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USGS2022

Using microbial source tracking to identify fecal contamination sources in Patchogue and Bellport Bays on Long Island, New York

The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Patchogue and Bellport Bays, two embayments on the south shore of Suffolk County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bays and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment. Analysis revealed that the most substantial source of fecal contamination to Patchogue and Bellport Bays was discharge from sites draining ponds and wetlands into the rivers and tributaries sampled, particularly during the summer months. Fecal coliform bacteria at sites where ponds and wetlands drain are increased by stormwater runoff, which is another substantial source of fecal contamination. Overall, canine- and waterfowl-associated bacterial contributions were prevalent in source samples in both bays. Human-associated markers were present in surface-water source samples and completely absent in receptor samples in Patchogue Bay. The Fireplace Neck receptor site in Bellport Bay had a human-associated marker present in the summer wet sample only. Human markers were detected at the sample site downstream from the Patchogue wastewater treatment plant but were associated with low fecal coliform concentrations, indicating that the wastewater treatment plant is not a likely source of fecal contamination to Patchogue Bay. The lack of human-associated marker detections within Patchogue and Bellport Bays in summer source samples coupled with low to no detections of fecal coliform, especially where small marinas are present in creeks and tributaries, suggest that boats do not substantially contribute fecal coliform bacteria to the bays. There was little evidence of groundwater-contributing fecal bacteria by direct discharge from the subsurface. Further, the sandy sediment alongside Patchogue and Bellport Bays is unlikely to contribute fecal coliform bacteria from the test host organisms when resuspended in the water column. A classification scheme was developed to convey the degree of fecal contamination to stakeholders and resource managers. Based on this classification scheme, the Corey Creek Near Middle Road, Patchogue River Near Division Street, and Swan River Mouth sampling sites were identified as locations that contribute substantial fecal contamination to Patchogue Bay. In Bellport Bay, the Culvert at Beaverdam Creek site was identified as the location contributing the most substantial fecal contamination.

New York↗

USGS2022

Using microbial source tracking to identify fecal contamination sources in Sag Harbor on Long Island, New York

The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Sag Harbor, an embayment complex on the northern shore of the south fork of Suffolk County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the harbor and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment. Analysis revealed that the most substantial source of fecal contamination to Sag Harbor was discharge from sites draining ponds and wetlands, particularly during the summer months. Fecal coliform bacteria at sites where ponds and wetlands drain are increased by stormwater runoff, which is another substantial source of fecal contamination. Human markers were detected in all four samples at the Sag Harbor Sewage Treatment Plant Outfall site but were associated with low fecal coliform concentrations, indicating that the sewage treatment plant is not a likely source of fecal contamination to the embayment. The Ligonee Brook Culvert, Paynes Creek near Marjorie Lane, and Otter Pond Culvert sites were identified as locations that contribute fecal contamination to Sag Harbor. These three locations had high fecal coliform bacteria concentrations in the summer, one of which was positive for canine microbial source tracking markers (Ligonee Brook Culvert), and another positive for waterfowl markers (Paynes Creek near Marjorie Lane). The absence of fecal coliform bacteria and human microbial source tracking markers in groundwater samples indicates that water from septic systems does not influence the harbor; however, elevated fecal coliform bacteria concentrations were not often detected. Further, the sandy sediment alongside Sag Harbor is unlikely to contribute fecal coliform bacteria when resuspended in the water column through tidal shifts or boat activity.

New York↗

USGS2022

Using microbial source tracking to identify fecal contamination sources in Great South Bay on Long Island, New York

The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering a part of Great South Bay (referred to as Great South Bay for the purposes of this report) near the hamlets of West Sayville, Sayville, and Bayport on the southern shore of Suffolk County on Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bay and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment. Analysis revealed that the most substantial source of fecal contamination to Great South Bay was discharge from sites draining ponds and wetlands into the tributaries sampled, Brown and Green Creeks, particularly during the summer months. Fecal coliform bacteria at sites where ponds and wetlands drain are increased by stormwater runoff, which is another substantial source of fecal contamination. Sites with high concentrations of fecal coliform bacteria in the summer exacerbated by stormwater include the Brown Creek Culvert at Middle Road, Mill Pond Culvert near South Street, and Green Creek Culvert near Montauk Highway sites. The canine Bacteroides (BacCan) marker was the most frequently detected microbial source tracking marker with 15 positive detections in surface water across the landscape, followed by the waterfowl Helicobacter (GFD) marker with 9 detections and the human Bacteroides (HF183) marker with 4 detections in surface water (excluding 2 detections in sediment). The ruminant Bacteroides (Rum2Bac) marker was not detected in any samples collected during this study. The detection frequency of BacCan was similar for all sampling conditions and seasons, suggesting canine influence is unrelated to weather events and is a year-round occurrence. BacCan was detected in 14 of 16 source samples and only 1 of 16 receptor samples, which suggests that canine fecal contamination is likely diluted in the bay. There was a similar amount of marker detections when comparing weather condition (wet or dry) and season (winter or summer), suggesting that fecal contamination was unrelated to weather events or time of year. Eight waterfowl marker detections were in samples collected during the winter, and only one during the summer, implicating seasonal avian fecal contamination throughout the embayment. The human marker was detected in only one surface-water receptor sample, during the wet winter sampling event at the Green Creek Mid-Bay site. Three of four human marker detections were in the samples collected during the wet winter sampling event, indicating that weather and season may influence the presence of human markers in Great South Bay, but human markers are not overly prevalent.

New York↗

USGS2022

Using microbial source tracking to identify fecal contamination sources in South Oyster Bay on Long Island, New York

The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering South Oyster Bay, a shallow embayment on the southern shore of Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bay and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment. Analysis revealed that the most substantial source of fecal contamination to South Oyster Bay was stormwater, particularly during the summer months. The highest frequency of fecal coliform detections in source sites were under wet summer conditions, and the highest fecal coliform concentrations were under wet summer conditions at the Cedar Creek near Bay Place and Unqua Lake Culvert sites (more than 16,000 most probable number per 100 milliliters each). The human-associated Bacteroides marker was the most frequently detected microbial source tracking marker in South Oyster Bay (50 percent positive detections). The human marker was detected at least twice in all surface water source and receptor sites, except for the Massapequa Lake East Culvert source site that did not have any positive human marker detections. Canine contamination was prolific at source sites but was associated with low fecal coliform concentrations in the winter months. All detections of the canine-associated Bacteroides marker were in samples collected during the winter season and were associated with fecal coliform concentrations below the reporting limit, indicating that birds are not a persistent source of fecal coliform to South Oyster Bay. The absence of fecal coliform and human markers in groundwater samples collected throughout the larger study area indicates that water from cesspools or septic tanks do not contribute fecal coliform to the bay. Further, microbial source tracking markers were not detected in the sandy sediment collected at Zachs Bay. Based a classification scheme developed to convey the degree of fecal contamination to stakeholders and resource managers, the Cedar Creek near Bay Place and Unqua Lake Culvert sites were identified as locations that contribute substantial fecal contamination to South Oyster Bay.

New York↗

USGS2001

Critical habitat for ovigerous Dungeness crabs

The Dungeness crab, Cancer magister , supports an important fishery in the northeastern Pacific Ocean, yet there is limited knowledge of ovigerous female brooding locations and brooding behavior. Our earlier research suggests that ovigerous crabs aggregate at the same brooding locations for many years. Within these locations, ovigerous females occur in high densities, with the majority of the aggregation buried within the sediment. These locations often have similar water depths and sediment types and appear to be critical for this life history stage. Our study was designed to examine the bathymetric distribution of Dungeness crabs in bays with and without sea otters at eight locations within the Glacier Bay area by conducting transects with a video-equipped manned submersible. Two of the bays investigated contained large aggregations of ovigerous females. At both sites the substrate was composed primarily of sand. However, only a small percentage of the 33 km of transects were classified as sand. These data suggest that sand substrate may be a limiting resource. Since crab brooding aggregations represent a large portion of the crab population within a small area, and because they are a critical component of Dungeness life history, areas with these characteristics need to be investigated, mapped, and protected from development or exploitation. The areas requiring protection from the impact of anthropogenic wastes, fishing, and logging activities could be quite small, thus limiting conflict with alternative users.

Conference Paper↗

USGS2000

Mapping and converting essential Federal Geographic Data Committee (FGDC) metadata into MARC21 and Dublin Core: towards an alternative to the FGDC Clearinghouse

The purpose of this article is to raise and address a number of issues related to the conversion of Federal Geographic Data Committee metadata into MARC21 and Dublin Core. We present an analysis of 466 FGDC metadata records housed in the National Biological Information Infrastructure (NBII) node of the FGDC Clearinghouse, with special emphasis on the length of fields and the total length of records in this set. One of our contributions is a 34 element crosswalk, a proposal that takes into consideration the constraints of the MARC21 standard as implemented in OCLC's World Cat and the realities of user behavior.

D-Lib↗

USGS2018

Digital Shoreline Analysis System (DSAS) version 5.0 user guide

Overview The Digital Shoreline Analysis System (DSAS) is a freely available software application that works within the Esri Geographic Information System (ArcGIS) software. DSAS computes rate-of-change statistics for a time series of shoreline vector data. DSAS version 5.0 (v5.0) was released in December 2018 and has been tested for compatibility with ArcGIS versions 10.4 and 10.5. It is supported on Windows 7 and Windows 10 operating systems. If you use it, please cite it as follows and make note of the current version: Himmelstoss, E.A., Farris, A.S., Henderson, R.E., Kratzmann, M.G., Ergul, Ayhan, Zhang, Ouya, Zichichi, J.L., Thieler, E. R., 2018, Digital Shoreline Analysis System (version 5.0): U.S. Geological Survey software release, https://code.usgs.gov/cch/dsas . This user guide describes the system requirements, installation procedures, and necessary inputs to establish measurement locations with DSAS-generated transects and compute rate-of-change calculations. Although the nomenclature for this software utility is based on use in a coastal environment, the DSAS application could be used to compute rates of change for any boundary-change problem that incorporates a clearly identified feature position at discrete times, such as glacier limits, river banks, or land use/cover boundaries.

Open-File Report↗

USGS2001

Statistical analysis of sand and gravel aggregate deposits of late Pleistocene Lake Bonneville, Utah

Sedimentary deposits of pluvial Lake Bonneville are an important source of sand and gravel suitable for aggregate and construction in Utah. Data on Lake Bonneville basin sand and gravel deposit thickness, volume, grain size, percent of fines, and durability were statistically analyzed to detect variations associated with geologic domains, geographic location, Lake Bonneville shorelines, and sand and gravel deposit type, and to construct quantitative deposit models. Analysis showed several trends; (1) sand and gravel in younger shorelines was slightly more durable and the deposits considerably larger in volume, (2) younger shorelines are also more likely to contain more than one genetic deposit type, (3) the volume of terrace deposits is larger than beach deposits, (4) terraces and beaches are generally thicker than spits and bars, (5) the northern part of the Bonneville Basin contains slightly more durable sand and gravel than the southern part of the basin and is more likely to contain deposits composed of more than one genetic deposit type, and (6) the Wasatch domain deposits are composed of more than one genetic deposit type more often than deposits of the Basin and Range domain. Three additional conclusions with immediate economic significance are; (1) the median sand and gravel deposit in the Wasatch domain, 360,000 m 3 (275,000 yd 3 ), is three times larger than that of the Basin and Range domain (120,000 m 3 [90,000 yd 3 ]), (2) the median deposit thickness in the Wasatch domain, 5.8 m (19.0 ft), is nearly twice that of the Basin and Range domain (3 m [10 ft]), and (3) the Wasatch domain also contains slightly larger diameter gravel. These three conclusions are significant because the trend for sand and gravel development in the Bonneville Basin is to move from the Wasatch domain to the Basin and Range domain. Smaller, thinner deposits with smaller diameter gravel will require more surface area to mine than would have been necessary in the Wasatch domain. The result is a higher cost for sand and gravel in construction projects in the Salt Lake City area, especially since the gravel must also be hauled further.

Idaho;Nevada;Utah↗

USGS1996

Integrating a geographic information system, a scientific visualization system and an orographic precipitation model

Investigating natural, potential, and man-induced impacts on hydrological systems commonly requires complex modelling with overlapping data requirements, and massive amounts of one- to four-dimensional data at multiple scales and formats. Given the complexity of most hydrological studies, the requisite software infrastructure must incorporate many components including simulation modelling, spatial analysis and flexible, intuitive displays. There is a general requirement for a set of capabilities to support scientific analysis which, at this time, can only come from an integration of several software components. Integration of geographic information systems (GISs) and scientific visualization systems (SVSs) is a powerful technique for developing and analysing complex models. This paper describes the integration of an orographic precipitation model, a GIS and a SVS. The combination of these individual components provides a robust infrastructure which allows the scientist to work with the full dimensionality of the data and to examine the data in a more intuitive manner.

IAHS-AISH Publication↗

USGS2009

Methodology for Estimation of Flood Magnitude and Frequency for New Jersey Streams

Methodologies were developed for estimating flood magnitudes at the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals for unregulated or slightly regulated streams in New Jersey. Regression equations that incorporate basin characteristics were developed to estimate flood magnitude and frequency for streams throughout the State by use of a generalized least squares regression analysis. Relations between flood-frequency estimates based on streamflow-gaging-station discharge and basin characteristics were determined by multiple regression analysis, and weighted by effective years of record. The State was divided into five hydrologically similar regions to refine the regression equations. The regression analysis indicated that flood discharge, as determined by the streamflow-gaging-station annual peak flows, is related to the drainage area, main channel slope, percentage of lake and wetland areas in the basin, population density, and the flood-frequency region, at the 95-percent confidence level. The standard errors of estimate for the various recurrence-interval floods ranged from 48.1 to 62.7 percent. Annual-maximum peak flows observed at streamflow-gaging stations through water year 2007 and basin characteristics determined using geographic information system techniques for 254 streamflow-gaging stations were used for the regression analysis. Drainage areas of the streamflow-gaging stations range from 0.18 to 779 mi2. Peak-flow data and basin characteristics for 191 streamflow-gaging stations located in New Jersey were used, along with peak-flow data for stations located in adjoining States, including 25 stations in Pennsylvania, 17 stations in New York, 16 stations in Delaware, and 5 stations in Maryland. Streamflow records for selected stations outside of New Jersey were included in the present study because hydrologic, physiographic, and geologic boundaries commonly extend beyond political boundaries. The StreamStats web application was developed cooperatively by the U.S. Geological Survey and the Environmental Systems Research Institute, Inc., and was designed for national implementation. This web application has been recently implemented for use in New Jersey. This program used in conjunction with a geographic information system provides the computation of values for selected basin characteristics, estimates of flood magnitudes and frequencies, and statistics for stream locations in New Jersey chosen by the user, whether the site is gaged or ungaged.

Scientific Investigations Report↗

USGS2002

Estimating the magnitude of peak flows at selected recurrence intervals for streams in Idaho

Methods for estimating magnitudes of peak flows at various recurrence intervals, needed for highway-structure and water-control design and planning, were developed for gaged and ungaged sites on streams throughout Idaho. Recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years were selected for analysis of peak flows. For gaged sites in Idaho, peak-flow estimates were calculated by fitting a log-Pearson Type III distribution to the annual peak-flow data for each site. Annual peak flows through 1997 were used in the analysis. Basin and climatic characteristics for these gaged sites were calculated from 1:24,000 digitalelevation models and various thematic data coverages using a geographic information system. Peak- flow data and basin and climatic characteristics for 333 gaged sites were combined to develop a database that was used for the analysis. To estimate the magnitude of peak flows at ungaged sites near gaged sites on the same stream, a method was developed on the basis of drainage-area ratios. To estimate the magnitude of peak flows for ungaged sites on unregulated and undiverted streams, two regional regression methods were developed. The first regression method, termed the regional regression method, used generalized least-squares regression to develop a set of predictive equations for estimating peak flows at selected recurrence intervals for seven hydrologic regions in Idaho. These regional regression equations related basin and climatic characteristics to peak flows. The regional regression equations were all functions of drainage area plus one or two other basin characteristics. Average errors of prediction for these regression equations ranged from +143 percent to 58.8 percent. The range of errors was narrowest, from about +51.9 to about 34.2, for region 5. Error ranges were usually narrower for the middle recurrence intervals than for the lower and upper recurrence intervals. A computer program was developed to calculate the magnitude of peak flows at each recurrence interval, the average error of prediction, and the 90-percent confidence interval for each ungaged site. The second regression method, termed the region-of-influence method, was used to develop a unique regression equation for each estimate that is based on a subset of gaged sites with values of basin and climatic characteristics similar to those for the ungaged sites. All 333 gages in the database were used to select the subset. Root-mean-squared errors for this method ranged from 55.5 percent to 72.4 percent. Differences in root-mean-squared errors between regional regression equations and the region-of-influence method were quite large. The average difference in root-mean-squared errors for the region-of-influence method was more than 10 percent greater than the average differences for the regional regression equations. For region 5, the average difference was greater than 20 percent. However, for region 8, the root-mean-squared errors were, in general, only slightly smaller for the region-of-influence method than for the regional regression equations. The region-of-influence method is not recommended for use in determining flood-frequency estimates for ungaged sites in Idaho because the results, overall, are less accurate and the calculations are more complex than those of regional regression equations. The regional regression equations were considered to be the primary method of estimating the magnitude and frequency of peak flows for ungaged sites in Idaho.

Idaho;Montana;Nevada;Oregon;Washington;Wyoming↗

USGS2015

The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls

Estimates of species' vital rates and an understanding of the factors affecting those parameters over time and space can provide crucial information for management and conservation. We used mark–recapture, reproductive output, and territory occupancy data collected during 1985–2013 to evaluate population processes of Northern Spotted Owls ( Strix occidentalis caurina ) in 11 study areas in Washington, Oregon, and northern California, USA. We estimated apparent survival, fecundity, recruitment, rate of population change, and local extinction and colonization rates, and investigated relationships between these parameters and the amount of suitable habitat, local and regional variation in meteorological conditions, and competition with Barred Owls ( Strix varia ). Data were analyzed for each area separately and in a meta-analysis of all areas combined, following a strict protocol for data collection, preparation, and analysis. We used mixed effects linear models for analyses of fecundity, Cormack-Jolly-Seber open population models for analyses of apparent annual survival (ϕ), and a reparameterization of the Jolly-Seber capture–recapture model (i.e. reverse Jolly-Seber; RJS) to estimate annual rates of population change (λ RJS ) and recruitment. We also modeled territory occupancy dynamics of Northern Spotted Owls and Barred Owls in each study area using 2-species occupancy models. Estimated mean annual rates of population change (λ) suggested that Spotted Owl populations declined from 1.2% to 8.4% per year depending on the study area. The weighted mean estimate of λ for all study areas was 0.962 (± 0.019 SE; 95% CI: 0.925–0.999), indicating an estimated range-wide decline of 3.8% per year from 1985 to 2013. Variation in recruitment rates across the range of the Spotted Owl was best explained by an interaction between total winter precipitation and mean minimum winter temperature. Thus, recruitment rates were highest when both total precipitation (29 cm) and minimum winter temperature (−9.5°C) were lowest. Barred Owl presence was associated with increased local extinction rates of Spotted Owl pairs for all 11 study areas. Habitat covariates were related to extinction rates for Spotted Owl pairs in 8 of 11 study areas, and a greater amount of suitable owl habitat was generally associated with decreased extinction rates. We observed negative effects of Barred Owl presence on colonization rates of Spotted Owl pairs in 5 of 11 study areas. The total amount of suitable Spotted Owl habitat was positively associated with colonization rates in 5 areas, and more habitat disturbance was associated with lower colonization rates in 2 areas. We observed strong declines in derived estimates of occupancy in all study areas. Mean fecundity of females was highest for adults (0.309 ± 0.027 SE), intermediate for 2-yr-olds (0.179 ± 0.040 SE), and lowest for 1-yr-olds (0.065 ± 0.022 SE). The presence of Barred Owls and habitat covariates explained little of the temporal variation in fecundity in most study areas. Climate covariates occurred in competitive fecundity models in 8 of 11 study areas, but support for these relationships was generally weak. The fecundity meta-analysis resulted in 6 competitive models, all of which included the additive effects of geographic region and annual time variation. The 2 top-ranked models also weakly supported the additive negative effects of the amount of suitable core area habitat, Barred Owl presence, and the amount of edge habitat on fecundity. We found strong support for a negative effect of Barred Owl presence on apparent survival of Spotted Owls in 10 of 11 study areas, but found few strong effects of habitat on survival at the study area scale. Climate covariates occurred in top or competitive survival models for 10 of 11 study areas, and in most cases the relationships were as predicted; however, there was little consistency among areas regarding the relative importance of specific climate covariates. In contrast, meta-analysis results suggested that Spotted Owl survival was higher across all study areas when the Pacific Decadal Oscillation (PDO) was in a warming phase and the Southern Oscillation Index (SOI) was negative, with a strongly negative SOI indicative of El Niño events. The best model that included the Barred Owl covariate (BO) was ranked 4 th and also included the PDO covariate, but the BO effect was strongly negative. Our results indicated that Northern Spotted Owl populations were declining throughout the range of the subspecies and that annual rates of decline were accelerating in many areas. We observed strong evidence that Barred Owls negatively affected Spotted Owl populations, primarily by decreasing apparent survival and increasing local territory extinction rates. However, the amount of suitable owl habitat, local weather, and regional climatic patterns also were related to survival, occupancy (via colonization rate), recruitment, and, to a lesser extent, fecundity, although there was inconsistency in regard to which covariates were important for particular demographic parameters or across study areas. In the study areas where habitat was an important source of variation for Spotted Owl demographics, vital rates were generally positively associated with a greater amount of suitable owl habitat. However, Barred Owl densities may now be high enough across the range of the Northern Spotted Owl that, despite the continued management and conservation of suitable owl habitat on federal lands, the long-term prognosis for the persistence of Northern Spotted Owls may be in question without additional management intervention. Based on our study, the removal of Barred Owls from the Green Diamond Resources (GDR) study area had rapid, positive effects on Northern Spotted Owl survival and the rate of population change, supporting the hypothesis that, along with habitat conservation and management, Barred Owl removal may be able to slow or reverse Northern Spotted Owl population declines on at least a localized scale.

California, Oregon, Washington↗

USGS1997

Applications of the U.S. Geological Survey's global land cover product

The U.S. Geological Survey (USGS), in partnership with several international agencies and universities, has produced a global land cover characteristics database. The land cover data were created using multitemporal analysis of advanced very high resolution radiometer satellite images in conjunction with other existing geographic data. A translation table permits the conversion of the land cover classes into several conventional land cover schemes that are used by ecosystem modelers, climate modelers, land management agencies, and other user groups. The alternative classification schemes include Global Ecosystems, the Biosphere Atmosphere Transfer Scheme, the Simple Biosphere, the USGS Anderson Level 2, and the International Geosphere Biosphere Programme. The distribution system for these data is through the World Wide Web (the web site address is: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html) or by magnetic media upon special request The availability of the data over the World Wide Web, in conjunction with the flexible database structure, allows easy data access to a wide range of users. The web site contains a user registration form that allows analysis of the diverse applications of large-area land cover data. Currently, applications are divided among mapping (20 percent), conservation (30 percent), and modeling (35 percent).

Acta Astronautica↗

USGS2007

USGS national surveys and analysis projects: Preliminary compilation of integrated geological datasets for the United States

The growth in the use of Geographic nformation Systems (GS) has highlighted the need for regional and national digital geologic maps attributed with age and rock type information. Such spatial data can be conveniently used to generate derivative maps for purposes that include mineral-resource assessment, metallogenic studies, tectonic studies, human health and environmental research. n 1997, the United States Geological Survey’s Mineral Resources Program initiated an effort to develop national digital databases for use in mineral resource and environmental assessments. One primary activity of this effort was to compile a national digital geologic map database, utilizing state geologic maps, to support mineral resource studies in the range of 1:250,000- to 1:1,000,000-scale. Over the course of the past decade, state databases were prepared using a common standard for the database structure, fields, attributes, and data dictionaries. As of late 2006, standardized geological map databases for all conterminous (CONUS) states have been available on-line as USGS Open-File Reports. For Alaska and Hawaii, new state maps are being prepared, and the preliminary work for Alaska is being released as a series of 1:500,000-scale regional compilations. See below for a list of all published databases.

Conference Paper↗

USGS1998

Geographic, temporal, and age-specific variation in diets of Glaucous Gulls in western Alaska

We collected boluses and food remains of adult Glaucous Gulls (Larus hyperboreus) at or near nests and chicks, and digestive tracts from adults at three sites on the Yukon-Kuskokwim Delta, Alaska that differed in proximity to marine and terrestrial foods. We observed both geographic and temporal variation in diet; gulls consumed proportionately more terrestrial prey after peak hatch in late June, and gulls near the coast consumed proportionately more marine prey than gulls at two inland areas. Goslings occurred in > 60% of all samples from these inland areas. We compared these data to those from a previous study in western Alaska and found no marked differences. Evidence for similar patterns of geographic and temporal variation in diet was found using measurements of stable-carbon and nitrogen isotopes in gull and prey tissues. Stable isotope analysis further revealed that adult gulls consumed proportionately more marine prey (saffron cod, Eleginus gracilis) than they fed to their young. Using isotopic models, we estimated that 7-22% and 10-23% of the diet of adult and juvenile Glaucous Gulls, respectively, was comprised of terrestrial species. In addition to significant age-related variation, dietary estimates varied among geographic areas and between pre- and post-hatch periods. Overall, our isotopic estimates of the contribution of terrestrial prey to the diet of Glaucous Gulls was less than what may be inferred from conventional methods of diet analysis. Our study emphasizes the benefit of combining stable-isotope and conventional analyses to infer temporal and geographic changes in diet of wild birds and other organisms.

Condor↗

USGS2022

Characteristics and sources of intense geoelectric fields in the United States: Comparative analysis of multiple geomagnetic storms

Intense geoelectric fields during geomagnetic storms drive geomagnetically induced currents in power grids and other infrastructure, yet there are limited direct measurements of these storm-time geoelectric fields. Moreover, most previous studies examining storm-time geoelectric fields focused on single events or small geographic regions, making it difficult to determine the typical source(s) of intense geoelectric fields. We perform the first comparative analysis of (a) the sources of intense geoelectric fields over multiple geomagnetic storms, (b) using 1-s cadence geoelectric field measurements made at (c) magnetotelluric survey sites distributed widely across the United States. Temporally localized intense perturbations in measured geoelectric fields with prominences (a measure of the relative amplitude of geoelectric field enhancement above the surrounding signal) of at least 500 mV/km were detected during geomagnetic storms with Dst minima ( Dst min ) of less than −100 nT from 2006 to 2019. Most of the intense geoelectric fields were observed in resistive regions with magnetic latitudes greater than 55° even though we have 167 sites located at lower latitudes during geomagnetic storms of −200 nT ≤ Dst min < −100 nT . Our study indicates intense short-lived (<1 min) and geoelectric field perturbations with periods on the order of 1–2 min are common. Most of these perturbations cannot be resolved with 1-min data because they correspond to higher frequency or impulsive phenomena that vary on timescales shorter than that sampling interval. The sources of geomagnetic perturbations inducing these intense geoelectric fields include interplanetary shocks, interplanetary magnetic field turnings, substorms, and ultralow frequency waves.

Space Weather↗

USGS2016

Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012

Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis. For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R 2 ) ranged from 85 to 91 percent. The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.

Alaska↗

USGS2006

Improving the design of acoustic and midwater trawl surveys through stratification, with an application to Lake Michigan prey fishes

Reliable estimates of fish biomass are vital to the management of aquatic ecosystems and their associated fisheries. Acoustic and midwater trawl surveys are an efficient sampling method for estimating fish biomass in large bodies of water. To improve the precision of biomass estimates from combined acoustic and midwater trawl surveys, sampling effort should be optimally allocated within each stage of the survey design. Based on information collected during fish surveys, we developed an approach to improve the design of combined acoustic and midwater trawl surveys through stratification. Geographic strata for acoustic surveying and depth strata for midwater trawling were defined using neighbor-restricted cluster analysis, and the optimal allocation of sampling effort for each was then determined. As an example, we applied this survey stratification approach to data from lakewide acoustic and midwater trawl surveys of Lake Michigan prey fishes. Precision of biomass estimates from surveys with and without geographic stratification was compared through resampling. Use of geographic stratification with optimal sampling allocation reduced the variance of Lake Michigan acoustic biomass estimates by 77%. Stratification and optimal allocation at each stage of an acoustic and midwater trawl survey should serve to reduce the variance of the resulting biomass estimates.

North American Journal of Fisheries Management↗