SEARCH · Search USGS

Results for “Environmental Software”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources

USGS2014

Virtual Beach 3: user's guide

Virtual Beach version 3 (VB 3 ) is a decision support tool that constructs site-specific statistical models to predict fecal indicator bacteria (FIB) concentrations at recreational beaches. VB 3 is primarily designed for beach managers responsible for making decisions regarding beach closures or the issuance of swimming advisories due to pathogen contamination. However, researchers, scientists, engineers, and students interested in studying relationships between water quality indicators and ambient environmental conditions will find VB 3 useful. VB 3 reads input data from a text file or Excel document, assists the user in preparing the data for analysis, enables automated model selection using a wide array of possible model evaluation criteria, and provides predictions using a chosen model parameterized with new data. With an integrated mapping component to determine the geographic orientation of the beach, the software can automatically decompose wind/current/wave speed and magnitude information into along-shore and onshore/offshore components for use in subsequent analyses. Data can be examined using simple scatter plots to evaluate relationships between the response and independent variables (IVs). VB 3 can produce interaction terms between the primary IVs, and it can also test an array of transformations to maximize the linearity of the relationship The software includes search routines for finding the "best" models from an array of possible choices. Automated censoring of statistical models with highly correlated IVs occurs during the selection process. Models can be constructed either using previously collected data or forecasted environmental information. VB 3 has residual diagnostics for regression models, including automated outlier identification and removal using DFFITs or Cook's Distances.

Report↗

USGS2007

Coastal Prairie Restoration Information System: Version 1 (Louisiana)

The Coastal Prairie Restoration Information System (CPR) is a Microsoft Access database that allows users to query and view data about Louisiana coastal prairie species. Less than 0.1% of Louisiana's coastal prairie vegetation remains in a relatively undisturbed condition. Encompassing as much as 1 million hectares of land, coastal prairie is a hybrid of coastal wetlands and tall grass prairie. Over 550 plant species have been identified in Louisiana's coastal prairies to date. Efforts to conserve and restore this endangered ecosystem are limited by the ability of workers to identify and access knowledge about this diverse group of plants. In this database, a variety of data are provided for each of 650 coastal prairie species in Louisiana. The database was developed at the U.S. Geological Survey National Wetlands Research Center by Larry Allain, with software development by Myra Silva. Additional funding was provided by the biology department of the University of Louisiana at Lafayette (ULL), the ULL Center for Environmental and Ecological Technology, and the National Science Foundation.

Data Series↗

USGS2022

Coupling validation effort with in situ bioacoustic data improves estimating relative activity and occupancy for multiple species with cross-species misclassifications

The increasing complexity and pace of ecological change requires natural resource managers to consider entire species assemblages. Acoustic recording units (ARUs) require minimal cost and effort to deploy and inform relative activity, or encounter rates, for multiple species simultaneously. ARU-based surveys require post-processing of the recordings via software algorithms that assign a species label to each recording. The automated classification process can result in cross-species misidentifications that should be accounted for when employing statistical modelling for conservation decision-making. Using simulation and ARU-based detection counts from 17 bat species in British Columbia, Canada, we investigate three strategies for adjusting statistical inference for species misclassification: (a) ‘coupling’ ambiguous and unambiguous detections by validating a subset of survey events post-hoc, (b) using a calibration dataset on the software algorithm's (in)accuracy for species identification or (c) specifying informative Bayesian priors on classification probabilities. We explore the impact of different Bayesian prior specifications for the classification probabilities on posterior estimation. We then consider how the quantity of data validated post-hoc impacts model convergence and resulting inferences for bat species relative activity as related to nightly conditions and yearly site occupancy after accounting for site-level environmental variables. Coupled methods resulted in less bias and uncertainty when estimating relative activity and species classification probabilities relative to calibration approaches. We found that species that were difficult-to-detect and those that were often inaccurately identified by the software required more validation effort than more easily detected and/or identified species. Our results suggest that, when possible, acoustic surveys should rely on coupled validated detection information to account for false-positive detections, rather than uncoupled calibration datasets. However, if the assemblage of interest contains a large number of rarely detected or less prevalent species, an intractable amount of effort may be required, suggesting there are benefits to curating a calibration dataset that is representative of the observation process. Our findings provide insights into the practical challenges associated with statistical analyses of ARU data and possible analytical solutions to support reliable and cost-effective decision-making for wildlife conservation/management in the face of known sources of observation errors.

British Columbia↗

USGS2019

Evaluating associations between environmental variables and Escherichia coli levels for predictive modeling at Pawtuckaway Beach in Nottingham, New Hampshire, from 2015 to 2017

From 2015 through 2017, the U.S. Geological Survey in cooperation with the New Hampshire Department of Health and Human Services and the New Hampshire Department of Environmental Services studied occurrences of high levels of Escherichia coli ( E. coli ) bacteria at the Pawtuckaway State Park Beach in Nottingham, New Hampshire. Historic data collected by the New Hampshire Department of Environmental Services indicated that E. coli concentrations in the water typically increased through the beach season to levels considered potentially harmful to beachgoers. During the three beach seasons that were studied, E. coli samples were collected three to four times per week, and water-quality and meteorological data were collected continuously. The Virtual Beach software was used to generate a predictive model for each year of the study (2015–2017), and the model for each of these years was tested with data from the other two. Additionally, data from all study years were combined to generate a comprehensive model to help identify independent variables that might characterize environmental conditions relative to E. coli levels during multiple seasons. The accuracy of the models in predicting the occurrence of high E. coli levels was marginal, but the models did provide insights into the likely mechanisms for increased E. coli levels during the seasons. Variables most important in explaining high E. coli levels were the presence of geese at the beach, the progression of the season, the number of visitors at the beach, and wind vectors relative to beach orientation.

New Hampshire↗

USGS2021

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distribution

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k -fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

Ecography↗

USGS2002

A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling

A modular approach to model design and construction provides a flexible framework in which to focus the multidisciplinary research and operational efforts needed to facilitate the development, selection, and application of the most robust distributed modelling methods. A variety of modular approaches have been developed, but with little consideration for compatibility among systems and concepts. Several systems are proprietary, limiting any user interaction. The US Geological Survey modular modelling system (MMS) is a modular modelling framework that uses an open source software approach to enable all members of the scientific community to address collaboratively the many complex issues associated with the design, development, and application of distributed hydrological and environmental models. Implementation of a common modular concept is not a trivial task. However, it brings the resources of a larger community to bear on the problems of distributed modelling, provides a framework in which to compare alternative modelling approaches objectively, and provides a means of sharing the latest modelling advances. The concepts and components of the MMS are described and an example application of the MMS, in a decision-support system context, is presented to demonstrate current system capabilities. Copyright ?? 2002 John Wiley and Sons, Ltd.

Hydrological Processes↗

USGS2010

Bayesian Inference: with ecological applications

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

Book↗

USGS2019

Let’s agree to disagree: Comparing auto-acoustic identification programs for northeastern bats

With the declines in abundance and changing distribution of white-nose syndrome–affected bat species, increased reliance on acoustic monitoring is now the new “normal.” As such, the ability to accurately identify individual bat species with acoustic identification programs has become increasingly important. We assessed rates of disagreement between the three U.S. Fish and Wildlife Service–approved acoustic identification software programs (Kaleidoscope Pro 4.2.0, Echoclass 3.1, and Bat Call Identification 2.7d) and manual visual identification using acoustic data collected during summers from 2003 to 2017 at Fort Drum, New York. We assessed the percentage of agreement between programs through pairwise comparisons on a total nightly count level, individual file level (e.g., individual echolocation pass call file), and grouped maximum likelihood estimate level (e.g., probability values that a species is misclassified as present when in fact it is absent) using preplanned contrasts, Akaike Information Criterion, and annual confusion matrices. Interprogram agreement on an individual file level was low, as measured by Cohen's Kappa (0.2–0.6). However, site-night level pairwise comparative analysis indicated that program agreement was higher (40–90%) using single season occupancy metrics. In comparing analytical outcomes of our different datasets (i.e., how comparable programs and visual identification are regarding the relationship between environmental conditions and bat activity), we determined high levels of congruency in the relative rankings of the model as well as the relative level of support for each individual model. This indicated that among individual software packages, when analyzing bat calls, there was consistent ecological inference beyond the file-by-file level at the scales used by managers. Depending on objectives, we believe our results can help users choose automated software and maximum likelihood estimate thresholds more appropriate for their needs and allow for better cross-comparison of studies using different automated acoustic software.

Journal of Fish and Wildlife Management↗

USGS1996

Benthic invertebrates of benchmark streams in agricultural areas of eastern Wisconsin — Western Lake Michigan drainages

This study describes the benthic invertebrate communities of 20 benchmark streams in agricultural areas of eastern Wisconsin. Streams with minimal adverse effects from human activity were selected from four agricultural areas with differing surficial deposits and bedrock types (relatively homogeneous units, or RHU's). Most aquatic invertebrate orders were well represented in the 20 benchmark stream samples; 217 species and 151 genera within 56 families were identified. Diptera was the best represented order (96 species), followed by Trichoptera (42 species) and Ephemeroptera (26 species). Diptera were the most abundant organisms in terms of numbers of individuals in the sample (28 percent of the total) followed by Trichoptera (25 percent) and Ephemeroptera (13 percent). Nine species of freshwater mussels were found, but only in 5 of the 20 benchmark streams. Community measures were calculated for the following: total number of individuals; number of species; number of families; Margalef's diversity index; percent dominant family; percent Ephemeroptera-Plecoptera-Trichoptera (EPT); ratio of EPT to Chironomidae; percent shredders; ratio of scrapers to collectors-gatherers-filterers; Hilsenhoff's Biotic Index; Hilsenhoff's family level biotic index; and mean tolerance value. The S AS statistical software package was used for calculations of variance and correlations, normality checks, and principal components analysis of these measures and to find relations between benthic-invertebrate data and environmental-setting, habitat, and water-quality data. Coefficients of variation within the RHU's were as great or greater than those for all 20 streams for most measures and RHU's. The specific taxa assemblages present at the sites did not show distinct differences between RHU's or similarities within the RHU's. The covariance and the Kruskal-Wallis tests showed that the benthic invertebrate measures were not related to RHU. These results all indicate that the combined effect of the RHU variables (bedrock geology, texture of surficial deposits, and land use/land cover) were not elemental in describing invertebrate communities in the study-area streams. A principal components analysis (PCA) was done on the 20 benchmark streams which used the invertebrate population measures as variables. A three-dimensional ordination plot of these components revealed that 18 of the 20 streams could be divided into three groups relative to stream size, available habitat, and water quality. The three classifications of streams include large, warmer streams with slight pollution; deep, mixed-water streams with minimal pollution; and small, cold, pristine headwater streams. The two streams not defined by the three PCA groupings were not suitable to represent benchmark conditions. One site lacked suitable quality habitat or sufficient nutrients to support a healthy population of invertebrates, causing low measures of diversity. The other site appeared to be affected by sedimentation and low flows. The classification groupings did not show any significant relations to percentage agricultural land use. Percentage of agricultural land use varied greatly within each group and the means for each group were similar. All streams in this study had some level of protection from agricultural practices in their basins. Although the intensity of agriculture is known to be a factor causing deterioration of invertebrate populations in past studies, the finding in this study indicated that the level of protection the stream received and other factors such as environmental setting and habitat could be more important to benthic invertebrates than the percentage of agriculture in the basin. Information gathered from these benchmark streams can be used as a regional reference for comparison with other streams in agricultural areas, based on communities of aquatic biota, habitat, and water quality.

Wisconsin↗

USGS2009

Mercury Loads in the South River and Simulation of Mercury Total Maximum Daily Loads (TMDLs) for the South River, South Fork Shenandoah River, and Shenandoah River: Shenandoah Valley, Virginia

Due to elevated levels of methylmercury in fish, three streams in the Shenandoah Valley of Virginia have been placed on the State's 303d list of contaminated waters. These streams, the South River, the South Fork Shenandoah River, and parts of the Shenandoah River, are downstream from the city of Waynesboro, where mercury waste was discharged from 1929-1950 at an industrial site. To evaluate mercury contamination in fish, this total maximum daily load (TMDL) study was performed in a cooperative effort between the U.S. Geological Survey, the Virginia Department of Environmental Quality, and the U.S. Environmental Protection Agency. The investigation focused on the South River watershed, a headwater of the South Fork Shenandoah River, and extrapolated findings to the other affected downstream rivers. A numerical model of the watershed, based on Hydrological Simulation Program-FORTRAN (HSPF) software, was developed to simulate flows of water, sediment, and total mercury. Results from the investigation and numerical model indicate that contaminated flood-plain soils along the riverbank are the largest source of mercury to the river. Mercury associated with sediment accounts for 96 percent of the annual downstream mercury load (181 of 189 kilograms per year) at the mouth of the South River. Atmospherically deposited mercury contributes a smaller load (less than 1 percent) as do point sources, including current discharge from the historic industrial source area. In order to determine how reductions of mercury loading to the stream could reduce methylmercury concentrations in fish tissue below the U.S. Environmental Protection Agency criterion of 0.3 milligrams per kilogram, multiple scenarios were simulated. Bioaccumulation of mercury was expressed with a site-specific exponential relation between aqueous total mercury and methylmercury in smallmouth bass, the indicator fish species. Simulations indicate that if mercury loading were to decrease by 98.9 percent from 189 to 2 kilograms per year, fish tissue methylmercury concentrations would drop below 0.3 milligrams per kilogram. Based on the simulations, the estimated maximum load of total mercury that can enter the South River without causing fish tissue methylmercury concentrations to rise above 0.3 milligrams per kilogram is 2.03 kilograms per year for the South River, and 4.12 and 6.06 kilograms per year for the South Fork Shenandoah River and Shenandoah River, respectively.

Scientific Investigations Report↗

USGS2013

NCWin — A Component Object Model (COM) for processing and visualizing NetCDF data

NetCDF (Network Common Data Form) is a data sharing protocol and library that is commonly used in large-scale atmospheric and environmental data archiving and modeling. The NetCDF tool described here, named NCWin and coded with Borland C + + Builder, was built as a standard executable as well as a COM (component object model) for the Microsoft Windows environment. COM is a powerful technology that enhances the reuse of applications (as components). Environmental model developers from different modeling environments, such as Python, JAVA, VISUAL FORTRAN, VISUAL BASIC, VISUAL C + +, and DELPHI, can reuse NCWin in their models to read, write and visualize NetCDF data. Some Windows applications, such as ArcGIS and Microsoft PowerPoint, can also call NCWin within the application. NCWin has three major components: 1) The data conversion part is designed to convert binary raw data to and from NetCDF data. It can process six data types (unsigned char, signed char, short, int, float, double) and three spatial data formats (BIP, BIL, BSQ); 2) The visualization part is designed for displaying grid map series (playing forward or backward) with simple map legend, and displaying temporal trend curves for data on individual map pixels; and 3) The modeling interface is designed for environmental model development by which a set of integrated NetCDF functions is provided for processing NetCDF data. To demonstrate that the NCWin can easily extend the functions of some current GIS software and the Office applications, examples of calling NCWin within ArcGIS and MS PowerPoint for showing NetCDF map animations are given.

Cartographic Journal↗

USGS1999

Inventory of selected freshwater-ecology studies from the New England Coastal Basins (Maine, New Hampshire, Massachusetts, Rhode Island), 1937-97

An inventory of published studies that address freshwater ecology within the New England Coastal Basins was created through computerized bibliographic literature searches and consultation with environmental agencies. Assembled papers were classified to associate their contents with one or more states, ecoregions, river basins, and ecological topics. Full references and their classifications were entered into a bibliographic software program and then exported to a data-base application to generate a checklist summary of study contents. This report presents a listing and classification of 154 selected studies, published between 1937 and 1997, that provide background knowledge and serve as general aquatic-ecology references for the New England Coastal Basins study area.

Maine, Massachusetts, New Hampshire, Rhode Island↗

USGS2012

Classifications for Coastal Wetlands Planning, Protection and Restoration Act site-specific projects: 2008 and 2009

The Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) funds over 100 wetland restoration projects across Louisiana. Integral to the success of CWPPRA is its long-term monitoring program, which enables State and Federal agencies to determine the effectiveness of each restoration effort. One component of this monitoring program is the analysis of high-resolution, color-infrared aerial photography at the U.S. Geological Survey's National Wetlands Research Center in Lafayette, Louisiana. Color-infrared aerial photography (9- by 9-inch) is obtained before project construction and several times after construction. Each frame is scanned on a photogrametric scanner that produces a high-resolution image in Tagged Image File Format (TIFF). By using image-processing software, these TIFF files are then orthorectified and mosaicked to produce a seamless image of a project area and its associated reference area (a control site near the project that has common environmental features, such as marsh type, soil types, and water salinities.) The project and reference areas are then classified according to pixel value into two distinct classes, land and water. After initial land and water ratios have been established by using photography obtained before and after project construction, subsequent comparisons can be made over time to determine land-water change. Several challenges are associated with the land-water interpretation process. Primarily, land-water classifications are often complicated by the presence of floating aquatic vegetation that occurs throughout the freshwater systems of coastal Louisiana and that is sometimes difficult to differentiate from emergent marsh. Other challenges include tidal fluctuations and water movement from strong winds, which may result in flooding and inundation of emergent marsh during certain conditions. Compensating for these events is difficult but possible by using other sources of imagery to verify marsh conditions for other dates in time.

Louisiana↗

USGS2013

Classifications for Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) site-specific projects: 2010

Louisiana↗

USGS2004

Map showing geology, oil and gas fields, and geologic provinces of the Caribbean Region

This CD-ROM compilation contains a map and associated spatial data showing surface geology, faults, oil and gas field centerpoints, and geologic provinces of the Caribbean region, draped over a shaded relief image of topography and bathymetry. The map is provided in the Environmental Systems Research Institute, Inc. (ESRI) ArcMap and ArcReader GIS formats, as well as in Adobe Acrobat Portable Document Format (PDF). On this CD-ROM, ESRI ArcReader and Adobe Acrobat Reader software provide a way to view and interact with the maps. The organization and user-friendly navigation of this CD-ROM ensure easy access to its maps and data by using the links on the right side of each page. A link to the USGS World Energy Project website is also provided to access the latest information, updates, and interactive maps, as they relate to this and other world energy products. In addition, system requirements, permission, and contact information can be found in the readme section of this product. Navigation of this product can be fully utilized with most web browsers (Internet Explorer 6.0/Netscape Navigator 7.1 or later recommended). Note: an Internet connection is necessary to view USGS website links, World Energy website, and the Caribbean Internet Map Service.

Open-File Report↗

USGS1996

A resource conservative procedure for comparison of dose-response relationships

The evaluation of effects of toxicants on a wildlife community can be complicated by varying responses among the community's constituent populations. Even within populations, considerable variability in dose-response relations may result from different avenues of exposure to the toxicant. Full-scale investigations of the dose-response relations among a variety of species and avenues of exposure can therefore be prohibitively expensive, whether this expense is measured by the number of experimental animals needed, by the human resources committed to the study, or by laboratory expenses. We propose an abbreviated protocol for investigations of multiple dose-response relations that is designed to limit these expenses. The protocol begins with the judicious choice of a baseline dose-response relation to be estimated by a full-scale study involving a minimum of five doses levels, with 10 subjects per dose level. This relation is then used as the basis for rapid screening of subsequent dose-response relations, which are compared to the baseline relation by testing for differences in the median effective dosages. These secondary studies can consist of as few as 14 animals exposed to the estimated median lethal concentration from the baseline study. We describe MS-DOS-compatible software available from the authors that can be used to analyze these data.

Environmental Toxicology and Chemistry↗

USGS1998

Estimation of octanol/water partition coefficients using LSER parameters

The logarithms of octanol/water partition coefficients, log Kow , were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for log Kow was 0.49. The regression equation was then used to estimate log Kow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

Advances in Environmental Research↗

USGS2020

msocc: Fit and analyse computationally efficient multi‐scale occupancy models in R

Environmental DNA (eDNA) sampling is a promising tool for the detection of rare and cryptic taxa, such as aquatic pathogens, parasites and invasive species. Environmental DNA sampling workflows commonly rely on multi‐stage hierarchical sampling designs that induce complicated dependencies within the data. This complex dependence structure can be intuitively modelled with Bayesian multi‐scale occupancy models. However, current software for such models are computationally demanding, impeding their use. We present an r package, msocc , that implements a data augmentation strategy to fit fully Bayesian, computationally efficient multi‐scale occupancy models. The msocc package allows users to fit multi‐scale occupancy models, to estimate and visualize posterior summaries of site, sample and replicate‐level occupancy, and to compare different models using Bayesian information criterion. Additionally, we provide a supplemental web application that allows users to investigate study design for multi‐scale occupancy models and acts as a graphical user interface to the msocc package. The utility of the msocc package is illustrated on a published dataset and the functions in msocc are compared to the primary Bayesian toolkit for multi‐scale occupancy modelling, eDNAoccupancy , using various computational benchmarks. These benchmarks indicate that msocc is capable of fitting models 50 times faster than eDNAoccupancy . We hope that access to software that efficiently fits, analyses and conducts study design investigations for multi‐scale occupancy models facilitates their implementation by the research and wildlife management communities.

Methods in Ecology and Evolution↗