Diel cycles are known to occur in all types of waters, and increasing studies indicate routine water samples may not provide an accurate snapshot in concentrations of trace elements and nutrients. Diel behavior in neutral to alkaline pH ranges is independent of streamflow variability and concentration. Extensive historical U.S. Geological Survey (USGS) water-quality data have been collected in the Eagle River Basin during daylight hours, which is defined as the period of time between one-half hour prior to sunrise and one-half hour after sunset. However, no USGS data have been collected throughout the nighttime, defined as the time between one-half hour after sunset and one-half hour prior to sunrise, making the evaluation of diel cycles impossible. To assess the importance of diel cycling within the Eagle River Basin, the USGS, in cooperation with Eagle River Watershed Council, developed a study to assess the mechanisms, patterns, and magnitude of change during the diel cycle for selected constituents. Water-quality monitors at five USGS streamgage sites (09065500, Gore Creek at Upper Station, near Minturn, Colorado, 09063000, Eagle River at Red Cliff, Colorado, 09064600, Eagle River near Minturn, Colorado, 09066325, Gore Creek above Red Sandstone Creek at Vail, Colorado, and 394220106431500, Eagle River below Milk Creek near Wolcott, Colorado) were deployed in 2017 to evaluate the water-quality field parameters and to determine if water conditions were favorable for the diel cycling of nutrients and trace elements. Based on the evaluation of water-quality parameters, three of the five sites were sampled for nutrient and trace-element concentrations in 2018 to confirm the presence and magnitude of diel cycling. Historical data were also analyzed to assess the effect of time of day on measured nutrient and trace-element concentrations. An assessment of the effect of land use on diel cycling was also investigated.
Measurable nutrients displayed a diel cycle at all three sites with the largest percentage change at the most downstream site (394220106431500), located on the Eagle River. More notable diel cycles at this site include filtered nitrate plus nitrite, which varied 179 percent, with concentrations from 0.24 to 0.67 milligrams per liter (mg/L) and filtered orthophosphate, which varied 71 percent, with concentrations from 0.07 to 0.12 mg/L. Filtered nitrate plus nitrite at site 09066325 varied 57 percent, ranging from 0.14 to 0.22 mg/L. Maximum concentrations occurred prior to noon, decreased through the afternoon (between noon and sunset), and increased during the night (between sunset and sunrise). That pattern is consistent with nutrient uptake in response to daytime (between sunrise and sunset) photosynthesis along with biologically driven denitrification and nitrification cycles. Nutrient concentrations at sites 09064600 and 09066325 were generally low and below laboratory reporting limits, which is the smallest measured concentration that nutrients could be measured by a given analytical method.
Trace-element concentrations were detectable at all sites with the largest percentage change at the most downstream site (394220106431500) and exhibited diel concentration variation from 11.6 to 284 percent. Appreciable diel cycles included filtered copper (0.98–1.40 micrograms per liter [µg/L], 42.9 percent), filtered zinc (less than [<] 4.00–5.50 µg/L, greater than [>] 37.5 percent), total manganese (9.70–19.5 µg/L, 101 percent), and total arsenic (0.30–0.40 µg/L, 33.3 percent). The largest percentage change in concentration was filtered manganese (2.84–10.9 µg/L, 284 percent). Diel cycles at site 09064600 ranged from 9.1 to 64.5 percent across the trace elements measured. Dissolved trace elements with appreciable diel cycles during the sampling period include filtered cadmium (0.09–0.12 µg/L, 33.3 percent), filtered copper (0.99–1.40 µg/L, 41.4 percent), and total arsenic (0.20–0.30 µg/L, 50 percent). The largest percentage change was filtered zinc (38.3–63.0 µg/L, 65 percent). Trace-element concentrations at site 09066325 were below laboratory reporting limits for many parameters, and no diel cycle could be assessed for these parameters. However, total recoverable iron, filtered barium, filtered manganese, and filtered selenium exhibited changes in concentrations of <10.0–19.4 µg/L (>94 percent), 115–121 µg/L (5 percent), 1.44–1.72 µg/L (19.4 percent), and 0.25–0.28 µg/L (12 percent), respectively. At sites 09064600 and 394220106431500, maximum trace-element concentrations occurred during nighttime with some variation regarding the timing of the peak. The exceptions to this were filtered copper, total arsenic, and filtered selenium, which had maximum concentrations around noon or as the sun disappeared below the horizon. The timing of minimum concentrations occurred in the afternoon for many trace elements, with filtered copper, total arsenic, and filtered selenium having minimum concentrations in the morning or just prior to the appearance of the sun.
Analysis of historical data also showed evidence of diel cycling. Historical samples collected from July through October were used to identify diel cycling in base-flow conditions. The resulting diel pattern in the median concentration for filtered manganese, filtered zinc at water-quality site 09064600, and filtered manganese and filtered nitrate plus nitrite at water-quality site 39422016431500 were consistent with the diel pattern in the September 2018 samples, and indicate time of day can bias sampling results even during daylight hours.
Diel cycling in the Eagle River Basin appears to be driven primarily by instream, biological processes. However, land use, particularly human effects downstream from urban areas, mining, and agriculture, may affect these processes. At some locations, diel variations in nutrient and trace-element concentrations are small enough to be of low concern. At other locations, however, variations in concentrations up to 284 percent in the data collected for this study and 214 percent in base-flow historical data, indicate daytime-only sampling, particularly in late afternoon, can underestimate daily average nutrient and trace-element concentrations. When feasible, the potential of diel cycling warrants consideration in sample design to account for the potential of diel cycles, or at a minimum, be recognized as a component of the river dynamic and the potential consequences that diel cycles may have in data interpretation and river management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215066","collaboration":"Prepared in cooperation with Eagle River Watershed Council","usgsCitation":"Richards, R.J., and Henneberg, M.F., 2021, Assessment of diel cycling in nutrients and trace elements in the Eagle River Basin, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5066, 36 p., \nhttps://doi.org/ 10.3133/ sir20215066.","productDescription":"Report: viii, 36 p.; 3 Databases","onlineOnly":"Y","ipdsId":"IP-116765","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":388128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5066/coverthb.jpg"},{"id":388129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5066/sir20215066.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
Native plants are the true green infrastructure we rely on for healthy, resilient, and biodiverse ecosystems. They protect us against climate change and natural disasters; create habitat for wildlife, rare species, and pollinators; and are vital for carbon sequestration. Without native plants, especially their seeds, we do not have the ability to restore functional ecosystems after natural disasters and mitigate the effects of climate change. Investing now in coordinated, research-driven native seed production is an efficient and cost-effective nature-based solution for improving ecosystem resilience in the face of the climate and extinction crisis. Federal government agencies (see list on page 13 and their partners are collaborating to increase the supply of native seeds for restoration through the National Seed Strategy for Rehabilitation and Restoration (National Seed Strategy) to get the right seed in the right place at the right time. The National Seed Strategy is a public-private collaboration to increase the supply of native seeds for restoration projects to ensure ecosystem resilience and the health and prosperity of future generations. Developed by the Plant Conservation Alliance (PCA) in 2015, the National Seed Strategy harnesses cross sector botanical expertise, supports rural, agricultural, minority, and tribal livelihoods, and provides training opportunities to our next generation of natural resource professionals to maintain and preserve our iconic habitats. This science-driven national effort is integral to the Nation’s conservation priorities, including the commitment to conserve 30% of America’s lands and waters by 2030 as outlined in Executive Order 14008 on Tackling the Climate Crisis at Home and Abroad. Moreover, the National Seed Strategy is recognized in the objectives of the 2021 DOI Invasive Species Strategic Plan (DOI 2021) and addresses national priorities such as climate change, wildland fire, and tribal engagement. The National Seed Strategy charts a course for federal, tribal, state, local and private partners to increase private and public sector coordination on native seed development, thereby accelerating the pace and scale of restoration. Success is being achieved through the establishment of nationwide networks of seed collectors, researchers to develop seed, farmers to grow native seed, nurseries and seed storage facilities to supply adequate quantities of appropriate seed, and restoration ecologists.
","language":"English","publisher":"Bureau of Land Management (National Operations Center)","usgsCitation":"Mccormick, M.L., Carr, A., DeAngelis, P., Olwell, M., Murray, R., and Park, M., 2021, National seed strategy progress report, 2015-2020: Progress Report, ii, 74 p.","productDescription":"ii, 74 p.","ipdsId":"IP-130943","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388718,"type":{"id":11,"text":"Document"},"url":"https://www.blm.gov/sites/blm.gov/files/docs/2021-08/Progress%20Report%2026Jul21.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mccormick, Molly Lutisha 0000-0002-4361-7567","orcid":"https://orcid.org/0000-0002-4361-7567","contributorId":265148,"corporation":false,"usgs":true,"family":"Mccormick","given":"Molly","email":"","middleInitial":"Lutisha","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Amanda N","contributorId":265150,"corporation":false,"usgs":false,"family":"Carr","given":"Amanda N","affiliations":[{"id":54608,"text":"Chicago Botanic Garden, Glencoe, IL","active":true,"usgs":false}],"preferred":false,"id":822326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Patricia","contributorId":265151,"corporation":false,"usgs":false,"family":"DeAngelis","given":"Patricia","email":"","affiliations":[{"id":54610,"text":"U.S. Fish and Wildlife Service, Falls Church, VA","active":true,"usgs":false}],"preferred":false,"id":822327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olwell, Margaret","contributorId":265152,"corporation":false,"usgs":false,"family":"Olwell","given":"Margaret","email":"","affiliations":[{"id":54611,"text":"Bureau of Land Management, Boise, ID","active":true,"usgs":false}],"preferred":false,"id":822328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murray, Regan","contributorId":265162,"corporation":false,"usgs":false,"family":"Murray","given":"Regan","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":822359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Park, Maggie","contributorId":265163,"corporation":false,"usgs":false,"family":"Park","given":"Maggie","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":822360,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224747,"text":"70224747 - 2021 - Cohesive sediment modeling in a shallow estuary: Model and environmental implications of sediment parameter variation","interactions":[],"lastModifiedDate":"2021-10-04T12:47:21.058229","indexId":"70224747","displayToPublicDate":"2021-08-20T07:44:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9372,"text":"Journal of Geophysical Research--Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Cohesive sediment modeling in a shallow estuary: Model and environmental implications of sediment parameter variation","docAbstract":"Numerical models of sediment transport in estuarine systems rely on parameter values that are often poorly constrained and can vary on timescales relevant to model processes. The selection of parameter values can affect the accuracy of model predictions, while environmental variation of these parameters can impact the temporal and spatial ranges of sediment fluxes, erosion, and deposition in the real world. We implemented a numerical model of San Pablo Bay, an embayment within San Francisco Bay, California, for November–December 2014, and compared model outputs to observations of water level, velocity, wave parameters, salinity, and suspended sediment concentration (SSC) in the shallow regions. Idealized model runs show that wind timing relative to the phase of the tides is the strongest control on sediment fluxes and bed erosion. We varied sediment erodibility in the outflow of the Petaluma River; while this causes erosion and deposition to vary strongly through the shallows system, total export from the shallows does not change. Model runs with realistic winds show that wind likely resuspends faster settling particles or allows for more particle flocculation; particle settling velocity controls system-wide sediment accumulation. At the margins of the system, the magnitude of SSC is closely tied to wind direction when winds occur during flood tide, but sediment deposition is less connected: Both bed evolution and SSC need to be considered in the prediction of marsh fate. Spatial patterns of light attenuation due to SSC is strongly tied to assumed settling velocity.
Wetlands provide ecological functionality by maintaining and promoting regional biodiversity supporting quality habitat for aquatic organisms. Globally, habitat loss, fragmentation and degradation due to increases in agricultural activities and urban development have reduced or altered geographically isolated wetlands, thus reducing biodiversity. The objective of this study was to assess the relative ecological function and integrity of natural ponds, excavated ponds and stormwater basins in the New Jersey Pinelands, located in the northeastern United States by comparing hydrologic conditions, water quality, pesticide concentrations (water, sediment and tissue) and wetland assemblages including amphibians. Twenty-four wetlands were selected based on surrounding land-use and sampled for a variety of abiotic and biotic variables. Abiotic and biotic wetland variables were similar between natural and excavated ponds, with notable differences between the ponds and stormwater basins. Natural and excavated ponds displayed characteristic Pinelands water quality (low pH, high organic carbon, and low pesticide concentrations), exhibited high ecological integrity and supported native ampbibians. Stormwater basins and degraded ponds surrounded by altered land-use exhibited degraded water quality (high pH, high pesticide concentrations) and were dominated by non-native and introduced plants and amphibians. Results from this study can broadly inform resource conservation strategies for amphibians and other communities with a diverse range of habitat requirements, particularly in areas where conservation and development are competing priorities. To conserve biodiversity in changing landscapes, wetlands with similar functionality and land-use characteristics need to be identified and managed to preserve water quality for species of conservation concern.
The U.S. Geological Survey, in cooperation with the Montana Department of Transportation, developed regression equations based on channel width to estimate peak-flow frequencies at ungaged sites in Montana. The equations are based on peak-flow data at streamgages through September 2011 (end of water year 2011), and channel widths measured in the field and from aerial photographs.
Active-channel width and bankfull width (channel widths) were measured in the field at 64 sites across Montana in 2017. Channel widths also were measured near 515 streamgages from aerial photographs. These new channel-width data, along with more than 438 historical channel-width measurements, are published in a separate data release.
Regression equations were developed using generalized least squares regression or weighted least squares regression. The channel-width regression equations can be used to estimate peak-flow frequencies (peak-flow magnitudes associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites in each of the eight hydrologic regions in Montana. Methods are presented for weighting estimates from the channel-width equations with estimates from equations using basin characteristics. The weighting technique can be used to reduce the standard error of prediction relative to that obtained using a single method. Several example problems covering a range of estimation scenarios also are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205142","collaboration":"Prepared in cooperation with Montana Department of Transportation","usgsCitation":"Chase, K.J., Sando, R., Armstrong, D.W., and McCarthy, P., 2021, Regional regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011 (ver. 1.1, September 2021): U.S. Geological Survey Scientific Investigations Report 2020–5142, 49 p., https://doi.org/10.3133/sir20205142.","productDescription":"Report: vi, 49 p.; Data Release; Dataset; Version History","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-102009","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":436235,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D7MEB1","text":"USGS data 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\"}}]}","edition":"Version 1.0: August 19, 2021; Version 1.1: September 20, 2021","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The Little Colorado River is a major tributary to the Colorado River with a confluence at the boundary between Marble and Grand Canyons within Grand Canyon National Park, Arizona. The bedrock gorge of the lower Little Colorado River is home to the largest known population of Gila cypha (humpback chub), an endangered fish endemic to the Colorado River Basin. Channel conditions might affect the spawning success of the humpback chub. Perennial base flow in the lower Little Colorado River deposits travertine, which forms dams and cascades. Geomorphic change in the lower Little Colorado River is controlled by the growth and collapse of travertine dams, debris flows from tributaries, and reworking of dams and debris fans by Little Colorado River floods.
A study was conducted by the U.S. Geological Survey, in cooperation with the Glen Canyon Dam Adaptive Management Program and the U.S. Fish and Wildlife Service, to document historical floods and geomorphic change in the lower Little Colorado River. For this study, we used historical and gaging records and hydraulic modeling of surveyed high-water marks from historical Little Colorado River floods to construct a peak-flow history of the lower Little Colorado River. We analyzed base-flow longitudinal profiles and historical photographs to determine changes in the longitudinal profile of the lower Little Colorado River from 1909 to 2019. The peak-flow magnitudes and the frequency of larger floods have declined since the late 1800s, and the longitudinal profile of the Little Colorado River has substantially changed between 1909 and 2019. Aggradation of as much as 6 meters in some reaches occurred between 1926 and 1992, mostly before the 1950s. This aggradation was caused largely by the documented growth of travertine dams continuing through at least 2013 at several locations. Other reaches were incised by as much as 10 meters between 1926 and 1992, but mostly before the 1950s, largely from the breaching of travertine dams. Travertine dams in the Little Colorado River have survived large flooding events and then later collapsed during floods of lower streamflow or even periods of base flow. The decline in peak-flow magnitude and frequency has changed the dominant geomorphic processes in this formerly dynamic reach. Large incision events have not been documented since the early 1950s; for this reason, the reach has only aggraded or remained stable since that time. This loss of geomorphic disturbance has likely affected, and will likely continue to affect, the spawning habitat of the endangered humpback chub in the lower Little Colorado River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215049","usgsCitation":"Unema, J.A., Topping, D.J., Kohl, K.A., Pillow, M.J., and Caster, J.J., 2021, Historical floods and geomorphic change in the lower Little Colorado River during the late 19th to early 21st centuries: U.S. Geological Survey Scientific Investigations Report 2021–5049, 34 p., https://doi.org/10.3133/sir20215049.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-113057","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5049/covrthb.jpg"},{"id":388148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5049/sir20215049.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VGWRV1","linkHelpText":"Topographic data, historical peak-stage data, and 2D flow models for the lowermost Little Colorado River, Arizona, USA, 2017"}],"country":"United States","state":"Arizona, Utah, New Mexico, Utah","otherGeospatial":"Lower Little Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.236328125,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 33.247875947924385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
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The U.S. Geological Survey, with the support of the International Joint Commission, and in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation, is leading a project that should improve information available to apportion water between Canada and the United States in the St. Mary and Milk River Basins. One component of the water budget, consumptive use of irrigation water (the amount of supplemental water used by crops), can be estimated at 100-meter resolution almost every week using imagery recorded by satellites from 1985 to present (2021) and weather data, when conditions permit. Better estimates of consumptive water use should improve understanding of water availability and use in the basin and should assist with water apportionment procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213042","collaboration":"Prepared in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, R., Friedrichs, M., and Senay, G.B., 2021, Using satellite imagery to estimate consumptive water use from irrigated lands in the Milk River Basin, United States and Canada: U.S. Geological Survey Fact Sheet 2021–3042, 2 p., https://doi.org/10.3133/fs20213042.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-130575","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3042/coverthb.jpg"},{"id":388102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3042/fs20213042.pdf","text":"Report","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"fs 2021–3042"}],"country":"Canada, United States","state":"Alberta, Montana, Saskatchewan","otherGeospatial":"Milk River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.851318359375,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 48.07807894349862\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211063","usgsCitation":"La Peyre, M.K., Marshall, D.A., and Sable, S.E., 2021, Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters: U.S. Geological Survey\nOpen-File Report 2021–1063, 40 p., https://doi.org/10.3133/ofr20211063.","productDescription":"Report: viii, 40p.; 3 Appendix Tables","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126014","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1063/images"},{"id":388041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1063/coverthb.jpg"},{"id":388042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1063"},{"id":388043,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.csv","text":"Table 1.1 (.csv)","size":"5.07 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388044,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.csv","text":"Table 2.1 (.csv)","size":"5.40 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388045,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.csv","text":"Table 3.1 (.csv)","size":"63.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"},{"id":388046,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.xlsx","text":"Table 1.1 (.xlsx)","size":"14.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388047,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.xlsx","text":"Table 2.1 (.xlsx)","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388048,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.xlsx","text":"Table 3.1 (.xlsx)","size":"46.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.177734375,\n 27.527758206861886\n ],\n [\n -82.6171875,\n 29.267232865200878\n ],\n [\n -83.8916015625,\n 30.372875188118016\n ],\n [\n -85.078125,\n 30.259067203213018\n ],\n [\n -86.7919921875,\n 30.826780904779774\n ],\n [\n -88.681640625,\n 30.675715404167743\n ],\n [\n -90.263671875,\n 30.486550842588485\n ],\n [\n -90.3076171875,\n 29.649868677972304\n ],\n [\n -91.62597656249999,\n 30.14512718337613\n ],\n [\n -94.3505859375,\n 29.916852233070173\n ],\n [\n -96.45996093749999,\n 29.036960648558267\n ],\n [\n -97.470703125,\n 27.800209937418252\n ],\n [\n -97.734375,\n 26.78484736105119\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.767578125,\n 27.01998400798257\n ],\n [\n -96.0205078125,\n 28.34306490482549\n ],\n [\n -94.39453125,\n 29.075375179558346\n ],\n [\n -92.94433593749999,\n 29.34387539941801\n ],\n [\n -92.2412109375,\n 29.075375179558346\n ],\n [\n -90.9228515625,\n 28.806173508854776\n ],\n [\n -89.033203125,\n 28.613459424004414\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -88.9013671875,\n 29.916852233070173\n ],\n [\n -87.4951171875,\n 29.80251790576445\n ],\n [\n -86.17675781249999,\n 29.99300228455108\n ],\n [\n -85.341796875,\n 29.19053283229458\n ],\n [\n -84.0673828125,\n 29.57345707301757\n ],\n [\n -83.4521484375,\n 28.998531814051795\n ],\n [\n -83.3642578125,\n 27.72243591897343\n ],\n [\n -82.4853515625,\n 26.667095801104814\n ],\n [\n -82.177734375,\n 27.527758206861886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Cooperative Fish and Wildlife Research Units
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Salinity (sodium chloride [NaCl]) is a prevalent and persistent contaminant that negatively affects freshwater ecosystems. Although most studies focus on effects of salinity from road salts (primarily NaCl), high-salinity wastewaters from energy extraction (wastewaters) could be more harmful because they contain NaCl and other toxic components. Many amphibians are sensitive to salinity, and their eggs are thought to be the most sensitive life-history stage. However, there are few investigations with salinity that include eggs and larvae sequentially in long-term exposures. We investigated the relative effects of wastewaters from a large energy reserve, the Williston Basin (USA), and NaCl on northern leopard (Rana pipiens) and boreal chorus (Pseudacris maculata) frogs. We exposed eggs and tracked responses through larval stages (for 24 days). Wastewaters and NaCl caused similar reductions in hatching and larval survival, growth, development, and activity, while also increasing deformities. Chorus frog eggs and larvae were more sensitive to salinity than leopard frogs, suggesting species-specific responses. Contrary to previous studies, eggs of both species were less sensitive to salinity than larvae. Our ecologically relevant exposures suggest that accumulating effects can reduce survival relative to starting experiments with unexposed larvae. Alternatively, egg casings of some species may provide some protection against salinity. Notably, effects of wastewaters on amphibians were predominantly due to NaCl rather than other components. Therefore, findings from studies with other sources of increased salinity (e.g., road salts) could guide management of wastewater-contaminated ecosystems, and vice versa, to mitigate effects of salinization.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5193","usgsCitation":"Tornabene, B., Breuner, C., and Hossack, B., 2021, Comparative effects of energy-related saline wastewaters and sodium chloride on hatching, survival, and fitness-associated traits of two amphibian species: Environmental Science & Technology, v. 40, no. 11, p. 3137-3147, https://doi.org/10.1002/etc.5193.","productDescription":"11 p.","startPage":"3137","endPage":"3147","ipdsId":"IP-129251","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":391083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","city":"Dagmar, Moran","otherGeospatial":"Pary Waterfowl Production Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.600830078125,\n 48.29781249243716\n ],\n [\n -104.04739379882812,\n 48.29781249243716\n ],\n [\n -104.04739379882812,\n 48.67101262432597\n ],\n [\n -104.600830078125,\n 48.67101262432597\n ],\n [\n -104.600830078125,\n 48.29781249243716\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58151245117188,\n 43.717519867330765\n ],\n [\n -110.32882690429688,\n 43.717519867330765\n ],\n [\n -110.32882690429688,\n 43.89591323557617\n ],\n [\n -110.58151245117188,\n 43.89591323557617\n ],\n [\n -110.58151245117188,\n 43.717519867330765\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":825937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breuner, Creagh","contributorId":268148,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":825938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":825939,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223350,"text":"70223350 - 2021 - Replacement of the typical artedi form of Coregonus artedi in Lake Huron by endemic shallow-water Ciscoes, including putative hybrids","interactions":[],"lastModifiedDate":"2021-12-10T16:38:40.642918","indexId":"70223350","displayToPublicDate":"2021-08-18T07:55:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Replacement of the typical artedi form of Coregonus artedi in Lake Huron by endemic shallow-water Ciscoes, including putative hybrids","title":"Replacement of the typical artedi form of Coregonus artedi in Lake Huron by endemic shallow-water Ciscoes, including putative hybrids","docAbstract":"Various ecomorphs of shallow-water Cisco Coregonus artedi were the dominant fish planktivores in each of the Great Lakes until invasive species and over fishing resulted in extirpations and extinctions. In this paper we describe the present morphological diversity and distribution of shallow-water Ciscoes in each of Lake Huron’s three basins: the main basin, Georgian Bay, and North Channel. Typical artedi, a formerly widespread ecomorph, which had supported the lake’s largest fishery, appears to have been extirpated from all three basins. Three types of shorthead ciscoes, a recently described and variable ecomorph, were extant. One type was morphologically robust and abundant along the north rim of the lake. The second type was large bodied, terete, short finned, and collected at only one location in the main basin. The third type consisted of putative shorthead cisco × typical artedi hybrids, which were widespread in Georgian Bay and the North Channel. Only the putative hybrids were regularly collected in midwater trawls, suggesting they were more-pelagic, which we attribute to an inferred partial ancestry with typical artedi. The putative shorthead cisco × typical artedi hybrids of Georgian Bay and the North Channel have replaced typical artedi to some degree, while shorthead ciscoes in the main basin, though possibly more abundant now than in the past, have not measurably replaced typical artedi. Even with the apparent extirpation of typical artedi, Lake Huron has a greater diversity of shallow-water Ciscoes than any of the other Great Lakes, which we attribute to its more-complex topography.
Noble gases record fluid interactions in multiphase subsurface environments through fractionation processes during fluid equilibration. Water in the presence of hydrocarbons at the subsurface acquires a distinct elemental signature due to the difference in solubility between these two fluids. We find the atmospheric noble gas signature in produced water is partially preserved after hydrocarbons production and water disposal to unlined ponds at the surface. This signature is distinct from meteoric water and can be used to trace oil-field water seepage into groundwater aquifers. We analyse groundwater (n = 30) and fluid disposal pond (n = 2) samples from areas overlying or adjacent to the Fruitvale, Lost Hills, and South Belridge Oil Fields in the San Joaquin Basin, California, USA. Methane (2.8 × 10−7 to 3 × 10−2 cm3 STP/cm3) was detected in 27 of 30 groundwater samples. Using atmospheric noble gas signatures, the presence of oil-field water was identified in 3 samples, which had equilibrated with thermogenic hydrocarbons in the reservoir. Two (of the three) samples also had a shallow microbial methane component, acquired when produced water was deposited in a disposal pond at the surface. An additional 6 samples contained benzene and toluene, indicative of interaction with oil-field water; however, the noble gas signatures of these samples are not anomalous. Based on low tritium and 14C contents (≤ 0.3 TU and 0.87–6.9 pcm, respectively), the source of oil-field water is likely deep, which could include both anthropogenic and natural processes. Incorporating noble gas analytical techniques into the groundwater monitoring programme allows us to 1) differentiate between thermogenic and microbial hydrocarbon gas sources in instances when methane isotope data are unavailable, 2) identify the carrier phase of oil-field constituents in the aquifer (gas, oil-field water, or a combination), and 3) differentiate between leakage from a surface source (disposal ponds) and from the hydrocarbon reservoir (either along natural or anthropogenic pathways such as faulty wells).
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, collects data pertaining to the surface-water resources of Missouri. Established in 1964, the Ambient Water-Quality Monitoring Network (AWQMN) consisted of 69 sites in 2017. Two additional sites from the National Water-Quality Program are included with the AWQMN sites for the analyses in this report. The sites are sampled typically from 2 to 12 times per year for physical properties, total suspended solids, nutrients, fecal indicator bacteria, and trace elements.
The period of analysis for this study was from 1993 through 2017 and data analysis included 71 sites and 15 water-quality constituents plus discharge. Data analysis involved retrieving the data, conditioning the data for analysis, analyzing the data for trends, and analyzing the monitoring network to determine if potential data gaps or data redundancies exist in the network. Results from these analyses can be used to help manage the monitoring network into the future.
Water-quality data were analyzed using several software packages to provide graphical and statistical information for interpretation of trends in the data at selected sites. Discharge data at selected sites were analyzed to determine the general trends during the analysis period and how the water-quality samples represented the range of daily mean discharges at each site. Water-quality data also were analyzed at selected sites to determine the relative sensitivity of selected sites and constituents to changes in data collection frequency. Trend analysis at selected sites using a simulated reduction in sampling frequency was completed to compare to trends obtained using monthly data to determine the potential degradation in the ability of determining trends from a reduced sampling frequency. The viability of using estimated discharge to evaluate long-term trends for sites with no continuous discharge was investigated. Data from sites were statistically compared in groups to determine the relative similarity (or difference) between sites for each water-quality constituent to identify potentially redundant sites in the monitoring network.
Discharge-weighted long-term trends during 1993 through 2017 were analyzed for 15 water-quality constituents at 58 sites and results indicated there were significant single- or two-period trends in about 17 percent of the analyses. Some trends indicated improvement and some trends indicated deterioration of the general water quality at some sites in the AWQMN. No trend was indicated in about 31 percent of the analyses. The constituents pH, specific conductance, and total phosphorus showed the most frequent significant trends, and each of the 15 constituents examined had a significant trend at one or more sites. A total of 42 sites indicated at least 1 constituent with a significant single- or two-period trend, and 10 sites indicated 6 or more significant trends.
Potential data gaps identified for computing discharge-weighted long-term trends in the monitoring network included the lack of collection of continuous discharge at 23 sites, insufficient sampling frequency for some constituents (dissolved chloride and total and dissolved lead and zinc) at some sites, insufficient temporal sample distribution (lack of at least one sample in each season per year) at some sites, and insufficient sampling frequency for some highly censored constituents (nutrients and total and dissolved lead and zinc) at some sites. Potential data gaps based on site spatial distribution were identified in 7 basins greater than 800 square miles.
Potential site redundancies were identified in 4 basins that had an area greater than 500 square miles with a site density greater than 2 sites per 1,000 square miles. Potential site redundancies also were identified for nine site pairs by observing statistical similarities in the constituent data distributions. Sampling frequency was investigated to determine if reducing the sampling frequency of select constituents could provide a statistically similar data distribution. At 28 of 71 sites, 11 constituents had sufficient data collection frequency (approximately monthly) to allow for the creation of simulated datasets of various reduced data collection frequency. For the selected monitoring network sites analyzed, the data distribution of a simulated sampling frequency of four times per year or greater, roughly evenly distributed over the year, was not significantly different than the data distribution of the original monthly sampling frequency. Sites analyzed using varying simulated sampling frequencies tended to be more sensitive to sampling frequency changes if they were in basins classified as large or very large size and tended to be least sensitive in basins classified as small and medium size in the Ozark Plateaus Province. Simulated reduced frequency sampling analysis indicated that the constituents and measurements most sensitive to changes in sampling frequencies were water temperature, dissolved oxygen, discharge, and dissolved nitrate, and least sensitive were pH, total suspended solids, dissolved phosphorus, and total phosphorus. Discharge-weighted long-term trend analysis was repeated at 22 sites for 11 constituents using a simulated quarterly sampling frequency, and matched about 46 percent of the significant single-period trends identified using monthly data and about 65 percent of the analyses that indicated no trend using the monthly data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215079","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079.","productDescription":"Report: xi, 75 p.; Data Release; Dataset; 11 Tables","numberOfPages":"91","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123674","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Many of the world's rivers are ice-covered during winter months but increasing evidence indicates that the extent of river ice will shift substantially as winters warm. However, our knowledge of rivers during winter lags far behind that of the growing season, limiting our understanding of how ice loss will affect rivers. Physical, chemical, and biological processes change from headwaters to large rivers; thus, we expect ice processes and resulting effects on the ecology of rivers could also vary with river size, as a result of the associated changes in geomorphology, temperature regimes, and connectivity. To conceptualize these relationships, we review typically disparate literature on ice processes and winter ecology and compare what is known in the smallest and largest rivers. In doing so, we show that our ability to link ice with ecology across river networks is made difficult by a primary focus on ice processes in larger rivers and a lack of study of ecosystem processes during winter. To address some of these gaps, we provide new scenarios of river ice loss and analyses of how the annual importance of winter gross primary productivity (GPP) varies with river size. We show projected ice loss varied with large-scale watershed characteristics such as north-south orientation and that the importance of winter to annual GPP was greatest in the smallest rivers. Finally, we highlight information needed to fill knowledge gaps on winter across river networks and improve our understanding of how rivers may change as climate and ice regimes shift.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006275","usgsCitation":"Thellman, A., Jankowski, K.J., Hayden, B., Yang, X., Dolan, W., Smits, A.P., and O’Sullivan, A.M., 2021, The ecology of river ice: JGR Biogeosciences, v. 126, no. 9, e2021JG006275, 28 p., https://doi.org/10.1029/2021JG006275.","productDescription":"e2021JG006275, 28 p.","ipdsId":"IP-126569","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Thellman, Audrey 0000-0003-3716-6664","orcid":"https://orcid.org/0000-0003-3716-6664","contributorId":265349,"corporation":false,"usgs":false,"family":"Thellman","given":"Audrey","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":822601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":822602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayden, Brian","contributorId":190917,"corporation":false,"usgs":false,"family":"Hayden","given":"Brian","email":"","affiliations":[],"preferred":false,"id":822603,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Xiao","contributorId":149701,"corporation":false,"usgs":false,"family":"Yang","given":"Xiao","affiliations":[],"preferred":false,"id":822604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dolan, Wayana 0000-0001-8405-4302","orcid":"https://orcid.org/0000-0001-8405-4302","contributorId":265350,"corporation":false,"usgs":false,"family":"Dolan","given":"Wayana","email":"","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":822605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smits, Adrianne P 0000-0001-9967-5419","orcid":"https://orcid.org/0000-0001-9967-5419","contributorId":217759,"corporation":false,"usgs":false,"family":"Smits","given":"Adrianne","email":"","middleInitial":"P","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":822606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Sullivan, Antoin M 0000-0003-0599-887X","orcid":"https://orcid.org/0000-0003-0599-887X","contributorId":265351,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Antoin","email":"","middleInitial":"M","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":822607,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223329,"text":"70223329 - 2021 - Optimization of a suite of flathead catfish (Pylodictis olivaris) microsatellite markers for understanding the population genetics of introduced populations in the northeast United States","interactions":[],"lastModifiedDate":"2021-08-24T12:03:00.263247","indexId":"70223329","displayToPublicDate":"2021-08-16T17:26:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":958,"text":"BMC Research Notes","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Optimization of a suite of flathead catfish (Pylodictis olivaris) microsatellite markers for understanding the population genetics of introduced populations in the northeast United States","title":"Optimization of a suite of flathead catfish (Pylodictis olivaris) microsatellite markers for understanding the population genetics of introduced populations in the northeast United States","docAbstract":"Flathead catfish are rapidly expanding into nonnative waterways throughout the United States. Once established, flathead catfish may cause disruptions to the local ecosystem through consumption and competition with native fishes, including species of conservation concern. Flathead catfish often become a popular sport fish in their introduced range, and so management strategies must frequently balance the need to protect native and naturalized fauna while meeting the desire to maintain or enhance fisheries. However, there are currently few tools available to inform management of invasive flathead catfish (Pylodictis olivaris). We describe a suite of microsatellite loci that can be used to characterize population structure, predict invasion history, and assess potential mitigation strategies for flathead catfish.
","language":"English","publisher":"Springer","doi":"10.1186/s13104-021-05725-2","usgsCitation":"White, S.L., Eackles, M.S., Wagner, T., Schall, M.K., Smith, G., Avery, J., and Kazyak, D., 2021, Optimization of a suite of flathead catfish (Pylodictis olivaris) microsatellite markers for understanding the population genetics of introduced populations in the northeast United States: BMC Research Notes, 341, 14 p., https://doi.org/10.1186/s13104-021-05725-2.","productDescription":"341, 14 p.","ipdsId":"IP-129433","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-021-05725-2","text":"Publisher Index Page"},{"id":388393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","noUsgsAuthors":false,"publicationDate":"2021-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":821770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schall, Megan K.","contributorId":115964,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":17758,"text":"Pennsylvania State Univ.","active":true,"usgs":false}],"preferred":false,"id":821771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Geoffrey","contributorId":199064,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":821772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Avery, Julian","contributorId":264623,"corporation":false,"usgs":false,"family":"Avery","given":"Julian","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":821773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821774,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228696,"text":"70228696 - 2021 - Arctic Ocean stratification set by sea level and freshwater inputs since the last ice age","interactions":[],"lastModifiedDate":"2022-03-18T15:04:58.07424","indexId":"70228696","displayToPublicDate":"2021-08-16T11:14:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Arctic Ocean stratification set by sea level and freshwater inputs since the last ice age","docAbstract":"Salinity-driven density stratification of the upper Arctic Ocean isolates sea-ice cover and cold, nutrient-poor surface waters from underlying warmer, nutrient-rich waters. Recently, stratification has strengthened in the western Arctic but has weakened in the eastern Arctic; it is unknown if these trends will continue. Here we present foraminifera-bound nitrogen isotopes from Arctic Ocean sediments since 35,000 years ago to reconstruct past changes in nutrient sources and the degree of nutrient consumption in surface waters, the latter reflecting stratification. During the last ice age and early deglaciation, the Arctic was dominated by Atlantic-sourced nitrate and incomplete nitrate consumption, indicating weaker stratification. Starting at 11,000 years ago in the western Arctic, there is a clear isotopic signal of Pacific-sourced nitrate and complete nitrate consumption associated with the flooding of the Bering Strait. These changes reveal that the strong stratification of the western Arctic relies on low-salinity inflow through the Bering Strait. In the central Arctic, nitrate consumption was complete during the early Holocene, then declined after 5,000 years ago as summer insolation decreased. This sequence suggests that precipitation and riverine freshwater fluxes control the stratification of the central Arctic Ocean. Based on these findings, ongoing warming will cause strong stratification to expand into the central Arctic, slowing the nutrient supply to surface waters and thus limiting future phytoplankton productivity.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41561-021-00789-y","usgsCitation":"Farmer, J.R., Sigman, D., Granger, J., Underwood, O.M., Frapiat, F., Cronin, T.M., Martinez-Garcia, A., and Haug, G.H., 2021, Arctic Ocean stratification set by sea level and freshwater inputs since the last ice age: Nature Geoscience, v. 14, p. 684-689, https://doi.org/10.1038/s41561-021-00789-y.","productDescription":"6 p.","startPage":"684","endPage":"689","ipdsId":"IP-118860","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":451157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41561-021-00789-y","text":"Publisher Index Page"},{"id":396117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic Ocean","volume":"14","noUsgsAuthors":false,"publicationDate":"2021-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Farmer, Jesse R.","contributorId":279531,"corporation":false,"usgs":false,"family":"Farmer","given":"Jesse","email":"","middleInitial":"R.","affiliations":[{"id":57270,"text":"1Department of Geosciences, Princeton University","active":true,"usgs":false}],"preferred":false,"id":835099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sigman, Daniel","contributorId":279532,"corporation":false,"usgs":false,"family":"Sigman","given":"Daniel","email":"","affiliations":[{"id":57270,"text":"1Department of Geosciences, Princeton University","active":true,"usgs":false}],"preferred":false,"id":835100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Granger, Julie","contributorId":279533,"corporation":false,"usgs":false,"family":"Granger","given":"Julie","affiliations":[{"id":57272,"text":"3Department of Marine Sciences, University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":835101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Underwood, Ona M.","contributorId":279660,"corporation":false,"usgs":false,"family":"Underwood","given":"Ona","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":835307,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frapiat, Francois","contributorId":279534,"corporation":false,"usgs":false,"family":"Frapiat","given":"Francois","email":"","affiliations":[{"id":57273,"text":"2Max-Planck Institute for Chemistry","active":true,"usgs":false}],"preferred":false,"id":835102,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":835103,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Martinez-Garcia, Alfredo","contributorId":279535,"corporation":false,"usgs":false,"family":"Martinez-Garcia","given":"Alfredo","email":"","affiliations":[{"id":57273,"text":"2Max-Planck Institute for Chemistry","active":true,"usgs":false}],"preferred":false,"id":835104,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haug, Gerald H.","contributorId":279536,"corporation":false,"usgs":false,"family":"Haug","given":"Gerald","email":"","middleInitial":"H.","affiliations":[{"id":57274,"text":"Max-Planck Institute for Chemistry","active":true,"usgs":false}],"preferred":false,"id":835105,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225600,"text":"70225600 - 2021 - Climate change effects on North American fish and fisheries to inform adaptation strategies","interactions":[],"lastModifiedDate":"2021-10-27T12:25:27.099518","indexId":"70225600","displayToPublicDate":"2021-08-16T07:22:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Climate change effects on North American fish and fisheries to inform adaptation strategies","docAbstract":"Climate change is a global persistent threat to fish and fish habitats throughout North America. Climate-induced modification of environmental regimes, including changes in streamflow, water temperature, salinity, storm surges, and habitat connectivity can change fish physiology, disrupt spawning cues, cause fish extinctions and invasions, and alter fish community structure. Reducing greenhouse emissions remains the primary mechanism to slow the pace of climate change, but local and regional management agencies and stakeholders have developed an arsenal of adaptation strategies to help partially mitigate the effects of climate change on fish. We summarize common stressors posed by climate change in North America, including (1) increased water temperature, (2) changes in precipitation, (3) sea level rise, and (4) ocean acidification, and present potential adaptation strategies that fishery professionals may apply to help vulnerable fish and fisheries cope with a changing climate. Although our adaptation strategies are primarily from North America, they have broader geographic applicability to fish and aquatic biota in other jurisdictions. These strategies provide opportunities for managers to mitigate the effects of climate change on fish and fish habitat while needed global policies to reduce greenhouse gas emissions emerge, which may offer more lasting solutions.
Previous studies have detected numerous organic contaminants and in vitro bioactivities in surface water from the South Platte River near Denver, Colorado, USA. To evaluate the temporal and spatial distribution of selected contaminants of emerging concern, water samples were collected throughout 2018 and 2019 at 11 sites within the S. Platte River and surrounding tributaries with varying proximities to a major wastewater treatment plant (WWTP). Water samples were analyzed for pharmaceuticals, pesticides, steroid hormones, and wastewater indicators and screened for in vitro biological activities. Multiplexed, in vitro assays that simultaneously screen for agonistic activity against 24 human nuclear receptors detected estrogen receptor (ER), peroxisome proliferator activated receptor-gamma (PPARγ), and glucocorticoid receptor (GR) bioactivities in water samples near the WWTP outflow. Targeted in vitro bioassays assessing ER, GR, and PPARγ agonism corroborated bioactivities for ER (up to 55 ± 9.7 ng/L 17β-estradiol equivalents) and GR (up to 156 ± 28 ng/L dexamethasone equivalents), while PPARγ activity was not confirmed. To evaluate the potential in vivo significance of the bioactive contaminants, sexually-mature fathead minnows were caged at six locations upstream and downstream of the WWTP for 5 days after which targeted gene expression analyses were performed. Significant up-regulation of male hepatic vitellogenin was observed at sites with corresponding in vitro ER activity. No site-related differences in GR-related transcript abundance were detected in female adipose or male livers, suggesting observed environmental concentrations of GR-active contaminants do not induce a detectable in vivo response. In line with the lack of detectable targeted in vitro PPARɣ activity, there were no significant effects on PPARɣ-related gene expression. Although the chemicals responsible for GR and PPAR-mediated bioactivities are unknown, results from the present study provide insights into the significance (or lack thereof) of these bioactivities relative to short-term in situ fish exposures.
The small and remote Easter Island (Rapa Nui) has a complex and still partially unknown history of human colonization and interactions with the environment. Previous research from sedimentary archives collected in the three freshwater bodies of Rapa Nui document dramatic environmental changes over the last two millennia. Yet, the characteristics of sediments and paleoenvironmental records are challenging to interpret, mainly due to poor temporal resolution, hiatuses and sediment mixing.
In this study, we reconstruct past changes in lithogenic inputs, weathering processes, redox conditions, productivity and water levels in the Rano Aroi wetland over the last 2000 years through the determination of major, trace and rare earth elements in a new peat core collected in 2017. The chronology is based on 8 14C AMS dates for the upper 1.5 m and provides decadal to multi-decadal resolution which is unprecedented for the island of Rapa Nui. The multielemental proxies depict seven distinct chronological phases marked by well-defined geochemical transitions. With only a few minor fluctuations, climate conditions were dry and the mire was mildly anoxic during the first millennium (0–1000 CE) to the arrival of the first Polynesians in Rapa Nui (800–1300 CE) and until ∼1400 CE, followed by wetter conditions afterwards. The record documents with unprecedented accuracy and resolution intense droughts occurring during the middle Little Ice Age between 1520 and 1710 CE, which may have been exacerbated by human activities and triggered dramatic cultural shifts. During the interval of first contact between the Rapanuis and Europeans, the climate changed to wetter conditions, followed by intense precipitations between 1790 and 1900 CE.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2021.107115","usgsCitation":"Roman, M., McWethy, D.B., Kehrwald, N., Osayuki Erhenhi, E., Myrbo, A.E., Ramirez Aliaga, J., Pauchard, A., Turetta, C., Barbante, C., Prebble, M., Argiriadis, E., and Battistel, D., 2021, A multi-decadal geochemical record from Rano Aroi (Easter Island/Rapa Nui): Implications for the environment, climate and humans during the last two millennia: Quaternary Science Reviews, v. 268, 107115, 19 p., https://doi.org/10.1016/j.quascirev.2021.107115.","productDescription":"107115, 19 p.","ipdsId":"IP-121878","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":387985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Rano Aroi","volume":"268","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roman, Marco","contributorId":202818,"corporation":false,"usgs":false,"family":"Roman","given":"Marco","email":"","affiliations":[{"id":36530,"text":"ECSIN -- European Center for the Sustainable Impact of Nanotechnology","active":true,"usgs":false}],"preferred":false,"id":821288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McWethy, David B.","contributorId":207232,"corporation":false,"usgs":false,"family":"McWethy","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":821289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kehrwald, Natalie 0000-0002-9160-2239","orcid":"https://orcid.org/0000-0002-9160-2239","contributorId":220636,"corporation":false,"usgs":true,"family":"Kehrwald","given":"Natalie","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":821290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osayuki Erhenhi, Evans","contributorId":264288,"corporation":false,"usgs":false,"family":"Osayuki Erhenhi","given":"Evans","email":"","affiliations":[{"id":37183,"text":"Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Italy","active":true,"usgs":false}],"preferred":false,"id":821291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Myrbo, Amy E.","contributorId":264289,"corporation":false,"usgs":false,"family":"Myrbo","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":54425,"text":"St. Croix Watershed Research Station, Science Museum of Minnesota, USA","active":true,"usgs":false}],"preferred":false,"id":821292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ramirez Aliaga, José M.","contributorId":264290,"corporation":false,"usgs":false,"family":"Ramirez Aliaga","given":"José M.","affiliations":[{"id":54426,"text":"Grupo Interdisciplinario de Investigacion Avanzada, Universidad de Playa Ancha, Viña Del Mar, Chile","active":true,"usgs":false}],"preferred":false,"id":821293,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pauchard, Anibal","contributorId":264291,"corporation":false,"usgs":false,"family":"Pauchard","given":"Anibal","affiliations":[{"id":54427,"text":"Institute of Ecology and Biodiversity, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":821294,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Turetta, Clara","contributorId":264292,"corporation":false,"usgs":false,"family":"Turetta","given":"Clara","email":"","affiliations":[{"id":54428,"text":"Institute of Polar Science – National Research Council ISP-CNR , Italy","active":true,"usgs":false}],"preferred":false,"id":821295,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barbante, Carlo","contributorId":202632,"corporation":false,"usgs":false,"family":"Barbante","given":"Carlo","email":"","affiliations":[{"id":36503,"text":"Department of Environmental Sciences, Infomatics, and Statistics, Ca'Foscari University of Venice, Via Torino 155, 30172 Mestre (VE), Italy","active":true,"usgs":false}],"preferred":false,"id":821296,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Prebble, Matthew","contributorId":213179,"corporation":false,"usgs":false,"family":"Prebble","given":"Matthew","email":"","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":821297,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Argiriadis, Elena","contributorId":207231,"corporation":false,"usgs":false,"family":"Argiriadis","given":"Elena","affiliations":[{"id":37489,"text":"University of Venice, Ca' Foscari","active":true,"usgs":false}],"preferred":false,"id":821298,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Battistel, Dario","contributorId":205865,"corporation":false,"usgs":false,"family":"Battistel","given":"Dario","email":"","affiliations":[{"id":37181,"text":"Department of Environmental Science, Informatics and Statistics, Ca' Foscari University of Venice, Italy","active":true,"usgs":false}],"preferred":false,"id":821299,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70227641,"text":"70227641 - 2021 - Characterization of water use and water balance for the croplands of Kansas using satellite, climate, and irrigation data","interactions":[],"lastModifiedDate":"2022-01-24T15:02:28.871949","indexId":"70227641","displayToPublicDate":"2021-08-13T08:59:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of water use and water balance for the croplands of Kansas using satellite, climate, and irrigation data","docAbstract":"Kansas is one of the most productive agricultural states in the United States, where agricultural irrigation is a primary user of underground and surface water. Because of low precipitation and declining groundwater levels in western and central Kansas, sustainable management of irrigation water resources is a critical issue in the agricultural productivity of the state. The objective of this study is to analyze and characterize the water use and water balance in the croplands of Kansas using satellite observations, meteorological data, and in situ irrigation water use records. We used actual evapotranspiration (ETa), precipitation, soil moisture, and irrigation water use to calculate water balance for Kansas in 2015 at scales of counties, climatic divisions, and groundwater management districts (GMD). The Operational Simplified Surface Energy Balance model was implemented to estimate 30-m resolution ETa. Results showed that the seasonal (May – September) precipitation, soil water storage change, and ETa are 528 mm, 80 mm, and 555 mm, respectively, on average of all croplands in the state. The annual net irrigation water consumption was 293 mm for irrigated croplands, indicating that irrigation water constitutes an substantial portion of the water supply in the state. The total volumetric irrigation water use was 3.24 km3 for all croplands within five GMDs in western and south-central Kansas, while only 0.38 km3 was outside of GMDs. The multiple regression models of ETa against precipitation and irrigation water use were statistically significant with R2 values of 0.71 and 0.87, respectively, at county and climate division scales. Regression models also indicated a higher rate of ETa response to irrigation water use than that to precipitation. Our study demonstrated the spatial patterns of crop water use and water balance in Kansas, which could provide useful information for management of irrigation agriculture and water resources for the state.
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\"}}]}","volume":"256","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":272078,"corporation":false,"usgs":false,"family":"Ji","given":"Lei","affiliations":[{"id":56342,"text":"ASRC Federal Data Solutions, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":831480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":831481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":199093,"corporation":false,"usgs":false,"family":"Friedrichs","given":"MacKenzie","affiliations":[],"preferred":false,"id":831482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":181608,"corporation":false,"usgs":false,"family":"Schauer","given":"Matthew","affiliations":[],"preferred":false,"id":831483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boiko, Olena 0000-0002-2007-7852","orcid":"https://orcid.org/0000-0002-2007-7852","contributorId":272079,"corporation":false,"usgs":false,"family":"Boiko","given":"Olena","email":"","affiliations":[{"id":56343,"text":"KBR, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":831484,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228894,"text":"70228894 - 2021 - Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway","interactions":[],"lastModifiedDate":"2022-02-23T13:28:46.986794","indexId":"70228894","displayToPublicDate":"2021-08-13T07:21:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway","docAbstract":"On the wintering grounds, wetland selection by waterfowl is influenced by spatiotemporal resource distribution. The ring-necked duck (Aythya collaris) winters in the southeastern United States where a disproportionate amount of Atlantic Flyway ring-necked duck harvest occurs. We quantified female ring-necked duck selection for wetland characteristics during and after the 2017–2018 and 2018–2019 waterfowl hunting seasons using discrete choice modeling under a Bayesian framework. Relative probability of selection was primarily influenced by characteristics at the local wetland scale. Relative probability of selection was higher for flooded agriculture and vegetated wetlands than open water and was positively influenced by wetland area during the winter. After the hunting season, the relative probability of selection decreased for flooded agriculture but increased for vegetated wetlands, and the effect of wetland area decreased in magnitude. We attribute changes in selection during and after the hunting season to dietary shifts related to migratory preparation, resource depletion, and reproductive pairing. Understanding the wetland characteristics that wintering waterfowl select, and the spatial scale at which selection occurs, is important for informing effective wetland management and waterfowl harvest practices.
Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams.
Visual encounter distance models are important tools for predicting how light and water clarity mediate visual predator-prey interactions that affect the structure and function of aquatic ecosystems at multiple spatial, temporal, and organizational scales. The two main varieties of visual encounter distance models, mechanistic and empirical, are used for similar purposes but take fundamentally different approaches to model development and have different strengths and weaknesses in terms of predictive accuracy, physical and biological interpretability of parameters, ability to incorporate outside information, and utility for knowledge transfer. To overcome weaknesses of existing mechanistic and empirical models and bridge the gap between approaches, we developed a generalized visual reaction distance model that relaxes assumptions of a widely-used mechanistic model that are violated in real predator-prey interactions. We compared the performance of the generalized visual reaction distance model to a widely used mechanistic model and an empirical visual encounter distance model by fitting models to data from four predator-prey experiments. The generalized visual reaction distance model substantially outperformed the other models in all cases based on fit to reaction distance data and presents an attractive alternative to prior models based on comparatively high predictive accuracy, use of interpretable parameters, and ability to incorporate outside information—characteristics that facilitate knowledge transfer.
Residual pit lakes from mining are often dangerous to sample for water quality. Thus, pit lakes may be rarely (or never) sampled. This study developed new technology in which water-sampling devices, mounted on an unmanned aerial vehicle (UAV), were used to sample three pit lakes in Nevada, USA, during 1 week in 2017. Water-quality datasets from two of the three pit lakes on public lands, Dexter and Clipper, are presented here. The current conditions of the Dexter pit lake were assessed by examining cation and anion concentration changes that have occurred over a 17-year period since the pit lake was last sampled in 2000. Data gathered during this sampling campaign assessed 2017 conditions of the Dexter and Clipper pit lakes by comparing constituent concentrations to the Nevada Division of Environmental Protection (NDEP) pit lake water-quality requirements, indicating that selenium concentrations exceeded regulatory standards. We compared our sampling data for Dexter lake to prior water-quality data from the Dexter pit lake collected in 1999 and 2000. This comparison for the Dexter pit lake indicates that evapoconcentration may have caused increasing cation and anion concentrations. This UAV sampling approach can potentially incorporate the use of additional multiparameter probes: pH, oxygen concentration, turbidity, or chlorophyll. Some limitations of this UAV water-sampling methodology are battery duration, weather conditions, and payload capacity.
The US Environmental Protection Agency's short-term freshwater effluent test methods include a fish (Pimephales promelas), a cladoceran (Ceriodaphnia dubia), and a green alga (Raphidocelis subcapitata). There is a recognized need for additional taxa to accompany the three standard species for effluent testing. An appropriate additional taxon is unionid mussels because mussels are widely distributed, live burrowed in sediment and filter particles from the water column for food, and exhibit high sensitivity to a variety of contaminants. Multiple studies were conducted to develop a relevant and robust short-term test method for mussels. We first evaluated the comparative sensitivity of two mussel species (Villosa constricta and Lampsilis siliquoidea) and two standard species (P. promelas and C. dubia) using two mock effluents prepared by mixing ammonia and five metals (cadmium, copper, nickel, lead, and zinc) or a field-collected effluent in 7-day exposures. Both mussel species were equally or more sensitive (more than two-fold) to effluents compared with the standard species. Next, we refined the mussel test method by first determining the best feeding rate of a commercial algal mixture for three age groups (1, 2, and 3 weeks old) of L. siliquoidea in a 7-day feeding experiment, and then used the derived optimal feeding rates to assess the sensitivity of the three ages of juveniles in a 7-day reference toxicant (sodium chloride [NaCl]) test. Juvenile mussels grew substantially (30%–52% length increase) when the 1- or 2-week-old mussels were fed 2 ml twice daily and the 3-week-old mussels were fed 3 ml twice daily. The 25% inhibition concentrations (IC25s) for NaCl were similar (314–520 mg Cl/L) among the three age groups, indicating that an age range of 1- to 3-week-old mussels can be used for a 7-day test. Finally, using the refined test method, we conducted an interlaboratory study among 13 laboratories to evaluate the performance of a 7-day NaCl test with L. siliquoidea. Eleven laboratories successfully completed the test, with more than 80% control survival and reliable growth data. The IC25s ranged from 296 to 1076 mg Cl/L, with a low (34%) coefficient of variation, indicating that the proposed method for L. siliquoidea has acceptable precision. Environ Toxicol Chem 2021;40:3392–3409. © 2021 SETAC