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At least 667 records · Page 37Linked to original sources
USGS2024

Predicted potential for aquatic exposure effects of per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania’s statewide network of streams

Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity and developmental abnormalities. To assess the ecological health risk of PFAS in Pennsylvania stream surface water, we conducted a comprehensive analysis that included both measured and predicted estimates. The potential combined exposure effects of 14 individual PFAS to aquatic biota were estimated using the sum of exposure-activity ratios ( Σ EARs) in 280 streams. Additionally, machine learning techniques were utilized to predict potential PFAS exposure effects in unmonitored stream reaches, considering factors such as land use, climate, and geology. Leveraging a tailored convolutional neural network (CNN), a validation accuracy of 78% was achieved, directly outperforming traditional methods that were also used, such as logistic regression and gradient boosting (accuracies of ~65%). Feature importance analysis highlighted key variables that contributed to the CNN’s predictive power. The most influential features highlighted the complex interplay of anthropogenic and environmental factors contributing to PFAS contamination in surface waters. Industrial and urban land cover, rainfall intensity, underlying geology, agricultural factors, and their interactions emerged as key determinants. These findings may help to inform biotic sampling strategies, water quality monitoring efforts, and policy decisions aimed to mitigate the ecological impacts of PFAS in surface waters.

Pennsylvania↗
USGS2018

Landsat time series analysis of fractional plant cover changes on abandoned energy development sites

Oil and natural gas development in the western United States has increased substantially in recent decades as technological advances like horizontal drilling and hydraulic fracturing have made extraction more commercially viable. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive. Understanding the rates, controls, and degree of recovery of these reclaimed well sites to a state similar to pre-development conditions is critical for energy development and land management decision processes. Here we use a multi-decadal time series of satellite imagery (Landsat 5, 1984–2011) to assess vegetation regrowth on 365 abandoned well pads located across the Colorado Plateau in Utah, Colorado, and New Mexico. We developed high-frequency time series of the Soil-Adjusted Total Vegetation Index (SATVI) for each well pad using the Google Earth Engine cloud computing platform. BFAST time-series models were used to fit temporal trends, identifying when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The time series metrics are used to calculate the relative fractional vegetation cover (RFVC) of each pad, a measure of post-abandonment vegetation cover relative to pre-drilling condition. Mean and median RFVC were 36% (s.d. 33%) and 26%, respectively, five years after abandonment, with one third of well pads having RFVC greater than 50%. Statistical analyses suggest that much of the high vegetation cover is associated with weedy invasive annual species such as cheatgrass (Bromus tectorum) and Russian thistle (Salsola spp.). Climate conditions and the year of abandonment also play a role, with increased cover in later years associated with a wetter period. Non-linear change at many pads suggests longer recovery times than would be estimated by linear extrapolation. New techniques implemented here address a complex response of cover change to soils, management, and climate over time, and can be extended to the operational monitoring of energy development across large areas.

International Journal of Applied Earth Observation↗
USGS2014

Causal networks clarify productivity-richness interrelations, bivariate plots do not

Perhaps no other pair of variables in ecology has generated as much discussion as species richness and ecosystem productivity, as illustrated by the reactions by Pierce (2013) and others to Adler et al.'s (2011) report that empirical patterns are weak and inconsistent. Adler et al. (2011) argued we need to move beyond a focus on simplistic bivariate relationships and test mechanistic, multivariate causal hypotheses. We feel the continuing debate over productivity–richness relationships (PRRs) provides a focused context for illustrating the fundamental difficulties of using bivariate relationships to gain scientific understanding. Pierce (2013) disputes Adler et al.'s (2011) conclusion that bivariate productivity–richness relationships (PRRs) are ‘weak and variable’. He argues, instead, that relationships in the Adler et al. data are actually strong and, further, that failure to adhere to the humped-back model (HBM; sensu Grime 1979) threatens scientists' ability to advise conservationists. Here, we show that Pierce's reanalyses are invalid, that statistically significant boundary relations in the Adler et al. data are difficult to detect when proper methods are used and that his advice neither advances scientific understanding nor provides the quantitative forecasts actually needed by decision makers. We begin by examining Grimes' HBM through the lens of causal networks. We first translate the ideas contained in the HBM into a causal diagram, which shows explicitly how multiple processes are hypothesized to control biomass production and richness and their interrelationship. We then evaluate the causal diagram using structural equation modelling and example data from a published study of meadows in Finland. Formal analysis rejects the literal translation of the HBM and reveals additional processes at work. This exercise shows how the practice of abstracting systems as causal networks (i) clarifies possible hypotheses, (ii) permits explicit testing and (iii) provides more powerful and useful predictions. Building on the Finnish meadow example, we contrast the utility of bivariate plots compared with structural equation models for investigating underlying processes. Simulations illustrate the fallibility of bivariate analysis as a means of supporting one theory over another, while models based on causal networks can quantify the sensitivity of diversity patterns to both management and natural constraints. A key piece of Pierce's critique of Adler et al.'s conclusions relies on upper boundary regression which he claims to reveal strong relationships between production and richness in Adler et al.'s original data. We demonstrate that this technique shows strong associations in purely random data and is invalid for Adler et al.'s data because it depends on a uniform data distribution. We instead perform quantile regression on both the site-level summaries of the data and the plot-level data (using mixed-model quantile regression). Using a variety of nonlinear curve-fitting approaches, we were unable to detect a significant humped-shape boundary in the Adler et al. data. We reiterate that the bivariate productivity–richness relationships in Adler et al.'s data are weak and variable. We urge ecologists to consider productivity–richness relationships through the lens of causal networks to advance our understanding beyond bivariate analysis. Further, we emphasize that models based on a causal network conceptualization can also provide more meaningful guidance for conservation management than can a bivariate perspective. Measuring only two variables does not permit the evaluation of complex ideas nor resolve debates about underlying mechanisms.

Functional Ecology↗
USGS2013

Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE; Version 1.0): web-based tools to assess the impact of sea level rise in south Florida

South Florida's Greater Everglades area is particularly vulnerable to sea level rise, due to its rich endowment of animal and plant species and its heavily populated urban areas along the coast. Rising sea levels are expected to have substantial impacts on inland flooding, the depth and extent of surge from coastal storms, the degradation of water supplies by saltwater intrusion, and the integrity of plant and animal habitats. Planners and managers responsible for mitigating these impacts require advanced tools to help them more effectively identify areas at risk. The U.S. Geological Survey's (USGS) Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE) Web site has been developed to address these needs by providing more convenient access to projections from models that forecast the effects of sea level rise on surface water and groundwater, the extent of surge and resulting economic losses from coastal storms, and the distribution of habitats. IMMAGE not only provides an advanced geographic information system (GIS) interface to support decision making, but also includes topic-based modules that explain and illustrate key concepts for nontechnical users. The purpose of this report is to familiarize both technical and nontechnical users with the IMMAGE Web site and its various applications.

Florida↗
USGS2021

A rapid assessment function to estimate common raven population densities: Implications for targeted management

Common raven ( Corvus corax ; raven) populations have increased over the past 5 decades within the western United States. Raven population increases have been largely attributed to growing resource subsidies from expansion of human enterprise. Concomitantly, managers are becoming increasingly concerned about elevated adverse effects on multiple sensitive prey species, damage to livestock and agriculture, and human safety. Managers could benefit from a rapid but reliable method to estimate raven densities across spatiotemporal scales to monitor raven populations more efficiently and inform targeted and adaptive management frameworks. However, obtaining estimates of raven density is data- and resource-intensive, which renders monitoring within an adaptive framework unrealistic. To address this need, we developed a rapid survey protocol for resource managers to estimate site-level density based on the average number of ravens per survey. Specifically, we first estimated raven densities at numerous field sites with robust distance sampling procedures and then used regression to investigate the relationship between those density estimates and the number of ravens per survey, which revealed a strong correlation ( R 2 = 0.86). For management application, we provide access to R function software through a web-based interface to estimate density using number of ravens per survey, which we refer to as a Rapid Assessment Function (RAF). Then, using a simulation analysis of data from sites with abundant surveys and the RAF, we estimated raven density based on different numbers of surveys to help inform how many surveys are needed to achieve reliable estimates within this rapid assessment. While more robust procedures of distance sampling are the preferred methods for estimating raven densities from count surveys, the RAF tool presented herein provides a reliable approximation for informing management decisions when managers are faced with resource and small sample size constraints.

California, Idaho, Nevada, Oregon↗
USGS2014

Uncertainty analysis of a groundwater flow model in east-central Florida

A groundwater flow model for east-central Florida has been developed to help water-resource managers assess the impact of increased groundwater withdrawals from the Floridan aquifer system on heads and spring flows originating from the Upper Floridan aquifer. The model provides a probabilistic description of predictions of interest to water-resource managers, given the uncertainty associated with system heterogeneity, the large number of input parameters, and a nonunique groundwater flow solution. The uncertainty associated with these predictions can then be considered in decisions with which the model has been designed to assist. The “Null Space Monte Carlo” method is a stochastic probabilistic approach used to generate a suite of several hundred parameter field realizations, each maintaining the model in a calibrated state, and each considered to be hydrogeologically plausible. The results presented herein indicate that the model’s capacity to predict changes in heads or spring flows that originate from increased groundwater withdrawals is considerably greater than its capacity to predict the absolute magnitudes of heads or spring flows. Furthermore, the capacity of the model to make predictions that are similar in location and in type to those in the calibration dataset exceeds its capacity to make predictions of different types at different locations. The quantification of these outcomes allows defensible use of the modeling process in support of future water-resources decisions. The model allows the decision-making process to recognize the uncertainties, and the spatial/temporal variability of uncertainties that are associated with predictions of future system behavior in a complex hydrogeological context.

Florida↗
USGS2020

Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for U.S. lakes and reservoirs

Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.

Environmental Monitoring and Assessment↗
USGS2007

Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: A case study of the Southwest Regional Gap Analysis Project (SWReGAP)

Land-cover mapping efforts within the USGS Gap Analysis Program have traditionally been state-centered; each state having the responsibility of implementing a project design for the geographic area within their state boundaries. The Southwest Regional Gap Analysis Project (SWReGAP) was the first formal GAP project designed at a regional, multi-state scale. The project area comprises the southwestern states of Arizona, Colorado, Nevada, New Mexico, and Utah. The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999-2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier). Partitioning of mapping responsibilities amongst the five collaborating states was organized around ecoregion-based "mapping zones". Over the course of 21/2 field seasons approximately 93,000 reference samples were collected directly, or obtained from other contemporary projects, for the land-cover modeling effort. The final map was made public in 2004 and contains 125 land-cover classes. An internal validation of 85 of the classes, representing 91% of the land area was performed. Agreement between withheld samples and the validated dataset was 61% (KHAT = .60, n = 17,030). This paper presents an overview of the methodologies used to create the regional land-cover dataset and highlights issues associated with large-area mapping within a coordinated, multi-institutional management framework. ?? 2006 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2006

Pigs on the plains: Institutional analysis of a Colorado water quality initiative

We used the Legal-Institutional Analysis Model (LIAM) and Advocacy Coalition Framework (ACF) to analyze the campaign over passage of the Colorado Hogs Rule, an initiative passed by the voters in 1998 to require regulation of swine production facilities in Colorado. Used in tandem, LIAM and ACF provided an opportunity to develop a robust understanding of the obstacles and opportunities that face water quality managers in a state-centered multi-organizational decision process. We found that combining the LIAM with the ACF enhanced the understanding that could be achieved by using either model in isolation. The predictive capacity of the LIAM would have been reduced without information from the ACF, and the ACF by itself would have missed the importance of a single-case study.

International Journal of Public Administration↗
USGS2025

Using probability difference to compare streamflow information of alternatives for efficient operation of monitoring networks

Efficient operation of streamflow monitoring networks requires investments in technology and labor that provide the greatest benefits from available resources. Economic analyses comparing the costs and benefits from different types of alternatives for monitoring have not been practical to implement. Streamflow information provides a generic measure of benefits that can be incorporated into operational decisions as an objective for monitoring networks. A methodology for comparing how accuracy, monitoring period, and monitoring instead of modeling affects streamflow information is developed from information-theoretic approaches for network design but contributes three novel features: (1) a probability-difference model for conditional probability of monotonically paired variables, (2) explicit discounting of unverified information that may exceed the accuracy of streamflow records, and (3) run analysis to account for non-stationarity in streamflow probabilities. Application of the methodology to the U.S. Geological Survey streamflow monitoring network indicates the value of monitoring period to reduce the uncertainty of streamflow probabilities and, thus, increase streamflow information. The methodology has important limitations, particularly for sites with non-perennial streamflow, but demonstrates that probability difference could be used to evaluate operational alternatives to increase the efficiency of monitoring networks.

PLOS Water↗
USGS2025

Environmental drivers of Greater Sage-grouse population trends over 25 years in Idaho, USA

Greater Sage-grouse ( Centrocercus urophasianus ) populations have been in decline for decades across much of the US Intermountain West. However, findings from 25 years of lek counts in Idaho indicate that some populations are stable or even increasing. After accounting for potential biases in past lek count data, we sought to explain the variability in population trends among all 70 lek clusters (i.e., populations) we identified in the state. For each population, we identified lek count troughs, or low-point years, that occurred between the mid-1990s and 2021 and used a regression slope of those abundance low points to quantify each population's trend over the 25-year time span. We related the 70 populations' slopes to climate, fire, topographic, vegetation, and landcover variables. Our analyses revealed that populations with negative trends tend to occur toward the ends of climate gradients (i.e., extremes of occupied habitats) and in locations with more wildfire, agriculture, and riparian landcover. Populations with positive trends generally occur in landscapes toward the middle of the climate gradient, with high amounts of low sagebrush ( Artemisia arbuscula ) landcover and intermediate amounts of riparian and agricultural landcover. Post hoc analysis indicated that the latter two drivers were strongly associated with high raven occupancy rates, which may contribute to the negative sage-grouse population trends we observed in areas with high riparian or agricultural landcover. When modeled separately for different regions however, various region-specific drivers were identified, including tree cover, annual herbaceous cover, and human development. This information can help guide sage-grouse habitat management decisions and set expectations for population recovery, given the diversity of habitats occupied by the species and the cyclic nature of sage-grouse populations.

Idaho↗
USGS2022

Framework for assessing and mitigating the impacts of offshore wind energy development on marine birds

Offshore wind energy development (OWED) is rapidly expanding globally and has the potential to contribute significantly to renewable energy portfolios. However, development of infrastructure in the marine environment presents risks to wildlife. Marine birds in particular have life history traits that amplify population impacts from displacement and collision with offshore wind infrastructure. Here, we present a broadly applicable framework to assess and mitigate the impacts of OWED on marine birds. We outline existing techniques to quantify impact via monitoring and modeling (e.g., collision risk models, population viability analysis), and present a robust mitigation framework to avoid, minimize, or compensate for OWED impacts. Our framework addresses impacts within the context of multiple stressors across multiple wind energy developments. We also present technological and methodological approaches that can improve impact estimation and mitigation. We highlight compensatory mitigation as a tool that can be incorporated into regulatory frameworks to mitigate impacts that cannot be avoided or minimized via siting decisions or alterations to OWED infrastructure or operation. Our framework is intended as a globally-relevant approach for assessing and mitigating OWED impacts on marine birds that may be adapted to existing regulatory frameworks in regions with existing or planned OWED.

Biological Conservation↗
USGS2019

Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields

In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.

Environmental Research Letters↗
USGS2025

Estimated ultimate recovery (EUR) Prediction for Eagle Ford Shale using integrated datasets and artificial neural networks

The estimated ultimate recovery (EUR) is an important parameter for forecasting oil and gas production and informing decisions regarding field development strategies. In this study, we combined site-specific geologic, completion, and operational parameters with the predictive capabilities of machine learning (ML) models to predict EURs of the wells for the Eagle Ford Marl Continuous Oil Assessment Unit. We developed an extensive dataset of wells that have produced from the lower and upper Eagle Ford Shale intervals and reduced the model complexity using principal component analysis. We tested the ML models and estimated the sensitivities of ML-predicted EURs to changes in the values of different input variables. The results of applying the optimized ML model to the Eagle Ford suggest that the approach developed in this study could be promising. The ML estimates of the EURs fit the DCA-based values with an R 2 ~ 0.9 and a mean absolute error of ~36 × 10 3 bbl. In the lower Eagle Ford Shale, the EUR estimates were found to be most sensitive to changes in porosity, net thickness of the interval, clay volume, and the API gravity of the oil; and that in the upper Eagle Ford Shale they were most sensitive to changes in the total organic carbon and water saturation, which suggests that it could be important to consider these parameters in assessing these intervals or close analogs.

Louisiana, Mississippi, Texas↗
USGS1995

Journal news

Statistical power (and conversely, Type II error) is often ignored by biologists. Power is important to consider in the design of studies, to ensure that sufficient resources are allocated to address a hypothesis under examination. Deter- mining appropriate sample size when designing experiments or calculating power for a statistical test requires an investigator to consider the importance of making incorrect conclusions about the experimental hypothesis and the biological importance of the alternative hypothesis (or the biological effect size researchers are attempting to measure). Poorly designed studies frequently provide results that are at best equivocal, and do little to advance science or assist in decision making. Completed studies that fail to reject Ho should consider power and the related probability of a Type II error in the interpretation of results, particularly when implicit or explicit acceptance of Ho is used to support a biological hypothesis or management decision. Investigators must consider the biological question they wish to answer (Tacha et al. 1982) and assess power on the basis of biologically significant differences (Taylor and Gerrodette 1993). Power calculations are somewhat subjective, because the author must specify either f or the minimum difference that is biologically important. Biologists may have different ideas about what values are appropriate. While determining biological significance is of central importance in power analysis, it is also an issue of importance in wildlife science. Procedures, references, and computer software to compute power are accessible; therefore, authors should consider power. We welcome comments or suggestions on this subject.

Journal of Wildlife Management↗
USGS2022

USGS invasive carp database management and integration support

Bigheaded carps (Bighead Carp and Silver Carp) tracking, monitoring, and contracted removal will continue throughout the Illinois River and Upper Mississippi River as part of an adaptive management effort to mitigate, control, and contain bigheaded carps. Other fish will also be tracked to maintain a holistic view of the transmitter distribution in the Upper Illinois River Waterway. To facilitate these actions, a need to compile and analyze invasive carp-related data from all agencies exists. Invasive carp-related data include all data sources that could inform the MRWG objectives or projects. These data, often in disparate formats, must be integrated into a common format that allows all agencies the opportunity to assess invasive carp monitoring, control, and removal efforts. Ensuring the interoperability of these datasets allows for their use in various analyzes and modeling efforts. Implementing an interoperable data management framework provides the mechanisms for end users to find and use integrated data. Integrating data for use in modeling and analysis furthers the partnership’s collective understanding of bigheaded carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts, assessing invasive carp abundance to support modeling efforts, informing deployment of control actions, etc.). An effective data management strategy will streamline the data update process, providing all agencies with timely data and analyses in support of informed decision-making processes.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗
USGS2024

USGS invasive carp database management and integration support

Invasive carp tracking, monitoring, and contracted removal will continue throughout the Illinois River and upper Mississippi River as part of an adaptive management effort to mitigate, control, and contain invasive carp. To facilitate these actions, a need to compile and analyze invasive carp-related data from all agencies exists. Invasive carp-related data include all data sources that could inform the MRWG objectives or projects. These data, often in disparate formats, must be integrated into a common format that allows all agencies the opportunity to assess invasive carp monitoring, control, and removal efforts. Ensuring the interoperability of these datasets allows for their use in various analyses and modeling efforts. Implementing an interoperable data management framework provides mechanisms for endusers to find and use integrated data. Integrating data for use in modeling and analysis furthers the partnership’s collective understanding of invasive carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts, assessing invasive carp abundance to support modeling efforts, informing deployment of control actions, etc.). An effective data management strategy will streamline the data update process, providing all agencies with timely data and analyses in support of informed decision-making processes.

Interim Summary Report↗
USGS2025

USGS invasive carp database management and integration support

Invasive carp tracking, monitoring, and contracted removal will continue throughout the Illinois River and upper Mississippi River as part of an adaptive management effort to mitigate, control, and contain invasive carp. To facilitate these actions, a need to compile and analyze invasive carp-related data from all agencies exists. Invasive carp-related data include all data sources that could inform the MRWG objectives or projects. This data, often in disparate formats, must be integrated into a common format that allows all agencies the opportunity to assess invasive carp monitoring, control, and removal efforts. Ensuring the interoperability of these datasets allows for their use in various analyses and modeling efforts. Implementing an interoperable data management framework provides mechanisms for endusers to find and use integrated data. Integrating data for use in modeling and analysis furthers the partnership’s collective understanding of invasive carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts; assessing invasive carp abundance to support modeling efforts; informing deployment of control actions; etc.). An effective data management strategy will streamline the data update process, providing all agencies with timely data and analyses in support of informed decision-making processes.

Interim Summary Report↗