No abstract available.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.ppat.1010624","usgsCitation":"Zipkin, E., and DiRenzo, G.V., 2022, Biodiversity is decimated by the cascading effects of the amphibian-killing chytrid fungus: PLoS Pathogens, v. 8, no. 7, e1010624, 7 p., https://doi.org/10.1371/journal.ppat.1010624.","productDescription":"e1010624, 7 p.","ipdsId":"IP-137551","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.ppat.1010624","text":"Publisher Index Page"},{"id":480776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Zipkin, Elise F.","contributorId":349049,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":923954,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":923955,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70233448,"text":"fs20223062 - 2022 - Mississippi and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T11:55:07.537702","indexId":"fs20223062","displayToPublicDate":"2022-07-20T21:20:22","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":"2022-3062","displayTitle":"Mississippi and Landsat","title":"Mississippi and Landsat","docAbstract":"Mississippi holds a significant place in the cultural and economic history of the United States. For example, the Magnolia State was a hotbed for the Delta blues, an early 20th century musical genre with tremendous cultural effects in the United States and around the world. The “Delta” in Delta blues is the Mississippi River Delta, the largest delta in the United States. The Mississippi River is one of the longest in the Nation, having 1,800 navigable miles that serve as a natural transportation corridor that is foundational to America’s economic fortunes.
This important waterway forms Mississippi’s western border, and the fertile alluvial soils that fan out across its western third served to bolster its early economy and remain key drivers of its agriculture sector. These soils continue to support cotton, soybeans, corn, and agricultural products for which Mississippi is a national leader: rice and farmed catfish.
Such heavy reliance on the land necessitates a strong understanding of the health of—and threats to—the landscape. Spikes in extreme heat and associated wildfire dangers, the increasing frequency of powerful hurricanes on the Gulf Coast, and annual tornadoes all stand as challenges to the Magnolia State’s land resources.
The U.S. Geological Survey Landsat program serves a critical role in mapping, monitoring, and understanding changes across Mississippi. Here are a few examples of how Landsat benefits the State.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Alabama’s warm climate and rich soil bolster its agriculture and timber industries, but they also offer ideal conditions for natural playgrounds, enjoyed by humans and the wildlife that call the open green spaces home. Alabama has 21 State parks and 11 national parks, monuments, and trails across its diverse geography.
Cotton is no longer king in the Cotton State, but it remains a part of the fabric of its multibillion-dollar agriculture sector. The State also produces poultry, cattle, calves, corn, lumber, soybeans, and catfish.
The State is home to Dauphin Island, the first land mass seen by migratory birds and pollinators upon their return from South America. Dauphin Island acts as a landing zone for hundreds of species, including sandpipers, plovers, and herons, and protects Alabama’s coastline. Since 1961, Dauphin Island has been the home of the Audubon Bird Sanctuary.
The varied landscapes of the Cotton State face challenges from climate change, land change, and extreme weather. Satellite imagery from the U.S. Geological Survey Landsat Program can offer insight and understanding in these and other areas. Here are some ways Landsat benefits Alabama.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
From the flat, rich soil of western Tennessee to the Appalachian Mountains in the east, and rolling hills in between, “the Volunteer State” enjoys a wealth of natural resources.
The Tennessee, Cumberland, and Mississippi Rivers supply economically crucial navigation routes, along with recreation for residents and visitors. Additionally, 14 million acres of hardwood and softwood forests cover roughly one-half of the State, contributing an estimated $24 billion and nearly 100,000 jobs to Tennessee’s economy. Within a span of more than 400 miles, the State’s diverse agricultural products include cotton, corn, soybeans, poultry, horses, cattle, goats, hay, vegetables, nursery crops, and tobacco.
Energy production is important to Tennessee and the region, and power sources range from coal and nuclear to hydroelectric sources. Tourism also is a key industry, and music attractions and historical sites are balanced by natural features such as the Great Smoky Mountains National Park, which recorded 14.1 million visits and ranked second for most visited National Park Service site in the United States in 2021.
Landsat imagery’s broad geographic scale and rich historical archive have proven useful to land managers and State agencies for monitoring natural resources. Here are several ways Landsat has benefited Tennessee.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
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
Delaware’s status as the first State to ratify the U.S. Constitution is a well-known point of pride. “The First State” is among Delaware’s nicknames, alongside “the Blue Hen State,” “the Diamond State,” and “the Small Wonder,” the last of which relates to Delaware’s diminutive land area—larger only than Rhode Island.
Less well known, perhaps, is Delaware’s geographic distinction as the State with the lowest average elevation. Most of its land area rises no more than 80 feet above sea level. In fact, about 32,000 acres of Cypress Swamp, sometimes called the Great Cypress Swamp, stretch across its southern border.
These low elevations put Delaware at particular risk of sea level rise associated with climate change. Sea levels are rising more quickly than average for the Mid-Atlantic Region, which includes Delaware. The State has seen its coastal waters rise more than 1 foot over the past century.
The Landsat Program’s 50-year archive of repeat Earth observations offers an indispensable record of land change along the Nation’s coastlines. Imagery collected by Landsat satellites can inform studies of the coastline losses, flooding extents, and land cover conversions that affect climate resilience in Delaware. Landsat data also can support plans to mitigate those effects. Here are a few examples of the ways Delaware benefits from Landsat.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Louisiana holds a unique historical, economic, and cultural position in the national consciousness. Its off-shore oil operations help fuel the U.S. economy. The Port of South Louisiana is the busiest in the United States by cargo volume; the nearby Port of New Orleans is the sixth busiest. The former French and Spanish colony served as a key connection to the Caribbean long before U.S. independence, and Louisiana’s multinational effects soon melded into a Creole culture that had an outsized effect on America.
That heritage remains a powerful draw for the tourism industry in Louisiana. Gulf Coast breezes carry the aromas of tropical flowers, sweet beignets, and savory crawfish through the 13 colorful blocks of New Orleans’ French Quarter. Interwoven with the sounds of jazz, rock, country music, and zydeco, the city’s charms delight more than 18 million visitors each year.
The proximity of the Gulf Coast and the city’s elevation, however—just 6.5 feet above sea level—also offer an ominous warning of the ever-present threat of climate change and natural disaster. Hurricane Katrina battered New Orleans in 2005, an incident tied to more than 1,800 deaths that marks one of the most notorious U.S. weather-related tragedies in the 21st century. Environmental changes have amplified threats from tropical storms. Through more frequent and powerful storms, sea level rise threatens low-lying areas such as Lake Charles and creates unpredictable weather patterns that threaten the cities and agricultural operations to the north.
Landsat data offer rich information that can aid in early warning, disaster response, and the monitoring of recovery from natural disasters. Its historic, unparalleled 50-year archive of repeat Earth observations also serves to guide resiliency plans and feeds modeling that can help States like Louisiana prepare for coming coastal and inland change. Here are just a few examples of how Landsat has been used to study and understand Louisiana.
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"nation\":\"USA \"}}]}","edition":"Version 1.0: July 20, 2022; Version 1.1: March 19, 2025","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Iowa is famous for plenty of reasons—its State Fair butter sculptures, its first-in-the-Nation presidential caucuses, and the Iowa Hawkeyes football team, whose mascot doubles as the State nickname—but “corn” might be the first word to cross the mind of a non-Iowan.
Iowa consistently leads the United States in corn production and in the production of hogs, which in turn consume a sizable share of the corn grown there. Corn also drives Iowa’s ethanol industry, which put nearly 4.5 billion gallons of fuel into the supply chain in 2020—more than any other State.
Iowa owes its agricultural dominance largely to its fertile soils, making land management decisions critical to its future. Changes to land cover, more intensive land use, unusual precipitation patterns, and temperature changes, coupled with an influx of extreme weather events—some of which can be tied to or exacerbated by climate change—have placed pressure on the productive farm ground of Iowa’s 99 counties.
Landsat Program satellites can be especially useful in the monitoring and management of croplands across the United States. Backed by a 50-year record of Earth surface change, Landsat satellites can detect the details of vegetation health by peering into the infrared and near-infrared parts of the electromagnetic spectrum. Iowa has long served as a proving ground for Landsat-based agricultural research, and its residents and leaders have benefited from that work. Here are a few examples of how the Landsat Program benefits Iowa.
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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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225039","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation, the Tug Hill Commission, the Jefferson County Soil and Water Conservation District, the Oswego County Soil and Water Conservation District, and the Tug Hill Land Trust","usgsCitation":"Miller, T.S., Fisher, B.N., and Kappel, W.M., 2022, Geohydrology and water quality of the northern and central parts of the Tug Hill glacial aquifer, Jefferson and Oswego Counties, north-central New York: U.S. Geological Survey Scientific Investigations Report 2022–5039, 54 p., https://doi.org/10.3133/sir20225039.","productDescription":"Report: ix, 54 p.; Data Releases: 2; Figures: 2; Tables: 4","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060485","costCenters":[{"id":474,"text":"New York Water Science 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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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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The rolling plains of Nebraska occupy a storied place in the American psyche. For those living outside the Midwest, the Cornhusker State may be seen as a symbol of the Nation’s heartland, cropped border to border, with country churches and barely standing barns to be found around every turn of its gravel roads.
Although the pioneer history and agricultural heritage of the 37th State lend credence to this idyllic view, Nebraska’s varied landscapes and modern economy make the reality of life in the State more complex than its rural image would suggest.
Agriculture remains Nebraska’s top industry, but manufacturing now represents 12 percent of the State’s gross domestic product. The financial services and insurance industries account for 8 percent of the gross domestic product in Nebraska, which is the headquarters of Berkshire Hathaway and Mutual of Omaha, the latter of which is named after Nebraska’s largest city, which grew nearly 12 percent between 2010 and 2020. Cropland is indeed a prominent feature in Nebraska, but the State also is home to 8 State parks, 5 national parks, 2 national forests, and 3 national grasslands. The grasses and dunes of the Nebraska Sand Hills that stretch across the north-central quarter of the State are a National Natural Landmark.
Data from the Landsat satellite program contribute to the study and management of Nebraska’s land in myriad ways, from monitoring crop productivity and aiding in rangeland management to tracking damage from floods, droughts, or hurricanes. Land cover maps produced using data pulled from the 50-year Landsat archive can offer important insights into urban growth, land use trends, and land change patterns. Here are a few examples of how Landsat has been used in Nebraska.
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12201 Sunrise Valley Drive
Reston, VA 20192
Wisconsin could be called a State of icons, and many of the icons can trace their roots to the abundant resources within its four borders. Big beer companies in Milwaukee that began in the 1800s made their beer from water from nearby lakes and rivers, kept it cool with ice from those same sources, and stored it in containers made of harvested wood from State forests. Dairy and cheese factories rely on milk from dairy farms with fields and pastures and generate billions in revenue for the State’s economy. The Wisconsin Dells, Door County, and the Northwoods draw tourists with their natural beauty and recreation opportunities.
Wisconsin contains more than 17 million acres of hardwood and coniferous forest, much of it on land reforested since the large-scale timber cutting of the 1800s and early 1900s. The Badger State boasts nearly 15,000 lakes within its borders and touches two Great Lakes—a bit of Lake Superior and a considerable length of western Lake Michigan. Wisconsin ranks second in the Nation for milk production, but it ranks first for cheese, cranberries, snap beans, and milk goats.
Data and imagery from Landsat Earth observation systems assist agencies and land managers in monitoring these resources and planning for future management. Here are several ways Landsat benefits Wisconsin.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Among important seabird breeding sites in the main Hawaiian Islands, Lehua Island offshore Niʻihau and Moku Manu Islets offshore Oʻahu support diverse and abundant seabird breeding populations. Both offshore islands provide excellent nesting habitat for surface-nesting boobies (Sula spp.) and terns but, of the two, only Moku Manu supports relatively large breeding populations of Sooty Tern (Onychoprion fuscatus) and Brown Noddy (Anous stolidus). Additionally, Ulupaʻu Crater, near Moku Manu on Oʻahu, is one of only a few sites within the eight main Hawaiian islands and the only site on the main island of Oʻahu that supports a nesting population of Red-footed Boobies (Sula sula). Despite their importance for informing renewable offshore energy planning off Hawaiʻi, robust and accurate seabird population survey data exist and are available for some locations (Lehua; Raine and others, 2021), but at Moku Manu and Ulupa‘u Crater, recent information are not yet available (E. VanderWerf, written commun. 2021). In this study, we completed comprehensive aerial photographic counts at these three sites for six surface-nesting seabird species present during the 2019 breeding season: Brown Booby (Sula leucogaster), Red-footed Booby, Masked Booby (S. dactylatra), Great Frigatebird (Fregata minor), Sooty Tern, and Brown Noddy. We estimated 5,782, 102, and 1,446 nesting pairs of Red-footed Boobies at Lehua, Ulupaʻu Crater, and Moku Manu, respectively. At Lehua and Moku Manu, we estimated 692 and 65 nesting pairs of Brown Boobies, respectively. At Moku Manu, we estimated 95 nesting pairs of Masked Boobies, one of only three nesting locales for this species in the main Hawaiian Islands. Based on digital photograph counts of sampled areas and area-based extrapolation, we estimated 17,938 terns (mostly Sooty Tern with fewer Brown Noddy) on Moku Manu. We observed Great Frigatebirds roosting at the two island sites, but we did not detect any sign of nesting for this species. We found that inter-observer counts for behavioral classifications (nesting, roosting, unknown) ranged in precision (D=0.13–0.31), but generally, counts among photographs accounting for all seabird targets (D=0.10–0.22) and for boobies classified as nesting (D=0.13–0.18) were more precise than for roosting and unknown categories (D=0.13–0.31), indicating that at least some of the variation in count precision relates to differences in how independent counters identified behavioral classifications. The nesting population sizes (and number of terns present on Moku Manu) present during aerial counts likely are minimum estimates because individuals among these species can exhibit asynchronous nesting phenologies, and not all members of the nesting populations would be expected to be attending the sites when we surveyed. The results of these counts provide current and accurate abundance estimates for these species that can serve as benchmarks for future management and monitoring and as important components of population-level assessments aimed at quantifying seabird vulnerability to potential offshore wind energy development in the main Hawaiian Islands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1161","collaboration":"Prepared in cooperation with Bureau of Ocean Energy Management Pacific OCS Region","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Adams, J., Kelsey, E.C., Stenske, J., and Felis, J.J., 2022, Aerial counts for surface-nesting seabirds at Lehua Island and Moku Manu Islet and Ulupaʻu Crater, Oʻahu, in 2019: Data Report 1161, 20 p., https://doi.org/10.3133/dr1161.","productDescription":"Report: vii, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-134422","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":404063,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1161/dr1161.xml"},{"id":404062,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1161/dr1161.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1161"},{"id":404061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1161/covrthb.jpg"},{"id":404064,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1161/images"},{"id":404066,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P434NO","text":"Digital imagery used for aerial counts for surface-nesting seabirds at Lehua Island and Moku Manu Islet and Ulupa’u Crater, O’ahu, in 2019","description":"Kelsey, E.C., Adams, J., Felis, J.J., Stenske, J.G., and Horton, C.A., 2022, Digital imagery used for aerial counts for surface-nesting seabirds at Lehua Island and Moku Manu Islet and Ulupa’u Crater, O’ahu, in 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9P434NO."}],"country":"United States","state":"Hawaii","otherGeospatial":"Lehua Island, Moku Manu Islet, Ulupaʻu Crater","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.1250457763672,\n 22.002265076386827\n ],\n [\n -160.06324768066406,\n 22.002265076386827\n ],\n [\n -160.06324768066406,\n 22.043003952961463\n ],\n [\n -160.1250457763672,\n 22.043003952961463\n ],\n [\n -160.1250457763672,\n 22.002265076386827\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.76092529296875,\n 21.4348539163072\n ],\n [\n -157.69775390625,\n 21.4348539163072\n ],\n [\n -157.69775390625,\n 21.49396356306447\n ],\n [\n -157.76092529296875,\n 21.49396356306447\n ],\n [\n -157.76092529296875,\n 21.4348539163072\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The clarity of Lake Tahoe, observed using a Secchi disk on a regular basis since the late 1960s, continues to be a sentinel metric of lake health. Water clarity is influenced by physical and biological processes and has declined in the five decades of monitoring, revealing differences between summer (June–September) and winter (December–March). This document summarizes key findings of a study of Lake Tahoe water clarity, including long-term variability and the relative importance of several influencing variables and processes.
This study, prepared in cooperation with the Nevada Division of Environmental Protection, focused on (1) an apparent divergence in clarity trends between summer and winter periods, (2) observed changes in in-lake physical and ecological variables that may influence or control seasonal and annual clarity trends, and (3) five research hypotheses regarding lake clarity that were developed by Lake Tahoe management agencies. Previously collected data were used to complete this study. Trend analysis confirmed that winter clarity stabilized (that is, there is no longer a statistically significant trend up or down) during the last 20 years. Evaluation of clarity for selected months in the 50-year Secchi disk clarity dataset showed that only two summer months, July and August, had statistically significant decreases in clarity from 2000–19. Different subsets of available data were analyzed to reveal the presence or absences of trends for each season, decade, and month.
Five hypotheses related to lake clarity were part of the study described by this report. Hypothesis 1 stated that clarity is controlled predominantly by the distribution and volumetric density of fine particles in suspension. This hypothesis was studied using available data describing in-lake fine (0–20 micrometers) particles from 2008–19. Water clarity was negatively correlated with in-lake particle abundance, with particles in the 1.0-4.6 μm range having the greatest effect, consistent with light-scattering theory. Estimated abundances of diatoms of the genus Cyclotella also were found to be negatively correlated with clarity.
Data limitations precluded a complete investigation of hypothesis 2, which stated that the observed improvements in winter water clarity are a response to decreasing fine suspended-sediment concentrations in the lake resulting from load reductions from upland sources in and near urbanized areas. Data describing fine-sediment loading from urban areas to the lake were only available since 2014, and only once or twice per month. A slight, statistically significant, negative correlation was identified between urban fine-particle loading and monthly lake clarity with a 4-month lag. Particle abundance in monitored streams is highly correlated with simultaneous particle abundance in the lake.
Hypothesis 3 stated that changing hydrodynamic conditions in the lake are increasing thermal stability and resistance to mixing. Trend analyses performed on stability index and buoyancy frequency time series computed from long-term observations of lake temperatures support the hypothesis that hydrodynamic conditions have evolved since 1969 to increase the lake’s resistance to mixing. The date of maximum mixing in winter has become progressively earlier in the year. Lake density stratification, defined using the stability index, is commencing earlier in the year and extending a month longer than in the early years of the monitoring program.
Hypothesis 4 stated that the trend of decreasing summer clarity is a result of earlier, prolonged, and more intense stratification. Statistically significant correlations were found between summer clarity and (1) date of onset of stratification, (2) duration of stratification, and (3) buoyancy frequency.
Hypothesis 5 stated that ecological (food web) interactions are causing changes in the trends of seasonal or annual clarity; data supporting hypothesis 5 were limited to examples from other systems and to intermittent monitoring of Lake Tahoe and Emerald Bay. The resulting narrative assessment was motivated by a 6-year study of Mysis shrimp disappearance and return in Emerald Bay. The available data and a large body of published literature are consistent with the inference that Mysis shrimp-induced food web changes are causing changes in the trends of seasonal or annual clarity. This food-web study focused on the relations between introduced Mysis shrimp, the native cladocerans (Daphnia and Bosmina) that were largely eliminated following Mysis introduction, and the effect on fine particles within the lake. The records of Mysis and other zooplankton data for Lake Tahoe are episodic and have large gaps. Consequently, statistical analyses could not be conducted to compare zooplankton data with other variables. The long-term record, however, indicates that the key effect was a change to the phytoplankton assemblage, where larger diatoms disappeared, likely due to Mysis grazing, only to be replaced by Cyclotella that are an order-of-magnitude smaller and have increased the abundance and volumetric density of total fine particles in suspension (biotic and abiotic).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225070","collaboration":"Prepared in cooperation with the Nevada Division of Environmental Protection","usgsCitation":"Naranjo, R., Work, P., Heyvaert, A., Schladow, G., Cortes, A., Watanabe, S., Tanaka, L., and Elci, S., 2022, Seasonal and long-term clarity trend assessment of Lake Tahoe, California–Nevada: U.S. Geological Survey Scientific Investigations Report 2022–5070, 86 p., https://doi.org/10.3133/sir20225070.","productDescription":"x, 86 p.","numberOfPages":"86","onlineOnly":"Y","ipdsId":"IP-120090","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":404038,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5070/covrthb.jpg"},{"id":404039,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5070/sir20225070.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404040,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5070/sir20225070.xml"},{"id":404041,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5070/images"}],"country":"United States","state":"California","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.201416015625,\n 38.90813299596705\n ],\n [\n -119.90753173828125,\n 38.90813299596705\n ],\n [\n -119.90753173828125,\n 39.30029918615029\n ],\n [\n -120.201416015625,\n 39.30029918615029\n ],\n [\n -120.201416015625,\n 38.90813299596705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Changing climate and human demographics in the world's mountains will have increasingly profound environmental and societal consequences across all elevations. Quantifying current human populations in and near mountains is crucial to ensure that any interventions in these complex social-ecological systems are appropriately resourced, and that valuable ecosystems are effectively protected. However, comprehensive and reproducible analyses on this subject are lacking. Here, we develop and implement an open workflow to quantify the sensitivity of mountain population estimates over recent decades, both globally and for several sets of relevant reporting regions, to alternative input dataset combinations. Relationships between mean population density and several potential environmental covariates are also explored across elevational bands within individual mountain regions (i.e. sub-mountain range scale). Globally, mountain population estimates vary greatly from 0.344 billion (<5% of the corresponding global total) to 2.289 billion (>31%) in 2015. A more detailed analysis using one of the population datasets (GHS-POP) revealed that in 35% of mountain sub-regions, population increased at least twofold over the 40-year period 19752015. The urban proportion of the total mountain population in 2015 ranged from 6% to 39%, depending on the combination of population and urban extent datasets used. At sub-mountain range scale, population density was found to be more strongly associated with climatic than with topographic and protected-area variables, and these relationships appear to have strengthened slightly over time. Such insights may contribute to improved predictions of future mountain population distributions under scenarios of future climatic and demographic change. Overall, our work emphasizes that irrespective of data choices, substantial human populations are likely to be directly affected by and themselves affect mountainous environmental and ecological change. It thereby further underlines the urgency with which the multitudinous challenges concerning the interactions between mountain climate and human societies under change must be tackled.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0271466","usgsCitation":"Thornton, J.M., Snethlage, M.A., Sayre, R., Urbach, D.R., Viviroli, D., Ehrlich, D., Muccione, V., Wester, P., Insarov, G., and Adler, C., 2022, Human populations in the world’s mountains: Spatio-temporal patterns and potential controls: PLoS ONE, v. 17, no. 7, e0271466, 17 p., https://doi.org/10.1371/journal.pone.0271466.","productDescription":"e0271466, 17 p.","ipdsId":"IP-134287","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":447051,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0271466","text":"Publisher Index Page"},{"id":409117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Thornton, James M.","contributorId":298797,"corporation":false,"usgs":false,"family":"Thornton","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":64685,"text":"Mountain Research Institute","active":true,"usgs":false}],"preferred":false,"id":856476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snethlage, Mark A.","contributorId":298798,"corporation":false,"usgs":false,"family":"Snethlage","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38838,"text":"Global Mountain Biodiversity Assessment","active":true,"usgs":false}],"preferred":false,"id":856477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":298799,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":856478,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urbach, Davnah R.","contributorId":298800,"corporation":false,"usgs":false,"family":"Urbach","given":"Davnah","email":"","middleInitial":"R.","affiliations":[{"id":38838,"text":"Global Mountain Biodiversity Assessment","active":true,"usgs":false}],"preferred":false,"id":856479,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Viviroli, Daniel","contributorId":298801,"corporation":false,"usgs":false,"family":"Viviroli","given":"Daniel","email":"","affiliations":[{"id":27368,"text":"University of Zurich","active":true,"usgs":false}],"preferred":false,"id":856480,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ehrlich, Daniele","contributorId":298802,"corporation":false,"usgs":false,"family":"Ehrlich","given":"Daniele","email":"","affiliations":[{"id":64686,"text":"Joint Research Center European Commission","active":true,"usgs":false}],"preferred":false,"id":856481,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Muccione, Veruska","contributorId":298803,"corporation":false,"usgs":false,"family":"Muccione","given":"Veruska","email":"","affiliations":[{"id":27368,"text":"University of Zurich","active":true,"usgs":false}],"preferred":false,"id":856482,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wester, Philippus","contributorId":298804,"corporation":false,"usgs":false,"family":"Wester","given":"Philippus","email":"","affiliations":[{"id":64687,"text":"International Center for Integrated Mountain Development","active":true,"usgs":false}],"preferred":false,"id":856483,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Insarov, Gregory","contributorId":298805,"corporation":false,"usgs":false,"family":"Insarov","given":"Gregory","affiliations":[{"id":49898,"text":"Russian Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":856484,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Adler, Carolina","contributorId":298806,"corporation":false,"usgs":false,"family":"Adler","given":"Carolina","affiliations":[{"id":64685,"text":"Mountain Research Institute","active":true,"usgs":false}],"preferred":false,"id":856485,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70233266,"text":"sir20225067 - 2022 - Occurrence of per- and polyfluoroalkyl substances and inorganic analytes in groundwater and surface water used as sources for public water supply in West Virginia","interactions":[],"lastModifiedDate":"2022-09-27T13:40:14.016525","indexId":"sir20225067","displayToPublicDate":"2022-07-20T11:40:00","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":"2022-5067","displayTitle":"Occurrence of Per- and Polyfluoroalkyl Substances and Inorganic Analytes in Groundwater and Surface Water Used as Sources for Public Water Supply in West Virginia","title":"Occurrence of per- and polyfluoroalkyl substances and inorganic analytes in groundwater and surface water used as sources for public water supply in West Virginia","docAbstract":"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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225067","collaboration":"Prepared in cooperation with the West Virginia Department of Environmental Protection, Division of Water and Waste Management and the West Virginia Department of Health and Human Resources, Bureau for Public Health","usgsCitation":"McAdoo, M.A., Connock, G.T., and Messinger, T., 2022, Occurrence of per- and polyfluoroalkyl substances and inorganic analytes in groundwater and surface water used as sources for public water supply in West Virginia: U.S. Geological Survey Scientific Investigations Report 2022–5067, 37 p., https://doi.org/10.3133/sir20225067.","productDescription":"Report: ix, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-139737","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center 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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
The U.S. Geological Survey Rocky Mountain Region hosted scientists, managers, program coordinators, and leadership team members for a virtual Science Exchange during September 15–17, 2020. The Science Exchange had 216 registered participants and included 48 talks over the 3-day period. Invited speakers presented information about the novel U.S. Geological Survey Earth Monitoring, Analysis, and Prediction (EarthMAP) concept. Scientists showcased their research and participated in discussions related to the EarthMAP concept and EarthMAP applications. In addition, the Colorado River Basin Pilot Project, one of the first EarthMAP Pilot Projects, was unveiled during the Science Exchange. This report provides synopses of session objectives and corresponding abstracts that were presented along with author affiliations and email address of the lead author. In addition, web links are provided for related programs and projects, and associated publications are referenced.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221040","usgsCitation":"Anderson, P.J., and Tillery, A.C., 2022, Presented abstracts from the U.S. Geological Survey 2020 Rocky Mountain Region Science Exchange (September 15–17, 2020): U.S. Geological Survey Open-File Report 2022–1040, 23 p., https://doi.org/10.3133/ofr20221040.","productDescription":"x, 23 p.","onlineOnly":"Y","ipdsId":"IP-132301","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":405563,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221040/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1040"},{"id":403936,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1040/ofr20221040.xml"},{"id":403935,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1040/images"},{"id":403934,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1040/ofr20221040.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1040"},{"id":403933,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1040/coverthb.jpg"}],"contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
P.O. Box 25046, Mail Stop 911
Denver, CO 80225
Water samples collected from 27 rivers and streams in Massachusetts were analyzed to characterize the presence and concentrations of per- and polyfluoroalkyl substances (collectively known as PFAS) in surface waters across the Commonwealth. Sampling sites were selected in urban rivers where PFAS were expected to be present, such as those that receive treated municipal wastewater, and in rural rivers that were not known to be affected by municipal wastewater. The samples were collected three times in 2020 from 64 sites, and were analyzed for 24 PFAS, 18 of which are included in the U.S. Environmental Protection Agency’s Method 537.1.
Samples were collected when the instantaneous flow of the rivers and streams were at base-flow condition to minimize PFAS input or dilution from stormwater runoff and overland flow. The analyses detected PFAS in samples from all 27 rivers and streams. The number of PFAS detected in each sample ranged from 2 to 16. Concentrations of individual PFAS ranged from no detectable concentrations (less than 1.74 nanograms per liter) to 109 nanograms per liter. Samples from sites associated with wastewater treatment facilities in urban areas had a larger number and variety of PFAS present, and at higher concentrations, than in samples from the more rural rivers. This report includes a summary of the chemical data and physical properties of both environmental and quality-control samples, and a description of procedures for the collection and processing of the samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1160","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Savoie, J.G., and Argue, D.M., 2022, Concentrations of per- and polyfluoroalkyl substances (PFAS) in selected rivers and streams in Massachusetts, 2020 (ver. 2.0, October 2023): U.S. Geological Survey Data Report 1160, 18 p., https://doi.org/10.3133/dr1160.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129431","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":421269,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1160/full","linkFileType":{"id":5,"text":"html"}},{"id":421265,"rank":6,"type":{"id":30,"text":"Data 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\"}}]}","edition":"Version 1.0: July 20, 2022; Version 1.1: February 17, 2023; Version 2.0: October 2, 2023","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Hypocenter estimation at active volcanoes improves our understanding of their magmatic systems and indicates changing conditions at depth for continuously monitored volcanoes. The most active volcano in the Cascades Range, Mount St. Helens, has a multi-decadal earthquake catalog and it shows an apparent change in the depth distribution of seismicity before and after the 2004–2008 dome-building eruption and unrest sequence. We use two new resources to evaluate the accuracy of hypocenters and consequently the change in depth distribution of seismicity before and after the 2004–2008 eruption. First, we deployed a dense array of 136 three-component nodal seismographs for one month in 2017, including sub-arrays on the newly extruded dome and crater floor. Second, for events recorded during this month, we located their hypocenters using a three-dimensional (3D) wavefront-tracking location solver and a recently developed tomography model derived from active and passive source data. The relocated hypocenters are generally shallower and more concentrated beneath the crater compared to their catalog locations. The mean hypocenter movement from catalog locations is 2.86 km, with an averaged depth shift of 2.53 km upward. Comparison between 2017 hypocenters located using all of the available phase picks and those located using only the catalog picks from the permanent network suggests the improved hypocenters mostly resulted from the location solver and the 3D velocity model, with smaller changes due to the dense three-component array. Applying the same hypocenter estimation method to phase picks for all events from 1997 to 2004 and 2008–2021, we found a concentration of seismicity between sea level and ~ 1 km above it before and after the 2004–2008 eruption. The new results suggest that the seismogenic structure in the shallow magmatic system quickly re-equilibrated to its earlier state after the dome-building eruption.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107629","usgsCitation":"Zhang, H., Glasgow, M., Schmandt, B., Thelen, W., Moran, S.C., and Thomas, A., 2022, Revisiting the depth distribution of seismicity before and after the 2004–2008 eruption of Mount St. Helens: Journal of Volcanology and Geothermal Research, v. 430, 107629, 10 p., https://doi.org/10.1016/j.jvolgeores.2022.107629.","productDescription":"107629, 10 p.","ipdsId":"IP-142680","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2022.107629","text":"Publisher Index Page"},{"id":463341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Untied States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.34522424954554,\n 46.31416654047442\n ],\n [\n -122.34522424954554,\n 46.084925777087534\n ],\n [\n -122.05723487869246,\n 46.084925777087534\n ],\n [\n -122.05723487869246,\n 46.31416654047442\n ],\n [\n -122.34522424954554,\n 46.31416654047442\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"430","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Han","contributorId":345700,"corporation":false,"usgs":false,"family":"Zhang","given":"Han","email":"","affiliations":[],"preferred":false,"id":917263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glasgow, Margaret 0000-0001-5637-5918","orcid":"https://orcid.org/0000-0001-5637-5918","contributorId":345691,"corporation":false,"usgs":false,"family":"Glasgow","given":"Margaret","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":917264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmandt, Brandon","contributorId":202750,"corporation":false,"usgs":false,"family":"Schmandt","given":"Brandon","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":917265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":917266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":224629,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Amanda","contributorId":195086,"corporation":false,"usgs":false,"family":"Thomas","given":"Amanda","affiliations":[],"preferred":false,"id":917268,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233355,"text":"ofr20221036 - 2022 - Intake efficiency field results for Federal Interagency Sedimentation Project bag samplers","interactions":[],"lastModifiedDate":"2022-07-21T10:55:22.5234","indexId":"ofr20221036","displayToPublicDate":"2022-07-20T08:30:00","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":"2022-1036","displayTitle":"Intake Efficiency Field Results for Federal Interagency Sedimentation Project Bag Samplers","title":"Intake efficiency field results for Federal Interagency Sedimentation Project bag samplers","docAbstract":"The Federal Interagency Sedimentation Project (FISP) standardizes and advances sediment science among federal agencies. It is important to ensure that the FISP bag samplers perform isokinetically under all tested and approved conditions and collect samples that are representative of the stream or river cross-section. A measure of a sampler’s isokinetic behavior is its intake efficiency, which is defined as the ratio of the velocity through the nozzle entrance of the sampler to the ambient stream velocity. The intake efficiencies of all FISP bag samplers and nozzle sizes were evaluated for this report. Samples were obtained across 31 U.S. Geological Survey streamflow-gaging stations between July 15, 2013, and June 17, 2020, where data were collected with all four bag samplers (US D-96, D-96-A1, D-99, and DH-2), each using various 3/16-inch, 1/4-inch, or 5/16-inch diameter nozzles.
Water temperature and ambient stream velocity outside the nozzle are two of several factors that are known to affect the intake efficiency of bag samplers. A regression curve was fitted to these data through LOWESS (locally weighted scatterplot smoothing), and a Kruskal-Wallis test was executed for the various samplers and nozzle sizes. Based on these results, there is no statistical evidence to indicate that water temperature and stream velocity have a noticeable effect on intake efficiency when the samplers are deployed under isokinetic conditions. Likewise, there is no statistical evidence to indicate that the type of bag sampler and nozzle diameter have a direct effect on intake efficiency.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221036","usgsCitation":"Manaster, A.E., Landers, M.N., and Straub, T.D., 2022, Intake efficiency field results for Federal Interagency Sedimentation Project bag samplers: U.S. Geological Survey Open-File Report 2022–1036, 27 p., https://doi.org/10.3133/ofr20221036.","productDescription":"Report: iv, 27 p.; Database","onlineOnly":"Y","ipdsId":"IP-134358","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":404073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1036/coverthb.jpg"},{"id":404075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1036/ofr20221036.pdf","text":"Report","size":"2.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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U.S. Geological Survey
405 North Goodwin Ave.
Urbana, IL 61801
Interannual consistency (an indicator of the strength of adjustments) in migration phenology of Golden Eagles (Aquila chrysaetos) in North America is most strongly associated with the breeding region, the season, and with late-season temperature on breeding and wintering grounds. Consistency was greatest in boreal spring migration and the breeding regions of eastern Canada. Using multi-year GPS tracks of 83 adults breeding in 3 spatially distant regions (Alaska, northeast Canada, and southeast Canada), we quantified the interannual consistency of migration phenology and wintering latitude within and among individuals tracked across multiple years and the repeatability (r) by breeding regions and seasons. By comparing regions and seasons, we found that consistency was highest (r > 0.85) for boreal spring migration in eastern Canada while Alaska had the lowest value (r < 0.15). Because seasonal consistency of migration phenology was only detected in eastern Canada, we conclude that seasonal features are not a primary constraint. While regional differences in consistency were not related to differences in migratory distances, they could be the result of genetic or habitat differences. We also found that temperatures warmer than the decadal average at the region of departure delayed the start of boreal spring migration by ~10 days and advanced boreal autumn migration by ~20 days. These results suggest that warmer temperatures would reduce residence time on breeding grounds, contrary to expectations and trends found in other studies. Wide variations in migratory strategies across a species distribution can add to the list of challenges for conservation but may give migrants the capacity to acclimate to environmental changes.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithology/ukac029","usgsCitation":"Maynard, L.D., Therrien, J., Lemaître, J., Booms, T.L., Miller, T.A., Katzner, T., Somershoe, S., Cooper, J., Sargent, R., and Lecomte, N., 2022, Interannual consistency of migration phenology is season- and breeding region-specific in North American Golden Eagles: Ornithology, v. 136, no. 4, ukac029, https://doi.org/10.1093/ornithology/ukac029.","productDescription":"ukac029","ipdsId":"IP-133671","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":488614,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1881361","text":"External Repository"},{"id":409985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"136","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Maynard, Laurie D","contributorId":299594,"corporation":false,"usgs":false,"family":"Maynard","given":"Laurie","email":"","middleInitial":"D","affiliations":[{"id":64900,"text":"Université de Moncton","active":true,"usgs":false}],"preferred":false,"id":858164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Therrien, Jean-François","contributorId":299595,"corporation":false,"usgs":false,"family":"Therrien","given":"Jean-François","affiliations":[{"id":51980,"text":"Hawk Mountain Sanctuary","active":true,"usgs":false}],"preferred":false,"id":858165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lemaître, Jérôme","contributorId":299596,"corporation":false,"usgs":false,"family":"Lemaître","given":"Jérôme","affiliations":[{"id":64902,"text":"Ministère des Forêts, de la Faune et des Parcs du Québec","active":true,"usgs":false}],"preferred":false,"id":858166,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booms, Travis L.","contributorId":199285,"corporation":false,"usgs":false,"family":"Booms","given":"Travis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":858167,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":858168,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858169,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Somershoe, Scott G.","contributorId":299597,"corporation":false,"usgs":false,"family":"Somershoe","given":"Scott G.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":858170,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cooper, Jeff","contributorId":199741,"corporation":false,"usgs":false,"family":"Cooper","given":"Jeff","affiliations":[{"id":35592,"text":"Virginia Department of Game and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":858171,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sargent, Robert","contributorId":288449,"corporation":false,"usgs":false,"family":"Sargent","given":"Robert","email":"","affiliations":[],"preferred":false,"id":858172,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lecomte, Nicolas","contributorId":131119,"corporation":false,"usgs":false,"family":"Lecomte","given":"Nicolas","email":"","affiliations":[],"preferred":false,"id":858173,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70233537,"text":"70233537 - 2022 - Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","interactions":[],"lastModifiedDate":"2022-09-15T14:17:56.583793","indexId":"70233537","displayToPublicDate":"2022-07-20T06:53:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","docAbstract":"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.