An important symbolic and subsistence animal for many Native American Tribes, the moose (Alces alces; mos in Algonquin, Penobscot language) has been under consistent threat in the northeastern United States because of winter tick (Dermacentor albipictus) parasitism over the past several decades, causing declines in moose populations throughout the region. This decline has raised concern for Tribes and agencies that are invested in moose. Given this concern, it is increasingly important to effectively monitor and develop strategies to manage winter ticks to address consistent population declines of moose due to winter ticks. The Penobscot Nation developed a novel strategy to sample questing winter ticks (i.e., ticks that are actively seeking hosts) using a plot-based sampling protocol that may be suitable for heterogeneous habitats. We deployed this protocol in the northeastern United States in 2022 during the tick questing period (Sep–Dec) on Penobscot Nation sovereign trust lands, the White Mountain National Forest and Umbagog National Wildlife Refuge, and western-central Massachusetts, USA. We analyzed the data using occupancy and N-mixture models. Detection probability peaked during mid-October and tick occupancy and abundance were greatest at sites with intermediate understory vegetation height. The sampling protocol was successful at sampling ticks in Massachusetts, where abundances were expected to be low, indicating that it may be useful for studies planning to monitor winter tick distribution and abundance in areas with sub-optimal moose habitat and where winter tick abundance is expected to be low. This approach may also benefit managers or researchers intending to monitor many species of hard ticks, and where imperfect detection is expected.
This cooperative study between the City of Durham Public Works Department, Stormwater Division and U.S. Geological Survey evaluated whether alternate monitoring strategies that incorporated samples collected across an increased range of streamflows would improve nutrient load estimates for Ellerbe and Sandy Creeks, two small, highly urbanized streams in the City of Durham, North Carolina. Water-quality and streamflow data collected between January 2009 and December 2020 were used to develop instream nutrient-load models using the U.S. Geological Survey R-LOADEST program. This study compared model results from two sampling scenarios: routine monthly (fixed frequency) sampling combined with targeted high-streamflow sampling (scenario A), and fixed frequency sampling only (scenario B).
Calibration diagnostic results were used to select the final, or most optimal, models. Most final models included seasonality terms to compensate for intra-annual variability in the data. Storm-runoff samples provided better definition at higher streamflows and improved the overall concentration versus flow relations for all constituents, except nitrate + nitrite. Uncertainties in the nutrient load estimates were lower and less variable for the scenario A tests compared to the scenario B tests.
Five time steps representing 12-, 9-, 7-, 6-, and 5-year subsets of the overall dataset were used to examine the effect of prediction period length on the computed loads and uncertainties. In focusing on the scenario A results, nutrient loads tended to be higher for the shorter time steps. These shorter time steps also produced higher errors, or uncertainty, in the load estimates compared to longer time steps. Evaluations of annual nutrient loads during 2016–20 indicated that the most consistent load estimates and tightest confidence intervals were obtained for longer 12- and 9-year time steps. Estimated loads were more variable and uncertain when based on the shorter 6- and 5-year time steps. The degree of uncertainty (standard error of prediction) in the nutrient load estimation results was influenced by sampling approach, calibration time step, and hydrologic characteristics during the model period of interest.
Director, South Atlantic Water Science Center
U.S. Geological Survey
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","tableOfContents":"Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106133","usgsCitation":"Alzraiee, A.H., and Niswonger, R.G., 2024, A probabilistic approach to training machine learning models using noisy data: Environmental Modelling & Software, v. 179, 106133, 15 p., https://doi.org/10.1016/j.envsoft.2024.106133.","productDescription":"106133, 15 p.","ipdsId":"IP-151600","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":439292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106133","text":"Publisher Index Page"},{"id":433694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259730,"text":"70259730 - 2024 - Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization","interactions":[],"lastModifiedDate":"2024-10-22T11:54:57.460967","indexId":"70259730","displayToPublicDate":"2024-07-08T06:53:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2351,"text":"Journal of Industrial Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization","docAbstract":"The low-carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics-informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics-informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector-specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.
","language":"English","publisher":"Wiley","doi":"10.1111/jiec.13517","usgsCitation":"Ryter, J.W., Bhuwalka, K., O’Rourke, M., Montanelli, L., Cohen-Tanugi, D., Roth, R., and Olivetti, E., 2024, Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization: Journal of Industrial Ecology, v. 28, no. 4, p. 709-726, https://doi.org/10.1111/jiec.13517.","productDescription":"18 p.","startPage":"709","endPage":"726","ipdsId":"IP-157688","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":466986,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jiec.13517","text":"Publisher Index Page"},{"id":463085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryter, John W. 0000-0002-0343-7553","orcid":"https://orcid.org/0000-0002-0343-7553","contributorId":345416,"corporation":false,"usgs":true,"family":"Ryter","given":"John","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":916487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bhuwalka, Karan 0000-0002-1963-6717","orcid":"https://orcid.org/0000-0002-1963-6717","contributorId":345417,"corporation":false,"usgs":false,"family":"Bhuwalka","given":"Karan","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Rourke, Michelena","contributorId":345418,"corporation":false,"usgs":false,"family":"O’Rourke","given":"Michelena","email":"","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":916489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montanelli, Luca 0000-0002-7784-7627","orcid":"https://orcid.org/0000-0002-7784-7627","contributorId":345419,"corporation":false,"usgs":false,"family":"Montanelli","given":"Luca","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cohen-Tanugi, David 0000-0003-2488-4819","orcid":"https://orcid.org/0000-0003-2488-4819","contributorId":345420,"corporation":false,"usgs":false,"family":"Cohen-Tanugi","given":"David","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roth, Richard","contributorId":257597,"corporation":false,"usgs":false,"family":"Roth","given":"Richard","affiliations":[{"id":52064,"text":"Materials Systems Lab, MIT","active":true,"usgs":false}],"preferred":false,"id":916492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olivetti, Elsa 0009-0005-4190-8319","orcid":"https://orcid.org/0009-0005-4190-8319","contributorId":345421,"corporation":false,"usgs":false,"family":"Olivetti","given":"Elsa","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916493,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261225,"text":"70261225 - 2024 - Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios","interactions":[],"lastModifiedDate":"2024-12-02T16:04:23.669174","indexId":"70261225","displayToPublicDate":"2024-07-08T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios","docAbstract":"Northern peatlands are a globally significant source of methane (CH4), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH4 production in these systems will be key to predicting annual emissions changes, with stable carbon isotopes (δ13C-CH4) being a powerful tool for characterizing these drivers. Given that δ13C signatures of CH4 are used in top-down atmospheric inversion models to partition sources, our ability to model CH4 production pathways and associated δ13C-CH4 signatures in peatland types impacted by a changing climate is critical. We sought to characterize the role of environmental conditions, including both hydrologic and vegetation patterns associated with permafrost thaw, on δ13C-CH4 signatures from a diverse set of high-latitude peatlands. We measured porewater and emitted CH4 stable isotopes, pH, and vegetation composition from five boreal-Arctic peatlands. Porewater δ13C-CH4 was strongly associated with peatland type, with δ13C enriched values obtained from more minerotrophic fens (-61.2 ± 9.1‰) compared to permafrost-free bogs (-74.1 ± 9.4‰) and raised permafrost bogs (-81.6 ± 11.5‰). Variation in porewater δ13C-CH4 was best explained by sedge cover, CH4 concentration, and the interactive effect of peatland type and pH (r2 = 0.50, p < 0.001). Emitted δ13C-CH4 varied greatly but was positively correlated with porewater δ13C-CH4, suggesting that porewater data can be used to predict changing emissions signatures from these systems. We calculated a weighted mean mixed atmospheric CH4 signature for northern peatlands of -65.3 ± 7‰ and show that this signature is more sensitive to landscape drying (4 to 10 % depletion in δ13C) than wetting (1.5 to 5% enrichment in δ13C) under permafrost thaw scenarios. Our results suggest northern peatland δ13C-CH4 signatures are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007837","usgsCitation":"Kuhn, M.A., Varner, R.K., McCalley, C.K., Perryman, C.R., Aurela, M., Burke, S.A., Chanton, J., Crill, P., DelGreco, J., Deng, J., Heffernan, L., Herrick, C., Hodgkins, S.B., Jones, C.P., Juutinen, S., Kane, E., Lamit, L.J., Larmola, T., Lilleskov, E., Olefeldt, D., Palace, M.W., Rich, V.I., Schulze, C., Shorter, J.H., Sullivan, F., Sonnentag, O., Turetsky, M., and Waldrop, M., 2024, Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios: Journal of Geophysical Research: Biogeosciences, v. 129, no. 7, e2023JG007837, 17 p., https://doi.org/10.1029/2023JG007837.","productDescription":"e2023JG007837, 17 p.","ipdsId":"IP-162425","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":466987,"rank":2,"type":{"id":40,"text":"Open 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McKenzie A.","contributorId":333403,"corporation":false,"usgs":false,"family":"Kuhn","given":"McKenzie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":919944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varner, Ruth K.","contributorId":346749,"corporation":false,"usgs":false,"family":"Varner","given":"Ruth","email":"","middleInitial":"K.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":919945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCalley, Carmody K.","contributorId":346731,"corporation":false,"usgs":false,"family":"McCalley","given":"Carmody","email":"","middleInitial":"K.","affiliations":[{"id":32390,"text":"Rochester Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":919946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perryman, Clarice R.","contributorId":346809,"corporation":false,"usgs":false,"family":"Perryman","given":"Clarice","email":"","middleInitial":"R.","affiliations":[{"id":82971,"text":"Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 USA","active":true,"usgs":false}],"preferred":false,"id":919947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aurela, Mika","contributorId":346810,"corporation":false,"usgs":false,"family":"Aurela","given":"Mika","affiliations":[{"id":82974,"text":"Finnish Meteorological Institute, Climate System Research, 00560 Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":919948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burke, Sophia A.","contributorId":346811,"corporation":false,"usgs":false,"family":"Burke","given":"Sophia","email":"","middleInitial":"A.","affiliations":[{"id":82971,"text":"Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 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Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 USA","active":true,"usgs":false}],"preferred":false,"id":919953,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Heffernan, Liam","contributorId":346816,"corporation":false,"usgs":false,"family":"Heffernan","given":"Liam","email":"","affiliations":[{"id":82978,"text":"Evolutionary Biology Centre, Department of Ecology and Genetics/Limnology, Uppsala University, Norbyvägen 18D, 752 36, Uppsala, Sweden.","active":true,"usgs":false}],"preferred":false,"id":919954,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Herrick, Christina","contributorId":346817,"corporation":false,"usgs":false,"family":"Herrick","given":"Christina","email":"","affiliations":[{"id":82976,"text":"Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH 03824 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Sari","contributorId":346820,"corporation":false,"usgs":false,"family":"Juutinen","given":"Sari","email":"","affiliations":[{"id":82974,"text":"Finnish Meteorological Institute, Climate System Research, 00560 Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":919958,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kane, Evan S.","contributorId":346821,"corporation":false,"usgs":false,"family":"Kane","given":"Evan S.","affiliations":[{"id":82980,"text":"College of Forest Resources and Environmental Science, Houghton, MI 49931, USA","active":true,"usgs":false}],"preferred":false,"id":919959,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Lamit, Louis J.","contributorId":346822,"corporation":false,"usgs":false,"family":"Lamit","given":"Louis","email":"","middleInitial":"J.","affiliations":[{"id":82981,"text":"Department of Biology, Syracuse University, NY, 13244, 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Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":919963,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Palace, Michael W.","contributorId":346826,"corporation":false,"usgs":false,"family":"Palace","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":82971,"text":"Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 USA","active":true,"usgs":false}],"preferred":false,"id":919964,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Rich, Virginia I.","contributorId":346827,"corporation":false,"usgs":false,"family":"Rich","given":"Virginia","email":"","middleInitial":"I.","affiliations":[{"id":18950,"text":"Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA","active":true,"usgs":false}],"preferred":false,"id":919965,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Schulze, Christopher","contributorId":346828,"corporation":false,"usgs":false,"family":"Schulze","given":"Christopher","email":"","affiliations":[{"id":82984,"text":"Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":919966,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Shorter, Joanne H.","contributorId":346829,"corporation":false,"usgs":false,"family":"Shorter","given":"Joanne","email":"","middleInitial":"H.","affiliations":[{"id":82985,"text":"Aerodyne Research, Inc., Billerica, MA 02421","active":true,"usgs":false}],"preferred":false,"id":919967,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Sullivan, Franklin","contributorId":346830,"corporation":false,"usgs":false,"family":"Sullivan","given":"Franklin","email":"","affiliations":[{"id":82986,"text":"Département de géographie, Université de Montréal, Montréal, Québec, Canada","active":true,"usgs":false}],"preferred":false,"id":919968,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Sonnentag, Oliver","contributorId":346831,"corporation":false,"usgs":false,"family":"Sonnentag","given":"Oliver","affiliations":[{"id":82987,"text":"Department of Ecology and Evolutionary Biology. University of Colorado Boulder. Boulder CO 80309","active":true,"usgs":false}],"preferred":false,"id":919969,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Turetsky, Merritt R.","contributorId":346832,"corporation":false,"usgs":false,"family":"Turetsky","given":"Merritt R.","affiliations":[],"preferred":false,"id":919970,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":919971,"contributorType":{"id":1,"text":"Authors"},"rank":28}]}} ,{"id":70255868,"text":"70255868 - 2024 - Climate change vulnerability of Arctic char across Scandinavia","interactions":[],"lastModifiedDate":"2024-07-09T11:52:23.159911","indexId":"70255868","displayToPublicDate":"2024-07-07T06:50:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change vulnerability of Arctic char across Scandinavia","docAbstract":"Climate change is anticipated to cause species to shift their ranges upward and poleward, yet space for tracking suitable habitat conditions may be limited for range-restricted species at the highest elevations and latitudes of the globe. Consequently, range-restricted species inhabiting Arctic freshwater ecosystems, where global warming is most pronounced, face the challenge of coping with changing abiotic and biotic conditions or risk extinction. Here, we use an extensive fish community and environmental dataset for 1762 lakes sampled across Scandinavia (mid-1990s) to evaluate the climate vulnerability of Arctic char (Salvelinus alpinus), the world's most cold-adapted and northernly distributed freshwater fish. Machine learning models show that abiotic and biotic factors strongly predict the occurrence of Arctic char across the region with an overall accuracy of 89 percent. Arctic char is less likely to occur in lakes with warm summer temperatures, high dissolved organic carbon levels (i.e., browning), and presence of northern pike (Esox lucius). Importantly, climate warming impacts are moderated by habitat (i.e., lake area) and amplified by the presence of competitors and/or predators (i.e., northern pike). Climate warming projections under the RCP8.5 emission scenario indicate that 81% of extant populations are at high risk of extirpation by 2080. Highly vulnerable populations occur across their range, particularly near the southern range limit and at lower elevations, with potential refugia found in some mountainous and coastal regions. Our findings highlight that range shifts may give way to range contractions for this cold-water specialist, indicating the need for pro-active conservation and mitigation efforts to avoid the loss of Arctic freshwater biodiversity.
The Coastal California Gnatcatcher (Polioptila californica californica), a federally threatened species, is a flagship species for regional conservation planning in southern California (USA). An inhabitant of coastal sage scrub vegetation, the gnatcatcher has declined in response to habitat loss and fragmentation, exacerbated by catastrophic wildfires. We documented the status of gnatcatchers throughout their California range and examined post-fire recovery of gnatcatchers and their habitat. We used GIS to develop a habitat suitability model for Coastal California Gnatcatchers using climate and topography covariates and selected over 700 sampling points in a spatially balanced manner. Bird and vegetation data were collected at each point between March and May in 2015 and 2016. Presence/absence of gnatcatchers was determined during three visits to points, using area searches within 150 x 150 m plots. We used an occupancy framework to generate Percent Area Occupied (PAO) by gnatcatchers, and analyzed PAO as a function of time since fire. At the regional scale in 2016, 23% of the points surveyed were occupied by gnatcatchers, reflecting the effect of massive wildfires in the last 15 years. Similarly, PAO in the post-fire subset of points was 24%, with the highest occupancy in unburned (last fire <2002) habitat. Positive predictors of occupancy included percent cover of California sagebrush (Artemisia californica), California buckwheat (Eriogonom fasciculatum), and sunflowers (Encelia spp., Bahiopsis laciniata), while negative predictors included laurel sumac (Malosma laurina) and total herbaceous cover; in particular, non-native grasses. Our findings indicate that recovery from wildfire may take decades, and provide information to speed up recovery through habitat restoration.
The National Water Model (NWM) provides critical analyses and projections of streamflow that support water management decisions. However, the NWM performs poorly in lower-elevation rivers of the western United States (US). The accuracy of the NWM depends on the fidelity of the model inputs and the representation and calibration of model processes and water sources. To evaluate the NWM performance in the western US, we compared observations of river water isotope ratios (18O
Parturition timing has long been a topic of interest in ungulate research. However, few studies have examined parturition timing at fine scale (e.g., <1 day). Predator activity and environmental conditions can vary considerably with diel timing, which may result in selective pressure for parturition to occur during diel times that maximize the likelihood of neonate survival. We monitored parturition events and early-life survival of elk (Cervus canadensis) and mule deer (Odocoileus hemionus) in Utah, USA to better understand diel timing of parturition in temperate ungulates. Diel timing of parturition was moderately synchronous among conspecifics and influenced by environmental variables on the date of parturition. For elk, parturition events were most common during the morning crepuscular period and generally occurred later (i.e., closer to 12:00) when a relatively large proportion of the moon was illuminated. For mule deer, parturition events were most common during the diurnal period and generally occurred later (i.e., closer to 15:00) on cold, wet dates. Diel timing of parturition did not influence neonate survival, but larger datasets may be required to verify the apparent lack of influence. Although additional work could evaluate alternative variables that might affect parturition timing, our data provide an improved and finer scale understanding of reproductive ecology and phenology in ungulates.
Managers invest substantial resources to promote recovery of declining anadromous fish stocks. Recovery strategies are manifold and often include management actions intended to stimulate somatic growth, increase in-river survival, and motivate juvenile outmigration during favorable environmental conditions. Evaluating the efficacy of these management actions is difficult, however, because monitoring data that explicitly track individuals from egg deposition to juvenile outmigration are typically lacking. We developed an integrated population model that links two different and often collected types of anadromous fish monitoring data: spawning ground surveys and rotary screw trap juvenile catch data. The integrated model accounts for incomplete detection and uses the two sources of data to estimate juvenile demographic parameters in a multistate framework. We evaluated the model's performance using simulated data under a range of conditions typically encountered in similar surveys. Simulation results indicated that the model estimated juvenile survival, growth, and movement with no-to-minimal bias (i.e., ≥ 50 % of simulations ± 0–0.05). As an example case study, we fit the model to empirical fall-run Chinook Salmon (Oncorhynchus tshawytscha) monitoring data collected in California's Central Valley, U.S.A. In doing so, we evaluated the influence of environmental conditions (e.g., discharge, water temperature) and habitat availability on juvenile demographic rates. We demonstrated that through our integrated approach we could estimate state transition probabilities that are typically inestimable for naturally produced, unmarked juvenile fish when using traditional statistical approaches to analyze these types of monitoring data. Furthermore, the structure of our model can serve as a useful foundation for decision-support models within adaptive management programs by directly linking management actions, decision-support-model predictions, and monitoring.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110780","usgsCitation":"Wohner, P.J., Duarte, A., and Peterson, J., 2024, An integrated analysis for estimation of survival, growth, and movement of unmarked juvenile anadromous fish: Ecological Modelling, v. 495, 110780, 9 p., https://doi.org/10.1016/j.ecolmodel.2024.110780.","productDescription":"110780, 9 p.","ipdsId":"IP-165440","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488126,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2024.110780","text":"Publisher Index Page"},{"id":485452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5906056592707,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.63419131375724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"495","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wohner, Patti J.","contributorId":338233,"corporation":false,"usgs":false,"family":"Wohner","given":"Patti","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":339254,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":935786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935787,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269686,"text":"70269686 - 2024 - A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","interactions":[],"lastModifiedDate":"2025-07-30T14:53:21.858355","indexId":"70269686","displayToPublicDate":"2024-07-03T09:49:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","docAbstract":"The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2024.110146","usgsCitation":"Knipper, K., Anderson, M., Bambach, N., Melton, F., Ellis, Z., Yang, Y., Volk, J.M., McElrone, A., Kustas, W.P., Roby, M., Carrara, W., Castro, S., Kilic, A., Fisher, J.B., Ruhoff, A., Senay, G.B., Morton, C., Saa, S., and Allen, R., 2024, A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards: Agricultural and Forest Meteorology, v. 355, 110146, 18 p., https://doi.org/10.1016/j.agrformet.2024.110146.","productDescription":"110146, 18 p.","ipdsId":"IP-167142","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493302,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2024.110146","text":"Publisher Index Page"},{"id":493185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.24346411280976,\n 35.36581404018972\n ],\n [\n -119.04389955946334,\n 35.98229970980546\n ],\n [\n -119.09665048217278,\n 36.489548538637436\n ],\n [\n -120.48964025685427,\n 37.8956809895726\n ],\n [\n -121.30837256179902,\n 39.0131951853744\n ],\n [\n -121.62152001465728,\n 39.13926422978071\n ],\n [\n -122.21612821856195,\n 38.932254764781106\n ],\n [\n -121.71286410491697,\n 37.92123720196997\n ],\n [\n -120.92135482117862,\n 37.048426115873994\n ],\n [\n -120.01788903644467,\n 36.065623999918515\n ],\n [\n -119.49460792137992,\n 35.23527037396478\n ],\n [\n -119.24346411280976,\n 35.36581404018972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"355","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Knipper, Kyle","contributorId":333373,"corporation":false,"usgs":false,"family":"Knipper","given":"Kyle","email":"","affiliations":[{"id":79855,"text":"USDA Agriculture Research Service","active":true,"usgs":false}],"preferred":false,"id":944430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":269899,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":944431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bambach, Nicolas","contributorId":358904,"corporation":false,"usgs":false,"family":"Bambach","given":"Nicolas","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":944432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melton, Forrest","contributorId":223919,"corporation":false,"usgs":false,"family":"Melton","given":"Forrest","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":944433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellis, Zac","contributorId":358905,"corporation":false,"usgs":false,"family":"Ellis","given":"Zac","affiliations":[{"id":85705,"text":"Olan Food Ingredients","active":true,"usgs":false}],"preferred":false,"id":944434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Yun","contributorId":333379,"corporation":false,"usgs":false,"family":"Yang","given":"Yun","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":944435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Volk, J. M.","contributorId":269921,"corporation":false,"usgs":false,"family":"Volk","given":"J.","middleInitial":"M.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":944436,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McElrone, Andrew J.","contributorId":358906,"corporation":false,"usgs":false,"family":"McElrone","given":"Andrew J.","affiliations":[{"id":85706,"text":"University of California Davis, USDA ARS","active":true,"usgs":false}],"preferred":false,"id":944437,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kustas, William P.","contributorId":29962,"corporation":false,"usgs":false,"family":"Kustas","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":944438,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Roby, Matthew","contributorId":358907,"corporation":false,"usgs":false,"family":"Roby","given":"Matthew","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":944439,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carrara, Will","contributorId":269951,"corporation":false,"usgs":false,"family":"Carrara","given":"Will","email":"","affiliations":[],"preferred":false,"id":944440,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Castro, Sebastian","contributorId":358908,"corporation":false,"usgs":false,"family":"Castro","given":"Sebastian","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":944441,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kilic, Ayse","contributorId":269913,"corporation":false,"usgs":false,"family":"Kilic","given":"Ayse","email":"","affiliations":[{"id":16587,"text":"University of Nebraska Lincoln","active":true,"usgs":false}],"preferred":false,"id":944442,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fisher, Joshua B.","contributorId":211503,"corporation":false,"usgs":false,"family":"Fisher","given":"Joshua","email":"","middleInitial":"B.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":944443,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Ruhoff, Anderson","contributorId":269919,"corporation":false,"usgs":false,"family":"Ruhoff","given":"Anderson","email":"","affiliations":[{"id":56044,"text":"Universidade Federal do Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":944444,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944445,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Morton, Charles","contributorId":178787,"corporation":false,"usgs":false,"family":"Morton","given":"Charles","affiliations":[],"preferred":false,"id":944446,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Saa, Sebastian","contributorId":358909,"corporation":false,"usgs":false,"family":"Saa","given":"Sebastian","affiliations":[{"id":85707,"text":"Almond Board of California","active":true,"usgs":false}],"preferred":false,"id":944447,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Allen, Richard G.","contributorId":358910,"corporation":false,"usgs":false,"family":"Allen","given":"Richard G.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":944448,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70255996,"text":"70255996 - 2024 - Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","interactions":[],"lastModifiedDate":"2024-07-30T14:55:12.863126","indexId":"70255996","displayToPublicDate":"2024-07-03T06:58:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","docAbstract":"The Southern California Borderland hosts a variety of geologic and oceanographic features that allow for diverse habitats to occur in a restricted region with a strong oxygen minimum zone (OMZ) and hard substrates. These include ferromanganese (FeMn) crusts and phosphorites targeted for deep-seabed mining in other regions. Baseline studies regarding hardground macro- (> 0.3 mm) and megafaunal (> 2 cm) invertebrates are lacking, although they contribute to understanding nutrient cycling and resilience of deep-sea communities to ocean deoxygenation, fishing, or mineral extraction. With the goal of understanding how substrate type, depth, and dissolved oxygen concentration influence invertebrate trophic structure, we surveyed δ13C and δ15N values of invertebrates on hard substrates on the Southern California Borderland margin along a depth gradient (120–2400 m) through the OMZ at inshore (< 100 km from shore) and offshore (100–250 km from shore) sites, using generalized additive models and community-level metrics. Macrofaunal isotopic values correlate with substrate type, exhibiting higher trophic diversity on FeMn crusts and specialized communities on phosphorites. Megafaunal isotopic values correlate with proximity to shore; animals offshore seem to depend more on phytoplanktonic production than animals inshore. In general, δ15N increased with decreasing dissolved oxygen and increasing depth, possibly due to remineralization processes within the OMZ and with depth. We discuss how feeding modes and community composition might influence the observed patterns. This study elucidates the importance of the environmental context in shaping invertebrate trophic structure on continental margins and provides baseline knowledge that may be useful in regions where these minerals are targeted for extraction.
Introduction: Habitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years.
Methods: We used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats.
Results: The simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon.
Discussion: We hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2024.1428697","usgsCitation":"Peterson, J., and Duarte, A., 2024, An evaluation of tradeoffs in restoring ephemeral vs. perennial habitats to conserve animal populations: Frontiers in Conservation Science, v. 5, 1428697, 12 p., https://doi.org/10.3389/fcosc.2024.1428697.","productDescription":"1428697, 12 p.","ipdsId":"IP-165963","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488153,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2024.1428697","text":"Publisher Index Page"},{"id":485520,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.15324571467127,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 38.645272222200845\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256509,"text":"70256509 - 2024 - Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","interactions":[],"lastModifiedDate":"2024-08-12T16:08:21.662133","indexId":"70256509","displayToPublicDate":"2024-07-02T10:55:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","docAbstract":"Once thought to be extirpated from the Chesapeake Bay, fall spawning runs of Atlantic Sturgeon Acipenser oxyrinchus have been rediscovered in the Marshyhope Creek (MC)–Nanticoke River (NR) system of Maryland, United States. High recapture rates in past telemetry surveys suggested a small population in the two connected tributaries. This study aims to generate estimates of abundance and understand within system connectivity for spawning runs in 2020 and 2021.
Data from mobile side-scan sonar surveys and detections of acoustically tagged adults on stationary telemetry receivers were analyzed in an integrated model to estimate spawning season abundance and examine run timing and system connectivity for this population. An array of acoustic receivers was deployed throughout the MC–NR system to monitor the movement of tagged fish during the spawning run period from mid-August to late October. Side-scan sonar surveys were conducted weekly in September in an area of high spawner aggregation to generate count data on spawning run abundance.
In 2020 and 2021, 32 (95% credible interval [CRI] = 23–47) and 70 (95% CRI = 49–105) Atlantic Sturgeon, respectively, used the MC–NR system. The lower estimate for 2020 coincided with an earlier end to the spawning run related to cooler September temperatures in that year.
In both years, high spawning run connectivity between MC and the upper NR was observed. Overall, run estimates supported previous hypotheses that the MC–NR system supports a very small population and that both MC and the upper NR serve as important areas for spawning activity.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10292","usgsCitation":"Coleman, N., Fox, D., Horne, A., Hostetter, N.J., Madsen, J., O’Brien, M., Park, I., Stence, C., and Secor, D., 2024, Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 16, no. 3, e10292, 16 p., https://doi.org/10.1002/mcf2.10292.","productDescription":"e10292, 16 p.","ipdsId":"IP-152527","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10292","text":"Publisher Index Page"},{"id":432489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay, Marshyhope Creek–Nanticoke River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.89424670173172,\n 38.22898121612636\n ],\n [\n -75.80356837182107,\n 38.3806027695843\n ],\n [\n -75.65092235600899,\n 38.5420516415999\n ],\n [\n -75.51423724108903,\n 38.538485023124736\n ],\n [\n -75.51310206970021,\n 38.701340540846786\n ],\n [\n -75.85480590117533,\n 38.71288976149938\n ],\n [\n -75.86506053032485,\n 38.57767393758033\n ],\n [\n -75.88558745668757,\n 38.40381670546191\n ],\n [\n -75.94442936459689,\n 38.33482586063296\n ],\n [\n -75.96657879053116,\n 38.253499920059284\n ],\n [\n -75.89424670173172,\n 38.22898121612636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Coleman, Nicholas","contributorId":340953,"corporation":false,"usgs":false,"family":"Coleman","given":"Nicholas","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, Dewayne","contributorId":340954,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","affiliations":[{"id":37219,"text":"Delaware State University","active":true,"usgs":false}],"preferred":false,"id":907729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horne, Ashlee","contributorId":340955,"corporation":false,"usgs":false,"family":"Horne","given":"Ashlee","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":907731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madsen, John","contributorId":340956,"corporation":false,"usgs":false,"family":"Madsen","given":"John","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Brien, Michael","contributorId":340957,"corporation":false,"usgs":false,"family":"O’Brien","given":"Michael","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Ian","contributorId":340958,"corporation":false,"usgs":false,"family":"Park","given":"Ian","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stence, Chuck","contributorId":340959,"corporation":false,"usgs":false,"family":"Stence","given":"Chuck","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907735,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Secor, David","contributorId":340960,"corporation":false,"usgs":false,"family":"Secor","given":"David","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907736,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257549,"text":"70257549 - 2024 - Population density and zooplankton biomass influence anadromous juvenile river herring growth in freshwater lakes","interactions":[],"lastModifiedDate":"2024-09-06T16:53:43.602215","indexId":"70257549","displayToPublicDate":"2024-07-02T09:46:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Population density and zooplankton biomass influence anadromous juvenile river herring growth in freshwater lakes","docAbstract":"Anadromous river herring populations, collectively alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis), have experienced a multi-century decline in abundance and distribution. These declines have been attributed in part to anthropogenic threats in freshwater ecosystems (e.g., habitat fragmentation, overharvest, water pollution, watershed development). An understanding of variability in juvenile productivity and growth is critical to developing restoration approaches. We characterized variability in juvenile river herring growth among 11 freshwater lakes in the northeastern USA. We used age estimates from otoliths and length measurements to calculate growth rates of juvenile river herring (n = 1452). We tested the effects of juvenile river herring densities, zooplankton (biomass and size), habitat area (based on thermocline depth), and water quality (temperature, nutrients, chlorophyll a) on juvenile growth. Mean monthly growth rates ranged from 0.56 to 1.41 mm/d and typically increased throughout the summer. Increased juvenile growth was best predicted by lower juvenile density (β = − 0.104, P < 0.001) and higher zooplankton biomass (β = 0.032, P < 0.05). Combined with information about juvenile densities and mortality, these results broaden the understanding of anadromous juvenile river herring productivity, provide information that can contribute to refining stock assessment and life cycle models, and help to better understand the potential impacts of habitat conservation and restoration decisions.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10641-024-01565-8","usgsCitation":"Devine, M., Bittner, S., Roy, A.H., Gahagan, B.I., Armstrong, M., and Jordaan, A., 2024, Population density and zooplankton biomass influence anadromous juvenile river herring growth in freshwater lakes: Environmental Biology of Fishes, v. 107, p. 755-770, https://doi.org/10.1007/s10641-024-01565-8.","productDescription":"16 p.","startPage":"755","endPage":"770","ipdsId":"IP-162984","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, 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Fisheries","active":true,"usgs":false}],"preferred":false,"id":910803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jordaan, Adrian","contributorId":343340,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910804,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257461,"text":"70257461 - 2024 - Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","interactions":[],"lastModifiedDate":"2024-09-06T16:40:27.763678","indexId":"70257461","displayToPublicDate":"2024-07-02T09:29:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5082,"text":"Ticks and Tick-borne Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","docAbstract":"Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.
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0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910479,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255685,"text":"sir20245050 - 2024 - Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","interactions":[],"lastModifiedDate":"2026-02-03T19:34:03.41537","indexId":"sir20245050","displayToPublicDate":"2024-07-02T07:51:23","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5050","displayTitle":"Water-Quality Trends in the Kansas River, Kansas, since Enactment of the Clean Water Act, 1972–2020","title":"Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","docAbstract":"The Clean Water Act was passed by Congress in 1972 to regulate pollution within the waters of the United States. The U.S. Geological Survey (USGS), in cooperation with the Kansas Department of Health and Environment (KDHE), the Kansas Water Office, the Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, compiled and analyzed historical streamflow and water-quality data collected by USGS and KDHE to characterize trends in water-quality constituents of interest because of their relation to water supply, drinking-water treatment, and sediment and nutrient transport, among others (total dissolved solids, chloride, ammonia, dissolved inorganic nitrogen [ammonia and nitrate plus nitrite], total nitrogen, orthophosphate, total phosphorus, total suspended solids, and fecal coliform bacteria) during mean- and low-flow conditions in the Kansas River since the passage of the Clean Water Act in 1972 through 2020. Trends in water-quality concentrations, or densities, and loads were analyzed using the Exploration and Graphics for RivER Trends R package and Weighted Regressions on Time, Discharge, and Season (WRTDS) model at upstream (Kansas River at Wamego, Kansas; USGS station 06887500) and downstream (Kansas River at De Soto, Kansas; USGS station 06892350) locations along the Kansas River using streamflow and water-quality data collected by the USGS and KDHE during 1972 through 2020. The Exploration and Graphics for RivER Trends Confidence Intervals R package and WRTDS bootstrap test estimated direction, uncertainty, and likelihood of trends in concentration and loads for each water-quality constituent of interest.
Downward trends in concentration and load were observed for 5 of the 9 water-quality constituents at both sites during mean-flow conditions during the study period. During low-flow conditions, 7 of the 9 constituents exhibited downward trends, possibly reflecting reductions in point-source contributions to the Kansas River. Downward trends in ammonia, dissolved inorganic nitrogen, and total nitrogen during mean- and low-flow conditions were observed at both Kansas River sites, which were similar to patterns observed nationally. Upward trends were generally observed for orthophosphate and total phosphorus, which were similar to patterns observed at sites in the Mississippi River Basin. Downward trends, or no trend, were observed for chloride. Upward and downward trends were observed for total dissolved solids. Downward trends in total suspended solids and fecal coliform bacteria were observed at both sites, which were also similar to patterns observed nationally. The long-term trend analyses in this report are an essential step to understanding how water-quality conditions have changed in the Kansas River since the passage of the Clean Water Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245050","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., Klager, B.J., and Stiles, T.C., 2024, Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020: U.S. Geological Survey Scientific Investigations Report 2024–5050, 29 p., https://doi.org/10.3133/sir20245050.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158483","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":430605,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WVZ8X1","text":"USGS data release","linkHelpText":"Water-quality data and computed flow-normalized and low-flow concentrations and loads in the Kansas River, Kansas, 1972–2020"},{"id":430602,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5050/images/"},{"id":430601,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.XML"},{"id":430599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5050/coverthb.jpg"},{"id":430600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5050"},{"id":430731,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245050/full"},{"id":499470,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117100.htm","linkFileType":{"id":5,"text":"html"}},{"id":430604,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.5,\n 40\n ],\n [\n -97.5,\n 38.75\n ],\n [\n -94.5,\n 38.75\n ],\n [\n -94.5,\n 40\n ],\n [\n -97.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March-May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large-scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High-resolution Weather Research and Forecasting coupled with Chemistry model (WRF-Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo-based drag-partition was used to explore land surface-atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub-tropical jet stream interaction was a common upper-level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast-directed pressure gradient upstream and over the source areas resulted in strong near-surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid-tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF-Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust-on-snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo-based drag partition provides a basis for additional dust-on-snow simulations to assess the hydrologic impact in the Southwest US.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JD040650","usgsCitation":"Dhital, S., Webb, N.P., Chappell, A., Kaplan, M.L., Nauman, T.W., Tyree, G.L., Duniway, M.C., Edwards, B.L., LeGrand, S.L., Letcher, T.W., Skiles, S.M., Naple, P., Chaney, N.W., and Cai, J., 2024, Synoptic analysis and WRF-Chem model simulation of dust events in the southwestern United States: JGR Atmospheres, v. 129, no. 13, e2023JD040650, 22 p., https://doi.org/10.1029/2023JD040650.","productDescription":"e2023JD040650, 22 p.","ipdsId":"IP-161089","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495722,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jd040650","text":"Publisher Index Page"},{"id":495406,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"southwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.01835497016609,\n 41.90825861082291\n ],\n [\n -121.01835497016609,\n 31.275127575649392\n ],\n [\n -102.51551924830923,\n 31.275127575649392\n ],\n [\n -102.51551924830923,\n 41.90825861082291\n ],\n [\n -121.01835497016609,\n 41.90825861082291\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"13","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Dhital, Saroj","contributorId":310520,"corporation":false,"usgs":false,"family":"Dhital","given":"Saroj","email":"","affiliations":[{"id":67202,"text":"USDA-ARS-Jornada Experimental Range. 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The earthquake, which occurred in the early days of both broadband seismic networks and the Internet, spurred advances in seismic monitoring, real-time systems, and development of data products. Motivated by the 30th anniversary of the earthquake, we present a brief retrospective of the earthquake and its impact, and reconsider both ground motions and the aftershock distribution using modern tools and best-available data. With improvements in instrumentation and analysis methodology, recent earthquakes continue to reveal increasing complexity of ground motions, fault systems, and earthquake ruptures. Even in the absence of data from state-of-the art instrumentation, a retrospective consideration of ground motion data from the Northridge earthquake reveals complexities beyond what could be characterized (and modeled) thirty years ago. Aftershock relocations for both the 1971 Sylmar and 1994 Northridge earthquakes also reveal an updated view of fault complexity. Our study does provide a cautionary tale regarding legacy data sets and research results that are not easily accessible, which can result in discrepancies between catalog data and products from best-available science. We also briefly describe outreach products produced as part of the anniversary commemoration.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320240012","usgsCitation":"Hough, S.E., Graves, R., Cochran, E.S., Yoon, C., Blair, J.L., Haefner, S., Wald, D.J., and Quitoriano, V., 2024, The 17 January 1994 Northridge, California, earthquake: A retrospective analysis: The Seismic Record, v. 4, no. 3, p. 151-160, https://doi.org/10.1785/0320240012.","productDescription":"10 p.","startPage":"151","endPage":"160","ipdsId":"IP-164904","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489848,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320240012","text":"Publisher Index Page"},{"id":482225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Northridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.60523974052802,\n 34.27362638562232\n ],\n [\n -118.60523974052802,\n 34.17466422238154\n ],\n [\n -118.47245060179975,\n 34.17466422238154\n ],\n [\n -118.47245060179975,\n 34.27362638562232\n ],\n [\n -118.60523974052802,\n 34.27362638562232\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yoon, Clara 0000-0003-4521-3889","orcid":"https://orcid.org/0000-0003-4521-3889","contributorId":222019,"corporation":false,"usgs":true,"family":"Yoon","given":"Clara","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blair, James Luke 0000-0002-6980-6446","orcid":"https://orcid.org/0000-0002-6980-6446","contributorId":213724,"corporation":false,"usgs":true,"family":"Blair","given":"James","email":"","middleInitial":"Luke","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haefner, Scott","contributorId":350679,"corporation":false,"usgs":true,"family":"Haefner","given":"Scott","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927578,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927579,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quitoriano, Vince 0000-0003-4157-1101 vinceq@usgs.gov","orcid":"https://orcid.org/0000-0003-4157-1101","contributorId":2582,"corporation":false,"usgs":true,"family":"Quitoriano","given":"Vince","email":"vinceq@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927580,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70275042,"text":"70275042 - 2024 - The early Earth as an analogue for exoplanetary biogeochemistry","interactions":[],"lastModifiedDate":"2026-04-13T15:58:45.228851","indexId":"70275042","displayToPublicDate":"2024-07-01T10:53:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3281,"text":"Reviews in Mineralogy and Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"The early Earth as an analogue for exoplanetary biogeochemistry","docAbstract":"Planet Earth has evolved over the past 4.5 billion years from an entirely anoxic planet with possibly a different tectonic regime to the oxygenated world with horizontal plate tectonics that we know today. For most of this time, Earth has been inhabited by a purely microbial biosphere albeit with seemingly increasing complexity over time. A rich record of this geobiological evolution over most of Earth’s history thus provides insights into the remote detectability of microbial life under a variety of planetary conditions. Here we leverage Earth’s geobiological record with the aim of (a) illustrating the current state of knowledge and key knowledge gaps about the early Earth as a reference point in exoplanet science research; (b) compiling biotic and abiotic mechanisms that controlled the evolution of the atmosphere over time; and (c) reviewing current constraints on the detectability of Earth’s early biosphere with state-of-the-art telescope technology. We highlight that life may have originated on a planet with a different (stagnant lid) tectonic regime and strong hydrothermal activity, and under these conditions, biogenic CH4 gas was perhaps the most detectable atmospheric biosignature. Oxygenic photosynthesis, which is responsible for essentially all O2 gas in the modern atmosphere, appears to have emerged concurrently with the establishment of modern plate tectonics and the emergence of continental crust, but O2 accumulation to modern levels only occurred late in Earth’s history, perhaps tied to the rise of land plants. Nutrient limitation in anoxic oceans, promoted by hydrothermal Fe fluxes, may have limited biological productivity and O2 production. N2O is an alternative biosignature that was perhaps significant on the redox-stratified Proterozoic Earth. We conclude that the detectability of atmospheric biosignatures on Earth was not only dependent on biological evolution but also strongly controlled by the evolving tectonic context.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2138/rmg.2024.90.14","usgsCitation":"Stüeken, E., Olson, S., Moore, E.K., and Foley, B., 2024, The early Earth as an analogue for exoplanetary biogeochemistry: Reviews in Mineralogy and Geochemistry, v. 90, no. 1, p. 515-558, https://doi.org/10.2138/rmg.2024.90.14.","productDescription":"44 p.","startPage":"515","endPage":"558","ipdsId":"IP-154232","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":502751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Stüeken, Eva","contributorId":369847,"corporation":false,"usgs":false,"family":"Stüeken","given":"Eva","affiliations":[{"id":87876,"text":"School of Earth & Environmental Sciences, University of St Andrews","active":true,"usgs":false}],"preferred":false,"id":959290,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olson, Stephanie","contributorId":369848,"corporation":false,"usgs":false,"family":"Olson","given":"Stephanie","affiliations":[{"id":87877,"text":"Department of Earth, Atmospheric, and Planetary Science, Purdue University","active":true,"usgs":false}],"preferred":false,"id":959291,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Elisha Kelly 0000-0002-9750-7769","orcid":"https://orcid.org/0000-0002-9750-7769","contributorId":334043,"corporation":false,"usgs":true,"family":"Moore","given":"Elisha","email":"","middleInitial":"Kelly","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":959292,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foley, Bradford","contributorId":369885,"corporation":false,"usgs":false,"family":"Foley","given":"Bradford","affiliations":[],"preferred":false,"id":959293,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273741,"text":"70273741 - 2024 - Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means","interactions":[],"lastModifiedDate":"2026-01-27T16:31:23.793842","indexId":"70273741","displayToPublicDate":"2024-07-01T10:29:44","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means","docAbstract":"The United States Geological Survey (USGS) coordinates the collection of hydrographic features derived from remotely sensed interferometric synthetic aperture radar (IfSAR) elevation and intensity data in Alaska. Hydrographic features are cartographic representations of surface water features such as stream, rivers, lakes, ponds, canals, etc. Collection and validation procedures involve complex automated and manual techniques that furnish snapshots of hydrographic vector data that exist during the IfSAR surveys. The dynamic nature of fluvial conditions warrants monitoring and updating hydrographic data, but extraction procedures for updates can be cost prohibitive. This paper overviews progress on automated workflows to extract hydrography from IfSAR data using deep learning methods trained and tested with USGS collected hydrography data. This research tests transfer learning methods on a well-performing U-net model trained on a 4600-square kilometer (sq km) base model area in northcentral Alaska. The base model is transferred and fine-tuned to regions in the target domain covering roughly 127,000 sq km. The target domain is subdivided into areas with similar hydrogeomorphic conditions using principal components and k-means clustering, and the base model is adaptively fine-tuned to each hydrogeomorphic class by selecting training watersheds from each cluster within the target domain. Results are compared with transfer learning that is fine-tuned with a random sample of watersheds in the target domain.","conferenceTitle":"Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS) 2024 Symposium","conferenceDate":"June 3-6, 2024","conferenceLocation":"Columbus, OH","language":"English","publisher":"The Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS)","usgsCitation":"Stanislawski, L., Shavers, E.J., Pastick, N.J., Thiem, P.T., Wang, S., Jaroenchai, N., Jiang, Z., Kronenfeld, B.J., Buttenfield, B.P., and Camerer, A., 2024, Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means, Cartography and Geographic Information Society (CaGIS) and the University Consortium for Geographic Information Science (UCGIS) 2024 Symposium, Columbus, OH, June 3-6, 2024, 45, 6 p.","productDescription":"45, 6 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0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":954511,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Camerer, Adam","contributorId":331850,"corporation":false,"usgs":false,"family":"Camerer","given":"Adam","email":"","affiliations":[{"id":26996,"text":"Missouri University of Science & Technology","active":true,"usgs":false}],"preferred":false,"id":954512,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70267248,"text":"70267248 - 2024 - Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models","interactions":[],"lastModifiedDate":"2025-05-20T14:56:34.636132","indexId":"70267248","displayToPublicDate":"2024-07-01T09:56:08","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models","docAbstract":"Traditionally, probabilistic seismic hazard analysis (PSHA) has focused on calculating ground motion hazard curves for elastic, 5%-damped pseudo spectral accelerations, Sa(T,5%), which are used as the basis for engineering design parameters and targets for ground motion selection and modification. However, structures and geotechnical systems can exhibit a wide range of damping ratios both above and below the 5% level, depending on the construction material, structural system, nonstructural elements, or subsurface soil properties. When spectral parameters at such damping levels are required for certain applications, 5%-damped accelerations have traditionally been extracted from PSHA-based hazard curves and adjusted outside of the hazard integral using damping scaling factors (DSF) such as those from Newmark & Hall (1982). Recent advances in the development of more rigorous and comprehensive damping scaling models (e.g., Rezaeian et al., 2014; Rezaeian et al., 2021) have allowed for the modeling of means and standard deviations of DSFs as functions of earthquake source and path properties for crustal, intraslab, and subduction interface tectonic environments. These DSF models can be applied to ground motion model (GMM) estimates of Sa(T,5%) for a given earthquake rupture scenario to produce a corresponding mean and standard deviation Sa at a specified damping ratio β, Sa(T,β). In this study, the DSF models of Rezaeian et al. (2014) and Rezaeian et al. (2021) are implemented within the U.S. Geological Survey National Seismic Hazard Model (NSHM) PSHA framework to calculate probabilistic hazard curves for spectral accelerations at damping ratios from 0.5% to 30%. The DSF models are applied directly to the mean and standard deviation of Sa(T,5%) predictions from each GMM in the NSHM logic tree. Resulting hazard curves and uniform hazard and risk spectra for Sa(T,β) are presented for several geographic locations and compared with corresponding spectra estimated using current design practices by applying the same DSFs outside of the PSHA calculation. Key differences between the two methods for estimating Sa(T,β) are discussed, and potential strategies are presented for the implementation and usage of the hazard-consistent Sa(T,β) in building codes. Comparing the results to those from DSFs used in current design practices that are mainly based on Newmark & Hall (1982) is not explored in this study.
","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30- July 5, 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Makdisi, A.J., Smith, D., Rezaeian, S., Powers, P.M., and Withers, K., 2024, Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30- July 5, 2024, 11 p.","productDescription":"11 p.","ipdsId":"IP-159606","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486139,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=23633&conference=18WCEE"},{"id":486214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Dallin","contributorId":355505,"corporation":false,"usgs":false,"family":"Smith","given":"Dallin","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":937508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Withers, Kyle B. 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937511,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267251,"text":"70267251 - 2024 - Effects of stochastically-simulated near-fault ground motions on soil liquefaction","interactions":[],"lastModifiedDate":"2025-05-20T14:51:11.280957","indexId":"70267251","displayToPublicDate":"2024-07-01T09:50:05","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Effects of stochastically-simulated near-fault ground motions on soil liquefaction","docAbstract":"The scarcity of historically recorded near-fault ground motions poses a challenge to systematically understanding the influence of near-fault effects on various types of seismic demands for engineering purposes. In particular, the current state of knowledge of the influence of ground-shaking intensity on soil liquefaction and its consequences does not specifically account for the effects of near-fault ground motion characteristics. In this study, the influence of near-fault ground motions on liquefaction triggering and lateral spreading are investigated using non-linear modeling of a hypothetical liquefiable soil column in the finite-element computational platform OpenSees subjected to simulated ground motion time series that represent strong earthquake shaking in the near field. The simulated ground motion time series and resulting datasets are based on a parametric stochastic model and are developed for a range of source and path parameters to represent a realistic variability of ground motion characteristics. Dependencies between ground motion intensity measures (IMs) and liquefaction demand parameters are investigated for near-fault pulse and nonpulse-like ground motion sets. Evolutionary IMs, such as cumulative absolute velocity (CAV) and the time-varying magnitude-adjusted peak ground acceleration (PGAM), are considered in developing liquefaction triggering probability density functions. Post-liquefaction triggering responses such as lateral spreading displacements are examined in relation to PGAM and CAV. The ground motion simulations are validated by comparing their liquefaction-capacity PGAM fragilities and post-triggering CAV vulnerability relationships to historical records from the 1994 Northridge earthquake in California, USA. Finally, a path forward for future studies that includes finding systematic differences in the IM-liquefaction demand relationships between near-fault and far-field stochastic ground motion sets is outlined.
","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30-July 5., 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Makdisi, A.J., Dabaghi, M., Brito Silveira, L., Rezaeian, S., Haynie, K.L., and Mason, H., 2024, Effects of stochastically-simulated near-fault ground motions on soil liquefaction, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30-July 5., 2024, 12 p.","productDescription":"12 p.","ipdsId":"IP-159292","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486140,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=25504&conference=18WCEE","linkFileType":{"id":5,"text":"html"}},{"id":486213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dabaghi, Mayssa","contributorId":221888,"corporation":false,"usgs":false,"family":"Dabaghi","given":"Mayssa","email":"","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":937513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brito Silveira, Lianne 0000-0002-8331-7104","orcid":"https://orcid.org/0000-0002-8331-7104","contributorId":355508,"corporation":false,"usgs":true,"family":"Brito Silveira","given":"Lianne","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":937514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haynie, Kirstie Lafon 0000-0001-9930-6736","orcid":"https://orcid.org/0000-0001-9930-6736","contributorId":289894,"corporation":false,"usgs":true,"family":"Haynie","given":"Kirstie","email":"","middleInitial":"Lafon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mason, Henry 0000-0003-4279-2854","orcid":"https://orcid.org/0000-0003-4279-2854","contributorId":293188,"corporation":false,"usgs":true,"family":"Mason","given":"Henry","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937517,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261254,"text":"70261254 - 2024 - 2024 Crustal Deformation Modeling Workshop report","interactions":[],"lastModifiedDate":"2024-12-04T15:17:20.396096","indexId":"70261254","displayToPublicDate":"2024-07-01T09:15:56","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"2024 Crustal Deformation Modeling Workshop report","docAbstract":"The 2024 Crustal Deformation Modeling Workshop was held June 10–14 at the Colorado School of Mines. The workshop included two days of tutorials on using PyLith for crustal deformation modeling, followed by three days of science talks and discussions. The workshop focused on four primary themes:
● Constraining long-term fault slip rates and their uncertainties using geodetic and geologic data;
● Earthquake cycle modeling with a focus on constraining models using seismic and geodetic data;
● Interaction of fluids and faulting; and
● Separating contributions of surface loading and tectonic loading in crustal deformation.
The complete agenda is available on the CIG website.
","conferenceTitle":"2024 Crustal Deformation Modeling Workshop","conferenceDate":"June 10-14, 2024","conferenceLocation":"Golden, CO","language":"English","publisher":"Computational Infrastructure for Geodynamics","usgsCitation":"Aagaard, B.T., Knepley, M., Lindsey, E., Materna, K.Z., Martens, H.R., and Williams, C., 2024, 2024 Crustal Deformation Modeling Workshop report, 2024 Crustal Deformation Modeling Workshop, Golden, CO, June 10-14, 2024, 4 p.","productDescription":"4 p.","ipdsId":"IP-172275","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":464748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":464724,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://geodynamics.org/resources/2113/supportingdocs"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":920126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knepley, Matthew","contributorId":304241,"corporation":false,"usgs":false,"family":"Knepley","given":"Matthew","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":920127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, Eric","contributorId":261913,"corporation":false,"usgs":false,"family":"Lindsey","given":"Eric","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":920128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Materna, Kathryn Z. 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":209697,"corporation":false,"usgs":false,"family":"Materna","given":"Kathryn","middleInitial":"Z.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":920129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martens, Hilary R","contributorId":215383,"corporation":false,"usgs":false,"family":"Martens","given":"Hilary","email":"","middleInitial":"R","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":920130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Charles 0000-0001-7435-9196","orcid":"https://orcid.org/0000-0001-7435-9196","contributorId":243027,"corporation":false,"usgs":false,"family":"Williams","given":"Charles","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":920131,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}