Layered intrusions are plutonic bodies of cumulates that form by the crystallization of mantle-derived melts. These intrusions are characterized by igneous layering distinguishable by shifts in mineralogy, texture, or composition. Layered intrusions have been fundamental to our understanding of igneous petrology; however, it is their status as important repositories of critical metals – such as platinum-group elements, chromium, and vanadium – that has predominantly driven associated research in recent decades. Many layered intrusions were emplaced during the Precambrian, predominantly at the margins of ancient cratons during intervals of supercontinent accretion and destruction. It appears that large, layered intrusions require rigid crust to ensure their preservation, and their geometry and layering is primarily controlled by the nature of melt emplacement.
Layered intrusions are best investigated by integrating observations from various length-scales. At the macroscale, intrusion geometries can be discerned, and their presence understood in the context of the regional geology. At the mesoscale, the layering of an intrusion may be characterized, intrusion-host rock contact relationships studied, and the nature of stratiform mineral occurrences described. At the microscale, the mineralogy and texture of cumulate rocks and any mineralization are elucidated, particularly when novel microtextural and mineral chemical datasets are integrated. For example, here we demonstrate how mesoscale observations and microscale datasets can be combined to understand the petrogenesis of the perplexing snowball oiks outcrop located in the Upper Banded Series of the Stillwater Complex. Our data suggest that the orthopyroxene oikocrysts did not form in their present location, but rather formed in a dynamic magma chamber where crystals were transported either by convective currents or within crystal-rich slurries.
Critical metals may be transported to the level of a nascent intrusion as dissolved components in the melt. Alternatively, ore minerals are entrained from elsewhere in a plumbing system, potentially facilitated by volatile-rich phases. There are many ore-forming processes propounded by researchers to occur at the level of emplacement; however, each must address the arrival of the ore mineral, its concentration of metals, and its accumulation into orebodies. In this contribution, several of these processes are described as well as our perspectives on the future of layered intrusion research.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2024.107615","usgsCitation":"Smith, W.D., Jenkins, M., Augustin, C.T., Virtanen, V.J., Vukmanovic, Z., and O’Driscoll, B., 2024, Layered intrusions in the Precambrian: Observations and perspectives: Precambrian Research, v. 415, 107615, 31 p., https://doi.org/10.1016/j.precamres.2024.107615.","productDescription":"107615, 31 p.","ipdsId":"IP-169762","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":466760,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2024.107615","text":"Publisher Index Page"},{"id":464288,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"415","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, William D.","contributorId":335361,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":918775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Michael 0000-0002-4261-409X mjenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-4261-409X","contributorId":172433,"corporation":false,"usgs":true,"family":"Jenkins","given":"Michael","email":"mjenkins@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":918776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Augustin, Claudia T.","contributorId":346348,"corporation":false,"usgs":false,"family":"Augustin","given":"Claudia","email":"","middleInitial":"T.","affiliations":[{"id":82834,"text":"Mineral Deposits Group, Department of Earth Sciences, Carleton University","active":true,"usgs":false}],"preferred":false,"id":918777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Virtanen, Ville J.","contributorId":346349,"corporation":false,"usgs":false,"family":"Virtanen","given":"Ville","email":"","middleInitial":"J.","affiliations":[{"id":82835,"text":"Institut des Sciences de la Terre d’Orléans; Department of Geosciences and Geography, University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":918778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vukmanovic, Zoja","contributorId":346350,"corporation":false,"usgs":false,"family":"Vukmanovic","given":"Zoja","email":"","affiliations":[{"id":82836,"text":"School of Environmental Sciences, University of East Anglia","active":true,"usgs":false}],"preferred":false,"id":918779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Driscoll, Brian","contributorId":346351,"corporation":false,"usgs":false,"family":"O’Driscoll","given":"Brian","email":"","affiliations":[{"id":35511,"text":"Department of Earth and Environmental Sciences, University of Ottawa","active":true,"usgs":false}],"preferred":false,"id":918780,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259587,"text":"70259587 - 2024 - Predictive understanding of stream salinization in a developed watershed using machine learning","interactions":[],"lastModifiedDate":"2024-10-23T16:27:38.970197","indexId":"70259587","displayToPublicDate":"2024-10-11T07:01:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Predictive understanding of stream salinization in a developed watershed using machine learning","docAbstract":"Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We compare the predictive performance of space- and time-unaware Random Forest models and space- and time-aware Recurrent Graph Convolution Neural Network models (KGE: 0.67 and 0.64, respectively) and use explainable artificial intelligence methods to interpret model predictions and understand salinization drivers. These models are applied to the Delaware River Basin, a developed watershed with diverse land uses that experiences anthropogenic salinization from winter deicer applications. These models capture seasonality for the winter first flush of deicers, and the streams with elevated predictions correspond well with indicators of deicer application. This result suggests that these models can be used to identify potential salinity-impaired streams for winter best management practices. Daily salinity predictions are driven primarily by land cover (urbanization) trends that may represent anthropogenic salinization processes and weather at time scales up to three months. Such modeling approaches are likely transferable to other watersheds and can be applied to further understand salinization risks and drivers.
The increasing prevalence of drought events in grasslands and shrublands worldwide potentially has impacts on soil organic carbon (SOC). We leveraged the International Drought Experiment to study how SOC, including particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) concentrations, responds to extreme drought treatments (1-in-100-year) for 1 to 5 years at 19 sites worldwide. In more mesic areas (aridity index > 0.65), SOC and POC concentrations decreased by 7.9% (±3.9) and 15.9% (±6.2) with drought, respectively, but there were no impacts on MAOC concentrations. However, drought had no impact on SOC, POC, or MAOC concentrations in drylands (aridity index < 0.65). The response of SOC to drought varied along an aridity gradient, concomitant with interannual precipitation variability and standing SOC concentration gradients. These findings highlight the differing response magnitudes of POC and MAOC concentrations to drought and the key regulating role of aridity.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adq2654","usgsCitation":"Shi, B., Delgado-Baquerizo, M., Knapp, A.K., Smith, M.D., Reed, S., Osborne, B.B., Carrillo, Y., Maestre, F.T., Zhu, Y., Chen, A., Wilkins, K.D., Holdrege, M.C., Kulmatiski, A., Picon-Cochard, C., Roscher, C., Power, S.A., Byrne, K.M., Churchill, A., Jentsch, A., Henry, H.A., Beard, K.H., Schuchardt, M.A., Eisenhauer, N., Otfinowski, R., Hautier, Y., Shen, H., Wang, Y., Wang, Z., Wang, C., Cusack, D., Petraglia, A., Carbognani, M., Forte, T., Flory, S., Hou, P., Zhang, T., Gao, W., and Sun, W., 2024, Aridity drives the response of soil total and particulate organic carbon to drought in temperate grasslands and shrublands: Science Advances, v. 10, no. 40, eadq2654 , 9 p., https://doi.org/10.1126/sciadv.adq2654.","productDescription":"eadq2654 , 9 p.","ipdsId":"IP-161119","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":490639,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adq2654","text":"Publisher Index Page"},{"id":490372,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"40","noUsgsAuthors":false,"publicationDate":"2024-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Baoku","contributorId":356764,"corporation":false,"usgs":false,"family":"Shi","given":"Baoku","affiliations":[{"id":85214,"text":"Institute of Grassland Science, Key Laboratory of Vegetation Ecology of the Ministry of Education, Jilin 17 Songnen Grassland Ecosystem National Observation and Research Station, Northeast Normal University, 18 Changchun, 130024, China","active":true,"usgs":false}],"preferred":false,"id":939987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Delgado-Baquerizo, 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Italy","active":true,"usgs":false}],"preferred":false,"id":940019,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Flory, S. Luke","contributorId":333448,"corporation":false,"usgs":false,"family":"Flory","given":"S. Luke","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":940020,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Hou, Pengli","contributorId":356774,"corporation":false,"usgs":false,"family":"Hou","given":"Pengli","affiliations":[{"id":85222,"text":"Institute of Grassland Science, Key Laboratory of Vegetation Ecology of the Ministry of Education, Jilin Songnen Grassland Ecosystem National Observation and Research Station, Northeast Normal University, Changchun, 130024, China","active":true,"usgs":false}],"preferred":false,"id":940021,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Zhang, Tao","contributorId":145845,"corporation":false,"usgs":false,"family":"Zhang","given":"Tao","email":"","affiliations":[{"id":16257,"text":"NOAA Earth System Research Laboratory, Boulder, Colorado","active":true,"usgs":false}],"preferred":false,"id":940022,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Gao, Weifeng","contributorId":356775,"corporation":false,"usgs":false,"family":"Gao","given":"Weifeng","affiliations":[{"id":85222,"text":"Institute of Grassland Science, Key Laboratory of Vegetation Ecology of the Ministry of Education, Jilin Songnen Grassland Ecosystem National Observation and Research Station, Northeast Normal University, Changchun, 130024, China","active":true,"usgs":false}],"preferred":false,"id":940023,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Sun, Wei","contributorId":289628,"corporation":false,"usgs":false,"family":"Sun","given":"Wei","email":"","affiliations":[{"id":62203,"text":"Institute of Grassland Science, School of Life Science, Northeast Normal University, Key Laboratory of Vegetation Ecology, Ministry of Education, Jilin Songnen Grassland Ecosystem National Observation and Research Station, Changchun, 130024, China","active":true,"usgs":false}],"preferred":false,"id":940024,"contributorType":{"id":1,"text":"Authors"},"rank":38}]}} ,{"id":70259791,"text":"70259791 - 2024 - Feeding habits and ecological implications of the invasive Flathead Catfish in the Susquehanna River basin, Pennsylvania","interactions":[],"lastModifiedDate":"2024-10-30T21:38:29.072988","indexId":"70259791","displayToPublicDate":"2024-08-22T10:18:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Feeding habits and ecological implications of the invasive Flathead Catfish in the Susquehanna River basin, Pennsylvania","docAbstract":"Flathead Catfish Pylodictis olivaris are a widespread aquatic invasive species within the United States and a recent invader in the Susquehanna River basin, Pennsylvania. Flathead Catfish are piscivores known to consume native and recreationally important fish species. In the mid-Atlantic United States, it is unknown how this invader is impacting food webs and which species may be at greatest predation risk. To address this knowledge gap, we DNA barcoded stomach contents collected from Flathead Catfish to identify prey species and elucidate potential predatory impacts of Flathead Catfish in the Susquehanna River.
We used a Bayesian hierarchical multivariate probit model to investigate if the probability of prey species occurrence in the diets of Flathead Catfish varied seasonally or was a function of Flathead Catfish length.
A total of 576 Flathead Catfish were collected during 2020–2021, with 241 individuals having recoverable stomach contents. In all, we identified 47 different prey species. The most common prey species were rusty crayfish Faxonius rusticus, Margined Madtom Noturus insignis, and shiners Notropis spp. While frequency of occurrence of prey species differed across Flathead Catfish length classes (<300 mm, 301–600 mm, 601–900 mm TL), rusty crayfish were commonly found (33.7–44.0% of diets) in stomachs of all size-classes.
We found that Flathead Catfish length and seasonality did influence occurrence probability differentially for several prey species. For example, Channel Catfish Ictalurus punctatus were more likely to appear in shorter Flathead Catfish while Smallmouth Bass Micropterus dolomieu appeared in larger Flathead Catfish. We demonstrate significant variation in Flathead Catfish predation, increasing our understanding of predator–prey dynamics, which is necessary to better manage and identify future impacts to aquatic communities in the Susquehanna River basin.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10480","usgsCitation":"Stark, S., Schall, M.K., Smith, G., Maloy, A., Coombs, J.A., Wagner, T., and Avery, J., 2024, Feeding habits and ecological implications of the invasive Flathead Catfish in the Susquehanna River basin, Pennsylvania: Transactions of the American Fisheries Society, v. 153, no. 5, p. 591-610, https://doi.org/10.1002/tafs.10480.","productDescription":"20 p.","startPage":"591","endPage":"610","ipdsId":"IP-160306","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466955,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10480","text":"Publisher Index Page"},{"id":463192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Susquehanna River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.13792639569971,\n 42.09066504733855\n ],\n [\n -78.27638072611447,\n 42.09066504733855\n ],\n [\n -78.27638072611447,\n 39.71793162556648\n ],\n [\n -75.13792639569971,\n 39.71793162556648\n ],\n [\n -75.13792639569971,\n 42.09066504733855\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"153","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Stark, Sydney","contributorId":343364,"corporation":false,"usgs":false,"family":"Stark","given":"Sydney","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":916708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schall, Megan K.","contributorId":274359,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":916709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Geoffrey D.","contributorId":224595,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey D.","affiliations":[{"id":40898,"text":"Pennsylvania Fish & Boat Commission","active":true,"usgs":false}],"preferred":false,"id":916710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloy, Aaron","contributorId":343773,"corporation":false,"usgs":false,"family":"Maloy","given":"Aaron","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":916711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coombs, Jason A.","contributorId":77039,"corporation":false,"usgs":true,"family":"Coombs","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":916712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":916713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Avery, Julian","contributorId":264623,"corporation":false,"usgs":false,"family":"Avery","given":"Julian","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":916714,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70263400,"text":"70263400 - 2024 - Status and performance of the ShakeAlert® earthquake early warning system: 2019-2023","interactions":[],"lastModifiedDate":"2025-02-10T16:47:16.140108","indexId":"70263400","displayToPublicDate":"2024-08-16T10:42:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Status and performance of the ShakeAlert® earthquake early warning system: 2019-2023","docAbstract":"The U.S. Geological Survey (USGS)‐operated ShakeAlert® system is the United States West Coast earthquake early warning system (Given et al., 2018). In this study we detail ShakeAlert’s performance during some of the largest events seen by the system thus far. Statewide public alerting using ShakeAlert messages was authorized in California in October 2019. Over the next few years, public alerts were expanded into Oregon and then into Washington (U.S. Geological Survey, 2024). ShakeAlert source results are routinely compared to the USGS Comprehensive Catalog (ComCat; Guy et al., 2015; U.S. Geological Survey, Earthquake Hazards Program, 2017), which contains the earthquake location and magnitude determined using complete waveform data. M 4.5 and larger is the threshold used for public alerting and was deliberately set below the level where damage is likely to compensate for cases where the system underestimates the magnitude. Between 17 October 2019 and 1 September 2023, the ShakeAlert system created 95 events with maximum magnitude estimates of M ≥4.5, the public alerting threshold. 94 of the 95 events were due to real earthquakes. Seven were categorized “false” per ShakeAlert’s internal definition that there was no matching catalog event within 100 km and 30 s of origin time; however, all but one of these were real earthquakes that were poorly located, primarily because they were at the edges of the seismic network. Three detected events were labeled “missed” because they were very poorly located (>100 km location error). In addition, the system did not produce solutions for four ComCat events M ≥4.5 (U.S. Geological Survey, Earthquake Hazards Program, 2017), which were all at the edge of the alerting and network boundaries. The ShakeAlert system has accurately detected the majority of earthquakes that have occurred within the operational region since completing the public rollout, and alerts from the system have been delivered to millions of cell phone users throughout the West Coast.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230259","usgsCitation":"Lux, A., Smith, D., Böse, M., McGuire, J., Saunders, J., Huynh, M., Stubailo, I., Andrews, J.R., Lotto, G., Crowell, B., Crane, S., Allen, R.M., Given, D.D., Hartog, R., Heaton, T., Husker, A., Marty, J., O'Driscoll, L., Tobin, H.J., McBride, S.K., and Toomey, D., 2024, Status and performance of the ShakeAlert® earthquake early warning system: 2019-2023: Bulletin of the Seismological Society of America, v. 114, no. 6, p. 3041-3062, https://doi.org/10.1785/0120230259.","productDescription":"22 p.","startPage":"3041","endPage":"3062","ipdsId":"IP-158989","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, 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Technology","active":true,"usgs":false}],"preferred":false,"id":926835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Huynh, Minh 0000-0002-5856-121X","orcid":"https://orcid.org/0000-0002-5856-121X","contributorId":350725,"corporation":false,"usgs":true,"family":"Huynh","given":"Minh","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stubailo, I.","contributorId":350737,"corporation":false,"usgs":false,"family":"Stubailo","given":"I.","affiliations":[{"id":83820,"text":"California Institute of Technology (Caltech) Seismological Laboratory","active":true,"usgs":false}],"preferred":false,"id":926837,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Andrews, Jennifer R 0000-0002-5679-5565","orcid":"https://orcid.org/0000-0002-5679-5565","contributorId":263435,"corporation":false,"usgs":false,"family":"Andrews","given":"Jennifer","email":"","middleInitial":"R","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":926838,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lotto, G.","contributorId":350738,"corporation":false,"usgs":false,"family":"Lotto","given":"G.","affiliations":[{"id":83821,"text":"University of Washington (UW), Seattle","active":true,"usgs":false}],"preferred":false,"id":926839,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Crowell, B.","contributorId":350722,"corporation":false,"usgs":false,"family":"Crowell","given":"B.","affiliations":[{"id":28238,"text":"Univ of Washington","active":true,"usgs":false}],"preferred":false,"id":926840,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Crane, S.","contributorId":350739,"corporation":false,"usgs":false,"family":"Crane","given":"S.","affiliations":[{"id":83822,"text":"Natural Resources Canada (NRCAN), Ottawa","active":true,"usgs":false}],"preferred":false,"id":926841,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Allen, R. M.","contributorId":36170,"corporation":false,"usgs":false,"family":"Allen","given":"R.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":926842,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Given, Douglas D. 0000-0002-3277-5121 doug@usgs.gov","orcid":"https://orcid.org/0000-0002-3277-5121","contributorId":201870,"corporation":false,"usgs":true,"family":"Given","given":"Douglas","email":"doug@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926843,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hartog, R.","contributorId":201873,"corporation":false,"usgs":false,"family":"Hartog","given":"R.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":926844,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Heaton, T.","contributorId":222141,"corporation":false,"usgs":false,"family":"Heaton","given":"T.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":926845,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Husker, A.","contributorId":350740,"corporation":false,"usgs":false,"family":"Husker","given":"A.","affiliations":[{"id":83820,"text":"California Institute of Technology (Caltech) Seismological Laboratory","active":true,"usgs":false}],"preferred":false,"id":926846,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Marty, J.","contributorId":350741,"corporation":false,"usgs":false,"family":"Marty","given":"J.","affiliations":[{"id":83823,"text":"UC Berkeley Seismological Laboratory (UCB)","active":true,"usgs":false}],"preferred":false,"id":926847,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"O'Driscoll, Leland","contributorId":350742,"corporation":false,"usgs":false,"family":"O'Driscoll","given":"Leland","affiliations":[{"id":83824,"text":"University of Oregon (UO), Eugene","active":true,"usgs":false}],"preferred":false,"id":926848,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tobin, Harold J.","contributorId":350743,"corporation":false,"usgs":false,"family":"Tobin","given":"Harold","middleInitial":"J.","affiliations":[{"id":83821,"text":"University of Washington (UW), Seattle","active":true,"usgs":false}],"preferred":false,"id":926849,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"McBride, Sara K. 0000-0002-8062-6542 skmcbride@usgs.gov","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":224627,"corporation":false,"usgs":true,"family":"McBride","given":"Sara","email":"skmcbride@usgs.gov","middleInitial":"K.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":926944,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Toomey, D.","contributorId":350744,"corporation":false,"usgs":false,"family":"Toomey","given":"D.","affiliations":[{"id":83824,"text":"University of Oregon (UO), Eugene","active":true,"usgs":false}],"preferred":false,"id":926850,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70257555,"text":"70257555 - 2024 - Population genetic structure and demographic history reconstruction of introduced flathead catfish (Pylodictis olivaris) in two US Mid-Atlantic rivers","interactions":[],"lastModifiedDate":"2024-09-06T18:18:10.540344","indexId":"70257555","displayToPublicDate":"2024-08-12T11:04:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"title":"Population genetic structure and demographic history reconstruction of introduced flathead catfish (Pylodictis olivaris) in two US Mid-Atlantic rivers","docAbstract":"Population genetic analysis of invasive populations can provide valuable insights into the source of introductions, pathways for expansion, and their demographic histories. Flathead catfish (Pylodictis olivaris) are a prolific invasive species with high fecundity, long-distance dispersal, and piscivorous feeding habits that can lead to declines in native fish populations. In this study, we analyse the genetics of invasive P. olivaris in the Mid-Atlantic region to assess their connectivity and attempt to reconstruct the history of introduced populations. Based on an assessment across 13 microsatellite loci, P. olivaris from the Susquehanna River system (N = 537), Schuylkill River (N = 33), and Delaware River (N = 1) have low genetic diversity (global Hobs = 0.504), although we detected no evidence of substantial inbreeding (FIS = −0.083 to 0.022). P. olivaris from these different river systems were genetically distinct, suggesting separate introductions. However, population structure was much weaker within each river system and exhibited a pattern of high connectivity, with some evidence of isolation by distance. P. olivaris from the Susquehanna and Schuylkill rivers showed evidence for recent genetic bottlenecks, and demographic models were consistent with historical records, which suggest that populations were established by recent founder events consisting of a small number of individuals. Our results show the risk posed by small introductions of P. olivaris, which can spread widely once a population is established, and highlight the importance of prevention and sensitive early detection methods to prevent the spread of P. olivaris in the future.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.15888","usgsCitation":"Waraniak, J., Eackles, M., Keagy, J., Smith, G., Schall, M., Stark, S., White, S.L., Kazyak, D.C., and Wagner, T., 2024, Population genetic structure and demographic history reconstruction of introduced flathead catfish (Pylodictis olivaris) in two US Mid-Atlantic rivers: Journal of Fish Biology, 14 p., https://doi.org/10.1111/jfb.15888.","productDescription":"14 p.","ipdsId":"IP-164536","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439216,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.15888","text":"Publisher Index Page"},{"id":433579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, Pennsylvania","noUsgsAuthors":false,"publicationDate":"2024-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin","contributorId":343350,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eackles, Michael S.","contributorId":343352,"corporation":false,"usgs":false,"family":"Eackles","given":"Michael S.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":910811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keagy, Jason","contributorId":343355,"corporation":false,"usgs":false,"family":"Keagy","given":"Jason","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Geoffrey D.","contributorId":343358,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey D.","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":910813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schall, Megan","contributorId":343361,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910814,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stark, Sydney","contributorId":343364,"corporation":false,"usgs":false,"family":"Stark","given":"Sydney","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910815,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":910816,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":910817,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910818,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256109,"text":"70256109 - 2024 - Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","interactions":[],"lastModifiedDate":"2024-07-22T11:47:51.294971","indexId":"70256109","displayToPublicDate":"2024-07-20T06:43:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","docAbstract":"In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km2), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km2/yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km2 scales under conditions without debris flows.
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":70257508,"text":"70257508 - 2024 - An experimental study of benthic habitat selection in yellow-phase American eels (Anguilla rostrata)","interactions":[],"lastModifiedDate":"2024-09-06T16:13:45.429755","indexId":"70257508","displayToPublicDate":"2024-05-14T09:06:32","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":"An experimental study of benthic habitat selection in yellow-phase American eels (Anguilla rostrata)","docAbstract":"In a laboratory experiment, we quantified microhabitat use of small yellow-phase American eels (Anguilla rostrata, n = 130, 224–338 mm TL) conditional on five benthic substrate types common to rivers within their geographic range. During nine, 4-day trials replicated with three aquaria, American eels were given a choice to burrow into five equally available benthic substrates: cobble (90–256 mm), gravel (4–16 mm), sand (0.125–1 mm), silt/clay (< 0.0625 mm), and leaf pack. Five American eels were used per aquarium for each trial, and individuals were used one time only. All eels were injected with PIT tags prior to the study, which allowed for determination of lengths and otolith-based ages of each individual following each trial. Leaf pack was selected with a significantly higher probability than other substrates (63 of 130 individuals). However, other substrates were also used (cobble, 21 of 130; silt/clay, 18 of 130; gravel, 16 of 130; and sand, 12 of 130). Length and age covariates were not associated with substrate selection. Selection of leaf pack habitat supports the importance of forested riparian zones and terrestrial organic material to yellow-phase American eels in riverine systems.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10641-024-01544-z","usgsCitation":"Braham, M., Welsh, S., and Smith, D., 2024, An experimental study of benthic habitat selection in yellow-phase American eels (Anguilla rostrata): Environmental Biology of Fishes, v. 107, p. 513-522, https://doi.org/10.1007/s10641-024-01544-z.","productDescription":"10 p.","startPage":"513","endPage":"522","ipdsId":"IP-127663","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Millville hydroelectric dam, Shenandoah River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.25894317307947,\n 39.22638050030608\n ],\n [\n -78.25894317307947,\n 38.94086151879799\n ],\n [\n -77.75103536336908,\n 38.94086151879799\n ],\n [\n -77.75103536336908,\n 39.22638050030608\n ],\n [\n -78.25894317307947,\n 39.22638050030608\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","noUsgsAuthors":false,"publicationDate":"2024-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Braham, Melissa","contributorId":343003,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, S.A. 0000-0003-0362-054X","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":10191,"corporation":false,"usgs":true,"family":"Welsh","given":"S.A.","affiliations":[],"preferred":false,"id":910570,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Dustin M.","contributorId":272979,"corporation":false,"usgs":false,"family":"Smith","given":"Dustin M.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":912567,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70254333,"text":"70254333 - 2024 - Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost","interactions":[],"lastModifiedDate":"2024-06-03T15:13:02.87373","indexId":"70254333","displayToPublicDate":"2024-04-05T06:28:58","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":"Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost","docAbstract":"Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere-Wave-Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process-based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.
In 2022, the U.S. Geological Survey (USGS) established the Saline Lake Ecosystems Integrated Water Availability Assessment (IWAAs) to monitor and assess the hydrology of terminal lakes in the Great Basin and the migratory birds and other wildlife dependent on those habitats. Scientists from across the USGS (with specialties in water quantity, water quality, limnology, avian biology, data science, landscape ecology, and science communication) formed the Saline Lake Ecosystems IWAAs Team. The team has developed this regional strategic science plan to guide data collection and assessment activities at terminal lakes in the Great Basin.
The U.S. Congress requested the USGS to establish the Saline Lake Ecosystems IWAAs in response to historically low water levels at terminal lakes and associated wetlands across the Great Basin. Not all Great Basin terminal lakes have high salinity; however, all terminal lakes occur in endorheic, closed, basins with no surface-water outflow. Low lake levels across the Great Basin are the result of increased water use for agriculture and municipalities, drought conditions, and a warming climate. Great Basin terminal lake water extents have decreased by as much as 90 percent over the last 150 years, and terminal lake wetlands have decreased in area by as much as 47 percent since 1984. Lake elevations and wetland areas are primarily supported by freshwater inputs from snowmelt feeding upgradient rivers, streams, and springs. These freshwater inputs have been severely reduced because of continued and increased surface-water diversions and surface-water capture through groundwater pumping for agriculture, mining, and public supply as well as unprecedented drought conditions and warming temperatures related to climate change.
Water quality, specifically salinity, is highly variable for terminal lakes of the Great Basin, and this variability is a result of the balance between freshwater inflow and evaporation. Variability of salinity at each of the terminal lakes can be affected by lake morphology, hydrogeologic features of the basin, annual variability in weather patterns, and changes in upgradient water use. Hypersaline terminal lakes provide abundant food resources such as brine shrimp and brine flies that support nesting and migrating birds. The density and composition of invertebrates are closely tied to lake salinity. Increased salinity can exceed the tolerance of invertebrates, severely limiting their biomass. In contrast, decreased salinity can lead to altered invertebrate community composition, reducing the abundance of optimal avian prey resources.
Great Basin terminal lake ecosystems, including open-water and adjacent aquatic and terrestrial environments, provide resources necessary to sustain many animal populations throughout the year. Although a variety of taxa use terminal lakes, these ecosystems are of acute importance for the millions of migratory waterbirds (for example, shorebirds, wading birds, and waterfowl) dependent on the network of terminal lakes and their associated wetlands. Migratory birds transiting the Pacific and Central Flyways use Great Basin terminal lake ecosystems throughout the year to feed, nest, and transit between wintering and breeding ranges. As such, successful conservation of birds and their habitats requires coordinated management of water and habitats across the Great Basin network of terminal lakes and wetlands.
The linkages between water availability and ecosystem vulnerability of terminal lakes in the Great Basin are not well understood. The vulnerability of terminal lakes is related to the factors driving change and adaptive capacity of the lake ecosystem. Saline lake ecosystems are vulnerable when changes in water quantity affect ecosystem function. Water quantity affects salinity, which affects food webs and habitat; these linkages can be investigated with water-quality and food web monitoring. Water quantity also affects inundated habitat, which can be quantified through remote sensing. It is necessary to quantify hydroclimatic and water use controls on water availability to terminal lakes to assess the response of the ecosystems. Remotely sensed data can provide a broad-scale and long-term synoptic view of terminal lake hydrologic characteristics, but ground observations are required to interpret changes in water quality and ecological functions. Some terminal lake basins have ongoing monitoring and modeling efforts within the Great Basin (for example, Great Salt Lake, Carson River Basin), yet most monitoring locations are hydrologically upgradient and too far away from lake inflows to provide an accurate assessment of hydrological trends for the lake ecosystems. Other terminal lakes have no long-term hydrological monitoring in their respective watersheds (for example, Lake Abert).
Ecological data collection in the Great Basin is also insufficient to understand how many birds exist on the landscape, how birds use the mosaic of terminal-lake habitats as an interconnected system, and how Great Basin terminal lakes are linked to the larger continental system of the Pacific and Central Flyways. Across agencies and organizations, tracking bird movement, abundance, and diversity is inconsistent, with some lakes having once- or twice-a-year bird survey efforts and a few locations having more intensive ecological data-gathering efforts (for example, Great Salt Lake, Lake Abert). Bridging hydrological and ecological information gaps will improve understanding of the trends in water supply and water quality, habitat availability and usage, and impacts on vulnerable waterbird species, all of which would be used by managers in coordinated conservation of this unique network of terminal-lake habitats.
The terminal lakes of the Great Basin are part of the Basin and Range physiographic province that extends from the Colorado Plateau on the east to the Sierra Nevada on the west, and from the Snake River Plain on the north to the Garlock fault and the Mojave block on the south. The Great Basin is larger than 650,000 square kilometers and encompasses most of the State of Nevada but also extends to western Utah, eastern California, southeastern Idaho, southwestern Wyoming, and southeastern Oregon. The climate is arid to semiarid with a hydrologic regime that is snowmelt dominated, providing as much as 75 percent of total annual runoff for the region. Terminal lakes of the Great Basin occupy the lowest areas of closed (endorheic) drainage basins, such that lake levels and water quality respond rapidly to surface-water inflow. Terminal lakes provide local and regional economic value to the States in the Great Basin, including mineral extraction, aquaculture, public works, and recreational uses. As an example, assessments of Great Salt Lake’s ecological health and economic impact find hemispheric importance for the former and regional importance for the latter. Great Salt Lake creates about 7,000 jobs and $2 billion of economic output per year, most of which would be lost with further declines in lake level.
The objectives of this Science Strategy are threefold: (1) to identify how changing water availability affects the quality, diversity, and abundance of habitats supporting continental waterbird populations; (2) to highlight the scientific monitoring and assessment needs of Great Basin terminal lakes; and (3) to support coordinated management and conservation actions to benefit those ecosystems, migratory birds, and other wildlife. There are long-term hydrological, ecological, and societal challenges associated with terminal lakes ecosystems in the Great Basin. This Science Strategy benefits partners by providing a conceptual model, nested at different spatial extents, that identifies key scientific information needs to inform coordinated implementation of management and conservation plans within and among hydrologic basins to address these complex challenges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1516","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Frus, R.J., Aldridge, C.L., Casazza, M.L., Eagles-Smith, C.A., Herring, G., Hynek, S.A., Jones, D.K., Kemp, S.K., Marston, T.M., Morris, C.M., Naranjo, R.C., Nell, C.S., O’Leary, D.R., Overton, C.T., Pulver, B.A., Reichert, B.E., Rumsey, C.A., Schuster, R., and Smith, C.D., 2023, Integrated science strategy for assessing and monitoring water availability and migratory birds for terminal lakes across the Great Basin, United States (ver. 1.1, May 2025): U.S. Geological Survey Circular 1516, 80 p., https://doi.org/10.3133/cir1516.","productDescription":"Report: v, 68 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-150954","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":493768,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115888.htm","linkFileType":{"id":5,"text":"html"}},{"id":486001,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1516/versionHistory.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version history"},{"id":423645,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q9LQ4B","text":"USGS data release","description":"USGS data release","linkHelpText":"Protected areas database of the United States (PAD-US) 3.0"},{"id":423641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1516/coverthb.jpg"},{"id":423642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1516/cir1516.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1516"},{"id":423644,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G5CGA7","text":"USGS data release","description":"USGS data release","linkHelpText":"Bibliography of hydrological and ecological research in the Great Basin terminal lakes, USA"},{"id":423646,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1516/images"},{"id":423647,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1516/cir1516.XML"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah, Wyoming","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.12391771230867,\n 44.77405073422017\n ],\n [\n -121.23711112053633,\n 42.97675607274502\n ],\n [\n -120.58454292500758,\n 41.114625765489706\n ],\n [\n -120.53050233969739,\n 39.38852576977999\n ],\n [\n -121.00002831582353,\n 38.53773263098702\n ],\n [\n -116.23219239369243,\n 34.14172761189964\n ],\n [\n -115.35960273905562,\n 33.921557217133085\n ],\n [\n -115.48689980188553,\n 34.74795954290249\n ],\n [\n -116.28333189096784,\n 36.47975443280677\n ],\n [\n -115.18568355055748,\n 36.991906804073\n ],\n [\n -114.24156554808329,\n 36.95599163233307\n ],\n [\n -113.37134736473482,\n 37.12638077251309\n ],\n [\n -110.81542598426338,\n 37.47470809636182\n ],\n [\n -109.75571598007943,\n 37.95141366251099\n ],\n [\n -109.25899321449629,\n 41.77121949987571\n ],\n [\n -109.56993210264781,\n 42.456264038882864\n ],\n [\n -111.91699262765405,\n 42.330119018284336\n ],\n [\n -115.47461542510021,\n 41.37630387146504\n ],\n [\n -117.71290028552804,\n 42.12092395037371\n ],\n [\n -119.12391771230867,\n 44.77405073422017\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: December 23, 2023; Version 1.1: April 2025","contact":"Director, Forest and Rangeland Ecosystem Science Center
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777 NW 9th Street, Suite 400
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Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The codex of the Endangered Species Act, volume II","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Bloomsbury Publishing","usgsCitation":"McGowan, C., Allan, N., and Smith, D.R., 2023, The species status assessment: A framework for assessing species status and risk to support endangered species management decisions, chap. of The codex of the Endangered Species Act, volume II, p. 87-102.","productDescription":"16 p.","startPage":"87","endPage":"102","ipdsId":"IP-138969","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":490777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Volume II","noUsgsAuthors":false,"publicationDate":"2023-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":3381,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":932174,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allan, Nathan","contributorId":340757,"corporation":false,"usgs":false,"family":"Allan","given":"Nathan","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":932175,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David R. 0000-0001-9560-5210 dvsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-9560-5210","contributorId":329849,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dvsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":932176,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250606,"text":"ofr20231090 - 2023 - Influence of a guide net on the presence and behavior of fish near the selective water withdrawal structure in Lake Billy Chinook, Oregon, 2022","interactions":[],"lastModifiedDate":"2026-02-18T21:58:29.869066","indexId":"ofr20231090","displayToPublicDate":"2023-12-19T10:10:34","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1090","displayTitle":"Influence of a Guide Net on the Presence and Behavior of Fish Near the Selective Water Withdrawal Structure in Lake Billy Chinook, Oregon, 2022","title":"Influence of a guide net on the presence and behavior of fish near the selective water withdrawal structure in Lake Billy Chinook, Oregon, 2022","docAbstract":"Imaging sonar was used to assess the influence of a fish guidance net, installed at the entrances to the selective water withdrawal (SWW) intake structure, in the forebay of Round Butte Dam, Oregon, on behavior, abundance, and timing of fish during the spring of 2022. The purposes of the SWW are (1) to direct surface currents in the forebay to attract and collect downriver migrating juvenile salmonid smolts (Chinook salmon [Oncorhynchus tshawytscha], sockeye salmon [O. nerka], and steelhead [O. mykiss]) from Lake Billy Chinook and (2) to enable operators of the SWW to withdraw water from surface and benthic elevations in the reservoir to manage downriver water temperatures. Part of the evaluation to determine how well the structure performs at collecting juvenile salmonids is (1) to regularly assess how fish are approaching the entrance, and (2) determine if operational flows and the installation of a guidance (lead) net near the SWW structure entrance can be optimized to increase the attraction of smolts present in the forebay of Lake Billy Chinook. The goal of this study was to provide data about the effects of the installation of a lead net on the movements and behaviors of juvenile salmonids near the entrance to the SWW to help inform decisions to improve downstream passage solutions.
Two imaging sonar units were deployed during the spring 2022 smolt out-migration period. One unit monitored fish movements near the south entrance and one unit monitored movements near the north entrance of the SWW, with the lead net between the two entrances. Both smolt and bull trout (Salvelinus confluentus)-size fish were regularly observed near the entrances, with greater abundances observed at night, corresponding with greater discharge through the SWW, as opposed to during the day when discharge was reduced. Smolt-size fish groups were primarily observed near the interior halves of each SWW entrance, and greater abundances of fish were observed at the south entrance. Increased counts of bull trout-size fish coincided with the increased abundances of smolt-size fish. Overall, the results indicate that (1) smolt-size fish were more abundant near the entrance of the SWW during periods of increased discharge, (2) bull trout-size fish were present at the SWW, and (3) a greater percentage of smolt-size fish were observed directed toward the entrances of the SWW during periods of increased discharge. The addition of the lead net may assist in orienting fish toward the entrances to the SWW.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231090","collaboration":"Prepared in cooperation with Portland General Electric","usgsCitation":"Smith, C.D., and Hatton, T.W., 2023, Influence of a guide net on the presence and behavior of fish near the selective water withdrawal structure in Lake Billy Chinook, Oregon, 2022: U.S. Geological Survey Open-File Report 2023–1090, 25 p., https://doi.org/10.3133/ofr20231090.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-155570","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":423749,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231090/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1090"},{"id":423751,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1090/ofr20231090.XML"},{"id":423750,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1090/Images"},{"id":423747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1090/ofr20231090.jpg"},{"id":423748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1090/ofr20231090.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1090"},{"id":500154,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115707.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Billy Chinook","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.64591331853623,\n 44.79048612033958\n ],\n [\n -121.64591331853623,\n 44.35814579483878\n ],\n [\n -121.03356092450966,\n 44.35814579483878\n ],\n [\n -121.03356092450966,\n 44.79048612033958\n ],\n [\n -121.64591331853623,\n 44.79048612033958\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
To understand patterns in CO2 partial pressure (PCO2) over time in wetlands’ surface water and porewater, we examined the relationship between PCO2 and land–atmosphere flux of CO2 at the ecosystem scale at 22 Northern Hemisphere wetland sites synthesized through an open call. Sites spanned 6 major wetland types (tidal, alpine, fen, bog, marsh, and prairie pothole/karst), 7 Köppen climates, and 16 different years. Ecosystem respiration (Reco) and gross primary production (GPP), components of vertical CO2 flux, were compared to PCO2, a component of lateral CO2 flux, to determine if photosynthetic rates and soil respiration consistently influence wetland surface and porewater CO2 concentrations across wetlands. Similar to drivers of primary productivity at the ecosystem scale, PCO2 was strongly positively correlated with air temperature (Tair) at most sites. Monthly average PCO2 tended to peak towards the middle of the year and was more strongly related to Reco than GPP. Our results suggest Reco may be related to biologically driven PCO2 in wetlands, but the relationship is site-specific and could be an artifact of differently timed seasonal cycles or other factors. Higher levels of discharge do not consistently alter the relationship between Reco and temperature normalized PCO2. This work synthesizes relevant data and identifies key knowledge gaps in drivers of wetland respiration.
This document serves to fulfill the Coregonine Threats Assessment Science Team’s charge of providing a written recommendation for a methodology to conduct threats assessments for Great Lakes coregonines within the Coregonine Restoration Framework (CRF). Through a series of team meetings that included presentations by experts on five candidate threats assessment frameworks followed by structured deliberations, we came to consensus to recommend the threats assessment framework used by Fisheries and Oceans Canada under Canada’s Species at Risk Act, with three modifications: (1) a conceptual modeling step, (2) the use of a “point spreading” approach to incorporate uncertainty when scoring threats, and (3) the use of a modified Delphi or “estimate-talk-estimate” approach when scoring key elements in the assessment. We recommend that this approach be applied to the spatial units delineated by the CRF Resolve Taxonomy and Gap Analysis science teams. In brief, the assessment process includes providing background information on the spatial unit and threats under assessment, constructing a conceptual model linking threats to key processes and vital rates, and scoring or ranking threats across six elements: likelihood of occurrence, level of impact, strength of evidence, unit-level threat occurrence, unit-level threat frequency, and unit-level threat extent. We provide detailed instructions for completing each step of the assessment and generating associated results, with particular attention paid to our suggested modifications.
The Coregonine Threats Assessment Science Team also conducted two test runs to assess the applicability and effectiveness of our recommended framework for Great Lakes coregonine populations and their threats. We conducted these test runs on two examples of Great Lakes coregonines that represented two extremes of data availability, as well as two different management contexts. We chose Kiyi (Coregonus kiyi) in Lake Ontario as an example of a data-poor, extirpated population, and we chose Cisco (Coregonus artedi) in Lake Superior as an example of a data-rich, extant population. We provide the results of these test runs in Appendices 1-2. We also describe the lessons we learned from these test runs throughout this document and highlighted them in the “Recommendations for avoiding challenges during application” section.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Honsey, A.E., Smith, D.R., Bronte, C.R., Cook, A., Drake, D.A., Gorsky, D., Johnson, T., Mandrak, N.E., Roberts, J., and Sitar, S.P., 2023, A proposed methodology for conducting threats assessments within the Great Lakes Coregonines restoration framework, 67 p.","productDescription":"67 p.","ipdsId":"IP-170470","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":462644,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.greatlakesciscoes.org/restoration-framework/planning","linkFileType":{"id":5,"text":"html"}},{"id":462665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Honsey, Andrew Edgar 0000-0001-7535-1321","orcid":"https://orcid.org/0000-0001-7535-1321","contributorId":295468,"corporation":false,"usgs":true,"family":"Honsey","given":"Andrew","email":"","middleInitial":"Edgar","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":915155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":915156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":915157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Andy","contributorId":344968,"corporation":false,"usgs":false,"family":"Cook","given":"Andy","affiliations":[{"id":6780,"text":"Ontario Ministry of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":915158,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drake, D. Andrew R.","contributorId":272033,"corporation":false,"usgs":false,"family":"Drake","given":"D.","email":"","middleInitial":"Andrew R.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":915159,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gorsky, Dimitry","contributorId":251650,"corporation":false,"usgs":false,"family":"Gorsky","given":"Dimitry","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":915160,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Timothy B.","contributorId":251690,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy B.","affiliations":[{"id":50374,"text":"Ontario Ministry of Natural Resources and Forests (OMNRF)","active":true,"usgs":false}],"preferred":false,"id":915161,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mandrak, Nicholas E.","contributorId":177869,"corporation":false,"usgs":false,"family":"Mandrak","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":915162,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915163,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sitar, Shawn P.","contributorId":181529,"corporation":false,"usgs":false,"family":"Sitar","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":915164,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70252767,"text":"70252767 - 2023 - Unifying the Neoarchean Lac des Iles Complex and implications for the petrogenesis of Pd-enriched noritic breccia pipes in ancient arcs","interactions":[],"lastModifiedDate":"2024-04-11T14:52:54.483847","indexId":"70252767","displayToPublicDate":"2023-12-01T09:43:26","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Unifying the Neoarchean Lac des Iles Complex and implications for the petrogenesis of Pd-enriched noritic breccia pipes in ancient arcs","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"14th International Platinum Symposium abstract volume","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"14th International Platinum Symposium","conferenceDate":"July 4-7, 2023","conferenceLocation":"Cardiff, Wales","language":"English","publisher":"Cardiff University","usgsCitation":"Smith, W.D., Fay, L., Djon, M., Jenkins, M., Lin, Y., Yao, Z.S., and Mungall, J.E., 2023, Unifying the Neoarchean Lac des Iles Complex and implications for the petrogenesis of Pd-enriched noritic breccia pipes in ancient arcs, in 14th International Platinum Symposium abstract volume, Cardiff, Wales, July 4-7, 2023, p. 207-210.","productDescription":"4 p.","startPage":"207","endPage":"210","ipdsId":"IP-151445","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":427702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427701,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://platinum2023.com/programme/","linkFileType":{"id":5,"text":"html"}}],"country":"Canada","state":"Ontario","otherGeospatial":"Lac des Iles Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.65579664256063,\n 49.3329417280701\n ],\n [\n -89.65579664256063,\n 49.03906141115314\n ],\n [\n -89.30733238781289,\n 49.03906141115314\n ],\n [\n -89.30733238781289,\n 49.3329417280701\n ],\n [\n -89.65579664256063,\n 49.3329417280701\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, William D.","contributorId":335361,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":898160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fay, L.","contributorId":335362,"corporation":false,"usgs":false,"family":"Fay","given":"L.","email":"","affiliations":[{"id":80380,"text":"Impala Canada","active":true,"usgs":false}],"preferred":false,"id":898161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Djon, M.L.","contributorId":335363,"corporation":false,"usgs":false,"family":"Djon","given":"M.L.","email":"","affiliations":[{"id":80380,"text":"Impala Canada","active":true,"usgs":false}],"preferred":false,"id":898162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, Michael 0000-0002-4261-409X mjenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-4261-409X","contributorId":172433,"corporation":false,"usgs":true,"family":"Jenkins","given":"Michael","email":"mjenkins@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":898163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lin, Y.","contributorId":267356,"corporation":false,"usgs":false,"family":"Lin","given":"Y.","affiliations":[],"preferred":false,"id":898164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yao, Z. S.","contributorId":335552,"corporation":false,"usgs":false,"family":"Yao","given":"Z.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":898165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mungall, James E. 0000-0001-9726-8545","orcid":"https://orcid.org/0000-0001-9726-8545","contributorId":269537,"corporation":false,"usgs":false,"family":"Mungall","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":898166,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250650,"text":"70250650 - 2023 - Plant size, latitude, and phylogeny explain within-population variability in herbivory","interactions":[],"lastModifiedDate":"2023-12-22T14:33:36.819866","indexId":"70250650","displayToPublicDate":"2023-11-09T07:16:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Plant size, latitude, and phylogeny explain within-population variability in herbivory","docAbstract":"Anadromous fish returning to the lower American River are restricted to 36 kilometers of free-flowing river between Nimbus Dam and American River’s confluence with the Sacramento River, California. Salmon in the American River provide an important freshwater recreational fishery. However, annual salmon production in the American River in recent years has been low relative to the mid-1990s (Surface Water Resources, Inc., 2001). To investigate the low production of fall-run Chinook salmon (Oncorhynchus tshawytscha), the Bureau of Reclamation requested that the U.S. Geological Survey apply the Stream Salmonid Simulator (S3) model to the population of fall-run Chinook salmon on the American River.
The American River was chosen among seven candidate Sacramento Basin rivers for S3 application. The American River was selected because of its management and public interest, recently low anadromous fish production, and rich time series of key demographic data needed for S3 application. Data that were not available, however, were empirical estimates on juvenile salmon habitat suitability in the American River. Therefore, a large component of applying S3 to the American River was devoted to the estimation of juvenile salmon habitat suitability and capacity. This entailed snorkeling the lower American River for 3 weeks in March 2021 during the early out-migration period for juvenile Chinook salmon. These efforts were fruitful and showed that the typically small fish (<55 millimeters) in the American River preferred much shallower depths than predicted by habitat suitability criteria derived from the literature for this population. Having empirical estimates on juvenile salmon in the American River provided a solid foundation from which to simulate the population using the S3 model.
The S3 model is a spatially explicit population model that runs on a daily time step to simulate redd superimposition, egg maturation, fry emergence and the subsequent growth, survival, and emigration of juvenile Chinook salmon from the river. The key features of this model relevant to this report include (1) a temperature-dependent bioenergetics model driving daily growth rates; (2) density-dependent dynamics that are influenced by the effect of flow on suitable habitat area; and (3) within-year habitat, river flow, and water temperature effects specific to spawning, egg incubation, and fry, parr, and smolt life stages. We used estimates of spawning escapement and geo-referenced redd locations to quantify the spatial and temporal distribution of female spawners for brood years 2014–19. These estimates of female spawners initiate the simulation of each year’s juvenile salmon emergence and emigration over a spatial domain extending from Nimbus Dam to the river’s confluence with the Sacramento River.
Using weekly estimates of juvenile salmon abundance and size (fork length) that passed the Watt Avenue fish trap (river kilometer 14.7), we calibrated the S3 model by estimating three key demographic parameters for each year, y: (1) Sy, the average daily survival probability, (2) M0y, the intercept for density-dependence in movement, representing the average daily probability of remaining in a habitat at zero abundance, and (3) Cy, the average daily proportion of maximum consumption. These parameters were obtained by minimizing the Mallow’s distance (Lupu and others, 2017) between distributions of weekly abundances and sizes of fish at the traps and weekly simulated abundances and sizes (by S3). Investigation of model fit showed excellent agreement between simulated annual abundances and the abundance of fish passing the fish trap. However, when we compared weekly abundances at the fish trap, S3 under-predicted peaks and over-predicted troughs in the time series of weekly abundances at the fish trap. Thus, some unknown within-year effects have yet to be identified and incorporated in the S3 model. Identifying these important effects and incorporating them in the S3 model would help explain the lack of fit between estimated and simulated weekly abundances.
We estimated parameters for 6 years that included a wide range of female spawner abundances (3,057–10,753) and water year types (Critical–Wet). We contrast our estimated parameters to the corresponding number of female spawners and the water year type for the Sacramento Valley. By happenstance, years having higher annual spawner abundances concurred with Critical to Dry water year types. Estimates of survival trended lower with higher spawner abundances and Critical to Dry conditions. In contrast, the extremely wet water year of 2017 had the lowest M0y, suggesting less density-dependence in fish movement, and the lowest Cy, suggesting lower average consumption in this year. When this high-flow year was excluded, a trend towards higher probabilities of fish remaining in a habitat at low abundance and lower proportions of maximum consumption was apparent from Critical to Wet conditions, but only 5 years of data were included. Except for 2017, daily proportions of maximum consumption were relatively high (Cy > 0.83), suggesting that fish were feeding at reasonably high proportions relative to the expected maximum consumption as defined by the “Wisconsin” bioenergetics model (Stewart and Ibarra, 1991).
Survival estimates from fry emergence to outmigration at the Sacramento River confluence were generally low when integrated over time. The highest daily survival probability was Sy = 0.93 in 2019, or 50 percent total mortality after 10 days. In contrast, our lowest daily survival probability was Sy = 0.74 in 2015, or 95 percent total mortality after 10 days. Consequently, even our highest estimated daily survival probability might be considered low. This is especially true given that Sy was estimated over a relatively short distance (<14.7 kilometers) from emergence to the Watt Avenue fish trap. Several factors, including our assumed and relatively high daily egg survival rate of 0.9975, could influence juvenile survival estimates. For example, an egg survival rate of 0.9975 results in 3-percent total mortality after 10 days. Egg mortality estimates used in S3 calibration were approximated from egg survivorship studies in the Yakima River, Washington (Johnson and others, 2012), and remains one of the greater uncertainties in S3 when estimating survival across life stages. By including bona fide estimates of egg survival in S3 simulations, the validity of the S3’s current daily egg survival rate could be assessed specifically for the American River. Tagging studies also could provide S3 with direct estimates of juvenile survival and movement; survival during egg incubation then could be estimated indirectly via model fitting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231060","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., Hatton, T.W., Smith, C.D., and Hannon, J.M., 2023, Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California: U.S. Geological Survey Open-File Report 2023–1060, 35 p., https://doi.org/10.3133/ofr20231060.","productDescription":"ix, 35 p.","onlineOnly":"Y","ipdsId":"IP-141661","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":421858,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.XML"},{"id":421857,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1060/images"},{"id":421856,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1060"},{"id":421855,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1060"},{"id":421854,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1060/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"American River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5064051133351,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.727216763718815\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
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Malheur Lake is the largest lake in the endorheic Harney Basin in southeastern Oregon. Since the 1990s, Malheur Lake—which averages depths of about 1 meter—has been in a degraded, turbid state lacking submergent and emergent vegetation. The goals of this study were to identify the major sources of sediment and nutrients to Malheur Lake to determine the importance of managing nutrients for lake restoration. Discrete water samples were analyzed for nutrient (total phosphorus, total nitrogen, orthophosphate, nitrate+nitrite, and ammonia) and suspended-sediment concentrations, and additional parameters including chlorophyll-a and phytoplankton biomass were measured in lake samples. Lake area fluctuated from a minimum of 3,300 hectares (ha) to a maximum of 11,300 ha in water years 2019 and 2020. In water year 2019, inflow from the tributaries created a 1,400-hectare area in the lake with low turbidity that persisted for multiple months. Land-use practices and water diversions along the tributaries affected the hydrographs and nutrient and suspended-sediment concentrations reaching the lake. As lake area increased, storage of sediment-associated constituents in the water column increased in excess of external loads because of resuspension. In 2019, 69 percent of the increase in suspended-sediment storage in the water column was attributed to internal resuspension and 31 percent was from external loading. Sediment was deposited as lake area decreased, and water-column storage decreased even as positive external loading continued. The internal resuspension, deposition, and external loading of suspended sediment likely is decreasing topographic heterogeneity in the lake. Concentrations of total phosphorus and orthophosphate are substantially higher than in the 1980s, and the lake is eutrophic. Phytoplankton in the lake was light limited in 2019–20, and restoration actions that prioritize vegetation establishment would reduce bioavailable nutrients for phytoplankton while increasing light in the water column.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235098","usgsCitation":"Smith, C.D., and Wood, T.M., 2023, Implications of water, sediment, and nutrient budgets for the restoration of a shallow, turbid lake in semiarid southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2023–5098, 31 p., https://doi.org/10.3133/sir20235098.","productDescription":"Report: viii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-136413","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":501065,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115406.htm","linkFileType":{"id":5,"text":"html"}},{"id":420867,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92ZBWJ5","text":"USGS data release","description":"USGS data release","linkHelpText":"Phytoplankton data for Malheur Lake, Oregon, 2018–2020"},{"id":420864,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5098/Images"},{"id":420861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5098/coverthb.jpg"},{"id":420862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5098/sir20235098.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5098"},{"id":420863,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235098/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5098"},{"id":420865,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5098/sir20235098.XML"},{"id":420866,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VJVRQ","text":"USGS data release","description":"USGS data release","linkHelpText":"Stage-volume-area table for Malheur Lake, Oregon, 2021"}],"country":"United States","state":"Oregon","otherGeospatial":"Malheur Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.58577782962396,\n 43.67074885904847\n ],\n [\n -119.58577782962396,\n 42.85909953384322\n ],\n [\n -118.17464011814371,\n 42.85909953384322\n ],\n [\n -118.17464011814371,\n 43.67074885904847\n ],\n [\n -119.58577782962396,\n 43.67074885904847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
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This paper applies a catch multiple survey analysis (CMSA) to Atlantic horseshoe crabs Limulus polyphemus in the Delaware Bay to generate robust population estimates for harvest management. Currently, horseshoe crabs along the U.S. Atlantic coast are harvested as bait for other fisheries and collected for their blood, which is used in a biomedical industry. The Delaware Bay is home to the largest population of horseshoe crabs and is a significant stopover for shorebirds to rebuild energy by consuming horseshoe crab eggs prior to completing their northward migration. To address this interrelationship, the Adaptive Resource Management (ARM) Framework has been used since 2013 to ensure that horseshoe crab harvest within the region takes into account the forage needs of migratory birds. Since its inception, the ARM Framework has used a single trawl survey's swept area-based population estimates of horseshoe crab relative abundance and a theoretical population model developed primarily from literature-derived values. With more data collected in the region in recent years and other sources of mortality that can now be quantified, a catch survey model can provide horseshoe crab population estimates going forward.
A CMSA was used to estimate male and female horseshoe crab population size for 2003–2021 using all quantifiable sources of mortality and three fishery-independent indices of abundance.
The CMSA results indicated that adult abundance of male and female horseshoe crabs was stable from 2003 to 2013 and then began to increase through 2017, a result that is consistent with stock rebuilding following a period of harvest restrictions as recommended by the ARM Framework. Population estimates were lower in recent years but remained above the levels estimated before implementation of the ARM Framework. In 2021, the CMSA estimated that there were over 6 million mature females and nearly 16 million mature male horseshoe crabs in the region.
The CMSA provides the best and most comprehensive population estimates of horseshoe crabs in Delaware Bay and will improve modeling efforts within the ARM Framework going forward.
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However, new technologies may also present challenges for decision makers due to the lack of a standardized process to assess technologies for consideration within management plans. Future technology development in fishery and water resources could benefit from a framework that assigns an appropriate technology development stage and defines the readiness for implementation. Technology Readiness Levels (TRL) are one established research and development scale used throughout engineering and related disciplines that could be applied to natural resource management tools. The TRL assess the maturity of a technology from nascent idea through a fully developed technology. Steps within this scale could provide a general framework for researchers to follow when planning and conducting studies, while similarly providing a standard scale for resource managers to assess readiness for technology transfer and implementation. 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