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At least 145 records · Page 8Linked to original sources
USGS2011

Dose-Response Calculator for ArcGIS

The Dose-Response Calculator for ArcGIS is a tool that extends the Environmental Systems Research Institute (ESRI) ArcGIS 10 Desktop application to aid with the visualization of relationships between two raster GIS datasets. A dose-response curve is a line graph commonly used in medical research to examine the effects of different dosage rates of a drug or chemical (for example, carcinogen) on an outcome of interest (for example, cell mutations) (Russell and others, 1982). Dose-response curves have recently been used in ecological studies to examine the influence of an explanatory dose variable (for example, percentage of habitat cover, distance to disturbance) on a predicted response (for example, survival, probability of occurrence, abundance) (Aldridge and others, 2008). These dose curves have been created by calculating the predicted response value from a statistical model at different levels of the explanatory dose variable while holding values of other explanatory variables constant. Curves (plots) developed using the Dose-Response Calculator overcome the need to hold variables constant by using values extracted from the predicted response surface of a spatially explicit statistical model fit in a GIS, which include the variation of all explanatory variables, to visualize the univariate response to the dose variable. Application of the Dose-Response Calculator can be extended beyond the assessment of statistical model predictions and may be used to visualize the relationship between any two raster GIS datasets (see example in tool instructions). This tool generates tabular data for use in further exploration of dose-response relationships and a graph of the dose-response curve.

Data Series↗
USGS2022

Spatially integrating microbiology and geochemistry to reveal complex environmental health issues: Anthrax in the contiguous United States

Maxent models were run using the B. anthracis presence data and/or the animal outbreak presence data. Models run using the animal outbreak data alone utilized two scales: the Outbreak State scale which included only states reporting animal anthrax outbreaks from 2001 to 2013 and the National scale which included all states in the contiguous United States. Three iterations of the environmental data were used and included the Sample Location dataset which utilized the environmental variable data with assigned latitude and longitude locations from the USGS NASGLP project; the Normalized dataset which scaled the environmental variables so that the values fell between 0 and 1; and the Interpolated dataset which provided an interpolation of the environmental variables averaged for each county and assigned to a point for that county at the centroid (rather than using the NASGLP latitude and longitude location). Two metrics were used to measure model performance including the widely used area under the curve (AUC) and an alternative method, the True Skill Statistic (TSS). The AUC gives the probability that a randomly chosen presence location has been correctly ranked higher than the absence/background site. AUC values at 0.5 or lower mean the ranking is no better than random, while the AUC values nearer to 1 mean the model is a better predictor. The TSS provides a comparison of how well the background predictions made by the model match the model results at the test dataset (presence) locations. TSS values near +1 means the model approaches perfect agreement, while values near −1 indicate the model is no better than random. Maxent models to determine the influence of environmental factors on the B. anthracis distribution using the PCR data yielded a low TSS, which suggested the model might be underfitting the data. This was not surprising due to the difficulty in recovering B. anthracis in soil samples as well as the samples themselves being discrete in nature and only capturing a snapshot in time. Therefore, the distribution of B. anthracis and its niche in the contiguous United States could not be determined in this study. However, efforts to investigate environmental factors that would have a higher potential of supporting an anthrax outbreak in wildlife and livestock yielded better results. Results showed that most of the Maxent models in this study performed best when using the Outbreak State scale. When the models were scaled up to the National scale, model performance declined, except for the Normalized variable dataset. At the Outbreak State scale, a large proportion of the area was predicted to be of higher probability for wildlife/livestock anthrax outbreaks, and the statistical measures assumed the model was underfitting the data. The model with the highest AUC and TSS scores for this study was the Outbreak State scale using Sample Location dataset (AUC = 0.918 and TSS = 0.82). Some of the variables found to be closely related to the occurrence of B. anthracis in this study included pH, drainage potential, and concentration of elements including Na, Ca, Sr, and Mg, which have also been found to be related to animal outbreaks or to the occurrence of B. anthracis in previous studies. The models in the current study indicated possible regions that have not had recent wildlife/livestock anthrax outbreaks but contained environmental conditions that could potentially support an outbreak if one were to occur (Michigan and Maine). This work provides an extension to the use of ecological niche modeling to outbreak potential in livestock/wildlife in the United States because it utilizes additional soil geochemistry data and has shown that further validation techniques, such as the TSS, should be considered in addition to AUC. Results from this study could be used by animal and public health officials to identify areas with a higher potential for anthrax outbreak in wildlife and livestock due to naturally occurring soil and environmental conditions.

Book chapter↗
USGS2021

Habitat suitability index model improvements

Habitat suitability index (HSI) models were developed for the 2023 Coastal Master Plan to evaluate the potential effects of coastal restoration and protection projects on habitat for key coastal fish, shellfish, and wildlife species. These species included: eastern oyster, brown shrimp, white shrimp, blue crab, crayfish, gulf menhaden, spotted seatrout, largemouth bass, American alligator, gadwall, mottled duck, brown pelican, seaside sparrow, and bald eagle. Most of these species were included in the 2017 Coastal Master Plan analyses, and the HSI models from that effort were refined and improved following the recommendations described in the technical memorandum: 2023 Coastal Master Plan Habitat Suitability Index Model Improvement Recommendations (Sable et al., 2019). In addition to model improvements, HSI models were created for seaside sparrow and bald eagle, both of which are new species for the master plan analyses. For the HSI models that are primarily literature-based, literature reviews were conducted for recent studies that could be used to improve the suitability index (SI) relationships that compose the models. As a result of this review, modifications were made to the salinity-related SIs of the oyster model including: expanding the time period used for salinity effects to spawning; adjusting the range of suitable annual average salinity to be more representative of Louisiana populations; and making oyster’s minimum salinity tolerance temperature dependent. In addition, a new SI was incorporated in the oyster HSI model that accounts for the effects of sediment deposition on oysters. The crayfish HSI model was improved by adjusting the time periods used for the SIs that describe the hydrology required for the crayfish life cycle, and the soil characteristics SI that was part of the 2017 crayfish model was removed because soil conditions do not appear to be limiting for crayfish burrow construction in coastal Louisiana. The other literature-based HSI models from the 2017 Coastal Master Plan, i.e., American alligator, gadwall, mottled duck, and brown pelican, were unchanged, with the exception of a small adjustment made to the suitability of forested wetlands for gadwall. Lastly, a literature-based HSI model was created for seaside sparrow that consists of SIs related to vegetated habitat type, marsh vegetation coverage, and marsh elevation. Statistical-based HSI models were developed for brown shrimp (both small and large juvenile stages), white shrimp (small and large juvenile stages), blue crab (juvenile stage), gulf menhaden (juvenile and adult stages), spotted seatrout (juvenile and adult stages), largemouth bass, and bald eagle. The bald eagle HSI model was developed from a bald eagle nest probability of occurrence model that related nest occurrence from survey data with land cover type. The resulting model showed that combinations of forested wetlands, flotant marsh, and open water habitats were most suitable for nesting bald eagles. The 2023 fish, shrimp, and blue crab HSI models were developed using new approaches for the formulation of the water quality and structural habitat SIs that compose the models. For the 2017 models, the water quality SI was derived using only generalized linear mixed models (GLMMs) to estimate the relationship between salinity, water temperature, and species’ catch. For the 2023 models, however, multiple GLMMs and generalized additive models (GAMMs) were created for each species or life stage. These alternative models were compared and a single model that performed well statistically and was ecologically reasonable was selected for the species’ water quality SI. The structural habitat SI was developed using a meta-analysis of published literature to estimate the relative importance of various estuarine habitats to the fish and shellfish species. The results of this analysis were then used to modify the 2017 structural habitat SI relationship to account for the added habitat value of submerged aquatic vegetation and oyster reefs, which are also important habitats for juvenile fish and shellfish. Similar to the 2017 fish, shrimp, and blue crab models, the water quality and structural habitat SIs were then combined to create the 2023 HSI models. The 2023 Coastal Master Plan HSI models were integrated with the Integrated Compartment Model (and are referred to as ICM-HSIs) and tested using environmental output from the 2017 Coastal Master Plan Future Without Action scenario. The tests showed that, in general, the models produced reasonable representations of species’ habitat distribution. Furthermore, the improvements made to the oyster, crayfish, fish, shrimp, and blue crab HSI models generally yielded more realistic results compared to the 2017 HSI models.

Report↗
USGS2022

Temporal segregation in spawning between native Yellowstone cutthroat trout and introduced rainbow trout

Hybridisation with introduced taxa poses a threat to native fish populations. Mechanisms of reproductive isolation can limit or prevent hybridisation between closely related species. Understanding how these mechanisms interact between the same species across geographically distinct occurrences of secondary contact, and how regional factors influence them, can inform our understanding of hybridisation as a threat and management actions to mitigate this threat. We used data collected on adult fish migration timing and approximate emergence timing of subsequent juvenile fish paired with genomic data to assess whether temporal isolation in the timing of spawning exists between Yellowstone cutthroat trout, rainbow trout and hybrids in the North Fork Shoshone River drainage in northwest Wyoming. We found evidence that Yellowstone cutthroat trout spawn, on average, two to four weeks later than rainbow trout and hybrids and two environmental covariates related to water temperature and discharge were associated with differences in spawning migration timing. Despite statistical support for Yellowstone cutthroat trout spawning later, disproportionately high numbers of rainbow trout and hybrids, paired with extended spawning seasons, lead to substantial overlap between all genotypes. Our results provide further evidence of temporal segregation in the timing of spawning as a mechanism of reproductive isolation between closely related species, but substantial spawning overlap suggests temporal segregation alone will not be enough to curtail hybridisation in conservation populations.

Wyoming↗
USGS2018

Evaluation of targeted and untargeted effects-based monitoring tools to assess impacts of contaminants of emerging concern on fish in the South Platte River, CO

Rivers in the arid Western United States face increasing influences from anthropogenic contaminants due to population growth, urbanization, and drought. To better understand and more effectively track the impacts of these contaminants, biologically-based monitoring tools are increasingly being used to complement routine chemical monitoring. This study was initiated to assess the ability of both targeted and untargeted biologically-based monitoring tools to discriminate impacts of two adjacent wastewater treatment plants (WWTPs) on Colorado's South Platte River. A cell-based estrogen assay ( in vitro , targeted) determined that water samples collected downstream of the larger of the two WWTPs displayed considerable estrogenic activity in its two separate effluent streams. Hepatic vitellogenin mRNA expression ( in vivo , targeted) and NMR-based metabolomic analyses ( in vivo , untargeted) from caged male fathead minnows also suggested estrogenic activity downstream of the larger WWTP, but detected significant differences in responses from its two effluent streams. The metabolomic results suggested that these differences were associated with oxidative stress levels. Finally, partial least squares regression was used to explore linkages between the metabolomics responses and the chemical contaminants that were detected at the sites. This analysis, along with univariate statistical approaches, identified significant covariance between the biological endpoints and estrone concentrations, suggesting the importance of this contaminant and recommending increased focus on its presence in the environment. These results underscore the benefits of a combined targeted and untargeted biologically-based monitoring strategy when used alongside contaminant monitoring to more effectively assess ecological impacts of exposures to complex mixtures in surface waters.

Colorado↗
USGS2018

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Proceedings of the National Academy of Sciences of↗
USGS2018

Hierarchical multi-population viability analysis

Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long-term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi-population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub-models. We present a case study in which we customized this model for historical population monitoring data (1985–2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi-pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi-population viability analysis bridges the gap between site-level field observations and population-level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.

Ecology↗
USGS2017

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis , and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua . Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea . We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

Mojave Desert↗
USGS2024

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

Spatial Statistics↗
USGS1998

Geospatial Technology Applications and Infrastructure in the Biological Resources Division

Executive Summary -- Automated spatial processing technology such as geographic information systems (GIS), telemetry, and satellite-based remote sensing are some of the more recent developments in the long history of geographic inquiry. For millennia, humankind has endeavored to map the Earth's surface and identify spatial relationships. But the precision with which we can locate geographic features has increased exponentially with satellite positioning systems. Remote sensing, GIS, thematic mapping, telemetry, and satellite positioning systems such as the Global Positioning System (GPS) are tools that greatly enhance the quality and rapidity of analysis of biological resources. These technologies allow researchers, planners, and managers to more quickly and accurately determine appropriate strategies and actions. Researchers and managers can view information from new and varying perspectives using GIS and remote sensing, and GPS receivers allow the researcher or manager to identify the exact location of interest. These geospatial technologies support the mission of the U.S. Geological Survey (USGS) Biological Resources Division (BRD) and the Strategic Science Plan (BRD 1996) by providing a cost-effective and efficient method for collection, analysis, and display of information. The BRD mission is 'to work with others to provide the scientific understanding and technologies needed to support the sound management and conservation of our Nation's biological resources.' A major responsibility of the BRD is to develop and employ advanced technologies needed to synthesize, analyze, and disseminate biological and ecological information. As the Strategic Science Plan (BRD 1996) states, 'fulfilling this mission depends on effectively balancing the immediate need for information to guide management of biological resources with the need for technical assistance and long-range, strategic information to understand and predict emerging patterns and trends in ecological systems.' Information sharing plays a key role in nearly everything BRD does. The Strategic Science Plan discusses the need to (1) develop tools and standards for information transfer, (2) disseminate information, and (3) facilitate effective use of information. This effort centers around the National Biological Information Infrastructure (NBII) and the National Spatial Data Infrastructure (NSDI), components of the National Information Infrastructure. The NBII and NSDI are distributed electronic networks of biological and geographical data and information, as well as tools to help users around the world easily find and retrieve the biological and geographical data and information they need. The BRD is responsible for developing scientifically and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources. Scientists also conduct important inventory and monitoring studies to maintain baseline information on these same resources. Research on those species for which the Department of the Interior (DOI) has trust responsibilities (including endangered species and migratory species) involves laboratory and field studies of individual animals and the environments in which they live. Researchboth tactical and strategicis conducted at the BRD's 17 science centers and 81 field stations, 54 Cooperative Fish and Wildlife Research Units in 40 states, and at 11 former Cooperative Park Study Units. Studies encompass fish, birds, mammals, and plants, as well as their ecosystems and the surrounding landscape. Biological Resources Division researchers use a variety of scientific tools in their endeavors to understand the causes of biological and ecological trends. Research results are used by managers to predict environmental changes and to help them take appropriate measures to manage resources effectively. The BRD Geospatial Technology Program facilitates the collection, analysis, and dissemination of data and informat

Information and Technology Report↗
USGS2001

Bird-landscape relations in the Chihuahuan Desert: Coping with uncertainties about predictive models

During the springs of 1995–1997, we studied birds and landscapes in the Chihuahuan Desert along part of the Texas–Mexico border. Our objectives were to assess bird–landscape relations and their interannual consistency and to identify ways to cope with associated uncertainties that undermine confidence in using such relations in conservation decision processes. Bird distributions were often significantly associated with landscape features, and many bird–landscape models were valid and useful for predictive purposes. Differences in early spring rainfall appeared to influence bird abundance, but there was no evidence that annual differences in bird abundance affected model consistency. Model consistency for richness (42%) was higher than mean model consistency for 26 focal species (mean 30%, range 0–67%), suggesting that relations involving individual species are, on average, more subject to factors that cause variation than are richness–landscape relations. Consistency of bird–landscape relations may be influenced by such factors as plant succession, exotic species invasion, bird species' tolerances for environmental variation, habitat occupancy patterns, and variation in food density or weather. The low model consistency that we observed for most species indicates the high variation in bird–landscape relations that managers and other decision makers may encounter. The uncertainty of interannual variation in bird–landscape relations can be reduced by using projections of bird distributions from different annual models to determine the likely range of temporal and spatial variation in a species' distribution. Stochastic simulation models can be used to incorporate the uncertainty of random environmental variation into predictions of bird distributions based on bird–landscape relations and to provide probabilistic projections with which managers can weigh the costs and benefits of various decisions. Uncertainty about the true structure of bird–landscape relations (structural uncertainty) can be reduced by ensuring that models meet important statistical assumptions, designing studies with sufficient statistical power, validating the predictive ability of models, and improving model accuracy through continued field sampling and model fitting. Uncertainty associated with sampling variation (partial observability) can be reduced by ensuring that sample sizes are large enough to provide precise estimates of both bird and landscape parameters. By decreasing the uncertainty due to partial observability, managers will improve their ability to reduce structural uncertainty.

Chihuahua, Coahuila, Nuevo León, Tamaulipas, Texas↗
USGS1988

Fossil diatoms and neogene paleolimnology

Diatoms have played an important role in the development of Neogene continental biostratigraphy and paleolimnology since the mid-19th Century. The history of progress in Quaternary diatom biostratigraphy has developed as a result of improved coring techniques that enable sampling sediments beneath existing lakes coupled with improved chronological control (including radiometric dating and varve enumeration), improved statistical treatment of fossil diatom assemblages (from qualitative description to influx calculations of diatom numbers or volumes), and improved ecological information about analogous living diatom associations. The last factor, diatom ecology, is the most critical in many ways, but progresses slowly. Fortunately, statistical comparison of modern diatom assemblages and insightful studies of the nutrient requirements of some common freshwater species are enabling diatom paleolimnologists to make more detailed interpretations of the Quaternary record than had been possible earlier, and progress in the field of diatom biology and ecology will continue to refine paleolimnological studies. The greater age and geologic setting of Tertiary diatomaceous deposits has prompted their study in the contexts of geologic history, biochronology and evolution. The distribution of diatoms of marine affinities in continental deposits has given geologists insights about tectonism and sea-level change, and the distribution of distinctive (extinct?) diatoms has found utilization both in making stratigraphic correlations between outcrops of diatomaceous deposits and in various types of biochronological studies that involve dating deposits in different areas. A continental diatom biochronologic scheme will rely upon evolution, such as the appearance of new genera within a family, in combination with regional environmental changes that are responsible for the wide distribution of distinctive diatom species. The increased use of the scanning electron microscope for the detailed descriptions of fossil diatoms will provide the basis for making more accurate correlations and identifications, and the micromorphological detail for speculations about evolutionary relationships.

Palaeogeography, Palaeoclimatology, Palaeoecology↗
USGS2012

Physical habitat, water quality, and riverine biological assemblages of selected reaches of the Sheyenne River, North Dakota, 2010

In 2010, data on physical habitat, water quality, and riverine biological assemblages were collected at selected reaches in four locations (Kleven, Sheyenne, Cooperstown, and West Fargo) on the Sheyenne River in east-central North Dakota. Three of the locations (Kleven, Sheyenne, and Cooperstown) are above Baldhill Dam and one location (West Fargo) is below Baldhill Dam on the Sheyenne River. The 2010 data provide information to establish a better understanding of the water-quality and ecological conditions of the Sheyenne River. Concerns were raised about the water-quality and ecological conditions of the Sheyenne River because of the interbasin transfer of water from nearby Devils Lake. The transfer of water from Devils Lake to the Sheyenne River occurs through the Devils Lake State Outlet near Peterson Coulee or, if lake elevations exceed 1,459 feet above National Geodetic Vertical Datum of 1929 (NGVD 29), through a natural outlet, Tolna Coulee. The field measurements of water-quality characteristics and results of chemical analyses generally are comparable to summary statistics calculated for Sheyenne River for 1980 through 2006. Overall, water-quality results show differences between the Kleven, Sheyenne, Cooperstown, and West Fargo reaches. Sulfate concentrations were less than the State of North Dakota criterion of 750 milligrams per liter for the upper Sheyenne River above Baldhill Dam and less than the criterion of 450 milligrams per liter for the lower Sheyenne River below Baldhill Dam. Arsenic concentrations at most reaches exceeded the U.S. Environmental Protection Agency drinking-water standard of 10 micrograms per liter. Nutrient concentrations (nitrogen, phosphorus) were higher in the upper Sheyenne River above Baldhill Dam than below Baldhill Dam where concentrations decreased by about half. In 2010, 35 families and 44 genera of benthic macroinvertebrates were collected and identified. On the basis of the index of biotic intergrity scores for benthic macroinvertebrate communities present in the Sheyenne River, all the reaches were determined to have condition classes of moderately disturbed to most disturbed. The benthic macroinvertebrate communities at the Cooperstown reaches were classed as moderately disturbed, whereas benthic macroinvertebrate communities at the Kleven, Sheyenne, West and Fargo reaches were most disturbed. During data collection, 37 genera and 165 species of periphyton (diatoms and soft-bodied algae) were collected and identified. In periphyton communities, similar taxa species were dominant in the Kleven, Sheyenne, and Cooperstown reaches, and different taxa species were dominant in the West Fargo reaches. For diatoms, the Kleven 3 reach had the lowest species richness value of 33.0, whereas the Cooperstown 8 reach had the highest species richness value of 57.0. For soft-bodied algae, the species richness values ranged from 8.0 at the Sheyenne 4 reach to 20.0 at the West Fargo 10 reach. During the fish collection, 32 species, representing 10 families, were collected in the Sheyenne River. All but two species are native to the Sheyenne River system. Common carp and white crappie are the two introduced species. Of the 32 species, 29 are tolerant to moderately tolerant to changes in water quality and habitat degradation, 16 species are tolerant to moderately tolerant to turbidity, and 16 species are tolerant to moderately tolerant to sensitivity to total dissolved solids, sulfate, and chloride. All fish species were categorized into four trophic groups. The largest group of 19 species was the insectivores (both benthic and general). The predator group consisted of seven species, and the omnivores consisted of six species. More fish were found in the lower Sheyenne River below Baldhill Dam than in the upper Sheyenne River above Baldhill Dam.

North Dakota↗
USGS2015

The Penobscot River and environmental contaminants: Assessment of tribal exposure through sustenance lifeways

EPA in collaboration with the Penobscot Indian Nation, U.S. Geological Survey (USGS), Agency for Toxic Substances and Disease Registry (ATSDR), and the U.S. Fish and Wildlife Service (USF&WS) collectively embarked on a four year research study to evaluate the environmental health of the riverine system by targeting specific cultural practices and using traditional science to conduct a preliminary contaminant screening of the flora and fauna of the Penobscot River ecosystem. This study was designed as a preliminary screening to determine if contaminant concentrations in fish, eel, snapping turtle, wood ducks, and plants in Regions of the Penobscot River relevant to where PIN tribal members hunt, fish and gather plants were high enough to be a health concern. This study was not designed to be a statistically validated assessment of contaminant differences among study sites or among species. The traditional methodology for health risk assessment used by the U. S. Environmental Protection Agency (EPA) is based on the use of exposure assumptions (e.g. exposure duration, food ingestion rate, body weight, etc.) that represent the entire American population, either as a central tendency exposure (e.g. average, median) or as a reasonable maximum exposure (e.g. 95% upper confidence limit). Unfortunately, EPA lacked exposure information for assessing health risks for New England regional tribes sustaining a tribal subsistence way of life. As a riverine tribe, the Penobscot culture and traditions are inextricably tied to the Penobscot River watershed. It is through hunting, fishing, trapping, gathering and making baskets, pottery, moccasins, birch-bark canoes and other traditional practices that the Penobscot culture and people are sustained. The Penobscot River receives a variety of pollutant discharges leaving the Penobscot Indian Nation (PIN) questioning the ecological health and water quality of the river and how this may affect the practices that sustain their way of life. The objectives of this Regional Applied Research Effort (RARE) study were to: Develop culturally sensitive methodologies for assessing the potential level of exposure tocontaminants that Penobscot Indian Nation tribal members may have from maintainingtribal sustenance practices. Conduct field surveys and laboratory analysis on targeted flora and fauna for chemicalexposure to dioxins/furans, polychlorinated biphenyls (PCBs), total mercury and methyl-mercury. Assist the Agency for Toxic Substances and Disease Registry (ATSDR) by providing thenecessary data to conduct a Public Health Assessment for the Penobscot Indian Nation. Establish protocols for assessing the level of exposure to PCBs, dioxins/furans and mercuryto PIN tribal members as a consequence of gathering tribal plants for medicinal andnutritional purposes; as well as consuming fish, wood duck, and snapping turtle as a primarysource of nutrition. Survey surface water, drinking water, and sediment from the Penobscot River and IndianIsland to assess the exposure of PIN tribal members to environmental genotoxicants thatcontinue cultural sustenance practices. This research initiative collected and analyzed sediment and biota to determine the level of contaminant exposure to Penobscot tribal members. Natural resource utilization patterns and exposure pathways were identified based on discussions with the Tribal elders. Identification of Tribal exposure factors (exposure pathways and contaminant concentrations) was essential for accurately assessing potential long-term Penobscot Indian Nation tribal members’ exposure. Based on this study, ATSDR’s Public Health Assessment (PHA) concluded that the Penobscot Indian Nation (PIN) tribal members who eat fish and snapping turtle at the ingestion levels suggested in the Wabanaki Traditional Cultural Lifeways Exposure Scenario Report (Wabanaki Exposure Scenario) may be exposed to harmful levels of mercury, dioxins/furans, dioxin-like PCBs, and ot

Maine↗
USGS2014

An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming

Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders. With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, social-value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.

Colorado;Wyoming↗
USGS2018

Estimation of stream conditions in tributaries of the Klamath River, northern California

Because of their critical ecological role, stream temperature and discharge are requisite inputs for models of salmonid population dynamics. Coho Salmon inhabiting the Klamath Basin spend much of their freshwater life cycle inhabiting tributaries, but environmental data are often absent or only seasonally available at these locations. To address this information gap, we constructed daily averaged water temperature models that used simulated meteorological data to estimate daily tributary temperatures, and we used flow differentials recorded on the mainstem Klamath River to estimate daily tributary discharge. Observed temperature data were available for fourteen of the major salmon bearing tributaries, which enabled estimation of tributary-specific model parameters at those locations. Water temperature data from six mid-Klamath Basin tributaries were used to estimate a global set of parameters for predicting water temperatures in the remaining tributaries. The resulting parameter sets were used to simulate water temperatures for each of 75 tributaries from 1980-2015. Goodness-of-fit statistics computed from a cross-validation analysis demonstrated a high precision of the tributary-specific models in predicting temperature in unobserved years and of the global model in predicting temperatures in unobserved streams. Klamath River discharge has been monitored by four gages that broadly intersperse the 292 kilometers from the Iron Gate Dam to the Klamath River mouth. These gages defined the upstream and downstream margins of three reaches. Daily discharge of tributaries within a reach was estimated from 1980-2015 based on drainage-area proportionate allocations of the discharge differential between the upstream and downstream margin. Comparisons with measured discharge on Indian Creek, a moderate-sized tributary with naturally regulated flows, revealed that the estimates effectively approximated both the variability and magnitude of discharge.

California↗
USGS2017

Long-term trends of surface-water mercury and methylmercury concentrations downstream of historic mining within the Carson River watershed

The Carson River is a vital water resource for local municipalities and migratory birds travelling the Pacific Flyway. Historic mining practices that used mercury (Hg) to extract gold from Comstock Lode ore has left much of the river system heavily contaminated with Hg, a practice that continues in many parts of the world today. Between 1998 and 2013, the United States Geological Survey (USGS) collected and analyzed Carson River water for Hg and methylmercury (MeHg) concentrations resulting in a sixteen year record of unfiltered total mercury (uf.THg), filtered (dissolved) Hg (f.THg), total methylmercury (uf.MeHg), filtered MeHg (f.MeHg), and particulate-bound THg (p.THg) and MeHg (p.MeHg) concentrations. This represents one of the longest continuous records of Hg speciation data for any riverine system, thereby providing a unique opportunity to evaluate long-term trends in concentrations and annual loads. During the period of analysis, uf.THg concentration and load trended downward at rates of −0.85% and −1.8% per year, respectively. Conversely, the f.THg concentration increased at a rate of 1.7% per year between 1998 and 2005, and 4.9% per year between 2005 and 2013. Trends in flow-normalized partition coefficients for both Hg and MeHg suggest a statistically significant shift from the particulate to the filtered phase. The upwardly accelerating f.THg concentration and observed shift from the solid phase to the aqueous phase among the pools of Hg and MeHg within the river water column signals an increased risk of deteriorating ecological conditions in the lower basin with respect to Hg contamination. More broadly, the 16-year trend analysis, completed 140 years after the commencement of major Hg releases to the Carson River, provides a poignant example of the ongoing legacy left behind by gold and silver mining techniques that relied on Hg amalgamation, and a cautionary tale for regions still pursuing the practice in other countries.

Nevada↗
USGS2022

FishStan: Hierarchical Bayesian models for fisheries

Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates). These relations and rates provide insight into populations and inputs for other models. For example, growth curves may vary across lakes showing fish populations differ due to management actions or underlying environmental conditions. A fisheries manager could use this information to set lake-specific harvest limits or an ecologist could use this information to test scientific hypotheses about fish populations. The above example also demonstrates how populations exist within hierarchical structures where sub-populations may be nested within a meta-population. More generally, these hierarchical structures may be both biological (e.g., different lakes or river pools) and statistical (e.g., correlated error structures). Currently, limited options exist for fitting these hierarchical models and people seeking to use them often must program their own implementations. Furthermore, many fisheries managers and researchers may not have Bayesian programming skills, but many can use interactive languages such as R. Additionally, programs such as JAGS often require long run times (e.g., hours if not days) to fit hierarchical models and programs such as Stan can be more difficult to program because it is a compiled language. We created fishStan to share hierarchical models for fisheries and ecology in an easy-to-use R package.

Journal of Open Source Software↗