Rhode Island is an oasis of natural calm surrounded by heavily urbanized East Coast areas, which may explain why the smallest State in the United States is such a popular tourist destination for residents of New York, Pennsylvania, and New Jersey, or perhaps its popularity is a measure of the Ocean State’s abundant wildlife and picturesque views. Although small in land area, Rhode Island claims the largest estuary in New England in the 147-square-mile Narragansett Bay. Locals and visitors feast on clams caught in the bay, trek to glimpse shorebirds, or boat to 1 of 30 islands.
As with any coastal State, the natural wonders of Rhode Island face threats related to sea level rise and warming ocean temperatures. State agencies also work to fend off foes like the invasive Lymantria dispar (Linnaeus, 1758; spongy moth) and protect the forests that cover more than one-half of Rhode Island.
The U.S. Geological Survey Landsat Program, with 50 years of recurring Earth observations from space, offers a unique and freely available public data source for the study of land and coastal change across Rhode Island and the United States. Here are just a few of the ways Landsat imagery has been used to benefit the State.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
At its widest point, a mere 80 miles separate the eastern and western borders of New Hampshire. Its northern and southern borders are just 175 miles apart. Even so, few States can boast as much rugged natural beauty per mile as the Nation’s fifth smallest.
Nestled within New Hampshire are 93 State parks teeming with moose, Ursus americanus (Pallas, 1780; black bears), coyotes, beavers, river otters, and foxes. The largest section of White Mountain National Forest cuts across north-central New Hampshire, drawing visitors to its lakes, streams, mountain peaks, and hardwood forests. New Hampshire also is home to Lake Winnipesaukee, the State’s largest lake, notable for its floating post offices, the annual “ice-out” contest that sees residents vying to guess the date its surface ice dissipates, and its supporting role in films such as “On Golden Pond” and “What About Bob?” However, the scenic forests of New Hampshire face challenges in the form of invasive species such as Lymantria dispar (Linnaeus, 1758; spongy moth), Adelges piceae (balsam woolly adelgid), and Agrilus planipennis (emerald ash borer). In recent years, New Hampshire’s lakes and streams have seen more cyanobacterial blooms as well.
The U.S. Geological Survey Landsat Program offers a consistent, reliable, and historically unmatched source of Earth observations that can aid in the mapping, monitoring, and management of New Hampshire’s land and water resources. Here are a few ways Landsat data have been used in the Granite State.
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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The northern and central parts of the Tug Hill glacial aquifer consist of a 29-mile-long, crescent-shaped, mixture of glaciofluvial, glaciolacustrine, and recent alluvial deposits of predominantly sand and gravel on the western side of the Tug Hill Plateau in Jefferson and Oswego Counties in north-central New York. Approximately 11,400 people are supplied by groundwater that is withdrawn from municipal and nonmunicipal wells in the northern and central parts of the aquifer. In addition, many farms, several industries, and a large New York State fish hatchery also rely on the water from the aquifer.
In the early 2000s, anticipated developmental pressures from potential new industries (including a proposed water-bottling plant in the central part of the Tug Hill glacial aquifer) and expansion of the Fort Drum military base north of Watertown (with the projected increase in population extending into the northern part of the aquifer) prompted the Tug Hill Commission, local municipal officials, and representatives from the New York State Department of Environmental Conservation to initiate a geohydrologic study with the U.S. Geological Survey. The information from this study is intended to help the state, counties, and local communities make sound policy decisions about their use of this large groundwater resource.
The northern part of the Tug Hill glacial aquifer is a combination of glaciofluvial outwash and alluvial sand and gravel in the Sandy Creek Valley northeast of Adams, New York, and mostly glaciolacustrine beach and deltaic sand or sand and gravel north and south of the village of Adams. The southern and eastern areas of the central part of the aquifer are composed mostly of glaciofluvial sediments such as kames, kame moraines, and kame terraces, whereas most of the western areas of the central part are composed mostly of glaciolacustrine sediments such as deltaic sand and beach sand and gravel.
The northern and central parts of the aquifer are unconfined. Recharge to the northern and central parts of the aquifer is from three main sources: (1) precipitation that falls directly onto the aquifer; (2) unchannelized runoff (overland flow) and groundwater from till and bedrock in the Tug Hill Plateau that seeps into the eastern side of the aquifer; and (3) streams that drain the Tug Hill Plateau and flow across and lose water to the aquifer. Groundwater discharges to springs, seeps, headwaters of streams, and wetlands in the middle area of the central part of the aquifer and along the entire western boundary of the northern and central parts of the aquifer; pumping wells; artificial ditches; and deeply incised streams in the northern and central parts of the aquifer. The groundwater discharge to such streams is critical in supporting the salmonid fishery in the central part of the aquifer.
Groundwater levels were measured on July 17, 2014, at 22 wells throughout the northern and central parts of the aquifer. Water-table contours were drawn on the basis of the measured July 2014 water levels, historical water-level data, and surface-water levels where surface water in the channels was expected to be hydraulically connected to the groundwater system. The water table generally slopes from east to west throughout the northern and central parts of the aquifer; this slope also indicates that the direction of groundwater flow is generally from east to west.
Water-quality samples were collected from 23 stream sites during base-flow conditions, and groundwater-quality and other types of environmental samples were collected from 20 wells in the northern and central parts of the Tug Hill glacial aquifer. The results of the sampling indicate that surface water and groundwater are generally of good quality.
Comparison of the median concentration values of major ions in groundwater samples indicated that hardness in the northern part of the aquifer was about twice as great, and concentrations of calcium and sodium were more than three times as great, as in the central part of the aquifer. As was the case with surface water, the much greater median concentrations in groundwater of calcium, hardness, and alkalinity in the northern part of the aquifer are due to the dissolution of limestone that underlies most of that area and to the high-carbonate content of the clasts in the sand and gravel. There was little to no difference among the median values for bromide, fluoride, silica, and iron in the two parts of the aquifer. Concentrations of most other major ions were slightly greater in the northern part than in the central part of the Tug Hill glacial aquifer, except for magnesium, whose concentration was greater in the central part. Median concentrations of nutrients were generally greatest in surface water and groundwater in the northern part of the aquifer.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Kansas seems synonymous with agriculture, and rightly so—87 percent of Kansas land is devoted to it. As a key contributor to the State’s economy, agriculture makes Kansas one of the top producers of wheat, grain sorghum, and cattle in the country, but the State at the geographic center of the conterminous United States contains much more than fields and pastures.
Deciduous woodlands sprawl throughout the east. Tallgrass prairie—the only extensive stand remaining in the country—covers the east-central Flint Hills with more than 500 species of plants, many of them wildflowers, including the Sunflower State’s nickname inspiration. Near the center of Kansas, Cheyenne Bottoms—the largest marsh in the interior United States at 41,000 acres—welcomes migrating birds, including the endangered Grus americana (Linnaeus, 1758; whooping crane), by the thousands in the spring and fall. To the south, the inland saltwater marshes of Quivira National Wildlife Refuge attract many more.
Farther west, chalk outcroppings like Castle Rock and Monument Rocks rise above the landscape as fossil-bearing remnants of a sea floor from millions of years ago. Oil and natural gas fields exist throughout the State. After two University of Kansas professors discovered helium in a sample from one natural gas well in 1905, Kansas became a substantial supplier. The Landsat Program provides tools for monitoring and managing our conservation lands and their many resources. Here are several ways Landsat helps Kansas.
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Per- and polyfluoroalkyl substances (PFAS) are widely observed anthropogenic compounds found in water supplies worldwide and increasingly linked with adverse health effects in humans. In 2019, the West Virginia Legislature recognized the contamination risk to public source-water supplies posed by PFAS and passed a resolution that required a statewide PFAS study. The purpose of the resolution was to understand the occurrence and distribution of PFAS contamination throughout the State’s rivers, lakes, and groundwater aquifers. The U.S. Geological Survey has worked in cooperation with the West Virginia Department of Environmental Protection and West Virginia Department of Health and Human Resources to collect raw-water samples at 279 public-water systems across West Virginia. Public-water systems sampled for this study were identified by the West Virginia Department of Health and Human Resources and included all community water systems in the State and all daycares and schools that operate their own water systems.
Raw source water was sampled for both groundwater and surface-water sites at the first available tap in the public-water system, prior to any treatment. One hundred and seventy-three samples were collected from groundwater sources and 106 samples were collected from surface-water sources. Parameters collected at the time of sampling included pH, specific conductance, water temperature, dissolved oxygen, turbidity, and alkalinity. PFAS was analyzed at all 279 sites, major ions and trace elements were analyzed at 272 sites, and nutrients were analyzed at 270 sites.
The type of source water used for public supply in West Virginia is generally dependent on geology with more groundwater sites sampled in high-yield aquifers such as karst and alluvium. Surface-water sites were more evenly distributed throughout the State and are often the only source used in areas underlain by lower-yielding fractured-rock aquifers. Twenty-four percent of the sites sampled for this study had at least 1 PFAS detected, 47 of which were in groundwater sources and 20 in surface-water sources. Five sites exceeded the U.S. Environmental Protection Agency’s health advisory for combined perfluorooctanoate and perfluorooctanesulfonate concentrations of 70 nanograms per liter. These sites were located in highly susceptible karst and alluvial groundwater aquifers on the east and west sides of the State.
Higher PFAS concentrations were more commonly found in groundwater than surface-water sources, and high concentrations and PFAS detections were generally concentrated in the Ohio River Valley and West Virginia’s eastern panhandle. PFAS was rarely detected in groundwater sites in fractured-rock aquifers and abandoned underground coal-mine aquifers in the Appalachian Plateaus Physiographic Province had very little PFAS detected. These data represent a baseline summary of source water in West Virginia. Additional studies may be needed to understand exposure to private homeowners with domestic-water sources, variability of PFAS concentrations over time, and PFAS in finished drinking water as evaluated by current and future drinking-water regulations.
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Virginia\",\"nation\":\"USA \"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Several species of fish in large lakes and marine environments have a pelagic larval stage, and are subject to variable transport that can ultimately regulate survival and recruitment success. Alewife, Alosa pseudoharengus, are subject to transport by complex coastal currents during their pelagic larval stage (~ 30 d). We assessed backward-trajectory simulations, consisting of a Lagrangian particle dispersion model linked to the Finite Volume Community Ocean Model, to estimate likely hatch locations of aged larval alewife collected from locations on both the eastern and western sides of Lake Michigan during July 2015. We used four deployments of three satellite-tracked drifter buoys in coastal waters to assess model skill in estimating the origin of a drifter from its final location. We found that the trajectories of drifters varied greatly, depending on wind events and associated coastal transport processes, including upwelling/downwelling and coastal jet currents. In 2 of 12 cases, the backward trajectory simulations failed to predict the drifter origin, associated with transport of 170 km in a narrow coastal jet current. In the remaining 10 cases, the known drifter origin was within 3.5 km of the spatial patch of predicted possible origins for a scenario of horizontal diffusivity (188 m2 s−1) consistent with the offshore model grid resolution. Modeled backward trajectories estimated that alewife originated from the same side of the lake where they were collected, within ~ 100 km of the collection site. Our paper demonstrates the utility of hydrodynamic models to estimate a region of origin for aged larval fish.
Overstory basal area, ericaceous shrub cover (Kalmia latifolia L. and Rhododendron maximum L.), and fuels (i.e., woody fuel loads and depths and O Horizon thickness) were assessed within Great Smoky Mountains National Park, USA, in 2003 − 2004. Due to recent wildfire activity within the southern Appalachian Mountain region (including Great Smoky Mountains National Park), the potential spread and expansion of ericaceous shrubs, and the impacts of the hemlock woolly adelgid (Adelges tsugae Annand) on eastern hemlock (Tsuga canadensis (L.) Carrière), these same ecosystem components were again assessed in 2019. Elevation and moisture regime (xeric, intermediate, and mesic) were included in this assessment as potential influential factors. An evaluation of repeated measurements from 40 plots suggested that O Horizon thickness did not change significantly over the 16-year period, but increased as elevation increased, and moisture regime (xeric O Horizon thickness > mesic O Horizon thickness) was a significant, related factor. The sum of 1-, 10-, and 100-h fuel loads (fuels less < 7.6 cm diameter) increased, whereas woody fuel depth decreased over the 16-year period. No significant changes in 1000-h fuel loads (> 7.6 cm diameter), total woody fuel loads, ericaceous shrub cover, total basal area, or live T. canadensis basal area were observed. Live T. canadensis basal area decreased with increasing elevation. Dead, standing T. canadensis basal area increased from 2003–2019, and that increase was most pronounced as elevation increased on xeric and intermediate sites. Overall, we found that: 1. hypothesized increases in total woody fuel loads and ericaceous shrub cover were not present; and 2. elevation and moisture regime were most related to observed changes in vegetation and fuel condition.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11676-022-01515-z","usgsCitation":"Coates, T., and Ford, W., 2022, Fuels and vegetation changes in southwestern, unburned portions of Great Smoky Mountains National Park, USA, 2003-2019: Journal of Forestry Research, v. 33, p. 1459-1470, https://doi.org/10.1007/s11676-022-01515-z.","productDescription":"12 p.","startPage":"1459","endPage":"1470","ipdsId":"IP-135457","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481079,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11676-022-01515-z","text":"Publisher Index Page"},{"id":480745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","otherGeospatial":"Great Smoky Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.9220143101735,\n 35.74602329026733\n ],\n [\n -83.9220143101735,\n 35.49903011724885\n ],\n [\n -83.02028585855729,\n 35.49903011724885\n ],\n [\n -83.02028585855729,\n 35.74602329026733\n ],\n [\n -83.9220143101735,\n 35.74602329026733\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, T. Adam","contributorId":348790,"corporation":false,"usgs":false,"family":"Coates","given":"T. Adam","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":923770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":923769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70260134,"text":"70260134 - 2022 - Infrasound observations and constraints on the 2018 eruption of Kīlauea Volcano, Hawaii","interactions":[],"lastModifiedDate":"2024-10-29T15:04:32.136121","indexId":"70260134","displayToPublicDate":"2022-07-19T09:59:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Infrasound observations and constraints on the 2018 eruption of Kīlauea Volcano, Hawaii","docAbstract":"The 2018 eruption of Kīlauea Volcano was a dynamic event involving explosions, collapses, and fountaining at multiple vents spread over tens of kilometers. The permanent infrasound network operated by the USGS Hawaiian Volcano Observatory (HVO) was well prepared to observe the collapse of the summit, and additional deployments permitted infrasound observations during fissuring in the lower East Rift Zone (LERZ). We provide a summary of infrasound observations, including lava lake spattering, collapses, explosions, rockfall, and lava fountaining, using seismicity and tilt at times to help constrain our interpretations. At the summit of Kīlauea Volcano, we document the process of partial caldera collapse and examine a set of “proto-collapse” events that precede the widely observed events but share many of the same qualities as the larger collapses. For the initial twelve collapse events, we compare the timing of collapse onset to other observations and illustrate the repeatable characteristics of the recorded waveforms and infrasound characteristics associated with each episode of caldera collapse. In the LERZ, we match the acoustic signals with visual observations, including fissure migration, explosions near fissures, and littoral explosions. Lastly, we document and discuss the performance of infrasound alarms during the 2018 Kīlauea eruption. In general, alarming became successful in detecting collapse events at the summit of the volcano after tuning and became a key discriminant in the initial determination of collapse events, especially when visual observations were not available.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01583-3","usgsCitation":"Thelen, W., Waite, G.P., Lyons, J.J., and David Fee, 2022, Infrasound observations and constraints on the 2018 eruption of Kīlauea Volcano, Hawaii: Bulletin of Volcanology, v. 84, 76, 24 p., https://doi.org/10.1007/s00445-022-01583-3.","productDescription":"76, 24 p.","ipdsId":"IP-131617","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.53549432982868,\n 19.57384518533665\n ],\n [\n -155.53549432982868,\n 19.32020786420226\n ],\n [\n -154.80441193441976,\n 19.32020786420226\n ],\n [\n -154.80441193441976,\n 19.57384518533665\n ],\n [\n -155.53549432982868,\n 19.57384518533665\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"84","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waite, Gregory P.","contributorId":146613,"corporation":false,"usgs":false,"family":"Waite","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":917133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"David Fee","contributorId":345625,"corporation":false,"usgs":false,"family":"David Fee","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":917135,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233192,"text":"fs20223053 - 2022 - North Dakota and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:02:48.50367","indexId":"fs20223053","displayToPublicDate":"2022-07-19T05:41:07","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3053","displayTitle":"North Dakota and Landsat","title":"North Dakota and Landsat","docAbstract":"The State of North Dakota once did not figure prominently in the Nation’s economy. The sparsely populated State supported food production, and hunters and anglers were drawn to its lakes, rivers, and wide-open spaces, but its economy was overshadowed by that of other States. However, the State and its prairie expanses recently rocketed from an economic afterthought to a national energy leader with the soaring production of oil and natural gas in the Bakken oil patch.
The Bakken development has been transformative for North Dakota’s landscapes in myriad ways. It has boosted economic output, drawn thousands of new residents to cities like Williston and Watford City, and led to a proliferation of oil and gas pads.
In the past two decades, North Dakota experienced other major changes, such as the expansion of the depressional wetlands of the Prairie Pothole Region on the eastern side of the State. These critical breeding areas for waterfowl, which stretch across Minnesota, South Dakota, North Dakota, and Canada, are home to more than 50 percent of North America’s migratory birds.
Changes from oil and gas production, urban development, and wetland resurgence can all be tracked over time using the unparalleled Earth observation record of the U.S. Geological Survey Landsat data archive. Its 50-year record of repeat imagery also aids in the monitoring, cataloging, and management of cropland, invasive insect species, and natural or human-made disaster recovery. Here are just a few examples of the benefits offered to North Dakota by the Landsat Program.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Natural resources have always been a strength for Indiana. Once largely covered by forest, the State now includes a mix of forest, farmland, wetlands, and small lakes. In fact, farms and forested areas make up more than 80 percent of the land. The Ohio River forms the southern border, and the south shore of Lake Michigan contrasts with urban development in the northwestern corner. Indiana Dunes National and State Parks reside just east of the Chicago metropolitan area.
Agriculture adds more than $30 billion to the economy in Indiana, which placed eighth in the country for agricultural exports at $4.6 billion in 2017. Indiana ranks in the top five States nationally for the production of corn and soybeans. The “Hoosier State” also grows sizable crops of popcorn, spearmint, peppermint, pumpkins, tomatoes, and watermelon. Additionally, hogs, cattle, dairy, and poultry contribute to the agricultural economy. Other industries important to Indiana include manufacturing, medicine, energy, and mining. Mineral sources vary from coal, building stone, and gypsum to sand, gravel, and shale.
Landsat can help monitor the condition of natural resources and the effects of extreme weather events. Here are several ways Landsat has benefited Indiana.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Numerous porphyry copper-molybdenum-gold and epithermal deposits define a belt that extends from Eastern Alaska to western Yukon, Canada. An orientation study conducted near the Taurus porphyry deposit was designed to test methods that require minimal sample collection, preparation, and analytical time to determine the viability of indicator mineral studies as a reconnaissance exploration method. Bulk stream sediments and altered and mineralized rocks were sieved to the 0.105−0.25 millimeter fraction (+140, −60 mesh) and passed over a shaking table to create a moderate to heavy mineral separate that was mounted in epoxy and subsequently analyzed using automated scanning electron microscope (SEM) techniques. Seven polished thin sections of core were also analyzed. Among the advantages of automated SEM techniques compared to visual mineral identification are that thousands of grains can be rapidly identified in each sample (about 1 hour per sample) and small quantities of indicator minerals that may be missed during traditional visual analyses can be detected. Automated SEM analyses of stream sediment and rock samples show that specific minerals (chalcopyrite, bornite, and jarosite) are indicators of potential mineralized areas. Svanbergite, an aluminum sulfate phosphate mineral, was identified in mineralized rocks and in nearly all stream sediment samples (up to 9 kilometers) downstream from the Taurus and other porphyry occurrences but not epithermal occurrences. It was not identified in areas with no known mineralization and thus it is possibly one of the best indicator minerals for porphyry copper (+/- molybdenum, gold) occurrences.
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Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 973
Denver, CO 80225
Eastern black rails (Laterallus jamaicensis jamaicensis) are among the rarest and least-studied birds in North America and were recently listed as threatened under the U.S. Endangered Species Act. Spatial models that predict habitat quality across the subspecies range are therefore needed to inform conservation, recovery, and monitoring efforts for this rare bird. We used data from 47,585 call-broadcast surveys collected at 7906 sites over a 3-decade period (1990s, 2000s, 2010s; 23 total years) to build species distribution models for eastern black rails. We used hierarchical Bayesian occupancy models and predictive model selection to develop multi-scale models that optimally predict habitat suitability for eastern black rails within tidal wetlands while also accounting for imperfect detection of these cryptic birds during field surveys. We also used raster regression techniques to translate model predictions into 30-m resolution maps of habitat suitability for eastern black rails within tidal wetlands along the eastern seaboard of the United States. The model predicted suitability of breeding habitat as a function of wetland attributes (e.g., cover of high marsh and terrestrial border), hydrologic modification, and disturbance from human development measured over multiple spatial scales. We also found differences in habitat relationships for eastern black rails when compared to models that included both North American subspecies of black rail. Important results included negative effects of shrub-scrub wetlands, and strong positive effects of high marsh, terrestrial border, and impoundments on breeding season occupancy. Our study provides an example of integrating detection-non-detection data and modern statistical methods to build predictive distribution models for an extremely rare species, while also providing rigorous predictions of breeding habitat quality for the eastern black rail within tidal wetlands. These models will facilitate optimal monitoring, habitat conservation, and recovery planning efforts for eastern black rails and provide a foundation for future research and conservation of this imperiled bird.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02222","usgsCitation":"Stevens, B., Conway, C.J., Luke, K., Weldon, A., Hand, C., Schwarzer, A., Smith, F., Watson, C., and Watts, B.D., 2022, Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems: Global Ecology and Conservation, v. 38, e02222, 12 p., https://doi.org/10.1016/j.gecco.2022.e02222.","productDescription":"e02222, 12 p.","ipdsId":"IP-136723","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02222","text":"Publisher Index Page"},{"id":430022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":903426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luke, Kirsten","contributorId":338653,"corporation":false,"usgs":false,"family":"Luke","given":"Kirsten","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weldon, Aimee","contributorId":338654,"corporation":false,"usgs":false,"family":"Weldon","given":"Aimee","email":"","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hand, Christy","contributorId":338655,"corporation":false,"usgs":false,"family":"Hand","given":"Christy","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarzer, Amy","contributorId":338656,"corporation":false,"usgs":false,"family":"Schwarzer","given":"Amy","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":903431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Fletcher","contributorId":338657,"corporation":false,"usgs":false,"family":"Smith","given":"Fletcher","email":"","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Watson, Craig","contributorId":338659,"corporation":false,"usgs":false,"family":"Watson","given":"Craig","email":"","affiliations":[{"id":81184,"text":"Atlanti Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903433,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watts, Bryan D.","contributorId":338660,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D.","affiliations":[{"id":37406,"text":"College of William & Mary","active":true,"usgs":false}],"preferred":false,"id":903434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70232967,"text":"70232967 - 2022 - Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin","interactions":[],"lastModifiedDate":"2022-07-14T13:18:11.726912","indexId":"70232967","displayToPublicDate":"2022-07-14T08:08:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11122,"text":"BMC Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin","docAbstract":"Background
Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae.
Results
A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi. No evidence was found for genetic diversification across depth in G. childressi, likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments.
Conclusions
These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
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2022 - 2021 assessment of the Joint Fire Science Program’s Fire Science Exchange Network","interactions":[],"lastModifiedDate":"2022-07-14T11:00:12.647841","indexId":"sir20225052","displayToPublicDate":"2022-07-13T13:30:00","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":"2022-5052","displayTitle":"2021 Assessment of the Joint Fire Science Program’s Fire Science Exchange Network","title":"2021 assessment of the Joint Fire Science Program’s Fire Science Exchange Network","docAbstract":"The U.S. Geological Survey (USGS), on behalf of the Joint Fire Science Program (JFSP), conducted an evaluation of the Fire Science Exchange Network (FSEN), which connects wildland fire scientists and practitioners through 15 individual exchanges across the United States to help address complex wildfire needs and challenges. The study was divided into two phases: The first phase was a literature review and synthesis from materials provided by the JFSP Board. Phase two, informed by the JFSP review, was an online survey sent to more than 16,000 exchange network users compiled from the electronic mailing lists for each exchange. Respondents were asked their opinions on the importance, quality, and delivery of information for 16 key fire science topics, the prioritization of FSEN objectives, and from where and to what extent respondents are gathering information on key topics. Overall, respondents believed that sharing information and building relationships are the most important objectives of the FSEN. Respondents believed the exchange network is successful in delivering information for many of the key science topics (for example, fire behavior, prescribed fire, firefighter safety, and incident management); gaps were identified in scientific resources available for some topics (for example, economic impacts, social science and human dimensions, Indigenous knowledge). Most respondents participated in one to two exchanges and relied heavily on their respondent location (the exchange in which they primarily live and [or] work) for information. Respondents also often relied on external sources outside of the exchange network. Regional patterns emerged in information gathering whereby respondents from exchanges in the western United States (for example, Northern Rockies, Southern Rockies, and Northwest) and respondents from exchanges in the eastern United States (for example, Southern, Oak Woodlands, and Tallgrass) frequently gathered information from each other.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20225052","collaboration":"Prepared in cooperation with the Joint Fire Science Program","usgsCitation":"Collins, N., Meldrum, J., Schuster, R., and Burkardt, N., 2022, 2021 assessment of the Joint Fire Science Program’s Fire Science Exchange Network: U.S. Geological Survey Scientific Investigations Report 2022–5052 (ver 1.1, July 2022), 109 p., https://doi.org/10.3133/sir20225052.","productDescription":"Report: xi, 108 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-132591","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":403599,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5052/versionHist.txt","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2022-5052 version history"},{"id":402468,"rank":6,"type":{"id":31,"text":"Publication 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The Old Erie Canal has undergone sedimentation and aquatic growth that have restricted flow and diminished the aesthetic quality of the canal during the nearly 200 years since its construction. During 2018–2019, the U.S. Geological Survey (USGS) in cooperation with the Madison County Planning Department and the New York State Canal Corporation conducted a study of the Old Erie Canal between the Town of DeWitt, New York, and its junction with the current Erie Canal of the New York State Canal System near Rome, N.Y. The study comprised bathymetric, velocity, and water-quality surveys and documentation of the canal infrastructure. The USGS established benchmarks and staff gages along the 30.8 miles of the canal study area to reference the water-surface level in the canal to the North American Vertical Datum of 1988 (NAVD 88). Bathymetric survey results indicated that during the time of the survey, the canal depths ranged from 1.26 feet (ft) to 7.33 ft between the Butternut and Durhamville aqueducts (with a mean depth of 3.52 ft). Shallow depths are located throughout the canal, but the section north of the Durhamville aqueduct was the shallowest, with depths ranging from 0.68 ft to 2.44 ft (and a mean depth of 1.36 ft). The reach-averaged water velocity was 0.28 feet per second. The system generally flows west to east from the Butternut aqueduct to the entrance to the Erie Canal.
Water-quality data (dissolved oxygen, water temperature, specific conductance, pH, and turbidity) were collected concurrently with the bathymetric survey (spring 2018) to characterize changes in water quality along the length of the canal. Specific-conductance values measured upstream from the hamlet of Kirkville, Manlius, N.Y. may reflect road salts being flushed into the canal through the Butternut and Limestone feeder system (designed to divert water from nearby creeks to supply water for the Old Erie Canal) from recent stormwater runoff. Increases in pH in the downstream direction are possibly caused by increasing amounts of aquatic vegetation. During the time of the survey, turbidity was highest near inflows from the canal feeder system and tributary inputs which were elevated by stormwater runoff that transported sediment into the canal.
The canal infrastructure was documented to provide a baseline assessment. The feeder system, designed to bring water into the canal, does not deliver flow when the creeks supplying water to those feeders are at base flow, but does bring water into the system when flows in the feeder creeks are elevated. A recent report provides an example of repair work completed on the Chittenango feeder to improve flow through the feeder into the canal (Welch and Madison County Planning Department, 1996). Two non-regulated tributaries, Meadow Brook and Pools Brook, consistently delivered flow to the canal. Outfalls where canal water discharges into nearby creeks were sealed in the Butternut and Limestone aqueducts. Outfalls in the Chittenango, Cowaselon, and Durhamville aqueducts were found with flashboards installed at an elevation that allows water to be discharged from the canal. These structures are designed to accept additional flashboards to raise the canal water surface with the potential to convey flow farther down the system. The general condition of the channel was open and navigable between Butternut aqueduct and Chittenago aqueduct. On the segment of the canal east of the Chittenango aqueduct, an increasing number of downed trees and tangled wads of vegetation affected flow and made navigation by boat difficult to the Durhamville aqueduct. North of the Durhamville aqueduct, numerous downed trees and an increased density of aquatic vegetation limited navigation by boat and reduced the flow rate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211125","usgsCitation":"Wernly, J.F., 2022, Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19: U.S. Geological Survey Open-File Report 2021–1125, 75 p., https://doi.org/10.3133/ofr20211125.","productDescription":"Report: viii, 75 p.; Data Release","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118164","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":402850,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1125/ofr20211125.XML"},{"id":402848,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1125/ofr20211125.pdf","text":"Report","size":"94.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1125"},{"id":402847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1125/coverthb.jpg"},{"id":402849,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QRL294","text":"USGS data release","linkHelpText":"Geospatial dataset of the characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to Rome, New York 2018–2019"},{"id":402851,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1125/images/"},{"id":403542,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20211125/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2021-1125"},{"id":501536,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113265.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Old Erie Canal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.13250732421875,\n 42.974511174899156\n ],\n [\n -76.11328125,\n 42.968984647488014\n ],\n [\n -75.9375,\n 42.96044267380142\n ],\n [\n -75.74798583984375,\n 42.96446257387128\n ],\n [\n -75.57769775390625,\n 43.002638523957906\n ],\n [\n -75.41839599609375,\n 43.1270477646888\n ],\n [\n -75.35522460937499,\n 43.207177786666655\n ],\n [\n -75.42388916015625,\n 43.271206115959785\n ],\n [\n -75.52001953125,\n 43.25920592943639\n ],\n [\n -75.65460205078125,\n 43.23920036180898\n ],\n [\n -75.92926025390625,\n 43.219188223481325\n ],\n [\n -76.09405517578125,\n 43.13105676219153\n ],\n [\n -76.18194580078124,\n 43.07891929985966\n ],\n [\n -76.18743896484375,\n 43.022721607058344\n ],\n [\n -76.13250732421875,\n 42.974511174899156\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
A series of large earthquakes in 1899 affected southeastern Alaska near Yakutat and Disenchantment Bays. The largest of the series, a MW 8.2 event on 10 September 1899, generated an ~12-m-high tsunami and as much as 14.4 m of coseismic uplift in Yakutat Bay, the largest coseismic uplift ever measured. Several complex fault systems in the area are associated with the Yakutat terrane collision with North America and the termination of the Fairweather strike-slip system, but because faults local to Yakutat Bay have been incompletely or poorly mapped, it is unclear which fault system(s) ruptured during the 10 September 1899 event. Using marine geophysical data collected in August 2012, we provide an improved tectonic framework for the Yakutat area, which advances our understanding of earthquake hazards. We combined 153 line km of 2012 high-resolution multichannel seismic (MCS) reflection data with compressed high-intensity radar pulse (Chirp) profiles, basin-scale MCS data, 2018 seafloor bathymetry, published geodetic models and thermochronology data, and previous measurements of coseismic uplift to better constrain fault geometry and subsurface structure in the Yakutat Bay area. We did not observe any active or concealed faults crossing Yakutat Bay in our high-resolution data, requiring faults to be located entirely onshore or nearshore. We interpreted onshore faults east of Yakutat Bay to be associated with the transpressional termination of the Fairweather fault system, forming a series of splay faults that exhibit a horsetail geometry. Thrust and reverse faults on the west side of the bay are related to Yakutat terrane underthrusting and collision with North America. Our results include an updated fault map, structural model of Yakutat Bay, and quantitative assessment of uncertainties for legacy geologic coseismic uplift measurements. Additionally, our results indicate the 10 September 1899 rupture was possibly related to stress loading from the earlier Yakutat terrane underthrusting event of 4 September 1899, with the majority of 10 September coseismic slip occurring on the Esker Creek system on the northwest side of Yakutat Bay. Limited (~2 m) coseismic or postseismic slip associated with the 1899 events occurred on faults located east of Yakutat Bay.
Germanium (Ge) is a metal used in emerging energy technologies, communications, and defense, and has been deemed critical by the United States due to its essential applications and scarce supply. Germanium is recovered as a byproduct of zinc (Zn) sulfides, and mining and processing of these materials lead to waste that could act both as a source of extractable Ge and a source for exposure to humans and ecosystems. Yet the distribution, speciation, and mineral hosts of Ge in mining-impacted areas are poorly understood. The Tar Creek Superfund Site, a former Zn mining area and Ge producer, is a natural laboratory to understand the environmental behavior and economic implications of Ge in mine wastes. We studied the distribution and behavior of Ge in solid wastes at the Tar Creek Superfund Site using bulk and microanalytical techniques. In wastes at this site we find that Ge has been redistributed from its original host, sphalerite (ZnS), to the fine-grained weathering product hemimorphite (Zn4Si2O7(OH)2·H2O), which impacts germanium's mobility, bioaccessibility, and potential for recovery. We provide chemical and mineralogical evidence of this redistribution, along with an evaluation of the oxidation state and molecular-scale substitution of Ge into sphalerite, hemimorphite, and quartz. Geochemical modeling shows that hemimorphite is more stable than sphalerite in waste piles and provides a stable secondary repository for Ge. However, hemimorphite is fine-grained, and if ingested or inhaled is readily soluble, with the potential to release Ge. Lastly, we discuss other sites internationally where similar behavior may be important. This study shows that weathering can have a significant impact on the distribution, speciation, and mineral hosts of Ge in mine wastes; directly influence mobilization from waste piles and subsequent availability to humans and ecosystems; and dictate metallurgical strategies to target Ge for recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2022.105341","usgsCitation":"White, S.J., Piatak, N.M., McAleer, R.J., Hayes, S.M., Seal,, R., Schaider, L.A., and Shine, J.P., 2022, Germanium redistribution during weathering of Zn mine wastes: Implications for environmental mobility and recovery of a critical mineral: Applied Geochemistry, v. 143, 105341, 12 p., https://doi.org/10.1016/j.apgeochem.2022.105341.","productDescription":"105341, 12 p.","ipdsId":"IP-127786","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":447162,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2022.105341","text":"Publisher Index Page"},{"id":435781,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZM36FG","text":"USGS data release","linkHelpText":"Mineral abundances within bulk and size-fractionated mine waste from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":435780,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HI7VKH","text":"USGS data release","linkHelpText":"Molecular speciation of Ge within sphalerite, hemimorphite, and quartz from mine waste from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":435779,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HHH5FL","text":"USGS data release","linkHelpText":"Geochemical, mineralogical, and molecular scale speciation characterization of mine wastes from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A. "},{"id":435778,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ALZZ3E","text":"USGS data release","linkHelpText":"Electron microprobe analyses of sphalerite and hemimorphite from mine wastes from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":435777,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MXFIQ","text":"USGS data release","linkHelpText":"Elemental concentrations for bulk and size-fractionated mine waste from the Tar Creek Superfund Site, Tri-State Mining District, Oklahoma, U.S.A."},{"id":404206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Tar Creek Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.40502929687499,\n 36.049098959065645\n ],\n [\n -94.658203125,\n 36.049098959065645\n ],\n [\n -94.658203125,\n 37.00255267215955\n ],\n [\n -96.40502929687499,\n 37.00255267215955\n ],\n [\n -96.40502929687499,\n 36.049098959065645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Sarah Jane 0000-0002-4055-8207","orcid":"https://orcid.org/0000-0002-4055-8207","contributorId":216796,"corporation":false,"usgs":true,"family":"White","given":"Sarah","email":"","middleInitial":"Jane","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piatak, Nadine M. 0000-0002-1973-8537 npiatak@usgs.gov","orcid":"https://orcid.org/0000-0002-1973-8537","contributorId":193010,"corporation":false,"usgs":true,"family":"Piatak","given":"Nadine","email":"npiatak@usgs.gov","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":847201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Sarah M. 0000-0001-5887-6492","orcid":"https://orcid.org/0000-0001-5887-6492","contributorId":208569,"corporation":false,"usgs":true,"family":"Hayes","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847202,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":847203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schaider, Laurel A.","contributorId":291960,"corporation":false,"usgs":false,"family":"Schaider","given":"Laurel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":847204,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shine, James P.","contributorId":178314,"corporation":false,"usgs":false,"family":"Shine","given":"James","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":847205,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232303,"text":"ofr20221006 - 2022 - U.S. Geological Survey coastal plain amplification virtual workshop","interactions":[],"lastModifiedDate":"2022-09-27T13:46:17.084014","indexId":"ofr20221006","displayToPublicDate":"2022-07-06T11:30:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1006","displayTitle":"U.S. Geological Survey Coastal Plain Amplification Virtual Workshop","title":"U.S. Geological Survey coastal plain amplification virtual workshop","docAbstract":"In early October of 2020, the U.S. Geological Survey (USGS) held a virtual workshop to discuss Gulf and Atlantic Coastal Plains site-response models. Earthquake researchers came together to assess (1) research related to proposed Coastal Plains amplification models and (2) USGS plans for implementing these models. Presentations spanned a broad range of topics from Atlantic and Gulf Coastal Plains geophysical properties including seismic velocity and attenuation, to ground motion amplification models and their impacts on seismic hazard. Interspersed with these presentations were discussions regarding the definition and extent of the Atlantic and Gulf Coastal Plains, potential complexities of wave propagation in the Atlantic and Gulf Coastal Plains, and problems that need to be overcome to implement various proposed site-response models. Based on feedback from this workshop, the USGS working group on Coastal Plain Amplification is considering applying published models that depend on sediment thickness. The working group is also exploring potential application of models that depend on the length of path traversed across the Coastal Plain, including the Gulf Coastal Plain ground-motion model adjustments from the Next Generation Attenuation Relationships for the Eastern United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221006","usgsCitation":"Boyd, O.S., Pratt, T.L., Chapman, M.C., Shumway, A., Rezaeian, S., Moschetti, M.P., and Petersen, M.D., 2022, U.S. Geological Survey coastal plain amplification virtual workshop: U.S. Geological Survey Open-File Report 2022–1006, 25 p., https://doi.org/10.3133/ofr20221006.","productDescription":"vi, 25 p.","onlineOnly":"Y","ipdsId":"IP-128818","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":402497,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.pdf","text":"Report","size":"849 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1006"},{"id":402498,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.xml"},{"id":402496,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1006/coverthb.jpg"},{"id":405344,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1006/images"},{"id":405560,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221006/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1006"}],"country":"United States","otherGeospatial":"Gulf and Atlantic Coast Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.716796875,\n 25.522614647623293\n ],\n [\n -81.03515625,\n 31.541089879585808\n ],\n [\n -75.5419921875,\n 35.35321610123823\n ],\n [\n -73.95996093749999,\n 40.38002840251183\n ],\n [\n -69.5654296875,\n 41.57436130598913\n ],\n [\n -70.48828125,\n 42.19596877629178\n ],\n [\n -70.224609375,\n 43.51668853502906\n ],\n [\n -66.97265625,\n 44.62175409623324\n ],\n [\n -68.2470703125,\n 45.920587344733654\n ],\n [\n -71.630859375,\n 43.45291889355465\n ],\n [\n -76.025390625,\n 40.613952441166596\n ],\n [\n -78.31054687499999,\n 38.54816542304656\n ],\n [\n -81.4306640625,\n 33.578014746143985\n ],\n [\n -83.056640625,\n 30.86451022625836\n ],\n [\n -92.98828125,\n 30.977609093348686\n ],\n [\n -96.328125,\n 30.29701788337205\n ],\n [\n -98.4375,\n 28.57487404744697\n ],\n [\n -98.0859375,\n 26.470573022375085\n ],\n [\n -97.20703125,\n 25.997549919572112\n ],\n [\n -96.9873046875,\n 27.371767300523047\n ],\n [\n -95.5810546875,\n 28.65203063036226\n ],\n [\n -93.8232421875,\n 29.305561325527698\n ],\n [\n -91.40625,\n 29.19053283229458\n ],\n [\n -89.9560546875,\n 28.8831596093235\n ],\n [\n -88.9892578125,\n 29.84064389983441\n ],\n [\n -86.17675781249999,\n 30.031055426540206\n ],\n [\n -84.111328125,\n 29.49698759653577\n ],\n [\n -83.1005859375,\n 28.76765910569123\n ],\n [\n -82.529296875,\n 26.82407078047018\n ],\n [\n -81.123046875,\n 25.045792240303445\n ],\n [\n -80.5078125,\n 24.726874870506972\n ],\n [\n -79.716796875,\n 25.522614647623293\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 966
Denver, CO 80225
Connecticut, the third-smallest State by land area, is the fourth most densely populated in the United States. Connecticut’s tightly packed cities serve as international hubs for the finance and insurance industries. These same urban enclaves host highly regarded institutions of higher learning, such as Yale, the University of Connecticut, and Quinnipiac and Wesleyan Universities.
Yet Connecticut has surprisingly varied natural features. Its residents are never far from pristine environments that teem with wildlife and convey the sounds, scents, and scenery of the natural world.
The Connecticut River, the longest in New England at 407 miles, cuts through the State’s center and drains into Long Island Sound, as do the Thames and Housatonic Rivers. Together, these rivers form an estuary spawning ground for hundreds of freshwater and saltwater aquatic species. North and east of the coastal lowlands that hug the estuary are the highlands of the Southern Taconic Range and the Berkshires. North and west of the coastline, smaller rivers and valleys weave through densely forested landscapes dotted with State parks and scenic lakes.
Imagery from the U.S. Geological Survey Landsat archive, a trusted public resource with more than 50 years of worldwide Earth observations, provides a powerful tool for analyzing the interplay of human habitation and natural landscapes in Connecticut. Landsat data also offer land managers a critical information source for managing damage from invasive species and monitoring the local landscape effects of a changing global climate.
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12201 Sunrise Valley Drive
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In Lake Erie, lake whitefish Coregonus clupeaformis supported lucrative fisheries before populations were decimated by overfishing and water quality degradation. In recent years, there has been a renewed interest in lake whitefish and management of the fishery they support. Lake whitefish spawn on several reefs throughout Lake Erie, but the relative recruitment dynamics and contributions of spawning groups to the fishery are not well understood. Modern high-throughput sequencing approaches offer new opportunities to census population diversity and to identify subtle differences among closely related populations. We used high-throughput sequencing data to evaluate the genetic structure and diversity of lake whitefish collected opportunistically across broad spatial scales in Lake Erie. Using RAD-capture (Rapture), we sequenced and genotyped individuals (N = 88) from the west, central, and east basin of Lake Erie at 120,268 single nucleotide polymorphisms (SNPs). Lake whitefish from Niagara and Crib Reefs (west basin) diverged from the three collections. Interestingly, these were the only lake whitefish collected during the act of spawning (late November), and all other fish were collected pre-spawn (August-early November). These results suggest that some lake whitefish spawning reefs may be reproductively isolated, though definition of these groups into stocks will require more intentional sampling during the act of spawning.
We assessed the influence of the severe mid-20th century population decline on genetic diversity in non-augmented peregrine falcon (Falco peregrinus) populations nesting within Alaska Arctic and eastern Interior. Microsatellite and mitochondrial DNA (mtDNA) data were analyzed for peregrine falcons sampled from three periods: pre-decline, decline, and post-decline. The influence of the decline on genetic diversity differed between the two locales. Alaska Arctic was characterized by shifts in mtDNA haplotype frequencies, increased inbreeding coefficient, reduction in effective population size, and increase in private haplotypes, and a signature of post-decline population growth was detected, while eastern Interior showed a reduction in haplotype diversity, no differences in allelic or haplotypic frequencies between pre- and post-decline periods, though pre-decline birds clustered away from the other two periods and allelic frequency differences were observed between decline and post-decline periods. Patterns in genetic diversity suggest populations recovered through recruitment from within and immigration.
A new 3-D resistivity model, estimated from inversion of magnetotelluric data, images crustal and upper-mantle structure of the Wyoming Province and adjacent areas. The Archean province is imaged as a coherent resistive domain, in sharp contrast to active tectonic domains of the western U.S. Prominent high-conductivity belts define the northern, eastern, and southern margins of the Wyoming Province and are interpreted as sutures marking the remnants of Paleoproterozoic orogens. The model results suggest the northern boundary of the Wyoming Province is located 150 km south of its traditional placement and adjacent to a composite orogen separating the Wyoming Province and Medicine Hat block. The eastern province boundary is clearly imaged along the Black Hills, whereas the western margin is obscured by Cenozoic extension and magmatism. An internal boundary within the Wyoming Province is interpreted to represent a Neoarchean suture; in stark contrast to Proterozoic sutures, though, it is not marked by a high-conductivity belt. This difference in conductivity is speculated to reflect changes in the subduction process through time. The absence of high-conductivity along Archean sutures appears to be global in nature and related to reduced continental freeboard in the Archean which limited continental weathering and the delivery of carbon-rich sediments to the seafloor. Although the entire Wyoming Province has been proposed to have undergone lithospheric modification that lessened its stability, the resistivity model suggests a thick lithospheric root remains in place except along its western margin. These results suggest that Archean cratons may be more resistant to lithospheric modification by influx of heat and fluids associated with extension and plumes than previously thought, and that metasomatism does not necessarily weaken the lithosphere and set a craton on the path to destruction.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36417.1","usgsCitation":"Bedrosian, P.A., and Frost, C.D., 2022, Geophysical extent of the Wyoming Province, western USA: Insights into ancient subduction and craton stability: Geological Society of America Bulletin, v. 135, no. 3-4, p. 725-742, https://doi.org/10.1130/B36417.1.","productDescription":"18 p.","startPage":"725","endPage":"742","ipdsId":"IP-136962","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/b36417.1","text":"Publisher Index Page"},{"id":501589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Montana, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.26750794943243,\n 48.72244507268985\n ],\n [\n -113.26750794943243,\n 39.45653001110685\n ],\n [\n -102.04436648017622,\n 39.45653001110685\n ],\n [\n -102.04436648017622,\n 48.72244507268985\n ],\n [\n -113.26750794943243,\n 48.72244507268985\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"135","issue":"3-4","noUsgsAuthors":false,"publicationDate":"2022-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":957812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frost, Carol D. 0000-0002-1674-2725","orcid":"https://orcid.org/0000-0002-1674-2725","contributorId":367851,"corporation":false,"usgs":false,"family":"Frost","given":"Carol","middleInitial":"D.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":957813,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236819,"text":"70236819 - 2022 - Egg retention in wild-caught Python bivittatus in the Greater Everglades Ecosystem, Florida, USA","interactions":[],"lastModifiedDate":"2022-09-19T15:52:06.211731","indexId":"70236819","displayToPublicDate":"2022-07-01T10:39:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1895,"text":"Herpetological Journal","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Egg retention in wild-caught Python bivittatus in the Greater Everglades Ecosystem, Florida, USA","title":"Egg retention in wild-caught Python bivittatus in the Greater Everglades Ecosystem, Florida, USA","docAbstract":"Retention of eggs in oviducts beyond the normal oviposition period is a common problem for captive reptiles, but the occurrence of egg retention in wild populations is largely unknown. The Burmese python (Python [molurus] bivittatus; Kühl 1820) is an oviparous snake native to south-eastern Asia that is now established in southern Florida. From 2011–2019, invasive Burmese pythons were opportunistically removed from Everglades National Park and Big Cypress National Preserve, humanely euthanised, and necropsied to determine reproductive condition. A total of 258 females of reproductive size were found to exhibit various stages of oviposition which generally aligned with purported annual reproductive timing. However, we encountered five pythons during the post-ovulatory period (Aug–Feb) showing signs of recent oviposition with retained eggs. Most of these cases comprised a small number of retained eggs, likely representing some portion of the total clutch. Because this condition is nearly absent in wild animal literature, our observations suggest retained eggs in wild snakes may be more common than previously assumed, possibly slowing or otherwise impacting population growth. However, we recognise that for an invasive species like the Burmese python in Florida, the egg retention rate may be higher in the non-native range compared to the native range due to maladaptation to novel habitats or environmental conditions. Additional research is needed to determine the exact causes of egg retention and investigate the implications for population dynamics on this and other snake species.
","language":"English","publisher":"British Herpetological Society","doi":"10.33256/32.3.109113","usgsCitation":"Anderson, G.E., Ridgley, F.N., Josimovich, J.M., Reed, R., Falk, B., Yackel Adams, A.A., and Currylow, A.F., 2022, Egg retention in wild-caught Python bivittatus in the Greater Everglades Ecosystem, Florida, USA: Herpetological Journal, v. 32, p. 109-113, https://doi.org/10.33256/32.3.109113.","productDescription":"5 p.","startPage":"109","endPage":"113","ipdsId":"IP-124676","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":447252,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.33256/32.3.109113","text":"Publisher Index Page"},{"id":406973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.771240234375,\n 25.075648445630527\n ],\n [\n -80.343017578125,\n 25.075648445630527\n ],\n [\n -80.343017578125,\n 26.657277674217585\n ],\n [\n -81.771240234375,\n 26.657277674217585\n ],\n [\n -81.771240234375,\n 25.075648445630527\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2022-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Gretchen Erika 0000-0002-5887-4961","orcid":"https://orcid.org/0000-0002-5887-4961","contributorId":271047,"corporation":false,"usgs":true,"family":"Anderson","given":"Gretchen","email":"","middleInitial":"Erika","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":852256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ridgley, Frank N.","contributorId":296701,"corporation":false,"usgs":false,"family":"Ridgley","given":"Frank","email":"","middleInitial":"N.","affiliations":[{"id":54678,"text":"Zoo Miami, Conservation and Research Department, 12400 SW 152nd St., Miami, Florida 33177, USA","active":true,"usgs":false}],"preferred":false,"id":852257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Josimovich, Jillian Maureen 0000-0002-7523-3496 jjosimovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7523-3496","contributorId":257058,"corporation":false,"usgs":true,"family":"Josimovich","given":"Jillian","email":"jjosimovich@usgs.gov","middleInitial":"Maureen","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":852258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reed, Robert 0000-0001-8349-6168","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":267796,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":852259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falk, Bryan G.","contributorId":296702,"corporation":false,"usgs":false,"family":"Falk","given":"Bryan G.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":852260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":852261,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Currylow, Andrea Faye 0000-0003-1631-8964","orcid":"https://orcid.org/0000-0003-1631-8964","contributorId":257055,"corporation":false,"usgs":true,"family":"Currylow","given":"Andrea","email":"","middleInitial":"Faye","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":852262,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270792,"text":"70270792 - 2022 - Standard operating protocol for mark and recapture monitoring of Brook Floater in streams","interactions":[],"lastModifiedDate":"2025-08-25T15:33:06.25202","indexId":"70270792","displayToPublicDate":"2022-07-01T10:32:28","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-142-2022","title":"Standard operating protocol for mark and recapture monitoring of Brook Floater in streams","docAbstract":"The Brook Floater (Alasmidonta varicosa) is a small (<100 mm) freshwater mussel (Family: Unionidae) found in streams of the eastern United States (U.S.) (Nedeau 2008). While there has been limited effort to document the status of Brook Floater across its range, there is evidence of Brook Floater range contraction and declining local abundances over recent decades (Wicklow et al. 2017, NatureServe 2021). Brook Floater is a Species of Greatest Conservation Need (SGCN) in 15 states (94% of range); listed as endangered, threatened, or special concern in nearly every state and province where it still occurs; and has been extirpated from two states (Rhode Island and Delaware). Brook Floater was petitioned for Federal listing under the U.S. Endangered Species Act; however, the listing was determined not to be warranted (U.S. FWS 2019), although it remains a Regional SGCN of very high concern in U.S. Fish & Wildlife Service (U.S. FWS) Regions 5 (Terwilliger 2015) and 4 (SEAFWA-WDC 2019) and is an At-Risk Species in U.S. FWS Region 5.
A critical component of understanding population declines is site-specific information about population density and demographics (e.g., growth, age structure) to assess population viability. This information had previously only been collected for a few populations of Brook Floater (e.g., Massachusetts Division of Fisheries & Wildlife, North Carolina Wildlife Resources Commission) and methods to collect these data varied from state to state, thus limiting comparisons across the range. In 2016, a competitive State Wildlife Grant (SWG) was awarded to develop a standardized monitoring technique that will aid in understanding differences in population viability across its range and assess changes in populations through time. The protocol described in this report was subsequently developed and tested by Massachusetts and Maine (2 sites in each state) and revised based on field experiences. Data collected using this protocol will allow for state managers to make informed decisions about management actions for Brook Floater.
Monitoring approaches are ideally designed to meet management objectives. Management objectives are specific, quantifiable outcomes that reflect the values of the decision makers and relate directly to the management decisions (Conroy and Peterson 2013). Lack of well-defined objectives hinders success of conservation and management actions because there are undefined metrics to determine when the objectives have been met (Yoccoz et al. 2001, Nichols and Thompson 2006). While monitoring to understand a system (i.e., status and trends; Reynolds et al. 2016) provides baseline information for developing management recommendations in the future, Nichols and Thompson (2006) criticize status and trends monitoring because of time lags associated with conservation and the costs and resource availability needed for surveillance, among other reasons. State partners in the Brook Floater SWG have a variety of different monitoring objectives (e.g., abundance/density, survival, recruitment) that depend on the population sizes and demographics.
There are many approaches for estimating population parameters such as density, age structure, recruitment, and growth rates. For example, presence/absence (i.e., multistate models), counts (i.e., multi-state models or Dail-Madsen model; Dail and Madsen 2011), and capture mark-recapture (CMR; e.g. Cormack-Jolly-Seber models; Lindberg and Rexstad 2002) are all approaches for assessing population status and viability.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css67282137","usgsCitation":"Sterrett, S., Roy, A.H., Hazelton, P., Swartz, B., Nedeau, E., Carmignani, J., and Skorupa, A., 2022, Standard operating protocol for mark and recapture monitoring of Brook Floater in streams: Cooperator Science Series CSS-142-2022, https://doi.org/10.3996/css67282137.","ipdsId":"IP-132939","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Sterrett, Sean","contributorId":360459,"corporation":false,"usgs":false,"family":"Sterrett","given":"Sean","affiliations":[{"id":69149,"text":"Massachusetts Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":947082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":947081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelton, Peter","contributorId":360462,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter","affiliations":[{"id":86008,"text":"Natural Heritage and Endangered Species Program","active":true,"usgs":false}],"preferred":false,"id":947083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swartz, Beth","contributorId":360463,"corporation":false,"usgs":false,"family":"Swartz","given":"Beth","affiliations":[{"id":86011,"text":"Maine Department of Inland Fisheries & Wildlife","active":true,"usgs":false}],"preferred":false,"id":947084,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nedeau, Ethan","contributorId":360464,"corporation":false,"usgs":false,"family":"Nedeau","given":"Ethan","affiliations":[{"id":86012,"text":"Biodrawversity","active":true,"usgs":false}],"preferred":false,"id":947085,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carmignani, Jason","contributorId":360465,"corporation":false,"usgs":false,"family":"Carmignani","given":"Jason","affiliations":[{"id":86008,"text":"Natural Heritage and Endangered Species Program","active":true,"usgs":false}],"preferred":false,"id":947086,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Skorupa, Ayla","contributorId":360466,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla","affiliations":[{"id":69149,"text":"Massachusetts Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":947087,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}