002-6980-6446","orcid":"https://orcid.org/0000-0002-6980-6446","contributorId":213724,"corporation":false,"usgs":true,"family":"Blair","given":"James","email":"","middleInitial":"Luke","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haefner, Scott","contributorId":350679,"corporation":false,"usgs":true,"family":"Haefner","given":"Scott","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927578,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927579,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quitoriano, Vince 0000-0003-4157-1101 vinceq@usgs.gov","orcid":"https://orcid.org/0000-0003-4157-1101","contributorId":2582,"corporation":false,"usgs":true,"family":"Quitoriano","given":"Vince","email":"vinceq@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927580,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273741,"text":"70273741 - 2024 - Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means","interactions":[],"lastModifiedDate":"2026-01-27T16:31:23.793842","indexId":"70273741","displayToPublicDate":"2024-07-01T10:29:44","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means","docAbstract":"The United States Geological Survey (USGS) coordinates the collection of hydrographic features derived from remotely sensed interferometric synthetic aperture radar (IfSAR) elevation and intensity data in Alaska. Hydrographic features are cartographic representations of surface water features such as stream, rivers, lakes, ponds, canals, etc. Collection and validation procedures involve complex automated and manual techniques that furnish snapshots of hydrographic vector data that exist during the IfSAR surveys. The dynamic nature of fluvial conditions warrants monitoring and updating hydrographic data, but extraction procedures for updates can be cost prohibitive. This paper overviews progress on automated workflows to extract hydrography from IfSAR data using deep learning methods trained and tested with USGS collected hydrography data. This research tests transfer learning methods on a well-performing U-net model trained on a 4600-square kilometer (sq km) base model area in northcentral Alaska. The base model is transferred and fine-tuned to regions in the target domain covering roughly 127,000 sq km. The target domain is subdivided into areas with similar hydrogeomorphic conditions using principal components and k-means clustering, and the base model is adaptively fine-tuned to each hydrogeomorphic class by selecting training watersheds from each cluster within the target domain. Results are compared with transfer learning that is fine-tuned with a random sample of watersheds in the target domain.","conferenceTitle":"Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS) 2024 Symposium","conferenceDate":"June 3-6, 2024","conferenceLocation":"Columbus, OH","language":"English","publisher":"The Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS)","usgsCitation":"Stanislawski, L., Shavers, E.J., Pastick, N.J., Thiem, P.T., Wang, S., Jaroenchai, N., Jiang, Z., Kronenfeld, B.J., Buttenfield, B.P., and Camerer, A., 2024, Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means, Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS) 2024 Symposium, Columbus, OH, June 3-6, 2024, 45, 6 p.","productDescription":"45, 6 p.","ipdsId":"IP-162419","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":499096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":499083,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cartogis.org/conferences/cagis2024/program/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":954503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":954504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":954505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":954506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Shaowen","contributorId":198966,"corporation":false,"usgs":false,"family":"Wang","given":"Shaowen","email":"","affiliations":[],"preferred":false,"id":954507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jaroenchai, Nattapon","contributorId":267318,"corporation":false,"usgs":false,"family":"Jaroenchai","given":"Nattapon","email":"","affiliations":[{"id":38021,"text":"University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":954508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Zhe","contributorId":267317,"corporation":false,"usgs":false,"family":"Jiang","given":"Zhe","email":"","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":954509,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":954510,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":954511,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Camerer, Adam","contributorId":331850,"corporation":false,"usgs":false,"family":"Camerer","given":"Adam","email":"","affiliations":[{"id":26996,"text":"Missouri University of Science & Technology","active":true,"usgs":false}],"preferred":false,"id":954512,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70267248,"text":"70267248 - 2024 - Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models","interactions":[],"lastModifiedDate":"2025-05-20T14:56:34.636132","indexId":"70267248","displayToPublicDate":"2024-07-01T09:56:08","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models","docAbstract":"Traditionally, probabilistic seismic hazard analysis (PSHA) has focused on calculating ground motion hazard curves for elastic, 5%-damped pseudo spectral accelerations, Sa(T,5%), which are used as the basis for engineering design parameters and targets for ground motion selection and modification. However, structures and geotechnical systems can exhibit a wide range of damping ratios both above and below the 5% level, depending on the construction material, structural system, nonstructural elements, or subsurface soil properties. When spectral parameters at such damping levels are required for certain applications, 5%-damped accelerations have traditionally been extracted from PSHA-based hazard curves and adjusted outside of the hazard integral using damping scaling factors (DSF) such as those from Newmark & Hall (1982). Recent advances in the development of more rigorous and comprehensive damping scaling models (e.g., Rezaeian et al., 2014; Rezaeian et al., 2021) have allowed for the modeling of means and standard deviations of DSFs as functions of earthquake source and path properties for crustal, intraslab, and subduction interface tectonic environments. These DSF models can be applied to ground motion model (GMM) estimates of Sa(T,5%) for a given earthquake rupture scenario to produce a corresponding mean and standard deviation Sa at a specified damping ratio β, Sa(T,β). In this study, the DSF models of Rezaeian et al. (2014) and Rezaeian et al. (2021) are implemented within the U.S. Geological Survey National Seismic Hazard Model (NSHM) PSHA framework to calculate probabilistic hazard curves for spectral accelerations at damping ratios from 0.5% to 30%. The DSF models are applied directly to the mean and standard deviation of Sa(T,5%) predictions from each GMM in the NSHM logic tree. Resulting hazard curves and uniform hazard and risk spectra for Sa(T,β) are presented for several geographic locations and compared with corresponding spectra estimated using current design practices by applying the same DSFs outside of the PSHA calculation. Key differences between the two methods for estimating Sa(T,β) are discussed, and potential strategies are presented for the implementation and usage of the hazard-consistent Sa(T,β) in building codes. Comparing the results to those from DSFs used in current design practices that are mainly based on Newmark & Hall (1982) is not explored in this study.
","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30- July 5, 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Makdisi, A.J., Smith, D., Rezaeian, S., Powers, P.M., and Withers, K., 2024, Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30- July 5, 2024, 11 p.","productDescription":"11 p.","ipdsId":"IP-159606","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486139,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=23633&conference=18WCEE"},{"id":486214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Dallin","contributorId":355505,"corporation":false,"usgs":false,"family":"Smith","given":"Dallin","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":937508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":937510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Withers, Kyle 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937511,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267251,"text":"70267251 - 2024 - Effects of stochastically-simulated near-fault ground motions on soil liquefaction","interactions":[],"lastModifiedDate":"2025-05-20T14:51:11.280957","indexId":"70267251","displayToPublicDate":"2024-07-01T09:50:05","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Effects of stochastically-simulated near-fault ground motions on soil liquefaction","docAbstract":"The scarcity of historically recorded near-fault ground motions poses a challenge to systematically understanding the influence of near-fault effects on various types of seismic demands for engineering purposes. In particular, the current state of knowledge of the influence of ground-shaking intensity on soil liquefaction and its consequences does not specifically account for the effects of near-fault ground motion characteristics. In this study, the influence of near-fault ground motions on liquefaction triggering and lateral spreading are investigated using non-linear modeling of a hypothetical liquefiable soil column in the finite-element computational platform OpenSees subjected to simulated ground motion time series that represent strong earthquake shaking in the near field. The simulated ground motion time series and resulting datasets are based on a parametric stochastic model and are developed for a range of source and path parameters to represent a realistic variability of ground motion characteristics. Dependencies between ground motion intensity measures (IMs) and liquefaction demand parameters are investigated for near-fault pulse and nonpulse-like ground motion sets. Evolutionary IMs, such as cumulative absolute velocity (CAV) and the time-varying magnitude-adjusted peak ground acceleration (PGAM), are considered in developing liquefaction triggering probability density functions. Post-liquefaction triggering responses such as lateral spreading displacements are examined in relation to PGAM and CAV. The ground motion simulations are validated by comparing their liquefaction-capacity PGAM fragilities and post-triggering CAV vulnerability relationships to historical records from the 1994 Northridge earthquake in California, USA. Finally, a path forward for future studies that includes finding systematic differences in the IM-liquefaction demand relationships between near-fault and far-field stochastic ground motion sets is outlined.
","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30-July 5., 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Makdisi, A.J., Dabaghi, M., Brito Silveira, L., Rezaeian, S., Haynie, K.L., and Mason, H., 2024, Effects of stochastically-simulated near-fault ground motions on soil liquefaction, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30-July 5., 2024, 12 p.","productDescription":"12 p.","ipdsId":"IP-159292","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486140,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=25504&conference=18WCEE","linkFileType":{"id":5,"text":"html"}},{"id":486213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dabaghi, Mayssa","contributorId":221888,"corporation":false,"usgs":false,"family":"Dabaghi","given":"Mayssa","email":"","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":937513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brito Silveira, Lianne 0000-0002-8331-7104","orcid":"https://orcid.org/0000-0002-8331-7104","contributorId":355508,"corporation":false,"usgs":true,"family":"Brito Silveira","given":"Lianne","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":937514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haynie, Kirstie Lafon 0000-0001-9930-6736","orcid":"https://orcid.org/0000-0001-9930-6736","contributorId":289894,"corporation":false,"usgs":true,"family":"Haynie","given":"Kirstie","email":"","middleInitial":"Lafon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mason, Henry 0000-0003-4279-2854","orcid":"https://orcid.org/0000-0003-4279-2854","contributorId":293188,"corporation":false,"usgs":true,"family":"Mason","given":"Henry","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937517,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261254,"text":"70261254 - 2024 - 2024 Crustal Deformation Modeling Workshop report","interactions":[],"lastModifiedDate":"2024-12-04T15:17:20.396096","indexId":"70261254","displayToPublicDate":"2024-07-01T09:15:56","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"2024 Crustal Deformation Modeling Workshop report","docAbstract":"The 2024 Crustal Deformation Modeling Workshop was held June 10–14 at the Colorado School of Mines. The workshop included two days of tutorials on using PyLith for crustal deformation modeling, followed by three days of science talks and discussions. The workshop focused on four primary themes:
● Constraining long-term fault slip rates and their uncertainties using geodetic and geologic data;
● Earthquake cycle modeling with a focus on constraining models using seismic and geodetic data;
● Interaction of fluids and faulting; and
● Separating contributions of surface loading and tectonic loading in crustal deformation.
The complete agenda is available on the CIG website.
","conferenceTitle":"2024 Crustal Deformation Modeling Workshop","conferenceDate":"June 10-14, 2024","conferenceLocation":"Golden, CO","language":"English","publisher":"Computational Infrastructure for Geodynamics","usgsCitation":"Aagaard, B.T., Knepley, M., Lindsey, E., Materna, K.Z., Martens, H.R., and Williams, C., 2024, 2024 Crustal Deformation Modeling Workshop report, 2024 Crustal Deformation Modeling Workshop, Golden, CO, June 10-14, 2024, 4 p.","productDescription":"4 p.","ipdsId":"IP-172275","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":464748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":464724,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://geodynamics.org/resources/2113/supportingdocs"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":920126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knepley, Matthew","contributorId":304241,"corporation":false,"usgs":false,"family":"Knepley","given":"Matthew","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":920127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, Eric","contributorId":261913,"corporation":false,"usgs":false,"family":"Lindsey","given":"Eric","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":920128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Materna, Kathryn Z. 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":209697,"corporation":false,"usgs":false,"family":"Materna","given":"Kathryn","middleInitial":"Z.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":920129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martens, Hilary R","contributorId":215383,"corporation":false,"usgs":false,"family":"Martens","given":"Hilary","email":"","middleInitial":"R","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":920130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Charles 0000-0001-7435-9196","orcid":"https://orcid.org/0000-0001-7435-9196","contributorId":243027,"corporation":false,"usgs":false,"family":"Williams","given":"Charles","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":920131,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272709,"text":"70272709 - 2024 - Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape","interactions":[],"lastModifiedDate":"2025-12-08T14:19:02.218112","indexId":"70272709","displayToPublicDate":"2024-07-01T08:57:37","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape","docAbstract":"Permafrost holds more than twice the amount of carbon currently in the atmosphere, but this large carbon reservoir is vulnerable to thaw and erosion under a rapidly changing Arctic climate. Convective storms are becoming increasingly common during Arctic summers and can amplify runoff and erosion. These extreme events, in concert with active layer deepening, may accelerate carbon loss from the Arctic landscape. However, we lack measurements of carbon fluxes during these events.\nRivers are sensitive to physical, chemical, and hydrological perturbations, and thus are excellent systems for studying landscape responses to thunderstorms. We present observations from the Canning River, Alaska, which drains the northern Brooks Range and flows across a continuous permafrost landscape to the Beaufort Sea. During summer 2022 and 2023 field campaigns, we opportunistically monitored river discharge, sediment, and organic carbon fluxes during several thunderstorms. During one notable storm, river discharge nearly doubled from ~130 m3/s to ~240 m3/s, suspended sediment flux increased 70-fold, and the particulate organic carbon (POC) flux increased 90-fold relative to non-storm conditions. Taken together, the river exported ~16 metric tons of POC over one hour of this sustained event, not including the additional flux of woody debris. Furthermore, the dissolved organic carbon (DOC) flux nearly doubled. Although these thunderstorm-driven fluxes are short-lived (hours to days), they play an outsized role in exporting organic carbon from Arctic rivers. Understanding how these extreme events impact river water, sediment, and carbon dynamics will help predict how Arctic climate change will modify the global carbon cycle.","conferenceTitle":"12th International Conference on Permafrost","conferenceDate":"June 16-20, 2024","conferenceLocation":"Whitehorse, Yukon","language":"English","publisher":"International Conference on Permafrost","doi":"10.52381/ICOP2024.104.1","usgsCitation":"Repasch, M., Arcuri, J., Overeem, I., Anderson, S.P., Anderson, R.G., and Koch, J.C., 2024, Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape, 12th International Conference on Permafrost, Whitehorse, Yukon, June 16-20, 2024, p. 341-348, https://doi.org/10.52381/ICOP2024.104.1.","productDescription":"8 p.","startPage":"341","endPage":"348","ipdsId":"IP-157679","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":497137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Canning River, North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.75,\n 70.25\n ],\n [\n -146.5,\n 70.25\n ],\n [\n -146.5,\n 68.5\n ],\n [\n -144.75,\n 68.5\n ],\n [\n -144.75,\n 70.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Repasch, Marisa 0000-0003-2636-9896","orcid":"https://orcid.org/0000-0003-2636-9896","contributorId":334190,"corporation":false,"usgs":false,"family":"Repasch","given":"Marisa","email":"","affiliations":[],"preferred":false,"id":951399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arcuri, Josie","contributorId":363269,"corporation":false,"usgs":false,"family":"Arcuri","given":"Josie","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":951400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overeem, Irina","contributorId":197487,"corporation":false,"usgs":false,"family":"Overeem","given":"Irina","email":"","affiliations":[],"preferred":false,"id":951401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Suzanne P. 0000-0002-6796-6649","orcid":"https://orcid.org/0000-0002-6796-6649","contributorId":172732,"corporation":false,"usgs":false,"family":"Anderson","given":"Suzanne","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":951402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Robert G.","contributorId":197569,"corporation":false,"usgs":false,"family":"Anderson","given":"Robert","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":951403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":951404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257116,"text":"70257116 - 2024 - Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin","interactions":[],"lastModifiedDate":"2024-08-12T13:52:05.405983","indexId":"70257116","displayToPublicDate":"2024-07-01T08:31:44","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NOS NCCOS 332","title":"Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin","docAbstract":"As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use.
Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.
","language":"English","publisher":"NOAA","doi":"10.25923/4jqw-mw29","usgsCitation":"Nisonson, H., Kiser, A.H., Gressler, B.P., Leight, A., and Young, J.A., 2024, Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin: NOAA Technical Memorandum NOS NCCOS 332, vi, 41 p., https://doi.org/10.25923/4jqw-mw29.","productDescription":"vi, 41 p.","ipdsId":"IP-163665","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":432484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patuxent River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.5870885061828,\n 38.29653642168418\n ],\n [\n -76.39765972760698,\n 38.2523876845089\n ],\n [\n -76.39469990294168,\n 38.39635283845547\n ],\n [\n -76.5219723635473,\n 38.51224538633858\n ],\n [\n -76.5930081555134,\n 38.75962947245472\n ],\n [\n -76.57524920752218,\n 38.93252018914461\n ],\n [\n -76.82683430406816,\n 39.192214789667304\n ],\n [\n -77.06635333905636,\n 39.45429197245687\n ],\n [\n -77.25054644375115,\n 39.48452671490274\n ],\n [\n -77.41561427713579,\n 39.40769859848646\n ],\n [\n -77.04882238288116,\n 39.139433495010024\n ],\n [\n -76.96002764292388,\n 39.04065023841653\n ],\n [\n -76.82979535765293,\n 38.90718862957951\n ],\n [\n -76.82091588365701,\n 38.66493779010759\n ],\n [\n -76.74100152907371,\n 38.412588059675414\n ],\n [\n -76.5870885061828,\n 38.29653642168418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nisonson, H","contributorId":342011,"corporation":false,"usgs":false,"family":"Nisonson","given":"H","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiser, Alexander H. 0000-0002-2871-0640","orcid":"https://orcid.org/0000-0002-2871-0640","contributorId":342012,"corporation":false,"usgs":true,"family":"Kiser","given":"Alexander","middleInitial":"H.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gressler, Benjamin P. 0000-0001-6639-8558","orcid":"https://orcid.org/0000-0001-6639-8558","contributorId":270167,"corporation":false,"usgs":true,"family":"Gressler","given":"Benjamin","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leight, A","contributorId":342013,"corporation":false,"usgs":false,"family":"Leight","given":"A","email":"","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":909481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259933,"text":"70259933 - 2024 - Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA","interactions":[],"lastModifiedDate":"2024-10-30T21:48:35.428948","indexId":"70259933","displayToPublicDate":"2024-07-01T07:09:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA","docAbstract":"Wind erosion and dust emission from drylands have large consequences for ecosystem function and human health. Wind erosion is naturally reduced by soil crusting and sheltering by non-erodible roughness elements such as plants. Land uses that reduce surface roughness and disturb the soil surface can dramatically increase dust emission. Extraction of oil and gas is a common and growing land use in the western United States (US) that removes vegetation and other roughness elements for construction of well pads and unpaved access roads, resulting in thousands of small (1–4 ha), discrete patches of unprotected soil. Here, we use a satellite albedo-based model to assess the effect of oil/gas activity on surface roughness in the Uinta-Piceance Basin, an area of the Upper Colorado River Basin (UCRB) with dense oil and natural gas development and modelled how the change in surface roughness could impact aeolian sediment flux and dust emission. We also investigated how regional drought influences the response of surface roughness to well pads and access roads. Oil/gas activity reduced surface roughness and increased modelled aeolian sediment flux at the landscape scale across much of the study region, resulting in a modest increase of 10 139 kg of dust per year, which is small relative to dust loads from a single regional dust event observed in the region, but downwind impact could be significant. The magnitude of surface roughness reductions by oil/gas activity was generally consistent among land cover types. However, in parts of the basin that had high cover of annual forbs and grasses, oil/gas activity was associated with larger surface roughness and smaller potential dust emission. Drought decreased surface roughness across disturbed and undisturbed sites, but there was no interactive effect of oil/gas activity and drought on surface roughness. These results suggest that oil/gas activity may increase sediment fluxes and likely contributes to dust emission from landscapes in the UCRB. Understanding how drought and land use change contribute to dust emissions will benefit mitigation of undesirable impacts of wind erosion and dust transport.
","language":"English","publisher":"British Society for Geomorphology","doi":"10.1002/esp.5887","usgsCitation":"Tyree, G.L., Chappell, A., Villarreal, M.L., Dhital, S., Duniway, M.C., Edwards, B., Faist, A., Nauman, T., and Webb, N., 2024, Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA: Earth Surface Processes and Landforms, v. 49, no. 11, p. 3292-3307, https://doi.org/10.1002/esp.5887.","productDescription":"16 p.","startPage":"3292","endPage":"3307","ipdsId":"IP-158320","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":466989,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5887","text":"Publisher Index Page"},{"id":463243,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.52852783555946,\n 41.270866482469955\n ],\n [\n -112.52852783555946,\n 37.65911929948223\n ],\n [\n -106.31026611680981,\n 37.65911929948223\n ],\n [\n -106.31026611680981,\n 41.270866482469955\n ],\n [\n -112.52852783555946,\n 41.270866482469955\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tyree, Gayle Loren 0000-0002-9949-6426","orcid":"https://orcid.org/0000-0002-9949-6426","contributorId":257744,"corporation":false,"usgs":true,"family":"Tyree","given":"Gayle","email":"","middleInitial":"Loren","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":916865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chappell, A.","contributorId":345530,"corporation":false,"usgs":false,"family":"Chappell","given":"A.","email":"","affiliations":[{"id":82618,"text":"Cardiff University, School of Earth and Ocean Sciences, Cardiff, Wales, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":916866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 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mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":916869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, B.L.","contributorId":345532,"corporation":false,"usgs":false,"family":"Edwards","given":"B.L.","email":"","affiliations":[{"id":80080,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":916870,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Faist, A.M.","contributorId":345533,"corporation":false,"usgs":false,"family":"Faist","given":"A.M.","email":"","affiliations":[{"id":82619,"text":"University of Montana, Department of Ecosystem and Conservation Sciences, Missoula, MT, USA","active":true,"usgs":false}],"preferred":false,"id":916871,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nauman, T.W.","contributorId":345534,"corporation":false,"usgs":false,"family":"Nauman","given":"T.W.","email":"","affiliations":[{"id":82620,"text":"USDA-NRCS National Soil Survey Center, Lincoln, NE, USA","active":true,"usgs":false}],"preferred":false,"id":916872,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Webb, N.P.","contributorId":345535,"corporation":false,"usgs":false,"family":"Webb","given":"N.P.","email":"","affiliations":[{"id":80080,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":916873,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70258442,"text":"70258442 - 2024 - Application of a workflow to determine the feasibility of using simulated streamflow for estimation of streamflow frequency statistics","interactions":[],"lastModifiedDate":"2024-09-17T11:40:02.349995","indexId":"70258442","displayToPublicDate":"2024-07-01T06:35:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Application of a workflow to determine the feasibility of using simulated streamflow for estimation of streamflow frequency statistics","docAbstract":"We developed Vp, Vs, Vp/Vs ratio, and Poisson’s ratio models of the uppermost crust (<4 km depth) from the eastern San Francisco (SF) Bay (California, USA) to near the Calaveras fault along a 15-km-long, linear profile. Upper crustal velocities are highly variable beneath, west, and well east of the Hayward fault. We observe eight notable features, from west to east: (1) Near San Francisco Bay, there is an ~2-km-wide structure with high Vp/Vs ratios (up to 5) and Poisson’s ratios (up to 0.48) extending from the surface to the base of our model, which we suggest the structure is a near-vertical fault that lies along a straight-line projection between the Silver Creek fault to the south and the Point Richmond fault to the north. The structure may be part of an ~90-km-long fault along the eastern SF Bay. (2) The western East Bay Plain, the lower lying area between the bay and the hills, includes up to 800 m of low-velocity sediments (Vp ~1600–3000 m/s, Vs ~500 m/s to ~1000 m/s), underlain by higher velocity basement rocks (Vp ~3000–5800 m/s; Vs ~1000–1500 m/s). (3) Between ~1 km and 3 km east of the Bay shoreline, sediments thin in a series of steps (likely faults) toward the Hayward fault. (4) Between ~3 km west and ~1 km east of the Hayward fault (at the East Chabot fault) at depths greater than 1 km, basement Vp (up to 6000 m/s) and Vs (up to 2800 m/s) are high, and Vp/Vs ratios (<2) and Poisson’s ratios (<0.3) are low, suggesting crystalline rocks. Furthermore, a near-vertical zone of low Vp/Vs ratios and Poisson’s ratios is between near-surface traces of the Hayward and East Chabot faults, likely corresponding to the San Leandro Gabbro of Ponce et al. (2003). (5) Eastward of the East Chabot fault in the upper 1.5 km, basement Vp (~3000 m/s to ~4200 m/s) and Vs (~1200–2000 m/s) are lower than those west of the fault. (6) In the eastern Hayward/Oakland Hills, there are zones of laterally varying, high- and low-velocity (Vp ~2500–3000 m/s) Jurassic–Cretaceous and Tertiary sediments in the shallow subsurface that likely extend much deeper than imaged. (7) Seismic energy that propagates westward from sources east of the Hayward fault (HF) appear weaker than energy that propagates eastward from sources west of the HF, suggesting that the HF acts as a partial barrier to shallow seismic energy propagation into the more populated eastern SF Bay area. (8) Unlike many fault zones, it appears that the active trace of the Hayward fault (in our study area) is not cored by a prominent, low-velocity zone relative to rocks to the east and west of the active trace. However, the active trace does mark a prominent change from relatively higher velocities to the west and lower velocities to the east.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1130/B36919.1","usgsCitation":"Catchings, R.D., Strayer, L.M., Chan, J.H., Goldman, M., McEvilly, A., and Suppe, J., 2024, Upper crustal seismic velocity structure of the Hayward fault zone, San Francisco Bay, California, USA: Results from the 2016 East Bay Seismic Experiment (EBSI-16): GSA Bulletin, v. 136, no. 7-8, p. 3261-3276, https://doi.org/10.1130/B36919.1.","productDescription":"16 p.","startPage":"3261","endPage":"3276","ipdsId":"IP-124842","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.49518270211846,\n 37.855388424415295\n ],\n [\n -122.49518270211846,\n 37.389789695119845\n ],\n [\n -121.96137507277817,\n 37.389789695119845\n ],\n [\n -121.96137507277817,\n 37.855388424415295\n ],\n [\n -122.49518270211846,\n 37.855388424415295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2024-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":928559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strayer, Luther M.","contributorId":139930,"corporation":false,"usgs":false,"family":"Strayer","given":"Luther","email":"","middleInitial":"M.","affiliations":[{"id":13318,"text":"California State University East Bay","active":true,"usgs":false}],"preferred":false,"id":928585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chan, Joanne H. 0000-0002-2065-2423 jchan@usgs.gov","orcid":"https://orcid.org/0000-0002-2065-2423","contributorId":178625,"corporation":false,"usgs":true,"family":"Chan","given":"Joanne","email":"jchan@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":928587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McEvilly, Andrian T.","contributorId":351006,"corporation":false,"usgs":false,"family":"McEvilly","given":"Andrian T.","affiliations":[],"preferred":false,"id":928588,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suppe, J.","contributorId":61178,"corporation":false,"usgs":true,"family":"Suppe","given":"J.","email":"","affiliations":[{"id":68365,"text":"Department of Earth and Atmospheric Sciences, University of Houston","active":true,"usgs":false}],"preferred":false,"id":928589,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257474,"text":"70257474 - 2024 - Predicting the response of a long-distance migrant to changing environmental conditions in winter","interactions":[],"lastModifiedDate":"2024-08-16T15:14:57.180412","indexId":"70257474","displayToPublicDate":"2024-06-29T10:10:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the response of a long-distance migrant to changing environmental conditions in winter","docAbstract":"Access to high-quality food is critical for long-distance migrants to provide energy for migration and arrival at breeding grounds in good condition. We studied effects of changing abundance and availability of a marine food, common eelgrass (Zostera marina L.), on an arctic-breeding, migratory goose, black brant (Brant bernicla nigricans Lawrence 1846), at a key non-breeding site, Bahía San Quintín, Mexico. Eelgrass, the primary food of brant, is consumed when exposed by the tide or within reach from the water's surface. Using an individual-based model, we predicted effects of observed changes (1991–2013) in parameters influencing food abundance and availability: eelgrass biomass (abundance), eelgrass shoot length (availability, as longer shoots more within reach), brant population size (availability, as competition greater with more birds), and sea level (availability, as less food within reach when sea level higher). The model predicted that the ability to gain enough energy to migrate was most strongly influenced by eelgrass biomass (threshold January biomass for migration = 60 g m−2 dry mass). Conversely, annual variation in population size (except for 1998), was relatively low, and variation in eelgrass shoot length and sea level were not strongly related to ability to migrate. We used observed data on brant body mass at Bahía San Quintín and annual survival to test for effects of eelgrass biomass in the real system. The lowest observed values of body mass and survival were in years when biomass was below 60 g m−2, although in some years of low biomass body mass and/or survival was higher. This suggests that the real birds may have some capacity to compensate to meet their energy demands when eelgrass biomass is low. We discuss consequences for brant population trends and conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11619","usgsCitation":"Stillman, R.A., Rivers, E., Gilkerson, W., Wood, K.A., Clausen, P., Deane, C., and Ward, D.H., 2024, Predicting the response of a long-distance migrant to changing environmental conditions in winter: Ecology and Evolution, v. 14, no. 7, e11619, 15 p., https://doi.org/10.1002/ece3.11619.","productDescription":"e11619, 15 p.","ipdsId":"IP-160623","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":439315,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11619","text":"Publisher Index Page"},{"id":434933,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T43R88","text":"USGS data release","linkHelpText":"Data from Black Brant (Branta bernicla nigricans) Overwintering in Three Lagoons Along the Baja California Peninsula, Mexico"},{"id":432860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Baja California","otherGeospatial":"Bahía San Quintín","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.06430118019601,\n 30.523772316473426\n ],\n [\n -116.06430118019601,\n 30.37195862378512\n ],\n [\n -115.92021973295668,\n 30.37195862378512\n ],\n [\n -115.92021973295668,\n 30.523772316473426\n ],\n [\n -116.06430118019601,\n 30.523772316473426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Stillman, Richard A.","contributorId":151661,"corporation":false,"usgs":false,"family":"Stillman","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":910500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivers, E.M.","contributorId":245657,"corporation":false,"usgs":false,"family":"Rivers","given":"E.M.","email":"","affiliations":[{"id":49249,"text":"Merkel & Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":910501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilkerson, W.","contributorId":245658,"corporation":false,"usgs":false,"family":"Gilkerson","given":"W.","affiliations":[{"id":49250,"text":"Wildfowl & Wetlands Trust","active":true,"usgs":false}],"preferred":false,"id":910502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wood, K. A.","contributorId":167726,"corporation":false,"usgs":false,"family":"Wood","given":"K.","email":"","middleInitial":"A.","affiliations":[{"id":24818,"text":"Department of Life and Environmental Sciences, Bournemouth University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":910503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clausen, P.","contributorId":245661,"corporation":false,"usgs":false,"family":"Clausen","given":"P.","email":"","affiliations":[{"id":49252,"text":"Department of Bioscience – Wildlife Ecology, Aarhus University","active":true,"usgs":false}],"preferred":false,"id":910505,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deane, C.","contributorId":342932,"corporation":false,"usgs":false,"family":"Deane","given":"C.","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":910506,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":910507,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255757,"text":"70255757 - 2024 - Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: A current view and future perspectives","interactions":[],"lastModifiedDate":"2024-07-03T12:02:59.820318","indexId":"70255757","displayToPublicDate":"2024-06-29T07:01:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: A current view and future perspectives","docAbstract":"Eruption source parameters (ESPs) are crucial for characterising volcanic eruptions and are essential inputs to numerical models used for hazard assessment. Key ESPs of explosive volcanic eruptions include plume height, mass eruption rate, eruption duration, and grain-size distribution. Some of these ESPs can be directly observed during an eruption, but others are difficult to measure in real-time, or indeed, accurately and precisely quantify afterwards. Estimates of ESPs for eruptions that cannot be observed, for example, due to the remote location of a volcano or poor weather conditions, are often defined using expert judgement and data from past eruptions, both from the volcano of interest and analogue volcanoes farther afield. Analysis of such information is time intensive and difficult, particularly during eruption response. These difficulties have resulted in the production of datasets to aid quick identification of ESPs prior to or during an eruption for use in operational response settings such as those at volcano observatories and Volcanic Ash Advisory Centres. These resources include the Mastin et al. (2009a) ESP dataset and the Catalogue of Icelandic Volcanoes and European Catalogue of Volcanoes aviation tables. Here, we review and compare these resources, which take different approaches to assigning ESPs. We identify future areas for development of these resources, highlighting the need for frequent updates as more knowledge of volcanic activity is gained and as modelling capabilities and requirements change.
The US faces multiple challenges in facilitating the safe, effective, and proactive use of fire as a landscape management tool. This intentional fire use exposes deeply ingrained communication challenges and distinct but overlapping strategies of prescribed fire, cultural burning, and managed wildfire. We argue for a new conceptual model that is organized around ecological conditions, capacity to act, and motivation to use fire and can integrate and expand intentional fire use as a tool. This result emerges from more considered collaboration and communication of values and needs to address the negative consequences of contemporary fire use. When applied as a communication and translation tool, there is potential to lower barriers to faster and more successful collaboration among stakeholders. Such improvements are a vital part of strategies to address climate adaptation, wildfire mitigation, and the well-being of ecosystems.
","language":"English","publisher":"Cell Press","doi":"10.1016/j.crsus.2024.100125","usgsCitation":"Russell, A., Fontana, N., Hoecker, T., Kamanu, A., Majumder, R., Stephens, J., Young, A., Cravens, A.E., Giardina, C., Hiers, K., Littell, J., and Terando, A., 2024, A fire-use decision model to improve the United States’ wildfire management and support climate change adaptation: Cell Reports Sustainability, v. 1, no. 6, 100125, 14 p., https://doi.org/10.1016/j.crsus.2024.100125.","productDescription":"100125, 14 p.","ipdsId":"IP-165285","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":487960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Davis","active":true,"usgs":false}],"preferred":false,"id":938273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoecker, Tyler","contributorId":355856,"corporation":false,"usgs":false,"family":"Hoecker","given":"Tyler","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":938274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kamanu, Alyssa","contributorId":355857,"corporation":false,"usgs":false,"family":"Kamanu","given":"Alyssa","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":938275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Majumder, Reetam","contributorId":355858,"corporation":false,"usgs":false,"family":"Majumder","given":"Reetam","affiliations":[{"id":7091,"text":"North Carolina State 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Science Center","active":true,"usgs":true}],"preferred":true,"id":938278,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Giardina, Christian ","contributorId":221117,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian ","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":938279,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hiers, Kevin","contributorId":212193,"corporation":false,"usgs":false,"family":"Hiers","given":"Kevin","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":938280,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":938281,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":938282,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70263882,"text":"70263882 - 2024 - Seismically detected cratering on Mars: Enhanced recent impact flux?","interactions":[],"lastModifiedDate":"2025-02-27T15:56:41.687204","indexId":"70263882","displayToPublicDate":"2024-06-28T08:52:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Seismically detected cratering on Mars: Enhanced recent impact flux?","docAbstract":"Seismic observations of impacts on Mars indicate a higher impact flux than previously measured. Using six confirmed seismic impact detections near the NASA InSight lander and two distant large impacts, we calculate appropriate scalings to compare these rates with lunar-based chronology models. We also update the impact rate from orbital observations using the most recent catalog of new craters on Mars. The snapshot of the current impact rate at Mars recorded seismically is higher than that found using orbital detections alone. The measured rates differ between a factor of 2 and 10, depending on the diameter, although the sample size of seismically detected impacts is small. The close timing of the two largest new impacts found on Mars in the past few decades indicates either a heightened impact rate or a low-probability temporal coincidence, perhaps representing recent fragmentation of a parent body. We conclude that seismic methods of detecting current impacts offer a more complete dataset than orbital imaging.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.adk7615","usgsCitation":"Daubar, I.J., Garcia, R., Stott, A.E., Fernando, B., Collins, G.S., Dundas, C., Wojcicka, N., Zenhausern, G., McEwen, A.S., Stahler, S., Golombek, M.P., Charalambous, C., Giardini, D., Lognonne, P., and Banerdt, W., 2024, Seismically detected cratering on Mars: Enhanced recent impact flux?: Science Advances, v. 10, no. 26, eadk7615, 9 p., https://doi.org/10.1126/sciadv.adk7615.","productDescription":"eadk7615, 9 p.","ipdsId":"IP-152454","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":487204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adk7615","text":"Publisher Index Page"},{"id":482566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"26","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daubar, Ingrid J.","contributorId":204233,"corporation":false,"usgs":false,"family":"Daubar","given":"Ingrid","email":"","middleInitial":"J.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":928853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Raphael F.","contributorId":351538,"corporation":false,"usgs":false,"family":"Garcia","given":"Raphael F.","affiliations":[{"id":34610,"text":"Universite de Toulouse","active":true,"usgs":false}],"preferred":false,"id":928854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stott, Alexander E.","contributorId":236698,"corporation":false,"usgs":false,"family":"Stott","given":"Alexander","email":"","middleInitial":"E.","affiliations":[{"id":47531,"text":"Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":928855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernando, Benjamin","contributorId":351539,"corporation":false,"usgs":false,"family":"Fernando","given":"Benjamin","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":928856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Gareth S.","contributorId":328863,"corporation":false,"usgs":false,"family":"Collins","given":"Gareth","email":"","middleInitial":"S.","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":928857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":928858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wojcicka, Natalia","contributorId":351540,"corporation":false,"usgs":false,"family":"Wojcicka","given":"Natalia","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":928859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zenhausern, Geraldine","contributorId":351541,"corporation":false,"usgs":false,"family":"Zenhausern","given":"Geraldine","affiliations":[{"id":12483,"text":"ETH 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Bruce","contributorId":351546,"corporation":false,"usgs":false,"family":"Banerdt","given":"W. Bruce","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":928867,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70255863,"text":"70255863 - 2024 - The National Ocean Biodiversity Strategy","interactions":[],"lastModifiedDate":"2025-01-24T20:43:15.098019","indexId":"70255863","displayToPublicDate":"2024-06-28T07:02:21","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"The National Ocean Biodiversity Strategy","docAbstract":"President Biden has been clear that the ocean is central to life on Earth. As he has proclaimed, “the ocean powers millions of jobs; feeds and sustains us; and is a rejuvenating source of inspiration, exploration, and recreation.” The Biden-Harris Administration has worked hard to fulfill the President’s goal to protect and conserve at least 30% of U.S. waters by 2030. The ocean faces increased threats from warming, overfishing, increased acidity, and loss of biodiversity. It is now more important than ever to sustain the many benefits that the ocean, coasts, and Great Lakes provide, including food, a favorable climate, recreation, physical and mental health, and for many, a sense of cultural identity. Ocean life represents an irreplaceable heritage, the foundation of a habitable planet, and a vast trove of resources. Keeping our ocean healthy requires reliable information on the changing status of these living organisms, the drivers of biodiversity change, and options for effectively addressing those drivers. Over 2 million species are estimated to live in the ocean, yet only about 240,000 species have been described by scientists. Most of the ocean’s benefits result from those diverse species interacting with one another and the environment they create. To protect and conserve the ocean, we as a nation need to make better use of existing knowledge and prioritize acquiring new biodiversity knowledge to enable better policy and management decisions. The ability to monitor ocean species and habitats has expanded dramatically over the past decade, with innovations in technology, genomics, taxonomy, big data management and sharing, artificial intelligence, and machine learning. Yet large fractions of the U.S. ocean remain almost unknown. The National Ocean Biodiversity Strategy (strategy) reflects the urgent need to leverage these advances. The goals of this strategy must be guided by the nation’s diverse voices and ways of knowing, in order to maximize effective and equitable stewardship of the ocean’s diverse life and its benefits to people. The strategy is intended as a guiding document for government to advance three overarching goals:
● Goal 1: Drive delivery of ocean biodiversity knowledge at the national scale. Objectives include developing an Implementation Plan for achieving the strategy’s three goals; establishing a coordination mechanism to manage the implementation; and documenting gaps in biodiversity knowledge and the benefits of ocean biodiversity to people and economies.
● Goal 2: Strengthen tools and institutions to deliver ocean biodiversity knowledge. Objectives include establishing a robust information pipeline to support indicators and dynamic maps of ocean biodiversity, from the coasts to the deep sea. This pipeline should include expanded observing systems and comprehensive data management; science and technology solutions to accelerate the availability of biodiversity information; and plans to leverage previous investments to rebuild and expand the nation’s human capital and infrastructure to sustain foundational taxonomy and biodiversity science.
● Goal 3: Protect, conserve, restore, and sustainably use ocean biodiversity. Objectives include expanding the collection, delivery, and use of biodiversity knowledge to inform actions that advance ocean protection, conservation, restoration, and sustainable development. Government should lead in establishing and incentivizing diverse partnerships across scales and sectors to implement those actions and should educate and involve the public to discover and value the nation’s diverse ocean life. Achieving these goals will require commitments across society: new federal and private investments, coordination across sectors to address climate and equity challenges, and engagement of Indigenous Knowledge holders and frontline communities as full partners throughout planning and implementation. The Subcommittee on Ocean Science and Technology (SOST) IWG-Biodiversity will begin developing an Implementation Plan to describe and direct specific actions to implement the strategy. Successful implementation of the strategy will harmonize and expand collection and delivery of timely knowledge on ocean life to all of society. The strategy will also enable evidence-based management and protection of the ocean. Advancing the strategy will build human and institutional capital and partnerships that support both existing mandates and new needs to rebuild and sustain biodiversity, achieve healthy ocean ecosystems, and manage living resources. Implementing the strategy will deliver knowledge for monitoring, modeling, forecasting, and assessments that support food security, public health, and cultural values, and that more effectively protect, conserve, and restore nature.
","language":"English","publisher":"White House Office of Science, Technology, and Policy (OSTP)","collaboration":"National Oceanic and Atmospheric Administration, Smithsonian, Bureau of Ocean Energy Management, University Corporation for Atmospheric Research, Environmental Protection Agency, Office of Naval Research, National Science Foundation, National Aeronautics and Space Administration","usgsCitation":"Canonico, G., Duffy, J., Edmonson, M., Fillingham, K., Benson, A., Bisson, K., Demopoulos, A., Hinchey, B., Matsumoto, K., Meyer, C., Price, J., Shen, E., Turner, W., Weise, M., Vander Woude, A., and Wenzel, L., 2024, The National Ocean Biodiversity Strategy, vi, 18 p.","productDescription":"vi, 18 p.","ipdsId":"IP-166759","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":430840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430823,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/06/NSTC_National-Ocean-Biodiversity-Strategy.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Canonico, Gabrielle","contributorId":217563,"corporation":false,"usgs":false,"family":"Canonico","given":"Gabrielle","email":"","affiliations":[{"id":39659,"text":"National Oceanographic and Atmospheric Administration, US Integrated Ocean Observing System, Silver Spring, MD, USA","active":true,"usgs":false}],"preferred":false,"id":905819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, J. 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2024 - Coyote use of prairie dog colonies is most frequent in areas used by American badgers","interactions":[],"lastModifiedDate":"2024-07-12T11:54:50.180369","indexId":"70255993","displayToPublicDate":"2024-06-28T06:52:29","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Coyote use of prairie dog colonies is most frequent in areas used by American badgers","docAbstract":"The consequences of intraguild predation on vulnerable subordinate species are an important consideration in the recovery of endangered species. In prairie ecosystems, coyotes (Canis latrans) are the primary predator of endangered black-footed ferrets (Mustela nigripes; hereafter, ferrets) and presumably compete for prairie dog (Cynomys spp.) prey. Coyote predation of ferrets is thought to occur at night when ferrets are active aboveground; however, the apparent source of competition, diurnal prairie dogs, are belowground and inaccessible to coyotes at this time, presenting a perplexing temporal mismatch between actual and expected times that coyotes and ferrets come into conflict. Our study used remote wildlife cameras, occupancy models, and overlap of circadian activity patterns to investigate how landscape features, prairie dog colony attributes, and attraction to sympatric species, i.e., American badgers (Taxidea taxus; hereafter, badgers) and lagomorphs (cottontail rabbits and jackrabbits) influence Coyote use of prairie dog colonies and potential Coyote–ferret interactions. We first evaluated Coyote use (i.e., occupancy) between prairie dog colonies and surrounding available grasslands, finding that coyotes whose home ranges include prairie dog colonies used colonies nearly twice as much as surrounding grasslands. Next, we investigated biotic and abiotic factors that may influence Coyote use and frequency of use (i.e., detection probability) on prairie dog colonies. We found high Coyote use across all areas on prairie dog colonies; however, their frequency of use increased in areas that were also used by badgers. High overlap between Coyote and badger activity patterns (81%) further supports the spatial use patterns revealed by our occupancy analysis, and badgers and coyotes are known to form hunting associations. Interspecific competition and overlapping patterns of resource use between badgers and ferrets have been documented in previous studies; our study supports these findings and suggests that Coyote attraction to badger activity may influence Coyote–ferret interactions.
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