The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.
A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.
The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.
In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.
The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.
The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.
A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.
Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.
The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.
Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The 1973 Oklahoma Water Law (82 OK Stat § 82-1020.5) requires that the Oklahoma Water Resources Board (OWRB) conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin (hereinafter referred to as an “aquifer”). The maximum annual yield allocated per acre of land is known as the equal-proportionate-share (EPS) pumping rate. At present (2021), the OWRB has not yet established a maximum annual yield and EPS pumping rate for the Salt Fork Red River aquifer. To provide updated information to the OWRB that could support evaluation and determination of an appropriate maximum annual yield, the U.S. Geological Survey (USGS), in cooperation with the OWRB, conducted a hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater availability in the Salt Fork Red River aquifer.
The Salt Fork Red River aquifer in Greer, Harmon, and Jackson Counties of southwestern Oklahoma is composed of about 274.5 square miles of alluvium and terrace deposits associated with the Salt Fork Red River. The mean annual recharge rate to the Salt Fork Red River aquifer for the period 1980–2015 was estimated to be about 2.94 inches per year, or 10.0 percent of the mean annual precipitation for the same period (29.4 inches per year). This 1980–2015 mean annual recharge rate is equivalent to a mean annual recharge rate of about 38,000 acre-feet per year (acre-ft/yr) for the Salt Fork Red River aquifer excluding about 19,764 acres comprising the Mulberry Creek and Horse Creek terraces. The mean annual recharge rates upgradient and downgradient from USGS streamgage 07300500 Salt Fork Red River at Mangum, Okla. (hereinafter referred to as the “Mangum gage”), apportioned by aquifer area (41.5 and 58.5 percent, respectively), were about 16,000 and 22,000 acre-ft/yr, respectively. Mean annual groundwater use for the study period (1980–2015) was 3,532.7 acre-ft/yr; about 77 percent of that groundwater use was for irrigation, and about 23 percent was for public supply. Most groundwater use for irrigation was associated with wells in the Martha terrace.
A hydrogeologic framework was developed for the Salt Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of the numerical groundwater-flow model of the Salt Fork Red River aquifer described in this report. A conceptual model for the Salt Fork Red River aquifer that reasonably represents the groundwater-flow system was developed to constrain the construction and calibration of the numerical model. The conceptual-model water budget estimated mean annual inflows to, and outflows from, the Salt Fork Red River aquifer for the period 1980–2015 and included a subaccounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from the Mangum gage.
The numerical groundwater-flow model of the Salt Fork Red River aquifer was constructed by using MODFLOW-2005 with the Newton formulation solver. The model of the Salt Fork Red River aquifer was spatially discretized into 1,050 rows, 1,125 columns, about 170,000 active cells measuring 200 by 200 feet (ft), and a single convertible layer. The model was temporally discretized into 432 monthly transient stress periods (each with two time steps to improve model stability). An initial steady-state stress period represented mean annual inflows to, and outflows from, the aquifer and produced a solution that was used as the initial condition for subsequent transient stress periods as well as some groundwater-availability scenarios. The model was calibrated to water-table-altitude observations at selected wells and base-flow observations at selected streamgages.
The simulated saturated thickness of the Salt Fork Red River aquifer was determined by subtracting the altitude of the aquifer base from the simulated water-table altitude at the end of the numerical-model period (2015). The simulated saturated thickness was more than 75 ft in a paleochannel in the Dodson terrace near the Texas border. The mean aquifer thickness (sum of saturated and unsaturated) was 49.62 ft, and the mean saturated thickness was 28.55 ft. A simulated mean transmissivity of 1,024 feet squared per day was computed from the calibrated hydraulic conductivity and saturated thickness of each cell. The simulated available water in storage at the end of the numerical-model period (2015) was 526,117 acre-feet (acre-ft); about 42 percent of that total was available upgradient from the Mangum gage, and about 58 percent of that total was available downgradient from the Mangum gage (including the Mangum terrace).
Three types of groundwater-availability scenarios were run using the calibrated numerical model. These scenarios were used to (1) estimate the EPS pumping rate that ensures a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought on base flow and groundwater storage. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were about 0.51, 0.48, and 0.48 acre-foot per acre per year, respectively. Given the 155,929-acre modeled aquifer area, these rates correspond to annual yields of about 78,800, 74,900, and 74,700 acre-ft/yr, respectively. For the 20-year EPS scenario, decreasing and increasing recharge by 10 percent resulted in a 6-percent change in the EPS pumping rate in both cases; for the 40- and 50-year EPS scenarios, decreasing and increasing recharge by 10 percent resulted in a 7-percent change in the EPS pumping rate in both cases.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the Salt Fork Red River aquifer and base flows in the Salt Fork Red River. The effects of well withdrawals were evaluated by quantifying differences in groundwater storage and base flow in four 50-year scenarios, which applied (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2015), (3) 2015 pumping rates, and (4) increasing demand pumping rates at simulated wells. The increasing demand pumping rates assumed a cumulative 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwestern Oklahoma. Groundwater storage after 50 years with no pumping was 535,000 acre-ft, or 8,900 acre-ft (1.7 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-table-altitude increase of 0.48 ft. Groundwater storage after 50 years of pumping at the mean rate for the study period (1980–2015) was 519,900 acre-ft, or 6,200 acre-ft (1.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.34 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 513,100 acre-ft, or 13,000 acre-ft (2.5 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.71 ft. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was 509,700 acre-ft, or 16,500 acre-ft (3.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.89 ft.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage. The period January 1983–December 1992 was chosen as the simulated drought period. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought) at the end of the simulated drought period (1992). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1983–92). Upstream inflows from the Salt Fork Red River, Turkey Creek, and Bitter Creek were reduced by 75 percent. Groundwater storage at the end of the drought period (1992) was 479,200 acre-ft, or 53,200 acre-ft (10.0 percent) less than the groundwater storage of the calibrated numerical model at the end of the drought period. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 2.9 ft. At the end of the 10-year hypothetical drought period, simulated base flows at the Mangum gage and USGS streamgage 07301110 Salt Fork Red River near Elmer, Okla., had decreased by about 80 and 70 percent, respectively.
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U.S. Geological Survey
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","tableOfContents":"Earthquake clustering (grouping in space and time) is a widely observed mode of strain release in the upper crust, although this behavior on individual faults is a departure from classic elastic rebound theory. In this study, we consider factors responsible for a cluster of earthquakes on the Bear River fault zone (BRF), a recently activated, 44-km-long normal fault on the eastern margin of Basin and Range extension in the Rocky Mountains. The entire surface-rupturing history of the BRF, as gleaned from paleoseismic and geomorphic observations, began only 4500 years ago and consists of at least three large events. Rupture of the BRF is spatially complex and is clearly conditioned by preexisting structure. In particular, where the south end of the fault intersects older thrust faults and upturned strata along the south-dipping flank of the Precambrian basement-cored Uinta arch, the main trace ends abruptly in a set of orthogonal splays that accommodate down-dropping of a large hanging-wall graben against the arch. We hypothesize that the geomechanically strong Uinta arch crustal block impeded the development of the BRF and, over time, enabled a significant accumulation of elastic strain energy, eventually giving rise to a pulse of strain release in the mid- to late Holocene. We surmise that variations in fault strength, both in space and time, is a cause of earthquake clustering on the BRF and on other faults that are structurally and tectonically immature. The first two earthquakes on the BRF occurred during the same period of time as a regional cluster of earthquakes in the Middle Rocky Mountains, suggesting that isolated faults in this slowly extending region interact through widespread changes in stress conditions.
Geophysical investigations documenting enhanced magnetic susceptibility (MS) within the water table fluctuation zone at hydrocarbon contaminated sites suggest that MS can be used as a proxy for investigating microbial mediated iron reduction during intrinsic bioremediation. Here, we investigated the microbial community composition over a 5-year period at a hydrocarbon-contaminated site that exhibited transient elevated MS responses. Our objective was to determine the key microbial populations in zones of elevated MS. We retrieved sediment cores from the petroleum-contaminated site near Bemidji, MN, United States, and performed MS measurements on these cores. We also characterized the microbial community composition by high-throughput 16S rRNA gene amplicon sequencing from samples collected along the complete core length. Our spatial and temporal analysis revealed that the microbial community composition was generally stable throughout the period of investigation. In addition, we observed distinct vertical redox zonations extending from the upper vadose zone into the saturated zone. These distinct redox zonations were concomitant with the dominant microbial metabolic processes as follows: (1) the upper vadose zone was dominated by aerobic microbial populations; (2) the lower vadose zone was dominated by methanotrophic populations, iron reducers and iron oxidizers; (3) the smear zone was dominated by iron reducers; and (4) the free product zone was dominated by syntrophic and methanogenic populations. Although the common notion is that high MS values are caused by high magnetite concentrations that can be biotically formed through the activities of iron-reducing bacteria, here we show that the highest magnetic susceptibilities were measured in the free-phase petroleum zone, where a methanogenic community was predominant. This field study may contribute to the emerging knowledge that methanogens can switch their metabolism from methanogenesis to iron reduction with associated magnetite precipitation in hydrocarbon contaminated sediments. Thus, geophysical methods such as MS may help to identify zones where iron cycling/reduction by methanogens is occurring.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2021.598172","usgsCitation":"Beaver, C.L., Atekwana, E.A., Bekins, B.A., Ntarlagiannis, D., Slater, L., and Rossbach, S., 2021, Methanogens and their syntrophic partners dominate zones of enhanced magnetic susceptibility at a petroleum contaminated site: Frontiers in Earth Science, v. 9, 598172, 18 p., https://doi.org/10.3389/feart.2021.598172.","productDescription":"598172, 18 p.","ipdsId":"IP-125198","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2021.598172","text":"Publisher Index Page"},{"id":384449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaver, Carol L.","contributorId":255451,"corporation":false,"usgs":false,"family":"Beaver","given":"Carol","email":"","middleInitial":"L.","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":812376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atekwana, Estella A.","contributorId":255452,"corporation":false,"usgs":false,"family":"Atekwana","given":"Estella","email":"","middleInitial":"A.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":812377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekins, Barbara A. 0000-0002-1411-6018 babekins@usgs.gov","orcid":"https://orcid.org/0000-0002-1411-6018","contributorId":1348,"corporation":false,"usgs":true,"family":"Bekins","given":"Barbara","email":"babekins@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":812378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ntarlagiannis, Dimitrios","contributorId":255453,"corporation":false,"usgs":false,"family":"Ntarlagiannis","given":"Dimitrios","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":812379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Slater, Lee D.","contributorId":255454,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":812380,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rossbach, Silvia","contributorId":255455,"corporation":false,"usgs":false,"family":"Rossbach","given":"Silvia","email":"","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":812381,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219910,"text":"70219910 - 2021 - The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed","interactions":[],"lastModifiedDate":"2021-04-19T11:51:47.992809","indexId":"70219910","displayToPublicDate":"2021-03-15T06:56:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed","docAbstract":"The Panola Mountain Research Watershed (PMRW) is a 41‐hectare forested catchment within the Piedmont Province of the Southeastern United States. Observations, experimentation, and numerical modelling have been conducted at Panola over the past 35 years. But to date, these studies have not been fully incorporated into a more comprehensive synthesis. Here we describe the evolving perceptual understanding of streamflow generation mechanisms at the PMRW. We show how the long‐term study has enabled insights that were initially unforeseen but are also unachievable in short‐term studies. In particular, we discuss how the accumulation of field evidence, detailed site characterization, and modelling enabled a priori hypotheses to be formed, later rejected, and then further refined through repeated field campaigns. The extensive characterization of the soil and bedrock provided robust process insights not otherwise achievable from hydrometric measurements and numerical modelling alone. We focus on two major aspects of streamflow generation: the role of hillslopes (and their connection to the riparian zone) and the role of catchment storage in controlling fluxes and transit times of water in the catchment. Finally, we present location‐independent hypotheses based on our findings at PMRW and suggest ways to assess the representativeness of PMRW in the broader context of headwater watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14127","usgsCitation":"Aulenbach, B.T., Hooper, R.P., van Meerveld, H.J., Burns, D., Freer, J.E., Shanley, J.B., Huntington, T., McDonnell, J.J., and Norman E. Peters, 2021, The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed: Hydrological Processes, v. 35, no. 4, e14127, 14 p., https://doi.org/10.1002/hyp.14127.","productDescription":"e14127, 14 p.","ipdsId":"IP-125152","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":385149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.78149414062499,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 33.25706340236547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Richard P 0000-0002-3329-9622","orcid":"https://orcid.org/0000-0002-3329-9622","contributorId":257488,"corporation":false,"usgs":false,"family":"Hooper","given":"Richard","email":"","middleInitial":"P","affiliations":[{"id":52045,"text":"Tufts University, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":814372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Meerveld, H. J. 0000-0002-7547-3270","orcid":"https://orcid.org/0000-0002-7547-3270","contributorId":257489,"corporation":false,"usgs":false,"family":"van Meerveld","given":"H.","email":"","middleInitial":"J.","affiliations":[{"id":52048,"text":"University of Zurich, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":814373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freer, James E. 0000-0001-6388-7890","orcid":"https://orcid.org/0000-0001-6388-7890","contributorId":188139,"corporation":false,"usgs":false,"family":"Freer","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":814375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McDonnell, Jeffery J. 0000-0002-3880-3162","orcid":"https://orcid.org/0000-0002-3880-3162","contributorId":62723,"corporation":false,"usgs":false,"family":"McDonnell","given":"Jeffery","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":814378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Norman E. Peters 0000-0002-0637-9424","orcid":"https://orcid.org/0000-0002-0637-9424","contributorId":207130,"corporation":false,"usgs":false,"family":"Norman E. Peters","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":814379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219224,"text":"70219224 - 2021 - Sea turtles across the North Pacific are exposed to perfluoroalkyl substances","interactions":[],"lastModifiedDate":"2021-04-01T11:54:08.775984","indexId":"70219224","displayToPublicDate":"2021-03-15T06:48:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Sea turtles across the North Pacific are exposed to perfluoroalkyl substances","docAbstract":"Perfluorinated alkyl substances (PFASs) are global, persistent, and toxic contaminants. We assessed PFAS concentrations in green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) turtles from the North Pacific. Fifteen compounds were quantified via liquid chromatography tandem mass spectrometry from 62 green turtle and 6 hawksbill plasma samples from Hawai’i, Palmyra Atoll, and the Northern Marianas Islands. Plasma from 14 green turtles severely afflicted with fibropapillomatosis, and eggs from 12 Hawaiian hawksbill nests from 7 females were analyzed. Perfluorooctane sulfonate (PFOS) predominated in green turtle plasma; perfluorononanoic acid (PFNA) predominated in hawksbill tissues. Concentrations were greater in hawksbill than green turtle plasma (p < 0.05), related to trophic differences. Green turtle plasma PFOS concentrations were related to human populations from highest to lowest: Hawai’i, Marianas, Palmyra. Influence on fibropapillomatosis was not evident. PFASs were maternally transferred to hawksbill eggs, with decreasing concentrations with distance from airports and with clutch order from one female. A risk assessment of PFOS showed concern for immunosuppression in Kailua green turtles and alarming concern for hawksbill developmental toxicity. Perfluoroundecanoic (PFUnA) and perfluorotridecanoic (PFTriA) acid levels were correlated with reduced emergence success (p < 0.05). Studies to further examine PFAS effects on sea turtle development would be beneficial.
Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.
Industrial chemical contamination within coastal regions of the Great Lakes can pose serious risks to wetland habitat and offshore fisheries, often resulting in fish consumption advisories that directly affect human and wildlife health. Mercury (Hg) is a contaminant of concern in many of these highly urbanized and industrialized coastal regions, one of which is the Saint Louis River estuary (SLRE), the second largest tributary to Lake Superior. The SLRE has legacy Hg contamination that drives high Hg concentrations within sediments, but it is unclear whether legacy-derived Hg actively cycles within the food web. To understand the relative contributions of legacy versus contemporary Hg sources in coastal zones, Hg, carbon, and nitrogen stable isotope ratios were measured in sediments and food webs of SLRE and the Bad River, an estuarine reference site. Hg stable isotope values revealed that legacy contamination of Hg was widespread and heterogeneously distributed in sediments of SLRE, even in areas lacking industrial Hg sources. Similar isotope values were found in benthic invertebrates, riparian spiders, and prey fish from SLRE, confirming legacy Hg reaches the SLRE food web. Direct comparison of prey fish from SLRE and the Bad River confirmed that Hg isotope differences between the sites were not attributable to fractionation associated with rapid Hg bioaccumulation at estuarine mouths, but due to the presence of industrial Hg within SLRE. The Hg stable isotope values of game fish in both estuaries were dependent on fish migration and diet within the estuaries and extending into Lake Superior. These results indicate that Hg from legacy contamination is actively cycling within the SLRE food web and, through migration, this Hg also extends into Lake Superior via game fish. Understanding sources and the movement of Hg within the estuarine food web better informs restoration strategies for other impaired Great Lakes coastal zones.
Watershed degradation due to invasion threatens downstream water flows and associated ecosystem services. While this topic has been studied across landscapes that have undergone invasive-driven state changes (e.g., native forest to invaded grassland), it is less well understood in ecosystems experiencing within-system invasion (e.g. native forest to invaded forest). To address this subject, we conducted an integrated ecological and ecohydrological study in tropical forests impacted by invasive plants and animals. We measured soil infiltration capacity in multiple fenced (i.e., ungulate-free)/unfenced and native/invaded forest site pairs along moisture and substrate age gradients across Hawaii to explore the effects of invasion on hydrological processes within tropical forests. We also characterized forest composition, structure and soil characteristics at these sites to assess the direct and vegetation-mediated impacts of invasive species on infiltration capacity. Our models show that invasive ungulates negatively affect soil infiltration capacity consistently across the wide moisture and substrate age gradients considered. Additionally, several soil characteristics known to be affected by invasive ungulates were associated with local infiltration rates, indicating that the long-term secondary effects of high ungulate densities in tropical forests may be stronger than effects observed in this study. The effect of invasive plants on infiltration was complex and likely to depend on their physiognomy within existing forest community structure. These results provide clear evidence for managers that invasive ungulate control efforts can improve ecohydrological function of mesic and wet forest systems critical to protecting downstream and nearshore resources and maintaining groundwater recharge.
Eurasian collared doves (Streptopelia decaocto) were introduced into Florida in the 1980s and have since established populations throughout the continental United States. Pigeon paramyxovirus-1 (PPMV-1), a species-adapted genotype VI Avian orthoavulavirus 1, has caused periodic outbreaks among collared doves in the U.S. since 2001 with outbreaks occasionally involving native doves. In California, PPMV-1 mortality events were first documented in Riverside County in 2014 with subsequent outbreaks in 23 additional counties from southern to northern California between 2015 and 2019. Affected collared doves exhibited torticollis and partial paralysis. Pale kidneys were frequently visible on gross necropsy (65.4%; 51/78) while lymphoplasmacytic interstitial nephritis often with acute tubular necrosis (96.0%; 24/25) and pancreatic necrosis (80.0%; 20/25) were common findings on histopathology. In total, PPMV-1 was confirmed by rRT-PCR and sequence analysis from oropharyngeal and/or cloacal swabs in 93.0% (40/43) of the collared doves tested from 16 California counties. In 2017, Avian orthoavulavirus 1 was confirmed in a native mourning dove (Zenaida macroura) found dead during a PPMV-1 outbreak in collared doves by rRT-PCR from formalin-fixed paraffin-embedded (FFPE) tissues, after the initial rRT-PCR from swabs failed to detect the virus. Molecular sequencing of the fusion protein of isolates collected from collared doves during outbreaks in 2014, 2016, and 2017 identified two distinct subgenotypes, VIa and VIn. Subgenotype VIn has been primarily isolated from collared doves in the southern U.S., while VIa has been isolated from mixed avian species in the northeastern U.S., indicating two independent introductions into California. While populations of collared doves are not expected to be substantially impacted by this disease, PPMV-1 may pose a threat to already declining populations of native columbids. This threat could be assessed by monitoring native and non-native columbids for PPMV-1. Based on our study, swab samples may not be sufficient to detect infection in native columbids and may require the use of non-traditional diagnostic approaches, such as FFPE tissues, to ensure virus detection.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.meegid.2021.104809","usgsCitation":"Rogers, K., Mete, A., Ip, H., Torchetti, M.K., Killian, M.L., and Crossley, B., 2021, Emergence and molecular characterization of pigeon Paramyxovirus-1 in non-native Eurasian collared doves (Streptopelia decaocto) in California, USA: Infection, Genetics and Evolution, v. 91, 104809, 8 p., https://doi.org/10.1016/j.meegid.2021.104809.","productDescription":"104809, 8 p.","ipdsId":"IP-123317","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":453094,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.meegid.2021.104809","text":"Publisher Index Page"},{"id":384623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.1142578125,\n 32.65787573695528\n ],\n [\n -114.47753906249999,\n 32.80574473290688\n ],\n [\n -114.169921875,\n 34.27083595165\n ],\n [\n -120.05859375,\n 39.13006024213511\n ],\n [\n -120.05859375,\n 42.09822241118974\n ],\n [\n -124.3212890625,\n 42.032974332441405\n ],\n [\n -124.365234375,\n 40.34654412118006\n ],\n [\n -123.74999999999999,\n 38.85682013474361\n ],\n [\n -121.904296875,\n 36.20882309283712\n ],\n [\n -120.62988281249999,\n 34.45221847282654\n ],\n [\n -117.1142578125,\n 32.65787573695528\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rogers, Krysta","contributorId":255719,"corporation":false,"usgs":false,"family":"Rogers","given":"Krysta","email":"","affiliations":[{"id":51652,"text":"Wildlife Investigations Laboratory, California Department of Fish and Wildlife, 1701 Nimbus Road Suite D, Rancho Cordova, CA 95670, U.S.A.;","active":true,"usgs":false}],"preferred":false,"id":812821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mete, Ash","contributorId":255720,"corporation":false,"usgs":false,"family":"Mete","given":"Ash","email":"","affiliations":[{"id":51653,"text":"California Animal Health and Food Safety Laboratory, University of California, Davis, West Health Sciences Drive, Davis, CA 95616, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":812822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ip, Hon S. 0000-0003-4844-7533","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":126815,"corporation":false,"usgs":true,"family":"Ip","given":"Hon S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":812823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torchetti, Mia K.","contributorId":252830,"corporation":false,"usgs":false,"family":"Torchetti","given":"Mia","email":"","middleInitial":"K.","affiliations":[{"id":50437,"text":"US Department of Agriculture – Veterinary Services, Ames, Iowa, USA","active":true,"usgs":false}],"preferred":false,"id":812824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Killian, Mary L.","contributorId":29685,"corporation":false,"usgs":false,"family":"Killian","given":"Mary","email":"","middleInitial":"L.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":812825,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crossley, Beate","contributorId":225109,"corporation":false,"usgs":false,"family":"Crossley","given":"Beate","email":"","affiliations":[{"id":41039,"text":"and California Animal Health and Food Safety Laboratory, University of California Davis, USA","active":true,"usgs":false}],"preferred":false,"id":812826,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218824,"text":"70218824 - 2021 - Comparative morphology of freshwater sculpin inhabiting different environmental conditions in the Chesapeake Bay headwaters","interactions":[],"lastModifiedDate":"2021-04-22T17:43:56.262386","indexId":"70218824","displayToPublicDate":"2021-03-13T06:46:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Comparative morphology of freshwater sculpin inhabiting different environmental conditions in the Chesapeake Bay headwaters","docAbstract":"We compared body morphology of two freshwater sculpin taxa that inhabit distinct environmental conditions in the Chesapeake Bay watershed of eastern North America: Potomac sculpin (C. girardi, Robins; PS) and checkered sculpin (C. sp. cf. girardi; CS). Both taxa are endemic to the study area, but PS are more broadly distributed than CS which are limited to karst groundwater-dominated streams in the central Potomac River basin. We examined preserved specimens from sites encompassing their geographic range (six sites per taxon) to evaluate taxonomic differences and environmental effects. Pelvic fin ray counts and body shape distinguished the study taxa. Morphological variation exhibited stronger relationships to environmental covariates (site elevation and basin size) in PS than CS as expected. In addition, the frequency of specimens with a united median chin pore increased with site elevation in PS (but not CS), suggesting thermal effects on preoperculomanibular canal development. However, contrary to expectation, PS did not exhibit greater among-population variation in body shape than CS, and this indicates the potential importance of unmeasured environmental differences among karst groundwater-dominated streams in the study area. Our study also indicated the utility of stream-level management units for CS, an undescribed species recognized as “critically imperiled” by state agencies.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-021-01078-8","usgsCitation":"Hitt, N.P., Kessler, K.G., Macmillan, H.E., Rogers, K.M., and Raesly, R.L., 2021, Comparative morphology of freshwater sculpin inhabiting different environmental conditions in the Chesapeake Bay headwaters: Environmental Biology of Fishes, v. 104, p. 309-324, https://doi.org/10.1007/s10641-021-01078-8.","productDescription":"16 p.","startPage":"309","endPage":"324","ipdsId":"IP-118788","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":384404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.76123046875,\n 37.19533058280065\n ],\n [\n -74.99267578125,\n 37.19533058280065\n ],\n [\n -74.99267578125,\n 39.791654835253425\n ],\n [\n -77.76123046875,\n 39.791654835253425\n ],\n [\n -77.76123046875,\n 37.19533058280065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"104","noUsgsAuthors":false,"publicationDate":"2021-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812292,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kessler, Karmann G. 0000-0001-5681-4909","orcid":"https://orcid.org/0000-0001-5681-4909","contributorId":242765,"corporation":false,"usgs":true,"family":"Kessler","given":"Karmann","email":"","middleInitial":"G.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Macmillan, Hannah Eisemann 0000-0002-5221-4989","orcid":"https://orcid.org/0000-0002-5221-4989","contributorId":255404,"corporation":false,"usgs":true,"family":"Macmillan","given":"Hannah","email":"","middleInitial":"Eisemann","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812294,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Karli M. 0000-0002-6188-7405","orcid":"https://orcid.org/0000-0002-6188-7405","contributorId":237955,"corporation":false,"usgs":true,"family":"Rogers","given":"Karli","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raesly, Richard L.","contributorId":172208,"corporation":false,"usgs":false,"family":"Raesly","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":13481,"text":"Department of Biology, Frostburg State University, 101 Braddock Road, Frostburg, MD","active":true,"usgs":false}],"preferred":false,"id":812296,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231651,"text":"70231651 - 2021 - Linking altered flow regimes to biological condition: An example using benthic macroinvertebrates in small streams of the Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2022-05-18T15:38:14.741057","indexId":"70231651","displayToPublicDate":"2021-03-12T10:34:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Linking altered flow regimes to biological condition: An example using benthic macroinvertebrates in small streams of the Chesapeake Bay watershed","docAbstract":"Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8–4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7–10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
","language":"English","publisher":"Springer Link","doi":"10.1007/s00267-021-01450-5","usgsCitation":"Maloney, K.O., Carlisle, D.M., Buchanan, C., Rapp, J.L., Austin, S.H., Cashman, M.J., and Young, J.A., 2021, Linking altered flow regimes to biological condition: An example using benthic macroinvertebrates in small streams of the Chesapeake Bay watershed: Environmental Management, v. 67, p. 1171-1185, https://doi.org/10.1007/s00267-021-01450-5.","productDescription":"15 p.","startPage":"1171","endPage":"1185","ipdsId":"IP-121380","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":453098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00267-021-01450-5","text":"Publisher Index Page"},{"id":400761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay 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timing is a key life-history characteristic that influences fitness and population performance. For migratory animals, however, appropriately timing birth on one seasonal range may be constrained by events occurring during other parts of the migratory cycle. We investigated how the use of capital and income resources may facilitate flexibility in reproductive phenology of migratory mule deer in western Wyoming, USA, over a 5-yr period (2015–2019). Specifically, we examined how seasonal interactions affected three interrelated life-history characteristics: fetal development, birth mass, and birth timing. Females in good nutritional condition at the onset of winter and those that migrated short distances had more developed fetuses (measured as fetal eye diameter in March). Variation in parturition date was explained largely by fetal development; however, there were up to 16 d of plasticity in expected birth date. Plasticity in expected birth date was shaped by income resources in the form of exposure to spring green-up. Although individuals that experienced greater exposure to spring green-up were able to advance expected birth date, being born early or late with respect to fetal development had no effect on birth mass of offspring. Furthermore, we investigated the trade-offs migrating mule deer face by evaluating support for existing theory that predicts that births should be matched to local peaks in resource availability at the birth site. In contrast to this prediction, only long-distance migrants that paced migration with the flush of spring green-up, giving birth shortly after ending migration, were able to match birth with spring green-up. Shorter-distance migrants completed migration sooner and gave birth earlier, seemingly trading off more time for offspring to grow and develop over greater access to resources. Thus, movement tactic had profound downstream effects on birth timing. These findings highlight a need to reconsider classical theory on optimal birth timing, which has focused solely on conditions at the birth site.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3334","usgsCitation":"Aikens, E., Dwinnell, S., LaSharr, T., Jakopak, R., Fralick, G., Randall, J., Kaiser, R., Thonhoff, M., Kauffman, M., and Monteith, K., 2021, Migration distance and maternal resource allocation determine timing of birth in a large herbivore: Ecology, v. 102, no. 6, e03334, 12 p., https://doi.org/10.1002/ecy.3334.","productDescription":"e03334, 12 p.","ipdsId":"IP-125404","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3334","text":"Publisher Index Page"},{"id":396917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.98388671874999,\n 41.91045347666418\n ],\n [\n -109.6875,\n 41.91045347666418\n ],\n [\n -109.6875,\n 43.628123412124616\n ],\n [\n -110.98388671874999,\n 43.628123412124616\n ],\n [\n -110.98388671874999,\n 41.91045347666418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Aikens, Ellen O.","contributorId":288165,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen O.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwinnell, Samantha P.H.","contributorId":288166,"corporation":false,"usgs":false,"family":"Dwinnell","given":"Samantha P.H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaSharr, Tayler N.","contributorId":288167,"corporation":false,"usgs":false,"family":"LaSharr","given":"Tayler N.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jakopak, Rhiannon P.","contributorId":288168,"corporation":false,"usgs":false,"family":"Jakopak","given":"Rhiannon P.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fralick, Gary L.","contributorId":288169,"corporation":false,"usgs":false,"family":"Fralick","given":"Gary L.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":837545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Randall, Jill","contributorId":288170,"corporation":false,"usgs":false,"family":"Randall","given":"Jill","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":837546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaiser, Rusty","contributorId":288171,"corporation":false,"usgs":false,"family":"Kaiser","given":"Rusty","affiliations":[{"id":56194,"text":"fs","active":true,"usgs":false}],"preferred":false,"id":837547,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thonhoff, Mark","contributorId":288174,"corporation":false,"usgs":false,"family":"Thonhoff","given":"Mark","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":837548,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837540,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Monteith, Kevin L.","contributorId":288177,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837549,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70218491,"text":"70218491 - 2021 - Expanding the repertoire of electron acceptors for the anaerobic oxidation of methane in carbonates in the Atlantic and Pacific Ocean","interactions":[],"lastModifiedDate":"2021-09-15T13:30:53.980925","indexId":"70218491","displayToPublicDate":"2021-03-12T08:32:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1956,"text":"ISME Journal","active":true,"publicationSubtype":{"id":10}},"title":"Expanding the repertoire of electron acceptors for the anaerobic oxidation of methane in carbonates in the Atlantic and Pacific Ocean","docAbstract":"Authigenic carbonates represent a significant microbial sink for methane, yet little is known about the microbiome responsible for the methane removal. We identify carbonate microbiomes distributed over 21 locations hosted by seven different cold seeps in the Pacific and Atlantic Oceans by carrying out a gene-based survey using 16S rRNA- and mcrA gene sequencing coupled with metagenomic analyses. Based on 16S rRNA gene amplicon analyses, these sites were dominated by bacteria affiliated to the Firmicutes, Alpha- and Gammaproteobacteria. ANME-1 and -2 archaeal clades were abundant in the carbonates yet their typical syntrophic partners, sulfate-reducing bacteria, were not significantly present. Based on mcrA amplicon analyses, the Candidatus Methanoperedens clades were also highly abundant. Our metagenome analysis indicated that methane oxidizers affiliated to the ANME-1 and -2, may be capable of performing complete methane- and potentially short-chain alkane oxidation independently using oxidized sulfur and nitrogen compounds as terminal electron acceptors. Gammaproteobacteria are hypothetically capable of utilizing oxidized nitrogen compounds and may be involved in syntrophy with methane-oxidizing archaea. Carbonate structures represent a window for a more diverse utilization of electron acceptors for anaerobic methane oxidation along the Atlantic and Pacific Margin.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41396-021-00918-w","usgsCitation":"Beckmann, S., Farag, I.F., Zhao, R., Christman, G., Prouty, N.G., and Biddle, J.F., 2021, Expanding the repertoire of electron acceptors for the anaerobic oxidation of methane in carbonates in the Atlantic and Pacific Ocean: ISME Journal, v. 15, p. 2523-2536, https://doi.org/10.1038/s41396-021-00918-w.","productDescription":"14 p.","startPage":"2523","endPage":"2536","ipdsId":"IP-119545","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41396-021-00918-w","text":"Publisher Index Page"},{"id":385081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2021-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Beckmann, Sabrina","contributorId":224434,"corporation":false,"usgs":false,"family":"Beckmann","given":"Sabrina","email":"","affiliations":[],"preferred":false,"id":811203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farag, Ibrahim F.","contributorId":252951,"corporation":false,"usgs":false,"family":"Farag","given":"Ibrahim","email":"","middleInitial":"F.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":811204,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhao, Rui","contributorId":252952,"corporation":false,"usgs":false,"family":"Zhao","given":"Rui","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":811205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christman, Glenn","contributorId":252954,"corporation":false,"usgs":false,"family":"Christman","given":"Glenn","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":811206,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811207,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Biddle, Jennifer F.","contributorId":224433,"corporation":false,"usgs":false,"family":"Biddle","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":811208,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220543,"text":"70220543 - 2021 - Multiple-scale relationships between vegetation, the wildland–urban interface, and structure loss to wildfire in California","interactions":[],"lastModifiedDate":"2021-05-20T12:08:44.207613","indexId":"70220543","displayToPublicDate":"2021-03-12T08:13:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Multiple-scale relationships between vegetation, the wildland–urban interface, and structure loss to wildfire in California","docAbstract":"Recent increases in destructive wildfires are driving a need for empirical research documenting factors that contribute to structure loss. Existing studies show that fire risk is complex and varies geographically, and the role of vegetation has been especially difficult to quantify. Here, we evaluated the relative importance of vegetation cover at local (measured through the Normalized Difference Vegetation Index) and landscape (as measured through the Wildland–Urban Interface) scales in explaining structure loss from 2013 to 2018 in California—statewide and divided across three regions. Generally, the pattern of housing relative to vegetation better explained structure loss than local-scale vegetation amount, but the results varied regionally. This is likely because exposure to fire is a necessary first condition for structure survival, and sensitivity is only relevant once the fire reaches there. The relative importance of other factors such as long-term climatic variability, distance to powerlines, and elevation also varied among regions. These suggest that effective fire risk reduction strategies may need to account for multiple factors at multiple scales. The geographical variability in results also reinforces the notion that “one size does not fit all”. Local-scale empirical research on specific vegetation characteristics relative to structure loss is needed to inform the most effective customized plan.
","language":"English","publisher":"MDPI","doi":"10.3390/fire4010012","usgsCitation":"Syphard, A.D., Rustigian-Romsos, H., and Keeley, J., 2021, Multiple-scale relationships between vegetation, the wildland–urban interface, and structure loss to wildfire in California: Fire, v. 4, no. 1, 12, 15 p., https://doi.org/10.3390/fire4010012.","productDescription":"12, 15 p.","ipdsId":"IP-127664","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4010012","text":"Publisher Index Page"},{"id":385764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.21142578125,\n 42.01665183556825\n ],\n [\n -124.47509765625,\n 40.49709237269567\n ],\n [\n -123.79394531249999,\n 38.92522904714054\n ],\n [\n -122.49755859375,\n 37.10776507118514\n ],\n [\n -120.4541015625,\n 34.470335121217474\n ],\n [\n -118.32275390624999,\n 33.779147331286474\n ],\n [\n -117.24609374999999,\n 32.58384932565662\n ],\n [\n -114.6533203125,\n 32.76880048488168\n ],\n [\n -114.5654296875,\n 32.93492866908233\n ],\n [\n -114.697265625,\n 33.15594830078649\n ],\n [\n -114.521484375,\n 33.97980872872457\n ],\n [\n -114.08203125,\n 34.252676117101515\n ],\n [\n -114.43359375,\n 34.813803317113155\n ],\n [\n -120.05859375,\n 39.07890809706475\n ],\n [\n -119.99267578124999,\n 42.00032514831621\n ],\n [\n -124.21142578125,\n 42.01665183556825\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Syphard, Alexandra D.","contributorId":8977,"corporation":false,"usgs":false,"family":"Syphard","given":"Alexandra","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":815956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rustigian-Romsos, Heather","contributorId":258207,"corporation":false,"usgs":false,"family":"Rustigian-Romsos","given":"Heather","email":"","affiliations":[{"id":52235,"text":"Conservation Biology Institute, Corvallis, OR 97333, USA","active":true,"usgs":false}],"preferred":false,"id":815957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":815958,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218782,"text":"sir20215014 - 2021 - Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020","interactions":[],"lastModifiedDate":"2021-03-15T11:42:02.813757","indexId":"sir20215014","displayToPublicDate":"2021-03-12T08:09:47","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":"2021-5014","displayTitle":"Extending Seasonal Discharge Records for Streamgage Sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through Water Year 2020","title":"Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020","docAbstract":"Daily mean discharge records are needed for management of selected streams in the Fortymile River Basin. The U.S. Geological Survey, in cooperation with the U.S. Bureau of Land Management, updated a technique for estimating seasonal (partial year) discharge at two short-record streamgage sites in the basin and evaluated the accuracy of the estimates. Daily mean discharge values were estimated for May 15–September 30, 1976–82 and 2006–18, for U.S. Geological Survey streamgage sites 15330000 (North Fork Fortymile River above Middle Fork near Franklin, Alaska) and 15331000 (Middle Fork Fortymile River near mouth near Chicken, Alaska). Relations between discharge for each study streamgage and an index streamgage on the main-stem Fortymile River (15348000, Fortymile River near Steele Creek, Alaska) for concurrent seasonal periods in 2019 and 2020 were developed using the maintenance of variance extension type 3 (MOVE.3) record extension technique. The MOVE.3 regressions were used to estimate daily mean discharges at the study streamgage sites for the selected season for the longer period of record of the index streamgage. Additionally, estimated records were generated from the regressions for the concurrent seasonal periods to evaluate the accuracy of the record extension techniques. The modified Nash-Sutcliffe efficiency coefficients for the estimated records were 0.53 for the North Fork Fortymile River (15330000) and 0.70 for the Middle Fork Fortymile River (15331000) streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215014","collaboration":"Prepared in cooperation with the U.S. Bureau of Land Management","usgsCitation":"Curran, J.H., 2021, Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020: U.S. Geological Survey Scientific Investigations Report 2021–5014, 11 p., https://doi.org/10.3133/sir20215014.","productDescription":"Report: v, 11 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-125266","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":384327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5014/sir20215014.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5014"},{"id":384361,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VCAOEZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Extended seasonal discharge records for selected streamgage sites in the Fortymile River Basin, Alaska, 1976-2020"},{"id":384326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5014/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"North Fork Fortymile River, Middle Fork Fortymile River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -146.46972656249997,\n 62.91523303947614\n ],\n [\n -141.0205078125,\n 62.91523303947614\n ],\n [\n -141.0205078125,\n 65.60387765860433\n ],\n [\n -146.46972656249997,\n 65.60387765860433\n ],\n [\n -146.46972656249997,\n 62.91523303947614\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Freshwater mussels share habitat and are parasites of freshwater fishes during the larval life stage. Therefore, models of fish biogeography may also explain the historical biogeography of freshwater mussels. We tested this assumption using predictions of three biogeographic models constructed for northern Gulf of Mexico drainages on a freshwater mussel species complex. Specifically, we tested (1) if speciation was due to vicariant events of fluctuating sea levels that separated lineages east‐west of the Mobile Basin (Central Gulf Coast speciation hypothesis), (2) if the timing of divergences occurred 8.5–3.5 MYA (Gulf Coast allopatric speciation model) and (3) if diversification in Mississippi River populations was recent and for evidence of population increase consistent with range expansion into northern deglaciated regions (Pleistocene glaciation model).
Eastern North America.
Freshwater mussels (Bivalvia: Unionidae), Lampsilis teres and L. floridensis.
We collected 249 specimens from 73 localities across the group's distribution. We used three molecular markers (COI, NDI & ITSI) to conduct time calibrated Bayesian phylogenetic analyses, phylogeographic analyses (AMOVA & SAMOVA) and demographic analyses including Bayesian skyline plots.
Lampsilis teres and L. floridensis are allopatric species whose distributions meet at the eastern edge of the Mobile Basin. Speciation was estimated to occur in the late Miocene. Populations from isolated river systems surrounding the Gulf of Mexico are almost all monophyletic. Mississippi drainage samples formed a shallow clade with recent diversification and showed evidence of recent population expansion.
The historical biogeography of the L. teres species complex is broadly consistent with tested ichthyofaunal models. The timing of speciation and intraspecific divergences correspond to low sea‐level events suggesting that Gulf Coast sea‐level fluctuations are responsible for dispersal (sea‐level recession) and subsequent cladogenesis (sea‐level inundation). Consistent with numerous other freshwater studies, we found the Mobile Basin to be a suture zone, which may be due to the narrow, offshore continental shelf.
The benthic foraminifers Bulimina denudata and Eggerelloides advenus are commonly abundant in offshore regions in the Pacific Ocean, especially in waste-discharge sites. The relationship between their abundance and standard macrofaunal sediment toxicity tests (amphipod survival and sea urchin fertilization) as well as sediment chemistry analyte measurements were determined for sediments collected in 1997 in Santa Monica Bay, California, USA, an area impacted by historical sewage input from the Hyperion Outfall primarily since the late 1950s. Very few surface samples proved to be contaminated based on either toxicity or chemistry tests and the abundance of B. denudata did not correlate with any of these. The abundance of E. advenus also did not correlate with toxicity, but positively correlated with total solids and negatively correlated with arsenic, beryllium, chromium, lead, mercury, nickel, zinc, iron, and TOC. In contrast, several downcore samples proved to be contaminated as indicated by both toxicity and chemistry data. The abundance of B. denudata positively correlated with amphipod survival and negatively correlated with arsenic, cadmium, unionized ammonia, and TOC; E. advenus negatively correlated with sea urchin fertilization success as well as beryllium, cadmium, and total PCBs. As B. denudata and E. advenus are tolerant of polluted sediments and their relative abundances appear to track those of macrofaunal toxicity tests, their use as cost- and time-effective marine sediment toxicity tests may have validity and should be further investigated.
","language":"English","publisher":"MDPI","doi":"10.3390/w13060775","usgsCitation":"McGann, M., 2021, Potential use of the benthic foraminifers Bulimina denudata and Eggerelloides advenus in marine sediment toxicity testing: Water, v. 13, no. 6, 775, 33 p., https://doi.org/10.3390/w13060775.","productDescription":"775, 33 p.","ipdsId":"IP-117658","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13060775","text":"Publisher Index Page"},{"id":385443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Los Angeles coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.58343505859374,\n 34.14590795200977\n ],\n [\n -118.85009765625,\n 33.708347493688414\n ],\n [\n -118.23486328125,\n 33.458942753687644\n ],\n [\n -117.77618408203124,\n 33.56199537293026\n ],\n [\n -118.28979492187499,\n 33.916013113401696\n ],\n [\n -118.98468017578125,\n 34.15045403191448\n ],\n [\n -119.34997558593749,\n 34.35023911062779\n ],\n [\n -119.58343505859374,\n 34.14590795200977\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815133,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222571,"text":"70222571 - 2021 - Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24","interactions":[],"lastModifiedDate":"2021-08-05T12:07:42.766418","indexId":"70222571","displayToPublicDate":"2021-03-12T07:04:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24","docAbstract":"A compilation is made of the largest and second-largest magnetic-storm-maximum intensities, −Dst1 and −Dst2, for solar cycles 14–24 (1902–2016) by sampling Oulu Dcx for cycles 19–24, using published −Dstm values for 4 intense storms in cycles 14, 15, and 18 (1903, 1909, 1921, 1946), and calculating 15 new storm-maximum −Dstm values (reported here) for cycles 14–18. Three different models are fitted to the cycle-ranked −Dst1 and −Dst2 values using a maximum-likelihood algorithm: A Gumbel model, an unconstrained Generalized-Extreme-Value model, and a Weibull model constrained to have a physically justified maximum storm intensity of −Dstm = 2500 nT. All three models are good descriptions of the data. Since the best model is not clearly revealed with standard statistical tests, inference is precluded of the source process giving rise to storm-maximum −Dstm values. Of the three candidate models, the constrained Weibull gives the lowest superstorm occurrence probabilities. Using the compiled data and the constrained Weibull model, a once-per-century storm intensity is estimated to be −Dst1 = 663 nT, with a bootstrap 68% confidence interval of [497, 694] nT. Similarly, the probability that a future storm will have an intensity exceeding that of the March 1989 superstorm, −Dstm > 565 nT, is 0.246 per cycle with a 68% confidence interval of [0.140, 0.311] per cycle. Noting (possibly slight) ambiguity in the rankings of storm intensities, using the same methods, but storms more intense than those identified for cycles 14–16, would yield a higher once-per-century intensity and a higher probability for a −Dstm > 565 nT storm.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020SW002579","usgsCitation":"Love, J.J., 2021, Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24: Space Weather, v. 19, no. 4, e2020SW002579, 25 p., https://doi.org/10.1029/2020SW002579.","productDescription":"e2020SW002579, 25 p.","ipdsId":"IP-124185","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":490073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020sw002579","text":"Publisher Index Page"},{"id":387701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820608,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70219433,"text":"70219433 - 2021 - Eastward expansion of Round Goby in New York: Assessment of detection methods and current range","interactions":[],"lastModifiedDate":"2021-04-06T12:01:57.537653","indexId":"70219433","displayToPublicDate":"2021-03-12T06:55:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Eastward expansion of Round Goby in New York: Assessment of detection methods and current range","docAbstract":"The Round Goby Neogobius melanostomus has spread rapidly around the Great Lakes region since its introduction to North America in 1990. In 2014, a specimen was captured in the New York State Canal System west of Utica, prompting concerns that Round Goby would soon reach the ecologically and economically valuable watersheds of Lake Champlain and the Hudson River estuary. The establishment of Round Goby populations elsewhere has been linked to a number of negative ecological consequences, yet methods for monitoring the invasion front of this species remain limited. The objectives of this study were to assess the current distribution of Round Goby in central New York and to determine the most effective methods for monitoring the invasion front. This was achieved by concurrently using benthic trawling, seining, minnow traps, and environmental DNA (eDNA) twice annually from 2016 to 2019 at 12 sites on the canal system between Oneida Lake and the Hudson River. Of the three traditional gear types, benthic trawling was the most effective method and captured Round Goby as far east as Utica by 2019. This finding suggests only minimal eastward expansion of Round Goby occurred between 2014 and 2019. Round Goby DNA was detected in water samples during all surveys in which individuals were captured with trawling, and the estimated concentration of DNA explained 69% of the variability in trawl catch. At multiple study sites, Round Goby DNA was identified during consecutive surveys before Round Goby were first captured with trawling. This suggests that in lotic waters, eDNA has the potential to forecast or serve as a sentinel for the expansion of Round Goby to new locations. Our results demonstrate the importance of using eDNA in a repeated sampling framework and supplementing eDNA sampling with some level of effort with traditional sampling methods.
Habitat studies that encompass a large portion of a species’ geographic distribution can explain characteristics that are either consistent or variable, further informing inference from more localized studies and improving management successes throughout the range. We identified landscape characteristics at Piping Plover nests at 21 sites distributed from Massachusetts to North Carolina and compared habitat selection patterns among the three designated U.S. recovery units (New England, New York–New Jersey, and Southern). Geomorphic setting, substrate type, and vegetation type and density were determined in situ at 928 Piping Plover nests (hereafter, used resource units) and 641 random points (available resource units). Elevation, beach width, Euclidean distance to ocean shoreline, and least-cost path distance to low-energy shorelines with moist substrates (commonly used as foraging habitat) were associated with used and available resource units using remotely sensed spatial data. We evaluated multivariate differences in habitat selection patterns by comparing recovery unit-specific Bayesian networks. We then further explored individual variables that drove disparities among Bayesian networks using resource selection ratios for categorical variables and Welch’s unequal variances t-tests for continuous variables. We found that relationships among variables and their connections to habitat selection were similar among recovery units, as seen in commonalities in Bayesian network structures. Furthermore, nesting Piping Plovers consistently selected mixed sand and shell, gravel, or cobble substrates as well as areas with sparse or no vegetation, irrespective of recovery unit. However, we observed significant differences among recovery units in the elevations, distances to ocean, and distances to low-energy shorelines of used resource units. Birds also exhibited increased selectivity for overwash habitats and for areas with access to low-energy shorelines along a latitudinal gradient from north to south. These results have important implications for conservation and management, including assessment of shoreline stabilization and habitat restoration planning as well as forecasting effects of climate change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3418","usgsCitation":"Zeigler, S.L., Gutierrez, B.T., Hecht, A., Plant, N., and Sturdivant, E., 2021, Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U.S. Atlantic coast: Ecosphere, v. 12, no. 3, e03418, 21 p., https://doi.org/10.1002/ecs2.3418.","productDescription":"e03418, 21 p.","ipdsId":"IP-123170","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3418","text":"Publisher Index Page"},{"id":407856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maryland, Massachusetts, new Jersey, New York, North Carolina, Rhode Island, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.06884765624999,\n 33.7243396617476\n ],\n [\n -75.47607421875,\n 34.92197103616377\n ],\n [\n -75.6298828125,\n 37.07271048132943\n ],\n [\n -73.95996093749999,\n 39.87601941962116\n ],\n [\n -73.54248046875,\n 40.54720023441049\n ],\n [\n -71.52099609375,\n 40.93011520598305\n ],\n [\n -70.02685546875,\n 41.47566020027821\n ],\n [\n -69.80712890625,\n 42.16340342422401\n ],\n [\n -70.81787109374999,\n 42.601619944327965\n ],\n [\n -71.21337890625,\n 42.439674178149424\n ],\n [\n -71.74072265625,\n 41.590796851056005\n ],\n [\n -73.98193359375,\n 41.04621681452063\n ],\n [\n -74.5751953125,\n 40.53050177574321\n ],\n [\n -74.37744140625,\n 40.027614437486655\n ],\n [\n -74.77294921875,\n 39.45316112807394\n ],\n [\n -75.38818359375,\n 39.85915479295669\n ],\n [\n -76.7724609375,\n 39.57182223734374\n ],\n [\n -77.255859375,\n 38.54816542304656\n ],\n [\n -77.2998046875,\n 36.89719446989036\n ],\n [\n -78.37646484375,\n 34.43409789359469\n ],\n [\n -78.06884765624999,\n 33.7243396617476\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeigler, Sara Lynn 0000-0002-5472-769X szeigler@usgs.gov","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":297200,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","email":"szeigler@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gutierrez, Benjamin T. 0000-0002-1879-7893 bgutierrez@usgs.gov","orcid":"https://orcid.org/0000-0002-1879-7893","contributorId":2924,"corporation":false,"usgs":true,"family":"Gutierrez","given":"Benjamin","email":"bgutierrez@usgs.gov","middleInitial":"T.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hecht, Anne","contributorId":297201,"corporation":false,"usgs":false,"family":"Hecht","given":"Anne","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel 0000-0002-5703-5672","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":81234,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sturdivant, Emily J.","contributorId":297196,"corporation":false,"usgs":false,"family":"Sturdivant","given":"Emily J.","affiliations":[{"id":56085,"text":"Woodwell Climate Research Center","active":true,"usgs":false}],"preferred":false,"id":853651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231209,"text":"70231209 - 2021 - A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells","interactions":[],"lastModifiedDate":"2022-05-03T13:37:01.222443","indexId":"70231209","displayToPublicDate":"2021-03-11T08:10:39","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":10741,"text":"Gulf Watch Alaska Long-Term Monitoring Program Synthesis Report","active":true,"publicationSubtype":{"id":3}},"chapter":"1","title":"A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells","docAbstract":"We use over 100 in situ and remotely sensed temperature datasets to investigate thermal variability within and across the intertidal nearshore, coastal and offshore waters of the northern Gulf of Alaska. For the years 1970 through 2019 we document a warming trend of 0.24±0.10 °C per decade for the coastal northern shelf (0-250 m depth average) and a Gulf-wide sea surface temperature (SST) trend of 0.25±0.11 °C per decade. The Gulf-wide SST trend in the last halfcentury is more than twice that of the 0.11±0.003 °C warming rate computed for 1900-2019. Decorrelation length scales vary regionally and correlation of synoptic scale fluctuations (less than one month) between two stations rapidly degrades with increasing station distance, accounting for less than 10% of the covariance for separations exceeding 100 km. In contrast, stations separated by as much as 500 km retain 50% of their covariance in common for seasonal and sub-seasonal fluctuations. While satellite-based measures often capture most of the daily SST anomaly in coastal and offshore waters, a significant portion of the variance (30-40%) can remain unresolved, even exceeding 75% in the nearshore realm. Similarly, the North Pacific and Gulf of Alaska leading modes of SST variability leave large fractions (25-50%) of the subseasonal thermal variance unresolved. These evaluations show the importance of in situ temperature records for studies that seek to understand mechanistic responses of marine organisms to habitat variability at biologically important time and space scales. We find that near-bottom temperature anomalies on the outer shelf vary inversely with surface temperatures and with near-bottom salinity, suggesting that thermal anomalies are also linked with nutrient flux anomalies. A case study of the recent Pacific marine heatwave and transition out of preceding cool years shows that the northern Gulf of Alaska surface temperatures (0-50 m) were elevated from 2014 to 2019 relative to the long-term record. Coastal temperatures warmed contemporaneously with offshore waters through the 2013 calendar year. In contrast, deep inner shelf waters (200-250 m) exhibited delayed warming relative to the surface and relative to deep waters offshore at the same depth. While offshore surface waters cooled from early 2014 into 1-2 Science Synthesis Final Report Gulf Watch Alaska, 2021 early 2016, the shelf continued to warm over this time as the effects of local air-sea and advective heat fluxes continued to permeate across the northern Gulf. These results highlight the importance of different heating mechanisms for surface and near-bottom waters across the northern Gulf of Alaska.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The Pacific marine heatwave: Monotoring during a major perturbation in the Gulf of Alaska","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Exxon Valdez Oil Spill Trustee Council","usgsCitation":"Danielson, S.L., Hennon, T.D., Monson, D., Suryan, R.M., Campbell, R.W., Baird, S.J., Holderied, K., and Weingartner, T., 2021, A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells: Gulf Watch Alaska Long-Term Monitoring Program Synthesis Report, 56 p.","productDescription":"56 p.","startPage":"1-1","endPage":"1-56","ipdsId":"IP-119985","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":400044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":400013,"type":{"id":15,"text":"Index Page"},"url":"https://gulfwatchalaska.org/resources/reports/science-synthesis-reports/"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.95703125,\n 54.87660665410869\n ],\n [\n -134.208984375,\n 58.401711667608\n ],\n [\n -140.712890625,\n 60.50052541051131\n ],\n [\n -147.568359375,\n 62.186013857194226\n ],\n [\n -153.017578125,\n 61.3546135846894\n ],\n [\n -161.89453125,\n 55.57834467218206\n ],\n [\n -164.443359375,\n 54.316523240258256\n ],\n [\n -162.24609375,\n 52.74959372674114\n ],\n [\n -143.7890625,\n 51.01375465718821\n ],\n [\n -132.626953125,\n 52.908902047770255\n ],\n [\n -130.95703125,\n 54.87660665410869\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Danielson, Seth L.","contributorId":256682,"corporation":false,"usgs":false,"family":"Danielson","given":"Seth","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":842031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hennon, Tyler D.","contributorId":291317,"corporation":false,"usgs":false,"family":"Hennon","given":"Tyler","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":842032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":842033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suryan, Rob M.","contributorId":291318,"corporation":false,"usgs":false,"family":"Suryan","given":"Rob","email":"","middleInitial":"M.","affiliations":[{"id":62685,"text":"Alaska Fisheries Science Center, NOAA","active":true,"usgs":false}],"preferred":false,"id":842034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, Rob W.","contributorId":251805,"corporation":false,"usgs":false,"family":"Campbell","given":"Rob","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":842035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baird, Steven J.","contributorId":12375,"corporation":false,"usgs":false,"family":"Baird","given":"Steven","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":842036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holderied, Kristine","contributorId":291319,"corporation":false,"usgs":false,"family":"Holderied","given":"Kristine","affiliations":[{"id":62686,"text":"Kasitsna Bay Laboratory, NOAA","active":true,"usgs":false}],"preferred":false,"id":842037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weingartner, Thomas","contributorId":291321,"corporation":false,"usgs":false,"family":"Weingartner","given":"Thomas","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":842038,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225526,"text":"70225526 - 2021 - Pore water exchange-driven inorganic carbon export from intertidal salt marshes","interactions":[],"lastModifiedDate":"2021-10-20T13:06:17.578828","indexId":"70225526","displayToPublicDate":"2021-03-11T08:02:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Pore water exchange-driven inorganic carbon export from intertidal salt marshes","docAbstract":"Respiration in intertidal salt marshes generates dissolved inorganic carbon (DIC) that is exported to the coastal ocean by tidal exchange with the marsh platform. Understanding the link between physical drivers of water exchange and chemical flux is a key to constraining coastal wetland contributions to regional carbon budgets. The spatial and temporal (seasonal, annual) variability of marsh pore water exchange and DIC export was assessed from a microtidal salt marsh (Sage Lot Pond, Massachusetts). Spatial variability was constrained from 224Ra : 228Th disequilibria across two hydrologic units within the marsh sediments. Disequilibrium between the more soluble 224Ra and its sediment-bound parent 228Th reveals significant pore water exchange in the upper 5 cm of the marsh surface (0–36 L m−2 d−1) that is most intense in low marsh elevation zones, driven by tidal overtopping. Surficial sediment DIC transport ranges from 0.0 to 0.7 g C m−2 d−1. The sub-surface sediment horizon intersected by mean low tide was disproportionately impacted by tidal pumping (20–80 L m−2 d−1) and supplied a seasonal DIC flux of 1.7–5.4 g C m−2 d−1. Export exceeded 10 g C m−2 d−1 for another marsh unit, demonstrating that fluxes can vary substantially across salt marshes under similar conditions within the same estuary. Seasonal and annual variability in marsh pore water exchange, constrained from tidal time-series of radium isotopes, was driven in part by variability in mean sea level. Rising sea levels will further inundate high marsh elevation zones, which may lead to greater DIC export.