Understanding how past coastal systems have evolved is critical to predicting future coastal change. Using over 12,000 trackline kilometers of recently collected, co-located multi-channel boomer, sparker and chirp seismic reflection profile data integrated with previously collected borehole and vibracore data, we define the upper (< 115 m below mean lower low water) seismic stratigraphic framework offshore of the Delmarva Peninsula, USA. Twelve seismic units and 11 regionally extensive unconformities (U1-U11) were mapped over 5900 km2 of North America's Mid-Atlantic continental shelf. We interpret U3, U7, U9, U11 as transgressive ravinement surfaces, while U1,2,4,5,6,8,10 are subaerial unconformities illustrating distinct periods of lower sea-level. Based on areal distribution, stratigraphic relationships and dating results (Carbon 14 and amino acid racemization estimates) from earlier vibracore and borehole studies, we interpret the infilled channels as late Neogene and Quaternary courses of the Susquehanna, Potomac, Rappahannock, York, James rivers and tributaries, and a broad flood plain. These findings indicate that the region's geologic framework is more complex than previously thought and that Pleistocene paleochannels are abundant in the Mid-Atlantic. This study synthesizes and correlates the findings of other Atlantic Margin studies and establishes a large-scale Quaternary framework that enables more detailed stratigraphic analysis in the future. Such work has implications for inner continental shelf systems tract evolution, the relationship between antecedent geology and modern coastal systems, assessments of eustacy, glacial isostatic adjustment, and other processes and forcings that play a role in passive margin evolution.
Ecological resources for fishes in stream food webs shift over space and time, providing a complex template of available resources that can be used for growth. We tracked water temperature in conjunction with young-of-year Coho Salmon size, growth, and diet in 2 streams with contrasting thermal regimes: a groundwater stream with colder temperatures and lower thermal variability all year and a surface-water stream with greater thermal variability and warmer summer temperatures more conducive to young-of-year salmon growth. We hypothesized that fry emergence would occur when rearing conditions are optimal for growth and that, all else being equal, summer fish growth will be greater in the surface-water stream. Previous work on Coho Salmon phenology in these streams showed that peak fry emergence occurred at the same time in early summer in both streams. We measured salmon fry emergence in relation to thermal variability and macroinvertebrate prey availability with subsequent tracking of somatic growth, diet, and body size during the 1st year of life in both streams. Macroinvertebrate prey availability was highest overall in the colder and thermally-stable groundwater stream than the surface-water stream. Prey availability was particularly high in the thalweg drift during peak fry emergence in the groundwater stream. There was no difference in Coho Salmon diet composition between streams, which included invertebrates from benthic, drift, and riparian habitats. We found no differences in young-of-year Coho Salmon body size, growth, or consumption between streams. Overall, our results suggest that large differences in thermal regimes do not necessarily translate to large differences in young-of-year Coho Salmon size, growth, or diet. Many variables can influence fish growth, and there is not always a direct connection between spatial and temporal dimensions of environmental variability and their cascading effects on young-of-year Coho Salmon growth during the 1st summer of life.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/710042","usgsCitation":"Campbell, E.Y., Dunham, J.B., and Reeves, G.H., 2020, Linkages between temperature, macroinvertebrates, and young-of-year Coho Salmon growth in surface-water and groundwater streams: Freshwater Science, v. 39, no. 3, p. 447-460, https://doi.org/10.1086/710042.","productDescription":"14 p.","startPage":"447","endPage":"460","ipdsId":"IP-106030","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":378502,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Campbell, E. Y.","contributorId":240894,"corporation":false,"usgs":false,"family":"Campbell","given":"E.","email":"","middleInitial":"Y.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":799062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":799063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reeves, G H","contributorId":240895,"corporation":false,"usgs":false,"family":"Reeves","given":"G","email":"","middleInitial":"H","affiliations":[{"id":48156,"text":"USFS-PNW Research Station","active":true,"usgs":false}],"preferred":false,"id":799064,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211057,"text":"70211057 - 2020 - Land-cover and climatic controls on water temperature, flow permanence, and fragmentation of Great Basin stream networks","interactions":[],"lastModifiedDate":"2020-07-16T20:06:22.256521","indexId":"70211057","displayToPublicDate":"2020-07-10T09:02:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Land-cover and climatic controls on water temperature, flow permanence, and fragmentation of Great Basin stream networks","docAbstract":"The seasonal and inter-annual variability of flow presence and water temperature within headwater streams of the Great Basin of the western United States limit the occurrence and distribution of coldwater fish and other aquatic species. To evaluate changes in flow presence and water temperature during seasonal dry periods, we developed spatial stream network (SSN) models from remotely sensed land-cover and climatic data that account for autocovariance within stream networks to predict the May to August flow presence and water temperature between 2015 and 2017 in two arid watersheds within the Great Basin: Willow and Whitehorse Creeks in southeastern Oregon and Willow and Rock Creeks in northern Nevada. The inclusion of spatial autocovariance structures improved the predictive performance of the May water temperature model when the stream networks were most connected, but only marginally improved the August water temperature model when the stream networks were most fragmented. As stream network fragmentation increased from the spring to the summer, the SSN models revealed a shift in the scale of processes affecting flow presence and water temperature from watershed-scale processes like snowmelt during high-runoff seasons to local processes like groundwater discharge during sustained seasonal dry periods.","language":"English","publisher":"MDPI","doi":"10.3390/w12071962","usgsCitation":"Gendaszek, A.S., Dunham, J.B., Torgersen, C.E., Hockman-Wert, D.P., Heck, M., Thorson, J.M., Mintz, J.M., and Allai, T., 2020, Land-cover and climatic controls on water temperature, flow permanence, and fragmentation of Great Basin stream networks: Water, v. 12, no. 7, 1962, 29 p., https://doi.org/10.3390/w12071962.","productDescription":"1962, 29 p.","ipdsId":"IP-113706","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":456057,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12071962","text":"Publisher Index Page"},{"id":436882,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZPBQVH","text":"USGS data release","linkHelpText":"Stream Temperature and Water Presence Models of Willow/Whitehorse and Willow/Rock Watersheds, Oregon and Nevada"},{"id":376309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.3212890625,\n 35.137879119634185\n ],\n [\n -114.521484375,\n 34.994003757575776\n ],\n [\n -113.73046875,\n 45.73685954736049\n ],\n [\n -124.93652343749999,\n 45.706179285330855\n ],\n [\n -124.3212890625,\n 35.137879119634185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986 agendasz@usgs.gov","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":3509,"corporation":false,"usgs":true,"family":"Gendaszek","given":"Andrew","email":"agendasz@usgs.gov","middleInitial":"S.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":792624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hockman-Wert, David P 0000-0003-2436-6237","orcid":"https://orcid.org/0000-0003-2436-6237","contributorId":228969,"corporation":false,"usgs":false,"family":"Hockman-Wert","given":"David","email":"","middleInitial":"P","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":792625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heck, Michael","contributorId":228970,"corporation":false,"usgs":false,"family":"Heck","given":"Michael","affiliations":[],"preferred":false,"id":792626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thorson, Justin Martin 0000-0001-7164-8777","orcid":"https://orcid.org/0000-0001-7164-8777","contributorId":228971,"corporation":false,"usgs":true,"family":"Thorson","given":"Justin","email":"","middleInitial":"Martin","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":792627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mintz, Jeffrey Michael 0000-0003-4345-366X","orcid":"https://orcid.org/0000-0003-4345-366X","contributorId":225149,"corporation":false,"usgs":true,"family":"Mintz","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":792628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Allai, Todd","contributorId":228972,"corporation":false,"usgs":false,"family":"Allai","given":"Todd","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":792629,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211891,"text":"70211891 - 2020 - Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2020-08-11T14:07:59.855212","indexId":"70211891","displayToPublicDate":"2020-07-10T09:01:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed","docAbstract":"Winter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (>26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass (p ≤0.01, R2 = 0.56) and with observed percent vegetative ground cover (p ≤0.01, R2 = 0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014–15, 2015–16, and 2017–18) for four Maryland counties. Results from winter 2017–18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit.
A field investigation intended to measure the potential for erosion of sediments beside the American and Sacramento Rivers near Sacramento, California, is described. The study featured two primary components: (1) drilling and soil sampling to reveal lithology, down to depths matching the local river thalweg, where possible, and (2) borehole erosion tests (BETs) as described by Briaud and others (2017) at many of the same locations. The latter test involved drilling a vertical hole, measuring its diameter profile, inserting a hollow drilling rod to almost the bottom of the hole, and pumping fluid through the drilling rod at a known discharge for a chosen time interval. The hole was then resurveyed to establish an erosion rate (change in borehole radius divided by duration of flow event) as a function of depth, and the test was repeated. This test was performed with water as the erosive fluid at 12 locations, with 1 test repeated with drilling mud. Lithology holes were drilled at these same locations and an additional five locations. Drilling operations took place on river left and river right on the American River and river left (left bank, when looking downstream) on the Sacramento River.
The drilling to acquire sediment samples and reveal lithology involved the use of a mobile drilling rig equipped with a 6-inch (in.) auger, a 140-pound pneumatic hammer to drive split spoon and Calmod samplers, and a piston to push Shelby tube samplers to obtain samples of clayey material. Blow count (hammer blows per 6-in. sampler advance) was recorded while sampling, and the process was logged using standard U.S. Army Corps of Engineers (USACE), Sacramento District procedures. Sediment samples were identified and described in the field per ASTM D2488 and then delivered to a USACE laboratory and to Texas A&M University for additional laboratory analysis.
The BETs were performed with the same drilling rig that performed the drilling for definition of lithology. In most instances, tests were limited to regions above the water table, to avoid slumping of the borehole and heaving sands pushing into the hole. Most of the tests featured sediments that were primarily silty sand or sandy silt.
The testing procedure involved comparing borehole profiles before and after passing an assumed constant discharge through a drilling rod to the bottom of the drilled hole. Discharge and water losses were logged during the testing procedure, and water losses into the walls of the drilled hole were typically less than 5 percent of the introduced volume. For the tests performed with water, the coefficient of variation of the discharge ranged from 4.5 to 28 percent, with a mean of 13 percent, but the mean discharge appeared to be reasonably steady over the typical test duration of 10–30 minutes. It was thus assumed that discharge was constant and water losses during the tests were neglected. Coefficients of variation of the discharge for the three tests performed with drilling mud were much higher (20–50 percent), but erosion rates were much smaller.
Resolution of the borehole caliper-reported diameter was 0.1 in. and several of the tests lasted for 10 minutes. With boreholes measured twice, before and after each test, and averaged, these numbers correspond to an apparent erosion rate (radius change divided by test duration) of 0.3 inches per hour (in/hr), which is a theoretical lower bound on what could be measured with this approach and equipment. In practice, 0.5 in/hr appears to be a more realistic lower bound on the detectable erosion rate, based on inspection of computed changes and erosion rates.
Three flow speeds (5, 8, and 12 feet per second; ft/s) were targeted for the tests. Because of equipment limitations, it was not possible in the field to reach an average of 12 ft/s throughout any given borehole, although much higher flow speeds were reached locally in some cases. Most tests featured at least two different flow rates, and the borehole was typically surveyed at least twice for each condition, to allow averaging to reduce the influence of random diameter measurement errors. Errors arising from out-of-round boreholes appeared to be uncommon.
Briaud and others (2017) recommend stepped increases in the flow rate during a borehole test. This approach was taken during initial testing but proved to be problematic. The drilled hole would be enlarged by the first (smaller) discharge, and then it would be difficult to reach the desired higher flow speed because of the larger annulus between the drilled hole and the drilling rod that supplied the water for testing. This was largely solved by starting with a high discharge and, in many cases, maintaining it for subsequent tests with the average flow speed decreasing as the hole enlarged.
Several different measures of erosion rate were computed and investigated by comparison to lithological profiles. The vertically averaged erosion rate for each hole was computed, but this result does not reveal vertical variability of erodibility; and the mean flow speed within the hole is not a good representation of the speed when attempting to determine a relationship between erosion rate and flow speed. Instead, for each 6-inch layer within the hole, vertically averaged erosion rates and local flow speeds were computed and plotted. Where possible, the soil type for each layer was identified. For later laboratory analysis, project protocol dictated collection of Shelby tube samples whenever clay was encountered.
Plots of erosion rate versus flow speed displayed scatter that indicate that several other factors influence the erosion potential of the soil. Blow count was not a good predictor variable; it is better correlated with soil type than erodibility.
Soils were classified as sand, silt, or clay, depending on which soil type dominated within a sample. In general, those classified as sand and silt did not reveal clear patterns allowing erosion rate to be computed directly from flow speed, but the test results define the range and bounds on the erosion rate. Results for clay were slightly clearer with the erosion rate increasing with flow speed, once a threshold had been reached. In this case, the erosion rate appeared to change near a speed of 7 ft/s; above this threshold, erosion rates jumped from less than 2 in/hr to greater than 3 in/hr.
Even for soils with similar classifications, large differences in erodibility were observed between sites and in different layers within an individual hole. One potential means of dealing with this problem would be to perform more tests at each site to allow establishment of relationships between flow speed and erodibility for individual layers within a borehole. The maximum number of tests performed at a site in this study was four, but in some cases, results are available for only one or two flow events. Comparison of data to a set of Erosion Function Apparatus tests that provide better resolution of the vertical variation in the erosion rate versus flow speed relationship would allow further investigation of this idea.
It was hypothesized that drilling mud could expand the utility of the test in soft sands by reducing the likelihood of slumping that would be interpreted as erosion. The one test that was performed with drilling mud indicated that it greatly reduced the erosion rate of the soils encountered. It yielded very different results from the test performed at the same site with water.
Erosion rate is often expressed as a function of shear stress applied to a soil. In order to compute shear stress on the walls of the drilled hole, one must assume a form for the relationship between flow speed and shear stress and select a friction factor that is often estimated empirically from head loss, observed water-surface profiles, surface roughness, or other data not available in this report. One methodology for computing shear stress from flow speed is discussed in this report, but the test results have been presented in terms of erosion rate versus flow speed to avoid assuming values that are not verifiable via the field data collected in this study. Erosion rate was computed from directly measured values (sequential borehole profiles) and flow speed was computed directly from measured quantities (discharge and borehole geometry).
The BET has seen limited application, primarily in clayey soils, whereas most of the soils encountered in this study were primarily sand or silt. The objective of the BET is to determine the erodibility of in situ soil below the ground or riverbed surface. The BET is simple in principle and has the advantage of revealing erodibility of in situ sediments below the ground or riverbed surface; it appears to be very useful in clayey soils, based on previously published work, but is more difficult to apply in sandy soils where slumping and water losses within the hole during testing are more likely to occur. The BET did reveal a large variation in the results both laterally and vertically, even for the same soil-type classification. It is thus recommended that the results be applied considering these spatial variations rather than attempting to universally assign an erosion-rate relationship to a particular soil type. Results have been provided showing the results by site and by sediment classification (sand, silt, and clay), to allow either approach. Where possible, it is important to rely on site-specific results because the erosion-rate relationship for a given soil type varied by site.
Data collected during this project have been made publicly available online via the U.S. Geological Survey (USGS) Sciencebase database. The measured borehole profiles, discharge, lithology log sheets, and photos are available in the data release that accompanies this report (see Work and Livsey (2019) in the “Selected References” section for the appropriate link).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205063","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Work, P., and Livsey, D., 2020, Sediment lithology and borehole erosion testing, American and Sacramento Rivers, California: U.S. Geological Survey Scientific Investigations Report 2020–5063, 92 p., https://doi.org/10.3133/sir20205063.","productDescription":"Report: vii, 92 p.; Data Release","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-110364","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376205,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5063/coverthb.jpg"},{"id":376206,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5063/sir20205063.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376207,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96MCT2Q","linkHelpText":"Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 3.0, July 2020)"}],"country":"United States","state":"California","city":"Sacramento","otherGeospatial":"American River, Sacramento Rivers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.7010498046875,\n 38.38903340675905\n ],\n [\n -121.22589111328126,\n 38.38903340675905\n ],\n [\n -121.22589111328126,\n 38.70694605159386\n ],\n [\n -121.7010498046875,\n 38.70694605159386\n ],\n [\n -121.7010498046875,\n 38.38903340675905\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Predictions of post‐wildfire flooding and debris flows are needed, typically with short lead times. Measurements of soil‐hydraulic properties necessary for model parameterization are, however, seldom available. This study quantified soil‐hydraulic properties, soil‐water retention, and selected soil physical properties within the perimeter of the 2017 Thomas Fire in California. The Thomas Fire burn scar produced catastrophic debris flows in January 2018, highlighting the need for improved prediction capability. Soil‐hydraulic properties were also indirectly estimated using relations tied to soil‐water retention. These measurements and estimates are examined in the context of parameterizing post‐wildfire hydrologic models. Tension infiltrometer measurements showed significant decreases (p < .05) in field‐saturated hydraulic conductivity (Kfs) and sorptivity (S) in burned areas relative to unburned areas. Wildfire effects on soil water‐retention were dominated by significant decreases in saturated soil‐water content (θS). The van Genuchten parameters α, N, and residual water content did not show significant wildfire effects. The impacts of the wildfire on hydraulic and physical soil properties were greatest in the top 1 cm, emphasizing that measurements of post‐fire soil properties should focus on the near‐surface. Reductions in Kfs, θs, and soil‐water retention in burned soils were attributed to fire‐induced decreases in soil structure evidenced by increases in dry bulk density. Sorptivity reductions in burned soils were attributed to increases in soil‐water repellency. Rapid post‐fire assessments of flash flood and debris flow hazards using physically‐based hydrologic models are facilitated by similarities between Kfs, S, and the Green–Ampt wetting front potential (ψf) with measurements at other southern CA burned sites. We suggest that ratios of burned to unburned Kfs (0.37), S (0.36), and ψf (0.66) could be used to scale unburned values for model parameterization. Alternatively, typical burned values (Kfs = 20 mm hr−1; S = 6 mm hr−0.5; ψf = 1.6 mm) could be used for model parameterization.
Environmental DNA (eDNA) analysis offers a promising tool for rapid and early detection of aquatic plant invasive species, but currently suffers from substantial unknowns that limit its widespread use in monitoring programs. We conducted the first study to test the factors related to eDNA-based detectability of 2 invasive aquatic plants, Egeria densa and Myriophyllum spicatum, over extended periods of time. Specifically, we examined how plant growth stage and abundance relate to detection in semi-natural and natural conditions. We conducted a mesocosm experiment over a 10-wk period to assess changes in eDNA detection as a function of plant growth and changing biomass. We also sampled lakes with varying species abundances and resampled a subset of lakes to test temporal variability in detection.
We used multilevel occupancy modeling to determine factors associated with detection and generalized linear mixed effects modeling to assess important predictors of eDNA concentration. In mesocosm experiments, we found that detection was less reliable while plants were actively growing but improved as a function of increasing senescence. Plant abundance in tanks was a poor predictor of detection in water samples. These findings were supported by field sampling, which resulted in higher detections for E. densa during senescence periods and only weak or ambiguous relationships between eDNA and total plant abundance in lakes for both species. Within lakes, proximity to shallow photic zones and discrete plant patches were associated with increased detections and concentrations of eDNA. However, detection at the lake scale (based on 4 sampling stations) was typically successful only at the highest levels of plant abundance. Detection and concentrations of eDNA were consistently lower for M. spicatum than for E. densa in the mesocosm experiment and field sampling, suggesting that overall detectability of aquatic invasive plants varies by species.
Our results support sampling during senescence periods to improve detection, but generally low levels of detection and weak relationships with plant abundance indicate that substantial hurdles remains to implement eDNA analysis for early detection of, and rapid response to, aquatic invasive plants.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/710106","usgsCitation":"Kuehne, L.M., Ostberg, C.O., Chase, D.M., Duda, J.J., and Olden, J., 2020, Use of environmental DNA to detect the invasive aquatic plants Myriophyllum spicatum and Egeria densa in lakes: Freshwater Science, v. 39, no. 3, p. 521-533, https://doi.org/10.1086/710106.","productDescription":"13 p.","startPage":"521","endPage":"533","ipdsId":"IP-112200","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":456072,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/710106","text":"Publisher Index Page"},{"id":436884,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BVKTO","text":"USGS data release","linkHelpText":"Detection of invasive aquatic plants Myriophyllum spicatum and Egeria densa in lakes using eDNA, field and mesocosm data"},{"id":378079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kuehne, Lauren M","contributorId":222591,"corporation":false,"usgs":false,"family":"Kuehne","given":"Lauren","email":"","middleInitial":"M","affiliations":[{"id":40565,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington 98195","active":true,"usgs":false}],"preferred":false,"id":797768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ostberg, Carl O. 0000-0003-1479-8458","orcid":"https://orcid.org/0000-0003-1479-8458","contributorId":220731,"corporation":false,"usgs":true,"family":"Ostberg","given":"Carl","middleInitial":"O.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chase, Dorothy M. 0000-0002-7759-2687","orcid":"https://orcid.org/0000-0002-7759-2687","contributorId":203926,"corporation":false,"usgs":true,"family":"Chase","given":"Dorothy","email":"","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olden, Julian D.","contributorId":202893,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":797772,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210883,"text":"sir20205046 - 2020 - Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018","interactions":[],"lastModifiedDate":"2020-07-08T15:30:55.392543","indexId":"sir20205046","displayToPublicDate":"2020-07-08T11:40:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5046","displayTitle":"Effects of Stormwater Runoff from Selected Bridge Decks on Conditions of Water, Sediment, and Biological Quality in Receiving Waters in South Carolina, 2013 to 2018","title":"Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018","docAbstract":"The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, investigated the effects of stormwater runoff from bridge decks on stream water quality conditions in South Carolina. The investigation assessed 5 bridges in 3 physiographic provinces in South Carolina (Piedmont, Upper Coastal Plain, and Lower Coast Plain) that had a range of bridge, traffic, and hydrologic characteristics. The five selected South Carolina bridge sites (coincident with U.S. Geological Survey stations) and corresponding highways were Lynches River at Effingham (station 02132000; U.S. Highway 52), North Fork Edisto River at Orangeburg (station 02173500; U.S. Highway 301), Turkey Creek above Huger (station 02172035; South Carolina Highway 41), South Fork Edisto River near Denmark (station 02173000; U.S. Highway 321), and Fishing Creek at Highway 5 below York (station 021473415; South Carolina Highway 5). Bridge decks at the selected sites used open chutes, scuppers, and downspouts to drain stormwater directly into the receiving water at evenly spaced intervals.
Stream water, sediment, and biological samples were collected and analyzed for a variety of constituents to evaluate the stream conditions for this study. Five to six stream samples were collected at transects upstream and downstream from each selected bridge site using the equal-width-increment technique during observable stormwater runoff. Routine samples of the receiving waters were collected 12 to 14 times at the upstream transect during nonstorm conditions. Samples were analyzed for physical properties, suspended sediment, nutrients, major ions, trace metals, polycyclic aromatic hydrocarbons, and Escherichia coli. Bridge-deck sediment and streambed sediment at upstream and downstream transects were collected once at each bridge site and analyzed for metals and semivolatile organic compounds that include polycyclic aromatic hydrocarbons. Benthic macroinvertebrate community surveys were conducted once using Hester-Dendy multiplate artificial substrate samplers deployed at multiple upstream and downstream transects concurrently.
Statistical analysis of the water-quality data determined that stormwater runoff from bridges did not significantly degrade physical properties, nor nutrient, trace-metal, Escherichia coli, and suspended-sediment concentrations at the selected sites beyond the variability at the upstream transect (no bridge influence) during the study period. During storm sampling at the bridge sites, water-quality conditions were statistically similar upstream and downstream from each bridge, except for greater turbidity, total nitrogen, and total organic nitrogen plus ammonia concentrations found downstream from the bridge site on Fishing Creek; higher total chromium concentrations detected downstream from the bridge site on Turkey Creek; and increased Escherichia coli concentrations found downstream from the bridge site on the North Fork Edisto River. Total recoverable lead, cadmium, and copper concentrations were the only trace metals that periodically exceeded the South Carolina Department of Health and Environmental Control freshwater aquatic-life criteria at some bridge sites (lead, copper, and cadmium in Turkey Creek; cadmium and lead in Fishing Creek; lead in the South Fork Edisto River and Lynches River), but the exceedances occurred more frequently during routine sampling upstream from the bridge sites than during storm sampling at upstream and downstream transects. In general, stormwater runoff from the bridge decks did not seem to be the major source of metal enrichment in receiving waters during the study period. North Fork and South Fork Edisto Rivers and Turkey Creek had only one storm sample that exceeded South Carolina Department of Health and Environmental Control recreational criterion for Escherichia coli at both the upstream and downstream locations, while Fishing Creek had more frequent exceedances. Polycyclic aromatic hydrocarbons were detected infrequently in the stream samples.
In general, sediment trace-metal concentrations were below the threshold and probable effect concentration at all bridge sites, except for the chromium concentration (45.1 milligrams per kilogram) detected upstream from the bridge site on Fishing Creek that exceeded the threshold effect concentration of 43.4 milligrams per kilogram. Based on enrichment ratios less than 1.5, bridge-deck runoff did not seem to be affecting trace-metal accumulation in the streambed sediment downstream from the bridge sites, except for lead at the bridge site on the Lynches River and manganese at the bridge site on Fishing Creek.
Individual polycyclic aromatic compound concentrations and the sum of 18 compounds did not exceed any threshold and probable effect concentrations, indicating polycyclic aromatic hydrocarbon concentrations in the streambed sediment at downstream and upstream transects were not likely to affect the health of benthic macroinvertebrate communities. Although the cumulative polycyclic aromatic hydrocarbon concentrations in downstream sediment at the sites on Turkey and Fishing Creeks were well below the threshold effect concentration of 1,610 micrograms per kilogram, the 3- to 100-fold increase in downstream concentrations indicated a strong probability of a bridge-deck runoff source.
Overall, benthic macroinvertebrate community health downstream from the bridge sites did not seem to be affected by bridge-deck runoff based on several multivariate analyses that indicated statistically similar benthic macroinvertebrate communities at upstream and downstream transects. Of the five bridge sites in this study, the site on Turkey Creek seemed to have the least healthy benthic macroinvertebrate communities because of the lowest Ephemeroptera, Plecoptera, and Trichoptera spp. (mayflies, stoneflies, and caddisflies, respectively) taxa, species richness, and diversity; and the highest biotic indices, indicative of poorer ecological health, at upstream and downstream transects. This ecological finding was not unexpected because of seasonal periods of negligible flow when dissolved-oxygen concentrations fell below 4 milligrams per liter during the study period. Of the five bridge sites in this study, the site on the South Fork Edisto River seemed to have healthier benthic macroinvertebrate communities because of the greater mean Ephemeroptera, Plecoptera, and Trichoptera spp. taxa; and lower mean biotic indices at upstream and downstream transects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205046","collaboration":"Prepared in cooperation with South Carolina Department of Transportation","usgsCitation":"Journey, C.A., Petkewich, M.D., Conlon, K.J., Caldwell, A.W., Clark, J.M., Riley, J.W., and Bradley, P.M., 2020, Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018: U.S. Geological Survey Scientific Investigations Report 2020–5046, 101 p., https://doi.org/10.3133/sir20205046.","productDescription":"xii, 101 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099513","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":376048,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes.xlsx","text":"Appendixes 1-3","size":"312 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":376047,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5046"},{"id":376046,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FXSV2Y","text":"USGS data release","linkHelpText":"Water-, Sediment-, and Biological-Quality Data for Waters Receiving Runoff from Five Bridges in South Carolina, 2013 to 2018"},{"id":376045,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5046/coverthb.jpg"},{"id":376051,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes_csv.zip","text":"Appendixes 1-3 (CSV)","size":"34.5 KB","linkFileType":{"id":6,"text":"zip"}}],"contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
When animals select areas to occupy, decisions involve trade-offs between the fitness benefits of obtaining critical resources and minimizing costs of biotic and abiotic factors that constrain their use. These processes can be more dynamic and complex for species inhabiting desert environments, where highly variable spatial and temporal distribution of precipitation can create high intra- and inter-annual variability in forage conditions and water availability, and thermal constraints can differ significantly among seasons and diel periods. We examined resource selection in desert bighorn sheep (Ovis canadensis mexicana) in Cabeza Prieta National Wildlife Refuge, Arizona, USA, at multiple spatial and temporal scales to gain insight into how a desert mammal responds to variations in climatic conditions. We used resource selection functions to test topographic, forage, and environmental features among seasons and diel periods, and between non-drought and drought conditions at the population and home-range scale. When precipitation was average, sheep selected for topographic features that were beneficial for predator avoidance (i.e., escape terrain—steep, rugged areas with high visibility) and locations near perennial water. When drought occurred, they ranged further from preferred escape terrain and perennial water, perhaps seeking forage conditions suitable to meet their nutritional requirements. On early (April–June) and late (July–September) summer days, sheep selected for more northerly aspects and locations with lower solar radiation, and in some periods, selection for these cooler areas coincided with periods when forage covariates, proximity to perennial water, and several topographic features were uninformative in resource selection models. These choices may be necessary trade-offs, foregoing good escape terrain and foraging areas, and access to water, for improved thermoregulation. This study highlights the importance of identifying resource selection at variable spatial and temporal scales when investigating the interrelationship between species and their environment. It provides insight into the dynamics of resource selection in desert mammals, and how they respond to constraints imposed on them by their environment. This work can serve to inform strategies for managing and conserving species living in arid environments when faced with climate change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3175","usgsCitation":"Gedir, J.V., Cain, J.W., Swetnam, T., Krausman, P.R., and Morgart, J.R., 2020, Extreme drought and adaptive resource selection by a desert mammal: Ecosphere, v. 11, no. 7, e03175, 19 p., https://doi.org/10.1002/ecs2.3175.","productDescription":"e03175, 19 p.","ipdsId":"IP-109452","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3175","text":"Publisher Index Page"},{"id":395633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Cabeza Prieta National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.96279907226562,\n 32.3822809650579\n ],\n [\n -112.97378540039062,\n 32.507445513754526\n ],\n [\n -113.13858032226562,\n 32.50860363229596\n ],\n [\n -113.14544677734375,\n 32.42402179265739\n ],\n [\n -113.66180419921875,\n 32.41590703229392\n ],\n [\n -113.75930786132811,\n 32.227904590766364\n ],\n [\n -113.51348876953125,\n 32.113985463263816\n ],\n [\n -113.40225219726562,\n 32.09071916431268\n ],\n [\n -113.29513549804688,\n 32.10351636222566\n ],\n [\n -113.27728271484374,\n 32.10467965495091\n ],\n [\n -113.21548461914062,\n 32.13724583390058\n ],\n [\n -113.14544677734375,\n 32.098863043145876\n ],\n [\n -113.08227539062499,\n 32.127942397192314\n ],\n [\n -113.08639526367188,\n 32.20582936513577\n ],\n [\n -112.994384765625,\n 32.20234331330286\n ],\n [\n -113.03146362304688,\n 32.287132632616384\n ],\n [\n -113.04519653320312,\n 32.288293580436644\n ],\n [\n -113.05755615234375,\n 32.36952297435149\n ],\n [\n -113.06716918945312,\n 32.377641904110355\n ],\n [\n -113.06442260742188,\n 32.397356268013105\n ],\n [\n -113.03695678710938,\n 32.397356268013105\n ],\n [\n -113.01223754882812,\n 32.38344069307763\n ],\n [\n -112.96279907226562,\n 32.3822809650579\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Gedir, Jay V.","contributorId":171735,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":833508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swetnam, Tyson","contributorId":213550,"corporation":false,"usgs":false,"family":"Swetnam","given":"Tyson","email":"","affiliations":[{"id":38787,"text":"University of Arizona , BIO5 Institute, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":833751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krausman, Paul R.","contributorId":31467,"corporation":false,"usgs":true,"family":"Krausman","given":"Paul","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":833509,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morgart, John R.","contributorId":10891,"corporation":false,"usgs":true,"family":"Morgart","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":833510,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211085,"text":"70211085 - 2020 - Nutrients and warming interact to force mountain lakes into unprecedented ecological state","interactions":[],"lastModifiedDate":"2020-07-14T15:25:23.400525","indexId":"70211085","displayToPublicDate":"2020-07-08T10:21:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Nutrients and warming interact to force mountain lakes into unprecedented ecological state","docAbstract":"While deposition of reactive nitrogen (N) in the 20th century has been strongly linked to changes in diatom assemblages in high-elevation lakes, pronounced and contemporaneous changes in other algal groups suggest additional drivers. We explored the origin and magnitude of changes in two mountain lakes from the end of the Little Ice Age at ca. 1850, to ca. 2010, using lake sediments. We found dramatic changes in algal community abundance and composition. While diatoms remain the most abundant photosynthetic organisms, concentrations of diatom pigments decreased while pigments representing chlorophytes increased 200-300% since ca. 1950 and total algal biomass more than doubled. Some algal changes began ca. 1900, but shifts in most sedimentary proxies accelerated ca. 1950 commensurate with many human-caused changes to the Earth system. In addition to N deposition, aeolian dust deposition may have contributed phosphorus. Strong increases in summer air and surface water temperatures since 1983 have direct and indirect consequences for high elevation ecosystems. Such warming could have directly enhanced nutrient use and primary production. Indirect consequences of warming include enhanced leaching of nutrients from geologic and cryosphere sources, particularly as glaciers ablate. While we infer causal mechanisms, changes in primary producer communities appear to be without historical precedent and are commensurate with the post-1950 acceleration of global change.","language":"English","publisher":"Royal Society","doi":"10.1098/rspb.2020.0304","usgsCitation":"Oleksy, I., Baron, J., Leavitt, P., and Spaulding, S., 2020, Nutrients and warming interact to force mountain lakes into unprecedented ecological state: Proceedings of the Royal Society B: Biological Sciences, v. 287, no. 1930, 10 p., https://doi.org/10.1098/rspb.2020.0304.","productDescription":"10 p.","ipdsId":"IP-107414","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456081,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://pure.qub.ac.uk/en/publications/9cd986f6-a0fd-43ed-9d88-caeaa66e9cf2","text":"Publisher Index Page"},{"id":436886,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MVU3CX","text":"USGS data release","linkHelpText":"Paleoecological data from The Loch and Sky Pond, Rocky Mountain National Park"},{"id":376361,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"287","issue":"1930","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Oleksy, Isabella A.","contributorId":228994,"corporation":false,"usgs":false,"family":"Oleksy","given":"Isabella A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":792732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baron, Jill S. 0000-0002-5902-6251","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":215101,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":792733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leavitt, Peter","contributorId":228995,"corporation":false,"usgs":false,"family":"Leavitt","given":"Peter","affiliations":[{"id":27547,"text":"University of Regina","active":true,"usgs":false}],"preferred":false,"id":792734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":792735,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211323,"text":"70211323 - 2020 - The grass is not always greener on the other side: Seasonal reversal of vegetation greenness in aspect-driven semiarid ecosystems","interactions":[],"lastModifiedDate":"2020-08-05T13:30:01.400372","indexId":"70211323","displayToPublicDate":"2020-07-08T10:07:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The grass is not always greener on the other side: Seasonal reversal of vegetation greenness in aspect-driven semiarid ecosystems","docAbstract":"Our current understanding of semiarid ecosystems is that they tend to display higher vegetation greenness on polar-facing slopes (PFS) than on equatorial-facing slopes (EFS). However, recent studies have argued that higher vegetation greenness can occur on EFS during part of the year. To assess whether this seasonal reversal of aspect-driven vegetation is a common occurrence, we conducted a global scale analysis of vegetation greenness on a monthly time scale over an 18-year period (2000-2017). We examined the influence of climate seasonality on the normalised difference vegetation index (NDVI) values of PFS and EFS at 60 different catchments with aspect-controlled vegetation located across all continents except Antarctica. Our results show that an overwhelming majority of sites (70%) display seasonal reversal, associated with transitions from water-limited to energy-limited conditions during wet winters. These findings highlight the need to consider seasonal variations of aspect-driven vegetation patterns in ecohydrology, geomorphology, and earth system models.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088918","usgsCitation":"Kumari, N., Saco, P.M., Rodriguez, J.F., Johnstone, S., Srivastava, A., Chun, K.P., and Yetemen, O., 2020, The grass is not always greener on the other side: Seasonal reversal of vegetation greenness in aspect-driven semiarid ecosystems: Geophysical Research Letters, v. 47, no. 15, e2020GL088918, 12 p., https://doi.org/10.1029/2020GL088918.","productDescription":"e2020GL088918, 12 p.","ipdsId":"IP-112051","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":456086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088918","text":"Publisher Index Page"},{"id":376685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumari, Nikul","contributorId":229650,"corporation":false,"usgs":false,"family":"Kumari","given":"Nikul","affiliations":[{"id":41698,"text":"Discipline of Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, Australia","active":true,"usgs":false}],"preferred":false,"id":793776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saco, Patricia M.","contributorId":229651,"corporation":false,"usgs":false,"family":"Saco","given":"Patricia","email":"","middleInitial":"M.","affiliations":[{"id":41698,"text":"Discipline of Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, Australia","active":true,"usgs":false}],"preferred":false,"id":793777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodriguez, Jose F.","contributorId":229652,"corporation":false,"usgs":false,"family":"Rodriguez","given":"Jose","email":"","middleInitial":"F.","affiliations":[{"id":41698,"text":"Discipline of Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, Australia","active":true,"usgs":false}],"preferred":false,"id":793778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnstone, Samuel 0000-0002-3945-2499","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":207545,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":793779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Srivastava, Ankur","contributorId":229653,"corporation":false,"usgs":false,"family":"Srivastava","given":"Ankur","email":"","affiliations":[{"id":41698,"text":"Discipline of Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, Australia","active":true,"usgs":false}],"preferred":false,"id":793780,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chun, Kwok P.","contributorId":202936,"corporation":false,"usgs":false,"family":"Chun","given":"Kwok","email":"","middleInitial":"P.","affiliations":[{"id":36553,"text":"Hong Kong Baptist University","active":true,"usgs":false}],"preferred":false,"id":793781,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yetemen, Omer","contributorId":229654,"corporation":false,"usgs":false,"family":"Yetemen","given":"Omer","email":"","affiliations":[{"id":41698,"text":"Discipline of Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, Australia","active":true,"usgs":false}],"preferred":false,"id":793782,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243728,"text":"70243728 - 2020 - Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes","interactions":[],"lastModifiedDate":"2023-05-18T14:02:55.45746","indexId":"70243728","displayToPublicDate":"2020-07-08T08:55:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9533,"text":"Soil Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes","docAbstract":"Soil erosion by water affects soil organic carbon (SOC) migration and distribution, which are important processes for defining ecosystem carbon sources and sinks. Little has been done to quantify soil carbon erosion in the three major basins in China, the Yangtze River, Yellow River and Pearl River Basins, which contain the most eroded areas. This research attempts to quantify the lateral movement of SOC based on spatial and temporal patterns of water erosion rates derived from an empirical Unit Stream Power Erosion Deposition Model (USPED) model. The water erosion rates simulated by the USPED model agreed reasonably with observations (R2 = 0.43, P < 0.01). We showed that regional water erosion ranged within 23.3–50 Mg ha–1 year–1 during 1992–2013, inducing the lateral redistribution of SOC caused by erosion in the range of 0.027–0.049 Mg C ha–1 year–1, and that caused by deposition of 0.0079–0.015 Mg C ha–1 year–1, in the three basins. The total eroded SOC was 0.006, 0.002 and 0.001 Pg year–1 in the Yangtze River, Yellow River and Pearl River Basins respectively. The net eroded SOC in the three basins was ~0.0075 Pg C year–1. Overall, the annual average redistributed SOC rate caused by erosion was greater than that caused by deposition, and the SOC loss in the Yangtze River Basin was greatest among the three basins. Our study suggests that considering both processes of erosion and deposition – as well as effects of topography, rainfall, land use types and their interactions – on these processes are important to understand SOC redistribution caused by water erosion.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/SR19325","usgsCitation":"Yang, Y., Zhu, Q., Liu, J., Li, M., Yuan, M., Chen, H., Peng, C., and Yang, Z., 2020, Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes: Soil Research, v. 58, no. 6, p. 540-550, https://doi.org/10.1071/SR19325.","productDescription":"11 p.","startPage":"540","endPage":"550","ipdsId":"IP-107196","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":417209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Yangtze River, Yellow River and Pearl River 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Yan 0000-0003-0858-7603","orcid":"https://orcid.org/0000-0003-0858-7603","contributorId":245232,"corporation":false,"usgs":false,"family":"Yang","given":"Yan","email":"","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":873092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false},{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false}],"preferred":false,"id":873093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Mingxu","contributorId":305521,"corporation":false,"usgs":false,"family":"Li","given":"Mingxu","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yuan, Minshu","contributorId":305515,"corporation":false,"usgs":false,"family":"Yuan","given":"Minshu","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873095,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Huai","contributorId":172942,"corporation":false,"usgs":false,"family":"Chen","given":"Huai","email":"","affiliations":[{"id":27125,"text":"State Key Lab of Soil Erosion and Dryland Framing, NW A&F Unv, Yangling, China","active":true,"usgs":false}],"preferred":false,"id":873096,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peng, Changhui","contributorId":197932,"corporation":false,"usgs":false,"family":"Peng","given":"Changhui","email":"","affiliations":[{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false},{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, 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The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base-flow discharge, but is not well-characterized at large regional to continental scales. We used a process-based inversion of CONUS-extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base-flow, and (d) long-term average water content in the unsaturated zone. CONUS-extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long-term average transmissivity for each subdomain model calibrated against water-levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first-order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long-term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways.
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0000-0002-8782-6627","orcid":"https://orcid.org/0000-0002-8782-6627","contributorId":339721,"corporation":false,"usgs":true,"family":"Zell","given":"Wesley","email":"","middleInitial":"O.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904936,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218793,"text":"70218793 - 2020 - Modeling the surface water and groundwater budgets of the US using MODFLOW-OWHM","interactions":[],"lastModifiedDate":"2021-03-12T13:20:11.840585","indexId":"70218793","displayToPublicDate":"2020-07-08T07:17:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the surface water and groundwater budgets of the US using MODFLOW-OWHM","docAbstract":"Assessments of groundwater and surface water budgets at a large scale, such as the contiguous United States, often separately analyze the complex dynamics linking the surface and subsurface categories of water resources. These dynamics include recharge and groundwater contributions to streamflow. The time-varying simulation of these complex hydrologic dynamics, across large spatial and temporal scales, remains a scientific challenge due to the complexity of the processes and data availability. In this study, groundwater fluxes and surface hydrologic processes are simulated across the contiguous US for 1950-2010. The simulation estimates the monthly water budget components, such as groundwater recharge, surface runoff, and evapotranspiration; streamflow in major rivers is routed while accounting for groundwater exchange. Human impacts are included through groundwater pumping, and climate variability is included, including variability in precipitation, temperature and potential evapotranspiration. The simulated groundwater level and river discharge have strong correlation with USGS observation wells and streamflow gages, with R2 values of 0.992 and 0.946, respectively. The simulated evapotranspiration is compared with three other published estimation methods, showing that it is able to capture the magnitude and seasonality of evapotranspiration over the Mississippi River basin. As such, the model is able to reasonably simulate the surface and groundwater budgets over the US, allowing for questions of the relative importance of climate and human impacts to be explored in the future.
As the Arctic warms and wildfire occurrence increases, talik formation in permafrost regions is projected to expand and affect the cycling of water and carbon. Yet, few unified field and modeling studies have examined this process in detail, particularly in areas of continuous permafrost. We address this gap by presenting multimethod, multiseasonal geophysical measurements of permafrost and liquid‐water content that reveal substantial talik development in response to recent wildfire in continuous permafrost of boreal Alaska. Results from observation‐based cryohydrogeologic model simulations suggest that predisturbance subsurface conditions are key factors influencing thaw response to fire disturbance and air temperature warming. Our high‐resolution integrated study illustrates enhanced vulnerability of boreal continuous permafrost, with observed talik formation that exceeds coarse‐scale model projections by ~100 years even under the most extreme future emissions scenario. Results raise important scaling questions for representing extreme permafrost thaw phenomena of growing widespread importance in large‐scale predictive models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087565","usgsCitation":"Rey, D., Walvoord, M.A., Minsley, B.J., Ebel, B., Voss, C., and Singha, K., 2020, Wildfire-initiated talik development exceeds current thaw projections: Observations and models from Alaska's continuous permafrost zone: Geophysical Research Letters, v. 47, no. 15, e2020GL087565, 11 p., https://doi.org/10.1029/2020GL087565.","productDescription":"e2020GL087565, 11 p.","ipdsId":"IP-116894","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456104,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl087565","text":"Publisher Index Page"},{"id":381213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Northeast Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.82031249999997,\n 64.77412531292873\n ],\n [\n -140.9765625,\n 64.77412531292873\n ],\n [\n -140.9765625,\n 70.37785394109224\n ],\n [\n -150.82031249999997,\n 70.37785394109224\n ],\n [\n -150.82031249999997,\n 64.77412531292873\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":806698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806699,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806700,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":806701,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211281,"text":"70211281 - 2020 - A national-scale assessment of mercury bioaccumulation in United States National Parks using dragonfly larvae as biosentinels through a citizen-science framework","interactions":[],"lastModifiedDate":"2020-07-22T15:35:59.640558","indexId":"70211281","displayToPublicDate":"2020-07-07T10:31:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"A national-scale assessment of mercury bioaccumulation in United States National Parks using dragonfly larvae as biosentinels through a citizen-science framework","docAbstract":"We conducted a national-scale assessment of mercury (Hg) bioaccumulation in aquatic ecosystems using dragonfly larvae as biosentinels, by developing a citizen science network to facilitate biological sampling. Implementing a carefully designed sampling methodology for citizen scientists, we developed an effective framework for landscape-level inquiry that might otherwise be resource limited. We assessed variation in dragonfly Hg concentrations across >450 sites spanning 100 US National Park Service units, and examined intrinsic and extrinsic factors associated with variation in Hg concentrations. Mercury concentrations ranged between 10.4-1,411 ng/g dry weight across sites and varied among habitat types. Dragonfly total Hg (THg) concentrations were up to 1.8-fold higher in lotic habitats than in lentic habitats, and 37% higher in waterbodies with abundant wetlands along their margins than those without wetlands. Mercury concentrations in dragonflies differed among families, but were correlated (R2>0.80) with each other, enabling adjustment to a consistent family to facilitate spatial comparisons among sampling units. Dragonfly THg concentrations were positively correlated with THg in both fish and amphibians from the same locations, indicating that dragonfly larvae are effective indicators of Hg bioavailability in aquatic food webs. Collectively, this continental-scale study demonstrates the utility of dragonfly larvae for estimating potential mercury risk to fish and wildlife in aquatic ecosystems and provides a framework for engaging citizen science as a component of landscape Hg monitoring programs.","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.0c01255","usgsCitation":"Eagles-Smith, C., Willacker, J., Nelson, S.J., Flanagan Pritz, C.M., Krabbenhoft, D.P., Chen, C.Y., Ackerman, J., Campbell Grant, E.H., and Pilliod, D.S., 2020, A national-scale assessment of mercury bioaccumulation in United States National Parks using dragonfly larvae as biosentinels through a citizen-science framework: Environmental Science and Technology, v. 54, no. 14, p. 8779-8790, https://doi.org/10.1021/acs.est.0c01255.","productDescription":"12 p.","startPage":"8779","endPage":"8790","ipdsId":"IP-117106","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science 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Maine","active":true,"usgs":false}],"preferred":false,"id":793492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flanagan Pritz, Collen M","contributorId":229537,"corporation":false,"usgs":false,"family":"Flanagan Pritz","given":"Collen","email":"","middleInitial":"M","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":793493,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":793494,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Celia Y.","contributorId":145630,"corporation":false,"usgs":false,"family":"Chen","given":"Celia","email":"","middleInitial":"Y.","affiliations":[{"id":16179,"text":"Dartmouth College, Hanover NH","active":true,"usgs":false}],"preferred":false,"id":793495,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":793496,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":793497,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":210334,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":793498,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211310,"text":"70211310 - 2020 - Beloniformes: Belonidae (Needlefishes) and Hemiramphidae (Halfbeaks)","interactions":[],"lastModifiedDate":"2020-07-23T15:16:27.184145","indexId":"70211310","displayToPublicDate":"2020-07-07T10:13:36","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Beloniformes: Belonidae (Needlefishes) and Hemiramphidae (Halfbeaks)","docAbstract":"The order Beloniformes (or Synentognathi) contains two suborders, six families, 37 genera, and about 235 species of atherinomorph fishes (Rosen & Parenti 1981; Collette et al. 1984; Collette 2004). Features common to these fishes include dorsal and anal fins on the rear half of the body, abdominal pelvic fins with six soft rays, no fin spines, lateral line running along the ventral edge of the body, an open nasal pit, and lower pharyngeal bones fused into a triangular plate (leading to the name Synentognathi). Two families, the Flying fishes (Exocoetidae) and the Sauries (Scomberesocidae) are restricted to marine waters but several genera of Needlefishes (Belonidae) and Halfbeaks (Hemiramphidae and Zenarchopteridae) are restricted to fresh waters and other genera contain estuarine, freshwater, and marine species. The family name Belonidae, based on the type genus Belone, means needle in reference to the unusually long and slender jaws of most Needlefishes. Similarly, the family name Hemiramphidae means half-beak, alluding to the conspicuous presence of a long slender lower jar and a short upper jaw in most species. Two species of Needlefishes (Belonidae, Strongylura) and two species of Halfbeaks (Hemiramphidae, Hyporhamphus) occur in North American fresh waters.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Freshwater fishes of North America, volume 2: Characidae to poeciliidae","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Bruce B. Collette, and Walsh, S., 2020, Beloniformes: Belonidae (Needlefishes) and Hemiramphidae (Halfbeaks), chap. of Freshwater fishes of North America, volume 2: Characidae to poeciliidae, v. 2, p. 449-462.","productDescription":"14 p.","startPage":"449","endPage":"462","ipdsId":"IP-077208","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":376667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376654,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/freshwater-fishes-north-america/table-of-contents"}],"volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bruce B. Collette","contributorId":229620,"corporation":false,"usgs":false,"family":"Bruce B. Collette","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":793694,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walsh, Stephen 0000-0002-1009-8537","orcid":"https://orcid.org/0000-0002-1009-8537","contributorId":214723,"corporation":false,"usgs":true,"family":"Walsh","given":"Stephen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":793695,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210855,"text":"ofr20201061 - 2020 - Continuous stream discharge, salinity, and associated data collected in the Lower St. Johns River and its tributaries, Florida, 2018","interactions":[],"lastModifiedDate":"2020-07-07T15:39:00.489347","indexId":"ofr20201061","displayToPublicDate":"2020-07-07T09:20:44","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1061","displayTitle":"Continuous Stream Discharge, Salinity, and Associated Data Collected in the Lower St. Johns River and Its Tributaries, Florida, 2018","title":"Continuous stream discharge, salinity, and associated data collected in the Lower St. Johns River and its tributaries, Florida, 2018","docAbstract":"The U.S. Army Corps of Engineers, Jacksonville District, plans to deepen the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel, beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the third annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project and contains information pertinent to the data collection during the 2018 water year, from October 2017 to September 2018. Changes to the network on the main stem of the St. Johns River include the addition of (1) three new stations to monitor water temperature and salinity at Racy Point, Shands Bridge, and above Buckman Bridge; (2) stage data collection at both Buckman Bridge and Dames Point Bridge; and (3) three additional parameters, namely stage, velocity, and streamflow direction, to the St. Johns River at Jacksonville and Dames Point Bridge.
Discharge and salinity varied widely during the data collection period, which included residual effects from Hurricane Irma in September 2017 and above-average rainfall for all counties in the project area over the 4-month period from April to July. The annual mean discharge at Durbin Creek was greatest among the tributaries, followed by annual mean discharges at Ortega River, Trout River, Cedar River, Julington Creek, Clapboard Creek, Broward River, Pottsburg Creek, and Dunn Creek. The annual mean discharge for each of the main-stem sites was higher in the 2018 water year than that of the previous 2 years of this study. Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and lowest at Durbin Creek and Ortega River, the sites farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which is expected. Relative to annual mean salinity calculated since the 2016 water year, annual mean salinity at all monitoring locations was lower for the 2018 water year, except for Durbin Creek, which was the same.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201061","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2020, Continuous stream discharge, salinity, and associated data collected in the Lower St. Johns River and its tributaries, Florida, 2018: U.S. Geological Survey Open-File Report 2020–1061, 34 p., https://doi.org/10.3133/ofr20201061.","productDescription":"viii, 34 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-107711","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375991,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1061/coverthb.jpg"},{"id":375992,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1061/ofr20201061.pdf","text":"Report","size":"25.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1061"}],"country":"United States","state":"Florida","otherGeospatial":"Lower St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.947021484375,\n 29.504159065872624\n ],\n [\n -81.00769042968749,\n 29.504159065872624\n ],\n [\n -81.00769042968749,\n 30.488917676126846\n ],\n [\n -81.947021484375,\n 30.488917676126846\n ],\n [\n -81.947021484375,\n 29.504159065872624\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Coastal wetlands are among the most productive habitats on Earth and sequester globally significant amounts of atmospheric carbon (C). Extreme rates of soil C accumulation are widely assumed to reflect efficient C storage. Yet the fraction of wetland C lost via hydrologic export has not been directly quantified, since comprehensive budgets including direct estimates of lateral C loss are lacking. We present a complete net ecosystem C budget (NECB), demonstrating that lateral losses of C are a major component of the NECB for the largest stable brackish tidal marsh on the U.S. Pacific coast. Mean annual net ecosystem exchange of CO2 with the atmosphere (NEE = −185 g C m2 year−1, negative NEE denoting ecosystem uptake) was compared to long-term soil C burial (87–110 g C m2 year−1), suggesting only 47–59% of fixed atmospheric C accumulates in soils. Consistently, direct monitoring in 2017–2018 showed NEE of −255 g C m−2 year−1, and hydrologic export of 105 g C m−2 year−1 (59% of NEE remaining on site). Despite their high C sequestration capacity, lateral losses from coastal wetlands are typically a larger fraction of the NECB when compared to other terrestrial ecosystems. Loss of inorganic C (the least measured NECB term) was 91% of hydrologic export and may be the most important term limiting C sequestration. The high productivity of coastal wetlands thus serves a dual function of C burial and estuarine export, and the multiple fates of fixed C must be considered when evaluating wetland capacity for C sequestration.
Mercury contamination in river systems due to historic and current Hg releases is a persistent concern for both wildlife and human health. In larger rivers, like the Ohio River, USA, it is difficult to directly link Hg discharges to bioaccumulation due to the existence of multiple industrial Hg sources as well as the varied dietary and migratory habits of biota. To better understand how industrial effluent influences the cycling and bioaccumulation of Hg within the Ohio River, Hg stable isotope analysis was applied to various nonbiological and biological media. High Hg concentrations in suspended particulate matter suggest this vector was the largest contributor of Hg to the water column, and distinct Hg source signatures were observed in effluent particulates from different industrial processes, such as chlor‐alkali activity (δ202Hg = −0.52‰) and coal power plant discharge (δ202Hg = −1.39‰). Despite this distinction, average sediments (δ202Hg = −1.00 ± 0.23‰) showed intermediate isotopic signatures that suggest the accumulation of a mixed Hg source driven by multiple industrial discharges. Biota in the system were shown to have a conserved range of δ202Hg and estimation approaches related these signatures back to particulate matter within Hannibal Pool. Mussels were found to conserve Hg isotopes signatures independently of food web drivers and served as ideal water column indicators of bioaccumulated Hg sources. This study highlights the complexity of Hg cycling within an industrialized river and shows that an isotope tracer approach can provide insight to water column sources of Hg. Integr Environ Assess Manag 2021;17:233−242. Published 2020. This article is a US Government work and is in the public domain in the USA.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/ieam.4308","usgsCitation":"Janssen, S., Patnode, K.A., Pluta, B.R., and Krabbenhoft, D.P., 2020, Insights into mercury source identification and bioaccumulation using stable isotope approaches in the Hannibal Pool of the Ohio River: Integrated Environmental Assessment and Management, v. 17, no. 1, p. 233-242, https://doi.org/10.1002/ieam.4308.","productDescription":"10 p.","startPage":"233","endPage":"242","ipdsId":"IP-119884","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456115,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8043245","text":"External Repository"},{"id":436892,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95QMNJ4","text":"USGS data release","linkHelpText":"Mercury concentrations and isotopic compositions in biota and sediments from the Hannibal Pool of the Ohio River"},{"id":381755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Hannibal Pool, Ohio River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.134033203125,\n 39.47860556892209\n ],\n [\n -80.61767578124999,\n 39.47860556892209\n ],\n [\n -80.61767578124999,\n 40.36328834091583\n ],\n [\n -81.134033203125,\n 40.36328834091583\n ],\n [\n -81.134033203125,\n 39.47860556892209\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patnode, Kathleen A.","contributorId":127355,"corporation":false,"usgs":false,"family":"Patnode","given":"Kathleen","email":"","middleInitial":"A.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":807371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pluta, Bruce R","contributorId":245948,"corporation":false,"usgs":false,"family":"Pluta","given":"Bruce","email":"","middleInitial":"R","affiliations":[{"id":49378,"text":"US EPA Hazardous Clean-up Division","active":true,"usgs":false}],"preferred":false,"id":807372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":807373,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217801,"text":"70217801 - 2020 - Improved fish counting method accurately quantifies high‐density fish movement in dual‐frequency identification sonar data files from a coastal wetland environment","interactions":[],"lastModifiedDate":"2021-02-03T21:20:22.369695","indexId":"70217801","displayToPublicDate":"2020-07-07T06:46:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Improved fish counting method accurately quantifies high‐density fish movement in dual‐frequency identification sonar data files from a coastal wetland environment","docAbstract":"There are many ways to quantify fish movement through shallow‐water habitats, but most noninvasive methods (e.g., visual counts) are not effective in turbid coastal wetland waters of the Great Lakes. Dual‐frequency identification sonar (DIDSON) technology (Sound Metrics) offers a noninvasive, hydroacoustic‐based approach to characterize fish movement in wetlands and other habitats by collecting highly detailed fish movement data regardless of light and water quality conditions. High‐resolution data can be analyzed to estimate fish movement in areas where visual observations are difficult. However, enumerating a complex mix of fish sizes by manually counting fish visible in echogram files requires training and is very time consuming. Therefore, four counting techniques were tested to estimate fish abundance from DIDSON echograms that were collected at a hydrologically reconnected coastal wetland in the Great Lakes. Briefly, the four counting methods were (1) manually viewing the entire length of the echogram (full‐hour manual count), (2) manually viewing subsections of the echogram before generating fish estimates by per‐minute average (subsample manual count), (3) using Echoview automated software to generate automated estimates, and (4) using DIDSON viewer software to generate automated estimates. Over 800 echogram‐hours were recorded over a 9‐month period at an open‐flow water control structure connecting a coastal wetland to a tributary to Lake Erie. Commercial fish tracking software (Echoview) and custom software scripts from Milne Technologies were used to semi‐automate fish count estimates for a small subset of data. Semi‐automated software counts were compared to manual counts of identical data files to assess differences in accuracy, cost, processing time, and counter effort. Semi‐automated fish count estimates using Echoview and custom pre‐ and postprocessing software scripts did not differ from baseline manual counts, suggesting that the semi‐automated count process could be a reliable tool to increase efficiency when processing large DIDSON data sets.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10451","usgsCitation":"Eggleston, M., Milne, S.W., Ramsay, M., and Kowalski, K., 2020, Improved fish counting method accurately quantifies high‐density fish movement in dual‐frequency identification sonar data files from a coastal wetland environment: North American Journal of Fisheries Management, v. 40, no. 4, p. 883-892, https://doi.org/10.1002/nafm.10451.","productDescription":"10 p.","startPage":"883","endPage":"892","ipdsId":"IP-108651","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":436893,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CMU62C","text":"USGS data release","linkHelpText":"DIDSON video collection of Coastal Lake Erie Wetland, Lucas Co, Ohio in 2011"},{"id":382918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.59277343749999,\n 40.78054143186033\n ],\n [\n -75.6298828125,\n 40.78054143186033\n ],\n [\n -75.6298828125,\n 49.55372551347579\n ],\n [\n -92.59277343749999,\n 49.55372551347579\n ],\n [\n -92.59277343749999,\n 40.78054143186033\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Eggleston, Michael R. 0000-0003-1068-3290","orcid":"https://orcid.org/0000-0003-1068-3290","contributorId":248759,"corporation":false,"usgs":true,"family":"Eggleston","given":"Michael R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809797,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milne, Scott W.","contributorId":248760,"corporation":false,"usgs":false,"family":"Milne","given":"Scott","email":"","middleInitial":"W.","affiliations":[{"id":40886,"text":"Milne Technologies","active":true,"usgs":false}],"preferred":false,"id":809798,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsay, Maxwell","contributorId":248761,"corporation":false,"usgs":false,"family":"Ramsay","given":"Maxwell","email":"","affiliations":[],"preferred":false,"id":809799,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809800,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211284,"text":"70211284 - 2020 - The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in high-elevation lakes","interactions":[],"lastModifiedDate":"2020-10-12T17:06:21.880134","indexId":"70211284","displayToPublicDate":"2020-07-06T10:20:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in high-elevation lakes","docAbstract":"Abstract\nClimate change is altering biogeochemical, metabolic, and ecological functions in lakes across the globe. Historically, mountain lakes in temperate regions have been unproductive due to brief ice-free seasons, a snowmelt-driven hydrograph, cold temperatures, and steep topography with low vegetation and soil cover. We tested the relative importance of winter and summer weather, watershed characteristics, and water chemistry as drivers of phytoplankton dynamics. Using boosted regression tree models for 28 mountain lakes in Colorado we examined regional, intra-seasonal, and inter-annual drivers of variability in chlorophyll a as a proxy for lake phytoplankton. Phytoplankton biomass was inversely related to the maximum snow water equivalent (SWE) of the previous winter, as others have found. However, even in years with average SWE, summer precipitation extremes and warming enhanced phytoplankton biomass. Peak seasonal phytoplankton biomass coincided with the warmest water temperatures and lowest nitrogen-to-phosphorus ratios. While links between snowpack, lake temperature, nutrients, and organic matter dynamics are increasingly recognized as critical drivers of change in high elevation lakes, our results highlight the additional influence of summer conditions on lake productivity in response to ongoing changes in climate. Continued changes in the timing, type, and magnitude of precipitation in combination with other global change drivers (e.g., nutrient deposition) will affect production in mountain lakes, potentially shifting these historically oligotrophic lakes toward new ecosystem states. Ultimately, a deeper understanding of these drivers and pattern at multiple scales will allow us to better anticipate ecological consequences of global change.","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3132","usgsCitation":"Oleksy, I., Beck, W., Lammers, R., Steger, C., Wilson, C., Christensen, K., Vincent, K., Johnson, P., and Baron, J., 2020, The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in high-elevation lakes: Ecology, v. 101, no. 10, e03132, 12 p., https://doi.org/10.1002/ecy.3132.","productDescription":"e03132, 12 p.","ipdsId":"IP-114262","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3132","text":"Publisher Index Page"},{"id":376637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Front Range of the Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.84228515625,\n 39.98132938627215\n ],\n [\n -105.01281738281249,\n 39.98132938627215\n ],\n [\n -105.01281738281249,\n 40.65563874006118\n ],\n [\n -105.84228515625,\n 40.65563874006118\n ],\n [\n -105.84228515625,\n 39.98132938627215\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"101","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Oleksy, Isabella A.","contributorId":229538,"corporation":false,"usgs":false,"family":"Oleksy","given":"Isabella A.","affiliations":[{"id":33412,"text":"Cary Institute for Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":793504,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, Whitney","contributorId":229539,"corporation":false,"usgs":false,"family":"Beck","given":"Whitney","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":793505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lammers, R.","contributorId":229540,"corporation":false,"usgs":false,"family":"Lammers","given":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793506,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steger, Cara","contributorId":229541,"corporation":false,"usgs":false,"family":"Steger","given":"Cara","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793507,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Cody","contributorId":229542,"corporation":false,"usgs":false,"family":"Wilson","given":"Cody","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793508,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Christensen, Kyle","contributorId":229543,"corporation":false,"usgs":false,"family":"Christensen","given":"Kyle","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793509,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vincent, Kim","contributorId":229544,"corporation":false,"usgs":false,"family":"Vincent","given":"Kim","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":793510,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Pieter","contributorId":229545,"corporation":false,"usgs":false,"family":"Johnson","given":"Pieter","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":793511,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Baron, Jill 0000-0002-5902-6241 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6241","contributorId":222907,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":793512,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211541,"text":"70211541 - 2020 - Hydrologic modeling to examine the influence of the forestry reclamation approach and climate change on mineland hydrology","interactions":[],"lastModifiedDate":"2020-07-30T15:25:29.367702","indexId":"70211541","displayToPublicDate":"2020-07-05T10:18:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic modeling to examine the influence of the forestry reclamation approach and climate change on mineland hydrology","docAbstract":"Forests in the Appalachian region of the U.S. are threatened by a variety of short- and long-term pressures, including climate change, invasive species, and resource extraction. Surface mining for coal is one of the most important drivers of land-use change in the region, reducing native forest cover, causing forest fragmentation, eliminating intact soil, and affecting water resources. The Forestry Reclamation Approach (FRA) has been demonstrated as a successful best practice for restoring forests on mine-impacted landscapes, but little information exists on how the practice will affect hydrologic processes. A study was initiated to examine soil-water movement, as in-situ saturated hydraulic conductivity (Ksat), combined with soil porosity to quantify the potential influence on streamflow of reclaimed mines relative to an unmined, forested control site in eastern Kentucky. We compared different reclamation techniques and time since reclamation to determine the extent to which hydrologic function can be restored. We also simulated evapotranspiration at the watershed scale as a function of reclamation technique for both historical and projected (2050) climate. Results indicate that conventional grassland reclamation critically changes how soil water transitions to streamflow, primarily due to Ksat variability that exceeds that measured for intact and FRA soils. Sites reclaimed using FRA exhibited a soil-water environment that was more similar to the unmined control. However, all reclaimed mine soils were thinner, retained and stored less soil water, and thus could provide less plant-available water during the growing season. The plant-available water stored in reclaimed landscapes may not be sufficient to support forest health and this is exacerbated by projected climate conditions. However, soil development under a combination of FRA techniques has the potential to mitigate this limitation.