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At least 1,099 records · Page 61Linked to original sources
USGS2023

Reconstructing missing data by comparing interpolation techniques: Applications for long-term water quality data

Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP) −1 , TN had a MAE of 0.39 mg (L-TN) −1 , and water velocity had a MAE of 0.10 m s −1 . The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗
USGS2024

Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins

The US Geological Survey (USGS) is selecting and prioritizing basins, known as Integrated Water Science basins, for monitoring and intensive study. Previous efforts to aid in this selection process include a scientifically defensible and quantitative assessment of basins facing human-caused water resource challenges (Van Metre et al. in Environmental Monitoring and Assessment , 192(7), 458 2020). In the present work, we explore this ranking process based on water quality considerations, specifically salinity and salinization. We selected top candidate basins to study salinity and salinization issues in 18 hydrologic regions that include 163 candidate basins. Our prioritization is based on quantitative assessment of sources of salinity, drivers of change, and receptors that must respond to those sources and drivers. Source terms represented in the prioritization include geology, depth to brackish groundwater, stream conductivity, chloride in precipitation, urban and agricultural land use, application of road salt as a deicer, and irrigation. Drivers represented in prioritization include changes in chemical weathering as a result of changes in rainwater chemistry. Receptors include measures of water stress, measurements of stream ecological health, and socioeconomic factors. In addition, we present research activities for the USGS on salinity and salinization that can be pursued in these basins including assessment of sources, pathways, and loadings; predicting and understanding changes in sources, peaks, and trends; understanding the components of salinity and mobilization of contaminants; understanding the relationship between salinization and changing ecosystems; and developing knowledge on the causes and distribution of groundwater salinity, brackish water resources, and challenges related to desalination.

Enviornmental Monitoring and Assessment↗
USGS2006

A review of methods to estimate cause-specific mortality in presence of competing risks

Estimating cause-specific mortality is often of central importance for understanding the dynamics of wildlife populations. Despite such importance, methodology for estimating and analyzing cause-specific mortality has received little attention in wildlife ecology during the past 20 years. The issue of analyzing cause-specific, mutually exclusive events in time is not unique to wildlife. In fact, this general problem has received substantial attention in human biomedical applications within the context of biostatistical survival analysis. Here, we consider cause-specific mortality from a modern biostatistical perspective. This requires carefully defining what we mean by cause-specific mortality and then providing an appropriate hazard-based representation as a competing risks problem. This leads to the general solution of cause-specific mortality as the cumulative incidence function (CIF). We describe the appropriate generalization of the fully nonparametric staggered-entry Kaplan–Meier survival estimator to cause-specific mortality via the nonparametric CIF estimator (NPCIFE), which in many situations offers an attractive alternative to the Heisey–Fuller estimator. An advantage of the NPCIFE is that it lends itself readily to risk factors analysis with standard software for Cox proportional hazards model. The competing risks–based approach also clarifies issues regarding another intuitive but erroneous “cause-specific mortality” estimator based on the Kaplan–Meier survival estimator and commonly seen in the life sciences literature.

Journal of Wildlife Management↗
USGS2020

Development and testing of species-specific quantitative PCR assays for environmental DNA applications

New, non-invasive methods for detecting and monitoring species presence are being developed to aid in fisheries and wildlife conservation management. The use of environmental DNA (eDNA) samples for detecting macrobiota is one such group of methods that is rapidly becoming popular and being implemented in national management programs. Here we focus on the development of species-specific targeted assays for probe-based quantitative PCR (qPCR) applications. Using probe-based qPCR offers greater specificity than is possible with primers alone. Furthermore, the ability to quantify the amount of DNA in a sample can be useful in our understanding of the ecology of eDNA and the interpretation of eDNA detection patterns in the field. Careful consideration is needed in the development and testing of these assays to ensure the sensitivity and specificity of detecting the target species from an environmental sample. In this protocol we will delineate the steps needed to design and test probe-based assays for the detection of a target species; including creation of sequence databases, assay design, assay selection and optimization, testing assay performance, and field validation. Following these steps will help achieve an efficient, sensitive, and specific assay that can be used with confidence. We demonstrate this process with our assay designed for populations of the mucket ( Actinonaias ligamentina ), a freshwater mussel species found in the Clinch River, USA.

JOVE Journal Of Visualized Experiments↗
USGS2003

Application of a multipurpose unequal probability stream survey in the Mid-Atlantic Coastal Plain

A stratified, spatially balanced sample with unequal probability selection was used to design a multipurpose survey of headwater streams in the Mid-Atlantic Coastal Plain. Objectives for the survey include unbiased estimates of regional stream conditions, and adequate coverage of unusual but significant environmental settings to support empirical modeling of the factors affecting those conditions. The design and field application of the survey are discussed in light of these multiple objectives. A probability (random) sample of 175 first-order nontidal streams was selected for synoptic sampling of water chemistry and benthic and riparian ecology during late winter and spring 2000. Twenty-five streams were selected within each of seven hydrogeologic subregions (strata) that were delineated on the basis of physiography and surficial geology. In each subregion, unequal inclusion probabilities were used to provide an approximately even distribution of streams along a gradient of forested to developed (agricultural or urban) land in the contributing watershed. Alternate streams were also selected. Alternates were included in groups of five in each subregion when field reconnaissance demonstrated that primary streams were inaccessible or otherwise unusable. Despite the rejection and replacement of a considerable number of primary streams during reconnaissance (up to 40 percent in one subregion), the desired land use distribution was maintained within each hydrogeologic subregion without sacrificing the probabilistic design.

Journal of the American Water Resources Associatio↗
USGS2014

From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments

Ecosystem services mapping and modeling has focused more on supply than demand, until recently. Whereas the potential provision of economic benefits from ecosystems to people is often quantified through ecological production functions, the use of and demand for ecosystem services has received less attention, as have the spatial flows of services from ecosystems to people. However, new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems. Our analysis includes five ecosystem services: carbon sequestration and storage, riverine flood regulation, sediment regulation for reservoirs, open space proximity, and scenic viewsheds. Each ecosystem service is characterized by different beneficiary groups and means of service flow. Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use. With the exception of the carbon sequestration service, regions that actually provided services to people, i.e., connected to beneficiaries via flow paths, amounted to 16-66% of those theoretically capable of supplying services, i.e., all ecosystems across the landscape. These results offer a more complete understanding of the spatial dynamics of ecosystem services and their effects, and may provide a sounder basis for economic valuation and policy applications than studies that consider only theoretical service provision and/or use.

Washington↗
USGS2000

Classification of river regimes: A context for hydroecology

Over the past 30 years, ecologists have demostrated the importance of flow and temperature as primary variables in driving running water, riparian and floodplain ecosystems. As it is important to assess the size and timing of discharge variations in relation to those in temperature, a method is proposed that uses multivariate techniques to separately classify annual discharge and temperature regimes according to their 'shape' and 'magnitude', and which then combines the classifications. This paper: (i) describes a generally applicable method; (ii) tests the method by applying it to riparian systems on four British rivers using a 20-year record (1977-97) of flow and air temperature: (iii) proposes a hydroecological interpretation of the classification; (iv) considers the degree to which the methodology might provide information to support the design of ecologically acceptable flow regimes. 'Regimes' are defined for discharge and air temperature using monthly mean data. The results of applying the classification procedure to four British rivers indicates that the 'typical' regimes for each of the four catchments are composite features produced by a small number of clearly defined annual types that reflect interannual variability in hydroclimatological conditions. Annual discharge patterns are dominated by three 'shape' classes (accounting for 94% of the station years: class A, early (November) peak; class B, intermediate (December-January) peak; and class C, late (March) peak) and one 'magnitude' class (70% of the station years fall into class 3, intermediate), with two subordinate 'magnitude' classes: low-flow years (18%) and high flow years (12%). For air temperature, annual patterns are classified evenly into three 'shape' and four 'magnitude' classes. It is argued that this variety of flow-temperature patterns is important for sustaining ecosystem integrity and for establishing benchmark flow regimes and associated frequencies to aid river management. Copyright ?? 2000 John Wiley & Sons, Ltd.Over the past 30 years, ecologists have demonstrated the importance of flow and temperature as primary variables in driving running water, riparian and floodplain ecosystems. As it is important to assess the size and timing of discharge variations in relation to those in temperature, a method is proposed that uses multivariate techniques to separately classify annual discharge and temperature regimes according to their `shape' and `magnitude', and which then combines the classifications. This paper: (i) describes a generally applicable method; (ii) tests the method by applying it to riparian systems on four British rivers using a 20-year record (1977-97) of flow and air temperature; (iii) proposes a hydroecological interpretation of the classification; (iv) considers the degree to which the methodology might provide information to support the design of ecologically acceptable flow regimes. `Regimes' are defined for discharge and air temperature using monthly mean data. The results of applying the classification procedure to four British rivers indicates that the `typical' regimes for each of the four catchments are composite features produced by a small number of clearly defined annual types that reflect interannual variability in hydroclimatological conditions. Annual discharge patterns are dominated by three `shape' classes (accounting for 94% of the station years: class A, early (November) peak; class B, intermediate (December-January) peak; and class C, late (March) peak) and one `magnitude' class (70% of the station years fall into class 3, intermediate), with two subordinate `magnitude' classes: low-flow years (18%) and high flow years (12%). For air temperature, annual patterns are classified evenly into three `shape' and four `magnitude' classes. It is argued that this variety of flow-temperature patterns is important for sustaining ecosystem integrity and for establishing benchmark flow regimes and associated frequencies to aid

Hydrological Processes↗
USGS2024

Estimation of contact time among animals from telemetry data

Continuous processes in most applications are measured discretely with error. This complicates the task of detecting intersections and the number of intersections between two continuous processes (i.e., when the processes have the same value). Intersections of continuous processes are scientifically important but challenging to estimate from data. For example, in the field of animal ecology, intersections of the paths of moving animals tracked with satellite technologies can be used to understand disease transmission. We illustrate how to quantify contact between animals using telemetry data (i.e., the recorded locations of an animal over time). We introduce our method to quantify contact time with accessible concepts from introductory stochastic process literature, such as Brownian motion. Then, we provide two data examples using white-tailed deer ( Odocoileus virginianus ) and mule deer ( Odocoileus hemionus) telemetry data in a region with high prevalence of chronic wasting disease. Our work provides a needed connection between existing model-based literature for animal movement and rule-based literature for animal interaction. Further, our work illustrates a unique statistical problem receiving minimal attention with broad applicability in human and livestock tracking.

The American Statistician↗
USGS2026

Combining individual and close-kin mark–recapture to design an effective wildlife population survey

Close-kin mark–recapture (CKMR) is a promising approach for assessing population size of species that have been difficult to survey using more traditional methods. Here, we combine individual and close-kin mark–recapture in a single modeling framework (ICKMR) and provide an example of study design using this approach for Pacific walrus ( Odobenus rosmarus divergens ). We develop the ICKMR model and test it using simulated datasets, then use properties of the pseudo-likelihood to investigate the expected precision in estimates of abundance with different proposed survey designs. Our motivating example, the Pacific walrus, is an ice-associated marine mammal found in the Bering and Chukchi seas, where it is an important resource for Indigenous peoples. Pacific walrus abundance declined in the late 20th century, and it is currently a species of conservation concern due to potential impacts of climate change, particularly the loss of sea ice. To reduce uncertainty in population size estimates, researchers undertook a genetic mark–recapture sampling campaign from 2013 to 2017 and collected tissue samples from over 8000 individuals. Another campaign of a similar scale is ongoing (2023–2028). While sample collection was designed for individual mark–recapture, advances in CKMR methods and associated molecular techniques mean that these samples could also be suitable for CKMR. The advantages of CKMR over mark–recapture include an increased effective sample size (because each individual tags itself and its parents, siblings, and offspring) and additional insights into demographic quantities of interest. To make best use of genetic samples, we combine individual mark–recapture (IMR) with CKMR (ICKMR) and investigate whether different sampling strategies can increase precision in estimates of abundance. Our modeling approach includes special considerations for walrus life history, including a multi-year inter-birth interval. We found that expected coefficients of variation (CVs) of the ICKMR estimates of abundance, adult female survival, juvenile female survival, and proportion of breeding females are lower than those expected from IMR alone, and with ICKMR, fewer years of sampling can be conducted to obtain sufficient precision in estimates of abundance. This work demonstrates the utility of ICKMR and could be applicable across a variety of taxa.

Ecology↗
USGS2005

Influence of topographic complexity on solar insolation estimates for the Colorado River, Grand Canyon, AZ

Rugged topography along the Colorado River in Glen and Grand Canyons, exemplifies features common to canyon-bound streams and rivers of the arid southwest. Physical relief influences regulated river systems, especially those that are altered, and have become partially reliant on aquatic primary production. We measured and modeled instantaneous solar flux in a topographically complex environment to determine where differences in daily, seasonal and annual solar insolation occurred in this river system. At a system-wide scale, topographic complexity generates a spatial and temporal mosaic of varying solar insolation. This solar variation is a predictable consequence of channel orientation, geomorphology, elevation angles and viewshed. Modeled estimates for clear conditions corresponded closely with observed measurements for both instantaneous photosynthetic photon flux density (PPFD: ??mol m-2 s-1) and daily insolation levels (relative error 2.3%, CI ??0.45, S.D. 0.3, n = 29,813). Mean annual daily insolation levels system-wide were estimated to be 36 mol m-2 d -1 (17.5 S.D.), and seasonally varied on average from 13.4-57.4 mol m-2 d-1, for winter and summer, respectively. In comparison to identical areas lacking topographic effect (idealized plane), mean daily insolation levels were reduced by 22% during summer, and as much as 53% during winter. Depending on outlying topography, canyon bound regions having east-west (EW) orientations had higher seasonal variation, averaging from 8.1 to 61.4 mol m-2 d-1, for winter and summer, respectively. For EW orientations, 70% of mid-channel sites were obscured from direct incidence during part of the year; and of these sites, average diffuse light conditions persisted for 19.3% of the year (70.5 days), and extended upwards to 194 days. This predictive model has provided an initial quantitative step to estimate and determine the importance of autotrophic production for this ecosystem, as well as a broader application for other canyon systems. ?? 2004 Published by Elsevier B.V.

Ecological Modelling↗
USGS2024

Informative priors can account for location uncertainty in stop-level analyses of the North American Breeding Bird Survey (BBS), allowing fine-scale ecological analyses

Ecological inferences are often based on the locations at which species are present, but many species records have substantial uncertainty in spatial metadata, limiting their utility for fine-scale analyses. This is especially prevalent in historical records such as museum specimens, and in some citizen-science data. For example, the North American Breeding Bird Survey (BBS) has 55+ years of bird data from regular transects (“routes”) across the continent but was not designed to capture the spatial component of point count events, limiting analyses of species-habitat relationships for which it would otherwise be well suited. We present a new methodology for quantifying location uncertainty in BBS records using digitized estimated stop locations, deriving the corresponding environmental covariate uncertainty distributions, and incorporating this information into hierarchical species distribution models using informative Bayesian priors. This approach allows for estimation of species–environment relationships in a way that fully accounts for underlying spatial uncertainty. We quantify stop-location uncertainty in BBS data across the central United States, model bird–land cover relationships in the upper Midwest, and validate our method by comparing posterior land cover estimates to known covariate values for a subset of GPS-digitized stop locations. We provide code for implementing this method in R. Posterior land cover estimates (forest, grass/hay, and developed land cover), based on our informative priors, were highly correlated with known land cover values from GPS-digitized stop locations. Our approach thus makes it possible to responsibly leverage large historic and citizen science databases, such as the BBS, for fine-scale ecological analyses.

Ornithological Applications↗
USGS2018

Implicit decision framing as an unrecognized source of confusion in endangered species classification

Legal classification of species requires scientific and values‐based components, and how those components interact depends on how people frame the decision. Is classification a negotiation of trade‐offs, a decision on how to allocate conservation efforts, or simply a comparison of the biological status of a species to a legal standard? The answers to problem‐framing questions such as these influence decision making in species classifications. In our experience, however, decision makers, staff biologists, and stakeholders often have differing perspectives of the decision problem and assume different framings. In addition to differences between individuals, in some cases it appears individuals themselves are unclear about the decision process, which contributes to regulatory paralysis, litigation, and a loss of trust by agency staff and the public. We present 5 framings: putting species in the right bin, doing right by the species over time, saving the most species on a limited budget, weighing extinction risk against other objectives, and strategic classification to advance conservation. These framings are inspired by elements observed in current classification practices. Putting species in the right bin entails comparing a scientific status assessment with policy thresholds and accounting for potential misclassification costs. Doing right by the species adds a time dimension to the classification decision, and saving the most species on a limited budget classifies a suite of species simultaneously. Weighing extinction risk against other objectives would weigh ecological or socioeconomic concerns in classification decisions, and strategic classification to advance conservation would make negotiation a component of classification. We view these framings as a means to generate thought, discussion, and movement toward selection and application of explicit classification framings. Being explicit about the decision framing could lead decision makers toward more efficient and defensible decisions, reduce internal confusion and external conflict, and support better collaboration between scientists and policy makers.

Conservation Biology↗
USGS1989

Structural marsh management research priorities

The paper presents a prioritized list of research issues related to structural marsh management developed by a multidisciplinary panel of regulatory agency representatives, landowners, and scientists. More than 75 issues were identified concerning landscape changes, influence on ecological processes (i.e., hydrologic, biologic, and edaphic factors), habitat quality, cumulative impacts, and management approach. These issues were prioritized and organized around six basic questions regulatory personnel must try to answer for each marsh management plan application. The six questions deal with the influence of marsh management on, in order of most immediate need, marsh loss and health, fisheries, wildlife, habitat change, water quality, and cumulative effects.

Conference Paper↗
USGS2023

Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia

Coastal resources are increasingly affected by erosion, extreme weather events, sea level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying effects on coastal landscapes because of the compounding of geologic, oceanographic, ecologic, and socioeconomic factors that exist at a given location. An assessment framework is introduced in this report that synthesizes existing datasets that cover the variability of the landscape, and hazards that may act on the landscape, to evaluate the likelihood of coastal change along the U.S. coastline on a decadal scale. The pilot study that aided in the development of the framework was run in the northeastern United States (from Maine to Virginia) and consists of datasets derived from a variety of Federal, State, and local sources. First, a decision-tree-based dataset was built that describes the resistance or integrity of the coastal landscape (called the fabric dataset for the purposes of this report) and includes land cover, elevation, slope, long-term (more than 50 years) shoreline change, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (for example, flooding, wave power, and probability of storm overwash) and persistent or perpetual hazards (for example, relative sea level rise rate, short-term [about 30-year] shoreline erosion rate, and storm recurrence interval). The fabric dataset was then merged with the coastal hazards databases, and a model training dataset made up of hundreds of polygons was generated from these combined data to support machine learning. The pilot study resulted in location-specific, 10-meter-resolution data classified into five raster datasets that include intrinsic characteristics of the coast used to determine the resistance of the landscape to change, the persistent and event hazards that act on the coast, the machine learning output (coastal change likelihood) based on the cumulative effects of the fabric and hazards datasets, and an estimate of the hazard type (event or persistent) that is the most likely to influence coastal change. Final outcomes are intended to be used as a first-order planning tool to determine which areas of the coast are more likely to change in response to future potential coastal hazards and to examine elements and drivers that make change in a location more likely.

Connecticut, Delaware, Maine, Maryland, Massachuse↗
USGS2017

Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question—How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km 2 ); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation—approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

Ecology and Evolution↗
USGS2007

Random forests for classification in ecology

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Ecology↗
USGS2022

Identifying monitoring information needs that support the management of fish in large rivers

Management actions intended to benefit fish in large rivers can directly or indirectly affect multiple ecosystem components. Without consideration of the effects of management on non-target ecosystem components, unintended consequences may limit management efficacy. Monitoring can help clarify the effects of management actions, including on non-target ecosystem components, but only if data are collected to characterize key ecosystem processes that could affect the outcome. Scientists from across the U.S. convened to develop a conceptual model that would help identify monitoring information needed to better understand how natural and anthropogenic factors affect large river fishes. We applied the conceptual model to case studies in four large U.S. rivers. The application of the conceptual model indicates the model is flexible and relevant to large rivers in different geographic settings and with different management challenges. By visualizing how natural and anthropogenic drivers directly or indirectly affect cascading ecosystem tiers, our model identified critical information gaps and uncertainties that, if resolved, could inform how to best meet management objectives. Despite large differences in the physical and ecological contexts of the river systems, the case studies also demonstrated substantial commonalities in the data needed to better understand how stressors affect fish in these systems. For example, in most systems information on river discharge and water temperature were needed and available. Conversely, information regarding trophic relationships and the habitat requirements of larval fishes were generally lacking. This result suggests that there is a need to better understand a set of common factors across large-river systems. We provide a stepwise procedure to facilitate the application of our conceptual model to other river systems and management goals.

Environmental Management↗
USGS2010

Water quality and hydrology of the Silver River Watershed, Baraga County, Michigan, 2005-08

The Silver River Watershed comprises about 69 square miles and drains part of northeastern Baraga County, Michigan. For generations, tribal members of the Keweenaw Bay Indian Community have hunted and fished in the watershed. Tribal government and members of Keweenaw Bay Indian Community are concerned about the effect of any development within the watershed, which is rural, isolated, and lightly populated. For decades, the area has been explored for various minerals. Since 2004, several mineral-exploration firms have been actively investigating areas within the watershed; property acquisition, road construction, and subsurface drilling have taken place close to tributary streams of the Silver River. The U.S. Geological Survey, in cooperation with Keweenaw Bay Indian Community, conducted a multi-year water-resources investigation of the Silver River Watershed during 2005-08. Methods of investigation included analyses of streamflow, water-quality sampling, and ecology at eight discrete sites located throughout the watershed. In addition, three continuous-record streamgages located within the watershed provided stage, discharge, specific conductance, and water-temperature data on an hourly basis. Water quality of the Silver River Watershed is typical of many streams in undeveloped areas of Upper Michigan. Concentrations of most analytes typically were low, although several exceeded applicable surface-water-quality standards. Seven samples had concentrations of copper that exceeded the Michigan Department of Environmental Quality standards for wildlife, and one sample had concentrations of cyanide that exceeded the same standards. Concentrations of total mercury at all eight sampling sites exceeded the Great Lakes Basin water-quality standard, but the ratio of methylmercury to total mercury was similar to the 5 to 10 percent found in most natural waters. Concentrations of arsenic and chromium in bed sediments were near the threshold-effect concentration. A qualitative ecological assessment of fishes and macroinvertebrates showed that intolerant salmonids were present at most sampled sites, and macroinvertebrate communities were indicative of near-excellent or excellent conditions at all eight sites. This baseline information will aid in an ongoing monitoring effort designed to protect the water resources of the

Scientific Investigations Report↗