The regal fritillary, Speyeria idalia (Drury), was once a common inhabitant of North American grassland communities. Regal fritillary populations are commonly reported to have a male biased adult sex ratio (ASR) throughout their range. We assessed the observed ASR of regal fritillary throughout an annual flight period, investigated how the overall density of both sexes changed, and tested effects of prescribed fire, grazing and haying management treatments on male and female density. We found that regal fritillary exhibited an observed 2:1 male biased ASR across the entire emergence period. Our analysis also revealed that male density peaked earlier than female density in the flight period. Point estimates of density indicated sites that received prescribed burning at the moderate fire-return interval supported ≥1.3 times greater density of males and ≥5.6 times greater density of females versus sites burned with short and long fire-return intervals. Additionally, this effect was enhanced when combined with grazing which showed males were ≥1.9 times and females had ≥1.2 times greater point estimates of density in sites that were grazed and burned at a moderate fire-return interval versus other sites. The relatively stable status of regal fritillary within our study region suggests that a 2:1 male to female ASR may be considered the model composition of populations throughout their range. Likewise, the dynamic nature of the ASR throughout the flight period highlights the importance of conducting surveys across the flight period. Finally, these results corroborate an increasing number of research results that reveal common prairie management practices, such as prescribed fire can be applied within sites that contain regal fritillary and continue to support stable populations.
Wetland ecosystems perform a multitude of services valued by society and provide critical habitat for migratory birds and other wildlife. Despite their importance, wetlands have been lost to different local, regional, and global drivers. Remaining wetlands are extremely sensitive to changing temperature and precipitation regimes. Management of grassland areas in wetland catchments may be an effective strategy for counteracting potentially negative impacts of climate change on wetlands. Our objective was to estimate the effects of climate changes on wetland hydrology, and to explore strategies for increasing surface-water inputs to wetlands. We coupled a field study with process-based simulation modeling of wetland-water levels. We found that climate change could decrease the number of wetlands that hold ponded water during the waterfowl breeding season by 14% under a hot wet scenario or 29% under a hot dry scenario if no upland-management actions were taken. Upland burning reduced pond losses to 9% (hot wet) and 26% (hot dry). Upland grazing resulted in the smallest loss of ponded wetlands, 6% loss under the hot-and-wet scenario and 22% loss under the hot-and-dry scenario. Overall, water inputs could be increased by either burning or grazing of upland vegetation thereby reducing pond losses during the waterfowl breeding season. While field results suggest that both grazing and burning can reduce the vegetative structure that could lead to increases in runoff in grassland catchments, our model simulations indicated that additional actions may be needed for managers to minimize future meteorologically driven water losses.
Uranium minerals are commonly found in soils and sediment across the United States at an average concentration of 2–4 mg/kg. Uranium occurs in the environment primarily in two forms, the oxidized, mostly soluble uranium(VI) form, or the reduced, sparingly soluble reduced uranium(IV) form. Here we describe subsurface geochemical conditions that result in low uranium concentrations in an alluvial aquifer with naturally occurring uranium in soils and sediments in the presence of complexing ligands under oxidizing conditions. Groundwater was saturated with respect to calcite and contained calcium (78–90 mg/L) with elevated levels of carbonate alkalinity (291–416 mg/L as HCO3−). X-ray adsorption near edge structure (XANES) spectroscopy identified that sediment-associated uranium was oxidized as a uranium(VI) form (85%). Calcite was the predominant mineral by mass in the ultrafine fraction in uranium-bearing sediments (>16 mg/kg). Groundwater geochemical modeling indicated calcite and/or a calcium-uranyl-carbonate mineral such as liebigite in equilibrium with groundwater. The δ13C (0.57‰ ± 0.15‰) was indicative of abiotic carbonate deposition. Thus, solid-phase uranium(VI) associated with carbonate is likely maintaining uranium(VI) groundwater levels below the maximum contaminant level (MCL; 30 µg/L), presenting a deposition mechanism for uranium attenuation rather than solely a means of mobilization.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.125089","usgsCitation":"Nolan, P., Bone, S., Campbell, K.M., Pannell, D., Healy, O., Stange, M., Bargar, J., and Weber, K., 2021, Uranium(VI) attenuation in a carbonate-bearing oxic alluvial aquifer: Journal of Hazardous Materials, v. 412, 125089, 11 p., https://doi.org/10.1016/j.jhazmat.2021.125089.","productDescription":"125089, 11 p.","ipdsId":"IP-124037","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":453886,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1807547","text":"Publisher Index Page"},{"id":436589,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98GDZFV","text":"USGS data release","linkHelpText":"X-ray diffraction data of sediment samples from Hastings, Nebraska"},{"id":382853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","city":"Hastings","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.49929809570312,\n 40.49030405370212\n ],\n [\n -98.27133178710938,\n 40.49030405370212\n ],\n [\n -98.27133178710938,\n 40.65980593837852\n ],\n [\n -98.49929809570312,\n 40.65980593837852\n ],\n [\n -98.49929809570312,\n 40.49030405370212\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"412","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nolan, PJ","contributorId":248603,"corporation":false,"usgs":false,"family":"Nolan","given":"PJ","email":"","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":809493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bone, S","contributorId":248604,"corporation":false,"usgs":false,"family":"Bone","given":"S","email":"","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":809494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Kate M. 0000-0002-8715-5544 kcampbell@usgs.gov","orcid":"https://orcid.org/0000-0002-8715-5544","contributorId":1441,"corporation":false,"usgs":true,"family":"Campbell","given":"Kate","email":"kcampbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":809495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pannell, David","contributorId":217709,"corporation":false,"usgs":false,"family":"Pannell","given":"David","email":"","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":809496,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Healy, O","contributorId":248605,"corporation":false,"usgs":false,"family":"Healy","given":"O","email":"","affiliations":[{"id":16602,"text":"University of Nebraska, Lincoln","active":true,"usgs":false}],"preferred":false,"id":809497,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stange, M","contributorId":248606,"corporation":false,"usgs":false,"family":"Stange","given":"M","email":"","affiliations":[{"id":49962,"text":"Hastings Utilities","active":true,"usgs":false}],"preferred":false,"id":809498,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bargar, J","contributorId":248607,"corporation":false,"usgs":false,"family":"Bargar","given":"J","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":809499,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weber, KA","contributorId":248608,"corporation":false,"usgs":false,"family":"Weber","given":"KA","email":"","affiliations":[{"id":16602,"text":"University of Nebraska, Lincoln","active":true,"usgs":false}],"preferred":false,"id":809500,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218764,"text":"70218764 - 2021 - Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska","interactions":[],"lastModifiedDate":"2021-03-12T14:36:45.473522","indexId":"70218764","displayToPublicDate":"2021-01-08T08:31:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7753,"text":"Frontiers in Earth Science","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska","docAbstract":"Accelerating erosion of the Alaska Beaufort Sea coast is increasing inputs of organic matter from land to the Arctic Ocean, and improved estimates of organic matter stocks in eroding coastal permafrost are needed to assess their mobilization rates under contemporary conditions. We collected three permafrost cores (4.5–7.5 m long) along a geomorphic gradient near Drew Point, Alaska, where recent erosion rates average 17.2 m year−1. Down-core patterns indicate that organic-rich soils and lacustrine sediments (12–45% total organic carbon; TOC) in the active layer and upper permafrost accumulated during the Holocene. Deeper permafrost (below 3 m elevation) mainly consists of Late Pleistocene marine sediments with lower organic matter content (∼1% TOC), lower C:N ratios, and higher δ13C values. Radiocarbon-based estimates of organic carbon accumulation rates were 11.3 ± 3.6 g TOC m−2 year−1 during the Holocene and 0.5 ± 0.1 g TOC m−2 year−1 during the Late Pleistocene (12–38 kyr BP). Within relict marine sediments, porewater salinities increased with depth. Elevated salinity near sea level (∼20–37 in thawed samples) inhibited freezing despite year-round temperatures below 0°C. We used organic matter stock estimates from the cores in combination with remote sensing time-series data to estimate carbon fluxes for a 9 km stretch of coastline near Drew Point. Erosional fluxes of TOC averaged 1,369 kg C m−1 year−1 during the 21st century (2002–2018), nearly doubling the average flux of the previous half-century (1955–2002). Our estimate of the 21st century erosional TOC flux year−1 from this 9 km coastline (12,318 metric tons C year−1) is similar to the annual TOC flux from the Kuparuk River, which drains a 8,107 km2 area east of Drew Point and ranks as the third largest river on the North Slope of Alaska. Total nitrogen fluxes via coastal erosion at Drew Point were also quantified, and were similar to those from the Kuparuk River. This study emphasizes that coastal erosion represents a significant pathway for carbon and nitrogen trapped in permafrost to enter modern biogeochemical cycles, where it may fuel food webs and greenhouse gas emissions in the marine environment.
Debris flows evolve in both time and space in complex ways, commonly starting as coherent failures but then quickly developing structures such as roll waves and surges. These processes are readily observed but difficult to study or quantify because of the speed at which they evolve. Many methods for studying debris flows consist of point measurements (e.g., flow height or basal stresses), which are inherently limited in spatial coverage and cannot fully characterize the spatiotemporal evolution of a flow. In this study, we use terrestrial lidar to measure debris-flow profiles at high sampling rates to examine debris-flow movement with high temporal and spatial precision and accuracy. We acquired measurements during gate-release experiments at the U.S. Geological Survey debris-flow flume, a unique experimental facility where debris flows can be artificially generated at a large scale. A lidar scanner was used to record repeat topographic profiles of the moving debris flows along the length of the flume with a narrow swath width (∼1 mm) at a rate of 60 Hz. The high-resolution lidar profiles enabled us to quantify flow front velocity of the debris flows and provided an unprecedented record of the development and evolution of the flow structure with a sub-second time resolution. The findings of this study demonstrate how to obtain quantitative measurements of debris-flow movement. In addition, the data help us to quantitatively define the development of a saltating debris-flow front and roll waves behind the debris-flow front. Such measurements may help constrain future modeling efforts.
Groundwater-derived solute fluxes to the ocean have long been assumed static and subordinate to riverine fluxes, if not neglected entirely, in marine isotope budgets. Here we present concentration and isotope data for Li, Mg, Ca, Sr, and Ba in coastal groundwaters to constrain the importance of groundwater discharge in mediating the magnitude and isotopic composition of terrestrially derived solute fluxes to the ocean. Data were extrapolated globally using three independent volumetric estimates of groundwater discharge to coastal waters, from which we estimate that groundwater-derived solute fluxes represent, at a minimum, 5% of riverine fluxes for Li, Mg, Ca, Sr, and Ba. The isotopic compositions of the groundwater-derived Mg, Ca, and Sr fluxes are distinct from global riverine averages, while Li and Ba fluxes are isotopically indistinguishable from rivers. These differences reflect a strong dependence on coastal lithology that should be considered a priority for parameterization in Earth-system models.
In support of nutrient reduction efforts, total phosphorus loads and yields were computed for the Turkey River at Garber, Iowa (U.S. Geological Survey station 05412500), for January 1, 2018, to April 30, 2020, based on continuously monitored turbidity sensor data. Sample data were used to create a total phosphorus turbidity-surrogate model. Streamflow-based total phosphorus models were used during periods of missing sensor data to obtain a more complete annual total phosphorus load. This report presents methods needed to accurately compute site-specific loads and track annual progress toward nutrient reduction goals within the State.
Annual total phosphorus loads for the Turkey River at Garber, Iowa, were 1,740 and 1,490 U.S. short tons for 2018 and 2019, respectively, with annual yields ranging from 3.01 to 3.53 pounds per acre per year, compared to a mean statewide yield of 0.73 pound per acre per year needed to achieve the total phosphorus-reduction goal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205131","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Garrett, J.D., 2021, The use of continuous water-quality time-series data to compute total phosphorus loadings for the Turkey River at Garber, Iowa, 2018–20: U.S. Geological Survey Scientific Investigations Report 2020–5131, 13 p., https://doi.org/10.3133/sir20205131.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119794","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5131/sir20205131.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5131"},{"id":381970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5131/coverthb.jpg"},{"id":382022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"}],"country":"United States","state":"Iowa","city":"Garber","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.27939224243163,\n 42.73276565598371\n ],\n [\n -91.24471664428711,\n 42.73276565598371\n ],\n [\n -91.24471664428711,\n 42.74953333969568\n ],\n [\n -91.27939224243163,\n 42.74953333969568\n ],\n [\n -91.27939224243163,\n 42.73276565598371\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The U.S. Geological Survey (USGS), in cooperation with the Rosebud Sioux Tribe, completed a study to characterize water-level fluctuations in observation wells to examine driving factors that affect water levels in and near the Rosebud Indian Reservation, which comprises all of Todd County. The study investigates concerns regarding potential effects of groundwater withdrawals and climate conditions on groundwater levels within an area that includes Todd County and a surrounding area that extends 10 miles north, east, and west of the county border. Characterization of water-level fluctuations in observation wells and relative driving factors was accomplished by statistical trend analysis.
Two statistical methods were used for analysis of temporal trends for climatic and hydrologic data. To determine which trend analysis to use, applicable datasets were tested for statistically significant short-term persistence (STP). In the absence of significant STP, existence of statistical trends was determined using the standard Mann-Kendall test for probability values less than or equal to 0.10 (90-percent confidence level); however, a modified Mann-Kendall test was used for datasets where statistically significant STP was detected. Trend magnitudes were computed using the Sen’s slope estimator.
Monthly data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were aggregated to obtain annual and seasonal datasets for total precipitation, minimum air temperature (Tmin), and maximum air temperature (Tmax) for the study area and a surrounding buffer area. Trend tests for total precipitation, Tmin, and Tmax were completed for annual and seasonal time series for water years 1956–2017, which is about 2 years before the earliest available water-level measurements. A 2-year offset was arbitrarily selected because scrutiny of water-level and precipitation data indicated that responses of groundwater levels for many of the observation wells lagged major changes in precipitation patterns by about 2 years. Statistically significant upward trends were detected for annual precipitation and annual Tmin for almost all of the study area and the surrounding buffer area. Statistically significant downward trends in Tmax were detected for a very small part of the study area; however, the sparse spatial coverage reduces confidence that these are true trends. Spatial distributions of statistically significant trends in seasonal climate data were generally similar to the annual trends, but with substantial differences in the spatial density of the trends.
Groundwater trends for 58 observation wells were analyzed for three separate water-level parameters (minimum, median, and maximum) because wells are measured sporadically and data are biased towards more frequent measurements during periods of heaviest irrigation demand. Trends in the time series of annual precipitation (from PRISM) starting 2 years earlier than for the associated water-level trend also were analyzed for the location of each individual observation well. Sen’s slope and Mann-Kendall probability values (p-values) were computed for the three water-level parameters and for the annual precipitation time series. Graphs showing results of trend analyses for each observation well also showed changes over time in the sum of licensed groundwater withdrawals within six specified radii (0.5, 1, 2, 3, 4, and 5 miles) of each well as a qualitative indicator of proximal groundwater demand.
Of all 58 observation wells considered, 28 wells had significant upward trends for at least one of the three water-level parameters, 11 wells had significant downward trends for at least one water-level parameter, and 19 wells did not have any significant trends. Significant upward trends in annual precipitation were detected for 48 of the 58 wells.
Results of trend analyses likely show the effects of groundwater withdrawals on water levels in the Ogallala aquifer in areas of substantial demand. Precipitation trends are significantly upward for 43 of the 48 wells completed in the Ogallala aquifer that were analyzed. Of the 48 Ogallala aquifer wells, 24 had significant upward trends for at least one water-level parameter (17 with all 3); however, 10 wells had statistically significant downward trends for at least one water-level parameter (8 with all 3 parameters). All but one of the wells with significant downward trends are located in the south-central part of the study area where licensed irrigation withdrawals are concentrated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205119","collaboration":"Prepared in cooperation with the Rosebud Sioux Tribe","usgsCitation":"Valseth, K.J., and Driscoll, D.G., 2021, Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017: U.S. Geological Survey Scientific Investigations Report 2020–5119, 46 p., https://doi.org/10.3133/sir20205119.","productDescription":"Report: v, 46 p.; 2 Appendixes; Data Release","onlineOnly":"Y","ipdsId":"IP-111377","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"},{"id":381910,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix2.pdf","text":"Appendix 2","size":"132 kB","description":"SIR 2020-5119 Appendix 2"},{"id":381909,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix1.pdf","text":"Appendix 1","size":"404 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119 Appendix 1"},{"id":381908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119.pdf","text":"Report","size":"4.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119"},{"id":381907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5119/coverthb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Rosebud Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.612548828125,\n 43.01268088642034\n ],\n [\n -99.8492431640625,\n 43.01268088642034\n ],\n [\n -99.8492431640625,\n 43.600284023536325\n ],\n [\n -101.612548828125,\n 43.600284023536325\n ],\n [\n -101.612548828125,\n 43.01268088642034\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
This report documents statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM). The U.S. Geological Survey developed SELDM and the statistics documented in this report in cooperation with the Federal Highway Administration to indicate the risk for stormwater flows, concentrations, and loads to exceed user-selected water-quality goals and the potential effectiveness of mitigation measures to reduce such risks. In SELDM, three treatment variables—hydrograph extension, volume reduction, and water-quality treatment—are simulated by using the trapezoidal distribution and the rank correlation with the associated runoff variables. This report describes methods for calculating the trapezoidal distribution statistics and rank correlation coefficients for these treatment variables and methods for estimating the minimum irreducible concentration (MIC), which is the lowest expected effluent concentration from a BMP site or a category of BMPs. These statistics are different from the statistics commonly used to characterize or compare BMPs; they are designed to provide a stochastic transfer function to approximate the quantity, duration, and quality of BMP effluent given the associated inflow values for a population of storm events.
Analyses for this study were done with data extracted from a modified copy of the December 2019 version of the International Stormwater Best Management Practices Database. Statistics for volume reduction, hydrograph extension, and water-quality treatment were developed with selected data. The medians of the best-fit statistics for selected constituents were used to construct generalized cumulative distribution functions for the three treatment variables. For volume reduction and hydrograph extension, selection of a Spearman’s rank correlation coefficient (rho) value that is the average of the median and maximum values for the BMP category may help generate realistic simulation results in SELDM. The median rho value may be selected to help generate realistic simulation results for water-quality treatment variables.
Water-quality treatment statistics, including trapezoidal ratios and MIC values, were developed for 51 runoff-quality constituents commonly measured in highway and urban runoff studies. Statistics were calculated for water-quality properties, sediment and solids, nutrients, major and trace inorganic elements, organic compounds, and biologic constituents.
Analysis of MIC values provides information to guide professional judgement for selecting values for simulating water quality at sites of interest. The MIC is a lower bound for BMP discharge concentrations and will therefore replace simulated BMP discharge concentrations below the selected value. A new method for estimating MIC values, the lognormal variate of inflow concentrations, was developed in this report and these statistics were calculated for individual constituents and constituent categories. Inflow quality is correlated to MIC values for some constituents, but regional soil concentrations were not strongly correlated to MIC values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205136","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136.","productDescription":"Report: 41 p.; 4 Tables; Data Release; Software Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119618","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":381933,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.04.txt","text":"Table 1.4","size":"89.4 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of correlations between the geometric mean concentration of inflows and selected minimum irreducible concentration estimates"},{"id":381930,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.01.txt","text":"Table 1.1","size":"91.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Median of selected treatment statistics for individual constituents"},{"id":381932,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.03.txt","text":"Table 1.3","size":"89.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the lognormal variate values of selected minimum irreducible concentrations"},{"id":381929,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X3ECTD","text":"USGS data release","linkHelpText":"Statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM)"},{"id":381927,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5136"},{"id":381928,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9XBPIOB","text":"USGS software release","linkHelpText":"- Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0"},{"id":381931,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.02.txt","text":"Table 1.2","size":"87.5 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the minimum irreducible concentration"},{"id":381926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5136/coverthb.jpg"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Kīlauea Volcano, on the Island of Hawaiʻi, is surrounded and permeated by active groundwater systems that interact dynamically with the volcanic system. A generalized conceptual model of Hawaiian hydrogeology includes high-level dike-impounded groundwater, very permeable perched and basal aquifers, and a transition (mixing) zone between freshwater and saltwater. Most high-level groundwater is associated with the low-permeability intrusive complexes that underlie volcanic rift zones and calderas and also act to compartmentalize the groundwater system. Hydrogeologic studies of Kīlauea in recent decades, accompanied by deep research drilling, have shown that high-level groundwater is more widespread than once understood, that permeability decreases dramatically at depth, particularly in rift zones, and that freshwater can occur at depths of as much as several kilometers below the local water table. Copious groundwater recharge causes near-surface conductive heat flow to be near zero over much of Kīlauea. Approximately 95 percent of groundwater discharge occurs offshore, accompanied by approximately 99 percent of the approximately 6,000 megawatts of heat supplied by magmatic intrusion. Here, we summarize current understanding of the groundwater system of Kīlauea Volcano and describe transient changes during the decade or more preceding the 2018 eruption sequence. The changes in groundwater chemistry and thermal structure beneath Kīlauea summit hold implications for volcanic-volatile transport and the potential for explosive volcanism. Between 2008 and 2018, the magma conduit beneath the lava lake likely created an adjacent zone of very hot rock that significantly delayed liquid groundwater inflow to the draining magma conduit. Sulfate concentrations in groundwater beneath Kīlauea summit, sampled at the National Science Foundation-funded drill hole 1.5 kilometers south-southwest of the lava lake, declined substantially between 2010 and present. This decline likely reflects, at least in part, the decreased effectiveness of volatile condensation and solution into groundwater (scrubbing). The vent opening in 2008 presumably focused volatile flux into the vicinity of the vent, and progressive drying of the surroundings further restricted interaction with the groundwater system. The decrease in sulfate concentrations in the drill hole between 2010 and 2018 likely reflects decreased effectiveness of scrubbing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867F","usgsCitation":"Hurwitz, S., Peek, S.E., Scholl, M.A., Bergfeld, D., Evans, W.C., Kauahikaua, J.P., Gingerich, S.B., Hsieh, P.A., Lee, R.L., Younger, E.F., and Ingebritsen, S.E., 2021, Groundwater dynamics at Kīlauea Volcano and vicinity, Hawaiʻi, chap. F of Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawaiʻi: U.S. Geological Survey Professional Paper 1867, 28 p., https://doi.org/10.3133/pp1867F.","productDescription":"Report: v, 28 p.; Data Release","numberOfPages":"28","ipdsId":"IP-113974","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":381967,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UCGT2F","linkHelpText":"Water level, temperature, and chemistry in a deep well on the summit of Kīlauea Volcano, Hawaiʻi"},{"id":381966,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1867/f/pp1867f.pdf","text":"Report","size":"24 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381965,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/f/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.68176269531253,\n 18.880300444535045\n ],\n [\n -154.7918701171875,\n 18.880300444535045\n ],\n [\n -154.7918701171875,\n 19.6348270888747\n ],\n [\n -155.68176269531253,\n 19.6348270888747\n ],\n [\n -155.68176269531253,\n 18.880300444535045\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720
River corridors supply a substantial proportion of the fresh water for societal and ecological needs. Individual functions of flowing (lotic) streams and rivers and ponded (lentic) waterbodies such as lakes and reservoirs are well-studied, but their collective functions are not as well understood. Here we bring together nationally consistent river corridor datasets to characterize the contributions of lotic and lentic features and to estimate changes over the past centuries. High-resolution datasets describing waterbodies across 10 million kilometers of the conterminous U.S. (CONUS) river network were classified by waterbody type and origin (historic vs. human-made or intensively managed), surface areal coverage, and degree of connectivity as estimated by a change in water residence timescale in river corridors. Four centuries of human disturbance drove large swings in river corridor makeup, with a transition toward more lotic systems caused by beaver extirpation and abandonment of waterwheel mill ponds by end of the nineteenth century. The twentieth century saw a vast expansion (49%) in river corridor areal coverage resulting from construction and management of small ponds and reservoirs for drinking water, hydropower, irrigation and livestock watering, and stormwater control. Water residence timescale in river corridors doubled or quadrupled over large areas, and more in specific locations, during the twentieth century as a result of the increased coverage of reservoirs and managed small ponds. Although reservoirs and lakes now dominate river corridor surface areas, we found that the growing number of small ponds impacts a greater proportion of network length through their influence on headwater streams where most water and chemical runoff enters the river corridor. We close with an agenda for integrated modeling of the physical, biogeochemical, and ecological drivers of river corridor functions, trajectories of change, and management opportunities.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2020.580727","usgsCitation":"Harvey, J., and Schmadel, N., 2021, The river corridor’s evolving connectivity of lotic and lentic waters: Frontiers in Water, v. 2, 580727, 17 p., https://doi.org/10.3389/frwa.2020.580727.","productDescription":"580727, 17 p.","ipdsId":"IP-123211","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453905,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2020.580727","text":"Publisher Index Page"},{"id":436599,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TCH5J7","text":"USGS data release","linkHelpText":"NHD-RC: Extension of NHDPlus Version 2.1 with high-resolution river corridor attributes"},{"id":436598,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TCH5J7","text":"USGS data 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0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmadel, Noah 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856272,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238836,"text":"70238836 - 2021 - Simulating water and heat transport with freezing and cryosuction in unsaturated soil: Comparing an empirical, semi-empirical and physically-based approach","interactions":[],"lastModifiedDate":"2022-12-14T15:25:50.400448","indexId":"70238836","displayToPublicDate":"2021-01-07T09:05:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Simulating water and heat transport with freezing and cryosuction in unsaturated soil: Comparing an empirical, semi-empirical and physically-based approach","docAbstract":"Freezing of unsaturated soil is an important process that influences runoff and infiltration in cold-climate regions. We used a simple numerical model to simulate water and heat transport with phase change in unsaturated soil via three different approaches: empirical, semi-empirical and physically based. We compared the performance and parameterization of each approach through testing on three experimental datasets. All approaches reproduced the observed unsaturated freezing process satisfactorily. The empirical cryosuction equation used in this study managed to capture observed cryosuction with a fixed empirical parameter value. The semi-empirical version therefore does not require calibration of a specific frozen soil related parameter. In view of simplicity, small computational demand and accurate performance, all three approaches are suitable for implementation in land-use schemes, catchment scale hydrological models, or multi-dimensional thermo-hydrological models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2021.103846","usgsCitation":"Stuurop, J.C., van der Zee, S.E., Voss, C., and French, H.K., 2021, Simulating water and heat transport with freezing and cryosuction in unsaturated soil: Comparing an empirical, semi-empirical and physically-based approach: Advances in Water Resources, v. 149, 103846, 16 p., https://doi.org/10.1016/j.advwatres.2021.103846.","productDescription":"103846, 16 p.","ipdsId":"IP-125325","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453908,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.advwatres.2021.103846","text":"Publisher Index Page"},{"id":410474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stuurop, Joris C","contributorId":299855,"corporation":false,"usgs":false,"family":"Stuurop","given":"Joris","email":"","middleInitial":"C","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":858860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van der Zee, Sjoerd E. A. T. M","contributorId":299856,"corporation":false,"usgs":false,"family":"van der Zee","given":"Sjoerd","email":"","middleInitial":"E. A. T. M","affiliations":[{"id":64966,"text":"Wageningen University, Monash University","active":true,"usgs":false}],"preferred":false,"id":858861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":858862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"French, Helen K","contributorId":299857,"corporation":false,"usgs":false,"family":"French","given":"Helen","email":"","middleInitial":"K","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":858863,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217200,"text":"70217200 - 2021 - Modeling hydrologic processes associated with soil saturation and debris flow initiation during the September 2013 storm, Colorado Front Range","interactions":[],"lastModifiedDate":"2021-05-13T15:55:57.045834","indexId":"70217200","displayToPublicDate":"2021-01-07T07:11:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Modeling hydrologic processes associated with soil saturation and debris flow initiation during the September 2013 storm, Colorado Front Range","docAbstract":"Seven days of extreme rainfall during September 2013 produced more than 1100 debris flows in the Colorado Front Range, about 78% of which occurred on south-facing slopes (SFS). Previously published soil moisture (volumetric water content) observations suggest that SFS were wetter than north-facing slopes (NFS) during the event, which contrasts with soil moisture patterns observed during normal conditions. Various causes have been hypothesized for the preferential saturation of SFS, but those hypotheses remain largely untested. Here, we analyze the soil moisture patterns using additional soil moisture observations, determine the hydrologic processes controlling the preferential saturation of SFS, and evaluate the importance of soil moisture in predicting the debris flow initiation sites. Soil moisture patterns are simulated using the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT + VS) model. Five hypotheses are tested that may have influenced the soil moisture reversal including higher rainfall rates, lower interception rates, lower saturated water content, thinner soils, and reduced deep drainage on SFS. The EMT + VS model is coupled with an infinite slope stability model to produce factor of safety maps. The hypotheses are tested by comparing the modeled soil moisture to soil moisture observations and the debris flow initiation sites. The results suggest that differences in interception and deep drainage between SFS and NFS were primarily responsible for producing wetter SFS, but the soil moisture pattern likely played a smaller role than vegetation and slope in determining where debris flows initiated. The final model predicts instability at approximately 72% of the observed debris flow initiation sites.
The Shalipayco Zn–Pb deposit, in central Peru, is composed of several stratabound orebodies, the largest of which are the Resurgidora and Intermedios, contained in carbonate rocks of the Upper Triassic Chambará Formation, Pucará group. Petrography suggests that a single ore-forming episode formed sphalerite and galena within vugs, open spaces, and fractures. Three-dimensional (3D) geological modeling has allowed division of the Chambará Formation into four members (Chambará I, II, III, and IV) that better define lithological controls on sulfide formation. Diagenetic replacement of evaporite minerals with the organic matter (OM) presence likely generated secondary porosity and H2S accumulation by bacterial sulfate reduction (BSR), providing ground preparation for the later Zn–Pb mineralizing event. The least-altered host rocks have C–O isotope compositions of 1.8 ± 0.1‰ (VPDB) and 29.9 ± 2.1‰ (VSMOW), respectively, within the Triassic marine carbonate ranges. Early dolomite contains lighter C–O composition (1.1 ± 0.9 and 23.8 ± 2.9‰, respectively) consistent with OM decomposition during burial diagenesis. Post-mineralization calcite has still lighter C–O composition (− 5.1 and 13.3‰, respectively), suggesting meteoric water that had migrated through organic-rich strata. The strontium isotopes of Mitu group basalts (0.709654–0.719669) indicate it as a possible, but not the unique source of strontium and probably of other metals. Highly negative sulfide sulfur isotope values (− 23.3 to − 6.2‰ (VCDT)) indicate a major component of the ore sulfur derived ultimately from BSR. However, multiple lines of evidence suggest that preexisting H2S underwent thermochemical redox cycling prior to ore formation. The influx of hot metalliferous brines to dolomitized zones containing trapped H2S is the preferred model for ore deposition at Shalipayco.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-020-01029-w","usgsCitation":"de Oliveira, S.B., Johnson, C.A., Juliani, C., Monteiro, L.V., Leach, D.L., and Caran, M.G., 2021, Geology and genesis of the Shalipayco evaporite-related Mississippi Valley-type Zn–Pb deposit, Central Peru: 3D geological modeling and C–O–S–Sr isotope constraints: Mineralium Deposita, v. 56, p. 1543-1562, https://doi.org/10.1007/s00126-020-01029-w.","productDescription":"20 p.","startPage":"1543","endPage":"1562","ipdsId":"IP-120554","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":382082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.640625,\n -11.695272733029402\n ],\n [\n -74.267578125,\n -11.695272733029402\n ],\n [\n -74.267578125,\n -9.96885060854611\n ],\n [\n -76.640625,\n -9.96885060854611\n ],\n [\n -76.640625,\n -11.695272733029402\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"de Oliveira, Saulo B 0000-0002-2149-1297","orcid":"https://orcid.org/0000-0002-2149-1297","contributorId":220732,"corporation":false,"usgs":false,"family":"de Oliveira","given":"Saulo","email":"","middleInitial":"B","affiliations":[{"id":40261,"text":"Nexa Resources","active":true,"usgs":false}],"preferred":false,"id":807911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":807912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Juliani, Caetano 0000-0002-0128-993X","orcid":"https://orcid.org/0000-0002-0128-993X","contributorId":220734,"corporation":false,"usgs":false,"family":"Juliani","given":"Caetano","email":"","affiliations":[{"id":40262,"text":"Universidade de Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":807913,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Monteiro, Lena VS 0000-0003-3999-026X","orcid":"https://orcid.org/0000-0003-3999-026X","contributorId":220735,"corporation":false,"usgs":false,"family":"Monteiro","given":"Lena","email":"","middleInitial":"VS","affiliations":[{"id":40262,"text":"Universidade de Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":807914,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leach, David L 0000-0001-6487-5584","orcid":"https://orcid.org/0000-0001-6487-5584","contributorId":220733,"corporation":false,"usgs":false,"family":"Leach","given":"David","email":"","middleInitial":"L","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":807915,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caran, Marianna G.N.","contributorId":247563,"corporation":false,"usgs":false,"family":"Caran","given":"Marianna","email":"","middleInitial":"G.N.","affiliations":[{"id":49578,"text":"Universidade de Sao Paulo, Brazil","active":true,"usgs":false}],"preferred":false,"id":807916,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215502,"text":"70215502 - 2021 - Modeling structural mechanics of oyster reef self-organization including environmental constraints and community interactions","interactions":[],"lastModifiedDate":"2021-01-22T21:57:13.479498","indexId":"70215502","displayToPublicDate":"2021-01-06T15:49:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Modeling structural mechanics of oyster reef self-organization including environmental constraints and community interactions","docAbstract":"Self-organization is a process of establishing and reinforcing local structures through feedbacks between internal population dynamics and external factors. In reef-building systems, substrate is collectively engineered by individuals that also occupy it and compete for space. Reefs are constrained spatially by the physical environment, and by mortality, which reduces production but exposes substrate for recruits. Reef self-organization therefore depends on efficient balancing of production and occupancy of substrate. To examine this, we develop a three-dimensional individual-based model (IBM) of oyster reef mechanics. Shell substrate is grown by individuals as valves, accumulates at the reef level, and degrades following mortality. Single restoration events and subsequent dynamics are simulated for a case study in South Carolina (USA). Variability in model processes is included on recruitment, spatial environmental constraints, and predation, over multiple independent runs and five predator community scenarios. The main goal for this study is to summarize trends in dynamics that are robust across this uncertainty, and from these generate new hypotheses and predictions for future studies. Simulation results demonstrate three phases following restoration: initial transient dynamics with considerable shell loss, followed by growth and saturation of the live population, and then saturation of settlement habitat several years later. Over half of simulations recoup initial shell losses as populations grow, while others continue in decline. The balance between population density, substrate supporting the reef, and exposed surfaces for settlement is mediated by overall population size and size structure, presence of predators, and relative amounts of live individuals and intact dead shells. The efficiency of settlement substrate production improves through time as population size structure becomes more complex, and the population of dead valves accumulates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2020.109389","usgsCitation":"Yurek, S., Eaton, M.J., Lavaud, R., Laney, R.W., DeAngelis, D., Pine, W.E., LaPeyre, M.K., Martin, J., Frederick, P., Wang, H., Lowe, M.R., Johnson, F., Camp, E.V., and Mordecai, R., 2021, Modeling structural mechanics of oyster reef self-organization including environmental constraints and community interactions: Ecological Modelling, v. 440, 109389, 15 p., https://doi.org/10.1016/j.ecolmodel.2020.109389.","productDescription":"109389, 15 p.","ipdsId":"IP-113110","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":382524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"440","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yurek, Simeon 0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216733,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":802526,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":802527,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lavaud, Romain","contributorId":200114,"corporation":false,"usgs":false,"family":"Lavaud","given":"Romain","email":"","affiliations":[],"preferred":false,"id":802528,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laney, R. Wilson","contributorId":243552,"corporation":false,"usgs":false,"family":"Laney","given":"R.","email":"","middleInitial":"Wilson","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":802529,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":802530,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pine, William E. III","contributorId":139959,"corporation":false,"usgs":false,"family":"Pine","given":"William","suffix":"III","email":"","middleInitial":"E.","affiliations":[{"id":13332,"text":"Uni. of Florida Department of Wildlife Ecology and Conservation","active":true,"usgs":false}],"preferred":false,"id":802531,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":802532,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":218445,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":802533,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Frederick, Peter C","contributorId":150013,"corporation":false,"usgs":false,"family":"Frederick","given":"Peter C","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":802534,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":221902,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":802535,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lowe, Michael R. 0000-0002-4645-9429","orcid":"https://orcid.org/0000-0002-4645-9429","contributorId":10539,"corporation":false,"usgs":true,"family":"Lowe","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":802536,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Johnson, Fred 0000-0002-5854-3695","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":217602,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":802537,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Camp, Edward V.","contributorId":173095,"corporation":false,"usgs":false,"family":"Camp","given":"Edward","email":"","middleInitial":"V.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":802538,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mordecai, Rua","contributorId":243553,"corporation":false,"usgs":false,"family":"Mordecai","given":"Rua","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":802539,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70219450,"text":"70219450 - 2021 - Genetic connectivity of the West Indian manatee in the southern range and limited evidence of hybridization with Amazonian manatees","interactions":[],"lastModifiedDate":"2021-04-08T13:09:52.102394","indexId":"70219450","displayToPublicDate":"2021-01-06T08:07:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Genetic connectivity of the West Indian manatee in the southern range and limited evidence of hybridization with Amazonian manatees","docAbstract":"The Antillean subspecies of the West Indian manatee is classified as endangered by the International Union for the Conservation of Nature (IUCN) Red List. In Brazil, the manatee population is listed as endangered with an estimated population size of 500–1,000. Historic hunting, recent habitat degradation, and fisheries bycatch have decreased the population size. The Amazonian manatee is listed as vulnerable by the IUCN with unknown population sizes within Brazil. The Antillean manatee occurs in sympatry with the Amazonian manatee in Brazil and hybridization has been previously indicated. To provide information on the genetic structure, diversity, and degree of hybridization in the sympatric zone near the Amazon River mouth, the mitochondrial DNA control region and 13 nuclear microsatellite markers were assessed on the two species. Samples were analyzed from the Antillean subspecies across its distribution in Brazil (n = 78) and from the Amazonian species (n = 17) at the Amazon River mouth and inland mainstem river. To assess the previously defined evolutionary significant units of Antillean manatees in the area, an additional 11 samples from Venezuela and Guyana were included. The Antillean manatee was found to be a single population in Brazil and had lower than average number of alleles (3.00), expected heterozygosity (0.34), and haplotype diversity (0.15) when compared to many other manatee populations. The low values may be influenced by the small population size and extended pressures from anthropogenic threats. Gene flow was identified with Venezuela/Guyana in admixed Antillean Brazil samples, although the two populations were found to be moderately divergent. The nuclear loci in Venezuela/Guyana Antillean manatee samples indicated high differentiation from the samples collected in the Amazon River (FST = 0.35 and RST = 0.18, p = 0.0001). No indication of nuclear hybridization was found except for a single sample, “Poque” that had been identified previously. The distribution of Antillean manatees in Brazil is extensive and the areas with unique habitat and threats would benefit from independent management and conservation actions. Gene flow, resulting in genetic diversity and long-term population stability, could be improved in the southern range through habitat restoration, and the establishments of travel corridors and protected areas, which are particularly important for successful parturition and neonatal calf survival.
Often overlooked and underfunded, ecological monitoring is an essential component of stream-restoration work. It helps practitioners to identify successful restoration practices, detect ineffective ones, and adjust their adaptive-management activities to improve efficacy (Bernhardt and Palmer 2011). Monitoring, along with research and modeling, are the three legs of the scientific stool that support ecosystem restoration and management. Monitoring tells us what is happening, research tells us why and how it is happening, and modeling provides insights about what can happen under different management alternatives.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Renewing our rivers: Stream corridor restoration in dryland regions","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of Arizona Press","usgsCitation":"Bunting, D., Barton, A.M., Bushman, B., Chernoff, B., Crawford, K., Dean, D.J., Gonzalez, E., Haney, J., Hinojosa-Huerta, O., Poulos, H.M., Renfrow, J., Richter, H., Sifuentes Lugo, C.A., Stromberg, J.C., Turner, D., Urbanczyk, K., and Briggs, M.K., 2021, Monitoring the results of stream corridor restoration, chap. 7 of Renewing our rivers: Stream corridor restoration in dryland regions, p. 313-390.","productDescription":"78 p.","startPage":"313","endPage":"390","ipdsId":"IP-118426","costCenters":[{"id":568,"text":"Southwest Biological Science 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M.","affiliations":[],"preferred":false,"id":808968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chernoff, Barry","contributorId":25701,"corporation":false,"usgs":true,"family":"Chernoff","given":"Barry","email":"","affiliations":[],"preferred":false,"id":808969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crawford, Kelon","contributorId":248362,"corporation":false,"usgs":false,"family":"Crawford","given":"Kelon","email":"","affiliations":[],"preferred":false,"id":808970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":131047,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":808971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gonzalez, Eduardo","contributorId":225181,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Eduardo","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":808972,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haney, Jeanmarie","contributorId":13192,"corporation":false,"usgs":true,"family":"Haney","given":"Jeanmarie","email":"","affiliations":[],"preferred":false,"id":808973,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hinojosa-Huerta, O.","contributorId":98445,"corporation":false,"usgs":true,"family":"Hinojosa-Huerta","given":"O.","affiliations":[],"preferred":false,"id":808974,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Poulos, Helen M.","contributorId":75271,"corporation":false,"usgs":true,"family":"Poulos","given":"Helen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":808975,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Renfrow, J","contributorId":245871,"corporation":false,"usgs":false,"family":"Renfrow","given":"J","email":"","affiliations":[{"id":49357,"text":"unknown (shows up at new 11th author in citation)","active":true,"usgs":false}],"preferred":false,"id":808976,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Richter, Holly E.","contributorId":26238,"corporation":false,"usgs":true,"family":"Richter","given":"Holly E.","affiliations":[],"preferred":false,"id":808977,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sifuentes Lugo, Carlos A.","contributorId":248363,"corporation":false,"usgs":false,"family":"Sifuentes Lugo","given":"Carlos","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808978,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stromberg, Juliet C.","contributorId":52280,"corporation":false,"usgs":true,"family":"Stromberg","given":"Juliet","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":808979,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Turner, Dale S.","contributorId":63742,"corporation":false,"usgs":true,"family":"Turner","given":"Dale S.","affiliations":[],"preferred":false,"id":808980,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Urbanczyk, K.","contributorId":40151,"corporation":false,"usgs":true,"family":"Urbanczyk","given":"K.","email":"","affiliations":[],"preferred":false,"id":808981,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Briggs, Mark K.","contributorId":177076,"corporation":false,"usgs":false,"family":"Briggs","given":"Mark","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":808982,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70218472,"text":"70218472 - 2021 - Broader impacts for ecologists: Biological soil crust as a model system for education","interactions":[],"lastModifiedDate":"2021-03-01T16:24:24.743096","indexId":"70218472","displayToPublicDate":"2021-01-05T10:19:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Broader impacts for ecologists: Biological soil crust as a model system for education","docAbstract":"Biological soil crusts (biocrusts) are a complex community of algae, cyanobacteria, lichens, bryophytes, and assorted bacteria, fungi, archaea, and bacteriophages that colonize the soil surface. Biocrusts are particularly common in drylands and are found in arid and semiarid ecosystems worldwide. While diminutive in size, biocrusts often cover large terrestrial areas, provide numerous ecosystem benefits, enhance biodiversity, and are found in multiple configurations and assemblages across different climate and disturbance regimes. Biocrusts have been a focus of many ecologists, especially those working in semiarid and arid lands, as biocrusts are foundational community members, play fundamental roles in ecosystem processes, and offer rare opportunities to study biological interactions at small and large spatial scales. Due to these same characteristics, biocrusts have the potential to serve as an excellent teaching tool. The purpose of this paper is to demonstrate the utility of biocrust communities as a model system in science education. Functioning as portable, dynamic mini ecosystems, biocrusts can be used to teach about organisms, biodiversity, biotic interactions, abiotic controls, ecosystem processes, and even global change, and can be easy to use in nearly every classroom setup. For example, education principles, such as evolution and adaptation to stress, or structure and function (patterns and processes) can be applied by bringing biocrusts into the classroom as a teaching tool. In addition, discussing the utility of biocrusts in the classroom – including theory, hypothesis testing, experimentation, and hands-on learning – this document also provides tips and resources for developing education tools and activities geared toward impactful learning.
","language":"English","publisher":"Frontiers Media SA","doi":"10.3389/fmicb.2020.577922","usgsCitation":"Faist, A.M., Antoninka, A.J., Barger, N.N., Bowker, M., Chaudhary, V.B., Havrilla, C.A., Huber-Saanwald, E., Reed, S., and Weber, B., 2021, Broader impacts for ecologists: Biological soil crust as a model system for education: Frontiers in Microbiology, v. 11, 577922, 6 p., https://doi.org/10.3389/fmicb.2020.577922.","productDescription":"577922, 6 p.","ipdsId":"IP-124195","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453931,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2020.577922","text":"Publisher Index Page"},{"id":383690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2021-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Faist, Alasha M.","contributorId":252912,"corporation":false,"usgs":false,"family":"Faist","given":"Alasha","email":"","middleInitial":"M.","affiliations":[{"id":50467,"text":"Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":811110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antoninka, Anita J.","contributorId":240674,"corporation":false,"usgs":false,"family":"Antoninka","given":"Anita","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":811111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barger, Nichole N.","contributorId":193039,"corporation":false,"usgs":false,"family":"Barger","given":"Nichole","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":811112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowker, Matthew A.","contributorId":240683,"corporation":false,"usgs":false,"family":"Bowker","given":"Matthew A.","affiliations":[],"preferred":false,"id":811113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chaudhary, V. Bala","contributorId":252913,"corporation":false,"usgs":false,"family":"Chaudhary","given":"V.","email":"","middleInitial":"Bala","affiliations":[{"id":50468,"text":"Department of Environmental Science and Studies, DePaul University Chicago IL, USA","active":true,"usgs":false}],"preferred":false,"id":811114,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Havrilla, Caroline A. 0000-0003-3913-0980","orcid":"https://orcid.org/0000-0003-3913-0980","contributorId":146326,"corporation":false,"usgs":true,"family":"Havrilla","given":"Caroline","email":"","middleInitial":"A.","affiliations":[{"id":16669,"text":"U of CO, Boulder","active":true,"usgs":false},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":811115,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huber-Saanwald, Elisabeth","contributorId":252914,"corporation":false,"usgs":false,"family":"Huber-Saanwald","given":"Elisabeth","email":"","affiliations":[{"id":50469,"text":"División de Ciencias Ambientales, Instituto Potosino de Investigación Científica y Tecnológica, San Luis Potosí, Mexico","active":true,"usgs":false}],"preferred":false,"id":811116,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811117,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weber, Bettina","contributorId":196800,"corporation":false,"usgs":false,"family":"Weber","given":"Bettina","email":"","affiliations":[],"preferred":false,"id":811118,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229708,"text":"70229708 - 2021 - Drivers of realized satellite tracking duration in marine turtles","interactions":[],"lastModifiedDate":"2022-03-17T13:30:26.92691","indexId":"70229708","displayToPublicDate":"2021-01-05T09:43:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of realized satellite tracking duration in marine turtles","docAbstract":"Background
Satellite tags have revolutionized our understanding of marine animal movements. However, tags may stop transmitting for many reasons and little research has rigorously examined tag failure. Using a long-term, large-scale, multi-species dataset, we evaluated factors influencing tracking duration of satellite tags to inform study design for future tracking studies.
Methods
We leveraged data on battery status transmitted with location data, recapture events, and number of transmission days to probabilistically quantify multiple potential causes of failure (i.e., battery failure, premature detachment, and tag damage/fouling). We used a combination of logistic regressions and an ordinary linear model including several predictor variables (i.e., tag type, battery life, species, sex, size, and foraging region).
Results
We examined subsets of data from 360 satellite tags encompassing 86,889 tracking days deployed on four species of marine turtles throughout the Gulf of Mexico, Caribbean, and Bahamas from 2008 to 2019. Only 4.1% of batteries died before failure due to other causes. We observed species-specific variation in how long tags remain attached: hawksbills retained 50% of their tags for 1649 days (95% CI 995–1800), loggerheads for 584 days (95% CI 400–690), and green turtles for 294 days (95% CI 198–450). Estimated tracking duration varied by foraging region (Caribbean: 385 days; Bahamas: 356; southern Gulf of Mexico [SGOM]: 276, northern Gulf of Mexico [NGOM]: 177). Additionally, we documented species-specific variation in estimated tracking duration among foraging regions. Based on sensor data, within the Gulf of Mexico, across species, we estimated that 50% of tags began to foul after 83 95% CI (70–120) days.
Conclusions
The main factor that limited tracking duration was tag damage (i.e., fouling and/or antenna breakage). Turtles that spent most of their time in the Gulf of Mexico had shorter tracking durations than those in the Bahamas and Caribbean, with shortest durations observed in the NGOM. Additionally, tracking duration varied by species, likely as a result of behaviors that damage tags. This information will help researchers, tag companies, permitting agencies, and funders better predict expected tracking durations, improving study designs for imperiled marine turtles. Our results highlight the heterogeneity in telemetry device longevity, and we provide a framework for researchers to evaluate telemetry devices with respect to their study objectives.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-020-00237-3","usgsCitation":"Hart, K., Guzy, J.C., and Smith, B., 2021, Drivers of realized satellite tracking duration in marine turtles: Movement Ecology, v. 9, 1, 14 p., https://doi.org/10.1186/s40462-020-00237-3.","productDescription":"1, 14 p.","ipdsId":"IP-121629","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453933,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-020-00237-3","text":"Publisher Index Page"},{"id":436601,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OXCKYI","text":"USGS data release","linkHelpText":"Tracking durations for marine turtles satellite tagged in Gulf of Mexico and Caribbean sites, 2008-2019"},{"id":397153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bahamas, Brazil, Honduras, Mexico, Nicaragua, United States, Virgin Islands","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Biscayne, Buck Island Reef National Monument, Caribbean Sea, Chandeleur Islands, Dry Tortugas, Everglades National Park, Gulf of Mexico, Gulf Shores, Pascagoula, Port Fourchon, Ship Shoal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.91015624999999,\n 26.194876675795218\n ],\n [\n -94.3505859375,\n 17.476432197195518\n ],\n [\n -91.40625,\n 17.853290114098012\n ],\n [\n -89.912109375,\n 20.34462694382967\n ],\n [\n -88.11035156249999,\n 20.3034175184893\n ],\n [\n -84.462890625,\n 14.477234210156519\n ],\n [\n -84.24316406249999,\n 12.039320557540572\n ],\n [\n -71.1474609375,\n 10.531020008464989\n ],\n [\n -67.7197265625,\n 9.579084335882534\n ],\n [\n -59.9853515625,\n 9.015302333420598\n ],\n [\n -57.74414062500001,\n 12.940322128384627\n ],\n [\n -59.45800781249999,\n 18.562947442888312\n ],\n [\n -67.8955078125,\n 19.72534224805787\n ],\n [\n -70.7080078125,\n 22.187404991398775\n ],\n [\n -74.5751953125,\n 23.725011735951796\n ],\n [\n -75.9814453125,\n 26.07652055985697\n ],\n [\n -79.89257812499999,\n 31.57853542647338\n ],\n [\n -80.8154296875,\n 31.50362930577303\n ],\n [\n -86.8798828125,\n 31.090574094954192\n ],\n [\n -90.04394531249999,\n 30.977609093348686\n ],\n [\n -93.4716796875,\n 30.56226095049944\n ],\n [\n -95.44921875,\n 30.107117887092357\n ],\n [\n -98.0859375,\n 27.761329874505233\n ],\n [\n -97.91015624999999,\n 26.194876675795218\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guzy, Jacquelyn C. 0000-0003-2648-398X","orcid":"https://orcid.org/0000-0003-2648-398X","contributorId":288520,"corporation":false,"usgs":true,"family":"Guzy","given":"Jacquelyn","email":"","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":838046,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217281,"text":"70217281 - 2021 - Toward physics-based nonergodic PSHA: A prototype fully-deterministic seismic hazard model for southern California","interactions":[],"lastModifiedDate":"2021-04-08T14:35:12.118897","indexId":"70217281","displayToPublicDate":"2021-01-05T08:16:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Toward physics-based nonergodic PSHA: A prototype fully-deterministic seismic hazard model for southern California","docAbstract":"We present a nonergodic framework for probabilistic seismic‐hazard analysis (PSHA) that is constructed entirely of deterministic, physical models. The use of deterministic ground‐motion simulations in PSHA calculations is not new (e.g., CyberShake), but prior studies relied on kinematic rupture generators to extend empirical earthquake rupture forecasts. Fully dynamic models, which simulate rupture nucleation and propagation of static and dynamic stresses, are still computationally intractable for the large simulation domains and many seismic cycles required to perform PSHA. Instead, we employ the Rate‐State earthquake simulator (RSQSim) to efficiently simulate hundreds of thousands of years of M≥6.5 earthquake sequences on the California fault system. RSQSim produces full slip‐time histories for each rupture, which, unlike kinematic models, emerge from frictional properties, fault geometry, and stress transfer; all intrinsic variability is deterministic. We use these slip‐time histories directly as input to a 3D wave‐propagation code within the CyberShake platform to obtain simulated Fmax=0.5 Hz ground motions. The resulting 3 s spectral acceleration ground motions closely match empirical ground‐motion model (GMM) estimates of median and variability of shaking. When computed over a range of sources and sites, the variability is similar to that of ergodic GMMs. Variability is reduced for individual pairs of sources and sites that repeatedly sample a single path, which is expected for a nonergodic model. This results in increased exceedance probabilities for certain characteristic ground motions for a source–site pair, while decreasing probabilities at the extreme tails of the ergodic GMM predictions. We present these comparisons and preliminary fully deterministic physics‐based RSQSim–CyberShake hazard curves, as well as a new technique for estimating within‐ and between‐event variability through simulation.
Heat has been widely applied to trace groundwater‐surface water exchanges in inland environments, but it is infrequently applied in coastal sediment where head oscillations induce periodicity in water flux magnitude/direction and heat advection. This complicates interpretation of temperatures to estimate water fluxes. We investigate the convolution of thermal and hydraulic signals to assess the viability of using heat as a tracer in environments with tidal head oscillations superimposed on submarine groundwater discharge. We first generate sediment temperature and head time series for conditions ranging from no tide to mega‐tidal using a numerical model (SUTRA) forced with periodic temperature and tidal head signals. We then analyze these synthetic temperature time series using heat tracing software (VFLUX2 and 1DTempPro) to evaluate if conventional terrestrial approaches to infer fluxes from temperatures are applicable for coastal settings. We consider high‐frequency water flux variability within a tidal signal and averaged over tidal signals. Results show that VFLUX2 analytical methods reasonably estimated the mean discharge fluxes in most cases but could not reproduce the flux variability within tidal cycles. The model results further reveal that high‐frequency time series of water fluxes varying in magnitude and direction can be accurately estimated if paired temperature and hydraulic head are analyzed using numerical models (e.g. 1DTempPro) that consider both dynamic hydraulic gradients and thermal signals. These results point to the opportunity to incorporate pressure sensors within heat tracing instrumentation to better assess sub‐daily flux oscillations and associated reactive processes.