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 Approximately 11,400 people are supplied by groundwater that is withdrawn from municipal and nonmunicipal wells in the northern and central parts of the aquifer.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
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
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.
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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
Concentrations of suspended sediment were measured in discrete samples and turbidity was continuously monitored at four U.S. Geological Survey streamgages in western Washington State, including one gage on the Sauk River; two gages on the Suiattle River, a tributary to the Sauk River; and one gage on Downey Creek, a tributary to the Suiattle River. The Suiattle River is a sediment-rich stream with headwaters on Glacier Peak, a glaciated volcano in the northern Cascade Range.
Contributions of suspended sediment to the Suiattle River from unglaciated tributaries, represented by Downey Creek, were compared to the contributions from Glacier Peak in the upper Suiattle River watershed. During summer 2017, a period for which complete records of discharge and sediment data were available for all three streamgages in the Suiattle River Basin, the suspended-sediment load from Downey Creek (drainage area [DA] 93 square kilometers [km2]) was 1,400 metric tons, which is equivalent to a sediment yield of about 15 metric tons per km2. During the same period, the suspended-sediment load from the upper Suiattle River (DA 176 km2) was 142,000 metric tons, or a sediment yield of about 800 metric tons per km2; and the suspended-sediment load from the lower Suiattle River (DA 733 km2) was 230,000 metric tons, or a sediment yield of about 300 metric tons per km2. The Downey Creek Basin accounts for 13 percent of the drainage area of the Suiattle River watershed but contributed only 0.6 percent of the suspended-sediment load over the summer of 2017 and water year 2017 (October 1, 2016–September 30, 2017). In contrast, the upper Suiattle River Basin, which accounts for 24 percent of the entire Suiattle River watershed, contributed 62 percent of the suspended-sediment load during the summer of 2017.
Given the short period for which data were collected, it cannot be known with certainty whether the above values are representative of long-term means. The relatively minor contribution of suspended sediment from Downey Creek, however, is consistent with the expectation that the upper Suiattle River, which drains Glacier Peak, is the dominant contemporary source of suspended sediment to the Sauk River. During summer 2016, the suspended-sediment load in the upper Siuattle River (180,000 metric tons) was more than double the estimated load in the lower Sauk River (80,000 metric tons), even though the upper Suiattle River represents only 10 percent of the total contributing area to the lower Sauk River Basin. This ratio of relative contribution is interpreted as an indication of transient storage of sediment along the Suiattle and Sauk Rivers between the two streamgaging stations. In the glaciated upper Suiattle River Basin, sediment is transported by annual glacial-melt processes in spring and summer months, deposited during the summer base-flow period, and then remobilized by fall and winter floods for delivery to the lower Sauk River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221056","collaboration":"Prepared in cooperation with the Sauk-Suiattle Indian Tribe","usgsCitation":"Jaeger, K.L., Anderson, S.W., Senter, C.A., Curran, C.A., and Morris, S., 2022, Relative contributions of suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017: U.S. Geological Survey Open-File Report 2022–1056, 18 p., https://doi.org/10.3133/ofr20221056.","productDescription":"v, 18 p.","onlineOnly":"Y","ipdsId":"IP-132545","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":403971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1056"},{"id":403970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1056/coverthb.jpg"},{"id":501781,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113307.htm","linkFileType":{"id":5,"text":"html"}},{"id":403972,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1056"},{"id":404178,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W74K0K","text":"USGS data release","description":"USGS data release","linkHelpText":"Suspended sediment and water temperature data in the Suiattle River and the Downey Creek Tributary, Washington for select time periods over 2013 - 2017 (ver. 2.0, October 2021)"},{"id":403974,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.XML"},{"id":403973,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1056/images"}],"country":"United States","state":"Washington","otherGeospatial":"Upper Suiattle River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.4,\n 48.0\n ],\n [\n -121.0,\n 48.0\n ],\n [\n -121.0,\n 48.4\n ],\n [\n -121.4,\n 48.4\n ],\n [\n -121.4,\n 48.0\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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Clear correlations between human and bird visual assessments of color have been documented, and are often assumed, despite fundamental differences in human and avian visual physiology and morphology. Analyses of plumage colors with avian perceptual models have shown widespread hidden inter-sexual and inter-specific color variation among passerines perceived as monochromatic to humans, highlighting the uncertainty of human vision to predict potentially relevant variation in color. Herein, we use reflectance data from 13 Larus gull species as an exemplar data set to study concordance between human vision and avian visual modeling of feather colors near, or below, the human threshold for discrimination. We found little evidence among gulls for sexual dichromatism hidden from human vision, but did find inter-specific color variation among gulls that is not seen by humans. Neither of these results were predictable a priori, and we reassert that reflectance measurements of actual feather colors, analyzed with avian relevant visual models, represent best practice when studying bird coloration.
","language":"English","publisher":"Finnish Zoological and Botanical Publishing Board","doi":"10.5735/086.059.0116","usgsCitation":"Eaton, M.D., Benites, P., Campillo, L., Wilson, R.E., and Sonsthagen, S.A., 2022, Gull plumages are, and are not, what they appear to human vision: Annales Zoologici Fennici, v. 59, no. 1, p. 187-203, https://doi.org/10.5735/086.059.0116.","productDescription":"7 p.","startPage":"187","endPage":"203","ipdsId":"IP-118461","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":404018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eaton, Muir D","contributorId":293231,"corporation":false,"usgs":false,"family":"Eaton","given":"Muir","email":"","middleInitial":"D","affiliations":[{"id":63252,"text":"Drake University","active":true,"usgs":false}],"preferred":false,"id":846813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benites, Pilar","contributorId":293232,"corporation":false,"usgs":false,"family":"Benites","given":"Pilar","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":846814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campillo, Luke","contributorId":293233,"corporation":false,"usgs":false,"family":"Campillo","given":"Luke","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":846815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Robert E.","contributorId":293234,"corporation":false,"usgs":false,"family":"Wilson","given":"Robert","email":"","middleInitial":"E.","affiliations":[{"id":63255,"text":"Nebraska State Museum","active":true,"usgs":false}],"preferred":false,"id":846816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":846817,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236158,"text":"70236158 - 2022 - Prioritizing pharmaceutical contaminants in Great Lakes tributaries using risk-based screening techniques","interactions":[],"lastModifiedDate":"2022-08-30T13:41:54.415555","indexId":"70236158","displayToPublicDate":"2022-07-19T08:37:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing pharmaceutical contaminants in Great Lakes tributaries using risk-based screening techniques","docAbstract":"In a study of 44 diverse sampling sites across 16 Great Lakes tributaries, 110 pharmaceuticals were detected of 257 monitored. The present study evaluated the ecological relevance of detected chemicals and identified heavily impacted areas to help inform resource managers and guide future investigations. Ten pharmaceuticals (caffeine, nicotine, albuterol, sulfamethoxazole, venlafaxine, acetaminophen, carbamazepine, gemfibrozil, metoprolol, and thiabendazole) were distinguished as having the greatest potential for biological effects based on comparison to screening-level benchmarks derived using information from two biological effects databases, the ECOTOX Knowledgebase and the ToxCast database. Available evidence did not suggest substantial concern for 75% of the monitored pharmaceuticals, including 147 undetected pharmaceuticals and 49 pharmaceuticals with screening-level alternative benchmarks. However, because of a lack of biological effects information, screening values were not available for 51 detected pharmaceuticals. Samples containing the greatest pharmaceutical concentrations and having the highest detection frequencies were from Lake Erie, southern Lake Michigan, and Lake Huron tributaries. Samples collected during low-flow periods had higher pharmaceutical concentrations than those collected during increased-flow periods. The wastewater-treatment plant effluent content in streams correlated positively with pharmaceutical concentrations. However, deviation from this correlation demonstrated that secondary factors, such as multiple pharmaceutical sources, were likely present at some sites. Further research could investigate high-priority pharmaceuticals as well as those for which alternative benchmarks could not be developed.
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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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Dakota\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
1. Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal’s location, movement characteristics, external environmental conditions, and internal physiological status.
2. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval.
3. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus(presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan.
4. We implement analyses using the MCP package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.13779","usgsCitation":"Wolfson, D., Andersen, D.E., and Fieberg, J., 2022, Using piecewise regression to identify biological phenomena in biotelemetry datasets: Journal of Animal Ecology, v. 91, no. 9, p. 1755-1769, https://doi.org/10.1111/1365-2656.13779.","productDescription":"15 p.","startPage":"1755","endPage":"1769","ipdsId":"IP-134564","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467175,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.13779","text":"Publisher Index Page"},{"id":466005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfson, David W.","contributorId":348002,"corporation":false,"usgs":false,"family":"Wolfson","given":"David W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":922812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fieberg, John R.","contributorId":348003,"corporation":false,"usgs":false,"family":"Fieberg","given":"John R.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":922813,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70233224,"text":"70233224 - 2022 - Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana","interactions":[],"lastModifiedDate":"2024-02-22T15:33:26.223523","indexId":"70233224","displayToPublicDate":"2022-07-18T09:19:44","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana","docAbstract":"The Acajutla debris-avalanche deposit is dated to about 40,000 cal BP. The dating is based on two 14C dates on pieces of wood from the debris-avalanche deposit recovered from a core at the Santa Águeda School Center. The debris-avalanche deposit overlies a 1.2-m-thick paleosol and four ash layers. One of these ash layers is geochemically correlated to the Los Chocoyos ash from Atitlán Caldera, while the others are possibly from eruptions of Coatepeque and Ilopango Calderas. The new data, collected in well reports from the years 1966 to 2019, include deposit thicknesses identified in 25 wells for water monitoring, bathymetric data from nautical charts, GEBCO’s grids, updated topographic data from LIDAR, and a reassessment of the deposit’s lateral limits. This new data will allow us to better constrain the volume of the Acajutla debris-avalanche deposit.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"Spanish","publisher":"COAMSS/OPAMSS","usgsCitation":"Garcia, A.V., Harpel, C., Hernandez, W., Escobar, D., Mixco, L.E., Lewis, C., and Scott Cummings, L., 2022, Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana, in Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional, p. R54-1-R54-4.","productDescription":"4 p.","startPage":"R54-1","endPage":"R54-4","ipdsId":"IP-141071","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":425867,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://opamss.org.sv/ova_doc/publicaciones-de-congreso-xiv-congreso-geologico-de-america-central/"},{"id":425868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"El Salvador","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.60202528025114,\n 13.862680525535808\n ],\n [\n -89.68026892718665,\n 13.862680525535808\n ],\n [\n -89.68026892718665,\n 13.783268152881789\n ],\n [\n -89.60202528025114,\n 13.783268152881789\n ],\n [\n -89.60202528025114,\n 13.862680525535808\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia, Angela V.","contributorId":293247,"corporation":false,"usgs":false,"family":"Garcia","given":"Angela","email":"","middleInitial":"V.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harpel, Christopher 0000-0001-8587-7845","orcid":"https://orcid.org/0000-0001-8587-7845","contributorId":204746,"corporation":false,"usgs":true,"family":"Harpel","given":"Christopher","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hernandez, Walter","contributorId":218214,"corporation":false,"usgs":false,"family":"Hernandez","given":"Walter","email":"","affiliations":[{"id":39782,"text":"Ministerio de Medio Ambiente y Recursos Naturales, San Salvador, El Salvador","active":true,"usgs":false}],"preferred":false,"id":846841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Escobar, Demetrio","contributorId":293248,"corporation":false,"usgs":false,"family":"Escobar","given":"Demetrio","email":"","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mixco, Luis E.","contributorId":293249,"corporation":false,"usgs":false,"family":"Mixco","given":"Luis","email":"","middleInitial":"E.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewis, Charles 0000-0001-5848-937X","orcid":"https://orcid.org/0000-0001-5848-937X","contributorId":293250,"corporation":false,"usgs":false,"family":"Lewis","given":"Charles","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":846844,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott Cummings, Linda","contributorId":293251,"corporation":false,"usgs":false,"family":"Scott Cummings","given":"Linda","email":"","affiliations":[{"id":63265,"text":"PaleoResearch Institute","active":true,"usgs":false}],"preferred":false,"id":846845,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236309,"text":"70236309 - 2022 - Tephrochronology of the Miocene Monterey and Modelo Formations, California","interactions":[],"lastModifiedDate":"2022-09-01T12:17:20.986718","indexId":"70236309","displayToPublicDate":"2022-07-17T07:15:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1727,"text":"GSA Special Papers","active":true,"publicationSubtype":{"id":10}},"title":"Tephrochronology of the Miocene Monterey and Modelo Formations, California","docAbstract":"Tuff beds have been known in the Miocene Monterey and Modelo Formations since the initial descriptions; however, age control and correlation is predominantly biostratigraphy. Here we combine tephrochronology and biostratigraphy in order to provide numerical age control for eight sedimentary sequences of the Monterey and Modelo Formations from Monterey, California to Orange County, California. We correlate 38 tuffs and tephra beds in the Monterey and Modelo Formations to 26 different dated tuffs found mainly in non-marine sequences in Nevada, Idaho and New Mexico. We also include geochemical data for an additional 19 tuffs in the Monterey and Modelo Formations for which there are no known correlative tuffs and geochemical data for 11 additional tuffs in other units that will add to the Miocene tephrostratigraphy. The identified tuffs range in age from 16 to 7 Ma with 31 tuffs erupted from volcanic centers of the Snake River Plain of northern Nevada to eastern Idaho. Twelve other tuffs erupted from the Southern Nevada Volcanic Field, one from the Sonoma Volcanic Field, north of San Francisco, and the eruptive source of 12 other tuffs is uncertain. These tuffs provide useful correlations of marine sequences deposited at varying depths along offshore Miocene California and possible insight into the distribution of air-fall tephra from so-called super eruptions","language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.2556(08)","usgsCitation":"Knott, J.R., Sarna-Wojcicki, A., Barron, J.A., Wan, E., Heizler, N., and Martinez, P., 2022, Tephrochronology of the Miocene Monterey and Modelo Formations, California: GSA Special Papers, https://doi.org/10.1130/2022.2556(08).","ipdsId":"IP-122368","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2022.2556(08)","text":"Publisher Index Page"},{"id":406061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knott, Jeffrey R. 0000-0002-4600-5961","orcid":"https://orcid.org/0000-0002-4600-5961","contributorId":218427,"corporation":false,"usgs":false,"family":"Knott","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":39844,"text":"CSU Fullerton, Department of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":850545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sarna-Wojcicki, Andrei M. 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":296073,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":850547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":296074,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":850548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heizler, Nancy","contributorId":296075,"corporation":false,"usgs":false,"family":"Heizler","given":"Nancy","email":"","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":850549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martinez, Priscilla","contributorId":296076,"corporation":false,"usgs":false,"family":"Martinez","given":"Priscilla","email":"","affiliations":[{"id":63349,"text":"California State University Fullerton","active":true,"usgs":false}],"preferred":false,"id":850550,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233571,"text":"70233571 - 2022 - Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","interactions":[],"lastModifiedDate":"2022-07-26T12:04:54.409941","indexId":"70233571","displayToPublicDate":"2022-07-16T06:59:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","docAbstract":"Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e., model transferability. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000 km2 fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R2 = 0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R2 ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.
Early detection and eradication of invasive plants are more cost-effective than managing well-established invasive plant populations and their impacts. However, there is high uncertainty around which taxa are likely to become invasive in a given area. Horizon scanning that combines a data-driven approach with rapid risk assessment and consensus building among experts can help identify invasion threats. We performed a horizon scan of potential invasive plant threats to Florida, USA—a state with a high influx of introduced species, conditions that are generally favorable for plant establishment, and a history of negative impacts from invasive plants. We began with an initial list of 2128 non-native plant taxa that are known invaders or crop pests. We built on previous invasive species horizon scans by developing data-based criteria to prioritize 100 taxa for rapid risk assessment. The semi-automated prioritization process included selecting taxa “on the horizon” (i.e., not yet in the target location and not on a noxious weed list) with climate matching, naturalization history, “weediness” record, and global commonness. We derived overall invasion risk scores with rapid risk assessment by evaluating the likelihood of each of the taxa arriving, establishing, and having an impact in Florida. Then, following a consensus-building discussion, we identified six plant taxa as high risk, with overall risk scores ranging from 75 to 100 out of a possible 125. The six taxa are globally distributed, easily transported to new areas, found in regions with climates similar to Florida’s, and can impact native plant communities, human health, or agriculture. Finally, we evaluated our initial and final lists for potential biases. Assessors tended to assign higher risk scores to taxa that had more available information. In addition, we identified biases towards four plant families and certain geographical regions of origin. Our horizon scan approach identified taxa conforming to metrics of high invasion risk and used a methodology refined for plants that can be applied to other locations.
Minerals occurring in marine environments span the globe and encompass a broad range of mineral categories, forming within varied geologic and oceanographic settings. They occur in coastal regions, either from the continuation or mechanical reworking of terrestrial mineralization, as well as in the deep ocean, from diagenetic, hydrogenetic, and hydrothermal processes. The oceans cover most of the Earth’s surface and as a result, any inventory of global resources is incomplete without the inclusion of marine minerals. This study by the U.S. Geological Survey reviews current knowledge regarding deep-ocean and coastal marine minerals within the marine areas surrounding Alaska, including the Alaska Outer Continental Shelf (OCS). For the purposes of this study, we have divided these areas in to eight regions: (1) Gulf of Alaska seamounts, (2) Chukchi Borderland, (3) Canada Basin, (4) Aleutian Arc, (5) Seward Peninsula, (6) Goodnews Bay, (7) Bristol Bay and Alaska Peninsula, and (8) southern and southeastern Alaska. The Alaska OCS encompasses several areas broadly conducive to marine mineral formation, including extensional basins resulting from an active subduction zone where massive sulfide deposits may form, deep abyssal plains with conditions that may lead to manganese nodule formation, seamounts that can provide substrate for the growth of ferromanganese crusts, and erosional settings and submerged continental crust where placer deposits are found. For deep-ocean hydrothermal minerals and manganese nodules, the Alaska OCS contains prospective regions, including the Canada Basin and the Aleutian Arc; however, no such minerals have yet been identified. We explore the probability that these minerals occur based on reviews of existing geologic and oceanographic data within the relevant sections. In regions far from shore data are limited. Deep-ocean ferromanganese crusts are known to occur in two regions: (1) the Gulf of Alaska seamounts and (2) the Chukchi Borderland in the Arctic Ocean. Limited sampling has occurred in both regions, and along the Chukchi Borderland the sampling was outside of the OCS and the U.S. Exclusive Economic Zone. Data relevant to coastal minerals is more extensive, and in some places fairly systematic sampling was conducted. Several nearshore placer deposits have been exploited for decades; however, the potential for nearshore extension of terrestrial ore deposits is less well considered. This contribution considers the state of knowledge regarding marine mineral occurrences within the Alaska regions and identifies the data gaps in order to help inform future marine mineral related research efforts around Alaska.
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Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
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":70232943,"text":"ofr20191023E - 2022 - Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium","interactions":[],"lastModifiedDate":"2026-03-25T16:59:26.525114","indexId":"ofr20191023E","displayToPublicDate":"2022-07-14T10:34:00","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":"2019-1023","chapter":"E","displayTitle":"Alaska Focus Area Definition for Data Acquisition for Potential Domestic Sources of Critical Minerals in Alaska for Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Magnesium, Manganese, Uranium, Vanadium, and Zirconium","title":"Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium","docAbstract":"Phase 3 of the Earth Mapping Resources Initiative (Earth MRI) focuses on geologic belts that are favorable for hosting mineral systems that could contain the critical minerals antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium. Prior phases of the Earth MRI program in Alaska focused only on rare earth elements, aluminum, cobalt, graphite, lithium, niobium, platinum-group metals, tantalum, tin, titanium, and tungsten. An additional 11 critical minerals planed for future phases of Earth MRI (As, Bi, Cs, Ga, Ge, In, Re, Rb, Sc, Sr, Te) are considered prospective in these focus areas. Together, Alaska focus areas address 22 of the 35 minerals or mineral material groups presently deemed critical. This report describes the methodology and techniques utilized to define focus areas for future data acquisition in Alaska; the conterminous United States are covered in a separate report.
Focus areas are identified using a mineral systems framework, which accounts for all the possible tectonic and geologic settings where co-genetic mineral deposits may form. These deposits contain many commodities, including byproduct and critical minerals. Large system-scale processes may be evaluated using such a framework to determine the influence they play on critical mineral endowment within the deposits. Analyzing larger mineral systems provides an integrated and broad context to determine how and where critical minerals are sourced, transported, and deposited in geologic systems.
Statewide geological, geochemical, geophysical, and mineral occurrence datasets informed the delineation of focus areas in Alaska. For some mineral systems, previously published data-driven prospectivity analyses for critical mineral-bearing deposit types provided the basis for focus areas. We report a total of 22 new focus areas that are prospective for phase 3 critical minerals. These new focus areas represent four different mineral systems that are known or suspected to occur in Alaska. An additional 55 focus areas that were previously identified for phase 1 and phase 2 commodities were also identified as being prospective for phase 3 critical minerals. Collectively, 102 focus areas in Alaska have known or suspected potential for hosting phase 1, phase 2, and (or) phase 3 critical minerals. These focus areas represent 17 different mineral systems also containing critical minerals that are planned for consideration in future Earth MRI phases. Thus, the focus areas delineated herein, and in previous reports for Alaska, are comprehensive for all critical minerals as presently defined and may be used to guide the collection of new geologic, geochemical, and geophysical data in the region.
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\"}}]}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The Earth Mapping Resources Initiative (Earth MRI) is conducted in phases to identify areas for acquiring new geologic framework data to identify potential domestic resources of the 35 mineral materials designated as critical minerals for the United States. This report describes the data sources and summary results for 13 critical minerals evaluated in the conterminous United States and Puerto Rico during phase 3 of the study (antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium). Phases 1 and 2 of the Earth MRI addressed aluminum, cobalt, graphite, lithium, niobium, platinum-group elements (PGEs), rare earth elements (REEs), tantalum, tin, titanium, and tungsten. Critical minerals in Alaska are covered in a separate report. No focus areas for phase 3 critical minerals are delineated for Hawaii.
The geologic, geochemical, topographic, and geophysical mapping provided by the Earth MRI documents geologic features that reflect the extent of individual mineral systems and provides information about critical mineral deposits that may not have been previously considered. The mineral-systems approach links critical mineral commodities to deposit types that represent the manifestations of large mineral systems.
Each of the 13 critical mineral commodities for phase 3 of the Earth MRI is discussed in terms of its importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types, and is accompanied by maps and tables listing examples of focus areas in the conterminous United States and Puerto Rico. Examples of important mineral systems for this group of 13 critical minerals include basin brine path systems for barite and fluorspar, Carlin-type systems and Coeur d’Alene systems for antimony, chemical weathering and volcanogenic seafloor systems for manganese, Climax-type systems for beryllium, mafic magmatic systems for chromium, marine evaporite systems for potash and magnesium, meteoric recharge systems for uranium, petroleum systems for helium, and placer systems for zirconium and hafnium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023D","collaboration":"Prepared in cooperation with the Association of American State Geologists","usgsCitation":"Hammarstrom, J.M., Dicken, C.L., Woodruff, L.G., Andersen, A.K., Brennan, S., Day, W.C., Drenth, B.J., Foley, N.K., Hall, S., Hofstra, A.H., McCafferty, A.E., Shah, A.K., and Ponce, D.A., 2022, Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico—Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium, chap. 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913 National Center
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