We surveyed for Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) along the upper San Luis Rey River, near Lake Henshaw, in Santa Ysabel, California, in 2020. Surveys were completed at four locations: three downstream from Lake Henshaw, where nest monitoring occurred from 2015 to 2019 (Rey River Ranch [RRR], Cleveland National Forest [CNF], Vista Irrigation District [VID]), and one at VID Lake Henshaw (VLH) that was previously surveyed in 2018 and 2019. There were 62 territorial flycatchers detected at 3 locations (RRR, CNF, VLH); no flycatchers were detected at VID. Within the former nest monitoring study area, 11 flycatchers, including 5 males and 6 females, were detected at RRR and CNF. In total, seven territories were established, consisting of six pairs (two polygynous groups consisting of two males each pairing with two different females) and one male of undetermined breeding status. Upstream from the former nest monitoring study area at VLH, we detected 51 flycatchers, including 24 males and 27 females. There were 28 territories established, containing 27 pairs (23 monogamous pairings and 4 confirmed polygynous pairings consisting of 1 male and 2 females) and 1 male of undetermined breeding status. Brown-headed cowbirds (Molothrus ater; cowbird) were detected at all four survey locations.
Flycatchers used four different habitat types in the survey area: (1) mixed willow riparian, (2) willow-oak, (3) willow-ash, and (4) willow-sycamore. Eighty percent of the flycatchers were detected in habitat characterized as mixed willow riparian, and 83 percent of the flycatchers were detected in habitat with greater than 50-percent native plant cover. Exotic vegetation was not prevalent in the survey area.
There were 17 flycatcher nests incidentally located during surveys: 2 were successful, 3 were seen with nestlings on the last visit, 10 failed, and the outcome of the remaining 2 nests was unknown. Five of these nests were parasitized by cowbirds. There were 10 juveniles detected during surveys: 2 at RRR and 8 at VLH.
Of the 17 banded flycatchers detected during surveys, 8 were resighted and confirmed to be adults that held territories in previous years. Seven flycatchers with a single dark blue federal band, indicating that they were banded as nestlings in the former nest monitoring study area downstream from Lake Henshaw, were resighted during surveys; 86 percent of these “natal” flycatchers held territories at VLH.
In 2020, we documented both adult and natal flycatchers moving from the former nest monitoring study area downstream from Lake Henshaw upstream to the habitat surrounding Lake Henshaw. Six natal flycatchers that were originally banded as nestlings and two adults that previously held territories downstream dispersed to Lake Henshaw in 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1140","usgsCitation":"Howell, S.L., and Kus, B.E., 2021, Distribution and abundance of Southwestern Willow Flycatchers (Empidonax traillii extimus) on the upper San Luis Rey River, San Diego County, California—2020 data summary: U.S. Geological Survey Data Series 1140, 11 p., https://doi.org/10.3133/ds1140.","productDescription":"vi, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-126374","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":388356,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1140/covrthb.jpg"},{"id":388357,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1140/ds1140.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388359,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ds/1140/images"},{"id":388358,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ds/1140/ds1140.xml"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.9769287109375,\n 33.15364887320581\n ],\n [\n -116.69403076171875,\n 33.15364887320581\n ],\n [\n -116.69403076171875,\n 33.29150775159364\n ],\n [\n -116.9769287109375,\n 33.29150775159364\n ],\n [\n -116.9769287109375,\n 33.15364887320581\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The eastern oyster, Crassostrea virginica, is a foundation species within US Gulf of Mexico (GoM) estuaries that has experienced substantial population declines. As changes from management and climate are expected to continue to impact estuarine salinity, understanding how local oyster populations might respond and identifying populations with adaptations to more extreme changes in salinity could inform resource management, including restoration and aquaculture programs. Wild oysters were collected from four estuarine sites from Texas [Packery Channel (PC): 35.5, annual mean salinity, Aransas Bay (AB): 23.0] and Louisiana [Calcasieu Lake (CL): 16.2, Vermilion Bay (VB): 7.4] and spawned. The progeny were compared in field and laboratory studies under different salinity regimes. For the field study, F1 oysters were deployed at low (6.4) and intermediate (16.5) salinity sites in Alabama. Growth and mortality were measured monthly. Condition index and Perkinsus marinus infection intensity were measured quarterly. For the laboratory studies, mortality was recorded in F1 oysters that were exposed to salinities of 2.0, 4.0, 20.0/22.0, 38.0 and 44.0 with and without acclimation. The results of the field study and laboratory study with acclimation indicated that PC oysters are adapted to high-salinity conditions and do not tolerate very low salinities. The AB stock had the highest plasticity as it performed as well as the PC stock at high salinities and as well as Louisiana stocks at the lowest salinity. Louisiana stocks did not perform as well as the Texas stocks at high salinities. Results from the laboratory studies without salinity acclimation showed that all F1 stocks experiencing rapid mortality at low salinities when 3-month oysters collected at a salinity of 24 were used and at both low and high salinities when 7-month oysters collected at a salinity of 14.5 were used.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coab065","usgsCitation":"Marshall, D., Casas, S., Walton, W., Rikard, F., Palmer, T., Breaux, N., La Peyre, M., Pollack, J., Kelly, M., and LaPeyre, J., 2021, Divergence in salinity tolerance of northern Gulf of Mexico eastern oysters under field and laboratory exposure: Conservation Physiology, v. 9, no. 1, coab065, 20 p., https://doi.org/10.1093/conphys/coab065.","productDescription":"coab065, 20 p.","ipdsId":"IP-124006","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coab065","text":"Publisher Index Page"},{"id":396698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Louisiana, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.998046875,\n 24.746831298412058\n ],\n [\n -86.5283203125,\n 30.278044377800153\n ],\n [\n -87.69287109375,\n 30.80791068136646\n ],\n [\n -89.62646484375,\n 30.278044377800153\n ],\n [\n -94.41650390625,\n 30.107117887092357\n ],\n [\n -97.58056640625,\n 28.420391085674304\n ],\n [\n -97.998046875,\n 24.746831298412058\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, D.A.","contributorId":287622,"corporation":false,"usgs":false,"family":"Marshall","given":"D.A.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":836955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casas, S.M.","contributorId":274910,"corporation":false,"usgs":false,"family":"Casas","given":"S.M.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":836956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton, W.C.","contributorId":287624,"corporation":false,"usgs":false,"family":"Walton","given":"W.C.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":836957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rikard, F.S.","contributorId":287626,"corporation":false,"usgs":false,"family":"Rikard","given":"F.S.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":836958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palmer, T.A.","contributorId":287629,"corporation":false,"usgs":false,"family":"Palmer","given":"T.A.","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":836959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breaux, N.","contributorId":287631,"corporation":false,"usgs":false,"family":"Breaux","given":"N.","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":836960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pollack, J.B.","contributorId":287633,"corporation":false,"usgs":false,"family":"Pollack","given":"J.B.","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":836962,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kelly, M.A.","contributorId":221161,"corporation":false,"usgs":false,"family":"Kelly","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":836963,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"LaPeyre, J.F.","contributorId":272909,"corporation":false,"usgs":false,"family":"LaPeyre","given":"J.F.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":836964,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223713,"text":"70223713 - 2021 - Disruption of the Francisella noatunensis orientalis pdpA gene results in virulence attenuation and protection in zebrafish","interactions":[],"lastModifiedDate":"2021-10-18T14:27:55.063182","indexId":"70223713","displayToPublicDate":"2021-08-23T07:32:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1987,"text":"Infection and Immunity","active":true,"publicationSubtype":{"id":10}},"title":"Disruption of the Francisella noatunensis orientalis pdpA gene results in virulence attenuation and protection in zebrafish","docAbstract":"Wetland creation is a common practice to mitigate for the loss of natural wetlands. However, there is still uncertainty about how effectively created wetlands replace habitat provided by natural wetlands. This uncertainty is due in part because post-construction monitoring of biological communities, and vertebrates especially, is rare and typically short-term (<5 years). We estimated occupancy of 4 amphibian species in 8 created mitigation wetlands, 7 impacted wetlands, and 7 reference wetlands in the Greater Yellowstone Ecosystem in Wyoming, USA. Mitigation wetlands were created to replace wetland habitat that was lost during road construction and ranged in age from 1 to 10 years when sampled. Impacted wetlands were natural wetlands partially filled by road construction and were adjacent to a highway. We sampled for amphibian larvae during 6 summers from 2013 to 2020 and used multi-species occupancy models that estimated detection and occupancy of each of 4 amphibian species to determine how amphibian responses changed over time, especially in mitigation wetlands. Occupancy did not differ between impacted and reference wetlands for any of the 4 amphibian species. Western Toads (Anaxyrus boreas) were most common (although briefly) in created wetlands, and occupancy of Columbia Spotted Frogs (Rana luteiventris), Western Tiger Salamanders (Ambystoma mavortium), and Boreal Chorus Frogs (Pseudacris maculata) was lower in created wetlands than in impacted or reference wetlands. Individual wetland area was positively associated with occupancy for all 4 species and wetland vegetation cover was positively associated with Boreal Chorus Frog and Columbia Spotted Frog occupancy; these results emphasize the importance of design characteristics when planning mitigation wetlands. The link between wetland age and occupancy was complex and included threshold and quadratic relationships for three of the four species, but only Boreal Chorus Frog occupancy was still increasing slowly at the end of our study. Our results indicate created wetlands did not attain the suitability of impacted and natural wetlands for local amphibians, even several years after construction. The complex relationships between wetland age and species-specific occupancy illustrate the importance of long-term monitoring in describing population responses to the construction of wetlands as mitigation for wetland loss.
California in the year 2020 experienced a record breaking number of large fires. Here, we place this and other recent years in a historical context by examining records of large fire events in the state back to 1860. Since drought is commonly associated with large fire events, we investigated the relationship of large fire events to droughts over this 160 years period.
This study shows that extreme fire events such as seen in 2020 are not unknown historically, and what stands out as distinctly new is the increased number of large fires (defined here as > 10,000 ha) in the last couple years, most prominently in 2020. Nevertheless, there have been other periods with even greater numbers of large fires, e.g., 1929 had the second greatest number of large fires. In fact, the 1920’s decade stands out as one with many large fires.
In the last decade, there have been several years with exceptionally large fires. Earlier records show fires of similar size in the nineteenth and early twentieth century. Lengthy droughts, as measured by the Palmer Drought Severity Index (PDSI), were associated with the peaks in large fires in both the 1920s and the early twenty-first century.
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\"}}]}","volume":"17","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Syphard, Alexandra D.","contributorId":8977,"corporation":false,"usgs":false,"family":"Syphard","given":"Alexandra","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":824142,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223129,"text":"ofr20201122 - 2021 - Structured decision making and optimal bird monitoring in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2021-08-23T13:45:32.769864","indexId":"ofr20201122","displayToPublicDate":"2021-08-20T14:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1122","displayTitle":"Structured Decision Making and Optimal Bird Monitoring in the Northern Gulf of Mexico","title":"Structured decision making and optimal bird monitoring in the northern Gulf of Mexico","docAbstract":"The avian conservation community struggles to design and implement large scale, long-term coordinated bird monitoring programs within the northern Gulf of Mexico due to the complexity of the conservation enterprise in the region; this complexity arises from the diverse stakeholders, multiple jurisdictions, complex ecological processes, myriad habitats, and over 500 species of birds using the region for at least some part of their annual cycle. In addition, long-term monitoring over large spatial scales is difficult because of the need for monitoring data to both (1) evaluate management and restoration outcomes, and (2) provide reliable information about the status and trends of bird populations over time.
To address these challenges, the Gulf of Mexico Avian Monitoring Network developed a problem statement:
“How can a cost-effective monitoring strategy for the Gulf Coast bird community and ecosystem be developed that evaluates ongoing conservation activities and chronic and acute threats; maximizes learning; and is flexible and holistic enough to detect novel ecological threats and evaluate new and emerging conservation activities?”
A structured decision-making framework was then used to articulate and quantify stakeholder values related to the problem statement. One use of the stakeholder values was to develop a regional, strategic plan for bird monitoring, which is presented elsewhere. A formal and complete decision support tool for conservation investments in monitoring and research guided by the stakeholder values is presented in this report. The technical aspects of the stakeholder value model and a portfolio analysis that could be used to guide decision making when allocating resources for monitoring activities is described. Whereas the decision analysis presented here could be useful to any decision maker faced with difficult choices about resource allocation, it is designed for decision makers who request monitoring study proposals and then determine which combination of proposals to fund. The portfolio decision support tool is designed to help funding agencies and organizations identify resource allocation strategies to maximize stated objectives.
To begin the decision analysis, an objectives hierarchy and quantitative performance metrics from the values of the Gulf of Mexico bird conservation community were created by a panel of regional stakeholders. Each fundamental objective and sub-objective in the hierarchy is composed of several performance metrics. To test the decision support tool, the authors evaluated a combination of monitoring study proposals written for the region and simulated proposals. Each proposal was scored against the performance metrics and used multi-attribute utility theory to combine the multiple objectives into a measure of total monitoring benefit. The total monitoring benefit and costs of each proposal were then used in a constrained optimization routine to identify optimal monitoring portfolios, that is, a combination of activities that maximizes monitoring benefits while meeting cost and other constraints of interest to stakeholders. A graphical solution based on the concept of Pareto efficiency, which is useful in situations when cost constraints and exact budgets are not known, is also provided. Finally, an evaluation of the sensitivity of the decision-making framework to the weights assigned to objectives by stakeholders is included. This decision support tool allows decision makers to identify an optimal suite of monitoring proposals with a transparent portfolio analysis that includes user-defined constraints (such as costs).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201122","collaboration":"Prepared in Cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Fournier, A.M.V., Wilson, R.R., Lyons, J.E., Gleason, J.S., Adams, E.M., Barnhill, L.M., Brush, J.M., Cooper, R.J., DeMaso, S.J., Driscoll, M.J.L., Eaton, M.J., Frederick, P.C., Just, M.G., Seymour, M.A., Tirpak, J.M, and Woodrey, M.S., 2021, Structured decision making and optimal bird monitoring in the northern Gulf of Mexico: U.S. Geological Survey Open-File Report 2020–1122, 62 p., https://doi.org/10.3133/ofr20201122.","productDescription":"Report: ix, 62 p.; 6 Companion Files","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100582","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387878,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.xlsm","text":"2. 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U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
Diel cycles are known to occur in all types of waters, and increasing studies indicate routine water samples may not provide an accurate snapshot in concentrations of trace elements and nutrients. Diel behavior in neutral to alkaline pH ranges is independent of streamflow variability and concentration. Extensive historical U.S. Geological Survey (USGS) water-quality data have been collected in the Eagle River Basin during daylight hours, which is defined as the period of time between one-half hour prior to sunrise and one-half hour after sunset. However, no USGS data have been collected throughout the nighttime, defined as the time between one-half hour after sunset and one-half hour prior to sunrise, making the evaluation of diel cycles impossible. To assess the importance of diel cycling within the Eagle River Basin, the USGS, in cooperation with Eagle River Watershed Council, developed a study to assess the mechanisms, patterns, and magnitude of change during the diel cycle for selected constituents. Water-quality monitors at five USGS streamgage sites (09065500, Gore Creek at Upper Station, near Minturn, Colorado, 09063000, Eagle River at Red Cliff, Colorado, 09064600, Eagle River near Minturn, Colorado, 09066325, Gore Creek above Red Sandstone Creek at Vail, Colorado, and 394220106431500, Eagle River below Milk Creek near Wolcott, Colorado) were deployed in 2017 to evaluate the water-quality field parameters and to determine if water conditions were favorable for the diel cycling of nutrients and trace elements. Based on the evaluation of water-quality parameters, three of the five sites were sampled for nutrient and trace-element concentrations in 2018 to confirm the presence and magnitude of diel cycling. Historical data were also analyzed to assess the effect of time of day on measured nutrient and trace-element concentrations. An assessment of the effect of land use on diel cycling was also investigated.
Measurable nutrients displayed a diel cycle at all three sites with the largest percentage change at the most downstream site (394220106431500), located on the Eagle River. More notable diel cycles at this site include filtered nitrate plus nitrite, which varied 179 percent, with concentrations from 0.24 to 0.67 milligrams per liter (mg/L) and filtered orthophosphate, which varied 71 percent, with concentrations from 0.07 to 0.12 mg/L. Filtered nitrate plus nitrite at site 09066325 varied 57 percent, ranging from 0.14 to 0.22 mg/L. Maximum concentrations occurred prior to noon, decreased through the afternoon (between noon and sunset), and increased during the night (between sunset and sunrise). That pattern is consistent with nutrient uptake in response to daytime (between sunrise and sunset) photosynthesis along with biologically driven denitrification and nitrification cycles. Nutrient concentrations at sites 09064600 and 09066325 were generally low and below laboratory reporting limits, which is the smallest measured concentration that nutrients could be measured by a given analytical method.
Trace-element concentrations were detectable at all sites with the largest percentage change at the most downstream site (394220106431500) and exhibited diel concentration variation from 11.6 to 284 percent. Appreciable diel cycles included filtered copper (0.98–1.40 micrograms per liter [µg/L], 42.9 percent), filtered zinc (less than [<] 4.00–5.50 µg/L, greater than [>] 37.5 percent), total manganese (9.70–19.5 µg/L, 101 percent), and total arsenic (0.30–0.40 µg/L, 33.3 percent). The largest percentage change in concentration was filtered manganese (2.84–10.9 µg/L, 284 percent). Diel cycles at site 09064600 ranged from 9.1 to 64.5 percent across the trace elements measured. Dissolved trace elements with appreciable diel cycles during the sampling period include filtered cadmium (0.09–0.12 µg/L, 33.3 percent), filtered copper (0.99–1.40 µg/L, 41.4 percent), and total arsenic (0.20–0.30 µg/L, 50 percent). The largest percentage change was filtered zinc (38.3–63.0 µg/L, 65 percent). Trace-element concentrations at site 09066325 were below laboratory reporting limits for many parameters, and no diel cycle could be assessed for these parameters. However, total recoverable iron, filtered barium, filtered manganese, and filtered selenium exhibited changes in concentrations of <10.0–19.4 µg/L (>94 percent), 115–121 µg/L (5 percent), 1.44–1.72 µg/L (19.4 percent), and 0.25–0.28 µg/L (12 percent), respectively. At sites 09064600 and 394220106431500, maximum trace-element concentrations occurred during nighttime with some variation regarding the timing of the peak. The exceptions to this were filtered copper, total arsenic, and filtered selenium, which had maximum concentrations around noon or as the sun disappeared below the horizon. The timing of minimum concentrations occurred in the afternoon for many trace elements, with filtered copper, total arsenic, and filtered selenium having minimum concentrations in the morning or just prior to the appearance of the sun.
Analysis of historical data also showed evidence of diel cycling. Historical samples collected from July through October were used to identify diel cycling in base-flow conditions. The resulting diel pattern in the median concentration for filtered manganese, filtered zinc at water-quality site 09064600, and filtered manganese and filtered nitrate plus nitrite at water-quality site 39422016431500 were consistent with the diel pattern in the September 2018 samples, and indicate time of day can bias sampling results even during daylight hours.
Diel cycling in the Eagle River Basin appears to be driven primarily by instream, biological processes. However, land use, particularly human effects downstream from urban areas, mining, and agriculture, may affect these processes. At some locations, diel variations in nutrient and trace-element concentrations are small enough to be of low concern. At other locations, however, variations in concentrations up to 284 percent in the data collected for this study and 214 percent in base-flow historical data, indicate daytime-only sampling, particularly in late afternoon, can underestimate daily average nutrient and trace-element concentrations. When feasible, the potential of diel cycling warrants consideration in sample design to account for the potential of diel cycles, or at a minimum, be recognized as a component of the river dynamic and the potential consequences that diel cycles may have in data interpretation and river management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215066","collaboration":"Prepared in cooperation with Eagle River Watershed Council","usgsCitation":"Richards, R.J., and Henneberg, M.F., 2021, Assessment of diel cycling in nutrients and trace elements in the Eagle River Basin, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5066, 36 p., \nhttps://doi.org/ 10.3133/ sir20215066.","productDescription":"Report: viii, 36 p.; 3 Databases","onlineOnly":"Y","ipdsId":"IP-116765","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":388128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5066/coverthb.jpg"},{"id":388129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5066/sir20215066.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
Retrospective eruption characterization is valuable for advancing our understanding of volcanic systems and evaluating our observational capabilities, especially with remote technologies (defined here as a space-borne system or non-local, ground-based instrumentation which include regional and remote infrasound sensors). In June 2019, the open-system Ulawun volcano, Papua New Guinea, produced a VEI 4 eruption. We combined data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System infrasound network, and GLD360 globally detected lightning with information from the local authorities and social media to characterize the pre-, syn- and post-eruptive behaviour. The Rabaul Volcano Observatory recorded ~24 h of seismicity and detected SO2 emissions ~16 h before the visually-documented start of the Plinian phase on 26 June at 04:20 UTC. Infrasound and SO2 detections suggest the eruption started during the night on 24 June 2019 at 10:39 UTC ~38 h before ash detections with a gas-dominated jetting phase. Local reports and infrasound detections show that the second phase of the eruption started on 25 June 19:28 UTC with ~6 h of jetting. The first detected lightning occurred on 26 June 00:14 UTC, and ash emissions were first detected by Himawari-8 at 01:00 UTC. Post-eruptive satellite imagery indicates new flow deposits to the south and north of the edifice and ash fall to the west and southwest. In particular, regional infrasound data provided novel insight into eruption onset and syn-eruptive changes in intensity. We conclude that, while remote observations are sufficient for detection and tracking of syn-eruptive changes, key challenges in data latency, acquisition, and synthesis must be addressed to improve future near-real-time characterization of eruptions at minimally-monitored or unmonitored volcanoes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107381","usgsCitation":"McKee, K., Smith, C.M., Reath, K., Snee, E., Maher, S., Matoza, R.S., Carn, S.A., Roman, D., Mastin, L.G., Anderson, K.R., Damby, D., Itikarai, I., Mulina, K., Saunders, S., Assink, J.D., de Negri Levia, R., and Perttu, A., 2021, Evaluating the state-of-the-art in remote volcanic eruption characterization Part II: Ulawun volcano, Papua New Guinea: Journal of Geophysical Research, v. 420, 107381, 14 p., https://doi.org/10.1016/j.jvolgeores.2021.107381.","productDescription":"107381, 14 p.","ipdsId":"IP-131054","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":451112,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://hdl.handle.net/11603/26623","text":"Publisher Index Page"},{"id":389733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Papua New 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}\n ]\n}","volume":"420","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Kathleen 0000-0003-3189-9189","orcid":"https://orcid.org/0000-0003-3189-9189","contributorId":265977,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","email":"","affiliations":[{"id":54848,"text":"Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":823930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra Marie 0000-0003-2653-4249 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-2653-4249","contributorId":257000,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","middleInitial":"Marie","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reath, 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Rodrigo 0000-0003-1283-2579","orcid":"https://orcid.org/0000-0003-1283-2579","contributorId":265983,"corporation":false,"usgs":false,"family":"de Negri Levia","given":"Rodrigo","email":"","affiliations":[{"id":54852,"text":"Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA; NDC-CTBT of the Chilean Nuclear Energy Commission, Chile","active":true,"usgs":false}],"preferred":false,"id":823953,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Perttu, Anna 0000-0003-3590-1549","orcid":"https://orcid.org/0000-0003-3590-1549","contributorId":265984,"corporation":false,"usgs":false,"family":"Perttu","given":"Anna","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":823946,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70223688,"text":"70223688 - 2021 - National seed strategy progress report, 2015-2020","interactions":[],"lastModifiedDate":"2021-09-02T11:30:49.37028","indexId":"70223688","displayToPublicDate":"2021-08-20T08:36:09","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":9317,"text":"Progress Report","active":true,"publicationSubtype":{"id":9}},"title":"National seed strategy progress report, 2015-2020","docAbstract":"Native plants are the true green infrastructure we rely on for healthy, resilient, and biodiverse ecosystems. They protect us against climate change and natural disasters; create habitat for wildlife, rare species, and pollinators; and are vital for carbon sequestration. Without native plants, especially their seeds, we do not have the ability to restore functional ecosystems after natural disasters and mitigate the effects of climate change. Investing now in coordinated, research-driven native seed production is an efficient and cost-effective nature-based solution for improving ecosystem resilience in the face of the climate and extinction crisis. Federal government agencies (see list on page 13 and their partners are collaborating to increase the supply of native seeds for restoration through the National Seed Strategy for Rehabilitation and Restoration (National Seed Strategy) to get the right seed in the right place at the right time. The National Seed Strategy is a public-private collaboration to increase the supply of native seeds for restoration projects to ensure ecosystem resilience and the health and prosperity of future generations. Developed by the Plant Conservation Alliance (PCA) in 2015, the National Seed Strategy harnesses cross sector botanical expertise, supports rural, agricultural, minority, and tribal livelihoods, and provides training opportunities to our next generation of natural resource professionals to maintain and preserve our iconic habitats. This science-driven national effort is integral to the Nation’s conservation priorities, including the commitment to conserve 30% of America’s lands and waters by 2030 as outlined in Executive Order 14008 on Tackling the Climate Crisis at Home and Abroad. Moreover, the National Seed Strategy is recognized in the objectives of the 2021 DOI Invasive Species Strategic Plan (DOI 2021) and addresses national priorities such as climate change, wildland fire, and tribal engagement. The National Seed Strategy charts a course for federal, tribal, state, local and private partners to increase private and public sector coordination on native seed development, thereby accelerating the pace and scale of restoration. Success is being achieved through the establishment of nationwide networks of seed collectors, researchers to develop seed, farmers to grow native seed, nurseries and seed storage facilities to supply adequate quantities of appropriate seed, and restoration ecologists.
","language":"English","publisher":"Bureau of Land Management (National Operations Center)","usgsCitation":"Mccormick, M.L., Carr, A., DeAngelis, P., Olwell, M., Murray, R., and Park, M., 2021, National seed strategy progress report, 2015-2020: Progress Report, ii, 74 p.","productDescription":"ii, 74 p.","ipdsId":"IP-130943","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388718,"type":{"id":11,"text":"Document"},"url":"https://www.blm.gov/sites/blm.gov/files/docs/2021-08/Progress%20Report%2026Jul21.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mccormick, Molly Lutisha 0000-0002-4361-7567","orcid":"https://orcid.org/0000-0002-4361-7567","contributorId":265148,"corporation":false,"usgs":true,"family":"Mccormick","given":"Molly","email":"","middleInitial":"Lutisha","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Amanda N","contributorId":265150,"corporation":false,"usgs":false,"family":"Carr","given":"Amanda N","affiliations":[{"id":54608,"text":"Chicago Botanic Garden, Glencoe, IL","active":true,"usgs":false}],"preferred":false,"id":822326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Patricia","contributorId":265151,"corporation":false,"usgs":false,"family":"DeAngelis","given":"Patricia","email":"","affiliations":[{"id":54610,"text":"U.S. Fish and Wildlife Service, Falls Church, VA","active":true,"usgs":false}],"preferred":false,"id":822327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olwell, Margaret","contributorId":265152,"corporation":false,"usgs":false,"family":"Olwell","given":"Margaret","email":"","affiliations":[{"id":54611,"text":"Bureau of Land Management, Boise, ID","active":true,"usgs":false}],"preferred":false,"id":822328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murray, Regan","contributorId":265162,"corporation":false,"usgs":false,"family":"Murray","given":"Regan","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":822359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Park, Maggie","contributorId":265163,"corporation":false,"usgs":false,"family":"Park","given":"Maggie","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":822360,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224747,"text":"70224747 - 2021 - Cohesive sediment modeling in a shallow estuary: Model and environmental implications of sediment parameter variation","interactions":[],"lastModifiedDate":"2021-10-04T12:47:21.058229","indexId":"70224747","displayToPublicDate":"2021-08-20T07:44:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9372,"text":"Journal of Geophysical Research--Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Cohesive sediment modeling in a shallow estuary: Model and environmental implications of sediment parameter variation","docAbstract":"Numerical models of sediment transport in estuarine systems rely on parameter values that are often poorly constrained and can vary on timescales relevant to model processes. The selection of parameter values can affect the accuracy of model predictions, while environmental variation of these parameters can impact the temporal and spatial ranges of sediment fluxes, erosion, and deposition in the real world. We implemented a numerical model of San Pablo Bay, an embayment within San Francisco Bay, California, for November–December 2014, and compared model outputs to observations of water level, velocity, wave parameters, salinity, and suspended sediment concentration (SSC) in the shallow regions. Idealized model runs show that wind timing relative to the phase of the tides is the strongest control on sediment fluxes and bed erosion. We varied sediment erodibility in the outflow of the Petaluma River; while this causes erosion and deposition to vary strongly through the shallows system, total export from the shallows does not change. Model runs with realistic winds show that wind likely resuspends faster settling particles or allows for more particle flocculation; particle settling velocity controls system-wide sediment accumulation. At the margins of the system, the magnitude of SSC is closely tied to wind direction when winds occur during flood tide, but sediment deposition is less connected: Both bed evolution and SSC need to be considered in the prediction of marsh fate. Spatial patterns of light attenuation due to SSC is strongly tied to assumed settling velocity.
Wetlands provide ecological functionality by maintaining and promoting regional biodiversity supporting quality habitat for aquatic organisms. Globally, habitat loss, fragmentation and degradation due to increases in agricultural activities and urban development have reduced or altered geographically isolated wetlands, thus reducing biodiversity. The objective of this study was to assess the relative ecological function and integrity of natural ponds, excavated ponds and stormwater basins in the New Jersey Pinelands, located in the northeastern United States by comparing hydrologic conditions, water quality, pesticide concentrations (water, sediment and tissue) and wetland assemblages including amphibians. Twenty-four wetlands were selected based on surrounding land-use and sampled for a variety of abiotic and biotic variables. Abiotic and biotic wetland variables were similar between natural and excavated ponds, with notable differences between the ponds and stormwater basins. Natural and excavated ponds displayed characteristic Pinelands water quality (low pH, high organic carbon, and low pesticide concentrations), exhibited high ecological integrity and supported native ampbibians. Stormwater basins and degraded ponds surrounded by altered land-use exhibited degraded water quality (high pH, high pesticide concentrations) and were dominated by non-native and introduced plants and amphibians. Results from this study can broadly inform resource conservation strategies for amphibians and other communities with a diverse range of habitat requirements, particularly in areas where conservation and development are competing priorities. To conserve biodiversity in changing landscapes, wetlands with similar functionality and land-use characteristics need to be identified and managed to preserve water quality for species of conservation concern.
No abstract available.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EO162330","usgsCitation":"Pratt, T.L., Chapman, M.C., Shah, A.K., Horton,, J., and Boyd, O.S., 2021, Ten years on from the quake that shook the nation’s capital: EOS, Transactions AGU, HTML Document, https://doi.org/10.1029/2021EO162330.","productDescription":"HTML Document","ipdsId":"IP-131539","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":451117,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021eo162330","text":"Publisher Index Page"},{"id":392849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Washington, D.C.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.519287109375,\n 37.94419750075404\n ],\n [\n -75.421142578125,\n 37.94419750075404\n ],\n [\n -75.421142578125,\n 39.62261494094297\n ],\n [\n -78.519287109375,\n 39.62261494094297\n ],\n [\n -78.519287109375,\n 37.94419750075404\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":828371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Martin C.","contributorId":139348,"corporation":false,"usgs":false,"family":"Chapman","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":828372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":828373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horton,, J. Wright Jr. 0000-0001-6756-6365","orcid":"https://orcid.org/0000-0001-6756-6365","contributorId":219824,"corporation":false,"usgs":true,"family":"Horton,","given":"J. Wright","suffix":"Jr.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":828374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":828375,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223197,"text":"sir20205142 - 2021 - Regional regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011","interactions":[],"lastModifiedDate":"2021-09-21T11:36:03.273884","indexId":"sir20205142","displayToPublicDate":"2021-08-19T15:56:48","publicationYear":"2021","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":"2020-5142","displayTitle":"Regional Regression Equations Based on Channel-Width Characteristics to Estimate Peak-Flow Frequencies at Ungaged Sites in Montana Using Peak-Flow Frequency Data through Water Year 2011","title":"Regional regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011","docAbstract":"The U.S. Geological Survey, in cooperation with the Montana Department of Transportation, developed regression equations based on channel width to estimate peak-flow frequencies at ungaged sites in Montana. The equations are based on peak-flow data at streamgages through September 2011 (end of water year 2011), and channel widths measured in the field and from aerial photographs.
Active-channel width and bankfull width (channel widths) were measured in the field at 64 sites across Montana in 2017. Channel widths also were measured near 515 streamgages from aerial photographs. These new channel-width data, along with more than 438 historical channel-width measurements, are published in a separate data release.
Regression equations were developed using generalized least squares regression or weighted least squares regression. The channel-width regression equations can be used to estimate peak-flow frequencies (peak-flow magnitudes associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites in each of the eight hydrologic regions in Montana. Methods are presented for weighting estimates from the channel-width equations with estimates from equations using basin characteristics. The weighting technique can be used to reduce the standard error of prediction relative to that obtained using a single method. Several example problems covering a range of estimation scenarios also are included.
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\"}}]}","edition":"Version 1.0: August 19, 2021; Version 1.1: September 20, 2021","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
We created this guide to introduce the user to the San Francisco Volcanic Field as a terrestrial analog site for planetary volcanic processes. For decades, the San Francisco Volcanic Field has been used to teach scientists to recognize the products of common types of volcanic eruptions and associated volcanic features. The volcanic processes and products observed in this volcanic field are like those observed on lunar and Martian surfaces. As a result, this region has been a favored location for training National Aeronautics and Space Administration astronauts and engineers since the Apollo missions.
Though the San Francisco Volcanic Field has more than 600 volcanic vents and flows, this guide will focus on S P Mountain (known locally as S P Crater, located ~30 miles north of Flagstaff, Arizona), one of the best preserved and most accessible of the volcanic cones and lava flows. S P Mountain presents both major types of basaltic eruptions—explosive and effusive—as well as some commonly associated tectonic landforms.
We assume that the user has a basic understanding of geologic concepts and terminology. For more specialized terminology, we include tables showing the classification scheme for lava compositions, styles of eruptions, and tephra sizes (tables 1, 2, and 3). If a further introduction or refresher in volcanological terminology is desired, we suggest reviewing such terms on the U.S. Geological Survey Volcano Science Center’s online glossary (https://volcanoes.usgs.gov/vsc/glossary/).
One term requires clarification at the start of this guide—the term cinder. The terms cinder and cinder cone are widely used to describe the material and edifice produced by lava fountains. However, the term comes from the mining and construction industries and has no clear or formal definition. The international committees in geology and volcanology have chosen the term tephra to be the general term to describe pyroclasts (material ejected through a volcanic explosion or from a volcanic vent). Therefore, in this guide, we use the term tephra rather than cinder.
This guide is outlined as follows:
Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The Little Colorado River is a major tributary to the Colorado River with a confluence at the boundary between Marble and Grand Canyons within Grand Canyon National Park, Arizona. The bedrock gorge of the lower Little Colorado River is home to the largest known population of Gila cypha (humpback chub), an endangered fish endemic to the Colorado River Basin. Channel conditions might affect the spawning success of the humpback chub. Perennial base flow in the lower Little Colorado River deposits travertine, which forms dams and cascades. Geomorphic change in the lower Little Colorado River is controlled by the growth and collapse of travertine dams, debris flows from tributaries, and reworking of dams and debris fans by Little Colorado River floods.
A study was conducted by the U.S. Geological Survey, in cooperation with the Glen Canyon Dam Adaptive Management Program and the U.S. Fish and Wildlife Service, to document historical floods and geomorphic change in the lower Little Colorado River. For this study, we used historical and gaging records and hydraulic modeling of surveyed high-water marks from historical Little Colorado River floods to construct a peak-flow history of the lower Little Colorado River. We analyzed base-flow longitudinal profiles and historical photographs to determine changes in the longitudinal profile of the lower Little Colorado River from 1909 to 2019. The peak-flow magnitudes and the frequency of larger floods have declined since the late 1800s, and the longitudinal profile of the Little Colorado River has substantially changed between 1909 and 2019. Aggradation of as much as 6 meters in some reaches occurred between 1926 and 1992, mostly before the 1950s. This aggradation was caused largely by the documented growth of travertine dams continuing through at least 2013 at several locations. Other reaches were incised by as much as 10 meters between 1926 and 1992, but mostly before the 1950s, largely from the breaching of travertine dams. Travertine dams in the Little Colorado River have survived large flooding events and then later collapsed during floods of lower streamflow or even periods of base flow. The decline in peak-flow magnitude and frequency has changed the dominant geomorphic processes in this formerly dynamic reach. Large incision events have not been documented since the early 1950s; for this reason, the reach has only aggraded or remained stable since that time. This loss of geomorphic disturbance has likely affected, and will likely continue to affect, the spawning habitat of the endangered humpback chub in the lower Little Colorado River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215049","usgsCitation":"Unema, J.A., Topping, D.J., Kohl, K.A., Pillow, M.J., and Caster, J.J., 2021, Historical floods and geomorphic change in the lower Little Colorado River during the late 19th to early 21st centuries: U.S. Geological Survey Scientific Investigations Report 2021–5049, 34 p., https://doi.org/10.3133/sir20215049.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-113057","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5049/covrthb.jpg"},{"id":388148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5049/sir20215049.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VGWRV1","linkHelpText":"Topographic data, historical peak-stage data, and 2D flow models for the lowermost Little Colorado River, Arizona, USA, 2017"}],"country":"United States","state":"Arizona, Utah, New Mexico, Utah","otherGeospatial":"Lower Little Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.236328125,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 33.247875947924385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The U.S. Geological Survey, with the support of the International Joint Commission, and in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation, is leading a project that should improve information available to apportion water between Canada and the United States in the St. Mary and Milk River Basins. One component of the water budget, consumptive use of irrigation water (the amount of supplemental water used by crops), can be estimated at 100-meter resolution almost every week using imagery recorded by satellites from 1985 to present (2021) and weather data, when conditions permit. Better estimates of consumptive water use should improve understanding of water availability and use in the basin and should assist with water apportionment procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213042","collaboration":"Prepared in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, R., Friedrichs, M., and Senay, G.B., 2021, Using satellite imagery to estimate consumptive water use from irrigated lands in the Milk River Basin, United States and Canada: U.S. Geological Survey Fact Sheet 2021–3042, 2 p., https://doi.org/10.3133/fs20213042.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-130575","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3042/coverthb.jpg"},{"id":388102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3042/fs20213042.pdf","text":"Report","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"fs 2021–3042"}],"country":"Canada, United States","state":"Alberta, Montana, Saskatchewan","otherGeospatial":"Milk River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.851318359375,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 48.07807894349862\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211063","usgsCitation":"La Peyre, M.K., Marshall, D.A., and Sable, S.E., 2021, Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters: U.S. Geological Survey\nOpen-File Report 2021–1063, 40 p., https://doi.org/10.3133/ofr20211063.","productDescription":"Report: viii, 40p.; 3 Appendix Tables","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126014","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1063/images"},{"id":388041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1063/coverthb.jpg"},{"id":388042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1063"},{"id":388043,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.csv","text":"Table 1.1 (.csv)","size":"5.07 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388044,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.csv","text":"Table 2.1 (.csv)","size":"5.40 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388045,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.csv","text":"Table 3.1 (.csv)","size":"63.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"},{"id":388046,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.xlsx","text":"Table 1.1 (.xlsx)","size":"14.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388047,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.xlsx","text":"Table 2.1 (.xlsx)","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388048,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.xlsx","text":"Table 3.1 (.xlsx)","size":"46.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.177734375,\n 27.527758206861886\n ],\n [\n -82.6171875,\n 29.267232865200878\n ],\n [\n -83.8916015625,\n 30.372875188118016\n ],\n [\n -85.078125,\n 30.259067203213018\n ],\n [\n -86.7919921875,\n 30.826780904779774\n ],\n [\n -88.681640625,\n 30.675715404167743\n ],\n [\n -90.263671875,\n 30.486550842588485\n ],\n [\n -90.3076171875,\n 29.649868677972304\n ],\n [\n -91.62597656249999,\n 30.14512718337613\n ],\n [\n -94.3505859375,\n 29.916852233070173\n ],\n [\n -96.45996093749999,\n 29.036960648558267\n ],\n [\n -97.470703125,\n 27.800209937418252\n ],\n [\n -97.734375,\n 26.78484736105119\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.767578125,\n 27.01998400798257\n ],\n [\n -96.0205078125,\n 28.34306490482549\n ],\n [\n -94.39453125,\n 29.075375179558346\n ],\n [\n -92.94433593749999,\n 29.34387539941801\n ],\n [\n -92.2412109375,\n 29.075375179558346\n ],\n [\n -90.9228515625,\n 28.806173508854776\n ],\n [\n -89.033203125,\n 28.613459424004414\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -88.9013671875,\n 29.916852233070173\n ],\n [\n -87.4951171875,\n 29.80251790576445\n ],\n [\n -86.17675781249999,\n 29.99300228455108\n ],\n [\n -85.341796875,\n 29.19053283229458\n ],\n [\n -84.0673828125,\n 29.57345707301757\n ],\n [\n -83.4521484375,\n 28.998531814051795\n ],\n [\n -83.3642578125,\n 27.72243591897343\n ],\n [\n -82.4853515625,\n 26.667095801104814\n ],\n [\n -82.177734375,\n 27.527758206861886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Cooperative Fish and Wildlife Research Units
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Recreational angling in the United States (US) is largely a personal hobby that scales up to a multibillion-dollar economic activity. Given dramatic changes to personal decisions and behaviors resulting from the COVID-19 pandemic, we surveyed recreational anglers across the US to understand how the pandemic may have affected their fishing motivations and subsequent activities. Nearly a quarter million anglers from 10 US states were invited to participate in the survey, and almost 18,000 responded. Anglers reported numerous effects of the pandemic, including fishing access restrictions. Despite these barriers, we found that the amount of fishing in the spring of 2020 was significantly greater—by about 0.2 trips per angler—than in non-pandemic springs. Increased fishing is likely associated with our result that most respondents considered recreational angling to be a COVID-19 safe activity. Nearly a third of anglers reported changing their motivation for fishing during the pandemic, with stress relief being more popular during the pandemic than before. Driven partly by the perceived safety of social fishtancing, recreational angling remained a popular activity for many US anglers during spring 2020.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0254652","usgsCitation":"Midway, S.R., Lynch, A.J., Peoples, B.K., Dance, M.A., and Caffey, R., 2021, COVID-19 influences on US recreational angler behavior: PLoS ONE, v. 16, no. 8, https://doi.org/10.1371/journal.pone.0254652.","productDescription":"e0254652, 16 p.","startPage":"e0254652","ipdsId":"IP-125141","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":451125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0254652","text":"Publisher Index Page"},{"id":388585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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Center","active":true,"usgs":true}],"preferred":true,"id":822090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peoples, Brandon K.","contributorId":177551,"corporation":false,"usgs":false,"family":"Peoples","given":"Brandon","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":822091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dance, Michael A.","contributorId":213049,"corporation":false,"usgs":false,"family":"Dance","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":822092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caffey, Rex","contributorId":264849,"corporation":false,"usgs":false,"family":"Caffey","given":"Rex","email":"","affiliations":[{"id":54570,"text":"Louisiana State U.","active":true,"usgs":false}],"preferred":false,"id":822093,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225617,"text":"70225617 - 2021 - Comparative effects of energy-related saline wastewaters and sodium chloride on hatching, survival, and fitness-associated traits of two amphibian species","interactions":[],"lastModifiedDate":"2021-10-28T13:38:24.352676","indexId":"70225617","displayToPublicDate":"2021-08-18T08:24:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Comparative effects of energy-related saline wastewaters and sodium chloride on hatching, survival, and fitness-associated traits of two amphibian species","docAbstract":"Salinity (sodium chloride [NaCl]) is a prevalent and persistent contaminant that negatively affects freshwater ecosystems. Although most studies focus on effects of salinity from road salts (primarily NaCl), high-salinity wastewaters from energy extraction (wastewaters) could be more harmful because they contain NaCl and other toxic components. Many amphibians are sensitive to salinity, and their eggs are thought to be the most sensitive life-history stage. However, there are few investigations with salinity that include eggs and larvae sequentially in long-term exposures. We investigated the relative effects of wastewaters from a large energy reserve, the Williston Basin (USA), and NaCl on northern leopard (Rana pipiens) and boreal chorus (Pseudacris maculata) frogs. We exposed eggs and tracked responses through larval stages (for 24 days). Wastewaters and NaCl caused similar reductions in hatching and larval survival, growth, development, and activity, while also increasing deformities. Chorus frog eggs and larvae were more sensitive to salinity than leopard frogs, suggesting species-specific responses. Contrary to previous studies, eggs of both species were less sensitive to salinity than larvae. Our ecologically relevant exposures suggest that accumulating effects can reduce survival relative to starting experiments with unexposed larvae. Alternatively, egg casings of some species may provide some protection against salinity. Notably, effects of wastewaters on amphibians were predominantly due to NaCl rather than other components. Therefore, findings from studies with other sources of increased salinity (e.g., road salts) could guide management of wastewater-contaminated ecosystems, and vice versa, to mitigate effects of salinization.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5193","usgsCitation":"Tornabene, B., Breuner, C., and Hossack, B., 2021, Comparative effects of energy-related saline wastewaters and sodium chloride on hatching, survival, and fitness-associated traits of two amphibian species: Environmental Science & Technology, v. 40, no. 11, p. 3137-3147, https://doi.org/10.1002/etc.5193.","productDescription":"11 p.","startPage":"3137","endPage":"3147","ipdsId":"IP-129251","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":391083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","city":"Dagmar, Moran","otherGeospatial":"Pary Waterfowl Production Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.600830078125,\n 48.29781249243716\n ],\n [\n -104.04739379882812,\n 48.29781249243716\n ],\n [\n -104.04739379882812,\n 48.67101262432597\n ],\n [\n -104.600830078125,\n 48.67101262432597\n ],\n [\n -104.600830078125,\n 48.29781249243716\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58151245117188,\n 43.717519867330765\n ],\n [\n -110.32882690429688,\n 43.717519867330765\n ],\n [\n -110.32882690429688,\n 43.89591323557617\n ],\n [\n -110.58151245117188,\n 43.89591323557617\n ],\n [\n -110.58151245117188,\n 43.717519867330765\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":825937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breuner, Creagh","contributorId":268148,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":825938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":825939,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223506,"text":"70223506 - 2021 - Genetic sequencing of Attwater's prairie chicken avian poxvirus and evaluation of its potential role in reticuloendotheliosis virus outbreaks","interactions":[],"lastModifiedDate":"2021-08-31T13:19:42.87042","indexId":"70223506","displayToPublicDate":"2021-08-18T08:15:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Genetic sequencing of Attwater's prairie chicken avian poxvirus and evaluation of its potential role in reticuloendotheliosis virus outbreaks","docAbstract":"Efforts to breed Attwater's prairie chickens (APC; Tympanuchus cupido attwateri) in captivity to supplement wild populations of this endangered bird have been negatively affected by infections with Avipoxvirus and reticuloendotheliosis virus (REV). Because REV can be integrated into the genome of fowlpox virus (FPV) and may be transmitted in that manner, identifying the source of avipox disease in APC is important to mitigate the impact of this virus. Tissue samples from APC were collected from breeding programs in Texas from 2016 to 2020. These samples consisted of 11 skin lesions and three internal organs from a total of 14 different birds that died of unknown causes or were euthanized. Avipoxvirus was detected by PCR and isolation in embryonating chicken eggs in all skin lesion samples but was not detected in internal organs. Using sequence analysis of FPV polymerase and 4b genes, we determined that 10 out of 11 Avipoxvirus detections resided within the fowlpox clade and a single sample resided within the canarypox clade. REV sequences were detected in all FPV positive samples and in all internal organ tissues but were not detected in the sample matching the canarypox clade. Analysis of REV sequences and PCR detection showed the REV infecting APC was consistent with REV-A and had little variability on analysis of the U3 region of the long terminal repeat. The results of this study indicate control of REV in APC breeding colonies may benefit by a vaccination program targeting FPV and REV. However, a commercially available vaccine for REV is not available at this time.