Explore the vast range of titles available.

Agricultural, industrial and urban development have all contributed to increased salinity in streams and rivers, but the likely effects of future development and climate change are unknown. I developed two empirical models to estimate how these combined effects might affect salinity by the end of this century (measured as electrical conductivity, EC). The first model predicts natural background from static (e.g. geology and soils) and dynamic (i.e. climate and vegetation) environmental factors and explained 78% of the variation in EC. I then compared the estimated background EC with current measurements at 2001 sites chosen probabilistically from all conterminous USA streams. EC was more than 50% greater at 34% of these sites. The second model predicts deviation of EC from background as a function of human land use and environmental factors and explained 60% of the variation in alteration from background. I then predicted the effects of climate and land use change on EC at the end of the century by replacing dynamic variables with published projections of future conditions based on the A2 emissions scenario. By the end of the century, the median EC is predicted to increase from 0.319 mS cm −1 to 0.524 mS cm −1 with over 50% of streams having greater than 50% increases in EC and 35% more than doubling their EC. Most of the change is related to increases in human land use, with climate change accounting for only 12% of the increase. In extreme cases, increased salinity may make water unsuitable for human use, but widespread moderate increases are likely a greater threat to stream ecosystems owing to the elimination of low EC habitats. This article is part of the theme issue ‘Salt in freshwaters: causes, ecological consequences and future prospects’.

Environmental DNA (eDNA) sampling from rivers has emerged as a promising new method for monitoring freshwater organisms of management concern. However, eDNA sampling cannot yet offer reliable estimates of a target species' abundance/biomass or confident determinations of a species' absence from a river segment. To unlock these abilities—and thereby greatly improve eDNA as a tool for management decision‐making—the influence of local environmental factors on eDNA fate must be better understood. At nine river sites across the central California coast, we added a known quantity of novel eDNA (Brook Trout, Salvelinus fontinalis) and collected eDNA at sequential downstream distances for qPCR analysis. We then used random forest modeling to identify the most important environmental factors to reach‐scale (≤ 200 m) sampling outcomes and characterize salmonid eDNA fate. Our final model identified six factors important to sampling outcomes, including five environmental factors (discharge, local catchment calcium oxide content, average depth of the sampling cross‐section, presence of pools, and impervious cover of the watershed) and one factor regarding our experimental design (the number of qPCR technical replicates). Our results highlight the notable effects of cross‐sectional area, turbulence, and catchment geology on eDNA fate, and we suggest the discharge and presence of pools as useful proxies for evaluating a site's favorability for eDNA recovery.

This book is about operations management and the strategic implementation of programs, techniques, and tools for reducing costs and improving quality. It not only covers the basics of operations management, but also explains how operations and process improvement relate to contemporary healthcare trends such as evidence-based medicine and pay-for-performance. The book's practical approach includes real-world examples to illustrate concepts and explanations of software tools that solve operational problems. Key Features: * Provides methodologies to align strategic and operational goals, including the use of project management tools and balanced-scorecard techniques to execute and monitor projects * Thoroughly explores process improvement tools, techniques, and programs, including Six Sigma, the Lean enterprise, and simulation * Applies performance improvement tools to supply chain management, scheduling, and other healthcare issues * Includes examples from a fictitious but realistic organization that illustrate important concepts discussed in each chapter * Includes chapter overviews, key terms and acronyms, discussion questions, and problems for each chapter * Provides a companion website that features Excel templates, Arena models, tutorials, exercises, PowerPoint presentations, and web links Included CD-Rom: The book explains and demonstrates the use of various software tools associated with problem solving and decision making including Microsoft Excel and Project. A version of Arena software is included in order to practice process modeling. Arena is a powerful simulation tool used by healthcare organizations to optimize patient flow, develop scheduling systems, and improve patient-care processes.

Salinization of surface waters is a global environmental issue that can pose a regional risk to freshwater organisms, potentially leading to high environmental and economic costs. Global environmental change including climate and land use change can increase the transport of ions into surface waters. We fit both multiple linear regression (LR) and random forest (RF) models on a large spatial dataset to predict Ca 2+ (266 sites), Mg 2+ (266 sites), and (357 sites) ion concentrations as well as electrical conductivity (EC—a proxy for total dissolved solids with 410 sites) in German running water bodies. Predictions in both types of models were driven by the major factors controlling salinity including geologic and soil properties, climate, vegetation and topography. The predictive power of the two types of models was very similar, with RF explaining 71–76% of the spatial variation in ion concentrations and LR explaining 70–75% of the variance. Mean squared errors for predictions were all smaller than 0.06. The factors most strongly associated with stream ion concentrations varied among models but rock chemistry and climate were the most dominant. The RF model was subsequently used to forecast the changes in EC that were likely to occur for the period of 2070 to 2100 in response to just climate change—i.e. no additional effects of other anthropogenic activities. The future forecasting shows approximately 10% and 15% increases in mean EC for representative concentration pathways 2.6 and 8.5 (RCP2.6 and RCP8.5) scenarios, respectively. This article is part of the theme issue ‘Salt in freshwaters: causes, ecological consequences and future prospects’.

Robust predictions of stream solute concentrations expected under natural (reference) conditions would help establish more realistic water quality standards and improve stream ecological assessments. Models predicting solute concentrations from environmental factors would also help identify the relative importance of different factors that influence water chemistry. Although data are available describing the major factors controlling water chemistry (i.e., geology, climate, atmospheric deposition, soils, vegetation, topography), geologic maps do not adequately convey how rocks vary in their chemical and physical properties. We addressed this issue by associating rock chemical and physical properties with geological map units to produce continuous maps of percentages of CaO, MgO, S, uniaxial compressive strength, and hydraulic conductivity for western United States lithologies. We used catchment summaries of these geologic properties and other environmental factors to develop multiple linear regression (LR) and random forest (RF) models to predict base flow electrical conductivity (EC), acid neutralization capacity (ANC), Ca, Mg, and SO4. Models were derived from observations at 1414 reference‐quality streams. RF models were superior to LR models, explaining 71% of the variance in EC, 61% in ANC, 92% in Ca, 58% in Mg, and 74% in SO4 when assessed with independent observations. The root‐mean‐square error for predictions on validation sites were all <11% of the range of observed values. The relative importance of different environmental factors in predicting stream chemistry varied among models, but on average rock chemistry > temperature > precipitation > soil = atmospheric deposition > vegetation > amount of rock/water contact > topography. Key Points We develop data describing chemical and physical attributes of bedrock geology Empirical models successfully predict stream chemistry from environmental data Geochemistry > temperature > precipitation in predicting stream chemistry

We used field-derived data from streams in Nevada, USA, to quantify relationships between stream biological condition, in-stream stressors, and potential sources of stress (land use). We used 2 freshwater macroinvertebrate-based indices to measure biological condition: a multimetric index (MMI) and an observed to expected (O/E) index of taxonomic completeness. We considered 4 categories of potential stressors: dissolved metals, total dissolved solids, nutrients, and flow alteration. For physicochemical factors that varied predictably across natural environmental gradients, we quantified potential stress as the site-specific difference between observed (O) and expected (E) levels of each factor (O–Estress). We then used 2 sets of Random Forest models to quantify relationships between: 1) biological condition and potential stressors, and 2) stressor values and land uses. The 2 indices of biological condition were differentially responsive to stressors, indicating that no single measure of biological condition could fully characterize assemblage response to stress. Total dissolved solids (as measured by electrical conductivity [EC]) and metal contamination were the stressors most strongly associated with biological degradation. The most likely sources of these stressors were agriculture, urban development, and mining. Our findings highlight the need to develop EC criteria for streams. Measures of biological condition and stress that account for natural variability should reduce errors of inference and increase confidence in causal analyses. This approach will require development of robust models capable of predicting physical and chemical reference conditions. Causal analyses for individual sites require appropriate hypotheses about which stressors and what levels of stress can cause biological degradation. Our study demonstrates the usefulness of field data collected from multiple sites within a region for developing these hypotheses.

Ecologically meaningful and scientifically defensible nutrient criteria are needed to protect the water quality of USA streams. Criteria based on our best understanding of naturally occurring nutrient concentrations would protect both water quality and aquatic biota. Previous approaches to predicting natural background nutrient concentrations have relied on some form of landscape categorization (e.g., nutrient ecoregions) to account for natural variability among water bodies. However, natural variation within these regions is so high that use of a single criterion can underprotect naturally occurring low-nutrient streams and overprotect naturally occurring high-nutrient steams. We developed Random Forest models to predict how baseflow concentrations of total P (TP) and total N (TN) vary among western USA streams in response to continuous spatial variation in nutrient sources, sinks, or other processes affecting nutrient concentrations. Both models were relatively accurate (root mean square errors <12% of the range of observations for independent validation sites) and made better predictions than previous models of natural nutrient concentrations. However, the models were not very precise (TP model: r2  =  0.46, TN model: r2  =  0.23). An analysis of the sources of variation showed that our models accounted for most of the spatial variation in nutrient concentrations, and much of the imprecision was caused by temporal or measurement variation. We applied 2 methods to determine upper prediction limits that incorporated model error and could be used as site-specific nutrient criteria. These site-specific candidate nutrient criteria better accounted for natural variation among sites than did criteria based on regional average conditions, would increase protection for streams with naturally low nutrient concentrations, and specified more attainable conditions for those streams with naturally higher nutrient concentrations.

The pallid sturgeon Scaphirhynchus albus , a large, long‐lived fish endemic to the Missouri and Mississippi River Basins, was listed as a federally endangered species in 1990 due to population declines driven by profound anthropogenic habitat alterations, including river fragmentation by dams and channelization. In contrast, its congener, the shovelnose sturgeon Scaphirhynchus platorynchus , remains common in large rivers and their tributaries, such as the Des Moines River. Historically, the pallid sturgeon’s range in Iowa has been limited to the Missouri River along the state’s western border. Recovery efforts, as outlined in the National Pallid Sturgeon Recovery Plan, have emphasized habitat restoration and conservation stocking to prevent extirpation and to support natural recruitment. Here, we document the first verified records of wild (nonstocked) pallid sturgeon in the Des Moines River, Iowa. This represents a potential expansion of the species’ known contemporary range and occurs within a tributary not previously identified as occupied habitat. This finding underscores the potential for stocked or wild individuals to disperse into novel river systems where previously undocumented habitat may be available. The finding has immediate conservation implications under the Endangered Species Act, prompting a reassessment of the Des Moines River’s management strategies and necessitating enhanced, targeted sampling efforts. It also raises concerns under the Act’s Similarity of Appearance clause, which governs the legal commercial take of shovelnose sturgeon in the pooled reach of the Upper Mississippi River downstream of the Des Moines River confluence, a potential migration corridor. The presence of pallid sturgeon in the Des Moines River demonstrates that even highly altered river systems can provide essential habitat, such as suitable spawning substrate, needed to support their life cycle, particularly when stream flow is managed to approximate natural hydrologic patterns.

Reference site networks should consist of minimally disturbed sites that collectively characterize the ranges of natural settings within a region. Compromise between reference-quality and representativeness is required. We evaluated how tradeoffs between reference-quality and regional representativeness affected applicability, performance, and interpretation of multi-metric (MMI) and Observed/Expected (O/E) indices developed for streams in eastern China. We emphasized reference-quality by applying the most-stringent objective criteria and expert-judgment to select reference-group1 (G1). We emphasized representativeness by applying the least-stringent criteria to select reference-group2 (G2) sites from different strata based on watershed size. We balanced reference-quality and representativeness in G3 by applying intermediate stringent criteria from each watershed size stratum used previously. Increasing representativeness using G2 improved index applicability to almost more than twice the number of test sites than when reference-quality maximized using G1. Bias in O/E index was almost eliminated only when reference-quality and representativeness balanced using G3. MMIs developed when reference-quality maximized using G1 eliminated all bias and had the highest precision. High-quality reference with limited representativeness affected the metrics selected for inclusion in MMIs and restricted the sites to which both types of indices could be applied. A balanced approach worked best in this instance and similar approaches should be tested in other regions.

The North Slope of Alaska contains arctic fish populations that are important for subsistence of local human populations, and are under threat from natural resource extraction and climate change. We designed and evaluated four quantitative PCR assays for the detection of environmental DNA from five Alaskan fish species present on the North Slope of Alaska: burbot ( Lota lota ), arctic char ( Salvelinus alpinus ), Dolly Varden ( Salvelinus malma ), arctic grayling ( Thymallus arcticus ), and slimy sculpin ( Cottus cognatus ). All assays were designed and tested for species specificity and sensitivity, and all assays detected target species from filtered water samples collected from the field. These assays will enable efficient and economical detection and monitoring of these species in lakes and rivers. This in turn will provide managers with improved knowledge of current distributions and future range shifts associated with climate and development threats, enabling more timely management.