A Comparison of the Temperature Regime of Short Stream Segments under Forested and Non‐Forested Riparian Zones at Eleven Sites Across North America

When forested riparian zones are cleared for agriculture or development, major changes can occur in the stream temperature regime and consequently in ecosystem structure and function. Our main objective was to compare the summer temperature regimes of streams with and without forest canopy cover at multiple sites. The secondary objective was to identify the components of the stream heat budget that had the greatest influence on the stream temperature regime. Paired stream reaches (one forested and one non‐forested or ‘open’) were identified at 11 sites distributed across the USA and Canada. Stream temperature was monitored at the upstream and downstream ends of 80 to 130‐m‐long reaches during summer, and five variables were calculated to describe the stream temperature regime. Overall, compared with forested reaches, open reaches tended to have significantly higher daily mean (mean difference = 0.33 ± 1.1°C) and daily maximum (mean difference = 1.0 ± 1.7°C) temperatures and wider daily ranges (mean difference = 1.1 ± 1.7°C). Mean and maximum daily net heat fluxes in open reaches tended to be greater (or less negative) than those in forested reaches. However, certain sites showed the opposite trends in some variables because of the following: (i) Daily mean and maximum temperatures were biased by differences in inflow temperature between paired reaches and (ii) inputs of cold groundwater exerted a strong influence on temperature. Modelling and regression results suggested that within sites, differences in direct solar radiation were mainly responsible for the observed differences in stream temperature variables at the daily scale. © 2014 The Authors. River Research and Applications published by John Wiley & Sons, Ltd.     

A CLIMATE‐BASED INDEX OF HISTORIC SPAWNING‐STREAM HYDROLOGY

Reproductive success of stream‐spawning Oncorhynchus fishes (Pacific salmon, rainbow trout, cutthroat trout and their allies) may be greatly affected by stream discharge or its covariate, stream temperature, during the spawning season. Because such data for the physical environment may not have been routinely collected as part of previous investigations of these fishes, identification of simple but robust indices of historic, seasonal stream discharge and temperature, using long‐term climate data sets, would be important, especially to investigations of historic population dynamics. This study examined statistical associations among several climate variables and the spawning‐season (approximately June) discharges and temperatures of Clear Creek, a Yellowstone Lake tributary used by spawning Yellowstone cutthroat trout, Oncorhynchus clarkii bouvieri (YCT), from the lake. Correlation analysis showed that total water‐year degree‐days (calculated on the basis of mean daily air temperature > 0°C) at Lake Village, on the lake's north shore, was a robust index (both negative and positive, respectively) of consecutive, total semi‐month metrics of creek discharge and temperature during the YCT spawning season. This study (and subsequent use of the Lake Village degree days metric as an environmental variable in a dynamic, age‐structured model of the lacustrine–adfluvial YCT population of Clear Creek) showed how exploratory analyses of the fragmentary but long‐term and regionally unique data sets for Clear Creek discharge and temperature revealed a simple but robust index of climate variation important to understanding the historic dynamics of Clear Creek's YCT population, which is a key spawning stock of Yellowstone Lake. In addition, the extensive statistical associations among the climate variables, along with the temporal trends in two key variables, broadly showed how climate varied across the Yellowstone Lake region during the past several decades. Those observations have implications for the historic, seasonal hydrology of all Yellowstone Lake tributaries used by spawning YCT. Copyright © 2011 John Wiley & Sons, Ltd.     

Statistical tools for thermal regime characterization at segment river scale: Case study of the Ste‐Marguerite River

Suitable thermal fish habitats are constrained by both maximum and minimum temperature tolerances. A multivariate and geostatistical approach was developed to estimate stream thermal characteristics at the river segment scale. Data from 22 temperature‐monitoring stations during summer 2007 were used to estimate monthly maximum temperature as well as thermal characteristics such as the number of events, the cumulative degree–days and the associated duration over specific temperature thresholds of 19 and 21°C. The probability of exceeding these temperature thresholds has also been interpolated. The methodology relies on the construction of a multivariate space using physiographic and hydrological characteristics of gauging stations as inputs in a canonical correlation analysis (CCA). A geostatistical interpolation technique, ordinary kriging, was subsequently used to perform interpolation in the physiographical space constructed using CCA. Results from this study were obtained for thermal characteristics estimated into two different interpolation spaces: (1) a 7 metrics space, and (2) an 8 metrics space. Cross‐validation technique has been performed and satisfactory results were obtained. Kriging thermal characteristics (magnitude and duration) into the 7 metric space for a 19°C threshold exceedance leads to best results with Relative Root Mean Square Error (RRMSE) ranging between 9.66 and 15.08%. The study shows that kriging in a multivariate space is a promising tool for water resources managers, especially in cases where risk mapping for lethal or sub‐lethal temperature thresholds may be required for a specific fish species. Copyright © 2010 John Wiley & Sons, Ltd.     

Arctic river temperature dynamics in a changing climate

Climate change in the Arctic is expected to have a major impact on stream ecosystems, affecting hydrological and thermal regimes. Although temperature is important to a range of in‐stream processes, previous Arctic stream temperature research is limited—focused on glacierised headwaters in summer—with limited attention to snowmelt streams and winter. This is the first high‐resolution study on stream temperature in north‐east Greenland (Zackenberg). Data were collected from five streams from September 2013 to September 2015 (24 months). During the winter, streams were largely frozen solid and water temperature variability low. Spring ice‐off date occurred simultaneously across all streams, but 11 days earlier in 2014 compared with 2015 due to thicker snow insulation. During summer, water temperature was highly variable and exhibited a strong relationship with meteorological variables, particularly incoming shortwave radiation and air temperature. Mean summer water temperature in these snowmelt streams was high compared with streams studied previously in Svalbard, yet was lower in Swedish Lapland, as was expected given latitude. With global warning, Arctic stream thermal variability may be less in summer and increased during the winter due to higher summer air temperature and elevated winter precipitation, and the spring and autumn ice‐on and ice‐off dates may extend the flowing water season—in turn affecting stream productivity and diversity.     

Thermal regime metrics and quantifying their uncertainty for North American streams

Understanding and characterizing thermal regimes is gaining popularity, but there has been little assessment of the sources and magnitudes of uncertainty among different thermal metrics. Understanding how the quantity of data influence estimates of metrics and the characterization of thermal regime is critical to resource management. We examine the influence of record length on the uncertainty of estimation for commonly used thermal metrics including mean annual maximum and minimum, timing of the annual maximum and minimum, mean annual temperature range, mean weekly maximum temperature, July maximum, minimum, and range. We selected 19 sites from U.S. Geological Survey hydrometric station network to represent stations with both small and large drainage areas across the ecoregions of the contiguous United States with at least 20 years of daily stream temperature data. We also selected 54 sites from Water Survey of Canada's hydrometric network with at least 7 years of sub‐daily data for the province of Ontario. Randomizing a progressively increasing set of years used to calculate estimates of each metric provided the percentile confidence bands that were compared with various thresholds of acceptable certainty. Bootstrap confidence bands quickly decreased in width with increasing record length and approached an acceptable level at an average of 12 years for daily data metrics. Metrics calculated using the sub‐daily data required approximately 3 years of data. The timing of annual minimum and maximum temperatures required the greatest amount of data to reduce bias to an acceptable level.     

Temporal Variations in Dissolved Oxygen Concentrations Observed in a Shallow Floodplain Aquifer

Dissolved oxygen (DO) concentrations play an important role in many groundwater biogeochemial processes, yet assessments of temporal variations are lacking. In this study, we examined daily DO concentrations using a continuously‐reading optical DO probe in a shallow floodplain setting in Iowa to (i) quantify fluctuations across two growing seasons; (ii) examine hydrologic controls on DO values; and (iii) model daily DO concentrations using easily measured variables. DO concentrations exhibited both rapid and long‐term changes in concentrations over time, rapidly increasing and decreasing more than 1 mg/l in response to precipitation recharge and stream stage increase over the span of several hours and days. On 40% of the monitoring days in this study, DO concentrations increased, on average, 0.2–0.4 mg/l from one day to the next. DO concentrations decreased approximately 5–6 mg/l from spring through late summer and fall, likely owing to microbial and root respiration. Daily DO concentrations were successfully modelled using a combination of hydrologic (groundwater level and river stage) and temperature variables (r² > 0.7). Improved understanding of temporal controls on groundwater DO patterns is needed to help clarify the dynamics of many biogeochemical processes. Copyright © 2014 John Wiley & Sons, Ltd.     

Modeling the Linkage Between River Water Quality and Landscape Metrics in the Chugoku District of Japan

The present study was conducted using secondary database, remote sensing, geographical information system (GIS) and multivariate analysis tools in order to develop Multiple Linear Regression (MLR) models that could be able to predict level of water quality variables using compositional and spatial attributes of land cover in the river basins. The study encompasses 21 river basins with 32 000 Km² area, located in the Chugoku district in West Japan. Biological Oxygen Demand (BOD), pH, Dissolved Oxygen (DO), Suspended Solid (SS), Total Nitrogen (TN), and Total Phosphorus (TP) were considered as water quality variables of the stream. Satellite data was used to generate the land cover map of the study area. MLR models were developed using the compositional (%) and spatial attributes (landscape metrics) of the land cover at watershed and class levels for representing the land cover-stream water quality linkage. The results of the MLR modeling using the land cover data at the class level revealed that 92%, 74% and 62% of the total variations in concentration of DO, pH and TP were explained by changes in the measure of the spatial attributes of the land cover at the class level in the study area. These models can help local and regional land managers to understand the relationships between the compositional attribute (%) and the spatial features of the land cover and river water quality and would be applied in formulating plan for watershed-level management.     

Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine,Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station

Forecasting stream flow is a very importance issue in water resources planning and management. The ability of three soft computing methods, least square support vector machine (LSSVM), fuzzy genetic algorithm (FGA) and M5 model tree (M5T), in forecasting daily and monthly stream flows of poorly gauged mountainous watershed using nearby hydro-meteorological data is investigated in the current study. In the first application, monthly stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. LSSVM provides slightly better forecasts than the FGA and M5T models. Stream flow and temperature inputs generally give better forecasts compared to other inputs. In the second application, daily stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. Better results are obtained from the models comprising only stream flow inputs. In general, a better accuracy is obtained from LSSVM models in relative to the FGA and M5T. The results indicate that the monthly and daily stream flows of Hunza can be accurately forecasted by using only nearby climatic data. In the third application, daily stream flows of Hunza river are forecasted using local stream flow and climatic data and the models’ accuracy is slightly increased in relative to the previous applications. LSSVM generally performs superior to the FGA and M5T in forecasting daily stream flow of Hunza river using local stream flow and climatic inputs.     

The hydroclimate niche: A tool for predicting and managing riparian plant community responses to streamflow seasonality

Habitat suitability is a consequence of interacting environmental factors. In riparian ecosystems, suitable plant habitat is influenced by interactions between stream hydrology and climate, hereafter referred to as “hydroclimate”. We tested the hypothesis that hydroclimate variables would improve the fit of ecological niche models for a suite of riparian species using occurrence data from the western United States. We focus on the climate conditions (temperature, precipitation and vapor pressure deficit) during the months of lowest and highest streamflow as integrative hydroclimate metrics of resource and stress levels. We found that the inclusion of hydroclimate variables improved model fit for all species in the western USA dataset. We then tested the utility of the improved habitat suitability models by projecting them onto a regulated segment of the Colorado River to assess potential impacts of streamflow seasonality on vegetation metrics of management concern. Species frequency derived from independent survey data in the Colorado River segment was significantly higher for species with predicted suitable habitat than for species without predicted suitable habitat. Under different simulated hydrographs for the Colorado River, overall species richness was predicted to be greatest with peak streamflows during summer, and native-to-non-native species ratios were predicted to be greatest with lowest streamflows in winter. Summer high flows were particularly associated with higher predicted habitat suitability for species that have increased in cover over recent decades (e.g., Pluchea sericea, Baccharis species). We conclude that hydroclimate covariates can be useful tools for predicting how riparian vegetation communities respond to changes in the seasonal timing of low and high streamflows.     

PAIRED STREAM–AIR TEMPERATURE MEASUREMENTS REVEAL FINE‐SCALE THERMAL HETEROGENEITY WITHIN HEADWATER BROOK TROUT STREAM NETWORKS

Previous studies of climate change impacts on stream fish distributions commonly project the potential patterns of habitat loss and fragmentation due to elevated stream temperatures at a broad spatial scale (e.g. across regions or an entire species range). However, these studies may overlook potential heterogeneity in climate change vulnerability within local stream networks. We examined fine‐scale stream temperature patterns in two headwater brook trout Salvelinus fontinalis stream networks (7.7 and 4.4 km) in Connecticut, USA, by placing a combined total of 36 pairs of stream and air temperature loggers that were approximately 300 m apart from each other. Data were collected hourly from March to October 2010. The summer of 2010 was hot (the second hottest on record) and had well below average precipitation, but stream temperature was comparable with those of previous 2 years because streamflow was dominated by groundwater during base‐flow conditions. Nonlinear regression models revealed stream temperature variation within local stream networks, particularly during warmest hours of the day (i.e. late afternoon to evening) during summer. Thermal variability was primarily observed between stream segments, versus within a stream segment (i.e. from confluence to confluence). Several cold tributaries were identified in which stream temperature was much less responsive to air temperature. Our findings suggested that regional models of stream temperature would not fully capture thermal variation at the local scale and may misrepresent thermal resilience of stream networks. Groundwater appeared to play a major role in creating the fine‐scale spatial thermal variation, and characterizing this thermal variation is needed for assessing climate change impacts on headwater species accurately. Copyright © 2013 John Wiley & Sons, Ltd.     

Applying spatial hydraulic principles to quantify stream habitat

Flow complexity plays an important role in stream ecology. Yet, a paucity of research exists with regard to quantifying flow complexity and relating it to the habitats that aquatic organisms utilize. Here we provide a generalized example of how two‐dimensional (2‐D) numerical hydraulic models and spatial hydraulic metrics can be used to simulate and quantify biologically important flow patterns within streams. A detailed topographic survey, incorporating meso‐scale topographic features (e.g. exposed boulders and bedrock outcrops) is performed for a small urbanized stream. The 2‐D hydraulic model RMA2 is then used to model the flow conditions within the stream reach. Model results demonstrate that the meso‐scale topographic features create highly complex flow patterns of potential biological importance. Recently developed spatial hydraulic metrics, based on hydraulic engineering principles (vorticity, circulation and kinetic energy gradients), are then used to quantify the various types of flow complexity found within the stream reach. In particular, spatial hydraulic metrics are used to quantify the stream reach's overall flow complexity and the flow complexity surrounding three chub mounds. A method for uniquely characterizing circulation zones is then developed and applied to five circulation zones within the study reach. The principles used in performing this study's topographic survey, spatial explicit hydraulic modelling and spatial hydraulic analyses, form a general framework for quantifying flow complexity in any stream. The ways in which using hydraulic models and spatial hydraulic metrics can help establish better habitat suitability criteria and design best management practices for use in stream and catchment area restoration projects is discussed. Copyright © 2005 John Wiley & Sons, Ltd.     

A Comparison of Connectivity Metrics on Watersheds and Implications for Water Management

Barriers within streams can affect riverine species' ability to access habitats and may reduce their population viability. Connectivity metrics attempt to quantify the impacts of barriers; however, little is known about their functioning when applied to dendritic habitats such as watersheds. Several graph‐theoretic connectivity metrics were calculated on rivers originating in the Luquillo Mountains of Puerto Rico. These metrics were classified into two primary groups: metrics that count weighted paths through the stream network and metrics that predict the flow of organisms through a stream reach. Representative metrics from each of these categories were suggested to model the effects of dams and water intakes, respectively. Copyright © 2014 John Wiley & Sons, Ltd.     

Quantifying uncertainty in estimation of hydrologic metrics for ecohydrological studies

Hydrologic metrics have been used extensively in ecology and hydrology to summarize the characteristics of riverine flow regimes at various temporal scales but there has been limited evaluation of the sources and magnitude of uncertainty involved in their computation. Variation in bias, precision and overall accuracy of these metrics influences the ability to correctly describe flow regimes, detect meaningful differences in hydrologic characteristics through time and space, and define flow‐ecological response relationships. Here, we examine the effects of two primary factors—discharge record length and time period of record—on uncertainty in the estimation of 120 separate hydrologic metrics commonly used by researchers to describe ecologically relevant components of the hydrologic regime. Metric bias rapidly decreased and precision and overall accuracy markedly increased with increasing record length, but tended to stabilize >15 years and did not change substantially >30 years. We found a strong positive relationship between the degree of overlap of discharge record and similarity in hydrologic metrics when based on 15‐ and 30‐year discharge periods calculated within a 36‐year temporal window (1965–2000), although hydrologic metrics calculated for a given stream gauge tended to vary only within a restricted range through time. Our study provides critical guidance for selecting an appropriate record length and temporal period of record given a degree of metric bias and precision deemed acceptable by a researcher. We conclude that: (1) estimation of hydrologic metrics based on at least 15 years of discharge record is suitable for use in hydrologic analyses that aim to detect important spatial variation in hydrologic characteristics; (2) metric estimation should be based on overlapping discharge records contained within a discrete temporal window (ideally >50% overlap among records); and (3) metric uncertainty varies greatly and should be accounted for in future analyses. Copyright © 2009 John Wiley & Sons, Ltd.     

LINKING RIVER MANAGEMENT TO SPECIES CONSERVATION USING DYNAMIC LANDSCAPE‐SCALE MODELS

Efforts to conserve stream and river biota could benefit from tools that allow managers to evaluate landscape‐scale changes in species distributions in response to water management decisions. We present a framework and methods for integrating hydrology, geographic context and metapopulation processes to simulate effects of changes in streamflow on fish occupancy dynamics across a landscape of interconnected stream segments. We illustrate this approach using a 482 km² catchment in the southeastern US supporting 50 or more stream fish species. A spatially distributed, deterministic and physically based hydrologic model is used to simulate daily streamflow for sub‐basins composing the catchment. We use geographic data to characterize stream segments with respect to channel size, confinement, position and connectedness within the stream network. Simulated streamflow dynamics are then applied to model fish metapopulation dynamics in stream segments, using hypothesized effects of streamflow magnitude and variability on population processes, conditioned by channel characteristics. The resulting time series simulate spatially explicit, annual changes in species occurrences or assemblage metrics (e.g. species richness) across the catchment as outcomes of management scenarios. Sensitivity analyses using alternative, plausible links between streamflow components and metapopulation processes, or allowing for alternative modes of fish dispersal, demonstrate large effects of ecological uncertainty on model outcomes and highlight needed research and monitoring. Nonetheless, with uncertainties explicitly acknowledged, dynamic, landscape‐scale simulations may prove useful for quantitatively comparing river management alternatives with respect to species conservation. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

A Statistical Model for Managing Water Temperature in Streams with Anthropogenic Influences

Streams in the Pacific Northwest (Oregon, Washington, British Columbia) face rising summer temperatures and increasing anthropogenic influence, with consequences for fish populations. Guidance is needed in small managed watersheds for setting reservoir release rates or for the restriction of water extractions to meet the needs of fish and aquatic ecosystems. Existing environmental flow methods focus on discharge rates and do not typically consider water temperatures, and detailed thermal models are too complex for widespread implementation. We used multiple logistic regression to develop statistical models for estimating the probability of exceeding a salmonid stream temperature threshold of 22 °C as a function of discharge and maximum daily air temperatures. Data required are air temperature, stream temperature and stream discharge over a minimum of one summer. The models are used to make minimum discharge recommendations under varying forecast weather conditions. The method was applied to nine streams in the Pacific Northwest. Minimum recommended discharge generally ranged from 23% to 86% of mean annual discharge and was higher than observed low flows in most streams. Comparison of the new method to existing methods for Fortune Creek in British Columbia indicated that total season discharge volumes could be reduced while meeting thermal requirements. For other streams, it was evident that high water temperatures cannot be managed by increasing discharge, as the discharge required would be greater than natural discharge and higher than achievable by management. The statistical method described in this paper allows for a risk‐based approach to discharge management for fish habitat needs.     

Machine-learning modeling of hourly potential thermal refuge area: A case study from the Sainte-Marguerite River (Quebec,Canada)

To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub-daily time-step in two tributary confluences of the Sainte-Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon (Salmo salar), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine-learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree-based and kernel-based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold-water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data-driven techniques in order to understand stream temperature heterogeneity at tributary confluences.     

Identifying Temperature Thresholds Associated with Fish Community Changes in British Columbia,Canada, to Support Identification of Temperature Sensitive Streams

We collected fish samples and measured physical habitat characteristics, including summer stream temperatures, at 156 sites in 50 tributary streams in two sampling areas (Upper Fraser and Thompson Rivers) in British Columbia, Canada. Additional watershed characteristics were derived from GIS coverages of watershed, hydrological and climatic variables. Maximum weekly average temperature (MWAT), computed as an index of summer thermal regime, ranged from 10 to 23 °C. High values of MWAT were associated with large, warm, low relief watersheds with a high lake influence. Measures of community similarity suggested that the fish community changed most rapidly through a lower transition zone at an MWAT of about 12 °C and an upper transition zone at an MWAT of about 19 °C. These results were confirmed using existing fisheries inventory data combined with predictions of MWAT from a landscape‐scale regression model for the Thompson River watershed. For headwater sites in the Chilcotin River watershed (which drains into the middle Fraser River), the relative dominance of bull trout versus rainbow trout (based on inventory data) decreased with increasing predicted MWAT although the distinction was not as clear as for the Thompson River sites. The fish communities in these watersheds can be characterized in terms of very cold water (bull trout and some cold water species), cold water (salmonids and sculpins) and cool water (minnows and some cold water salmonids). The two transition zones (ca 12 and 19 °C) can be used to identify thresholds where small changes in stream temperature can be expected to lead to large changes in fish communities. Such clear, quantifiable thresholds are critical components of a management strategy designed to identify and protect vulnerable fish communities in streams where poor land use practices, alone or in combination with climatic change, can lead to changes in stream temperatures. Copyright © 2015 John Wiley & Sons, Ltd.     

黄河上游河道一维河道水温模型和经验公式法对比

为对比不同方法在黄河上游水温模拟中的适用性,分别用一维河道水温模型和经验公式法(四次多项式、三次多项式和幂函数)对兰州以上的黄河上游干支流共8个主要水文站日平均水温和月平均水温进行模拟,并采用平均绝对误差、均方根误差和纳什系数对模拟的精度进行检验。结果表明:四次多项式在黄河干流(除黄河沿站)日平均水温模拟中精度最高,幂函数在支流湟水的日平均水温模拟中精度最高;四次多项式适合黄河干流水文站的月平均水温模拟,幂函数更适合于支流湟水的月平均水温模拟;经验公式法在黄河干流日平均水温模拟中精度高于一维河道水温模型,更适合黄河上游干流段的日平均水温模拟。     

BENIGN USE OF SALT SLUGS ON AQUATIC MACROINVERTEBRATES: MEASURING DISCHARGE WITH SALT DURING AN AQUATIC ECOLOGY STUDY

Pulsed salt tracer injections (salt slugs) are widely used for measuring discharge in streams, particularly in small streams. However, salt slug usage in stream ecology studies is limited, possibly due to concerns that salt injections may affect biotic results. We used salt slug measurements concomitantly with macroinvertebrate sampling over the course of a summer field season and show that the realistic use of salt slugs to measure discharge at our sites was benign with respect to several common biotic community metrics. Salt slug discharge measurements may warrant more usage as a component of stream ecology studies. Copyright © 2011 John Wiley & Sons, Ltd.     

HOW PROXIMITY OF LAND USE AFFECTS STREAM FISH AND HABITAT

This study quantified the unique variation in stream fish and habitat and a land use disturbance index (LDI) at a variety of spatial scales: catchment, eight riparian polygons that varied in width and length (e.g. 50 m to all upstream reaches), upstream polygons of 1.6 and 3.2 km and the residual upland area of each site watershed not accounted for by each polygon. The analyses confirmed a hockey stick‐shaped relationship between the fish community and the LDI, with sensitive species only present below an LDI of 11. The largest variation for most metrics was explained by the largest polygons, suggesting that local riparian conditions were not as important predictors of stream condition. LDI in upland areas, where zero‐order streams occur, was also an important predictor of fish biomass and taxa richness. Contrary to expected, additive models with both catchment and riparian corridors provided minimal increases in predictive power, and no improvement in model performance occurred when data sets were stratified into sites below the LDI threshold. Finally, there was considerable covariation in the template and stressor predictor variables that made it difficult to quantify the unique variation in biological and physical responses accounted for by land use. That the 1600‐m proximal polygon provided the best predictor of the fish community and temperature is supportive of there being some proximal effects of land use. Overall, our findings suggest that stream management must consider processes that occur in the entire upstream catchment and the entire riparian corridor, including the headwaters for success. Copyright © 2012 John Wiley & Sons, Ltd.