Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.
This report reflects on the past, present, and potential future of community and citizen science (CCS) in the Elwha River watershed, with particular focus on the years before and after a major restoration event: the removal of two dams that had impacted the river system for a century. We ask: how does CCS feature in the Elwha story and how could it feature? We use the term CCS to reference the broad range of ways in which members of the public might participate in authentic science and monitoring processes, including students and both paid and unpaid interns: participants are individuals contributing to scientific projects without prior formal training in the topic.
Removal of the Elwha dams was a large-scale, complex project, and communities had an important role to play: the Lower Elwha Klallam Tribe (LEKT) and other local groups were a large part of the original drive to remove the dams. Some funding and policy requirements for monitoring are ending, but there is still much to learn from the changes happening in the Elwha, requiring ongoing research and monitoring. In 2022, the Elwha scientific community came together in a multifaceted effort called the “Elwha ScienceScape” to mark the ten-year anniversary of dam removal and to plan for future monitoring. One of ScienceScape’s priorities is expanding CCS efforts, and because the Elwha dam removal is a powerful international symbol of large-scale watershed restoration, ScienceScape is well-positioned to inform and emphasize the potential role of CCS in dam removal worldwide.
This report presents insights about Elwha CCS from an academic literature review and discussions with scientists and many others that have been working in the Elwha. We found that the history of CCS on the Elwha is important but understated, with few scientific papers acknowledging support by volunteers of various kinds. Recent and ongoing CCS projects on the Elwha tend to be focused on biological phenomena, and most are associated with educational opportunities (across many types of institutions) and paid internships. We also noted that most Elwha CCS projects required volunteers with particular pre-existing skill sets (e.g., botanical knowledge) or time to impart specialized training (e.g. boat use), leading many projects towards engagement with a smaller number of volunteers.
Partners working in the Elwha are considering a wide range of potential new CCS projects, and these ideas are in varying stages of development. Many new projects would broaden public involvement in terms of the opportunities available and increase the variety of focal topics for research and monitoring. This increased breadth is promising: there are indications that the local community’s interests also range widely, from fish recovery after dam removal to dam removal impacts on humans.
Elwha CCS projects have encountered some challenges and barriers, including the administrative burden of coordinating volunteers and managing liability concerns. But Elwha ScienceScape scientists are committed to the value that CCS brings both to the research itself as well as to those who participate in these projects. CCS can be a way to increase equity in science and engage people who would not otherwise participate in research, and in many cases the research simply wouldn’t be possible without their help. Support with project administration, volunteer management, and data management could help in expanding CCS efforts and broadening their inclusivity. More systematic tracking of CCS projects to assess how they contribute to research and to community and participant benefit could be helpful in establishing and maintaining a long-term CCS strategy in the Elwha.
","language":"English","publisher":"UC Davis Center for Community and Citizen Science","doi":"10.58076/C64W2B","usgsCitation":"Eitzel, M.V., Morley, S.A., Behymer, C., Meyer, R., Kagley, A., Ballard, H.L., Jadallah, C., Duda, J.J., Jennings, L., Miller, I.M., Stapleton, J., Shaffer, A., Miller, A., Shafroth, P., and Blackie, B., 2023, Community and citizen science on the Elwha River: Past, present, and future, 22 p., https://doi.org/10.58076/C64W2B.","productDescription":"22 p.","ipdsId":"IP-149069","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":414976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wshington","otherGeospatial":"Elwha River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.62525330687644,\n 47.71956268467267\n ],\n [\n -123.42458708492356,\n 47.71956268467267\n ],\n [\n -123.42458708492356,\n 48.15187321706955\n ],\n [\n -123.62525330687644,\n 48.15187321706955\n ],\n [\n -123.62525330687644,\n 47.71956268467267\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eitzel, M. 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of Raramuri Criollo vs. Angus cattle grazing California Chaparral and Colorado Plateau shrublands","docAbstract":"Selecting livestock genetics adapted to arid environments, such as Criollo cattle, is one of several strategies recommended for decreasing the vulnerability to climate change of ranching in the southwestern USA. Our objective was to determine whether desirable foraging traits of Criollo cattle previously documented in the Chihuahuan Desert, held true in two of the most climate-vulnerable ecosystems of the Southwest. We conducted a study at Rancho Corta Madera (RCM) in southern California and Dugout Ranch (DR) in southeast Utah. Twenty mature cows, 10 Raramuri Criollo and 10 Red or Black Angus, were monitored with GPS collars during multiple seasons between 2018 and 2021. Geolocation data were used to compute daily distance traveled (km*d−1), movement velocity (m*min−1), path sinuosity (SI), time spent grazing, resting, or traveling (h*d−1), and area of the pasture explored (ha*d−1) as well as to calculate selection of vegetation cover types (E, Ivlev's Electivity Index) by cows of each breed. The effects of breed, season, year, and pasture on each of these metrics were modeled with repeated measures analyses of variance. At both ranches, statistically detectable differences (P ≤ 0.05) between breeds were observed for most behavior metrics during the dormant season. Conversely, few breed differences were observed during the growing season. Criollo cattle exhibited greater relative preference for a number of shrub dominated vegetation types at both ranches, and similar relative selection of grassland dominated sites compared to Angus counterparts. At both ranches, Criollo cattle exhibited similar or less relative preference for riparian areas vs. Angus counterparts. Breed divergence vs. convergence of foraging behaviors during the dormant vs. growing seasons, previously observed in the Chihuahuan Desert, was documented at both sites. Positive system outcomes associated with foraging traits of Criollo cattle could be expected to occur more broadly across the Southwest.
Earthquake ground motion processing for next-generation attenuation (NGA) projects required human inspection to select high-pass corner frequencies (fcHP), which is time-intensive and subjective. With growth in the number of recordings per event and interest in enhancing repeatability, we sought to develop automated procedures for fcHP selection. These procedures consider signal-to-noise ratio (SNR) and non-physical features in the displacement time series that indicate high- and/or low-frequency noise effects. The procedures are implemented in a US Geological Survey software package (gmprocess). We extend previous procedures for SNR-based corner frequency selection to also check for low-frequency artifacts in the displacement record using a polynomial fit to improve fcHP selection. We evaluate the performance of the SNR and polynomial fit criteria using recordings from the 2020 M5.1 North Carolina and the 2013 M4.7 Southern Ontario earthquakes. Data processed with the SNR-only criteria can have low fcHP and displacement drift; the displacement check increases fcHP and reduces displacement drift.
Well ER-5-3-2 is part of a well network designed to monitor long-term water levels and radionuclide concentrations downgradient from underground nuclear tests that occurred in Frenchman Flat, an area of the U.S. Department of Energy Nevada National Security Site in southern Nevada. Interpretation of monitoring records for well ER-5-3-2 was confounded by previously unexplained water-level fluctuations in the well hydrograph. This study integrated geologic, hydrologic, and water-chemistry data to evaluate potential stresses and hydrologic conditions that likely affected the well ER-5-3-2 hydrograph. Numerical groundwater models were applied to evaluate four model scenarios: (1) wellbore leakage without recharge, (2) wellbore leakage with recharge, (3) equilibration to vertical heterogeneities between shallow (low transmissivity) and deep (higher transmissivity) carbonate zones, and (4) equilibration to lateral heterogeneities in carbonate rocks.
Meteoric recharge was not the cause of the 21-foot (ft) water-level rise in well ER-5-3-2 from 2001 to 2011 or the 4-ft decline from 2012 to 2016. Based on observed water-level fluctuations in nearby wells, the water-level rise and decline from recharge for these periods was less than 3 and 1 ft, respectively. The lateral-heterogeneity scenario is based on the assumption that the 21-ft water-level rise from 2001 to 2011 was a natural water-level reequilibration following the pumping-induced depressurization of a large volume of high transmissivity and low-storage carbonate rock that is surrounded by low transmissivity and high-storage carbonate rock. The lateral-heterogeneity scenario was discounted because simulated water levels cannot match the well ER-5-3-2 hydrograph. Underground nuclear testing and temperature effects were discounted based on hydraulic connections and water-temperature data.
Wellbore-leakage scenarios are based on the assumption that the water-level rise was sustained from leakage rates required to cause a localized mounding in the carbonate system near well ER-5-3-2, where the carbonate transmissivity is 530 square feet per day. Even though simulated and measured water levels compare favorably for scenarios of wellbore leakage with and without recharge, large volumes (178–184 million gallons) of groundwater from volcanic rocks would be required to leak into the carbonate system, which is not supported by water-chemistry data.
An alternative conceptualization of wellbore leakage is based on the assumption that the 21-ft water-level rise from 2001 to 2011 was sustained by the hydraulic disconnection of well ER-5-3-2 from the carbonate system. The disconnection occurred several months after a constant-rate test in well ER-5-3-2 when carbonate rocks were hydraulically disconnected from the well by either (1) the shifting of sloughed fill in the open hole or (2) the encrusting of carbonate precipitate in the well screen. The hydraulic disconnection effectively sealed the well and caused a 21-ft water-level rise from wellbore leakage during 2001–11. In this case, total wellbore leakage from 2001 to 2011 was about 50 gallons. The 4-ft water-level decline from 2012 to 2016 was conceptualized to have occurred from the slow breaking of the seal and reconnection of the well to the carbonate system. This alternative conceptualization of wellbore leakage was consistent with water-chemistry analyses because the computed wellbore leakage (50 gallons) was small relative to purged volumes (30,000–40,000 gallons) for sampling, and the water chemistry would not be expected to change.
The shallow-deep carbonate scenario provided another explanation for the well ER-5-3-2 hydrograph. This scenario is based on the assumption that well-construction effects and vertical heterogeneity of the carbonate system explain the ER-5-3-2 water-level trend. Well-construction effects are attributed to a temporary clogging of the open interval below the well screen that was opened during pumping events, which affected the hydraulic connection of deep transmissive carbonate rocks to the wellbore. The 21-ft water-level rise from 2001 to 2011 was a natural equilibration to shallow, low-transmissivity carbonate rocks during a period when the lower open interval was clogged. The 4-ft decline from 2012 to 2016 represents equilibration between the shallow and deep intervals, because of a partial unclogging of the connection between the two intervals. The low water levels from 2016 to 2021 resulted from pumping for sampling and an unclogging of the open interval so that the low head in the deep carbonate dominated the water level. Despite potential well-construction effects, from either a wellbore leakage or shallow-deep carbonate scenario, samples collected from well ER-5-3-2 are representative of the carbonate system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225132","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-EM0004969","usgsCitation":"Jackson, T.R., and Frus, R.J., 2023, Evaluation of potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada: U.S. Geological Survey Scientific Investigations Report 2022–5132, 35 p., https://doi.org/10.3133/sir20225132.","productDescription":"Report: viii, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-139917","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":413963,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95C0NG5","text":"MODFLOW 6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada","description":"Jackson, T.R., and Frus, R.J., 2023, MODFLOW 6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada: U.S. Geological Survey data release, available at https://doi.org/10.5066/P95C0NG5."},{"id":500485,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114613.htm","linkFileType":{"id":5,"text":"html"}},{"id":413973,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225132/full"},{"id":413962,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5132/images"},{"id":413961,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5132/sir20225132.xml"},{"id":413960,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5132/sir20225132.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":413959,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5132/covrthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Frenchman Flat","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.05668643871044,\n 36.549912612507626\n ],\n [\n -116.05668643871044,\n 35.84481987187543\n ],\n [\n -115.27945901304658,\n 35.84481987187543\n ],\n [\n -115.27945901304658,\n 36.549912612507626\n ],\n [\n -116.05668643871044,\n 36.549912612507626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil properties, groundwater flow, land use, and other features that describe the hydrogeology and geochemistry of the aquifer system. Four models were developed to predict the probability of manganese concentrations greater than a detection level of 10 micrograms per liter (μg/L) and greater than three threshold concentrations (50, 150, and 300 μg/L) relevant to drinking-water quality. One model was developed to predict the probability of 1,4-dioxane concentrations greater than a detection level of 0.07 μg/L. The 1,4-dioxane model was limited geographically to Suffolk County because of data availability. Predictions were made for two layers in the upper glacial aquifer and three layers in the Magothy aquifer, which are the upper two of the three major aquifers of the Long Island aquifer system.
The objective of the study described in this report was to demonstrate the application of the methods rather than to develop precise estimates of manganese or 1,4-dioxane concentrations at any given location. The predictive models developed in the study are considered preliminary in the sense that they are an initial effort at developing these kinds of models specifically for Long Island. The models could be improved by the inclusion of additional data, by the use of methods to improve the modeling of infrequent high concentrations of manganese and 1,4-dioxane (above threshold concentrations), and by including more explanatory variables that specifically describe conditions and contaminant sources on Long Island. Nonetheless, the distribution of model predictions and the influence of explanatory variables in the models were consistent with the expected relations between contaminant concentrations and groundwater-flow-system characteristics and the distribution of manmade sources.
Mapped predictions indicated that manganese detections were more probable in the upper glacial aquifer and along the southern shore of Long Island, consistent with the distribution of anoxic conditions in groundwater in the Long Island aquifer system. Manganese was infrequently predicted at concentrations greater than thresholds of concern for drinking-water quality in any of the aquifer layers. Detections of 1,4-dioxane were predicted in the western, more highly developed parts of Suffolk County, in the upper glacial aquifer and the top and middle layers of the Magothy aquifer, and in northwestern Suffolk County in the bottom layer of the Magothy aquifer. Although preliminary in nature and based on limited data, these mapped predictions can be used to generally identify areas where manganese and 1,4-dioxane may be present at concentrations of concern to prioritize areas for future monitoring and to guide future modeling and mapping efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225120","programNote":"National Water Quality Program","usgsCitation":"DeSimone, L.A., 2023, Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5120, 34 p., https://doi.org/10.3133/sir20225120.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-133571","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":414438,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5120/images/"},{"id":414437,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.XML"},{"id":414436,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225120/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5120"},{"id":500463,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114612.htm","linkFileType":{"id":5,"text":"html"}},{"id":414439,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90AT9YG","text":"USGS data release","linkHelpText":"Data and model archive for preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York"},{"id":414434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5120/coverthb.jpg"},{"id":414435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.pdf","text":"Report","size":"5.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5120"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.05146015978082,\n 40.628474760922984\n ],\n [\n -73.96502494019428,\n 40.542103435896706\n ],\n [\n -73.54649650851006,\n 40.545560430280744\n ],\n [\n -73.20985407432973,\n 40.61811609149555\n ],\n [\n -72.74128419972719,\n 40.738867255336714\n ],\n [\n -72.19082832762075,\n 40.90411303840304\n ],\n [\n -71.79504600635465,\n 41.08266452105815\n ],\n [\n -72.259066658874,\n 41.20257103045407\n ],\n [\n -72.71853808930965,\n 41.00374947032347\n ],\n [\n -73.1643618534946,\n 41.010615404965876\n ],\n [\n -73.52375039809249,\n 40.948796241204036\n ],\n [\n -73.76485916851937,\n 40.873160815851264\n ],\n [\n -73.87858972060721,\n 40.79055078444986\n ],\n [\n -74.01961560519632,\n 40.72163048139012\n ],\n [\n -74.05146015978082,\n 40.68714353955147\n ],\n [\n -74.05146015978082,\n 40.628474760922984\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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The U.S. Geological Survey (USGS) Fundamental Science Practices (FSP) are a set of standard principles fundamental to how USGS conducts and carries out its science activities and how resulting information products and data are reviewed, approved, and released. These policies, practices, philosophical premises, and operational principles serve as the foundation for all USGS research and monitoring activities and apply to all levels of the organization. The strength and future of the USGS depend on following these practices. USGS FSP were initiated in 2006 and fully implemented in 2009 to consolidate and standardize science practices across multiple scientific mission areas and science disciplines within the USGS.
Fundamental Science Practices Advisory Council
","tableOfContents":"Field measurements of Rn-222 fluxes from the tops and bottoms of compacted clay radon barriers were used to calculate effective Rn diffusion coefficients (DRn) at four uranium waste disposal sites in the western United States to assess cover performance after more than 20 years of service. Values of DRn ranged from 7.4 × 10−7 to 6.0 × 10−9 m2/s, averaging 1.42 × 10−7. Water saturation (SW) from soil cores indicated that there was relatively little control of DRn by SW, especially at higher moisture levels, in contrast to estimates from most steady-state diffusion models. This is attributed to preferential pathways intrinsic to construction of the barriers or to natural process that have developed over time including desiccation cracks, root channels, and insect burrows in the engineered earthen barriers. A modification to some models in which fast and slow pathway DRn values are partitioned appears to give a good representation of the data; 4% of the fast pathway was needed to fit the data regression. For locations with high Sw and highest DRn (and fluxes) at each site, the proportion of fast pathway ranged from 1.7% to 34%, but for many locations with lower fluxes, little if any fast pathway was needed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvrad.2023.107140","usgsCitation":"Fuhrmann, M., Caldwell, T., Likos, W.J., Waugh, W.J., Williams, M.M., and Benson, C.H., 2023, Evolving radon diffusion through earthen barriers at uranium waste disposal sites: Journal of Environmental Radioactivity, v. 262, 107140, 7 p., https://doi.org/10.1016/j.jenvrad.2023.107140.","productDescription":"107140, 7 p.","ipdsId":"IP-140005","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":444139,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2424456","text":"External Repository"},{"id":414702,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"262","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fuhrmann, Mark","contributorId":293204,"corporation":false,"usgs":false,"family":"Fuhrmann","given":"Mark","email":"","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":867453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Todd 0000-0003-4068-0648","orcid":"https://orcid.org/0000-0003-4068-0648","contributorId":217924,"corporation":false,"usgs":true,"family":"Caldwell","given":"Todd","email":"","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867454,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Likos, William J. 0000-0001-8177-6625","orcid":"https://orcid.org/0000-0001-8177-6625","contributorId":303390,"corporation":false,"usgs":false,"family":"Likos","given":"William","email":"","middleInitial":"J.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":867455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waugh, W. Jodi","contributorId":303391,"corporation":false,"usgs":false,"family":"Waugh","given":"W.","email":"","middleInitial":"Jodi","affiliations":[{"id":65785,"text":"RSI Entech","active":true,"usgs":false}],"preferred":false,"id":867456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Morgan M.","contributorId":303392,"corporation":false,"usgs":false,"family":"Williams","given":"Morgan","email":"","middleInitial":"M.","affiliations":[{"id":65785,"text":"RSI Entech","active":true,"usgs":false}],"preferred":false,"id":867457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Benson, Craig H. 0000-0001-8871-382X","orcid":"https://orcid.org/0000-0001-8871-382X","contributorId":303394,"corporation":false,"usgs":false,"family":"Benson","given":"Craig","email":"","middleInitial":"H.","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":867458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266729,"text":"70266729 - 2023 - Multistage hierarchical capture–recapture models","interactions":[],"lastModifiedDate":"2025-05-12T14:59:43.43047","indexId":"70266729","displayToPublicDate":"2023-03-20T09:57:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"title":"Multistage hierarchical capture–recapture models","docAbstract":"Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual-level data. A variety of implementations exist for such models, including integrated likelihood, parameter-expanded data augmentation, and combinations of those. Capture–recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture–recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two datasets resulting from capture–recapture studies of different species.
","language":"English","publisher":"Wiley","doi":"10.1002/env.2799","usgsCitation":"Hooten, M., Schwob, M., Johnson, D., and Ivan, J., 2023, Multistage hierarchical capture–recapture models, v. 34, no. 6, e2799, 14 p., https://doi.org/10.1002/env.2799.","productDescription":"e2799, 14 p.","ipdsId":"IP-129867","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":936613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwob, Michael 0000-0001-6367-2013","orcid":"https://orcid.org/0000-0001-6367-2013","contributorId":315373,"corporation":false,"usgs":false,"family":"Schwob","given":"Michael","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":936614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Devin","contributorId":346945,"corporation":false,"usgs":false,"family":"Johnson","given":"Devin","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":936615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ivan, Jacob S.","contributorId":200243,"corporation":false,"usgs":false,"family":"Ivan","given":"Jacob S.","affiliations":[],"preferred":false,"id":936616,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241606,"text":"70241606 - 2023 - A targeted annual warning system developed for the conservation of a sagebrush indicator species","interactions":[],"lastModifiedDate":"2023-03-24T12:07:59.966599","indexId":"70241606","displayToPublicDate":"2023-03-20T07:02:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A targeted annual warning system developed for the conservation of a sagebrush indicator species","docAbstract":"A fundamental goal of population ecologists is to identify drivers responsible for temporal variation in abundance. Understanding whether variation is associated with environmental stochasticity or anthropogenic disturbances, which are more amenable to management action, is crucial yet difficult to achieve. Here, we present a hierarchical monitoring framework that models rates of change in abundance from spatially structured populations and identifies when local declines fall out of synchrony with trends at larger spatial scales. Importantly, the framework provides signals that alert managers to the categorical significance of observed declines while avoiding signals where declines result from drivers operating at larger spatial scales (e.g., periodic reductions in primary productivity owing to drought). We demonstrate utility through application to a rapidly declining sagebrush (Artemisia spp.) indicator species (greater sage-grouse; Centrocercus urophasianus) using 30 years (1990–2019) of count data collected from greater than 4,400 leks (habitual breeding sites) distributed across the western United States. Results revealed population declines, immediately preceding triggers (2–4-year period), ranging between 58 and 68%. Conversely, population trends unassociated with triggers showed little-to-no sign of decline. Retrospective application of the monitoring framework indicated an average annual rate of 1.7% of leks or 1.3% of neighborhood clusters (lek aggregations) would have required management intervention to reverse range-wide declines and stabilize the U.S. population as a whole.
There is inconsistent evidence that stream restoration projects lead to recovery of ecosystem attributes, especially stream biota. While some assessments have documented desired changes in fish community metrics in the first years following restoration, longer-term studies have not always corroborated these findings. In this study, we used data and monitoring reports submitted to federal regulators by stream mitigation consultants to examine whether in-stream restoration activities led to changes in fish community attributes at 23 compensatory mitigation projects representing 53 sampling sites in Georgia, United States over 7 years of post-restoration monitoring. Modeling results indicated that abundance and species richness of fishes generally increased in the first years after restoration before decreasing to baseline levels by the seventh year. This pattern was consistent for models considering sensitive fish taxa, as well as at sites across a range of agricultural and forested land cover percentages. However, the effect of restoration on species richness was dampened in larger streams and at more urbanized locations. A community trajectory analysis supported the findings that fish community change was transitory at most sites. Remote estimation of canopy cover change at restoration sites suggested that the hump-shaped response may be driven by increased light availability during the immediate-post restoration period, followed by subsequent re-shading of stream channels by riparian plantings. Our analysis indicates that reach-level manipulation of streams should not be expected to induce long-term changes in fish communities, and that publicly available monitoring reports may be leveraged to address questions of stream restoration efficacy.
Assemblage patterns and processes of aquatic vegetation in most large floodplain rivers are not well understood, particularly after plant recovery. Identifying vegetation types, which are recurring plant groupings based on species composition, diversity, and abundances, can describe plant assembly patterns and environmental drivers that aid conservation planning and management. We used a 22-year dataset (n = 18,000 sampling plots) to identify aquatic vegetation types during an “early phase” and “late phase” of plant recovery at multiple spatial scales nested within a 500-km river reach of the Upper Mississippi River, USA. We hypothesized that vegetation types varied according to scale because of the stark environmental differences among riverine habitats and differing regional species pools along the river's latitudinal gradient, and that the late phase of recovery had developed several new vegetation types. We first used cluster analyses at multiple spatiotemporal scales to identify the number of vegetation types and their characteristics, such as indicator species, species compositions and abundances, and diversity index. Then we applied a multivariate regression to pinpoint environmental factors (such as hydrodynamics, system productivity, local habitat, and water quality) that structured those vegetation types. Clustering revealed that ~90% of plots irrespective of recovery phase were not classified into vegetation types, which indicated that most aquatic sampling plots are unique in species composition and unpredictable. However, impounded areas upriver from dams had matured five vegetation types: lotus (Nelumbo lutea Willd.), submersed (a mix of 11 common submersed species), watercelery (Vallisneria americana Michx.), arrowheads (Sagittaria rigida Pursh and Sagittaria latifolia Willd.), and a diverse community (with high diversity indices and multiple life forms). The vegetation types were associated with three environmental gradients related to inundation depth and duration, system productivity, and water clarity. These five vegetation types are known to be of high ecological value to fish and wildlife and thus targets for restoration, for example, the watercelery community is principal forage for migrating canvasback ducks (Aythya valisineria) along the Mississippi River flyway. Our results provide insights on vegetation assembly during recovery and aid habitat conservation by providing quantitative, environmental targets for restoration.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4468","usgsCitation":"Larson, D.M., Carhart, A., and Lund, E., 2023, Aquatic vegetation types identified during early and late phases of vegetation recovery in the Upper Mississippi River: Ecosphere, v. 14, no. 3, e4468, 20 p., https://doi.org/10.1002/ecs2.4468.","productDescription":"e4468, 20 p.","ipdsId":"IP-131066","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":444170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4468","text":"Publisher Index Page"},{"id":415663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.10976435882736,\n 41.60685944282207\n ],\n [\n -89.99975982612516,\n 42.224983847864536\n ],\n [\n -90.81533578734795,\n 43.0853039521503\n ],\n [\n -91.17295281697338,\n 44.10941290093686\n ],\n [\n -92.4919662081708,\n 44.74832598668567\n ],\n [\n -92.97405022349875,\n 45.04742613671087\n ],\n [\n -93.13038965400605,\n 44.71333048187006\n ],\n [\n -91.91569538208077,\n 44.03813254031178\n ],\n [\n -91.35537860580692,\n 43.326638741150475\n ],\n [\n -91.34212433361859,\n 42.710247775085435\n ],\n [\n -90.44333088312968,\n 42.162313420549964\n ],\n [\n -90.79716133996195,\n 41.5831628575645\n ],\n [\n -90.10976435882736,\n 41.60685944282207\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, Danelle M. 0000-0001-6349-6267","orcid":"https://orcid.org/0000-0001-6349-6267","contributorId":228838,"corporation":false,"usgs":true,"family":"Larson","given":"Danelle","email":"","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":865662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carhart, Alicia 0000-0002-9977-8124","orcid":"https://orcid.org/0000-0002-9977-8124","contributorId":223884,"corporation":false,"usgs":false,"family":"Carhart","given":"Alicia","email":"","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":865663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lund, Eric","contributorId":221777,"corporation":false,"usgs":false,"family":"Lund","given":"Eric","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":865664,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70248233,"text":"70248233 - 2023 - Assessing potential effects of climate change on highway-runoff flows and loads in southern New England by using planning-level space-for-time analyses","interactions":[],"lastModifiedDate":"2023-09-05T12:11:07.324945","indexId":"70248233","displayToPublicDate":"2023-03-19T07:06:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16697,"text":"Transportation Research Record, Journal of the Transportation Research Board.","active":true,"publicationSubtype":{"id":10}},"title":"Assessing potential effects of climate change on highway-runoff flows and loads in southern New England by using planning-level space-for-time analyses","docAbstract":"Coastal habitats can play an important role in climate change mitigation. As Louisiana implements its climate action plan and the restoration and risk-reduction projects outlined in its 2017 Louisiana Coastal Master Plan, it is critical to consider potential greenhouse gas (GHG) fluxes in coastal habitats. This study estimated the potential climate mitigation role of existing, converted, and restored coastal habitats for years 2005, 2020, 2025, 2030, and 2050, which align with the Governor of Louisiana's GHG reduction targets. An analytical framework was developed that considered (1) available scientific data on net ecosystem carbon balance fluxes per habitat and (2) habitat areas projected from modeling efforts used for the 2017 Louisiana Coastal Master Plan to estimate the net GHG flux of coastal area. The coastal area was estimated as net GHG sinks of −38.4 ± 10.6 and −43.2 ± 12.0 Tg CO2 equivalents (CO2e) in 2005 and 2020, respectively. The coastal area was projected to remain a net GHG sink in 2025 and 2030, both with and without the implementation of Coastal Master Plan projects (means ranged from −25.3 to −34.2 Tg CO2e). By 2050, with model-projected wetland loss and conversion of coastal habitats to open water due to coastal erosion and relative sea level rise, Louisiana's coastal area was projected to become a net source of GHG emissions both with and without the Coastal Master Plan projects. However, in the year 2050, the Louisiana Coastal Master Plan project implementation was projected to avoid the release of +8.8 ± 1.3 Tg CO2e compared with an alternative with no action. Reduction in current and future stressors to coastal habitats, including impacts from sea level rise, as well as the implementation of restoration projects could help to ensure coastal areas remain a natural climate solution.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2847","usgsCitation":"Baustian, M.M., Liu, B., Moss, L.C., Dausman, A., and Pahl, J.W., 2023, Climate change mitigation potential of Louisiana's coastal area: Current estimates and future projections: Ecological Applications, v. 23, no. 4, e2847, 22 p.; Data Release, https://doi.org/10.1002/eap.2847.","productDescription":"e2847, 22 p.; Data Release","ipdsId":"IP-147080","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444174,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2847","text":"Publisher Index Page"},{"id":415413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419359,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Z2MZV","text":"A subset of 2017 Louisiana Coastal Master Plan model output to estimate climate change mitigation potential of Louisiana’s coastal area"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.59801925944635,\n 30.689846184930914\n ],\n [\n -93.59801925944635,\n 28.629249419941743\n ],\n [\n -89.0431895005861,\n 28.629249419941743\n ],\n [\n -89.0431895005861,\n 30.689846184930914\n ],\n [\n -93.59801925944635,\n 30.689846184930914\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Bingqing","contributorId":304014,"corporation":false,"usgs":false,"family":"Liu","given":"Bingqing","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":868937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moss, Leland C.","contributorId":272644,"corporation":false,"usgs":false,"family":"Moss","given":"Leland","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":868938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dausman, Alyssa","contributorId":223766,"corporation":false,"usgs":false,"family":"Dausman","given":"Alyssa","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":868939,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pahl, James W.","contributorId":304017,"corporation":false,"usgs":false,"family":"Pahl","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":868940,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243534,"text":"70243534 - 2023 - Uses of epistemic uncertainties in the USGS National Seismic Hazard Models","interactions":[],"lastModifiedDate":"2023-05-16T18:21:23.693721","indexId":"70243534","displayToPublicDate":"2023-03-18T06:34:47","publicationYear":"2023","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":"Uses of epistemic uncertainties in the USGS National Seismic Hazard Models","docAbstract":"The Big River in southeast Missouri drains the largest historical lead mining area in the United States. Ongoing releases of metal contaminated sediments into this river are well documented and are suspected of suppressing freshwater mussel populations. We characterized the spatial extent of metal contaminated sediments and evaluated its relationship with mussel populations in the Big River. Mussels and sediments were collected at 34 sites with potential metal effects and 3 reference sites. Analysis of sediment samples showed that lead (Pb) and zinc (Zn) concentrations were 1.5 to 65 times greater than background concentrations in the reach extending 168 km downstream from Pb mining releases. Mussel abundance decreased acutely downstream from these releases where sediment Pb concentrations were highest and increased gradually as Pb sediment concentrations attenuated downstream. We compared current species richness with historical survey data from three reference rivers with similar physical habitat characteristics and human effects, but without Pb-contaminated sediment. Big River species richness was on average about one-half that expected based on reference stream populations and was 70–75 % lower in reaches with high median Pb concentrations. Sediment Zn and cadmium, and particularly Pb, had significant negative correlations with species richness and abundance. The association of sediment Pb concentrations with mussel community metrics in otherwise high-quality habitat indicates that Pb toxicity is likely responsible for depressed mussel populations observed within the Big River. We used concentration-response regressions of mussel density verses sediment Pb to determine that the Big River mussel community is adversely affected when sediment Pb concentrations are above 166 ppm, the concentration associated with 50 % decreases in mussel density. Based on this assessment of metals concentrations sediment and mussel fauna, our findings indicate that sediment in approximately 140 km of the Big River with suitable habitat has a toxic effect to mussels.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.162743","usgsCitation":"Roberts, A.D., Besser, J.M., Hundley, J., Mosby, D., Rosenberger, A.E., Bouska, K.L., Simmons, B., McMurray, S.E., Faiman, S., and Lueckenhoff, L., 2023, An assessment of the relation between metal contaminated sediment and freshwater mussel populations in the Big River, Missouri: Science of the Total Environment, v. 876, 162743, 15 p., https://doi.org/10.1016/j.scitotenv.2023.162743.","productDescription":"162743, 15 p.","ipdsId":"IP-138980","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":427520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Big 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and links to earthquake behavior","docAbstract":"Fault geometry affects the initiation, propagation, and cessation of earthquake rupture, as well as, potentially, the statistical behavior of earthquake sequences. We analyze 18,250 (−0.27 < M < 4.4) earthquakes of the 2016–2019 Cahuilla, California, swarm and, for the first time, use these high‐resolution earthquake locations to map, in detail, the roughness across an active fault surface at depth. We find that the strike‐slip fault is 50% rougher in the slip‐perpendicular direction than parallel to slip. 3D mapping of fault roughness at seismogenic depths suggests that roughness varies by a factor of 8 for length scales of 1 km. We observe that the largest earthquake (M 4.4) occurred where there is significant fault complexity and the highest measured roughness. We also find that b‐values are weakly positively correlated with fault roughness. Following the largest earthquake, we observe a distinct population of earthquakes with comparatively low b‐values occurring in an area of high roughness within the rupture area of the M 4.4 earthquake. Finally, we measure roughness at multiple scales and find that the fault is self‐affine with a Hurst exponent of 0.52, consistent with a Brownian surface.
Introduction: Beta diversity represents changes in community composition among locations across a landscape. While the effects of human activities on beta diversity are becoming clearer, few studies have considered human effects on the three dimensions of beta diversity: taxonomic, functional, and phylogenetic. Including anthropogenic factors and multiple dimensions of biodiversity may explain additional variation in stream fish beta diversity, providing new insight into how metacommunities are structured within different spatial delineations.
Methods: In this study, we used a 350 site stream fish abundance dataset from South Carolina, United States to quantify beta diversity explainable by spatial, natural environmental, and anthropogenic variables. We investigated three spatial delineations: (1) a single whole-state metacommunity delineated by political boundaries, (2) two metacommunities delineated by a natural geomorphic break separating uplands from lowlands, and (3) four metacommunities delineated by natural watershed boundaries. Within each metacommunity we calculated taxonomic, functional, and phylogenetic beta diversity and used variation partitioning to quantify spatial, natural environmental, and anthropogenic contributions to variations in beta diversity.
Results: We explained 25–81% of the variation in stream fish beta diversity. The importance of these three factors in structuring metacommunities differed among the diversity dimensions, providing complementary perspectives on the processes shaping beta diversity in fish communities. The effect of spatial, natural environmental, and anthropogenic factors varied among the spatial delineations, which indicate conclusions drawn from variation partitioning may depend on the spatial delineation chosen by researchers.
Discussion: Our study highlights the importance of considering human effects on metacommunity structure, quantifying multiple dimensions of beta diversity, and careful consideration of user-defined metacommunity boundaries in beta diversity analyses.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1077994","usgsCitation":"Stocsynski, L., Scott, M.C., Bower, L.M., and Peoples, B.K., 2023, Effects of environment and metacommunity delineation on multiple dimensions of stream fish beta diversity: Frontiers in Ecology and Evolution, v. 11, 1077994, 16 p., https://doi.org/10.3389/fevo.2023.1077994.","productDescription":"1077994, 16 p.","ipdsId":"IP-142686","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/fevo.2023.1077994","text":"Publisher Index Page"},{"id":433012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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Carolina\",\"nation\":\"USA \"}}]}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Stocsynski, Lauren","contributorId":341032,"corporation":false,"usgs":false,"family":"Stocsynski","given":"Lauren","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":907830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, Mark C.","contributorId":341033,"corporation":false,"usgs":false,"family":"Scott","given":"Mark","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":907831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bower, Luke Max 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Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Increasing hypoxia on global coral reefs under ocean warming","docAbstract":"Ocean deoxygenation is predicted to threaten marine ecosystems globally. However, current and future oxygen concentrations and the occurrence of hypoxic events on coral reefs remain underexplored. Here, using autonomous sensor data to explore oxygen variability and hypoxia exposure at 32 representative reef sites, we reveal that hypoxia is already pervasive on many reefs. Eighty-four percent of reefs experienced weak to moderate (≤153 µmol O2 kg−1 to ≤92 µmol O2 kg−1) hypoxia and 13% experienced severe (≤61 µmol O2 kg−1) hypoxia. Under different climate change scenarios based on four Shared Socioeconomic Pathways (SSPs), we show that projected ocean warming and deoxygenation will increase the duration, intensity and severity of hypoxia, with more than 94% and 31% of reefs experiencing weak to moderate and severe hypoxia, respectively, by 2100 under SSP5-8.5. This projected oxygen loss could have negative consequences for coral reef taxa due to the key role of oxygen in organism functioning and fitness.
","language":"English","publisher":"Nature","doi":"10.1038/s41558-023-01619-2","usgsCitation":"Pezner, A.K., Courtney, T.A., Barkley, H., Chou, W., Chu, H., Clements, S.M., Cyronak, T., DeGrandpre, M.D., Kekuewa, S.A., Kline, D.I., Liang, Y., Martz, T.R., Mitarai, S., Page, H.N., Rintoul, M.S., Smith, J.E., Soong, K., Takeshita, Y., Tresguerres, M., Wei, Y., Yates, K.K., and Andersson, A.J., 2023, Increasing hypoxia on global coral reefs under ocean warming: Nature Climate Change, v. 13, p. 403-409, https://doi.org/10.1038/s41558-023-01619-2.","productDescription":"7 p.","startPage":"403","endPage":"409","ipdsId":"IP-138099","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467117,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://oist.repo.nii.ac.jp/record/2000571/files/Pezner_MS_edits_clean_coauthors.pdf","text":"External 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U.S. Geological Survey
640 Grassmere Park Drive
Nashville, TN 37211
On 20 December 2020, after more than 2 years of quiescence at Kīlauea Volcano, Hawaiʻi, renewed volcanic activity in the summit crater caused boiling of the water lake over a period of ∼90 min. The resulting water-rich, electrified plume rose to 11–13 km above sea level, which is among the highest plumes on record for Kīlauea. Although conventional models would infer a high mass flux from explosive magma-water interaction, the plume was not associated with an infrasound signal indicative of “explosive” activity, nor did it produce a measurable ash-fall deposit. We use multisensor data to characterize lava-water interaction and plume generation during this opening phase of the 2020–21 eruption. Satellite, weather radar, and eyewitness observations revealed that the plume was rich in water vapor and hydrometeors but transported less ash than expected from its maximum height. Volcanic lightning flashes detected by ground-based cameras were confined to freezing altitudes of the upper cloud, suggesting that the ice formation drove the electrification of this plume. The low acoustic energy from lava-water interaction points to a weakly explosive style of hydrovolcanism. Heat transfer calculations show that the lava to water heat flux was sufficient to boil the lake within 90 min. Limited mixing of lava and water inhibited major steam explosions and fine fragmentation. Results from one-dimensional plume modeling suggest that the models may underpredict plume height due to overestimation of crosswind air-entrainment. Our findings shed light on an unusual style of volcanism in which weakly explosive lava-water interaction generated an outsized plume.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GC010718","usgsCitation":"Cahalan, R.C., Mastin, L.G., Van Eaton, A.R., Hurwitz, S., Smith, A., Dufek, J., Solovitz, S.A., Patrick, M.R., Schmith, J., Parcheta, C., Thelen, W., and Downs, D.T., 2023, Dynamics of the December 2020 ash-poor plume formed by lava-water interaction at the summit of Kilauea Volcano, Hawaii: Geochemistry, Geophysics, Geosystems, v. 24, no. 3, e2022GC010718, 23 p., https://doi.org/10.1029/2022GC010718.","productDescription":"e2022GC010718, 23 p.","ipdsId":"IP-145500","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":489752,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gc010718","text":"Publisher Index Page"},{"id":481272,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.2803669612127,\n 19.458319847666203\n ],\n [\n -155.2803669612127,\n 19.37101672587596\n ],\n [\n -155.17107998542878,\n 19.37101672587596\n ],\n [\n -155.17107998542878,\n 19.458319847666203\n ],\n [\n -155.2803669612127,\n 19.458319847666203\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Cahalan, Ryan Cain 0000-0002-3322-0654","orcid":"https://orcid.org/0000-0002-3322-0654","contributorId":302355,"corporation":false,"usgs":true,"family":"Cahalan","given":"Ryan","email":"","middleInitial":"Cain","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":925179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":925181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - 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2023 - Crossing the threshold: Invasive grasses inhibit forest restoration on Hawaiian islands","interactions":[],"lastModifiedDate":"2024-01-08T17:12:25.046632","indexId":"70250816","displayToPublicDate":"2023-03-15T11:07:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Crossing the threshold: Invasive grasses inhibit forest restoration on Hawaiian islands","docAbstract":"Forest removal for livestock grazing is a striking example of human-caused state change leading to a stable, undesirable invasive grass system that is resistant to restoration efforts. Understanding which factors lead to resilience to the alternative grass state can greatly benefit managers when planning forest restoration. We address how thresholds of grass cover and seed rain might influence forest recovery in a restoration project on Hawaiʻi Island, USA. Since the 1980s, over 400,000 Acacia koa (koa) trees have been planted across degraded pasture, and invasive grasses still dominate the understory with no native woody-plant recruitment. Between this koa/grass matrix are remnant native Metrosideros polymorpha (ʻōhiʻa) trees beneath which native woody plants naturally recruit. We tested whether there were threshold levels of native woody understory that accelerate recruitment under both tree species by monitoring seed rain at 40 trees (20 koa and ʻōhiʻa) with a range of native woody understory basal area (BA). We found a positive relationship between total seed rain (but not bird-dispersed seed rain) and native woody BA and a negative relationship between native woody BA and grass cover, with no indication of threshold dynamics. We also experimentally combined grass removal levels with seed rain density (six levels) of two common understory species in plots under koa (n = 9) and remnant ʻōhiʻa (n = 9). Few seedlings emerged when no grass was removed despite adding seeds at densities two to 75 times higher than naturally occurring. However, seedling recruitment increased two to three times once at least 50% of grass was removed. Existing survey data of naturally occurring seedlings also supported a threshold of grass cover below which seedlings were able to establish. Thus, removal of all grasses is not necessary to achieve system responses: Even moderate reductions (~50%) can increase rates of native woody recruitment. The nonlinear thresholds found here highlight how incremental changes to an inhibitory factor lead to limited restoration success until a threshold is crossed. The resources needed to fully eradicate an invasive species may be unwarranted for state change, making understanding where thresholds lie of the utmost importance to prioritize resources.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2841","usgsCitation":"Rehm, E.M., D'Antonio, C., and Yelenik, S.G., 2023, Crossing the threshold: Invasive grasses inhibit forest restoration on Hawaiian islands: Ecological Applications, v. 33, no. 4, e2841, 16 p., https://doi.org/10.1002/eap.2841.","productDescription":"e2841, 16 p.","ipdsId":"IP-146381","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":444204,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2841","text":"Publisher Index Page"},{"id":435410,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98OUTPW","text":"USGS data release","linkHelpText":"Hakalau Forest NWR seedling and substrate data, 2015"},{"id":424193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hakalau Forest National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.33984563763397,\n 19.888564964992852\n ],\n [\n -155.33984563763397,\n 19.7594436528868\n ],\n [\n -155.2208910771554,\n 19.7594436528868\n ],\n [\n -155.20855397426797,\n 19.931097090422853\n ],\n [\n -155.33984563763397,\n 19.888564964992852\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Rehm, Evan M","contributorId":216487,"corporation":false,"usgs":false,"family":"Rehm","given":"Evan","email":"","middleInitial":"M","affiliations":[{"id":39457,"text":"University of California at Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":891660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D'Antonio, Carla M.","contributorId":27992,"corporation":false,"usgs":false,"family":"D'Antonio","given":"Carla M.","affiliations":[],"preferred":false,"id":891661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":891662,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70254295,"text":"70254295 - 2023 - Development of a benchmark eddy flux evapotranspiration dataset for evaluation of satellite-driven evapotranspiration models over the CONUS","interactions":[],"lastModifiedDate":"2024-05-17T14:39:17.114319","indexId":"70254295","displayToPublicDate":"2023-03-15T09:36:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Development of a benchmark eddy flux evapotranspiration dataset for evaluation of satellite-driven evapotranspiration models over the CONUS","docAbstract":"A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2023.109307","usgsCitation":"Volk, J.M., Huntington, J., Melton, F.M., Allen, R., Anderson, M.C., Fisher, J.B., Kilic, A., Senay, G.B., Halverson, G., Knipper, K., Minor, B., Pearson, C., Wang, T., Yang, Y., Evett, S.R., French, A.N., Jasoni, R.L., and Kustas, W.P., 2023, Development of a benchmark eddy flux evapotranspiration dataset for evaluation of satellite-driven evapotranspiration models over the CONUS: Agricultural and Forest Meteorology, v. 331, 109307, 15 p., https://doi.org/10.1016/j.agrformet.2023.109307.","productDescription":"109307, 15 p.","ipdsId":"IP-147475","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":444209,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2023.109307","text":"Publisher Index 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DMWW uses groundwater and surface water as raw water sources to supply the City of Des Moines and surrounding communities. In response to current and future demands, DMWW is in need of a thorough understanding of local groundwater resources, specifically the Des Moines River alluvial aquifer. The Des Moines River alluvial aquifer is hydraulically connected to the Des Moines River and consists of alluvial deposits and glacial outwash sands and gravels. To ensure a sustainable groundwater supply, additional information to better understand and manage groundwater availability within the Des Moines River alluvial aquifer would be beneficial. Beginning in 2018, DMWW partnered with the U.S. Geological Survey to construct a groundwater-flow model to increase understanding of the hydrologic system in the Des Moines area. The model hydrogeologic framework will be enhanced by using multiple geophysical methods of data collection in the Des Moines River, Beaver Creek, and the Des Moines River alluvial aquifer that could provide a better understanding of the geology in the model area.
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