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Disentangling the effects of natural factors and human disturbances on freshwater systems is essential for understanding the distributions and composition of biological communities and their relationship with physicochemical and biological factors. As the spatial extent of ecological investigations increases from local to global scales, efforts to account for the increasing influence of natural factors become more important. This article synthesizes the current knowledge and commonly used approaches for disentangling these effects on aquatic systems. New understanding has been facilitated by the availability of large-scale geospatial landscape databases that facilitate regional analyses and classifications in conjunction with novel approaches to identify reference conditions and statistical partitioning analyses. This synthesis begins with a summary of how natural factors and human disturbances interactively affect aquatic systems. It then provides an overview of why it is essential to separate the effects of natural factors and human disturbances and a description of examples of landscape databases that make the separation of natural and human factors feasible. It last synthesizes currently-used common approaches for separating the effects of natural factors from human disturbances. Our synthesis assembles representative approaches to disentangling human disturbances in one place to provide new insights that stimulate integrated uses of multiple approaches and the development of new approaches so that management actions can be taken to protect and restore aquatic ecosystem health. [ABSTRACT FROM AUTHOR]

Land use change and other human disturbances have significant impacts on physicochemical and biological conditions of stream systems. Meanwhile, linking these disturbances with hydrology and water quality conditions is challenged due to the lack of high-resolution datasets and the selection of modeling techniques that can adequately deal with the complex and nonlinear relationships of natural systems. This study addresses the above concerns by employing a watershed model to obtain stream flow and water quality data and fill a critical gap in data collection. The data were then used to estimate fish index of biological integrity (IBI) within the Saginaw Bay basin in Michigan. Three methods were used in connecting hydrology and water quality variables to fish measures including stepwise linear regression, partial least squares regression, and fuzzy logic. The IBI predictive model developed using fuzzy logic showed the best performance with the R = 0.48. The variables that identified as most correlated to IBI were average annual flow, average annual organic phosphorus, average seasonal nitrite, average seasonal nitrate, and stream gradient. Next, the predictions were extended to pre-settlement (mid-1800s) land use and climate conditions. Results showed overall significantly higher IBI scores under the pre-settlement land use scenario for the entire watershed. However, at the fish sampling locations, there was no significant difference in IBI. Results also showed that including historical climate data have strong influences on stream flow and water quality measures that interactively affect stream health; therefore, should be considered in developing baseline ecological conditions. [ABSTRACT FROM AUTHOR]

Fisheries management programs, such as the National Fish Habitat Action Plan (NFHAP), urgently need a nationwide spatial framework and database for health assessment and policy development to protect and improve riverine systems. To meet this need, we developed a spatial framework and database using National Hydrography Dataset Plus (I-.100,000-scale); http://www.horizon-systems.com/nhdplus). This framework uses interconfluence river reaches and their local and network catchments as fundamental spatial river units and a series of ecological and political spatial descriptors as hierarchy structures to allow users to extract or analyze information at spatial scales that they define. This database consists of variables describing channel characteristics, network position/connectivity, climate, elevation, gradient, and size. It contains a series of catchment-natural and human-induced factors that are known to influence river characteristics. Our framework and database assembles aü river reaches and their descriptors in one place for the first time for the conterminous United States. This framework and database provides users with the capability of adding data, conducting analyses, developing management scenarios and regulation, and tracking management progresses at a variety of spatial scales. This database provides the essential data needs for achieving the objectives of NFHAP and other management programs. The downloadable beta version database is available at http://ec2-184-73-40-15.compute-1.amazonaws.com/nfhap/main/. [ABSTRACT FROM PUBLISHER]

Freshwater streams are critical resources that provide multiple benefits to humans and aquatic biota alike. As climate changes, it is projected that changes to the hydrological cycle and water temperatures will affect individual biota and aquatic ecosystems as a whole. The goal of this study was to determine the extent of climate change impacts on stream ecosystems as represented by four commonly used stream health indicators (Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT), Family Index of Biotic Integrity (FIBI), Hilsenhoff Biotic Index (HBI), and fish Index of Biotic Integrity (IBI)). Seven watersheds in Michigan were selected based on stream thermal regimes. The Soil and Water Assessment Tool was used to simulate streamflow and pollutant loads. Important variables for each thermal class were selected using a Bayesian variable selection method and used as inputs to adaptive neuro-fuzzy inference systems models of EPT, FIBI, HBI, and IBI. Finally, an ensemble of climate models from the Coupled Model Intercomparison Project Phase 5 were used to determine the impacts of climate on the stream health in 2020–2040 compared to 1980–2000. The risk of declining stream health was determined using cumulative distribution functions. A stream temperature regression model was also developed to assess potential changes in stream thermal regimes, which could cause shifts in composition of aquatic communities. Several flow regime variables, including those related to flow variability, duration of extreme events, and timing were mainly affected by changing climate. At the watershed scale, most indicators were relatively insensitive to changing climate and the magnitude of stream health decline was low. However, at the reach scale, there are many instances of high risk and large magnitude of declines in the stream health indicators. At the same time, several streams experienced changes in thermal class, mostly transitioning from cold-transitional and cool streams to warm streams. This research demonstrated the applicability of the stream health modeling process in performing a climate change impacts assessment. [ABSTRACT FROM AUTHOR]

With increasing human population and urbanization, tourism in natural reserves and other protected lands is growing. It is critical to monitor and assess the impacts of tourism on ecosystem health. However, there is a general lack of information on biological communities in natural reserves of developing countries and of tools for assessing human impacts. In the present study, we investigated macroinvertebrate assemblages in nine lakes in Jiuzhaigou Natural Reserve of China. Both benthic (20 dips of D-net) and light-trap samples (2 h) were collected at each lake and all benthic specimens and adults of Ephemeroptera, Plecoptera, and Trichoptera (EPT) were identified and counted. Water temperature and water quality variables were measured on site or in the Lab. Seventy taxa were recorded and dominated by dipterans and caddisflies. Light traps contributed 47% of taxa richness and 66% of EPT richness at the lakes. Detrended Correspondence Analysis showed that water temperature and tourism index were strongly associated with the changes of assemblage composition. Taxa richness and EPT richness calculated for the composite samples (benthic + light trap) were well fit with Poisson generalized linear model (adjusted R 2 = 0.83 and 0.85, respectively), generally decreasing with increasing elevation, tourism index, and total-N. Tourism index was ranked as the top predictor for EPT richness based on multiple model weights, and elevation for taxa richness. In comparison, when based on benthic samples, neither of the metrics could be fitted with the seven environmental variables selected. These findings highlight the benefit of combined use of the sampling methods for lake monitoring and offered an analytical guide to developing biological indicators of lake ecosystem health in protected areas. [ABSTRACT FROM AUTHOR]

Variable selection is a critical step in development of empirical stream health prediction models. This study develops a framework for selecting important in-stream variables to predict four measures of biological integrity: total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, family index of biotic integrity (FIBI), Hilsenhoff biotic integrity (HBI), and fish index of biotic integrity (IBI). Over 200 flow regime and water quality variables were calculated using the Hydrologic Index Tool (HIT) and Soil and Water Assessment Tool (SWAT). Streams of the River Raisin watershed in Michigan were grouped using the Strahler stream classification system (orders 1–3 and orders 4–6), k -means clustering technique (two clusters: C1 and C2), and all streams (one grouping). For each grouping, variable selection was performed using Bayesian variable selection, principal component analysis, and Spearman's rank correlation. Following selection of best variable sets, models were developed to predict the measures of biological integrity using adaptive-neuro fuzzy inference systems (ANFIS), a technique well-suited to complex, nonlinear ecological problems. Multiple unique variable sets were identified, all which differed by selection method and stream grouping. Final best models were mostly built using the Bayesian variable selection method. The most effective stream grouping method varied by health measure, although k -means clustering and grouping by stream order were always superior to models built without grouping. Commonly selected variables were related to streamflow magnitude, rate of change, and seasonal nitrate concentration. Each best model was effective in simulating stream health observations, with EPT taxa validation R 2 ranging from 0.67 to 0.92, FIBI ranging from 0.49 to 0.85, HBI from 0.56 to 0.75, and fish IBI at 0.99 for all best models. The comprehensive variable selection and modeling process proposed here is a robust method that extends our understanding of watershed scale stream health beyond sparse monitoring points. [ABSTRACT FROM AUTHOR]

Abstract: Anthropogenic activities such as agricultural practices can have large effects on the ecological components and overall health of stream ecosystems. Therefore, having a better understanding of those effects and relationships allows for better design of mitigating strategies. The objectives of this study were to identify influential stream variables that correlate with macroinvertebrate indices using biophysical and statistical models. The models developed were later used to evaluate the impact of three agricultural management practices on stream integrity. Our study began with the development of a high-resolution watershed model for the Saginaw River watershed in Michigan for generating in-stream water quality and quantity data at stream reaches with biological sampling data. These in-stream data were then used to explain macroinvertebrate measures of stream health including family index of biological integrity (FamilyIBI), Hilsenhoff biotic index (HBI), and the number of Ephemeroptera, Plecoptera , and Trichoptera taxa (EPTtaxa). Two methods (stepwise linear regression and adaptive neuro-fuzzy inference systems (ANFIS)) were evaluated for developing predictive models for macroinvertebrate measures. The ANFIS method performed the best on average and the final models displayed the highest R2 and lowest mean squared error (MSE) for FamilyIBI (R2 =0.50, MSE=29.80), HBI (R2 =0.57, MSE=0.20), and EPTtaxa (R2 =0.54, MSE=6.60). Results suggest that nutrient concentrations have the strongest influence on all three macroinvertebrate measures. Consistently, average annual organic nitrogen showed the most significant association with EPTtaxa and HBI. Meanwhile, the best model for FamilyIBI included average annual ammonium and average seasonal organic phosphorus. The ANFIS models were then used in conjunction with the Soil and Water Assessment Tool to forecast and assess the potential effects of different best management practices (no-till, residual management, and native grass) on stream integrity. Based on the model predictions, native grass resulted in the largest improvement for all macroinvertebrate measures. [Copyright &y& Elsevier]