Multi-organizational principal investigators formed a U.S. Geological Survey (USGS) Powell Center Working Group (WG), Tsunami Source Standardization for Hazards Mitigation in the United States, to develop a comprehensive series of sources capable of generating tsunamis that could impact U.S. state and territory coastal areas using probabilistic tsunami hazard analysis (PTHA). PTHA results are commonly used to provide consistent tsunami hazard information for use in engineering and risk assessment and, to a lesser extent, hazard response planning. Following an initial weeklong planning meeting in April 2018, designed to establish the WG’s scope, a series of weeklong meetings devoted to aspects of tsunami hazards placed emphasis on assessment of various tsunami sources, including subduction zones in Alaska, the Atlantic and Caribbean, Cascadia, and the Pacific Basin, as well as landslides in Alaska, the Atlantic, and the Caribbean. The final meeting in the series discussed tsunami sources from crustal faults. These meetings, each with a regional geographic focus, were designed to incorporate reviews and feedback from subject matter experts (SMEs) in academia, private industry, and federal, state, and local governmental organizations. Incorporating consensus from SMEs is important because the results derived from the tsunami source models will be used to inform the public about potential hazards from tsunamis related to safety concerns. This paper describes the USGS Powell Center meeting in March 2023, devoted specifically to developing a PTHA for tsunami sources in the Pacific Ocean Basin other than the Alaska–Aleutian and Cascadia subduction zones that were addressed during previous WG meetings.
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However, studies have shown species responding in surprising ways. In this study, we aim to understand the impact of global change on species ranges by considering both climate and habitat changes. Using occupancy analysis of acoustic survey data in the mountains of the northeastern United States, we tested specific predictions of range responses to warming (shifting upslope), precipitation change (shifting downslope), and forest composition change (shifting downslope). We found that American red squirrels Tamiasciurus hudsonicus, key nodes in northern North American food webs, are not tracking increasing temperatures upslope, despite substantial warming in recent decades. Structural equation modeling indicates that red squirrel abundance is primarily influenced by red-spruce forest cover, which has shifted downslope with recovery from historical logging and acid deposition. Accounting for the multiple dimensions of global change will enable better predictions and more effective conservation strategies.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.07560","usgsCitation":"Morelli, T.L., Hallworth, M.T., Duclos, T., Ells, A., Faccio, S., Foster, J., McFarland, K.P., Nislow, K., Ralston, J., Ratnaswamy, M., Deluca, W.V., and Siren, A., 2025, Does habitat or climate change drive species range shifts?: Ecography, v. 2025, no. 6, e07560, 11 p., https://doi.org/10.1111/ecog.07560.","productDescription":"e07560, 11 p.","ipdsId":"IP-159616","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":486086,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":489021,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.07560","text":"Publisher Index Page"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, Vermont","otherGeospatial":"northeastern United 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Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha−1) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha−1) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha−1, while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R2 = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj. R2 = 0.748, RMSE = 13.10 kg ha−1 from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling (n = 1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R2 = 0.700–0.769, RMSE = 10.70–15.40 kg ha−1). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R2 = 0.765, RMSE = 12.37 kg ha−1), management variables (species, season, adj. R2 = 0.772, and RMSE = 12.13 kg ha−1), and biophysical variables (height, fractional ground cover, adj. R2 = 0.818, and RMSE = 10.29 kg ha−1). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.
","language":"English","publisher":"Wiley","doi":"10.1002/agj2.70011","usgsCitation":"Thieme, A., Jennewein, J., Hively, W.D., Lamb, B.T., Whitcraft, A., Mirsky, S., Reberg-Horton, S., and Justice, C., 2025, Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops: Agronomy Journal, v. 117, no. 1, e70011, 22 p., https://doi.org/10.1002/agj2.70011.","productDescription":"e70011, 22 p.","ipdsId":"IP-166011","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":497994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/agj2.70011","text":"Publisher Index Page"},{"id":491812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Eastern Shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.72279086706524,\n 39.59745381238187\n ],\n [\n -76.72279086706524,\n 38.03247531099828\n ],\n [\n -75.71750552954822,\n 38.03247531099828\n ],\n [\n -75.71750552954822,\n 39.59745381238187\n ],\n [\n -76.72279086706524,\n 39.59745381238187\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"117","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":942060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":942061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. 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However, geothermal potential across the Great Basin region is thought to be much higher for hidden or blind geothermal systems. Accordingly, exploration techniques that can locate geothermal resources that lack surface thermal features could support future development of these systems. The goal of the INGENIOUS project is to reduce exploration risk and discover new, economically viable hidden geothermal systems in the Great Basin region. This paper summarizes the efforts and preliminary results of blind geothermal resource prospecting in Utah as part of the larger INGENIOUS project. The Lund North site in the Basin and Range province of southwest Utah was designated as the fourth detailed study site for the project. The site was initially identified from the preliminary Play Fairway Analysis (PFA) geothermal favorability map, which is based on various data elements, and the identification of favorable structural settings across Utah. This site contains existing legacy data including a thermal gradient borehole with a heat-flow value exceeding 220 mW/m2. New data collected at this site include terrestrial gravity, magnetotellurics, transient electromagnetics, fluid geochemistry, geologic mapping, UAV-based lidar, paleomagnetism, aeromagnetic surveys, and a shallow temperature survey. Preliminary data and model interpretations corroborate the presence of a large, east-dipping, northeast-southwest-trending normal fault on the edge of a typical Basin and Range graben. Quaternary fault mapping refined by high-resolution lidar surveys suggests a complex step over fault geometry. Utilizing multi-disciplinary datasets for further site assessment at Lund North will facilitate the development of a local-scale PFA and geothermal conceptual model. This new data will be used to site successive thermal gradient drilling to verify heat flow with temperatures at depth at the Lund North site and further investigate the existence of a local geothermal system.
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issues","interactions":[],"lastModifiedDate":"2025-01-10T15:47:05.625225","indexId":"70262010","displayToPublicDate":"2025-02-15T08:40:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Molluscan isotope sclerochronology in marine palaeoclimatology: Taxa, technique and timespan issues","docAbstract":"Study of the accretionary biomineralised hardparts of organisms (sclerochronology) can make a useful contribution to palaeoclimatology. Ontogenetic sequences of isotopic data (δ18O and Δ47 values) from the shells of marine molluscs are a source of information on seasonal sea-surface temperatures that can be used for detailed and rigorous evaluation of the outputs of numerical climate models. In situations where there is significant seasonality, and where shell preservation is adequate, accurate information about winter and summer surface temperature can be obtained from shallow-water benthic forms (bivalves and gastropods), in particular the early ontogeny of fast-growing species. Accurate information about winter surface temperature can also be obtained from individuals that lived at mid-shelf depths (20–40 m), but summer seafloor values from these need upward adjustment to derive a plausible surface temperature if thermal stratification of the water column occurs in this season. Ontogenetic δ18O profiles from planktonic pteropod gastropods are a potential source of insight into seasonal surface temperatures in the ocean basins; these organisms merit investigation for provision of information to complement shelf data.
Temperature profiles constructed from shell δ18O require an estimate of the δ18O value of ambient seawater, which can be derived by back-calculation from the Δ47-temperature supplied by the same shell material. Alternatively, through appropriate sampling and data processing, seasonal temperatures can be obtained directly from Δ47 profiles. Climate parameters are defined in terms of the mean state over a period of 30 consecutive years, a statistic (e.g., for seasonal temperatures) which can be derived from the long isotopic temperature records obtainable from bivalve species that live for many tens or hundreds of years. Efforts should be made to acquire such records, especially averaged data from crossdated shells, to specify climate parameters for precise times in the past. Information for precise times would be of particular value for icehouse intervals like the late Cenozoic, characterised by high frequency (high amplitude) climate fluctuation. Short records from non-crossdated shells can nevertheless provide useful insights into climate, particularly if a large dataset is obtained, supplying a reliable picture of the mean state and range of variation in climate parameters over the interval represented by the shells.
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simulator","interactions":[],"lastModifiedDate":"2025-05-05T14:11:27.139592","indexId":"70266342","displayToPublicDate":"2025-02-14T09:08:16","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"A new groundwater energy transport model for the MODFLOW hydrologic simulator","docAbstract":"Heat transport in the subsurface is an important aspect of research related to the effects of a warming climate on ecological services (i.e., cold-water refugia); the development of geothermal resources for energy banking schemes (i.e., aquifer thermal energy storage [ATES]); and the effects of temperature on other aspects of groundwater quality, such as nutrient cycling. Historically, simulation of heat transport using the MODFLOW groundwater simulator and related codes was performed by scaling the input parameters of a solute-transport model to emulate heat transport. However, that approach required additional pre- and post-processing of input and output and could not account for the variation in effective thermal storage and transport properties during transient, unsaturated flow, for example. True heat-transport capabilities in the context of MODFLOW were first introduced in a variant called USG-Transport. More recently, a new groundwater energy-transport (GWE) model type has been added to MODFLOW 6, the core version of the MODFLOW hydrologic simulator. GWE supports the simulation of heat transport on structured or unstructured grids as well as within and between features of advanced packages that represent streams, lakes, multi-aquifer wells, and the unsaturated zone. GWE is integrated within MODFLOW 6 and is accessible through the FloPy Python package and the MODFLOW 6 application programming interface (API). An example simulation demonstrates conduction between grid cells through both the water and the solid aquifer material, including thermal bleeding from saturated overburden cells into a groundwater flow field.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.13470","usgsCitation":"Morway, E.D., Provost, A.M., Langevin, C.D., Hughes, J.D., Russcher, M.J., Chen, C., and Lin, Y., 2025, A new groundwater energy transport model for the MODFLOW hydrologic simulator: Groundwater, v. 63, no. 3, p. 409-421, https://doi.org/10.1111/gwat.13470.","productDescription":"13 p.","startPage":"409","endPage":"421","ipdsId":"IP-172581","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":487945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13470","text":"Publisher Index Page"},{"id":485372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":935684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Provost, Alden M. 0000-0002-4443-1107 aprovost@usgs.gov","orcid":"https://orcid.org/0000-0002-4443-1107","contributorId":2830,"corporation":false,"usgs":true,"family":"Provost","given":"Alden","email":"aprovost@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":935685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":935686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":935687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russcher, Martijn J. 0000-0001-8799-6514","orcid":"https://orcid.org/0000-0001-8799-6514","contributorId":272524,"corporation":false,"usgs":false,"family":"Russcher","given":"Martijn","email":"","middleInitial":"J.","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":935688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Chieh-Ying","contributorId":354439,"corporation":false,"usgs":false,"family":"Chen","given":"Chieh-Ying","affiliations":[{"id":38021,"text":"University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":935689,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lin, Yu-Feng 0000-0001-6454-0901","orcid":"https://orcid.org/0000-0001-6454-0901","contributorId":302351,"corporation":false,"usgs":false,"family":"Lin","given":"Yu-Feng","email":"","affiliations":[{"id":65462,"text":"Illinois Water Resources Center, University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":935690,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70263597,"text":"70263597 - 2025 - Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa","interactions":[],"lastModifiedDate":"2025-02-19T15:57:20.28773","indexId":"70263597","displayToPublicDate":"2025-02-14T08:47:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa","docAbstract":"From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320240040","usgsCitation":"Yoon, C., Skoumal, R., Michael, A.J., Jolly, A., Ellis, A.P., Downs, D.T., Dotray, P., Deligne, N.I., Chang, J., Bennington, N.L., Wech, A., Haney, M.M., Hotovec-Ellis, A.J., Lutu-McMoore, E., and Langkilde, M., 2025, Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa: The Seismic Record, v. 5, no. 1, p. 83-96, https://doi.org/10.1785/0320240040.","productDescription":"14 p.","startPage":"83","endPage":"96","ipdsId":"IP-170130","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320240040","text":"Publisher Index Page"},{"id":482217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"American Somoa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -169.52912354628728,\n -14.200315944676532\n ],\n [\n -169.52912354628728,\n -14.276188945189261\n ],\n [\n -169.4027807728498,\n -14.276188945189261\n ],\n [\n -169.4027807728498,\n -14.200315944676532\n ],\n [\n -169.52912354628728,\n -14.200315944676532\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-14","publicationStatus":"PW","contributors":{"authors":[{"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":927463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skoumal, Robert","contributorId":217693,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jolly, A.D. 0000-0003-1020-9062","orcid":"https://orcid.org/0000-0003-1020-9062","contributorId":296487,"corporation":false,"usgs":true,"family":"Jolly","given":"A.D.","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellis, Andria P. 0000-0003-2543-0640","orcid":"https://orcid.org/0000-0003-2543-0640","contributorId":305983,"corporation":false,"usgs":true,"family":"Ellis","given":"Andria","email":"","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927467,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927468,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dotray, Peter 0000-0003-2984-455X","orcid":"https://orcid.org/0000-0003-2984-455X","contributorId":215611,"corporation":false,"usgs":true,"family":"Dotray","given":"Peter","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927469,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science 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0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927473,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927474,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hotovec-Ellis, Alicia J. 0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":927475,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lutu-McMoore, Elinor","contributorId":341954,"corporation":false,"usgs":false,"family":"Lutu-McMoore","given":"Elinor","email":"","affiliations":[{"id":81814,"text":"NOAA National Weather Service Pago Pago Office","active":true,"usgs":false}],"preferred":false,"id":927476,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Langkilde, Marcus","contributorId":350957,"corporation":false,"usgs":false,"family":"Langkilde","given":"Marcus","affiliations":[{"id":83889,"text":"NOAA NWS Pago Pago","active":true,"usgs":false}],"preferred":false,"id":927477,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70264632,"text":"70264632 - 2025 - Establishing historical baselines of arthropod assemblages using rodent paleomiddens","interactions":[],"lastModifiedDate":"2025-03-18T15:19:02.044943","indexId":"70264632","displayToPublicDate":"2025-02-14T08:06:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Establishing historical baselines of arthropod assemblages using rodent paleomiddens","docAbstract":"Aim
Arthropods are under-represented in paleoecological studies but are critical agents in ecological processes. Despite rigorous documentation of diverse arthropod assemblages from ancient rodent (or paleo) middens worldwide, their use for studying ancient arthropod diversity has stalled in recent decades. Here, we review published studies to identify how paleomidden arthropods can be leveraged to address significant questions in ecology and synthesise spatiotemporal trends in ancient arthropod diversity.
Location
Global with a focus on the Americas.
Time Period
From ~40,000 to 100 years before present.
Major Taxa Studied
Fossil arthropods preserved in rodent paleomiddens.
Methods
We identified four primary objectives in ecology that can be advanced using paleomidden arthropod fossils: the reconstruction of ancient biodiversity, the elucidation of mechanisms and processes driving arthropod range shifts, the incorporation of arthropods into ancient trophic webs, and the assessment of adaptive responses to changing environments.
Results
We identified 20 papers reporting arthropod remains, of which 14 provided data suitable for quantitative analyses. Although no significant differences in arthropod community diversity were found across broad geographic regions, substantial differences in community structure were observed. In North America, we also detected a decline in arthropod diversity from 10 to 5 ka BP, driven primarily by the absence of high-diversity assemblages during this period.
Main Conclusions
Paleomidden arthropods remain an underutilised ecological resource that can be leveraged to improve the inferential scope of arthropod research across spatiotemporal gradients. Collaboration between paleo- and neoecologists could help expand the sampling of ancient and modern arthropod communities, particularly those associated with paleomiddens, to create high-resolution datasets spanning important periods in biogeographical history. Paleomidden arthropods will remain a fundamental component for understanding arthropod biodiversity changes in the past, present, and future.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.70006","usgsCitation":"Braasch, J., Betancourt, J.L., Dezerald, O., Peiris, U., Tapia-Rozas, M., Villagra, C., Latorre, C.L., and Gonzalez, A., 2025, Establishing historical baselines of arthropod assemblages using rodent paleomiddens: Global Ecology and Biogeography, v. 34, no. 2, e70006, 12 p., https://doi.org/10.1111/geb.70006.","productDescription":"e70006, 12 p.","ipdsId":"IP-172669","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":488330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/geb.70006","text":"Publisher Index Page"},{"id":483474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Braasch, Joseph","contributorId":352381,"corporation":false,"usgs":false,"family":"Braasch","given":"Joseph","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":931016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":931017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dezerald, Olivier","contributorId":336824,"corporation":false,"usgs":false,"family":"Dezerald","given":"Olivier","email":"","affiliations":[{"id":80869,"text":"DECOD (Ecosystem Dynamics and Sustainability), INRAE, Institut Agro, IFREMER, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":931018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peiris, Udari","contributorId":352382,"corporation":false,"usgs":false,"family":"Peiris","given":"Udari","affiliations":[{"id":84194,"text":"Pontificia Universidad Católica de Chile, Santiago","active":true,"usgs":false}],"preferred":false,"id":931019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tapia-Rozas, Maura","contributorId":352383,"corporation":false,"usgs":false,"family":"Tapia-Rozas","given":"Maura","affiliations":[{"id":84194,"text":"Pontificia Universidad Católica de Chile, Santiago","active":true,"usgs":false}],"preferred":false,"id":931020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Villagra, Cristian","contributorId":352384,"corporation":false,"usgs":false,"family":"Villagra","given":"Cristian","affiliations":[{"id":84195,"text":"Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":931021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Latorre, Claudio L.","contributorId":331520,"corporation":false,"usgs":false,"family":"Latorre","given":"Claudio","email":"","middleInitial":"L.","affiliations":[{"id":79225,"text":"Pontificia Universidad Católica de Chile, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":931022,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzalez, Angelica L.","contributorId":336826,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Angelica L.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":931023,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263500,"text":"sir20245127 - 2025 - Groundwater age estimates for the Mississippi River Valley alluvial aquifer based on tracer data collected during 2018–20","interactions":[],"lastModifiedDate":"2025-07-21T18:22:10.201666","indexId":"sir20245127","displayToPublicDate":"2025-02-13T14:55:22","publicationYear":"2025","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":"2024-5127","displayTitle":"Groundwater Age Estimates for the Mississippi River Valley Alluvial Aquifer Based on Tracer Data Collected During 2018–20","title":"Groundwater age estimates for the Mississippi River Valley alluvial aquifer based on tracer data collected during 2018–20","docAbstract":"This study characterized groundwater age across the Mississippi River Valley alluvial aquifer (MRVA). Groundwater samples from 69 MRVA wells and 19 wells in Tertiary units of the Mississippi embayment aquifer system (MEAS) were analyzed for sulfur hexafluoride (SF6), tritium (3H), helium (He), and (or) carbon-14 of dissolved inorganic carbon (14C). The age distributions of 89 samples were estimated by fitting lumped parameter models to processed tracer concentrations with the U.S. Geological Survey software TracerLPM. Mean ages of MRVA groundwater samples ranged from 12 to 22,000 years, with a median of 140 years. Mean ages of MEAS groundwater samples ranged from 230 to 52,000 or more years, with a median of 13,500 years. The spatial distribution of MRVA groundwater ages was found to be influenced by depth, inflow of groundwater from deeper units, and soil saturated hydraulic conductivity. In parts of the MRVA, the spatial distribution of MRVA groundwater ages was found to be influenced by annual recharge and (or) annual groundwater pumpage.
Director, Lower Mississippi-Gulf Water Science Center, U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Relative sensitivities and responses of juvenile white sturgeon (Acipenser transmontanus), Hyalella azteca, two families of mayfly (Ephemerellidae, Heptageniidae), one family of caddisfly (Brachycentridae), and a natural community of benthic macroinvertebrates (BMI) to multiple metals are predicted using previously collected laboratory and field samples and a metal mixture model. Biological responses in single metal exposures are used to parameterize toxicity functions, which include accumulations of hydrogen and selected metals on biological receptors, intrinsic potencies of hydrogen and metals, sensitivities of organisms, and times of exposure. The model then is used to predict responses in multiple metal laboratory exposures and field-collected porewater. The following sensitivity sequence in porewater was determined based on endpoints of survival or total abundance: juvenile white sturgeon greater than (>) Ephemerellidae family > Hyalella azteca > Heptageniidae family about equal to (~) benthic macroinvertebrate community > Brachycentridae family. The fraction of porewater samples that are predicted to have adverse impacts on benthic biota (20-percent or greater negative response) depends on organism sensitivities and metal toxicities, and ranges from 44 to 48 percent for juvenile white sturgeon, 23 to 26 percent for the Ephemerellidae family, 16 to 22 percent for Hyalella azteca, 5 to 8 percent for the Heptageniidae family and BMI community, and 0 percent for the caddisfly family. The most toxic porewater in the upper Columbia River Basin (UCR) is at the backwater bar site at Deadmans Eddy and China Bend. The model also indicates that the element responsible for the most toxic conditions in UCR porewater is copper for all organisms, except Hyalella azteca and the metal-insensitive Brachycentridae family. Copper and lead result in the most toxic conditions for Hyalella azteca. This approach and results can aid in assessing metal toxicity and its potential risk to aquatic biota in ecosystems impacted by historical mining activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255001","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Balistrieri, L.S., 2025, Sensitivity of benthic biota and toxicity of cadmium, cobalt, copper, nickel, lead, and zinc mixtures in Washington, United States, and British Columbia, Canada: U.S. Geological Survey Scientific Investigations Report 2025–5001, 19 p., https://doi.org/10.3133/sir20255001.","productDescription":"v, 19 p.","onlineOnly":"Y","ipdsId":"IP-168170","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":492689,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118446.htm","linkFileType":{"id":5,"text":"html"}},{"id":482001,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5001/images"},{"id":482002,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5001/sir20255001.XML"},{"id":482000,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255001/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5001"},{"id":481999,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5001/sir20255001.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5001"},{"id":481998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5001/coverthb.jpg"}],"country":"Canada, United States","state":"British Columbia, Washington","otherGeospatial":"Upper Columbia River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.333,\n 49.5\n ],\n [\n -118.333,\n 49.5\n ],\n [\n -118.333,\n 48.6\n ],\n [\n -117.333,\n 48.6\n ],\n [\n -117.333,\n 49.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
This study addresses a significant gap in understanding the features of the south-central Cascadia subduction zone, a region characterized by complex geologic, tectonic, and seismic transitions both offshore and onshore. Unlike other segments along this margin, this area lacks a 3-D velocity model to delineate its structural and geological features on a fine scale. To address this void, we developed a high-resolution 3-D P-wave velocity model using active source seismic data from ship-borne seismic shots recorded on temporary and permanent onshore seismic stations and ocean-bottom seismometers. Our model shows velocity variations across the region with distinct velocity-depth profiles for the Siletz, Franciscan, and Klamath terranes in the overlying plate. We identified seaward dipping high-velocity static backstops associated with the Siletz and Klamath terranes, situated near the shoreline and further inland, respectively. Regions of reduced crustal velocity are associated with crustal faults. Moreover, there is significant along-strike depth variation in the subducting slab, which is about 4 km deeper near the thick, dense Siletz terrane and becomes shallower near the predominantly less-dense Franciscan terrane. This highlights a sudden tectonic and geologic transition at the southern boundary of the Siletz terrane. Our velocity model also indicates slightly increased hydration, though still minimal, in both the oceanic crust and the upper mantle of the subducting plate compared to other parts of the margin.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JB029525","usgsCitation":"Ashraf, A., Hooft, E., Toomey, D., Trehu, A., Nolan, S., Wirth, E.A., and Ward, K.M., 2025, A high-resolution 3-D P-wave velocity structure of the south-central Cascadia subduction zone from wide-angle shore-crossing seismic refraction data: JGR Solid Earth, v. 130, e2024JB029525, 23 p., https://doi.org/10.1029/2024JB029525.","productDescription":"e2024JB029525, 23 p.","ipdsId":"IP-165201","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487643,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2024JB029525","text":"External Repository"},{"id":482045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.05502627728873,\n 50\n ],\n [\n -130,\n 50\n ],\n [\n -130,\n 40\n ],\n [\n -122,\n 40\n ],\n [\n -122,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","noUsgsAuthors":false,"publicationDate":"2025-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ashraf, Asif","contributorId":350925,"corporation":false,"usgs":false,"family":"Ashraf","given":"Asif","affiliations":[{"id":80428,"text":"Univ. of Oregon","active":true,"usgs":false}],"preferred":false,"id":927325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooft, Emilie","contributorId":350929,"corporation":false,"usgs":false,"family":"Hooft","given":"Emilie","affiliations":[{"id":80428,"text":"Univ. of Oregon","active":true,"usgs":false}],"preferred":false,"id":927326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Toomey, Douglas 0000-0003-2873-4084","orcid":"https://orcid.org/0000-0003-2873-4084","contributorId":206934,"corporation":false,"usgs":false,"family":"Toomey","given":"Douglas","affiliations":[{"id":27574,"text":"UO","active":true,"usgs":false}],"preferred":false,"id":927327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trehu, Anne 0000-0002-5349-3289","orcid":"https://orcid.org/0000-0002-5349-3289","contributorId":242894,"corporation":false,"usgs":false,"family":"Trehu","given":"Anne","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":927328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nolan, Sarah","contributorId":350932,"corporation":false,"usgs":false,"family":"Nolan","given":"Sarah","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":927329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wirth, Erin A. 0000-0002-8592-4442","orcid":"https://orcid.org/0000-0002-8592-4442","contributorId":207853,"corporation":false,"usgs":true,"family":"Wirth","given":"Erin","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927330,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ward, Kevin M.","contributorId":206723,"corporation":false,"usgs":false,"family":"Ward","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":927331,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70269042,"text":"70269042 - 2025 - Accounting for non-random samples with distance sampling to estimate population density","interactions":[],"lastModifiedDate":"2025-07-15T14:30:48.364427","indexId":"70269042","displayToPublicDate":"2025-02-12T09:28:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for non-random samples with distance sampling to estimate population density","docAbstract":"Fishing gear entanglements can compromise health and lower survival and reproductive output of wildlife, which can slow population growth or cause population declines. However, entanglements may go unobserved, making it difficult to quantify their effects on individuals' vital rates and a population's trajectory. Fishing gear entanglements are a leading cause of death for North Atlantic right whales, Eubalaena glacialis, an endangered species whose population has declined substantially over the last decade. Key sources of observation error have not been addressed when quantifying the effects of entanglement on this species. We formulated a hidden Markov model that integrated 28 years of photo identification records, visual health assessments, entanglement assessments, and hormone assays to estimate body condition dynamics, and entanglement, survival, and reproductive rates of female North Atlantic right whales. The model also accounted for observation errors, including entanglements and reproductive events that were unobserved. Whales entangled in fishing gear were drastically more likely to experience declines in body condition and had lower survival rates than whales that were not entangled. Additionally, only whales in good body condition became pregnant. Between 1994 and 2021 entanglements reduced the expected number of calves born by 12.9% (95% credible interval: 8.2%–19.6%) and the expected number of females alive at the end of 2021 by 18.5% (95% credible interval: 8.6%–29.4%). Fishing gear entanglements have reduced the North Atlantic right whale population's capacity to recover and have increased the importance of the health and survival of the remaining living whales. Therefore, reducing the risk of fishing gear entanglements is likely necessary for the population to recover.
","language":"English","publisher":"Zoological Society of London","doi":"10.1111/acv.13016","usgsCitation":"Crum, N.J., Gowan, T., Hostetler, J.A., Schick, R., Knowlton, A., Pettis, H., Hamilton, P., and Rolland, R., 2025, Unobserved individual and population level impacts of fishing gear entanglements on North Atlantic right whales: Animal Conservation, https://doi.org/10.1111/acv.13016.","ipdsId":"IP-151853","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":487726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/acv.13016","text":"Publisher Index Page"},{"id":483051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Crum, Nathan J.","contributorId":200016,"corporation":false,"usgs":false,"family":"Crum","given":"Nathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":929958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gowan, Timothy A.","contributorId":335405,"corporation":false,"usgs":false,"family":"Gowan","given":"Timothy A.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":929959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hostetler, J. A. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":11319,"corporation":false,"usgs":true,"family":"Hostetler","given":"J.","middleInitial":"A.","affiliations":[],"preferred":true,"id":929960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schick, Robert S.","contributorId":352043,"corporation":false,"usgs":false,"family":"Schick","given":"Robert S.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":929961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knowlton, Amy R.","contributorId":352046,"corporation":false,"usgs":false,"family":"Knowlton","given":"Amy R.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":929962,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pettis, Heather M.","contributorId":352049,"corporation":false,"usgs":false,"family":"Pettis","given":"Heather M.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":929963,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hamilton, Philip K.","contributorId":352052,"corporation":false,"usgs":false,"family":"Hamilton","given":"Philip K.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":929964,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rolland, Rosalind M.","contributorId":352055,"corporation":false,"usgs":false,"family":"Rolland","given":"Rosalind M.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":929965,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70264730,"text":"70264730 - 2025 - An unexplained tsunami: Was there megathrust slip during the 2020 Mw7.6 Sand Point, Alaska, earthquake?","interactions":[],"lastModifiedDate":"2025-03-21T15:08:55.785308","indexId":"70264730","displayToPublicDate":"2025-02-12T07:59:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17454,"text":"Seismica","active":true,"publicationSubtype":{"id":10}},"title":"An unexplained tsunami: Was there megathrust slip during the 2020 Mw7.6 Sand Point, Alaska, earthquake?","docAbstract":"On October 19, 2020, the Mw7.6 Sand Point earthquake struck south of the Shumagin Islands in Alaska. Moment tensors indicate the earthquake was primarily strike-slip, yet the event produced an enigmatic tsunami that was larger and more widespread than expected for an earthquake of that magnitude and mechanism. Using a suite of hydrodynamic, seismic, and geodetic modeling techniques, we explore plausible causes of the tsunami. We find that strike-slip models consistent with the moment tensor orientation cannot produce the observed tsunami. Hydrodynamic inversion of sea surface deformation from deep ocean and tide gauge data suggest seafloor deformation more closely matches a megathrust, rather than a strike-slip, source. Static slip inversions, using sea level and Global Navigation Satellite System data, allow for a portion of co-seismic megathrust slip that can explain tsunamigenesis. Combining all available geophysical datasets to model the kinematic rupture, we show that considerable, relatively slow, megathrust slip is allowable in the Shumagin segment, concurrent with strike-slip faulting. We hypothesize that the slow megathrust rupture does not contribute much seismic radiation allowing it to previously go unnoticed with traditional seismic monitoring.
","language":"English","publisher":"McGill","doi":"10.26443/seismica.v4i1.1336","usgsCitation":"Santellanes, S., Goldberg, D.E., Koch, P., Melgar, D., Yeck, W.L., Crowell, B.W., and Lin, J., 2025, An unexplained tsunami: Was there megathrust slip during the 2020 Mw7.6 Sand Point, Alaska, earthquake?: Seismica, v. 4, no. 1, p. 1-13, https://doi.org/10.26443/seismica.v4i1.1336.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-156090","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":488362,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v4i1.1336","text":"Publisher Index Page"},{"id":483661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Sand Point","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.8287429707048,\n 55.46546760592042\n ],\n [\n -160.8287429707048,\n 55.238590952487215\n ],\n [\n -160.19346063892195,\n 55.238590952487215\n ],\n [\n -160.19346063892195,\n 55.46546760592042\n ],\n [\n -160.8287429707048,\n 55.46546760592042\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Santellanes, Sean R. 0000-0003-4181-437X","orcid":"https://orcid.org/0000-0003-4181-437X","contributorId":347848,"corporation":false,"usgs":false,"family":"Santellanes","given":"Sean R.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":931475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldberg, Dara Elyse 0000-0002-0923-3180","orcid":"https://orcid.org/0000-0002-0923-3180","contributorId":289891,"corporation":false,"usgs":true,"family":"Goldberg","given":"Dara","email":"","middleInitial":"Elyse","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":931476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Pablo","contributorId":294680,"corporation":false,"usgs":false,"family":"Koch","given":"Pablo","email":"","affiliations":[{"id":63624,"text":"National Seismological Center, University of Chile","active":true,"usgs":false}],"preferred":false,"id":931477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melgar, Diego","contributorId":341315,"corporation":false,"usgs":false,"family":"Melgar","given":"Diego","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":931478,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":931479,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crowell, Brendan W.","contributorId":184207,"corporation":false,"usgs":false,"family":"Crowell","given":"Brendan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":931480,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lin, Jiun-Ting 0000-0002-8134-9612","orcid":"https://orcid.org/0000-0002-8134-9612","contributorId":347849,"corporation":false,"usgs":false,"family":"Lin","given":"Jiun-Ting","affiliations":[{"id":13621,"text":"Lawrence Livermore National Laboratory","active":true,"usgs":false}],"preferred":false,"id":931481,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266780,"text":"70266780 - 2025 - Terrebonne Basin, Gulf of Mexico gas hydrate resource evaluation and 3-D modeling of basin-scale sedimentation, salt tectonics, and hydrate system evolution since the early Miocene","interactions":[],"lastModifiedDate":"2025-05-13T16:16:28.57162","indexId":"70266780","displayToPublicDate":"2025-02-11T11:10:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Terrebonne Basin, Gulf of Mexico gas hydrate resource evaluation and 3-D modeling of basin-scale sedimentation, salt tectonics, and hydrate system evolution since the early Miocene","docAbstract":"In assessing methane hydrate as a potential transitional energy source, quantification of in-place volumes of gas is a first step—and yet, global, regional, and even local estimates of gas volumes contained within hydrate are highly variable, including within the extensively-studied Gulf of Mexico (GoM) gas hydrate province. Here, we construct the first 3-D basin and hydrocarbon system model of the Terrebonne Basin (Walker Ridge protraction area, northern GoM) to estimate in-place resources contained as gas within hydrate, as well as to provide the first estimate of in-place resources contained within free gas accumulations in the basin. The resultant model-predicted 3-D distributions (and saturations) of hydrate are consistent with independent seismic interpretation and borehole observations. This study reveals both sedimentation-driven and salt diapir-driven gas hydrate recycling produce elevated hydrate saturations in the basin (“recycling” occurs when relative upward shifts of the gas hydrate stability zone (GHSZ) cause hydrate dissociation, generating buoyant free gas that may reaccumulate as higher-saturation hydrate at the new base of GHSZ). An important finding of this study (particularly given that, globally, the source of methane in marine hydrate systems tends to be poorly understood) is that microbial sources can explain most or all hydrate occurrences in Terrebonne Basin. We calculate that ∼32 × 109 m3 (∼1.1 1012 ft3) of methane gas is trapped within hydrate accumulations throughout Terrebonne Basin, while nearly 2 × 109 m3 (∼67 × 109 ft3) of methane occurs as free gas at high saturations within sandy reservoirs beneath the GHSZ and ∼35 × 109 m3 (∼1.2 × 1012 ft3) occurs as free gas at low saturations within muddy units beneath the GHSZ. In total, our calibrated 3-D model predicts that nearly 70 × 109 m3 (∼2.5 × 1012 ft3) of microbial methane is trapped as gas hydrate and free gas in the Terrebonne Basin.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2025.107330","usgsCitation":"Dafov, L., Burton, Z.F., Haines, S.S., Hosford Scheirer, A., Masurek, N., Boswell, R., Frye, M., Seol, Y., and Graham, S., 2025, Terrebonne Basin, Gulf of Mexico gas hydrate resource evaluation and 3-D modeling of basin-scale sedimentation, salt tectonics, and hydrate system evolution since the early Miocene: Marine and Petroleum Geology, v. 176, 107330, 18 p., https://doi.org/10.1016/j.marpetgeo.2025.107330.","productDescription":"107330, 18 p.","ipdsId":"IP-167026","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":488201,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpetgeo.2025.107330","text":"Publisher Index Page"},{"id":485832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"176","noUsgsAuthors":false,"publicationDate":"2025-02-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Dafov, Laura","contributorId":354989,"corporation":false,"usgs":false,"family":"Dafov","given":"Laura","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":936752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burton, Zachary FM","contributorId":302277,"corporation":false,"usgs":false,"family":"Burton","given":"Zachary","email":"","middleInitial":"FM","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":936753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":936754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hosford Scheirer, Allegra","contributorId":67995,"corporation":false,"usgs":true,"family":"Hosford Scheirer","given":"Allegra","affiliations":[],"preferred":false,"id":936755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Masurek, Nicole","contributorId":354998,"corporation":false,"usgs":false,"family":"Masurek","given":"Nicole","affiliations":[{"id":84692,"text":"Schlumberger Center of Excellence for Petroleum Systems Modeling","active":true,"usgs":false}],"preferred":false,"id":936756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boswell, Ray","contributorId":354999,"corporation":false,"usgs":false,"family":"Boswell","given":"Ray","affiliations":[{"id":84695,"text":"US DOE","active":true,"usgs":false}],"preferred":false,"id":936757,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frye, Matthew","contributorId":197799,"corporation":false,"usgs":false,"family":"Frye","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":936758,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Seol, Yongkoo","contributorId":195139,"corporation":false,"usgs":false,"family":"Seol","given":"Yongkoo","email":"","affiliations":[],"preferred":false,"id":936759,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Graham, Stephan A.","contributorId":293143,"corporation":false,"usgs":false,"family":"Graham","given":"Stephan A.","affiliations":[{"id":63235,"text":"Stanford Univeristy","active":true,"usgs":false}],"preferred":false,"id":936760,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263918,"text":"70263918 - 2025 - Road salt collection and redistribution at an urban rain garden on sandy soil, Gary, Indiana","interactions":[],"lastModifiedDate":"2025-02-28T15:33:47.700472","indexId":"70263918","displayToPublicDate":"2025-02-11T09:28:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Road salt collection and redistribution at an urban rain garden on sandy soil, Gary, Indiana","docAbstract":"Rain gardens installed as green infrastructure to divert storm runoff from entering combined sewers also collect dissolved constituents and particulates. An urban rain garden in northwestern Indiana, USA, was continuously monitored from November 2019 to May 2021 to evaluate the fate of dissolved constituents entering the rain garden in runoff. Physical and chemical properties of soils in the rain garden were also monitored, along with underlying groundwater. Linear regression models relating specific conductance to chloride concentration indicated that the 0.0371-ha (3998 square feet) rain garden collected approximately 1490 kg (3285 pounds) of road salt from the surrounding 0.2228 ha (24,500 square feet) of impervious surfaces. Soils and groundwater were seasonally affected by road salt application but carryover from year to year was not indicated. Rain garden soil permeability (5.20 × 10−5 to 9.72 × 10−5 m/s) remained unchanged during the study period and soil organic carbon generally increased under native vegetation. The results suggest that a rain garden built on sandy soil can divert substantial quantities of runoff and dissolved constituents from combined sewers; however, chloride is transported to sub-infrastructure groundwater that eventually discharges to adjacent waterways with concentrations lower than those observed in runoff.
","language":"English","publisher":"MDPI","doi":"10.3390/w17040510","usgsCitation":"Bayless, E.R., Naylor, S., Lampe, D.C., Story, A., and Artz, C., 2025, Road salt collection and redistribution at an urban rain garden on sandy soil, Gary, Indiana: Water, v. 17, no. 4, 510, 25 p., https://doi.org/10.3390/w17040510.","productDescription":"510, 25 p.","ipdsId":"IP-126325","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":489968,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w17040510","text":"Publisher Index Page"},{"id":482638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","city":"Gary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.33710281880745,\n 41.603842124498556\n ],\n [\n -87.33710281880745,\n 41.60260759598816\n ],\n [\n -87.33586794894438,\n 41.60260759598816\n ],\n [\n -87.33586794894438,\n 41.603842124498556\n ],\n [\n -87.33710281880745,\n 41.603842124498556\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Bayless, E. Randall 0000-0002-0357-3635","orcid":"https://orcid.org/0000-0002-0357-3635","contributorId":42586,"corporation":false,"usgs":true,"family":"Bayless","given":"E.","email":"","middleInitial":"Randall","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Naylor, Shawn 0000-0003-0710-1560","orcid":"https://orcid.org/0000-0003-0710-1560","contributorId":333771,"corporation":false,"usgs":true,"family":"Naylor","given":"Shawn","email":"","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lampe, David C. 0000-0002-8904-0337 dclampe@usgs.gov","orcid":"https://orcid.org/0000-0002-8904-0337","contributorId":2441,"corporation":false,"usgs":true,"family":"Lampe","given":"David","email":"dclampe@usgs.gov","middleInitial":"C.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Story, Amy A 0000-0003-1926-9811","orcid":"https://orcid.org/0000-0003-1926-9811","contributorId":350865,"corporation":false,"usgs":true,"family":"Story","given":"Amy A","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929089,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Artz, Caleb Colyer 0009-0002-3049-6062","orcid":"https://orcid.org/0009-0002-3049-6062","contributorId":350866,"corporation":false,"usgs":true,"family":"Artz","given":"Caleb Colyer","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929090,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267221,"text":"70267221 - 2025 - Filling the gaps: A Bayesian mixture model for imputing missing soil water content data","interactions":[],"lastModifiedDate":"2025-05-16T15:31:40.63399","indexId":"70267221","displayToPublicDate":"2025-02-10T08:27:47","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Filling the gaps: A Bayesian mixture model for imputing missing soil water content data","docAbstract":"Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water-limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well (R2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap-fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.70004","usgsCitation":"Ogle, K., Reich, E., Samuels-Crow, K., Litvak, M., Bradford, J., Schlaepfer, D.R., and Devan, M., 2025, Filling the gaps: A Bayesian mixture model for imputing missing soil water content data: Ecohydrology, v. 18, no. 1, e70004, 17 p., https://doi.org/10.1002/eco.70004.","productDescription":"e70004, 17 p.","ipdsId":"IP-163652","costCenters":[{"id":49226,"text":"Northwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":490127,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eco.70004","text":"External Repository"},{"id":486071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.07993996238093,\n 37.04473109056555\n ],\n [\n -109.07993996238093,\n 31.382778545525298\n ],\n [\n -103.04352625964134,\n 31.382778545525298\n ],\n [\n -103.04352625964134,\n 37.04473109056555\n ],\n [\n -109.07993996238093,\n 37.04473109056555\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ogle, Kiona","contributorId":248351,"corporation":false,"usgs":false,"family":"Ogle","given":"Kiona","email":"","affiliations":[],"preferred":false,"id":937346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reich, Emma","contributorId":355440,"corporation":false,"usgs":false,"family":"Reich","given":"Emma","affiliations":[{"id":84751,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":937347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Samuels-Crow, Kimberly","contributorId":289104,"corporation":false,"usgs":false,"family":"Samuels-Crow","given":"Kimberly","email":"","affiliations":[{"id":62051,"text":"School of Informatics, Computing, and Cyber Systems; Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":937348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Litvak, Marcy","contributorId":216915,"corporation":false,"usgs":false,"family":"Litvak","given":"Marcy","affiliations":[{"id":39549,"text":"University of New Mexico: Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":937349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937350,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937351,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Devan, Megan","contributorId":355441,"corporation":false,"usgs":false,"family":"Devan","given":"Megan","affiliations":[{"id":84752,"text":"Department of Biology, University of New Mexico, Albuquerque, New Mexico, 87131, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":937352,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266568,"text":"70266568 - 2025 - A real-time fish detection system for partially dewatered fish to support selective fish passage","interactions":[],"lastModifiedDate":"2025-05-09T14:23:25.631013","indexId":"70266568","displayToPublicDate":"2025-02-09T09:15:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"A real-time fish detection system for partially dewatered fish to support selective fish passage","docAbstract":"Recent advances in fish transportation technologies and deep machine learning-based fish classification have created an opportunity for real-time, autonomous fish sorting through a selective passage mechanism. This research presents a case study of a novel application that utilizes deep machine learning to detect partially dewatered fish exiting an Archimedes Screw Fish Lift (ASFL). A MobileNet SSD model was trained on images of partially dewatered fish volitionally passing through an ASFL. Then, this model was integrated with a network video recorder to monitor video from the ASFL. Additional models were also trained using images from a similar fish scanning device to test the feasibility of this approach for fish classification. Open source software and edge computing design principles were employed to ensure that the system is capable of fast data processing. The findings from this research demonstrate that such a system integrated with an ASFL can support real-time fish detection. This research contributes to the goal of automated data collection in a selective fish passage system and presents a viable path towards realizing optical fish sorting.
","language":"English","publisher":"MDPI","doi":"10.3390/s25041022","usgsCitation":"Gregory, J., Miehls, S.M., Eickholt, J., and Zielinski, D., 2025, A real-time fish detection system for partially dewatered fish to support selective fish passage: Sensors, v. 25, no. 4, 1022, 23 p., https://doi.org/10.3390/s25041022.","productDescription":"1022, 23 p.","ipdsId":"IP-174633","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":490109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s25041022","text":"Publisher Index Page"},{"id":485641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Swan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.71462217594592,\n 45.40023637231536\n ],\n [\n -83.78015729775288,\n 45.40023637231536\n ],\n [\n -83.78015729775288,\n 45.337445622308564\n ],\n [\n -83.71462217594592,\n 45.337445622308564\n ],\n [\n -83.71462217594592,\n 45.40023637231536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Gregory, Jonathan","contributorId":354850,"corporation":false,"usgs":false,"family":"Gregory","given":"Jonathan","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":936574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eickholt, Jesse","contributorId":205582,"corporation":false,"usgs":false,"family":"Eickholt","given":"Jesse","email":"","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":936576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zielinski, Daniel","contributorId":245798,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":936577,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263464,"text":"70263464 - 2025 - Applications of unoccupied aerial systems (UAS) in landscape ecology: A review of recent research, challenges and emerging opportunities","interactions":[],"lastModifiedDate":"2025-02-12T14:53:37.848481","indexId":"70263464","displayToPublicDate":"2025-02-08T08:43:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Applications of unoccupied aerial systems (UAS) in landscape ecology: A review of recent research, challenges and emerging opportunities","docAbstract":"Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications.
The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research.
We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function.
We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software.
We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-024-02040-6","usgsCitation":"Villarreal, M.L., Bishop, T., Sankey, T., Smith, W.K., Burgess, M.A., Caughlin, T., Gillan, J.K., Havrilla, C., Huang, T., LeBeau, R., Norton, C., Sankey, J., Scholl, V.M., Von Nonn, J.W., and Yao, E., 2025, Applications of unoccupied aerial systems (UAS) in landscape ecology: A review of recent research, challenges and emerging opportunities: Landscape Ecology, v. 40, 43, 32 p., https://doi.org/10.1007/s10980-024-02040-6.","productDescription":"43, 32 p.","ipdsId":"IP-165776","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":487638,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-024-02040-6","text":"Publisher Index 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recovery goals. However, because current field data cannot be gathered about these species, efforts to identify suitable habitat are often complicated and controversial. Here we present a case study examining a novel approach for identifying suitable habitat for the locally extirpated brown bear, known as the “grizzly,” in California, USA. Once home to a large population of grizzlies, the last credible sighting of a grizzly in California was in 1924, but recent discussions have explored a reintroduction. To study potential grizzly bear habitat suitability in California, we use a multi-model approach in which we consider three indirect methods, or “analogies,” each of which incorporates both environmental and social variables. In the “historical analogy,” we used a unique record of human encounters and museum specimens to build a habitat model of grizzlies in California 150 years ago. In the “geographic analogy,” we used data about contemporary brown bears in Europe and North America to identify potential suitable habitat in California. In the “taxonomic analogy,” we examined data from other large carnivores currently extant in California to draw conclusions about grizzlies. Because these methods yield varying results, we adopt a process known in the social sciences as “triangulation” to compare them. We show that a triangulation approach can improve our understanding of potential suitable habitat, clarifying the strengths and weaknesses of various methods and producing robust yet conservative estimates. Our findings suggest that large areas of suitable habitat for grizzlies exist in California. However, we note that place-based social science research and long-term investment in co-existence would be necessary to maintain suitability. The “analogy and triangulation” approach demonstrates the value of multi-model approaches for habitat suitability, especially for reintroductions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2025.110989","usgsCitation":"Mcinturff, M.C., Alagona, P., Cooper, S., Gaynor, K., Anderson, S., Forbes, E.S., Heilmayr, R., Hiroyasu, E., Kendall, B., Mychajliw, A.M., and Hardesty-Moore, M., 2025, Triangulating habitat suitability for the locally extirpated California grizzly bear: Biological Conservation, v. 303, 110989, 15 p., https://doi.org/10.1016/j.biocon.2025.110989.","productDescription":"110989, 15 p.","ipdsId":"IP-168408","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486514,"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\": 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Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Possible influence of water level management on nutrient flux in nearshore sediments of Kabetogama Lake, Minnesota, USA","docAbstract":"Lake water level fluctuations are an important factor driving variation in many ecosystem processes. The nearshore sediments that are periodically exposed and re-inundated can develop distinct physical and chemical characteristics, especially in relationship to the organic matter content of the sediments and the particle size distribution. These sediment characteristics in turn can alter the flux of nitrogen (N) and phosphorus (P) from sediments into the water column when sediments are inundated. Here, we used intact sediment core experiments across a range of sediment inundation frequencies to estimate the effect of inundation frequency on sediment nutrient flux in Kabetogama Lake, Minnesota, USA. We observed associations between elevation or inundation frequency and some sediment characteristics, but in a structural equation model, inundation frequency and the sediment properties we measured were poorly related to inorganic nutrient flux. On the other hand, inundation frequency did have a moderate association with organic N and P flux from sediments, which could be due to decay of terrestrial organic matter that accumulates on exposed sediments. We used our parameterized structural equation model to estimate how three different water level management regimes employed over the past 50 years could influence organic N and P flux from sediments. The models suggested more recent water level management regimes reduced organic N and P flux by 9%–13% and 5.9%–9.8%, respectively. Nearshore sediment flux could sustain and influence harmful algal blooms that occur in this lake, and these fluxes could be influenced by water level management.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70176","usgsCitation":"Larson, J.H., Bailey, S., Maki, R., Christensen, V., Stelzer, E., Smith, J., LeDuc, J.F., and McWhorter, S., 2025, Possible influence of water level management on nutrient flux in nearshore sediments of Kabetogama Lake, Minnesota, USA: Ecosphere, v. 16, no. 2, e70176, 22 p., https://doi.org/10.1002/ecs2.70176.","productDescription":"e70176, 22 p.","ipdsId":"IP-160471","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70176","text":"Publisher Index Page"},{"id":482500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.1323728221501,\n 48.530999864005054\n ],\n [\n -93.1323728221501,\n 48.39653345056249\n ],\n [\n -92.70028521395359,\n 48.39653345056249\n ],\n [\n -92.70028521395359,\n 48.530999864005054\n ],\n [\n -93.1323728221501,\n 48.530999864005054\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":928643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":928644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":928645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, James C.","contributorId":351486,"corporation":false,"usgs":false,"family":"Smith","given":"James C.","affiliations":[{"id":82351,"text":"U.S. National Park Service (NPS)","active":true,"usgs":false}],"preferred":false,"id":928646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LeDuc, Jamie F.","contributorId":178241,"corporation":false,"usgs":false,"family":"LeDuc","given":"Jamie","email":"","middleInitial":"F.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":928647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McWhorter, Seth","contributorId":351487,"corporation":false,"usgs":false,"family":"McWhorter","given":"Seth","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":928648,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263394,"text":"70263394 - 2025 - ShakeAlert® version 3: Expected performance in large earthquakes","interactions":[],"lastModifiedDate":"2025-03-25T15:55:45.307964","indexId":"70263394","displayToPublicDate":"2025-02-06T10:17:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"ShakeAlert® version 3: Expected performance in large earthquakes","docAbstract":"The ShakeAlert earthquake early warning (EEW) system partners along with U.S. Geological Survey (USGS) licensed operators deliver EEW alerts to the public and trigger automated systems when a significant earthquake is expected to impact California, Oregon, or Washington. ShakeAlert’s primary goal is to provide usable warning times before the arrival of damaging shaking. EEW is most likely to achieve this goal in large‐magnitude earthquakes. In recent years, ShakeAlert has gone through a series of upgrades to its underlying scientific algorithms aimed at improved performance during large earthquakes. Version 3 of this software recently went live in the production system and includes improvements to all algorithms. The main seismic algorithms that detect an earthquake and characterize its location, magnitude, and fault rupture orientation are faster than older versions. Other key changes include: using real‐time geodetic data to characterize the magnitude growth in large earthquakes; the introduction of an alert pause procedure to compromise between speed near the epicenter and improved accuracy at larger distances; and the inclusion of a nonergodic site‐response model in the ground‐motion predictions. ShakeAlert has achieved its primary goal of usable warning times before strong shaking at some locations in real‐time operations in recent M 6 earthquakes. Using offline tests, we demonstrate usable warning times are possible for many sites with peak shaking values of modified Mercalli intensity (MMI) 7–8 in M 7+ earthquakes and also for many MMI 8–9 sites in M 8+ earthquakes. ShakeAlert partners use a variety of MMI and magnitude thresholds in deciding when to alert their users within bounds set by the USGS. Our study shows that there is room to raise the magnitude thresholds up to about M 5.5 without adversely affecting performance in large earthquakes. The ground‐motion criteria are more complex owing to a significant drop‐off in warning times between the MMI 4 and 5 levels of predicted shaking. However, widely used ShakeAlert products, such as the MMI 3 and 4 contour products, can provide sufficiently long warning times before strong shaking in moderate‐to‐great earthquakes to enable a range of protective actions.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240189","usgsCitation":"McGuire, J., Ulberg, C., Lux, A., Bose, M., Andrews, J., Smith, D., Crowell, B., Murray, J.R., Henson, I., Hartog, R., Felizardo, C., Huynh, M., Aranha, M., Parker, G.A., Baltay Sundstrom, A.S., Murray, M.H., Biasi, G., Guiwits, S., Saunders, J., Good, A., Santillan, V., Scrivener, C., Szeliga, W., Melbourne, T., Kress, V., de Groot, R., McBride, S., Given, D.D., Allen, R., Heaton, T.H., Husker, A.L., Thomas, V., Tobin, H., Jha, S., and Bunn, J., 2025, ShakeAlert® version 3: Expected performance in large earthquakes: Bulletin of the Seismological Society of America, v. 115, no. 2, p. 533-561, https://doi.org/10.1785/0120240189.","productDescription":"29 p.","startPage":"533","endPage":"561","ipdsId":"IP-167694","costCenters":[{"id":237,"text":"Earthquake Science 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2025 - Time-varying rates of organic and inorganic mass accumulation in southeast Louisiana marshes: Relationships to sea-level anomalies and tropical storms","interactions":[],"lastModifiedDate":"2025-05-28T14:23:50.819907","indexId":"70267513","displayToPublicDate":"2025-02-06T09:19:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Time-varying rates of organic and inorganic mass accumulation in southeast Louisiana marshes: Relationships to sea-level anomalies and tropical storms","docAbstract":"Louisiana's coastal wetlands are complex systems that require a continuous input of organic and inorganic material to keep pace with relative sea-level rise. Coastal restoration projects such as sediment diversions are being implemented to mitigate land loss and increase availability of inorganic sediment to coastal wetlands, and marshes specifically rely on organic material to build soil volume and maintain surface elevation. Interannual-to-decadal sea-level anomalies such as hurricanes can affect marsh accretion, mineral deposition, and plant productivity. In this light, complex ecogeomorphic feedback controls whether a marsh surface is sustainable or eroded/drowns. This study performs some of the first differential vertical accretion rates (VARs) and organic and inorganic mass accumulation rates (MARs) over time in SE Louisiana marshes determined from the 210Pb Constant Rate of Supply model, coupled with standard 137Cs VARs. These accumulation rates over the past ∼100 years were measured from a total of six brackish and salt marsh locations in Barataria Basin near the proposed Mid-Barataria Sediment Diversion. They were then related to interannual sea-surface elevations at Grand Isle, Louisiana, over the last ∼60 years and recorded hurricane activity in the delta. Results show VARs range from 0.63 cm/y to 1.69 cm/y and total MARs range from 0.11 to 0.43 g/cm2/y. Temporally, VARs and MARs (total, inorganic, and organic) are characterized by gradual increases in rates with decreasing age along with episodic peaks in VARs and MARs. The findings of this study indicate that no relationship occurs between sea-level anomalies and VARs or organic and inorganic MARs; however, a strong relationship appears to occur between major hurricanes to VAR and MAR contributions. Furthermore, high water content (81 ± 8%) and organic-rich soils in the sediment cores highlight the significance of belowground biomass and associated pore volume in maintaining marsh elevation in the study area.
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