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At least 127 records · Page 7Linked to original sources
USGS2009

Downsizer - A Graphical User Interface-Based Application for Browsing, Acquiring, and Formatting Time-Series Data for Hydrologic Modeling

The U.S. Geological Survey Downsizer is a computer application that selects, downloads, verifies, and formats station-based time-series data for environmental-resource models, particularly the Precipitation-Runoff Modeling System. Downsizer implements the client-server software architecture. The client presents a map-based, graphical user interface that is intuitive to modelers; the server provides streamflow and climate time-series data from over 40,000 measurement stations across the United States. This report is the Downsizer user's manual and provides (1) an overview of the software design, (2) installation instructions, (3) a description of the graphical user interface, (4) a description of selected output files, and (5) troubleshooting information.

Open-File Report↗
USGS2008

An Overview of the GIS Weasel

This fact sheet provides a high-level description of the GIS Weasel, a software system designed to aid users in preparing spatial information as input to lumped and distributed parameter environmental simulation models (ESMs). The GIS Weasel provides geographic information system (GIS) tools to help create maps of geographic features relevant to the application of a user?s ESM and to generate parameters from those maps. The operation of the GIS Weasel does not require a user to be a GIS expert, only that a user has an understanding of the spatial information requirements of the model. The GIS Weasel software system provides a GIS-based graphical user interface (GUI), C programming language executables, and general utility scripts. The software will run on any computing platform where ArcInfo Workstation (version 8.1 or later) and the GRID extension are accessible. The user controls the GIS Weasel by interacting with menus, maps, and tables.

Fact Sheet↗
USGS2024

Predicting the odds of chronic wasting disease with Habitat Risk software

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer ( Odocoileus virginianus ) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.

Tennessee↗
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↗
USGS2019

User’s manual for the Draper climate-distribution software suite with data‑evaluation tools

Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily point observations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are included in this manual. The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically a drainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input and output files with defined formats and naming conventions. A shell application also is available to manage multiple runs of the Draper application. Other applications in the Draper Suite include (1) a tool to find and interactively remove outliers in the input data, (2) a tool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time-series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a workflow involving physically based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).

Techniques and Methods↗
USGS2003

Fitting population models from field data

The application of population and community ecology to solving real-world problems requires population and community dynamics models that reflect the myriad patterns of interaction among organisms and between the biotic and physical environments. Appropriate models are not hard to construct, but the experimental manipulations needed to evaluate their defining coefficients are often both time consuming and costly, and sometimes environmentally destructive, as well. In this paper we present an empirical approach for finding the coefficients of broadly inclusive models without the need for environmental manipulation, demonstrate the approach with both an animal and a plant example, and suggest possible applications. Software has been developed, and is available from the senior author, with a manual describing both field and analytic procedures.

Ecological Modelling↗
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↗
USGS2020

Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality

As part of a U.S. Geological Survey water-quality study started in 2018, in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency, a publicly available software package called R–QWTREND was developed for analyzing trends in stream-water quality. The R–QWTREND package is a collection of functions written in R, an open source language and a general environment for statistical computing and graphics. The package uses a parametric time-series model to express logarithmically transformed concentration in terms of flow-related variability, trend, and serially correlated model errors. Flow-related variability captures natural variability in concentration on the basis of concurrent and antecedent streamflow. The trend identifies systematic changes in concentration in terms of potential step trends, piecewise monotonic trends, or user-specified trends. Maximum likelihood estimation is used to estimate model parameters and determine the best-fit trend model. This report describes the time-series model and statistical methodology behind R–QWTREND and provides formal documentation for installing and using the package.

Open-File Report↗
USGS2006

Hierarchical models of animal abundance and occurrence

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

Journal of Agricultural, Biological, and Environme↗
USGS2016

Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data

The Everglades Depth Estimation Network (EDEN), with over 240 real-time gaging stations, provides hydrologic data for freshwater and tidal areas of the Everglades. These data are used to generate daily water-level and water-depth maps of the Everglades that are used to assess biotic responses to hydrologic change resulting from the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The generation of EDEN daily water-level and water-depth maps is dependent on high quality real-time data from water-level stations. Real-time data are automatically checked for outliers by assigning minimum and maximum thresholds for each station. Small errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the stations. Correcting these small errors in the data often is time consuming and water-level data may not be finalized for several months. To provide daily water-level and water-depth maps on a near real-time basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous water-level data. The Automated Data Assurance and Management (ADAM) software uses inferential sensor technology often used in industrial applications. Rather than installing a redundant sensor to measure a process, such as an additional water-level station, inferential sensors, or virtual sensors, were developed for each station that make accurate estimates of the process measured by the hard sensor (water-level gaging station). The inferential sensors in the ADAM software are empirical models that use inputs from one or more proximal stations. The advantage of ADAM is that it provides a redundant signal to the sensor in the field without the environmental threats associated with field conditions at stations (flood or hurricane, for example). In the event that a station does malfunction, ADAM provides an accurate estimate for the period of missing data. The ADAM software also is used in the quality assurance and quality control of the data. The virtual signals are compared to the real-time data, and if the difference between the two signals exceeds a certain tolerance, corrective action to the data and (or) the gaging station can be taken. The ADAM software is automated so that, each morning, the real-time EDEN data are compared to the inferential sensor signals and digital reports highlighting potential erroneous real-time data are generated for appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

Florida↗
USGS2007

Development of the Hydroecological Integrity Assessment Process for Determining Environmental Flows for New Jersey Streams

The natural flow regime paradigm and parallel stream ecological concepts and theories have established the benefits of maintaining or restoring the full range of natural hydrologic variation for physiochemical processes, biodiversity, and the evolutionary potential of aquatic and riparian communities. A synthesis of recent advances in hydroecological research coupled with stream classification has resulted in a new process to determine environmental flows and assess hydrologic alteration. This process has national and international applicability. It allows classification of streams into hydrologic stream classes and identification of a set of non-redundant and ecologically relevant hydrologic indices for 10 critical sub-components of flow. Three computer programs have been developed for implementing the Hydroecological Integrity Assessment Process (HIP): (1) the Hydrologic Indices Tool (HIT), which calculates 171 ecologically relevant hydrologic indices on the basis of daily-flow and peak-flow stream-gage data; (2) the New Jersey Hydrologic Assessment Tool (NJHAT), which can be used to establish a hydrologic baseline period, provide options for setting baseline environmental-flow standards, and compare past and proposed streamflow alterations; and (3) the New Jersey Stream Classification Tool (NJSCT), designed for placing unclassified streams into pre-defined stream classes. Biological and multivariate response models including principal-component, cluster, and discriminant-function analyses aided in the development of software and implementation of the HIP for New Jersey. A pilot effort is currently underway by the New Jersey Department of Environmental Protection in which the HIP is being used to evaluate the effects of past and proposed surface-water use, ground-water extraction, and land-use changes on stream ecosystems while determining the most effective way to integrate the process into ongoing regulatory programs. Ultimately, this scientifically defensible process will help to quantify the effects of anthropogenic changes and development on hydrologic variability and help planners and resource managers balance current and future water requirements with ecological needs.

Scientific Investigations Report↗
USGS2007

Prediction of lethal/effective concentration/dose in the presence of multiple auxiliary covariates and components of variance

Predictors of the percentile lethal/effective concentration/dose are commonly used measures of efficacy and toxicity. Typically such quantal-response predictors (e.g., the exposure required to kill 50% of some population) are estimated from simple bioassays wherein organisms are exposed to a gradient of several concentrations of a single agent. The toxicity of an agent may be influenced by auxiliary covariates, however, and more complicated experimental designs may introduce multiple variance components. Prediction methods lag examples of those cases. A conventional two-stage approach consists of multiple bivariate predictions of, say, medial lethal concentration followed by regression of those predictions on the auxiliary covariates. We propose a more effective and parsimonious class of generalized nonlinear mixed-effects models for prediction of lethal/effective dose/concentration from auxiliary covariates. We demonstrate examples using data from a study regarding the effects of pH and additions of variable quantities 2???,5???-dichloro-4???- nitrosalicylanilide (niclosamide) on the toxicity of 3-trifluoromethyl-4- nitrophenol to larval sea lamprey (Petromyzon marinus). The new models yielded unbiased predictions and root-mean-squared errors (RMSEs) of prediction for the exposure required to kill 50 and 99.9% of some population that were 29 to 82% smaller, respectively, than those from the conventional two-stage procedure. The model class is flexible and easily implemented using commonly available software. ?? 2007 SETAC.

Environmental Toxicology and Chemistry↗
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↗
USGS2015

Social Values for Ecosystem Services, version 3.0 (SolVES 3.0): documentation and user manual

The geographic information system (GIS) tool, S ocial V alues for E cosystem S ervices (SolVES), was developed to incorporate quantified and spatially explicit measures of social values into ecosystem service assessments. SolVES 3.0 continues to extend the functionality of SolVES, which was designed to assess, map, and quantify the social values of ecosystem services. Social values—the perceived, nonmarket values the public ascribes to ecosystem services, particularly cultural services, such as aesthetics and recreation—can be evaluated for various stakeholder groups. These groups are distinguishable by their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with previous versions, SolVES 3.0 derives a quantitative 10-point, social-values metric—the value index—from a combination of spatial and nonspatial responses to public value and preference surveys. The tool also calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. SolVES 3.0 is integrated with Maxent maximum entropy modeling software to generate more complete social-value maps and offer robust statistical models describing the relationship between the value index and explanatory environmental variables. A model’s goodness of fit to a primary study area and its potential performance in transferring social values to similar areas using value-transfer methodology can be evaluated. SolVES 3.0 provides an improved public-domain tool for decision makers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among ecosystem services in a variety of physical and social contexts ranging from forest and rangeland to coastal and marine.

Open-File Report↗
USGS2020

Social Values for Ecosystem Services, version 4.0 (SolVES 4.0)—Documentation and user manual

The geographic information system tool, Social Values for Ecosystem Services (SolVES), was developed to incorporate quantified and spatially explicit measures of social values into ecosystem service assessments. SolVES 4.0 provides an open-source version of SolVES, which was designed to assess, map, and quantify the social values of ecosystem services. Social values—the perceived, nonmarket values the public ascribes to ecosystem services, particularly cultural services, such as aesthetics and recreation—can be evaluated for various stakeholder groups. These groups are distinguishable by factors such as their attitudes and preferences regarding public uses (for example, motorized recreation and logging). As with previous versions, SolVES 4.0 derives a quantitative 10-point, social-values metric—the value index—from a combination of spatial and nonspatial responses to public value and preference surveys. The tool also calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. SolVES 4.0 has been developed with Python using a QGIS user interface and a PostgreSQL database for required data. SolVES is integrated with Maxent maximum entropy modeling software to generate more complete social-value maps and offer robust statistical models describing the relation between the value index and explanatory environmental variables. A model’s goodness of fit to a primary study area and its potential performance in transferring social values to similar areas using value-transfer methods can be evaluated. SolVES 4.0 provides an improved open-source, public-domain tool for decision makers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among ecosystem services in a variety of biophysical and social contexts including mountain, forest, coastal, riparian, agricultural, and urban environments around the globe.

Techniques and Methods↗
USGS2012

Social values for ecosystem services (SolVES): Documentation and user manual, version 2.0

In response to the need for incorporating quantified and spatially explicit measures of social values into ecosystem services assessments, the Rocky Mountain Geographic Science Center (RMGSC), in collaboration with Colorado State University, developed a geographic information system (GIS) application, Social Values for Ecosystem Services (SolVES). With version 2.0 (SolVES 2.0), RMGSC has improved and extended the functionality of SolVES, which was designed to assess, map, and quantify the perceived social values of ecosystem services. Social values such as aesthetics, biodiversity, and recreation can be evaluated for various stakeholder groups as distinguished by their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with the previous version, SolVES 2.0 derives a quantitative, 10-point, social-values metric, the Value Index, from a combination of spatial and nonspatial responses to public attitude and preference surveys and calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. Additionally, SolVES 2.0 integrates Maxent maximum entropy modeling software to generate more complete social value maps and to produce robust statistical models describing the relationship between the social values maps and explanatory environmental variables. The performance of these models can be evaluated for a primary study area, as well as for similar areas where primary survey data are not available but where social value mapping could potentially be completed using value-transfer methodology. SolVES 2.0 also introduces the flexibility for users to define their own social values and public uses, model any number and type of environmental variable, and modify the spatial resolution of analysis. With these enhancements, SolVES 2.0 provides an improved public domain tool for decisionmakers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among different ecosystem services in a variety of physical and social contexts ranging from forest and rangeland to coastal and marine.

Open-File Report↗
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↗
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↗