Background
Colorado River Cutthroat Trout (CRCT; Oncorhynchus clarkii pleuriticus) conservation populations may be at risk from wildfire and post-fire debris flows hazards.
Aim
To predict burn severity and potential post-fire debris flow hazard classifications to CRCT conservation populations before wildfires occur.
Methods
We used remote sensing, spatial analyses, and machine learning to model 28 wildfire incidents (2016–2020) and spatially predict burn severity from pre-wildfire environmental factors to evaluate the likelihood (%) and volume (m3) hazard classification of post-fire debris flow.
Key results
Burn severity was best predicted by fuels, followed by topography, physical ecosystem conditions, and weather (mean adjusted R2 = 0.54). Predictions of high or moderate burn severity covered 1.1 (15% of study area) and 1.5 (19% of study area) million ha, respectively, and varied by watershed. Combined high or moderate debris flow hazard classification included 80% of stream reaches with conservation populations and 97% of conservation population point nodes.
Conclusions
Predicted burn severity and potential post-fire debris flow indicated moderate to high hazard for CRCT conservation populations native to the Green and Yampa rivers of the Upper Colorado River Basin.
Implications
Future management actions can incorporate predicted burn severity and potential post-fire debris flow to mitigate impacts to CRCT and other at-risk resource values before a wildfire occurs.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WF23199","usgsCitation":"Wells, A.G., Yackulic, C., Kostelnik, J., Bock, A.R., Zuellig, R.E., Carlisle, D.M., Roberts, J., Rogers, K., and Munson, S.M., 2024, Before the fire: Predicting burn severity and potential post-fire debris-flow hazards to Colorado River Cutthroat Trout (Oncorhynchus clarkii pleuriticus) conservation populations: International Journal of Wildland Fire, v. 33, WF23199, 19 p., https://doi.org/10.1071/WF23199.","productDescription":"WF23199, 19 p.","ipdsId":"IP-160628","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":481050,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wf23199","text":"Publisher Index Page"},{"id":480839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.0498046875,\n 44.731125592643274\n ],\n [\n -111.1376953125,\n 42.114523952464246\n ],\n [\n -112.78564453124999,\n 41.902277040963696\n ],\n [\n -112.6318359375,\n 38.03078569382294\n ],\n [\n -111.9287109375,\n 36.756490329505176\n ],\n [\n -110.1708984375,\n 35.8356283888737\n ],\n [\n -106.63330078125,\n 35.55010533588552\n ],\n [\n -106.787109375,\n 35.88905007936091\n ],\n [\n -104.83154296875,\n 38.77121637244273\n ],\n [\n -104.96337890625,\n 40.93011520598305\n ],\n [\n -111.0498046875,\n 44.731125592643274\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationDate":"2024-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Adam Gerhard 0000-0001-9675-4963","orcid":"https://orcid.org/0000-0001-9675-4963","contributorId":270137,"corporation":false,"usgs":true,"family":"Wells","given":"Adam","email":"","middleInitial":"Gerhard","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":924578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":924579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kostelnik, Jaime 0000-0002-1817-5461","orcid":"https://orcid.org/0000-0002-1817-5461","contributorId":300717,"corporation":false,"usgs":true,"family":"Kostelnik","given":"Jaime","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":924580,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924581,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zuellig, Robert E. 0000-0002-4784-2905 rzuellig@usgs.gov","orcid":"https://orcid.org/0000-0002-4784-2905","contributorId":1620,"corporation":false,"usgs":true,"family":"Zuellig","given":"Robert","email":"rzuellig@usgs.gov","middleInitial":"E.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924582,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - 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2024 - Triggering the 2022 eruption of Mauna Loa","interactions":[],"lastModifiedDate":"2024-11-14T15:40:19.077739","indexId":"70260892","displayToPublicDate":"2024-11-12T09:35:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Triggering the 2022 eruption of Mauna Loa","docAbstract":"Distinguishing periods of intermittent unrest from the run-up to eruption is a major challenge at volcanoes around the globe. Comparing multidisciplinary monitoring data with mineral chemistry that records the physical and spatio-temporal evolution of magmas fundamentally advances our ability to forecast eruptions. The recent eruption of Mauna Loa, Earth’s largest active volcano, provides a unique opportunity to differentiate unrest from run-up and improve forecasting of future eruptions. After decades of intermittent seismic and geodetic activity over 38 years of repose, Mauna Loa began erupting on 27 November 2022. Here we present a multidisciplinary synthesis that tracks the spatio-temporal evolution of precursory activity by integrating mineral and melt chemistry, fluid inclusion barometry, numerical modeling of mineral zoning, syn-eruptive gas plume measurements, the distribution and frequency of earthquake hypocenters, seismic velocity changes, and ground deformation. These diverse data indicate that the eruption occurred following a 2-month period of sustained magma intrusion from depths of 3–5 km up to 1–2 km beneath the summit caldera, providing a new model of the plumbing system at this very high threat volcano. Careful correlation of both the geochemistry and instrumental monitoring data improves our ability to distinguish unrest from the run-up to eruption by providing deeper understanding of the both the monitoring data and the magmatic system—an approach that could be applied at other volcanic systems worldwide.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-024-52881-7","usgsCitation":"Lynn, K.J., Downs, D.T., Trusdell, F., Wieser, P.E., Rangel, B., McDade, B.R., Hotovec-Ellis, A.J., Bennington, N.L., Anderson, K.R., Ruth, D.C., DeVitre, C., Ellis, A.P., Nadeau, P.A., Clor, L., Kelly, P.J., Dotray, P., and Chang, J., 2024, Triggering the 2022 eruption of Mauna Loa: Nature Communications, v. 15, 9451, 12 p., https://doi.org/10.1038/s41467-024-52881-7.","productDescription":"9451, 12 p.","ipdsId":"IP-166194","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-024-52881-7","text":"Publisher Index Page"},{"id":464028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa","geographicExtents":"{\n \"type\": 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Here we outline a methodology to quantify multi-gas storage resources in depleted conventional gas reservoirs for usage in assessments by the United States Geological Survey (USGS) at the scale of sedimentary basins. The methodology consists first of quantifying accessible pore volume in a depleted reservoir for natural gas storage using up to three equations. Input data are derived from commonly reported or estimated reservoir parameters and natural gas production volumes, and equations may be combined in linear models to improve pore volume estimates. Storage estimates from these equations are tested and validated for 31 reservoirs in the Michigan Basin Province, USA that were previously converted to underground gas storage facilities and have known (federally reported) natural gas storage capacities. Secondly, natural gas storage capacities can be transformed via fluid substitution calculations to estimate the storage resources for non-native fluids, applied here for, CO2, H2, and methane-H2 blends, accounting for molecule-specific deviations from ideal gas behavior at reservoir pressures and temperatures as well as differing storage efficiencies. Importantly, the storage of non-native fluids may not be appropriate in all depleted gas reservoir settings due to potential risks like leakage, in particular in the case of H2 storage, requiring additional knowledge of caprock sealing capacity. Given this caveat, we demonstrate the fluid substitution method for natural gas reservoirs of the Northern Niagaran Reef and Southern Niagaran Reef USGS plays in the Michigan Basin Province, as these trends of Silurian pinnacle reefs are capped with tight-sealing evaporite facies. The deterministic equations outlined from this methodology can be incorporated into future probabilistic USGS gas storage assessments for CO2, H2, and natural gas resources in the United States.
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D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918323,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263800,"text":"70263800 - 2024 - Detection probabilities of Flathead Catfish in small Kansas impoundments","interactions":[],"lastModifiedDate":"2025-03-26T15:57:43.955275","indexId":"70263800","displayToPublicDate":"2024-11-11T09:47:29","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Detection probabilities of Flathead Catfish in small Kansas impoundments","docAbstract":"A primary challenge of Flathead Catfish Pylodictis olivaris management is uncertainty associated with sampling strategies and resulting ambiguity in population-level information. Assessment of impoundment and environmental conditions that affect detection probability may aid in reducing sample variance and benefit inferences regarding changes to Flathead Catfish populations. We sampled eight small impoundments in Kansas (37–114 surface ha) using low-frequency electrofishing in summer, 2021. We revisited sites nine times over three months using an occupancy modeling framework to estimate the influence of impoundment and environmental conditions on detection probability of Flathead Catfish. We employed an information theoretic approach and ranked models built with impoundment as a random effect and three environmental variables predicted to influence detection of Flathead Catfish in small impoundments. Detection probability across all populations was 0.526 (SE = 0.020) and was influenced by water temperature, mean depth of the impoundment, and proportion of impoundment sampled. Generally, detection probability increased with all measured variables. The inclusion of detection probability in assessments of Flathead Catfish in small impoundments can inform interpretation of catch-related metrics. Further, variable detection suggests collection of multiple samples during a defined sampling period might be more suitable for characterizing populations than a single sample.
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Kowalewski, Kramer, N., Lundgren, S., and Spurgeon, J.J., 2024, Detection probabilities of Flathead Catfish in small Kansas impoundments: Journal of Fish and Wildlife Management, v. 15, no. 2, p. 530-536, https://doi.org/10.3996/jfwm-23-057.","productDescription":"7 p.","startPage":"530","endPage":"536","ipdsId":"IP-156562","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":490091,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-057","text":"Publisher Index Page"},{"id":482449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Parks","active":true,"usgs":false}],"preferred":false,"id":928334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waters, Micah J.","contributorId":351289,"corporation":false,"usgs":false,"family":"Waters","given":"Micah J.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Salazar, Vanessa","contributorId":351290,"corporation":false,"usgs":false,"family":"Salazar","given":"Vanessa","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928336,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lucas K. Kowalewski","contributorId":351291,"corporation":false,"usgs":false,"family":"Lucas K. Kowalewski","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928337,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kramer, Nicholas W.","contributorId":351292,"corporation":false,"usgs":false,"family":"Kramer","given":"Nicholas W.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928338,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lundgren, Seth A.","contributorId":351293,"corporation":false,"usgs":false,"family":"Lundgren","given":"Seth A.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":928339,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spurgeon, Jonathan J. 0000-0002-6888-5867","orcid":"https://orcid.org/0000-0002-6888-5867","contributorId":304259,"corporation":false,"usgs":true,"family":"Spurgeon","given":"Jonathan","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":928340,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260886,"text":"70260886 - 2024 - Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance","interactions":[],"lastModifiedDate":"2025-03-11T14:49:17.072232","indexId":"70260886","displayToPublicDate":"2024-11-11T08:52:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance","docAbstract":"The Uniform California Earthquake Rupture Forecast version 3-Time Dependent depicts California’s seismic faults and their activity. Its logic tree has 5760 leaves. Considering 30 more model combinations related to ground motion produces 172,800 distinct models representing so-called epistemic uncertainties. To calculate risk to a portfolio of buildings, one also considers millions of earthquakes and spatially correlated ground-motion variability. We offer a tree-trimming technique that retains the probability distribution of portfolio loss and identifies the leading sources of uncertainty for further study. We applied it to a California statewide building portfolio and various levels of nonexceedance probability between one in 100 and one in 2500. We trimmed the logic tree from 172,800 leaves to as few as 15. The result: a supercomputer that would otherwise run 24 h to estimate the distribution of one-in-250-year loss can calculate it in moments with the reduced-order model. Others can use the reduced-order model to calculate risk to different California portfolios, and scientists can prioritize study to reduce the remaining epistemic uncertainty.
","language":"English","publisher":"Sage","doi":"10.1177/87552930241280401","usgsCitation":"Porter, K., Milner, K.R., and Field, E.H., 2024, Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance: Earthquake Spectra, v. 41, no. 1, p. 636-653, https://doi.org/10.1177/87552930241280401.","productDescription":"19 p.","startPage":"636","endPage":"653","ipdsId":"IP-161498","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":464026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Porter, Keith","contributorId":191074,"corporation":false,"usgs":false,"family":"Porter","given":"Keith","affiliations":[],"preferred":false,"id":918429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":918430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":52242,"corporation":false,"usgs":true,"family":"Field","given":"Edward","email":"field@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":918431,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263253,"text":"70263253 - 2024 - A decade of shaking in the Garden City: The dynamics of preparedness, perceptions, and beliefs in Canterbury, New Zealand, and implications for earthquake information","interactions":[],"lastModifiedDate":"2025-02-03T16:41:31.554672","indexId":"70263253","displayToPublicDate":"2024-11-10T09:24:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20065,"text":"Frontiers Communication - Disaster Communications","active":true,"publicationSubtype":{"id":10}},"title":"A decade of shaking in the Garden City: The dynamics of preparedness, perceptions, and beliefs in Canterbury, New Zealand, and implications for earthquake information","docAbstract":"This study explored earthquake preparedness over time - before, during, and 10 years after the Canterbury Earthquake Sequence (CES) in Aotearoa New Zealand (NZ). Surveys of Canterbury residents were conducted in 2009, 2013 and again in 2021, using variables derived from Community Engagement Theory (CET). The surveys measured earthquake perceptions and beliefs, participation and engagement, and preparedness actions. Results were compared across the three samples. Findings indicate that perceptions and beliefs (e.g. risk perception, outcome expectancy beliefs), and types of preparedness actions taken (e.g. collection of survival items, structural preparedness, community and agency relationships), differed over time, depending on people’s experiences before, during, and after the CES. For example, during and after the CES people were more likely to believe that preparing provided a benefit to daily life, but less likely to think it could reduce property damage, perhaps due to people’s experiences of disruption and damage during the earthquakes. An understanding of such dynamics can assist with the provision and timing of risk and preparedness information. This study highlights the importance of providing applicable and actionable preparedness information, that is relevant to people’s experiences, throughout an earthquake sequence. Such information might evolve and change in focus over time depending on risks and needs. Focus could also be given to information that builds peoples beliefs and capacities to undertake preparedness in evolving situations. Understanding preparedness in the context of different experiences and timeframes is useful in helping update models such as the CET, where the dynamics of time might be better incorporated.","language":"English","publisher":"Frontiers in Communication","doi":"10.17605/OSF.IO/4T9U6","usgsCitation":"Becker, J., Hudson-Doyle, E.E., Vinnell, L., McBride, S., Paton, D., and Johnston, D.A., 2024, A decade of shaking in the Garden City: The dynamics of preparedness, perceptions, and beliefs in Canterbury, New Zealand, and implications for earthquake information: Frontiers Communication - Disaster Communications, v. 9, 1410333, 17 p., https://doi.org/10.17605/OSF.IO/4T9U6.","productDescription":"1410333, 17 p.","ipdsId":"IP-166034","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":481617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Canterbury","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 173.90311410586924,\n -41.90491335110837\n ],\n [\n 173.0671112994046,\n -42.34762205459946\n ],\n [\n 172.65824372717864,\n -42.12977595965312\n ],\n [\n 171.5759622163327,\n -42.76805561899313\n ],\n [\n 169.58271546684648,\n -43.891078936023206\n ],\n [\n 169.8484364471409,\n -44.994495013131015\n ],\n [\n 171.2960770344631,\n -44.972998012300614\n ],\n [\n 171.40906022229615,\n -44.44185763613985\n ],\n [\n 171.86277407338306,\n -44.13592354193222\n ],\n [\n 173.19397839209154,\n -43.90197178118605\n ],\n [\n 172.9675920785376,\n -43.298534792782036\n ],\n [\n 173.37940278385906,\n -42.951952400210985\n ],\n [\n 174.11640295427313,\n -42.004263620426265\n ],\n [\n 173.90311410586924,\n -41.90491335110837\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Becker, Julia S.","contributorId":217541,"corporation":false,"usgs":false,"family":"Becker","given":"Julia S.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":926027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudson-Doyle, Emma E. 0000-0002-2878-0972","orcid":"https://orcid.org/0000-0002-2878-0972","contributorId":240959,"corporation":false,"usgs":false,"family":"Hudson-Doyle","given":"Emma","email":"","middleInitial":"E.","affiliations":[{"id":13571,"text":"Massey University","active":true,"usgs":false}],"preferred":false,"id":926028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vinnell, Lauren","contributorId":292282,"corporation":false,"usgs":false,"family":"Vinnell","given":"Lauren","email":"","affiliations":[{"id":13571,"text":"Massey University","active":true,"usgs":false}],"preferred":false,"id":926029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paton, Douglas","contributorId":350440,"corporation":false,"usgs":false,"family":"Paton","given":"Douglas","affiliations":[{"id":12877,"text":"Charles Darwin University","active":true,"usgs":false}],"preferred":false,"id":926031,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnston, David A.","contributorId":64637,"corporation":false,"usgs":false,"family":"Johnston","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":6956,"text":"GNS Science/Massey University","active":true,"usgs":false}],"preferred":false,"id":926032,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70260228,"text":"sir20245088 - 2024 - Inset groundwater-flow models for the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas","interactions":[],"lastModifiedDate":"2025-12-22T21:29:22.991335","indexId":"sir20245088","displayToPublicDate":"2024-11-08T12:11:54","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5088","displayTitle":"Inset Groundwater-Flow Models for the Cache and Grand Prairie Critical Groundwater Areas, Northeastern Arkansas","title":"Inset groundwater-flow models for the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas","docAbstract":"The water resources in the Mississippi alluvial plain, located in parts of Missouri, Kentucky, Tennessee, Mississippi, Louisiana, and Arkansas, supports a multibillion-dollar agricultural industry that relies heavily on pumping of groundwater for irrigation of crops and aquaculture. The primary source of groundwater for agricultural-related pumping is the Mississippi River Valley alluvial aquifer, which has declined in storage for decades; secondary groundwater sources include the middle Claiborne aquifer and Wilcox aquifer system. Two areas in northeastern Arkansas that lie within the Mississippi alluvial plain, part of the Cache and Grand Prairie regions, have been designated as Critical Groundwater Areas owing to decades of groundwater declines that resulted from past and current water use. The multidisciplinary Mississippi Alluvial Plain project, led by the U.S. Geological Survey, and funded by their Water Availability and Use Science Program, included objectives to develop numerical groundwater models in focus regions, including the part of the Cache and Grand Prairie regions of northeastern Arkansas. Two inset models were developed using the child model capabilities of MODFLOW 6, the U.S. Geological Survey’s Modular Hydrologic Model simulation software. Both models, called the Cache model and Grand Prairie model, simulated the groundwater system and surface-water/groundwater interactions for the Mississippi River Valley alluvial aquifer and underlying Tertiary-age aquifers and confining units to the Midway confining unit. Each model was spatially discretized into 500-meter x 500-meter orthogonal cells on a grid with 5-meter constant-thickness vertical layers that represented the Mississippi River Valley alluvial aquifer and increasing thickness layers for the aquifers and confining units below the alluvial aquifer. The Cache and Grand Prairie models were calibrated with the PEST++ iterative ensemble smoother Version 5 and employed high dimensional parameterization schemes of 13,740 and 30,436 parameters, respectively. The Cache mean absolute residual for groundwater-level observations within each model domain for the priority well was 1.58 meters. Grand Prairie mean absolute residuals for the alluvial aquifer and middle Claiborne aquifer groundwater-level observations were 2.71 and 10.78 meters, respectively. The groundwater budgets for the Cache and Grand Prairie models were characterized by substantial outflows to irrigation wells, which constituted about 52 and 54 percent of all outflows, with the primary source of water to those wells being releases from unconfined aquifer storage.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245088","programNote":"Water Availability and Use Science Program","usgsCitation":"Traylor, J.P., Duncan, L.L., Leaf, A.T., Weisser, A.R., Dietsch, B.J., and Guira, M., 2024, Inset groundwater-flow models for the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas: U.S. Geological Survey Scientific Investigations Report 2024–5088, 152 p., https://doi.org/10.3133/sir20245088.","productDescription":"Report: xi, 152 p.; 13 Figures: 8.50 x 11.00 inches; Data Release; Dataset","numberOfPages":"168","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155030","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497914,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117774.htm","linkFileType":{"id":5,"text":"html"}},{"id":463425,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HZWI8S","text":"USGS data release","linkHelpText":"Simulations of the groundwater-flow system in the Cache and Grand Prairie Critical Groundwater Areas, northeastern Arkansas"},{"id":463426,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245088/full"},{"id":463424,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":463419,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5088/coverthb.jpg"},{"id":463422,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5088/images/"},{"id":463420,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5088/sir20245088.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5088"},{"id":463421,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5088/sir20245088.XML"},{"id":463423,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5088/downloads/","text":"Layered figures","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arkansas","otherGeospatial":"Cache and Grand Prairie Critical Groundwater Areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.4216070279204,\n 35.763468234275166\n ],\n [\n -92.62077527136867,\n 35.763468234275166\n ],\n [\n -92.62077527136867,\n 33.579387250010626\n ],\n [\n -90.4216070279204,\n 33.579387250010626\n ],\n [\n -90.4216070279204,\n 35.763468234275166\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Peak-flow (flood) frequency analysis is essential to water-resources management applications, including the design of critical infrastructure such as bridges and culverts, and floodplain mapping. Federal guidelines for performing peak-flow flood frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C, which documents the guidelines for determining annual peak streamflow frequency, is that, for basins without major hydrologic alterations (for example, regulation, diversion, and urbanization), statistical properties of the distribution of annual peak streamflows are stationary; that is, the mean, variance, and skew are constant through time. Nonstationarity is a statistical property of a peak-flow series such that the long-term (on the order of decades) distributional properties change one or more times either gradually or abruptly through time. Individual nonstationarities may be attributed to one source such as flow regulation, land-use change, or climate but are often the result of a combination of sources, making detection and attribution of nonstationarities challenging.
In response to a growing concern regarding nonstationarity in peak streamflows in the region, the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, assessed the potential nonstationarity in peak streamflows in the north-central United States. This chapter characterizes the effects of natural hydroclimatic shifts and potential climate change on annual peak streamflows in the State of South Dakota. Annual peak and daily streamflow as well as model-simulated gridded climatic data were examined for temporal monotonic trends, change points, and other statistical properties indicative of changing climatic and environmental conditions.
Changes in annual peak and daily flows were evaluated among 13, 35, and 81 qualifying U.S. Geological Survey streamgages for the 75-, 50-, and 30-year trend periods through water year 2020 (the period from October 1, 2019, to September 30, 2020) in South Dakota, respectively. No qualifying streamgages were in the 100-year trend period in the State. Statistical tests for autocorrelation (independent and identically distributed assumption), monotonic trends, and change points in the median and scale are analyzed to evaluate potential stationarity violations (nonstationarity) for performing at-site peak-flow flood-frequency analysis. The trends are reported using a likelihood approach as an alternative to simply reporting significant trends with an arbitrary p-value cutoff point.
A distinct east-west spatial pattern of likely upward and downward monotonic trends and change points, respectively, was detected in 75- and 50-year trend periods, but an inconsistent spatial pattern was detected in the 30-year trend period. Additionally, change points in the median annual peak streamflows were detected in the late 1970s and early 1980s in the western part of the State, but in the east, the change point was more commonly detected in 1992–93. A similar east-west spatial pattern of likely upward and downward trends was detected in the annual peak-flow timing, the day of the year of the annal peak streamflow. In the western part of the State, the annual peak streamflows are arriving earlier, but in the east, the annual peak streamflows are arriving later. A peaks-over-threshold (POT) analysis where, on average, there are two events per year (POT2) and four events per year (POT4) was also used to evaluate changes in the frequency (count) of daily streamflows exceeding the threshold. Similar to detected changes in the annual peak streamflow, an east-west likely upward or downward change corresponding to an increase or decrease, respectively, in the frequency of daily streamflow greater than a POT2 and POT4 threshold was detected.
A monthly water-balance model was used to evaluate hydroclimatic variation in annual and seasonal precipitation, snowfall, potential evapotranspiration, and soil moisture storage for all qualifying streamgages in the 75-, 50-, and 30-year trend periods. Detected trends in the annual hydroclimatic metrics for the 75- and 50-year trend periods indicate a spatially consistent statewide increase in precipitation, decrease in snowfall, increase in potential evapotranspiration, and increase in soil moisture storage. Furthermore, detected trends in seasonal precipitation in the 75- and 50-year trend periods highlight a pronounced change in precipitation in winter and later into the summer season, especially in the 50-year trend period in the eastern part of the State. Statewide increases in seasonal soil moisture storage were also detected, highlighting year-round increasing flood magnitudes, particularly in the eastern part of the State.
Based on the results of these stationarity tests for the qualifying streamgages in South Dakota among the 75-, 50-, and 30-year trend periods, consistent temporal and spatial patterns of nonstationarity were detected among the 75- and 50-year trend periods. Furthermore, when nonstationarity is detected in daily streamflow, increased streamflow and volume (increasing frequency in POT), as well as potentially bridge scour, may have implications on culvert and highway design in the eastern part of South Dakota. Thus, when performing at-site peak-flow flood-frequency analyses in South Dakota, potential nonstationarities and alternative approaches are important considerations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064I","collaboration":"Prepared in cooperation with the South Dakota Department of Transportation","usgsCitation":"Barth, N.A., and Sando, S.K., 2024, Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020, chap. I of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 70 p., https://doi.org/10.3133/sir20235064I.","productDescription":"Report: x, 70 p.; Data Release; Dataset","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146340","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":497916,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117775.htm","linkFileType":{"id":5,"text":"html"}},{"id":463794,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064I/full"},{"id":463793,"rank":6,"type":{"id":30,"text":"Data 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U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Fluid inclusion microthermometry and Raman spectroscopy of fluid inclusions in quartz veins from the Pennsylvanian rocks of the Anthracite belt, eastern Pennsylvania support a deep burial model of coalification in favor of focused orogenic hot fluid flow. High-temperature (250 to 255 °C) trapping of CH4 ± CO2 saturated aqueous fluids and CH4 ± CO2 inclusions indicate fluid trapping at depths of 11.5 to 13.4 km under a cover of Pennsylvanian to Permian(?) syntectonic load. In the folded rocks to the south of the Anthracite belt, CH4 ± CO2 fluid inclusions indicate a sediment load that was up to 16.3 km thick. Re-equilibrated aqueous fluid inclusions from veins in Silurian through Devonian rocks give the same range of trapping conditions but a wide range of fluid salinities suggesting that folding, fracturing, and meteoric recharge resulted in the intermixing of fluids from throughout the stratigraphic succession.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2024.104646","usgsCitation":"Evans, M.A., and Jubb, A., 2024, Deep syntectonic burial of the Anthracite belt, Eastern Pennsylvania: International Journal of Coal Geology, v. 295, 104646, 27 p., https://doi.org/10.1016/j.coal.2024.104646.","productDescription":"104646, 27 p.","ipdsId":"IP-164265","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75,\n 41.75\n ],\n [\n -80,\n 41.75\n ],\n [\n -80,\n 39.5\n ],\n [\n -75,\n 39.5\n ],\n [\n -75,\n 41.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Mark A.","contributorId":197411,"corporation":false,"usgs":false,"family":"Evans","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":918370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918369,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270845,"text":"70270845 - 2024 - Biological feasibility of introducing bighorn sheep to the Jicarilla Apache Nation","interactions":[],"lastModifiedDate":"2025-08-28T15:11:50.278531","indexId":"70270845","displayToPublicDate":"2024-11-08T09:59:02","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-159-2024","title":"Biological feasibility of introducing bighorn sheep to the Jicarilla Apache Nation","docAbstract":"The biological feasibility of introducing Rocky Mountain bighorn sheep (Ovis canadensis canadensis) to the Dulce area of the Jicarilla Apache Nation (JAN) depends on availability and condition of potential habitat and the potential for disease risk, as pneumonia is the largest current threat to wild sheep populations. We modeled quality and quantity of potential bighorn sheep habitat incorporating the three most recent fire scars around Dulce, determined potential winter range within preferred habitat, assessed on the ground vegetation characteristics, and examined potential for disease transmission via risk of contact with domestic sheep and goats. Most of the area of interest for this study has a suitability value ≥ 50%, with approximately 23-29% of the study area considered preferred, or high-quality habitat for bighorn sheep. High-quality habitat for Rocky Mountain bighorn sheep is defined as being within 300 m of escape terrain, within 1.6 km of water, and containing ≤ 30% shrub and tree cover. Of this, approximately 43-56% of potential preferred habitat qualifies as winter range, which is mostly concentrated in the narrow valley bottoms where roads are commonly located and the south-facing slopes surrounding valleys. Analysis of field-collected vegetation data indicate most of the existing forage within the surveyed area to be of moderate or high forage value to bighorn sheep, but horizontal visibility, predominantly in the form of shrubs, is more obscured than what bighorn sheep prefer for most of the area of interest. Maximum shrub and tree cover is also the most limiting factor in the suitability model, primarily due to the prevalence of dense shrub regeneration, particularly Gambel oak, which occurs after high severity burns. The largest quantities of high-quality potential bighorn sheep habitat within the study area occur in unburned areas or those burnt by the predominantly low-moderate severity Amargo fire in 2021. Relative to risk of contact with domestic sheep and goats that can transmit lethal pneumonia-causing pathogens, potentially causing an introduction effort to fail, there is high risk because of the proximity to two hobby-subsistence herds (< 3 km away).
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/10.3996/css73616801","usgsCitation":"Thompson, C.J., and Cain, J.W., 2024, Biological feasibility of introducing bighorn sheep to the Jicarilla Apache Nation: Cooperator Science Series CSS-159-2024, 80 p., https://doi.org/10.3996/10.3996/css73616801.","productDescription":"80 p.","ipdsId":"IP-167003","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":494875,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/biological-feasibility-introducing-bighorn-sheep-jicarilla-apache-nation","linkFileType":{"id":5,"text":"html"}},{"id":495006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jicarilla Apache Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.01771249033892,\n 36.99249959403626\n ],\n [\n -107.5487950989812,\n 36.99249959403626\n ],\n [\n -107.5487950989812,\n 36.05621273485714\n ],\n [\n -106.01771249033892,\n 36.05621273485714\n ],\n [\n -106.01771249033892,\n 36.99249959403626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Cara J.","contributorId":360559,"corporation":false,"usgs":false,"family":"Thompson","given":"Cara","middleInitial":"J.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":947210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":947211,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70266388,"text":"70266388 - 2024 - Effects of release strategy, source population, and age on reintroduced scaled quail reproduction","interactions":[],"lastModifiedDate":"2025-05-06T15:11:55.444491","indexId":"70266388","displayToPublicDate":"2024-11-08T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of release strategy, source population, and age on reintroduced scaled quail reproduction","docAbstract":"Translocation is one strategy to reestablish populations of scaled quail (Callipepla squamata). Initial reproductive success post-translocation is important for establishing short-lived species such as quail, but factors influencing reproductive success are poorly understood. We evaluated the effect of source population and variation in delayed release strategy (1−9 weeks) on nest initiation and nest survival of wild-caught, translocated scaled quail. We trapped and translocated scaled quail in 2016–2017 from source populations in the Edwards Plateau and Rolling Plains ecoregions of Texas, USA, to a large contiguous (>40,000 ha) release site in Knox County, Texas. We used a multi-state mark-recapture model with state uncertainty to test for effects of release treatment, source population, age, release location, and year on nest initiation and survival. Increased length of holding time decreased re-nesting effort. Yearlings were more likely to initiate nests than adults and the probability of re-nesting was lower during the year with drought conditions. There was no effect of source population on any of the parameters we evaluated. Future scaled quail reintroduction efforts may benefit from prioritizing translocation of yearlings and conducting translocations when drought conditions are not forecasted.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22660","usgsCitation":"Ruzicka, R., Rollins, D., Kendall, W.L., and Doherty, P.F., 2024, Effects of release strategy, source population, and age on reintroduced scaled quail reproduction: Journal of Wildlife Management, v. 88, no. 8, e22660, 15 p., https://doi.org/10.1002/jwmg.22660.","productDescription":"e22660, 15 p.","ipdsId":"IP-163958","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490103,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22660","text":"Publisher Index Page"},{"id":485451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Knox County","otherGeospatial":"Edwards Plateau, Rolling Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.06618223574085,\n 33.8841524446256\n ],\n [\n -100.06618223574085,\n 33.291492418907126\n ],\n [\n -99.37113169882426,\n 33.291492418907126\n ],\n [\n -99.37113169882426,\n 33.8841524446256\n ],\n [\n -100.06618223574085,\n 33.8841524446256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruzicka, Rebekah E.","contributorId":354109,"corporation":false,"usgs":false,"family":"Ruzicka","given":"Rebekah E.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935800,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rollins, Dale","contributorId":140708,"corporation":false,"usgs":false,"family":"Rollins","given":"Dale","email":"","affiliations":[],"preferred":false,"id":935801,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doherty, Paul F. 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From 2018 through 2020 the U.S. Geological Survey and U.S. Army Corps of Engineers led a series of calls and meetings, and a workshop to develop a draft UMRS hydrogeomorphic change conceptual model and hierarchical classification scheme. This project was funded through an Upper Mississippi River Restoration 2018 science in support of restoration proposal entitled, “Conceptual Model and Hierarchical Classification of Hydrogeomorphic Settings in the Upper Mississippi River System.” This report documents the background leading up to and the major findings from the workshop. The resulting conceptual model focuses on the drivers and boundary conditions that affect the major hydrogeomorphic processes along the valley corridor using a continuum of spatial and temporal scales and resolutions. The draft hierarchical classification was based on three existing and three new nested geospatial datasets that ultimately can be used to characterize hydrogeomorphic settings that span the UMRS valley corridor. The conceptual model and hierarchical classification will help characterize recent (mid-1990s through mid-2010s) decadal-scale processes and sources for potential hydrogeomorphic change that span a range of spatial scales from watershed hydrology and sediment sources to channel hydraulics and sediment transport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241051","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Fitzpatrick, F.A., Rogala, J.T., Hendrickson, J.S., Sawyer, L., Stone, J., Erwin, S., Brauer, E.J., and Vaughan, A.A., 2024, Upper Mississippi River System hydrogeomorphic change conceptual model and hierarchical classification: U.S. Geological Survey Open-File Report 2024–1051, 24 p., https://doi.org/10.3133/ofr20241051.","productDescription":"vi, 24 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154820","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":463608,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1051/ofr20241051.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1051"},{"id":463607,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1051/coverthb.jpg"},{"id":463611,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241051/full"},{"id":463609,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1051/ofr20241051.XML"},{"id":463610,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1051/images/"},{"id":497918,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117772.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, South Dakota, Wisconsin","otherGeospatial":"Upper Mississippi River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.50342683246095,\n 40.74721296729754\n ],\n [\n -86.22485822523954,\n 41.12212957640983\n ],\n [\n -86.47633619015835,\n 41.49085772032035\n ],\n [\n -87.70997806528162,\n 41.54161184649007\n ],\n [\n -87.96780499446993,\n 42.13710516972478\n ],\n [\n -88.45256695171824,\n 43.2601947483673\n ],\n [\n -88.90796053029793,\n 43.953704040844826\n ],\n [\n -89.31966888436784,\n 43.88619768580165\n ],\n [\n -88.82098034404291,\n 45.84620970391856\n ],\n [\n -89.77751644898966,\n 46.14284357021026\n ],\n [\n -92.3836871160787,\n 46.36717699384113\n ],\n [\n -93.57654031001726,\n 47.5843117955113\n ],\n [\n -96.69363739805445,\n 47.520997934475815\n ],\n [\n -96.5298693029927,\n 46.26717241479707\n ],\n [\n -97.88294379082475,\n 46.288663271101996\n ],\n [\n -96.96809715353103,\n 45.32804185048016\n ],\n [\n -95.79932895452089,\n 43.51835880034011\n ],\n [\n -93.73471399415848,\n 39.80187490699902\n ],\n [\n -90.99752932951242,\n 36.93622008574626\n ],\n [\n -89.6631770431625,\n 36.64968232220457\n ],\n [\n -89.28540446277691,\n 37.166100746132656\n ],\n [\n -87.50342683246095,\n 40.74721296729754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Rapid-response petrological monitoring is a major advance for volcano observatories, allowing them to build and validate models of plumbing systems that supply eruptions in near-real-time. The depth of magma storage has recently been identified as high-priority information for volcanic observatories, yet this information is not currently obtainable via petrological monitoring methods on timescales relevant to eruption response. Fluid inclusion barometry (using micro-thermometry or Raman spectroscopy) is a well-established petrological method to estimate magma storage depths and has been proposed to have potential as a rapid-response monitoring tool, although this has not been formally demonstrated. To address this deficiency, we performed a near-real-time rapid-response simulation for the September 2023 eruption of Kīlauea, Hawaiʻi. We show that Raman-based fluid inclusion barometry can robustly determine reservoir depths within a day of receiving samples — a transformative timescale that has not previously been achieved by petrological methods. Fluid inclusion barometry using micro-thermometric techniques has typically been limited to systems with relatively deep magma storage (>0.4 g/cm3 or >7 km) where measurements of CO2 density are easy and accurate because the CO2 fluid homogenizes into the liquid phase. Improvements of the accuracy of Raman spectroscopy measurements of fluids with low CO2 density over the past couple of decades has enabled measurements of fluid inclusions from shallower magmatic systems. However, one caveat of examining shallower systems is that the fraction of H2O in the fluid may be too high to reliably convert CO2 density to pressure. To test the global applicability of rapid response fluid inclusion barometry, we compiled a global melt inclusion dataset (>4000 samples) and calculate the fluid composition at the point of vapor saturation (XH2O). We show that fluid inclusions in crystal-hosts from mafic compositions (<57 wt. % SiO2) — likely representative of magmas recharging many volcanic systems worldwide — trap fluids with XH2O low enough to make fluid inclusion barometry useful at many of the world’s most active and hazardous mafic volcanic systems (e.g., Iceland, Hawaiʻi, Galápagos Islands, East African Rift, Réunion, Canary Islands, Azores, Cabo Verde).
","language":"English","publisher":"Oxford Academic","doi":"10.1093/petrology/egae119","usgsCitation":"DeVitre, C., Wieser, P.E., Bearden, A.T., Richie, A., Rangel, B., Gleeson, M., Grimsich, J., Lynn, K.J., Downs, D.T., Deligne, N.I., and Mulliken, K.M., 2024, Depths in a day - A new era of rapid-response Raman-based barometry using fluid inclusions: Journal of Petrology, v. 65, no. 11, egae119, 15 p., https://doi.org/10.1093/petrology/egae119.","productDescription":"egae119, 15 p.","ipdsId":"IP-158109","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466776,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egae119","text":"Publisher Index Page"},{"id":464235,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.2575579892722,\n 19.41802727097236\n ],\n [\n -155.2575579892722,\n 19.40813592330987\n ],\n [\n -155.2414055520364,\n 19.40813592330987\n ],\n [\n -155.2414055520364,\n 19.41802727097236\n ],\n [\n -155.2575579892722,\n 19.41802727097236\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"65","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-11-07","publicationStatus":"PW","contributors":{"authors":[{"text":"DeVitre, Charlotte","contributorId":346229,"corporation":false,"usgs":false,"family":"DeVitre","given":"Charlotte","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":918713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wieser, Penny E. 0000-0002-1070-8323","orcid":"https://orcid.org/0000-0002-1070-8323","contributorId":272601,"corporation":false,"usgs":false,"family":"Wieser","given":"Penny","email":"","middleInitial":"E.","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":918714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bearden, Alexander T.","contributorId":346329,"corporation":false,"usgs":false,"family":"Bearden","given":"Alexander","email":"","middleInitial":"T.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richie, Araela","contributorId":346330,"corporation":false,"usgs":false,"family":"Richie","given":"Araela","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rangel, Berenise","contributorId":346222,"corporation":false,"usgs":false,"family":"Rangel","given":"Berenise","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":918717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gleeson, Matthew","contributorId":346331,"corporation":false,"usgs":false,"family":"Gleeson","given":"Matthew","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918718,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grimsich, John","contributorId":346332,"corporation":false,"usgs":false,"family":"Grimsich","given":"John","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918719,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918720,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":918721,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"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 Center","active":true,"usgs":true}],"preferred":true,"id":918722,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mulliken, Katherine M. 0000-0003-4190-5060","orcid":"https://orcid.org/0000-0003-4190-5060","contributorId":217810,"corporation":false,"usgs":false,"family":"Mulliken","given":"Katherine","email":"","middleInitial":"M.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":918723,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70260413,"text":"sir20245035 - 2024 - Potential corrosivity of untreated groundwater in Louisiana","interactions":[],"lastModifiedDate":"2025-12-22T21:37:33.869696","indexId":"sir20245035","displayToPublicDate":"2024-11-06T10:30:11","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5035","displayTitle":"Potential Corrosivity of Untreated Groundwater in Louisiana","title":"Potential corrosivity of untreated groundwater in Louisiana","docAbstract":"Corrosive groundwater can cause lead, copper, and other metals to leach from pipes and plumbing fixtures in water distribution systems. Metals, if ingested, could lead to serious health implications to the nearly 2.9 million people in Louisiana who obtain their drinking water from groundwater sources. Four indices—the Langelier Saturation Index (LSI), Ryznar Stability Index (RSI), Puckorius Scaling Index (PSI), and the Potential to Promote Galvanic Corrosion (PPGC)—in addition to an analysis which normalized the results from the existing indices, the Combined Index (CI), were used to assess the corrosivity of groundwater in Louisiana and identify areas within eight major aquifers and aquifer systems with moderate to high corrosivity potential. The purpose of this study is to provide State and local governments, public water system managers, and the nearly 500,000 private well owners in Louisiana with information needed to manage drinking-water supplies and mitigate potential health risks related to leaching of metals from water pipes and fixtures.
The average scores of untreated groundwater samples from approximately 375 wells by index are as follows: LSI, −1.28; RSI, 9.78; PSI, 9.34; and CI, 4.14. The PPGC does not produce a numerical score, but the total percentage of class counts can be used to assign a classification; overall, samples in Louisiana were classified as significant concern. The percentages of groundwater samples from wells classified as potentially corrosive, by index, are as follows: LSI, 53 percent; RSI, 94 percent; PSI, 81 percent; PPGC, 98 percent; and CI, 81 percent. The percentages of samples classified as indeterminate, by index, are as follows: LSI, 46 percent; RSI, 5 percent; PSI, 12 percent; PPGC, 0 percent; and CI, 18 percent.
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"nation\":\"USA \"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
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","tableOfContents":"Rocks dredged from water depths of 1,605, 2,500, 3,300, and 3,400 m in the Arctic Ocean included Paleozoic continental rocks pervasively mineralized during the Neogene by hydrothermal Fe and Mn oxides. Samples were recovered in three dredge hauls from the Chukchi Borderland and one from Mendeleev Ridge north of Alaska and eastern Siberia, respectively. Many of the rocks were so pervasively altered that the protolith could not be identified, while others had volcanic, plutonic, and metamorphic protoliths. The mineralized rocks were cemented and partly to wholly replaced by the hydrothermal oxides. The Amerasia Basin, where the Chukchi Borderland and Mendeleev Ridge occur, supports a series of faults and fractures that serve as major zones of crustal weakness. We propose that the stratabound hydrothermal deposits formed through the flux of hydrothermal fluids along Paleozoic and Mesozoic faults related to block faulting along a rifted margin during minor episodes of Neogene tectonism and were later exposed at the seafloor through slumping or other gravity processes. Tectonically driven hydrothermal circulation most likely facilitated the pervasive mineralization along fault surfaces via frictional heating, hydrofracturing brecciation, and low- to moderate temperature Fe- and Mn-rich hydrothermal fluids, which mineralized the crushed, altered, and brecciated rocks.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC010996","usgsCitation":"Hein, J.R., Mizell, K., and Gartman, A., 2024, Neogene hydrothermal Fe- and Mn-oxide mineralization of Paleozoic continental rocks, Amerasia Basin, Arctic Ocean: Geochemistry, Geophysics, Geosystems, v. 25, no. 11, e2023GC010996, 27 p., https://doi.org/10.1029/2023GC010996.","productDescription":"e2023GC010996, 27 p.","ipdsId":"IP-167891","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466778,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc010996","text":"Publisher Index Page"},{"id":463785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Amerasia basin, Arctic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -150,\n 72\n ],\n [\n -179.9,\n 74.5\n ],\n [\n -179.9,\n 85\n ],\n [\n -137.09374278427933,\n 80.73796302105899\n ],\n [\n -115,\n 73\n ],\n [\n -150,\n 72\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.9,\n 85\n ],\n [\n 165,\n 85\n ],\n [\n 172,\n 74.5\n ],\n [\n 179.9,\n 74.5\n ],\n [\n 179.9,\n 85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":140835,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mizell, Kira 0000-0002-5066-787X kmizell@usgs.gov","orcid":"https://orcid.org/0000-0002-5066-787X","contributorId":4914,"corporation":false,"usgs":true,"family":"Mizell","given":"Kira","email":"kmizell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gartman, Amy 0000-0001-9307-3062 agartman@usgs.gov","orcid":"https://orcid.org/0000-0001-9307-3062","contributorId":177057,"corporation":false,"usgs":true,"family":"Gartman","given":"Amy","email":"agartman@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":918143,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263425,"text":"70263425 - 2024 - Assessing and implementing the concept of Blue Economy in Laurentian Great Lakes fisheries: Lessons from coupled human and natural systems","interactions":[],"lastModifiedDate":"2025-02-11T15:30:07.537954","indexId":"70263425","displayToPublicDate":"2024-11-06T08:22:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":865,"text":"Aquatic Ecosystem Health & Management","active":true,"publicationSubtype":{"id":10}},"title":"Assessing and implementing the concept of Blue Economy in Laurentian Great Lakes fisheries: Lessons from coupled human and natural systems","docAbstract":"Inland fisheries often receive little to no attention in global discussions about sustainable development. The consequences of overlooking inland fisheries in sustainability dialogues are increasingly problematic as fisheries stressors (e.g., overharvest, species invasion, climate change, habitat modification) intensify. Elevating the global profile of inland fisheries requires an approach for quantifying and clearly conveying the ecological, economic, and societal values of these systems. One such approach involves the Blue Economy, a multifaceted concept initially used to describe the intersection of marine conservation and sustainable use of marine resources for economic growth. Although conceptually powerful, the Blue Economy has rarely been applied to inland waters and fisheries. To address this knowledge gap, we conceptualized Laurentian Great Lakes fisheries from a Blue Economy perspective. In particular, we evaluated the utility of the coupled human and natural systems (CHANS) framework for characterizing the ecological, economic, and societal values of Laurentian Great Lakes fisheries and associated contributions to the Blue Economy (e.g., human livelihoods, food security, recreation, conservation, economic prosperity). There are numerous opportunities to leverage CHANS methods (e.g., metacoupling, telecoupling) and associated mathematical models to advance fisheries science, inform fisheries management, and ultimately move toward a Blue Economy in the Laurentian Great Lakes. To that end, we demonstrated applications of CHANS methods, discussed strategies for communicating with stakeholders, and provided insights for navigating challenges to developing a Blue Economy in the Laurentian Great Lakes—a model that could be used in the African Great Lakes and other large ecosystems in the world.","language":"English","publisher":"BioOne","doi":"10.14321/aehm.027.02.74","usgsCitation":"Carlson, A.K., Leonard, N., Munawar, M., and Taylor, W., 2024, Assessing and implementing the concept of Blue Economy in Laurentian Great Lakes fisheries: Lessons from coupled human and natural systems: Aquatic Ecosystem Health & Management, v. 27, no. 2, p. 74-84, https://doi.org/10.14321/aehm.027.02.74.","productDescription":"11 p.","startPage":"74","endPage":"84","ipdsId":"IP-154893","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":481930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Laurentian Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.35414421298296,\n 47.69798628255907\n ],\n [\n -92.55498641328694,\n 46.29425302711437\n ],\n [\n -86.95944652656331,\n 46.00505185703939\n ],\n [\n -88.62282303782777,\n 43.92021164632037\n ],\n [\n -87.50058525109819,\n 41.25347232284972\n ],\n [\n -85.72809349640711,\n 42.11694323111757\n ],\n [\n -85.91733849617773,\n 43.493974004776575\n ],\n [\n -84.89672932891068,\n 44.873597615031386\n ],\n [\n -83.29102906108528,\n 43.6549891230233\n ],\n [\n -83.78159647699495,\n 41.31847374004327\n ],\n [\n -79.95408136104199,\n 41.39215071916226\n ],\n [\n -75.87519895574565,\n 43.702579861752696\n ],\n [\n -79.58470723400637,\n 45.3214766230865\n ],\n [\n -84.23758896005683,\n 48.14783853442674\n ],\n [\n -88.84558873081599,\n 49.05585075889549\n ],\n [\n -91.35414421298296,\n 47.69798628255907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Andrew Kenneth 0000-0002-6681-0853","orcid":"https://orcid.org/0000-0002-6681-0853","contributorId":340581,"corporation":false,"usgs":true,"family":"Carlson","given":"Andrew","email":"","middleInitial":"Kenneth","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":926955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leonard, Nancy J.","contributorId":350769,"corporation":false,"usgs":false,"family":"Leonard","given":"Nancy J.","affiliations":[{"id":20304,"text":"Pacific States Marine Fisheries Commission","active":true,"usgs":false}],"preferred":false,"id":926956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munawar, Mohiuddin","contributorId":350770,"corporation":false,"usgs":false,"family":"Munawar","given":"Mohiuddin","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":926957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, William W.","contributorId":350772,"corporation":false,"usgs":false,"family":"Taylor","given":"William W.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":926958,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261558,"text":"70261558 - 2024 - Insights on arc magmatic systems drawn from natural melt inclusions and crystallization experiments at PH2O=800 MPa under oxidizing conditions","interactions":[],"lastModifiedDate":"2024-12-16T14:12:30.156142","indexId":"70261558","displayToPublicDate":"2024-11-06T06:57:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Insights on arc magmatic systems drawn from natural melt inclusions and crystallization experiments at PH2O=800 MPa under oxidizing conditions","title":"Insights on arc magmatic systems drawn from natural melt inclusions and crystallization experiments at PH2O=800 MPa under oxidizing conditions","docAbstract":"Whole rock compositions at Buldir Volcano, western Aleutian arc, record a strong, continuous trend of iron depletion with decreasing MgO, classically interpreted as a calc-alkaline liquid line of descent. In contrast, olivine-hosted melt inclusions have higher total iron (FeO*) than whole rocks and show little change in FeO* with decreasing MgO. To investigate this discrepancy and determine the conditions required for strong iron depletion, we conducted oxygen fugacity (ƒO2) buffered, water-saturated crystallization experiments at 800 MPa and ƒO2 = QFM + 1.6 ± 0.4 (1σ) (where QFM refers to the quartz-fayalite-magnetite buffer) on a high-Al, basaltic starting material modeled after a Buldir lava. Experimental conditions were informed by olivine-hosted melt inclusions that record minimum entrapment pressures as high as 570 MPa, >6 wt % H2O, and ƒO2 of QFM + 1.4 (±0.2), making Buldir one of the most oxidized and wettest arc volcanoes documented globally. The experiments produce melts with Si-enrichment and Fe-depletion signatures characteristic of evolved, calc-alkaline magmas at the lowest MgO, although FeO* remains roughly constant over most of the experimental temperature range. Experiments saturate CrAl-spinel and olivine at 1160°C, followed by clinopyroxene and Al-spinel at 1085°C, hornblende at 1060°C, and, finally, plagioclase and magnetite between 1040°C and 960°C. Hornblende crystallization, not magnetite, generates the largest increase in SiO2 and largest decrease in FeO* in coexisting melts. Compositions of melt inclusions are consistent with experimental melts and reflect crystallization of a basaltic parent magma at high PH2O. In contrast, the whole rock compositional trends are influenced by magma mixing and phenocryst redistribution and accumulation. The crystallization experiments and natural liquids (melt inclusions and groundmass glass) from Buldir suggest that for an oxidized, hydrous primary basalt starting composition, significant Fe depletion from the melt will not occur until intermediate to late stages of magma crystallization (< ~4.5 wt % MgO). We conclude that the Buldir whole rock trend cannot be reproduced by crystallization at arc-relevant oxygen fugacities and is not a true liquid line of descent, warranting caution when interpreting volcanic trends globally.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egae117","usgsCitation":"Andrys, J.L., Cottrell, E., Kelley, K., Waters, L.E., and Coombs, M.L., 2024, Insights on arc magmatic systems drawn from natural melt inclusions and crystallization experiments at PH2O=800 MPa under oxidizing conditions: Journal of Petrology, v. 65, no. 12, egae117, 23 p., https://doi.org/10.1093/petrology/egae117.","productDescription":"egae117, 23 p.","ipdsId":"IP-166029","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":465141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"65","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Andrys, Janine L.","contributorId":347200,"corporation":false,"usgs":false,"family":"Andrys","given":"Janine","email":"","middleInitial":"L.","affiliations":[{"id":52668,"text":"Boise State","active":true,"usgs":false}],"preferred":false,"id":921044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cottrell, Elizabeth","contributorId":347203,"corporation":false,"usgs":false,"family":"Cottrell","given":"Elizabeth","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":921045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Katherine A.","contributorId":347206,"corporation":false,"usgs":false,"family":"Kelley","given":"Katherine A.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":921046,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waters, Laura E.","contributorId":347209,"corporation":false,"usgs":false,"family":"Waters","given":"Laura","email":"","middleInitial":"E.","affiliations":[{"id":34868,"text":"New Mexico Institute of Mining and Technology","active":true,"usgs":false}],"preferred":false,"id":921047,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coombs, Michelle L. 0000-0002-6002-6806 mcoombs@usgs.gov","orcid":"https://orcid.org/0000-0002-6002-6806","contributorId":2809,"corporation":false,"usgs":true,"family":"Coombs","given":"Michelle","email":"mcoombs@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":921048,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268340,"text":"70268340 - 2024 - River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements","interactions":[],"lastModifiedDate":"2025-06-23T14:40:02.847232","indexId":"70268340","displayToPublicDate":"2024-11-05T09:37:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7942,"text":"Earth Surface Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements","docAbstract":"Measuring suspended-sand fluxes in rivers remains a scientific challenge due to their high spatial and temporal variability. To capture the vertical and lateral gradients of concentration in the cross-section, measurements with point samples are performed. However, the uncertainty related to these measurements is rarely evaluated, as few studies of the major sources of error exist. Therefore, the aim of this study is to develop a method to determine the cross-sectional sand flux and estimate its uncertainty. This SDC (for sand discharge computing) method combines suspended-sand concentrations from point samples with ADCP (acoustic Doppler current profiler) high-resolution depth and velocity measurements. The MAP (for multitransect averaged profile) method allows obtaining an average of several ADCP transects on a regular grid, including the unmeasured areas. The suspended-sand concentrations are integrated vertically by fitting a theoretical exponential suspended-sand profile to the data using Bayesian modeling. The lateral integration is based on the water depth as a proxy for the local bed shear stress to evaluate the bed concentration and sediment diffusion along the river cross-section. The estimation of uncertainty combines ISO standards and semi-empirical methods with a Bayesian approach to estimate the uncertainty due to the vertical integration. The new method is applied to data collected in four rivers under various hydro-sedimentary conditions: the Colorado, Rhône, Isère, and Amazon rivers, with computed flux uncertainties ranging between 18 % and 32 %. The relative difference between the suspended-sand flux in 21 cases calculated with the proposed SDC method compared to the International Organization for Standardization (ISO) 4363 standard method ranges between −40 % and +23 %. This method that comes with a flexible, open-source code is the first to propose an applicable uncertainty estimation that could be adapted to other flux computation methods.
","language":"English","publisher":"European Geophysical Union","doi":"10.5194/esurf-12-1243-2024","usgsCitation":"Marggraf, J., Dramais, G., Le Coz, J., Calmel, B., Camenen, B., Topping, D.J., Santini, W., Pierrefeu, G., and Lauters, F., 2024, River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements: Earth Surface Dynamics, v. 12, no. 6, p. 1243-1266, https://doi.org/10.5194/esurf-12-1243-2024.","productDescription":"24 p.","startPage":"1243","endPage":"1266","ipdsId":"IP-123898","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":491495,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esurf-12-1243-2024","text":"Publisher Index Page"},{"id":491101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Marggraf, Jessica","contributorId":350702,"corporation":false,"usgs":false,"family":"Marggraf","given":"Jessica","affiliations":[{"id":83813,"text":"RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940855,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dramais, Guillaume","contributorId":357236,"corporation":false,"usgs":false,"family":"Dramais","given":"Guillaume","affiliations":[{"id":85354,"text":"1RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940856,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Le Coz, Jerome","contributorId":350703,"corporation":false,"usgs":false,"family":"Le Coz","given":"Jerome","affiliations":[{"id":83813,"text":"RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940857,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calmel, Blaise","contributorId":357237,"corporation":false,"usgs":false,"family":"Calmel","given":"Blaise","affiliations":[{"id":85354,"text":"1RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940858,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camenen, Benoit","contributorId":238956,"corporation":false,"usgs":false,"family":"Camenen","given":"Benoit","email":"","affiliations":[{"id":47840,"text":"Scientist, IRSTEA, Lyon, France","active":true,"usgs":false}],"preferred":false,"id":940859,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":940860,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Santini, William","contributorId":357238,"corporation":false,"usgs":false,"family":"Santini","given":"William","affiliations":[{"id":85355,"text":"IRD-GET, Institut de Recherche pour le Développement, Laboratoire GET (IRD, CNRS, UPS, CNES), Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":940861,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pierrefeu, Gilles","contributorId":238958,"corporation":false,"usgs":false,"family":"Pierrefeu","given":"Gilles","email":"","affiliations":[{"id":47841,"text":"Senior Engineer, CNR, Lyon, France","active":true,"usgs":false}],"preferred":false,"id":940862,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lauters, François","contributorId":357239,"corporation":false,"usgs":false,"family":"Lauters","given":"François","affiliations":[{"id":85356,"text":"Service Etudes Eau Environnement, EDF, 134 Chemin de l'étang, Saint Martin Le Vinoux, 38950, France","active":true,"usgs":false}],"preferred":false,"id":940863,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260957,"text":"70260957 - 2024 - Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy","interactions":[],"lastModifiedDate":"2024-12-10T15:37:31.896607","indexId":"70260957","displayToPublicDate":"2024-11-05T09:14:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy","docAbstract":"Making timely management decisions is often hindered by uncertainty. Monitoring reduces two key types of uncertainty. First, it serves to reduce structural uncertainty of how the system works and provides support for expectations of how a system works. Second, it serves to reduce parametric uncertainty of the drivers of system dynamics. By combining monitoring data and quantitative models, we can reduce structural and parametric uncertainty. To demonstrate this, we focus on the Shenandoah salamander (Plethodon shenandoah), a United States Federally Endangered Species. Early work suggested that P. shenandoah extinction risk results from competition with a conspecific (Plethodon cinereus). However, more recent work has found equivocal support for this claim, instead suggesting that abiotic factors, such as moisture and temperature, drive P. shenandoah persistence. Using long-term monitoring data, we find that while competition may play a part in P. shenandoah extinction risk, measures of surface moisture are better predictors of occupancy dynamics. Further, we find decreased detection rates of P. shenandoah when P. cinereus is present, suggesting a conflation of detection probability with actual competition, which cautions against making inference from unadjusted observations of occurrence. Using multiple lines of inquiry allows for more robust understanding of system drivers in the face of high uncertainty, increasing opportunities to manage extinction risk.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00442-024-05631-x","usgsCitation":"Werba, J.A., DiRenzo, G.V., Brand, A., and Campbell Grant, E.H., 2024, Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy: Oecologia, v. 206, p. 305-316, https://doi.org/10.1007/s00442-024-05631-x.","productDescription":"12 p.","startPage":"305","endPage":"316","ipdsId":"IP-148195","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":464231,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Blue Ridge Mountains, Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.60935855780521,\n 38.68748637317097\n ],\n [\n -78.60935855780521,\n 38.31093059502825\n ],\n [\n -78.13191468279777,\n 38.31093059502825\n ],\n [\n -78.13191468279777,\n 38.68748637317097\n ],\n [\n -78.60935855780521,\n 38.68748637317097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"206","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Werba, Jo Avital 0000-0002-5295-7790","orcid":"https://orcid.org/0000-0002-5295-7790","contributorId":338728,"corporation":false,"usgs":true,"family":"Werba","given":"Jo","email":"","middleInitial":"Avital","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":918691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":918692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brand, Adrianne 0000-0003-2664-0041","orcid":"https://orcid.org/0000-0003-2664-0041","contributorId":304281,"corporation":false,"usgs":true,"family":"Brand","given":"Adrianne","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":918693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":918694,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261018,"text":"70261018 - 2024 - Ceanothus: Taxonomic patterns in life history responses to fire","interactions":[],"lastModifiedDate":"2024-12-10T15:36:47.620254","indexId":"70261018","displayToPublicDate":"2024-11-05T08:14:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Ceanothus: Taxonomic patterns in life history responses to fire","docAbstract":"Premise: Ceanothus (Rhamnaceae) is a large genus of shrubs that dominate California chaparral and are resilient to fires. Persistence is ensured by resprouting and/or seedling recruitment from dormant seed banks. Some species do both and others, the obligate seeders, are entirely dependent on seedling recruitment. The distribution of these two modes within the genus is poorly documented.
Methods: We used all available publications that document species responses to fire and filled most gaps in the literature based on extensive field studies of more than 60 recent wildfires in California.
Results: The genus is divided into two subgenera, Ceanothus and Cerastes. Ceanothus is widely considered to comprise mostly resprouting species and Cerastes to consist of only obligate seeders. The subgenus Ceanothus includes resprouting species throughout their range from the eastern United States and Midwest to western United States. Within the California Floristic Province (CFP), a few species are unique in producing massive lignotubers that develop from repeated fires; however, within the CFP, the majority of species in this subgenus do not resprout and are obligate seeders. Two have disjunct subspecies that are facultative seeders or obligate seeders.
Conclusions: Previously, speciation in this genus was contended to have occurred in the late Miocene within the CFP. The syndrome of obligate seeding is most strongly represented in this region, and we hypothesize that evolution of this syndrome was a response to increased predictability of fire driven by the Mediterranean climate and the long interval between fires.
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Using remote sensing data and hydrological modeling, the 1-km runoff field simulated by VegET, and routed with mizuRoute, covers over 64,000 river segments in Africa. The VegET model simulates runoff based on vegetation and soil moisture dynamics, while mizuRoute processes this runoff through a detailed river network. Performance metrics show strong model reliability, with R² ranging from 0.5 to 0.9, NSE between 0.6 and 0.9, and KGE from 0.5 to 0.8. The total annual average discharge for Africa is quantified at 3238.1 km³.year-1, with contributions to various oceanic basins: 989.9 km³.year-1 to the North Atlantic, primarily from West African rivers like the Senegal, Gambia, Volta, and Niger; 1313.7 km³.year-1 to the South Atlantic, largely from the Congo River; 212.5 km³.year-1 to the Mediterranean Sea, predominantly from the Nile River; and 722.0 km³.year-1 to the Indian Ocean, with substantial inputs from rivers such as the Zambezi. This VegDischarge v1 is valuable for policymakers, stakeholders, and researchers to better understand water availability, its temporal and spatial variations, that impact water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience mitigation strategies.
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We developed standardized indices of relative abundance from long–term (1980–2016), semi-structured monitoring data (eBird) for eight species: Brown Pelican (Pelecanus occidentalis), Double-Crested Cormorant (Nannopterum auritum), White Ibis (Eudocimus albus), Wood Stork (Mycteria americana), Piping Plover (Charadrius melodus), American Oystercatcher (Haematopus palliatus), Clapper Rail (Rallus crepitans), and Northern Gannet (Morus bassanus). Following a period of stable or declining abundance from the 1980s through the 1990s, most species have shown stable or slightly upward trends through the late 2000s; Brown Pelican and Piping Plover have shown some evidence of recent declines. Species–specific correlations between abundance indices developed from presence/absence data and those developed from count data were positive for all species and ranged from 0.53 to 0.86. Dynamic factor analysis identified common trends in abundance among several species, in particular, Brown Pelican, Double–Crested Cormorant, and White Ibis. Model performance was improved with inclusion of an indicator of sea level rise, but not forage fish abundance or temperature, indicating habitat availability mediated by changing water levels may explain some of the underlying abundance trends. Our results provide baseline information on long–term trends for several important coastal birds that can help inform research, monitoring and conservation efforts in the U.S. South Atlantic region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsma.2024.103886","usgsCitation":"Craig, J., Siegfried, K., Cheshire, R., Karnauskas, M., and Jodice, P.G., 2024, Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic: Regional Studies in Marine Science, v. 80, 103886, 14 p., https://doi.org/10.1016/j.rsma.2024.103886.","productDescription":"103886, 14 p.","ipdsId":"IP-159778","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":490995,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsma.2024.103886","text":"Publisher Index Page"},{"id":484853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.63812689012559,\n 24.981195366830136\n ],\n [\n -79.74779294326453,\n 26.53588076270711\n ],\n [\n -81.03973835977342,\n 29.95801011198671\n ],\n [\n -81.11631302916135,\n 31.396072044056297\n ],\n [\n -79.30822669011008,\n 33.150807571917255\n ],\n [\n -76.01718491915142,\n 34.68241756177868\n ],\n [\n -75.87971328976948,\n 36.709011056016635\n ],\n [\n -78.23890913908112,\n 36.73325180728209\n ],\n [\n -80.34864182194866,\n 34.55119531258265\n ],\n [\n -83.32101866451728,\n 32.39789594150341\n ],\n [\n -81.81661133200153,\n 28.946064586607065\n ],\n [\n -80.63812689012559,\n 24.981195366830136\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"80","noUsgsAuthors":false,"publicationDate":"2024-11-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Craig, J.K.","contributorId":353621,"corporation":false,"usgs":false,"family":"Craig","given":"J.K.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siegfried, K.I.","contributorId":353620,"corporation":false,"usgs":false,"family":"Siegfried","given":"K.I.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cheshire, R.T.","contributorId":353622,"corporation":false,"usgs":false,"family":"Cheshire","given":"R.T.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karnauskas, M.","contributorId":353623,"corporation":false,"usgs":false,"family":"Karnauskas","given":"M.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934112,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70260490,"text":"70260490 - 2024 - The state of the science and practice of stream restoration in the Chesapeake: Lessons learned to inform better implementation, assessment and outcomes","interactions":[],"lastModifiedDate":"2024-11-05T16:47:15.828836","indexId":"70260490","displayToPublicDate":"2024-11-04T10:44:08","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":17129,"text":"STAC Workshop Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"24-006","title":"The state of the science and practice of stream restoration in the Chesapeake: Lessons learned to inform better implementation, assessment and outcomes","docAbstract":"The Chesapeake Bay Program’s (CBP) Science and Technical Advisory Committee (STAC) organized and led a workshop on the science and practice of stream restoration in order to summarize the state of knowledge in order to identify ways to improve stream restoration outcomes. 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This conflict in goals is a shortcoming of the currently most common regulatory driver for stream restoration (reducing downstream loads of N, P, and sediment) that could be addressed directly through diversifying goals to include biotic uplift, as biological benefit is an assumed condition for the permitting and crediting of stream restoration projects. It is also likely that current understanding of stressors and drivers of stream ecosystem health is insufficient, and that reach-scale restoration focused on geomorphic restoration is not removing the actual sources of stream health impairment that may arise in the upstream watershed. More science could help to identify how to improve the ecological condition of streams through management. The outcome of stream restoration monitoring has revealed that while geomorphic and hydrodynamic functions of stream restoration projects may be achieved, biotic stream function improvements remain elusive. As such, ensuring uplift may be achieved by avoiding restoration projects that risk resources in higher-quality streams and riparian corridors. Reach-scale restoration often does not effectively mitigate the watershed-scale stressors of stream ecosystems. 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