Life history theory provides a framework to understand environmental change based on species strategies for survival and reproduction under stable, cyclical, or stochastic environmental conditions. We evaluated environmental predictors of fish life history strategies in 20 streams intersecting a national park within the Potomac River basin in eastern North America. We sampled stream sites during 2018–2019 and collected 3801 individuals representing 51 species within 10 taxonomic families. We quantified life history strategies for species from their coordinates in an ordination space defined by trade-offs in spawning season duration, fecundity, and parental care characteristic of opportunistic, periodic, and equilibrium strategies. Our analysis revealed important environmental predictors: Abundance of opportunistic strategists increased with low-permeability soils that produce flashy runoff dynamics and decreased with karst terrain (carbonate bedrock) where groundwater inputs stabilize stream flow and temperature. Conversely, abundance of equilibrium strategists increased in karst terrain indicating a response to more stable environmental conditions. Our study indicated that fish community responses to groundwater and runoff processes may be explained by species traits for survival and reproduction. Our findings also suggest the utility of life history theory for understanding ecological responses to destabilized environmental conditions under global climate change.
The United States Department of Energy, the MH21-S Research Consortium of Japan, and the United States Geological Survey are collaborating to enable gas hydrate scientific drilling and extended-duration reservoir response testing on the Alaska North Slope. To feasibly execute such a test, a location is required that is accessible from existing roads and gravel pads and that can be occupied without disrupting ongoing industry operations. A review of potential locations meeting these criteria determined the likely occurrence of gas hydrate in two fine-grained marginal-marine sands of Tertiary age in the vicinity of the inactive “Kuparuk State 7-11-12” exploration pad in the western Prudhoe Bay Unit (PBU). Existing well and seismic data for that site were insufficient to preclude the potential for free gas occurrence within the deeper (and most prospective) target sand. Therefore, with support from the PBU Working Interest Owners, Alaska Department of Natural Resources, and Petrotechnical Resources Alaska, the Hydrate-01 Stratigraphic Test Well (STW) was drilled in December 2018 to confirm the suitability of the site for future gas hydrate scientific testing. The Hydrate-01 well was successfully drilled to −3290 ft (1003 m) subsea vertical depth at a bottom hole location of approximately 900 ft (∼275 m) east of the surface location. The drilling program featured acquisition of a full suite of logging while drilling data, the collection of side-wall pressure cores, and the installation of distributed temperature and distributed acoustic sensor fiber-optic cables. The log data acquired confirmed the occurrence of gas hydrate at high saturation in two target sands. Integrated evaluation of log and sidewall core data provide petrophysical and geomechanical property information that allow for potential reservoir response to depressurization to be simulated. The deeper “B1 sand” is deemed to be most favorable for reservoir response testing as a result of confirmed gas hydrate occurrence in sediments of high intrinsic permeability, location within 100 ft (30 m) of the base of gas hydrate stability, and minimal risk for direct communication with permeable water-bearing (hydrate-free) zones. The shallower “D1 sand” provides a secondary target that is differentiated by colder in situ temperatures and the interpreted direct hydraulic communication to a lower section of non-hydrate-bearing, water-saturated sand. The Hydrate-01 log data also confirm the occurrence of at least one sub-seismic fault in close proximity to the B1 sand reservoir. To better image the distribution of the gas-hydrate-bearing reservoir sections and associated faults, a three-dimensional (3D) vertical seismic profile was conducted in early 2019 using the distributed acoustic sensors installed as part of the Hydrate-01 STW completion. Detailed two-dimensional (2D) and 3D geologic models have been constructed to enable numerical simulations to inform the planning for potential future scientific tests of reservoir response to depressurization at the site.
Barkley Canyon is one of the few known sites worldwide with the occurrence of thermogenic gas seepage and formation of structure-II and structure-H gas hydrate mounds on the seafloor. This site is the location of continuous seafloor monitoring as part of the Ocean Networks Canada (ONC) cabled observatory off the west coast off Vancouver Island, British Columbia, Canada. We combine repeat remotely operated vehicle (ROV) seafloor video observations, mapping with an autonomous underwater vehicle (AUV), ship-, ROV-, and AUV-based identification of gas flares, as well as seismic and Chirp data to investigate the distribution of fluid migration pathways. Geologically, the site with the prominent gas hydrate mounds and associated fluid seepage is covering an area of ∼0.15 km2 and is situated on a remnant of a rotated fault block that had slipped off the steep flanks of the north-east facing canyon wall. The gas hydrate mounds, nearly constant in dimension over the entire observation period, are associated with gas and oil seepage and surrounded by debris of chemosynthetic communities and authigenic carbonate. The formation of gas hydrate at and near the seafloor requires additional accommodation space created by forming blisters at the seafloor that displace the regular sediments. An additional zone located centrally on the rotated fault block with more diffuse seepage (∼0.02 km2 in extent) has been identified with no visible mounds, but with bacterial mats, small carbonate concretions, and clam beds. Gas venting is seen acoustically in the water column up to a depth of ∼300 m. However, acoustic water-column imaging during coring and ROV dives showed rising gas bubbles to much shallower depth, even <50 m, likely a result of degassing of rising oil droplets, which themselves cannot be seen acoustically. Combining all observations, the location of the gas hydrate mounds is controlled by a combination of fault-focused fluid migration from a deeper reservoir and fluid seepage along more permeable strata within the rotated slope block. Fluids must be provided continuously to allow the sustained presence of the gas hydrate mounds at the seafloor.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.852853","usgsCitation":"Reidel, M., Scherwath, M., Romer, M., Paull, C., Lundsten, E., Caress, D.W., Brewer, P., Pohlman, J., Lapham, L.L., Chapman, N., Whiticar, M., Spence, G.D., Enkin, R., and Douglas, K., 2022, Barkley Canyon gas hydrates: A synthesis based on two decades of seafloor observation and remote sensing: Frontiers in Earth Science, v. 10, 852853, 25 p., https://doi.org/10.3389/feart.2022.852853.","productDescription":"852853, 25 p.","ipdsId":"IP-137853","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.852853","text":"Publisher Index Page"},{"id":399810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Barkley Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.6667,\n 48\n ],\n [\n -125.8,\n 48\n ],\n [\n -125.8,\n 48.5\n ],\n [\n -126.6667,\n 48.5\n ],\n [\n -126.6667,\n 48\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Reidel, M.","contributorId":290694,"corporation":false,"usgs":false,"family":"Reidel","given":"M.","email":"","affiliations":[{"id":62473,"text":"GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":841589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scherwath, M.","contributorId":290695,"corporation":false,"usgs":false,"family":"Scherwath","given":"M.","affiliations":[{"id":62475,"text":"Ocean Networks Canada, University of Victoria, Victoria, BC, Canada","active":true,"usgs":false}],"preferred":false,"id":841590,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romer, M.","contributorId":290696,"corporation":false,"usgs":false,"family":"Romer","given":"M.","email":"","affiliations":[{"id":62476,"text":"MARUM - Center for Environmental Sciences and Department of Geosciences at the University of Bremen, Bremen, Germany","active":true,"usgs":false}],"preferred":false,"id":841591,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paull, C. 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Half of Washington is forested, and its orchards grow more than one-half of the apples sold in the United States. Rivers are important to the State, particularly the Columbia River, the largest North American river that flows into the Pacific Ocean. Dozens of dams have been constructed in the river basin, including the largest hydroelectric producer in the Nation, the Grand Coulee Dam.
Washington’s diverse landscapes also change in various ways over time. Some are relatively steady, like urban expansion in the populous Puget Sound region. Others can be sudden, like the Mount Saint Helens eruption in 1980—one of the largest volcanic eruptions that caused the largest landslide in U.S. history. Although landscape change may be hard to detect at any one time, the Landsat program provides an objective view of it over decades. Landsat not only allows one to see what Mount Saint Helens looked like before and after the eruption, but also how the area has evolved since.
Here are a few examples of how Landsat benefits Washington.
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\"}}]}","edition":"Version 1.0: April 25, 2022; Version 1.1: January 24, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Eastern oysters (Crassostrea virginica) are a critical ecological and commercial resource in the northern Gulf of Mexico facing changing environmental conditions from river management and climate change. In Louisiana, USA, development of restored reefs, and off-bottom aquaculture would benefit from the identification of locations supportive of sustainable oyster populations (i.e., metapopulations) and high consistent production. This study defines four oyster resource zones across coastal Louisiana based on environmental conditions known to affect oyster survival, growth, and reproduction. Daily data from 2015 to 2019 were interpolated to generate salinity and temperature profiles across Louisiana's estuaries, which were then used to classify zones based on monthly and annual salinity mean and variance. Zones were classified as supportive of (1) broodstock sanctuary reefs (i.e., support reproductive populations), (2) productive reefs during dry (salty) years, (3) productive reefs during wet (fresh) years, and (4) off-bottom aquaculture development. Of the 38,000 km2 investigated, over 11,000 km2 of potential oyster zone area was identified across the Louisiana coast. The Broodstock Sanctuary Zone was the smallest (∼540 km2), as salinity variance limited this zone in many areas, as it is driven largely by riverine inputs across many estuaries. Located up-estuary (Dry Restoration Zone) and down-estuary (Wet Restoration Zone) of the Broodstock Sanctuary Zone, Dry and Wet Restoration Zone areas covered ∼2400 km2 and ∼3900 km2, respectively. Mapped reefs in Louisiana currently exist largely within the Dry Restoration zones, suggesting a potential strategy to focus reef development in Wet Restoration zones to ensure reef network sustainability through years with high precipitation and river inflow. The off-bottom Aquaculture Zone was the largest (∼6400 km2) zone identified, with much of this area located more down-estuary and off-shore. Accounting for variable water quality conditions enables the development of a network of reefs resilient to environmental variability, and more stable areas for consistent off-bottom aquaculture production. Spatial planning and identification of oyster resource zones reduces focus on individual reef success and supports management of oyster metapopulation outcomes, while identifying zones supportive of off-bottom aquaculture.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ocecoaman.2022.106178","usgsCitation":"Swam, L.M., Couvillion, B., Callam, B., La Peyre, J., and La Peyre, M., 2022, Defining oyster resource zones across coastal Louisiana for restoration and aquaculture: Ocean and Coastal Management, v. 225, 106178, 11 p., https://doi.org/10.1016/j.ocecoaman.2022.106178.","productDescription":"106178, 11 p.","ipdsId":"IP-134836","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499824,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/animalsciences_pubs/2261","text":"External Repository"},{"id":433380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.28531382800476,\n 30.084673161811594\n ],\n [\n -89.8627113518053,\n 30.384080583724042\n ],\n [\n -90.30515265860349,\n 30.512183704575193\n ],\n [\n -90.81872980801528,\n 30.23163703531037\n ],\n [\n -91.21166619938886,\n 30.068757329434987\n ],\n [\n -93.742248457641,\n 30.376322644227812\n ],\n [\n -93.94164092204788,\n 29.613593061579024\n ],\n [\n -92.27979250199853,\n 29.44209715796825\n ],\n [\n -91.10243386209395,\n 29.085644613779976\n ],\n [\n -89.98832868271369,\n 28.980943971169282\n ],\n [\n -88.89450433276538,\n 28.983731357632564\n ],\n [\n -89.28531382800476,\n 30.084673161811594\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"225","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swam, Lauren M.","contributorId":341585,"corporation":false,"usgs":false,"family":"Swam","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Couvillion, Brady 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":222810,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":908656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Callam, Brian","contributorId":341586,"corporation":false,"usgs":false,"family":"Callam","given":"Brian","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Jerome F.","contributorId":341587,"corporation":false,"usgs":false,"family":"La Peyre","given":"Jerome F.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908655,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262285,"text":"70262285 - 2022 - Sandhill crane colt survival in Minnesota","interactions":[],"lastModifiedDate":"2025-01-22T15:23:44.846418","indexId":"70262285","displayToPublicDate":"2022-04-22T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Sandhill crane colt survival in Minnesota","docAbstract":"Age-structured population models require reliable estimates of cohort-specific survival rates, yet vital rates of younger age classes are often difficult to estimate because of the logistical challenges of monitoring young animals. As part of a study of sandhill cranes Antigone canadensis in the zone of contact between breeding distributions of the Eastern Population and Midcontinent Population in Minnesota, we monitored first summer survival of 34 sandhill cranes (hereafter colts) by using very-high-frequency and global positioning system–global system for mobile communications transmitters. We estimated daily survival probabilities from 19 to 120 d posthatch by using a generalized linear model accounting for interval censoring, resulting in an estimated period survival rate of 0.52 (90% CI, 0.36–0.71) over summer (100 d). Estimated daily probabilities of survival increased as colts became older and fledged (at 70–75 d posthatch), when they presumably became less vulnerable to predation. Causes of mortality were mostly unknown aside from one case of a collision with a vehicle. There is a scarcity of published colt survival rate estimates for sandhill cranes, and what is available varies widely by study site. Region-specific sandhill crane colt survival rate estimates can inform future management efforts and inform population dynamics research and overall natural history knowledge of sandhill cranes.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-21-097","usgsCitation":"Severud, W., Wolfson, D., Fieberg, J., and Andersen, D.E., 2022, Sandhill crane colt survival in Minnesota: Journal of Fish and Wildlife Management, v. 13, no. 2, p. 494-501, https://doi.org/10.3996/jfwm-21-097.","productDescription":"8 p.","startPage":"494","endPage":"501","ipdsId":"IP-135672","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481088,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-097","text":"Publisher Index Page"},{"id":480919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Aitkin County, Becker County, Cass County, Clearwater County, Mahnomen County, Todd 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William J.","contributorId":348747,"corporation":false,"usgs":false,"family":"Severud","given":"William J.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfson, David","contributorId":348748,"corporation":false,"usgs":false,"family":"Wolfson","given":"David","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fieberg, John","contributorId":348749,"corporation":false,"usgs":false,"family":"Fieberg","given":"John","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923738,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230620,"text":"70230620 - 2022 - Pre-breeding foraging ecology of three tern species nesting in the Gulf of Maine","interactions":[],"lastModifiedDate":"2022-04-19T14:56:56.175554","indexId":"70230620","displayToPublicDate":"2022-04-19T09:24:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Pre-breeding foraging ecology of three tern species nesting in the Gulf of Maine","docAbstract":"A variety of seabird species migrate annually from wintering grounds in the Southern Hemisphere to the Gulf of Maine, USA to breed and raise their young. Post-migration, adult seabirds depend on the spatio-temporal match of reliable food resources to replenish energy reserves before breeding. However, the conditions during this critical window of time are becoming increasingly uncertain given the magnitude and pace at which climate change is impacting the Gulf of Maine region. We investigated the pre-breeding foraging ecology of Arctic Terns (Sterna paradisaea), Common Terns (S. hirundo), and the federally endangered Roseate Tern (S. dougallii) by analyzing stable carbon (δ13C) and nitrogen (δ15N) isotopes in eggshell tissues collected from seven islands in the Gulf of Maine from 2016 to 2018. Results show at the interspecific level, adult foraging patterns are consistent with expectations based on chick diets. At interisland and interannual scales, variation in isotopic values and niche breadths suggest foraging habits are highly localized. Although uncertainty remains, interannual trends also suggest warmer ocean conditions are either affecting tern foraging behaviors and/or prey resource availability during the late spring and early summer. Overall, results provide new information on adult tern foraging ecology in an important breeding area experiencing rapid environmental change.
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,{"id":70228749,"text":"sir20215144 - 2022 - Surface-water-quality data to support implementation of revised freshwater aluminum water-quality criteria in Massachusetts, 2018–19","interactions":[],"lastModifiedDate":"2026-02-23T18:31:13.191429","indexId":"sir20215144","displayToPublicDate":"2022-04-12T13:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5144","displayTitle":"Surface-Water-Quality Data to Support Implementation of Revised Freshwater Aluminum Water-Quality Criteria in Massachusetts, 2018–19","title":"Surface-water-quality data to support implementation of revised freshwater aluminum water-quality criteria in Massachusetts, 2018–19","docAbstract":"The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, performed a study to inform the development of the department’s guidelines for the collection and use of water-chemistry data to support calculation of site-dependent aluminum criteria values. The U.S. Geological Survey collected and analyzed discrete water-quality samples at four wastewater-treatment facilities and seven water-treatment facilities in eastern and central Massachusetts from April 2018 through May 2019.
For each of the 11 facilities considered, water-quality samples were collected from treatment-plant effluent and receiving-water bodies. Samples were collected for laboratory analysis of major ions (calcium and magnesium ions are used to calculate total hardness), dissolved organic carbon (DOC), total organic carbon (TOC), and total recoverable aluminum. Field parameters for pH, temperature, and specific conductance were measured in situ concurrently with sample collection.
Water-quality conditions differed among monitoring stations. The highest pH values were observed for stations on the Assabet River that receive effluent discharges from wastewater-treatment facilities (the Westborough, Marlborough, Hudson, and Maynard wastewater-treatment facilities). High DOC concentrations (greater than 10 mg/L) were measured in water bodies associated with large areas of riparian wetlands—Lily Pond (Cohasset) and Third Herring Brook (Hanover), and low DOC concentrations (less than 2.5 mg/L) were measured at three water bodies in central Massachusetts—Hocomonco Pond (Westborough), Wyman Pond (Fitchburg), and Monoosnoc Brook (Leominster). Wyman Pond (Fitchburg), Monoosnoc Brook (Leominster), and Lily Pond (Cohasset) also had low pH values and low total hardness concentrations.
The monthly discrete pH, DOC, and total hardness data for selected stations on receiving-water bodies were used in the U.S. Environmental Protection Agency Aluminum Criteria Calculator Version 2.0 to estimate site-dependent total recoverable aluminum concentrations that—if not exceeded—would be expected to protect fish, invertebrates, and other aquatic life from adverse effects associated with acute and chronic aluminum exposures. The U.S. Environmental Protection Agency Calculator output provides values for the acute criterion, defined as the criterion maximum concentration (CMC), an estimate of the highest aluminum concentration in surface water to which an aquatic community can be exposed briefly without resulting in an unacceptable effect. This output also provides values for the chronic criterion, defined as the criterion continuous concentration (CCC), an estimate of the highest concentration of aluminum in surface water to which an aquatic community can be exposed indefinitely without resulting in an unacceptable effect. To determine aluminum criteria values typically evaluated for use as protective water-quality criteria, the monthly instantaneous CMC and CCC values were used to calculate the minimum, 5th percentile, and 10th percentile CMC and CCC values for selected monitoring stations.
The monthly instantaneous aluminum CMC and CCC values generated using the EPA Calculator varied among stations. Aluminum CMC and CCC values were highest for four ambient (upstream) stations on the Assabet River associated with wastewater-treatment facilities (Westborough, Marlboro, Hudson, and Maynard). Aluminum CMC and CCC values were lower for stations associated with water-treatment facilities, and lowest for selected ambient stations on Lily Pond, Monoosnoc Brook, and Wyman Pond associated with water-treatment facilities in Cohasset, Leominster, and Fitchburg, respectively. For many stations, the highest CMC and CCC instantaneous aluminum criteria values generated using the U.S. Environmental Protection Agency Calculator were for months during the growing season for algae and aquatic macrophytes (April or May through September or October) and the lowest values were for months during the nongrowing season (October or November through March or April), indicating the importance of collecting water-quality data during the nongrowing season.
Aluminum CMC and CCC values generated by the U.S. Environmental Protection Agency Calculator are sensitive to variations in the input parameters (pH, DOC, and total hardness). Aluminum solubility is particularly affected by pH. To characterize diel and seasonal variations in pH, multiparameter water-quality monitors recording continuous (15-minute interval) water temperature and pH were installed in the receiving-water body for one station near each facility upstream from the effluent discharge (in rivers) or at a station outside the immediate effect of effluent discharge (in ponds). Continuous water temperature and pH data were collected from April or May 2018 through November or December 2018. Continuous pH data indicated that the pond stations and Assabet River stations had large diel variations in pH during the growing season. Continuous pH data were used together with discrete DOC and total hardness data to evaluate the potential effect of diel variations in pH on calculated site-dependent aluminum criteria values. For the 11 stations, diel variations in pH were determined to correspond to differences in the 10th percentile of CMC values by a median of 160 μg/L, ranging from 0 to 610 μg/L, and differences in the 10th percentile of CCC values by a median of 40 μg/L, ranging from 15 to 210 μg/L. The low monthly instantaneous CMC and CCC values that have the greatest effect on the minimum, 5th percentile, and 10th percentile aluminum values tend to result during the nongrowing season (October or November through March or April) when the range of diel variations in pH is small, thus minimizing the effect of diel variations in pH on the lowest CMC and CCC values.
Historical water-quality data on organic carbon in Massachusetts streams were investigated using data retrieved from the USGS National Water Information System database. An assessment of the availability of historical pH, DOC, and hardness data indicated that more data were available for TOC than for DOC. A linear regression equation was developed for the relation between DOC and TOC concentrations to inform the potential use of available data to evaluate water-quality conditions at additional sites across Massachusetts where only pH, hardness, and TOC data are available. DOC and TOC concentrations were well correlated in the 223 samples in which both constituents were analyzed, and the equation had a coefficient of determination (R2) equal to 0.93.
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10 Bearfoot Road
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Groundwater-level declines and limited quantitative knowledge of the groundwater-flow system in the Harney Basin prompted a cooperative study between the U.S. Geological Survey and the Oregon Water Resources Department to evaluate the groundwater-flow system and budget. This report provides a hydrologic budget of the Harney Basin groundwater system that includes separate groundwater budgets for upland and lowland areas to avoid double counting water that recharges in the uplands, discharges to streams and springs in the uplands, flows downstream to the lowlands, and recharges the lowland groundwater system. Lowlands generally represent the conterminous valleys within the center of the basin, including floodplains of the major streams and uplands represent all other areas in the basin.
The upland groundwater budget is minimally affected by groundwater development and generally represents the budget of the natural system. In upland areas during 1982–2016, mean-annual recharge totaled 288,000 acre-feet (acre-ft) and mean-annual discharge totaled 239,000 acre-ft, resulting in a net recharge of 49,000 acre-ft. Upland groundwater recharge occurs as infiltration of precipitation and snowmelt and was estimated using the USGS Soil-Water-Balance model calibrated to estimates of runoff, evapotranspiration (ET), base flow, and snow-water equivalent. Groundwater discharge to streams is the predominant discharge mechanism in upland areas and was estimated as 225,000 acre-feet per year (acre-ft/yr) during 1982–2016 using hydrograph separation and summer low-flow estimates in streamgaged watersheds and a linear relation between estimated streamflow and base flow in ungaged watersheds. The remaining upland discharge occurs through springs (14,000 acre-ft/yr) that either emerge downgradient of locations where groundwater discharge to streams was estimated or are routed to irrigated areas. Spring discharge was estimated as a compilation of current and historical measurements. The net upland recharge, which is 17 percent of total upland recharge, ultimately recharges lowland areas as groundwater flow from uplands to lowlands.
The lowland groundwater budget for the Harney Basin represents a combination of natural conditions and human activity as more than 99 percent of groundwater development has occurred either inside or within 2 miles of the lowland boundary. In lowland areas during 1982–2016, mean annual groundwater recharge totaled 173,000 acre-ft and groundwater discharge totaled 283,000 acre-ft, indicating discharge exceeded recharge by more than 60 percent.
Excluding groundwater pumping, the lowland groundwater budget is more in balance with a mean annual recharge of 165,000 acre-ft and a mean annual discharge of 131,000 acre-ft during 1982–2016. The 23-percent difference between non-pumping recharge and discharge mostly represents the cumulative uncertainty in the estimates of the various groundwater budget components but also likely includes a small reduction in natural groundwater discharge captured by pumping. Lowland groundwater is predominantly recharged by infiltration of surface water (116,000 acre-ft/yr) through streams, floodwater, and irrigation, with a lesser amount as groundwater inflow from uplands and minimal recharge beneath Malheur and Harney Lakes. Recharge from streams and floodwater (natural and irrigation) was estimated using a balance of measured and estimated surface-water inflow to and outflow from lowland areas including streamflow, springflow, and ET where a portion of surface-water inflow to lowland areas is comprised of upland discharge to streams and springs. Groundwater ET (119,000 acre-ft/yr) is the predominant natural discharge mechanism in lowland areas and was estimated as the mean from two remote-sensing based approaches incorporating groundwater ET measurements from other similar basins and 23 years (1987–2015) of Landsat imagery. Discharge of lowland groundwater into Malheur and Harney Lakes is about 700 acre-ft/yr and is represented in groundwater ET estimates. The remaining natural groundwater discharge from lowland areas issues from Sodhouse Spring (8,900 acre-ft/yr) and as groundwater flow to the Malheur River Basin through Virginia Valley (3,100 acre-ft/yr). The relatively large amount of groundwater discharged to springs in Warm Springs Valley (25,000 acre-ft/yr) is accounted for in groundwater ET estimates. Natural groundwater discharge in lowland areas of the Harney Basin has remained relatively constant during the last 80 years based on comparisons with estimates north of Malheur Lake and west of Harney Lake published in the 1930s.
Annual net amount of groundwater pumped (pumpage) from the Harney Basin during 2017–18 averaged 144,000 acre-ft. The net value is the difference between pumpage (about 152,000 acre-ft/yr) and reinfiltration of groundwater pumped for irrigation and non-irrigation purposes (about 8,000 acre-ft/yr). Net pumpage was estimated in concurrent studies that compiled groundwater-use data and coupled reported groundwater pumpage data from wells with remote-sensing-based ET estimates from groundwater-irrigated fields. Total pumpage for irrigation has increased from about 54,000 acre-ft/yr during 1991–92 to 145,000 acre-ft/yr during 2017–18. Presently, pumpage is greatest in the lowland region north of Malheur Lake (81,000 acre-ft/yr), with lesser amounts to the north and northwest of Harney Lake (41,000 acre-ft/yr) and to the south and east of Malheur Lake (22,000 acre-ft/yr).
During this study, mean annual lowland groundwater discharge (including pumpage) exceeded mean annual recharge, indicating that the lowland hydrologic budget is out of balance. Net groundwater pumpage during 2017–18 is similar to groundwater discharge from all other sources in the lowlands and is four times the imbalance between non-pumping lowland recharge and discharge (34,000 acre-ft/yr). Declining groundwater levels at depth across many parts of the Harney Basin lowlands indicate that pumpage is depleting aquifer storage and is likely capturing a small amount of natural groundwater discharge to springs and ET in some lowland areas. If pumping continues, aquifer storage depletion will continue until the capture rate of natural discharge to springs and ET is equal to the pumping rate. If groundwater development occurs in upland areas and reduces either the streamflow or groundwater inflow to lowland areas, the deficit in the lowland water budget will increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215128","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Garcia, C.A., Corson-Dosch, N.T., Beamer, J.P., Gingerich, S.B., Grondin, G.H., Overstreet, B.T., Haynes, J.V., and Hoskinson, M.D., 2021, Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon (ver. 1.1, November 2022): U.S. Geological Survey Scientific Investigations Report 2021–5128, 144 p., https://doi.org/10.3133/sir20215128.","productDescription":"Report: xiii, 144 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-119839","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502128,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112849.htm","linkFileType":{"id":5,"text":"html"}},{"id":398083,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QABFML","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data–Hydrologic budget of the Harney Basin groundwater system, Oregon"},{"id":398082,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94NH4D8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil- Water-Balance (SWB) model archive used to simulate mean annual upland recharge from infiltration of precipitation and snowmelt in Harney Basin, Oregon, 1982–2016"},{"id":409214,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5128/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5128 Version History"},{"id":398080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5128/coverthb2.jpg"},{"id":398081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5128/sir20215128.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5128"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.08056640625,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 2022; Version 1.1: November 2022","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Applications of genetic-based estimates of population size are expanding, especially for species for which traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (NS) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, therefore eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of NS, is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of NS using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases for which data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating NS. We applied results of the simulation analysis to evaluate NˆS for a population of federally endangered Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA. Our results indicated that NS is likely to be three orders of magnitude lower compared with historic breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate NS and can help to guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2602","usgsCitation":"White, S.L., Sard, N.M., Brundage III, H., Johnson, R.L., Lubinski, B.A., Eackles, M.S., Park, I.A., Fox, D.A., and Kazyak, D., 2022, Evaluating sources of bias in pedigree-based estimates of breeding population size: Ecological Applications, v. 32, no. 5, e2602, 13 p., https://doi.org/10.1002/eap.2602.","productDescription":"e2602, 13 p.","ipdsId":"IP-114638","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448228,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2602","text":"Publisher Index Page"},{"id":398310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.794677734375,\n 39.614152077002664\n ],\n [\n -74.542236328125,\n 39.614152077002664\n ],\n [\n -74.542236328125,\n 41.40153558289846\n ],\n [\n -75.794677734375,\n 41.40153558289846\n ],\n [\n -75.794677734375,\n 39.614152077002664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-17","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":839957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sard, Nicholas M","contributorId":289872,"corporation":false,"usgs":false,"family":"Sard","given":"Nicholas","email":"","middleInitial":"M","affiliations":[{"id":48660,"text":"SUNY Oswego","active":true,"usgs":false}],"preferred":false,"id":839958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brundage III, Harold M","contributorId":289873,"corporation":false,"usgs":false,"family":"Brundage III","given":"Harold M","affiliations":[{"id":62274,"text":"Environmental Research and Consulting Inc","active":true,"usgs":false}],"preferred":false,"id":839959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839961,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":839962,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Ian A","contributorId":289876,"corporation":false,"usgs":false,"family":"Park","given":"Ian","email":"","middleInitial":"A","affiliations":[{"id":62277,"text":"DNREC","active":true,"usgs":false}],"preferred":false,"id":839963,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fox, Dewayne A.","contributorId":117052,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","email":"","middleInitial":"A.","affiliations":[{"id":12970,"text":"Department of Agriculture and Natural Resources, Delaware State University","active":true,"usgs":false}],"preferred":false,"id":839964,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":839965,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230428,"text":"70230428 - 2022 - Young basalt fields of the Mojave Desert","interactions":[],"lastModifiedDate":"2022-04-13T13:35:33.773805","indexId":"70230428","displayToPublicDate":"2022-04-01T08:33:00","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Young basalt fields of the Mojave Desert","docAbstract":"Basalt, a mafic volcanic rock common in mid-ocean islands and in several continental settings, is melted from upper mantle rocks in many cases and thus provides information on mantle conditions. Basalt lava fields, some decorated with cinder cones, are scattered around the Mojave Desert. Only a few basalt fields have been well studied, so we undertook a compilation of basalt fields that are younger than ~12 Ma to examine space-time patterns. Cima volcanic field is unique in having eruptions that span ~7.5 MY, including the youngest eruption in the Mojave Desert at ~12 ka. Other fields probably erupted over short timespans of decades to hundreds of years based on analogy with modern eruptions, with few exceptions. We find that all basalt fields except Cima are restricted to the active eastern California shear zone, and many lie on active faults, indicating a direct relation between faulting and volcanism. Area and volume of lava is greatest for those fields associated with dextral faults, which may be attributed to less shear stress across those faults as compared to sinistral faults. Xenolith-bearing basalts that include chunks of mantle and deep crustal rocks are known in a few locations from the eastern San Bernardino Mountains to Cima and have a wide range in age.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Desert Symposium Inc","usgsCitation":"Miller, D., and Buesch, D.C., 2022, Young basalt fields of the Mojave Desert, in Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings, p. 63-73.","productDescription":"11 p.","startPage":"63","endPage":"73","ipdsId":"IP-132547","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":398643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398613,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.3282470703125,\n 33\n ],\n [\n -115.5,\n 33\n ],\n [\n -115.5,\n 35.36217605914681\n ],\n [\n -118.3282470703125,\n 35.36217605914681\n ],\n [\n -118.3282470703125,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, David M. 0000-0003-3711-0441","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":238721,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":840407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":840408,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230431,"text":"70230431 - 2022 - Post-Early Miocene silicic volcanism in the northern Mojave Desert, California","interactions":[],"lastModifiedDate":"2022-04-13T13:21:26.717806","indexId":"70230431","displayToPublicDate":"2022-04-01T08:14:16","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Post-Early Miocene silicic volcanism in the northern Mojave Desert, California","docAbstract":"Silicic volcanism that postdates widespread early Miocene volcanism in the Mojave Desert is underappreciated. We compiled age, petrographic, and geochemical data for volcanic rocks in a wide swath of the desert south of the Garlock fault using an age threshold of post-18.8 Ma, approximately the limit of the earlier Miocene volcanism as marked by the eruption of the widespread Peach Spring Tuff. In addition to the well-known young basaltic volcanic centers not considered in this paper, several dozen silicic volcanic edifices are known or likely to be younger than 18.8 Ma. Several examples of rhyolite tuffs and basalt lava in middle Miocene basin occur in sequences of the Barstow Formation and its correlatives. Dacite domes are common in the Calico Mountains, dated at ~17 Ma, and similar, but mostly undated, domes are scattered nearby in the Barstow area and east of the Calico Mountains. North of Barstow, chains of rhyolite domes and scattered dacite domes are known. A few of these domes and flows are dated in the range of 13-7 Ma. Farther north, the Lava Mountains have several volcanic sequences from 12 to 7 Ma and ranging in composition from basalt to rhyolite. Farther east and west are more rhyolite and dacite domes, in general undated, as well as the extensive ~17.8 Ma Woods Mountains rhyolite center. Sparse geochemical data for the silicic rocks indicate distinct rhyolite and dacite groups, and rare andesite. Understanding of these potentially young silicic volcanic rocks is hampered by poor age control and geochemical data, but more study holds promise for better understanding the origins of volcanism in the Mojave Desert.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Desert Symposium Inc","usgsCitation":"Miller, D., Gans, P.B., Felger, T.J., and Vazquez, J.A., 2022, Post-Early Miocene silicic volcanism in the northern Mojave Desert, California, in Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings, p. 124-141.","productDescription":"18 p.","startPage":"124","endPage":"141","ipdsId":"IP-137943","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":398642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398614,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org"}],"country":"United States","state":"California, Nevada","otherGeospatial":"northern Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 34\n ],\n [\n -114,\n 34\n ],\n [\n -114,\n 37\n ],\n [\n -119,\n 37\n ],\n [\n -119,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, David M. 0000-0003-3711-0441","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":238721,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":840409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gans, Phillip B 0000-0003-0373-9639","orcid":"https://orcid.org/0000-0003-0373-9639","contributorId":204410,"corporation":false,"usgs":false,"family":"Gans","given":"Phillip","email":"","middleInitial":"B","affiliations":[{"id":36937,"text":"Dept of Earth Science, Univ of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":840410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Felger, Tracey J. 0000-0003-0841-4235 tfelger@usgs.gov","orcid":"https://orcid.org/0000-0003-0841-4235","contributorId":290175,"corporation":false,"usgs":true,"family":"Felger","given":"Tracey","email":"tfelger@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":840411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":840412,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230103,"text":"70230103 - 2022 - Poplar Island: Understanding the development of a beneficial use restoration site","interactions":[],"lastModifiedDate":"2022-03-30T14:39:14.395853","indexId":"70230103","displayToPublicDate":"2022-03-29T09:26:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1462,"text":"Ecological Restoration","active":true,"publicationSubtype":{"id":10}},"title":"Poplar Island: Understanding the development of a beneficial use restoration site","docAbstract":"Poplar Island, like many other islands throughout the Chesapeake Bay, eroded from 460 hectares in 1847 to only 1.5 hectares by the 1990’s. However, the U.S. Army Corps of Engineers, Maryland Department of Transportation, and numerous other state and federal agencies selected this site as the location of a beneficial use project aimed at restoring remote island habitat in the Chesapeake Bay using clean dredge material. While monitoring efforts since the beginning of restoration efforts have documented extensive use of Poplar Island by numerous species across multiple taxa, these efforts have previously lacked context regarding underlying habitat patterns. However, such information is especially important on a site like Poplar Island where habitat distribution and availability can change dramatically between years due to ongoing construction efforts. To address this information gap we digitized annual aerial imagery of Poplar Island from 2006-2017 into 20 habitat classes. The resulting data layers demonstrate the transition of cells along the eastern side of the island from undeveloped cells to planted marsh cells, which matches trends seen in avian monitoring data. Similarly, our data display changes in the distribution of specific resources such as sand across the island, and how individual locations of interest such as constructed habitat islands have evolved over time. We believe that these data will provide critical insight into the factors influencing wildlife distribution patterns on Poplar Island, and will allow for the identification of management actions that may either be targeted or avoided in the planning of future beneficial use projects.","language":"English","publisher":"University of Wisconsin Press","doi":"10.3368/er.40.1.17","usgsCitation":"Prosser, D., Sullivan, J.D., Wall, J.L., Buck, E., Taylor, J.F., Callahan, C.R., and McGowan, P.C., 2022, Poplar Island: Understanding the development of a beneficial use restoration site: Ecological Restoration, v. 40, p. 17-24, https://doi.org/10.3368/er.40.1.17.","productDescription":"8 p.","startPage":"17","endPage":"24","ipdsId":"IP-128662","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":397857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Talbot","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.39360427856445,\n 38.74250272111668\n ],\n [\n -76.3553237915039,\n 38.74250272111668\n ],\n [\n -76.3553237915039,\n 38.78339443129763\n ],\n [\n -76.39360427856445,\n 38.78339443129763\n ],\n [\n -76.39360427856445,\n 38.74250272111668\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":839037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":839038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wall, Jennifer L.","contributorId":205845,"corporation":false,"usgs":false,"family":"Wall","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":839039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buck, Evan J","contributorId":265821,"corporation":false,"usgs":false,"family":"Buck","given":"Evan J","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":839040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, John F.","contributorId":80890,"corporation":false,"usgs":false,"family":"Taylor","given":"John","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":839041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Callahan, Carl R.","contributorId":205289,"corporation":false,"usgs":false,"family":"Callahan","given":"Carl","email":"","middleInitial":"R.","affiliations":[{"id":37073,"text":"USFWS, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":839042,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGowan, Peter C.","contributorId":13867,"corporation":false,"usgs":false,"family":"McGowan","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":839043,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230841,"text":"70230841 - 2022 - Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","interactions":[],"lastModifiedDate":"2022-04-26T14:00:19.627318","indexId":"70230841","displayToPublicDate":"2022-03-29T08:51:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","docAbstract":"A previously completed mineral resources assessment of the Texas Coastal Plain indicated the potential for the future discovery of uranium resources. Geoenvironmental assessments that include the hydrogeologic framework can be used as a tool to understand the potential effects of mining operations. The hydrogeologic framework for this study focused on the composite hydrogeologic unit of the tract permissive for the occurrence of uranium consisting of the upper part of the Miocene-age Fleming Formation/Lagarto Clay, Pliocene-age Goliad and Pleistocene-age Willis Sands, Pleistocene-age Lissie and Beaumont Formations, and Holocene-age alluvial sediments (fluvial alluvium and eolian sand deposits). This composite hydrogeologic unit, which contains the Chicot and Evangeline aquifers of the Gulf Coast aquifer system, is intended for inclusion in a regional-scale geoenvironmental assessment of as yet undiscovered uranium resources. This article provides (1) a brief literature review describing the geologic and hydrogeologic settings, (2) the methodology used to develop a composite hydrogeologic framework, and (3) descriptions and maps of the land-surface altitude, composite hydrogeologic unit base and midpoint depth, water-level altitude, depth of water, unsaturated and saturated zone thickness, and transmissivity and hydraulic conductivity. A composite hydrogeologic unit, created by combining geologic and hydrogeologic data and maps for individual geologic and hydrogeologic units, is intended for use as a tool in a geoenvironmental assessment to evaluate potential contaminant migration through various avenues. Potential applications include using the hydrogeologic framework as an input into a geoenvironmental assessment to help estimate the potential for (1) runoff of contaminants into surface water, (2) infiltration of contaminants into the groundwater (aquifers), or (3) movement of contaminants from the mining area through wind, groundwater-flow, or streamflow in a given permissive tract. The procedures outlined in this paper also provide a method for developing hydrogeologic frameworks that can be applied in other areas where mining may occur.
","language":"English","publisher":"MDPI","doi":"10.3390/min12040420","usgsCitation":"Teeple, A., Becher, K.D., Walton-Day, K., Humberson, D.G., and Gallegos, T., 2022, Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain: Minerals, v. 12, no. 4, 420, 29 p., https://doi.org/10.3390/min12040420.","productDescription":"420, 29 p.","ipdsId":"IP-136336","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":448338,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12040420","text":"Publisher Index Page"},{"id":435904,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AZMEU0","text":"USGS data release","linkHelpText":"Data used for developing a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain"},{"id":399663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.84521484375,\n 29.707139348134145\n ],\n [\n -93.69140625,\n 30.29701788337205\n ],\n [\n -93.55957031249999,\n 30.996445897426373\n ],\n [\n -93.84521484375,\n 31.74685416292141\n ],\n [\n -94.0869140625,\n 32.287132632616384\n ],\n [\n -94.19677734375,\n 32.97180377635759\n ],\n [\n -95.537109375,\n 32.62087018318113\n ],\n [\n -96.7236328125,\n 30.86451022625836\n ],\n [\n -97.9541015625,\n 29.34387539941801\n ],\n [\n -99.11865234374999,\n 27.994401411046148\n ],\n [\n -99.36035156249999,\n 27.332735136859146\n ],\n [\n -99.29443359375,\n 26.745610382199022\n ],\n [\n -98.67919921875,\n 26.254009699865737\n ],\n [\n -98.06396484375,\n 26.03704188651584\n ],\n [\n -97.42675781249999,\n 25.958044673317843\n ],\n [\n -97.00927734375,\n 25.760319754713887\n ],\n [\n -97.1630859375,\n 26.54922257769204\n ],\n [\n -97.1630859375,\n 27.547241546253268\n ],\n [\n -95.8447265625,\n 28.5941685062326\n ],\n [\n -94.41650390625,\n 29.305561325527698\n ],\n [\n -93.84521484375,\n 29.707139348134145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becher, Kent D 0000-0002-3947-0793","orcid":"https://orcid.org/0000-0002-3947-0793","contributorId":290642,"corporation":false,"usgs":false,"family":"Becher","given":"Kent","email":"","middleInitial":"D","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":841446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humberson, Delbert G 0000-0001-6789-9135","orcid":"https://orcid.org/0000-0001-6789-9135","contributorId":240891,"corporation":false,"usgs":false,"family":"Humberson","given":"Delbert","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":841448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":841449,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230415,"text":"70230415 - 2022 - Mapping aquifer salinity gradients and effects of oil field produced water disposal using geophysical logs: Elk Hills, Buena Vista and Coles Levee Oil Fields, San Joaquin Valley, California","interactions":[],"lastModifiedDate":"2022-04-12T11:46:20.251679","indexId":"70230415","displayToPublicDate":"2022-03-28T06:39:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Mapping aquifer salinity gradients and effects of oil field produced water disposal using geophysical logs: Elk Hills, Buena Vista and Coles Levee Oil Fields, San Joaquin Valley, California","docAbstract":"The effects of oil and gas production on adjacent groundwater quality are becoming a concern in many areas of the United States. As a result, it has become increasingly important to identify which aquifers require monitoring and protection. In this study, we map the extent of groundwater with less than 10,000 mg/L TDS both laterally and vertically near the Elk Hills, Buena Vista and Coles Levee Oil Fields in the San Joaquin Valley, California and note evidence of effects of produced water disposal on salinity within the Tulare aquifer. Subsurface maps showing the depth at which groundwater salinity is less than 10,000 mg/L (or Base 10K) in the Tulare aquifer are generated using geophysical logs and verified by comparison to water sample analyses. The depth to Base 10K ranges from 240 m (800 ft) in Elk Hills to 800 m (2650 ft) in the adjacent Buena Vista syncline and is 670 m (2,200 ft) deep in the Coles Levee area to the east. Log-calculated salinities show a relatively smooth increase with depth prior to disposal activities whereas salinities calculated from logs collected near and after disposal activities show a more variable salinity profile with depth. The effect of produced water injection is represented by log resistivity profiles that change from low resistivity at the base of the sand to higher resistivity near the top due to density differences between the saline produced water and the brackish groundwater within each sand. Continued post-disposal logging in new wells in the 18G disposal area on the south flank of Elk Hills shows that injected water has migrated approximately 1,200 m (4,000 ft) downdip (south) over a period of 20 years since the inception of disposal activity.
Many tiger beetles (Family Cicindelidae) are critically imperiled due to their dependence on small patches of suitable habitat that are frequently threatened by natural and anthropogenic disturbances. In the eastern United States, conservation of three tiger beetles - Habroscelimorpha dorsalis dorsalis, H. dorsalis media, and Ellipsoptera puritana - has been inhibited by the absence of population genetic information that is needed for effective recovery planning and potential reintroductions. Using microsatellite panels, we performed population genetic analyses and compared patterns in diversity and differentiation within and between taxa. Nearly all collections of the three taxa had less observed heterozygosity than expected under Hardy-Weinberg Equilibrium, and there was a strong latitudinal gradient in genetic diversity in H. d. dorsalis distributed along the eastern and western shores of the Chesapeake Bay. We also found clear spatial patterns of genetic differentiation which reflected strong isolation-by-distance within all three taxa and between collections of H. d. dorsalis and H. d. media. However, there was evidence of admixture in current (mouth of the Chesapeake Bay) and former (coastal New Jersey) contact zones of H. d. dorsalis and H. d. media. Taken together, our study suggests that relatively few adult tiger beetles may maintain many populations, and that gene flow among nearby habitat patches is common in all three taxa – a characteristic that may help tiger beetles persist in dynamic coastal environments. Results of our analyses can be used to support conservation and management by identifying the spatial scale of metapopulation connectivity and locating populations at the greatest risk of extirpation.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10592-022-01440-y","usgsCitation":"Kazyak, D., Aunins, A.W., White, S.L., Eackles, M.S., and Knisley, C.B., 2022, Population genetics of three at-risk tiger beetles Habroscelimorpha dorsalis dorsalis, H. d. media, and Ellipsoptera puritana: Conservation Genetics, v. 23, p. 623-638, https://doi.org/10.1007/s10592-022-01440-y.","productDescription":"16 p.","startPage":"623","endPage":"638","ipdsId":"IP-128201","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":399082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay, Eastern Virginia Shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.2119140625,\n 36.59788913307022\n ],\n [\n -75.41015624999999,\n 36.59788913307022\n ],\n [\n -75.41015624999999,\n 39.740986355883564\n ],\n [\n -77.2119140625,\n 39.740986355883564\n ],\n [\n -77.2119140625,\n 36.59788913307022\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2022-03-26","publicationStatus":"PW","contributors":{"authors":[{"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":840978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aunins, Aaron 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":840979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":840980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":840981,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knisley, C. Barry","contributorId":290423,"corporation":false,"usgs":false,"family":"Knisley","given":"C.","email":"","middleInitial":"Barry","affiliations":[{"id":62424,"text":"Randolph-Macon (emeritus)","active":true,"usgs":false}],"preferred":false,"id":840982,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230098,"text":"70230098 - 2022 - Arsenic in private well water and birth outcomes in the United States","interactions":[],"lastModifiedDate":"2022-03-29T11:50:35.732821","indexId":"70230098","displayToPublicDate":"2022-03-26T06:46:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic in private well water and birth outcomes in the United States","docAbstract":"Prenatal exposure to drinking water with arsenic concentrations >50 μg/L is associated with adverse birth outcomes, with inconclusive evidence for concentrations ≤50 μg/L. In a collaborative effort by public health experts, hydrologists, and geologists, we used published machine learning model estimates to characterize arsenic concentrations in private wells—federally unregulated for drinking water contaminants—and evaluated associations with birth outcomes throughout the conterminous U.S.
Using several machine learning models, including boosted regression trees (BRT) and random forest classification (RFC), developed from measured groundwater arsenic concentrations of ∼20,000 private wells, we characterized the probability that arsenic concentrations occurred within specific ranges in groundwater. Probabilistic model estimates and private well usage data were linked by county to all live birth certificates from 2016 (n = 3.6 million). We evaluated associations with gestational age and term birth weight using mixed-effects models, adjusted for potential confounders and incorporated random intercepts for spatial clustering.
We generally observed inverse associations with term birth weight. For instance, when using BRT estimates, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 5 μg/L was associated with a −1.83 g (95% CI: −3.30, −0.38) lower term birth weight after adjusting for covariates. Similarly, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 10 μg/L was associated with a −2.79 g (95% CI: −4.99, −0.58) lower term birth weight. Associations with gestational age were null.
In this largest epidemiologic study of arsenic and birth outcomes to date, we did not observe associations of modeled arsenic estimates in private wells with gestational age and found modest inverse associations with term birth weight. Study limitations may have obscured true associations, including measurement error stemming from a lack of individual-level information on primary water sources, water arsenic concentrations, and water consumption patterns.
Massachusetts is the seventh smallest U.S. State in land area, but its size is surpassed by its contributions to U.S. history and the economy, its academic and medical expertise, and its natural features. The Atlantic Ocean to the east gives the “Bay State” more than 1,500 miles of coastline that were important in past fishing and maritime trade industries and in the tourism industry of today for destinations such as Boston, Cape Cod, Nantucket, and Martha’s Vineyard. Forests cover roughly 60 percent of the State, which owns 315,000 acres of forests in parklands, reserves, and woodlands.
Massachusetts celebrates many strengths, but its strengths can be vulnerable to environmental change. A robust population of 7 million translates to a density of more than 800 people per square mile; in Boston, the density rises to more than 18,000 people per square mile. Urban temperature hotspots can increase health risks to residents.
Although the whims of wind and water have long reshaped coastlines, climate change-induced sea-level rise and severe storms can amplify coastal effects. In forests, changes in temperature or precipitation can provide more favorable conditions for invasive species.
State and local governments have been taking steps to address climate change. Here are several ways Landsat has benefited the residents of Massachusetts.
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\"}}]}","edition":"Version 1.0: March 24, 2022; Version 1.1: January 23, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Data were collected at two monitoring sites along the Little Arkansas River in south-central Kansas that bracket most of the easternmost part of the Equus Beds aquifer. The data were used as part of the city of Wichita’s aquifer storage and recovery project to evaluate source water quality. The U.S. Geological Survey, in cooperation with the City of Wichita, has continued to monitor the water quality of these sites through 2019 to update previously published regression-based models using continuously measured physicochemical properties and discretely sampled water-quality constituents of interest. The purpose of this report is to provide an update of the previously published linear regression models that have been used to continuously compute estimates of water-quality constituent concentrations or densities at these two sites. Water-quality constituent model updates include those for dissolved and suspended solids, suspended-sediment concentration, hardness, alkalinity, primary ions (bicarbonate, calcium, sodium, chloride, and sulfate), nutrients (total Kjeldahl nitrogen and total phosphorus), total organic carbon, indicator bacteria (Escherichia coli and fecal coliform bacteria), a trace element (arsenic), and a pesticide (atrazine).
Regression analyses were used to develop surrogate models that related continuously measured physicochemical properties, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Specific conductance was an explanatory variable for dissolved solids, primary ions, and atrazine. Turbidity was an explanatory variable for total suspended solids and sediment, nutrients, total organic carbon, and indicator bacteria. Streamflow and water temperature were explanatory variables for dissolved arsenic. Seasonal components were included as explanatory variables for atrazine models. The amount of variance explained by most of the updated models was within 5 percent of previously published models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221010","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2022, Documentation of models describing relations between continuous real-time and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998–2019: U.S. Geological Survey Open-File Report 2022–1010, 34 p., https://doi.org/10.3133/ofr20221010.","productDescription":"Report: vii, 34 p.; 2 Appendixes; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-126572","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":397345,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221010/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397333,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397331,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River at Highway 50 near Halstead, Kansas (Halstead Site; U.S. Geological Survey Station Number 07143672)"},{"id":501756,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112715.htm","linkFileType":{"id":5,"text":"html"}},{"id":397330,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1010/images"},{"id":397332,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Model Archive Summaries for the Little Arkansas River near Sedgwick, Kansas (Sedgwick Site; U.S. Geological Survey Station Number 07144100)"},{"id":397329,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.XML"},{"id":397328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1010/ofr20221010.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1010"},{"id":397327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1010/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 37.714244967649265\n ],\n [\n -97.1667,\n 38.533333\n ],\n [\n -98.1667,\n 38.533333\n ],\n [\n -98.1667,\n 37.714244967649265\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Coregonine fishes are important to Laurentian Great Lakes food webs and fisheries and are central to basin-wide conservation initiatives. In Lake Ontario, binational management objectives include conserving and restoring spawning stocks of cisco (Coregonus artedi) and lake whitefish (C. clupeaformis), but the spatial extent of contemporary coregonine spawning habitat and the environmental factors regulating early life success are not well characterized. In Spring 2018, we conducted a binational ichthyoplankton assessment to describe the spatial extent of coregonine spawning habitat across Lake Ontario. We then quantified the relative importance of a suite of biophysical variables hypothesized to influence coregonine early life success using generalized additive mixed models and multimodel inference. Between April 10 and May 14, we conducted 1,092 ichthyoplankton tows and captured 2,350+ coregonine larvae across 17 sampling areas, predominantly within embayments. Although 95% of catches were in the eastern basin, coregonine larvae were also found in historical south shore spawning areas. Most coregonine larvae were cisco; <6% were lake whitefish. Observed catches of both species across sampling areas were strongly and similarly associated with ice cover duration, but the importance of site-specific characteristics varied, such as distance to shore and site depth for cisco and lake whitefish, respectively. These results suggest that regional-scale climatic drivers and local environmental habitat characteristics interact to regulate early life stage success. Furthermore, strong regional and cross-species variation in larval distributions emphasize the importance of lake-wide assessments for monitoring both the current eastern basin populations and potential expansions into western Lake Ontario habitats.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.07.009","usgsCitation":"Taylor A. Brown, Sethi, S., Lars G. Rudstam, Jeremy P. Holden, Michael J. Connerton, Dimitry Gorsky, Curtis T. Karboski, Chalupnicki, M., Nicholas M. Sard, Roseman, E., Scott E. Prindle, Matthew J. Sanderson, Thomas M. 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Evans","affiliations":[{"id":83364,"text":"St. Mary's College","active":true,"usgs":false}],"preferred":false,"id":923453,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cooper, Amanda","contributorId":348575,"corporation":false,"usgs":false,"family":"Cooper","given":"Amanda","affiliations":[],"preferred":false,"id":923606,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Reinhart, Daren J.","contributorId":348576,"corporation":false,"usgs":false,"family":"Reinhart","given":"Daren J.","affiliations":[],"preferred":false,"id":923607,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Cameron David","contributorId":348436,"corporation":false,"usgs":false,"family":"Cameron David","affiliations":[{"id":62863,"text":"Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":923455,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923454,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70240411,"text":"70240411 - 2022 - Evaluation of post-stocking dispersal and mortality of juvenile lake trout Salvelinus namaycush in Lake Ontario using acoustic telemetry","interactions":[],"lastModifiedDate":"2023-02-07T13:01:59.14599","indexId":"70240411","displayToPublicDate":"2022-03-20T06:58:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of post-stocking dispersal and mortality of juvenile lake trout Salvelinus namaycush in Lake Ontario using acoustic telemetry","docAbstract":"Wild reproduction by stocked lake trout Salvelinus namaycush in Lake Ontario has yet to produce a self-sustaining population, requiring a reliance on stocking. Once released, age-1 juvenile lake trout are not typically surveyed until age-2, creating a gap in knowledge of fine-scale post-release behaviors. A method to track fine-scale movements and estimate mortality of juvenile lake trout could complement standard survey methods and benefit management decisions regarding stocking locations. We used acoustic telemetry to estimate post-stocking mortality and observe fine-scale spatial and temporal movements of 38 hatchery-reared, age-1 lake trout from an offshore stocking site in the eastern basin of Lake Ontario from 2017 to 2018. Cumulative post-stocking mortality was estimated at 5.3%, 10.5%, and 26.3% after one week, one month and one year, respectively. The majority of lake trout (68.4%) emigrated from the stocking location within two months and entered deep water (∼50 m) once warm-water incursions at the stocking site exceeded lake trout thermal preferences (15 °C). Lake trout made large movements (i.e., median 1.9 km, maximum 12.4 km straight-line distance) within the first hour post-release and had an average swimming speed of 1.64 km‧hr−1over the first day. There was no statistically significant relationship between total distance traveled and time of day, although distance traveled tended to be greater during crepuscular and dark periods compared to daylight. Our results provide a conservative estimate of post-release mortality and reveal behaviors of hatchery-reared juvenile lake trout that may be helpful when selecting stocking locations beneficial to restoration program goals.
Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.
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Kerr Reservoir and Lake Gaston. The Raleigh terrane (located on the eastern side of the map) contains Neoproterozoic to early Paleozoic(?) polydeformed, amphibolite-facies gneisses and schists. The Carolina slate belt of the Carolina terrane (located in the central part of the map) contains Neoproterozoic metavolcanic and metasedimentary rocks at greenschist facies. Although locally complicated, the slate-belt structure mapped across the South Boston map area is generally a broad, complex anticlinorium of the Hyco Formation (here called the Chase City anticlinorium) and is flanked to the west and east by synclinoria, which are cored by the overlying Aaron and Virgilina Formations. The western flank of the Carolina terrane (located in the western-central part of the map) contains similar rocks at higher metamorphic grade. This terrane includes epidote-amphibolite-facies to amphibolite-facies gneisses of the Neoproterozoic Country Line complex, which extends north-northeastward across the map. The Milton terrane (located on the western side of the map) contains Ordovician amphibolite-facies metavolcanic and metasedimentary gneisses of the Cunningham complex.
Crosscutting relations and fabrics in mafic to felsic plutonic rocks constrain the timing of Neoproterozoic to late Paleozoic deformations across the Piedmont. In the eastern part of the map, a 5- to 9-kilometer-wide band of tectonic elements that contains two late Paleozoic mylonite zones (Nutbush Creek and Lake Gordon) and syntectonic granite (Buggs Island pluton) separates the Raleigh and Carolina terranes. Amphibolite-facies, infrastructural metaigneous and metasedimentary rocks east of the Lake Gordon mylonite zone are generally assigned to the Raleigh terrane. In the western part of the map area, a 5- to 8-kilometer-wide band of late Paleozoic tectonic elements includes the Hyco and Clover shear zones, syntectonic granitic sheets, and amphibolite-facies gneisses along the western margin of the Carolina terrane at its boundary with the Milton terrane. This band of tectonic elements is also the locus for early Mesozoic extensional faults associated with the early Mesozoic Scottsburg, Randolph, and Roanoke Creek rift basins.
The map shows fluvial terrace deposits of sand and gravel on hills and slopes near the Roanoke and Dan Rivers. The terrace deposits that are highest in altitude are the oldest. Saprolite regolith is spatially associated with geologic source units and is not shown separately on the map.
Mineral resources in the area include gneiss and granite quarried for crushed stone, tungsten-bearing vein deposits of the Hamme district, and copper and gold deposits of the Virgilina district. Surface-water resources are abundant and include rivers, tributaries, the John H. Kerr Reservoir, and Lake Gaston. Groundwater flow is concentrated in saprolite regolith, along fractures in the crystalline bedrock, and along fractures and bedding-plane partings in the Mesozoic rift basins.
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