Patterns of spatial autocorrelation in stream water chemistry

Characterizing Spatiotemporal Variability in Phosphorus Export across the United States through Bayesian Hierarchical Modeling

Phosphorus inputs from anthropogenic activities are subject to hydrologic (riverine) export, causing water quality problems in downstream lakes and coastal systems. Nutrient budgets have been developed to quantify the amount of nutrients imported to and exported from various watersheds. However, at large spatial scales, estimates of hydrologic phosphorus export are usually unavailable. This study develops a Bayesian hierarchical model to estimate annual phosphorus export across the contiguous United States, considering agricultural inputs, urban inputs, and geogenic sources under varying precipitation conditions. The Bayesian framework allows for a systematic updating of prior information on export rates using an extensive calibration data set of riverine loadings. Furthermore, the hierarchical approach allows for spatial variation in export rates across major watersheds and ecoregions. Applying the model, we map hotspots of phosphorus loss across the United States and characterize the primary factors driving these losses. Results emphasize the importance of precipitation in determining hydrologic export rates for various anthropogenic inputs, especially agriculture. Our findings also emphasize the importance of phosphorus from geogenic sources in overall river export.

High-resolution freshwater dissolved calcium and pH data layers for Canada and the United States

Freshwater ecosystems are biologically important habitats that provide many ecosystem services. Calcium concentration and pH are two key variables that are linked to multiple chemical processes in these environments, influence the biology of organisms from diverse taxa, and can be important factors affecting the distribution of native and non-native species. However, it can be challenging to obtain high-resolution data for these variables at regional and national scales. To address this data gap, water quality data for lakes and rivers in Canada and the continental USA were compiled and used to generate high-resolution (10 × 10 km) interpolated raster layers, after comparing multiple spatial interpolation approaches. This is the first time that such data have been made available at this scale and resolution, providing a valuable resource for research, including projects evaluating risks from environmental change, pollution, and invasive species. This will aid the development of conservation and management strategies for these vital habitats.

Predicting Fecal Indicator Bacteria Using Spatial Stream Network Models in A Mixed-Land-Use Suburban Watershed in New Jersey, USA

Good water quality safeguards public health and provides economic benefits through recreational opportunities for people in urban and suburban environments. However, expanding impervious areas and poorly managed sanitary infrastructures result in elevated concentrations of fecal indicator bacteria and waterborne pathogens in adjacent waterways and increased waterborne illness risk. Watershed characteristics, such as urban land, are often associated with impaired microbial water quality. Within the proximity of the New York-New Jersey-Pennsylvania metropolitan area, the Musconetcong River has been listed in the Clean Water Act's 303 (d) List of Water Quality-Limited Waters due to high concentrations of fecal indicator bacteria (FIB). In this study, we aimed to apply spatial stream network (SSN) models to associate key land use variables with E. coli as an FIB in the suburban mixed-land-use Musconetcong River watershed in the northwestern New Jersey. The SSN models explicitly account for spatial autocorrelation in stream networks and have been widely utilized to identify watershed attributes linked to deteriorated water quality indicators. Surface water samples were collected from the five mainstem and six tributary sites along the middle section of the Musconetcong River from May to October 2018. The log₁₀ geometric means of E. coli concentrations for all sampling dates and during storm events were derived as response variables for the SSN modeling, respectively. A nonspatial model based on an ordinary least square regression and two spatial models based on Euclidean and stream distance were constructed to incorporate four upstream watershed attributes as explanatory variables, including urban, pasture, forest, and wetland. The results indicate that upstream urban land was positively and significantly associated with the log₁₀ geometric mean concentrations of E. coli for all sampling cases and during storm events, respectively (p < 0.05). Prediction of E. coli concentrations by SSN models identified potential hot spots prone to water quality deterioration. The results emphasize that anthropogenic sources were the main threats to microbial water quality in the suburban Musconetcong River watershed. The SSN modeling approaches from this study can serve as a novel microbial water quality modeling framework for other watersheds to identify key land use stressors to guide future urban and suburban water quality restoration directions in the USA and beyond.

Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation

Multiple studies have been conducted to identify the complex and diverse relationships between stream ecosystems and land cover. However, these studies did not consider spatial dependency inherent from the systemic structure of streams. Therefore, the present study aimed to analyze the relationship between green/urban areas and topographical variables with biological indicators using regression tree analysis, which considered spatial autocorrelation at two different scales. The results of the principal components analysis suggested that the topographical variables exhibited the highest weights among all components, including biological indicators. Moran′s I values verified spatial autocorrelation of biological indicators; additionally, trophic diatom index, benthic macroinvertebrate index, and fish assessment index values were greater than 0.7. The results of spatial autocorrelation analysis suggested that a significant spatial dependency existed between environmental and biological indicators. Regression tree analysis was conducted for each indicator to compensate for the occurrence of autocorrelation; subsequently, the slope in riparian areas was the first criterion of differentiation for biological condition datasets in all regression trees. These findings suggest that considering spatial autocorrelation for statistical analyses of stream ecosystems, riparian proximity, and topographical characteristics for land use planning around the streams is essential to maintain the healthy biological conditions of streams.

Variation in stream network relationships and geospatial predictions of watershed conductivity

Secondary salinization, the increase of anthropogenically-derived salts in freshwaters, threatens freshwater biota and ecosystems, drinking water supplies, and infrastructure. The various anthropogenic sources of salts and their locations in a watershed may result in secondary salinization of river and stream networks through multiple inputs. We developed a watershed predictive assessment to investigate the degree to which topology, land-cover, and land-use covariates affect stream specific conductivity (SC), a measure of salinity. We used spatial stream network models to predict SC throughout an Appalachian stream network in a watershed affected by surface coal mining. During high-discharge conditions, 8 to 44% of stream km in the watershed exceeded the SC benchmark of 300 μS/cm, which is meant to be protective of aquatic life in the Central Appalachian ecoregion. During low-discharge conditions, 96 to 100% of stream km exceeded the benchmark. The 2 different discharge conditions altered the spatial dependency of SC among the stream monitoring sites. During most low discharges, SC was a function of upstream-to-downstream network distances, or flow-connected distances, among the sites. Flow-connected distances are indicative of upstream dependencies affecting stream SC. During high discharge, SC was related to both flow-connected distances and flow-unconnected distances (i.e., distances between sites on different branches of the network). Flow-unconnected distances are indicative of processes on adjacent branches and their catchments affecting stream SC. With sites distributed from headwaters to the watershed outlet, the extent of impacts from secondary salinization could be better spatially predicted and assessed with spatial stream network models than with models assuming spatial independence. Importantly, the assessment also recognized the multi-scale spatial relationships that can occur between the landscape and stream network.

Modelling spatial and temporal variability of water quality from different monitoring stations using mixed effects model theory

Polder-type agricultural catchments within river deltas are specific land formations which management is highly demanding from several aspects. The close contact with the coastal sea may additionally affect the quality of adjacent marine environment. This study uses the case of the Lower Neretva Valley (LNV) to test the efficiency of applying Linear Mixed Effect (LME) theory in modelling spatial and temporal variations of surface and groundwater quality within a polder-type agricultural catchment. The methodology uses linear regressive techniques while taking into account spatial and temporal autocorrelation of residuals. The objective was to assess and model the spatial and temporal variability of the quality of surface- and ground-waters, in order to predict the impact of natural processes and human activities. A dataset of physicochemical properties of surface and groundwater quality of the LNV, recorded monthly in the period 2009-2017, was used to model the spatial and temporal variations of water salinity and nitrate concentrations. The network of water quality monitoring sites covers four polders on five thousand hectares of agricultural land, including the following types of water bodies: river streams, lateral canals, pumping stations, drainage canals and groundwater. The method of data analysis, based on LME theory with correlated spatial and temporal residuals, takes also into account the heteroscedasticity of the variance associated with each type of water quality monitoring station. The two Linear Mixed Effects models proposed for the prediction of electrical conductivity and nitrate concentration in the surface waters and groundwater, proved to be efficient at adequately reproducing the heterogeneity and complexity of the study area. However, the prediction of nitrate concentration in the water was not equally satisfactory of the one of electrical conductivity due to the large variation in nutrient concentrations. To improve spatial prediction, the density of monitoring network should be increased.

Drivers and spatial structure of abiotic and biotic properties of lakes, wetlands, and streams at the national scale

Broad-scale studies have improved our ability to make predictions about how freshwater biotic and abiotic properties will respond to changes in climate and land use intensification. Further, fine-scaled studies of lakes, wetlands, or streams have documented the important role of hydrologic connections for understanding many freshwater biotic and abiotic processes. However, lakes, wetlands, and streams are typically studied in isolation of one another at both fine and broad scales. Therefore, it is not known whether these three freshwater types (lakes, wetlands, and streams) respond similarly to ecosystem and watershed drivers nor how they may respond to future global stresses. In this study, we asked, do lake, wetland, and stream biotic and abiotic properties respond to similar ecosystem and watershed drivers and have similar spatial structure at the national scale? We answered this question with three U.S. conterminous data sets of freshwater ecosystems. We used random forest (RF) analysis to quantify the multi-scaled drivers related to variation in nutrients and biota in lakes, wetlands, and streams simultaneously; we used semivariogram analysis to quantify the spatial structure of biotic and abiotic properties and to infer possible mechanisms controlling the ecosystem properties of these freshwater types. We found that abiotic properties responded to similar drivers, had large ranges of spatial autocorrelation, and exhibited multi-scale spatial structure, regardless of freshwater type. However, the dominant drivers of variation in biotic properties depended on freshwater type and had smaller ranges of spatial autocorrelation. Our study is the first to document that drivers and spatial structure differ more between biotic and abiotic variables than across freshwater types, suggesting that some properties of freshwater ecosystems may respond similarly to future global changes.

Microplastic ingestion by riverine macroinvertebrates

Although microplastics are a recognised pollutant in marine environments, less attention has been directed towards freshwater ecosystems despite their greater proximity to possible plastic sources. Here, we quantify the presence of microplastic particles (MPs) in river organisms upstream and downstream of five UK Wastewater Treatment Works (WwTWs). MPs were identified in approximately 50% of macroinvertebrate samples collected (Baetidae, Heptageniidae and Hydropsychidae) at concentrations up to 0.14 MP mg tissue^-1 and they occurred at all sites. MP abundance was associated with macroinvertebrate biomass and taxonomic family, but MPs occurred independently of feeding guild and biological traits such as habitat affinity and ecological niche. There was no increase in plastic ingestion downstream of WwTW discharges averaged across sites, but MP abundance in macroinvertebrates marginally increased where effluent discharges contributed more to total runoff and declined with increasing river discharge. The ubiquity of microplastics within macroinvertebrates in this case study reveals a potential risk from MPs entering riverine food webs through at least two pathways, involving detritivory and filter-feeding, and we recommend closer attention to freshwater ecosystems in future research.

Effects of connectivity and watercourse distance on temporal coherence patterns in a tropical reservoir

Temporal coherence exists when environmental variables measured at different spatial locations vary synchronously over time. This is an important property to be analyzed because levels of coherence may indicate the role of regional and local processes in determining population and ecosystem dynamics. Also, studies on temporal coherence may guide the optimal allocation of sampling effort. We analyzed a dataset from a monitoring program undertaken at a tropical reservoir (Peixe Angical Reservoir, State of Tocantins, Brazil) to test three predictions. First, coherence should be a common pattern in the reservoir considering that sampling sites were distributed in a single water body and over a small spatial extent. Second, coherence was expected to decline with increasing watercourse distance and to increase with hydrological connectivity. Third, abiotic variables should exhibit higher coherence than biological variables. Twenty limnological variables were monitored at 14 sites and for 31 months. We found significant levels of coherence for all variables, supporting our first prediction. Watercourse distances, hydrological connectivity, or both were significant predictors of coherence for 17 environmental variables. In all these cases, the signs of the coefficients were in the direction predicted. Interestingly, for some environmental variables (color, turbidity, alkalinity, and total phosphorus), hydrological connectivity was even more important in predicting coherence than watercourse distance. The view that abiotic variables should exhibit higher coherence than biological variables was supported. Our analyses revealed that precipitation was an important factor inducing coherence of a key set of environmental variables, highlighting the role of regional processes in ecosystem dynamics.

Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Passive detection of Pb in water using rock phosphate agarose beads

In this study, passive detectors for Pb were prepared by immobilizing powdered rock phosphate in agarose beads. Rock phosphate has been used to treat Pb-contaminated waters and soil by fixing the metal as an insoluble pyromorphite mineral. Under lab conditions, Pb was rapidly adsorbed from aqueous solution by the beads over time, consistent with the acidic dissolution of rock phosphate, the precipitation of pyromorphite within the pore space of the agarose gel matrix, and surface exchange reactions. Net accumulation of Pb occurred when beads were exposed to simulated periodic releases of Pb over time. Under field conditions, beads in mesh bags were effective at detecting dissolved Pb being transported as surface runoff from a site highly contaminated with Pb. Rates of Pb accumulation in beads under field conditions appeared to be correlated with the frequency of storm events and total rainfall. The rock phosphate agarose bead approach could be an inexpensive way to carry out source-tracking of Pb pollution, to verify the successful remediation of sites with Pb-contaminated soil, and to routinely monitor public water systems for potential Pb contamination.

IMPROVING PREDICTIVE MODELS OF IN-STREAM PHOSPHORUS CONCENTRATION BASED ON NATIONALLY-AVAILABLE SPATIAL DATA COVERAGES

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1290 km² watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest that SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.

Validation and comparison of geostatistical and spline models for spatial stream networks

Scientists need appropriate spatial-statistical models to account for the unique features of stream network data. Recent advances provide a growing methodological toolbox for modelling these data, but general-purpose statistical software has only recently emerged, with little information about when to use different approaches. We implemented a simulation study to evaluate and validate geostatistical models that use continuous distances, and penalised spline models that use a finite discrete approximation for stream networks. Data were simulated from the geostatistical model, with performance measured by empirical prediction and fixed effects estimation. We found that both models were comparable in terms of squared error, with a slight advantage for the geostatistical models. Generally, both methods were unbiased and had valid confidence intervals. The most marked differences were found for confidence intervals on fixed-effect parameter estimates, where, for small sample sizes, the spline models underestimated variance. However, the penalised spline models were always more computationally efficient, which may be important for real-time prediction and estimation. Thus, decisions about which method to use must be influenced by the size and format of the data set, in addition to the characteristics of the environmental process and the modelling goals. ©2015 The Authors. Environmetrics published by John Wiley & Sons, Ltd.

Dispersal ability determines the role of environmental, spatial and temporal drivers of metacommunity structure

Recently, community ecologists are focusing on the relative importance of local environmental factors and proxies to dispersal limitation to explain spatial variation in community structure. Albeit less explored, temporal processes may also be important in explaining species composition variation in metacommunities occupying dynamic systems. We aimed to evaluate the relative role of environmental, spatial and temporal variables on the metacommunity structure of different organism groups in the Upper Paraná River floodplain (Brazil). We used data on macrophytes, fish, benthic macroinvertebrates, zooplankton, periphyton, and phytoplankton collected in up to 36 habitats during a total of eight sampling campaigns over two years. According to variation partitioning results, the importance of predictors varied among biological groups. Spatial predictors were particularly important for organisms with comparatively lower dispersal ability, such as aquatic macrophytes and fish. On the other hand, environmental predictors were particularly important for organisms with high dispersal ability, such as microalgae, indicating the importance of species sorting processes in shaping the community structure of these organisms. The importance of watercourse distances increased when spatial variables were the main predictors of metacommunity structure. The contribution of temporal predictors was low. Our results emphasize the strength of a trait-based analysis and of better defining spatial variables. More importantly, they supported the view that “all-or- nothing” interpretations on the mechanisms structuring metacommunities are rather the exception than the rule.

Spatial interpolation of fine particulate matter concentrations using the shortest wind-field path distance

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health.

Network analysis reveals multiscale controls on streamwater chemistry

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.

Flexible regression models over river networks

Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of confluences. Suitable models for this type of data have been constructed based on covariance functions. The aim of the paper is to place the focus on underlying spatial trends by adopting a regression formulation and using methods which allow smooth but flexible patterns. Specifically, kernel methods and penalized splines are investigated, with the latter proving more suitable from both computational and modelling perspectives. In addition to their use in a purely spatial setting, penalized splines also offer a convenient route to the construction of spatiotemporal models, where data are available over time as well as over space. Models which include main effects and spatiotemporal interactions, as well as seasonal terms and interactions, are constructed for data on nitrate pollution in the River Tweed. The results give valuable insight into the changes in water quality in both space and time.

Identifying the spatial scale of land use that most strongly influences overall river ecosystem health score

Catchment and riparian degradation has resulted in declining ecosystem health of streams worldwide. With restoration a priority in many regions, there is an increasing interest in the scale at which land use influences stream ecosystem health. Our goal was to use a substantial data set collected as part of a monitoring program (the Southeast Queensland, Australia, Ecological Health Monitoring Program data set, collected at 116 sites over six years) to identify the spatial scale of land use, or the combination of spatial scales, that most strongly influences overall ecosystem health. In addition, we aimed to determine whether the most influential scale differed for different aspects of ecosystem health. We used linear-mixed models and a Bayesian model-averaging approach to generate models for the overall aggregated ecosystem health score and for each of the five component indicators (fish, macroinvertebrates, water quality, nutrients, and ecosystem processes) that make up the score. Dense forest close to the survey site, mid-dense forest in the hydrologically active near-stream areas of the catchment, urbanization in the riparian buffer, and tree cover at the reach scale were all significant in explaining ecosystem health, suggesting an overriding influence of forest cover, particularly close to the stream. Season and antecedent rainfall were also important explanatory variables, with some land-use variables showing significant seasonal interactions. There were also differential influences of land use for each of the component indicators. Our approach is useful given that restoring general ecosystem health is the focus of many stream restoration projects; it allowed us to predict the scale and catchment position of restoration that would result in the greatest improvement of ecosystem health in the regions streams and rivers. The models we generated suggested that good ecosystem health can be maintained in catchments where 80% of hydrologically active areas in close proximity to the stream have mid-dense forest cover and moderate health can be obtained with 60% cover.

Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods

This study attempts to determine the scale-dependent hierarchical spatial variation and longitudinal distributions of Sicyopterus japonicus year round. The distribution of S. japonicus in the Datuan Stream in northern Taiwan was surveyed during the fall and winter 2007, as well as the spring and summer of 2008. The spatial structure of S. japonicus density was modeled using geostatistics. The longitudinal distributions of S. japonicus density were then estimated using kriging and hydrology distance with nested variogram models. Variography results indicate that nested variogram models could reflect the hierarchical structure in the spatial variation of seasonal S. japonicus density, with the small, median, and large ranges representing three nested scales. Models for the four seasons were consistent in that they shared the same shape of variogram models with various ranges and sill values. This model shape consistency implies stationary spatial correlations in the longitudinal fish distribution across the four seasons. The Kriging geostatistical method based on the multiple scales nested variogram models also provided robust estimates of S. japonicus densities at unsampled sections. We conclude that S. japonicus densities exhibit hierarchical patterns and variation in the four seasons along the study stream. Geostatistical methods with a nested variograms and hydrological distance are a highly effective means of delineating the hierarchical structure in longitudinal patterns of S. japonicus density in each season, providing estimates of the S. japonicus density for hierarchically structured spatial distributions and expanding knowledge of S. japonicus beyond the limits imposed by spatial and temporal scales.

Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network

Mountain streams provide important habitats for many species, but their faunas are especially vulnerable to climate change because of ectothermic physiologies and movements that are constrained to linear networks that are easily fragmented. Effectively conserving biodiversity in these systems requires accurate downscaling of climatic trends to local habitat conditions, but downscaling is difficult in complex terrains given diverse microclimates and mediation of stream heat budgets by local conditions. We compiled a stream temperature database (n = 780) for a 2500-km river network in central Idaho to assess possible trends in summer temperatures and thermal habitat for two native salmonid species from 1993 to 2006. New spatial statistical models that account for network topology were parameterized with these data and explained 93% and 86% of the variation in mean stream temperatures and maximas, respectively. During our study period, basin average mean stream temperatures increased by 0.38 degrees C (0.27 degrees C/decade), and maximas increased by 0.48 degrees C (0.34 degrees C/decade), primarily due to long-term (30-50 year) trends in air temperatures and stream flows. Radiation increases from wildfires accounted for 9% of basin-scale temperature increases, despite burning 14% of the basin. Within wildfire perimeters, however, stream temperature increases were 2-3 times greater than basin averages, and radiation gains accounted for 50% of warming. Thermal habitat for rainbow trout (Oncorhynchus mykiss) was minimally affected by temperature increases, except for small shifts towards higher elevations. Bull trout (Salvelinus confluentus), in contrast, were estimated to have lost 11-20% (8-16%/decade) of the headwater stream lengths that were cold enough for spawning and early juvenile rearing, with the largest losses occurring in the coldest habitats. Our results suggest that a warming climate has begun to affect thermal conditions in streams and that impacts to biota will be specific to both species and context. Where species are at risk, conservation actions should be guided based on considerations of restoration opportunity and future climatic effects. To refine predictions based on thermal effects, more work is needed to understand mechanisms associated with biological responses, climate effects on other habitat features, and habitat configurations that confer population resilience.

A mixed-model moving-average approach to geostatistical modeling in stream networks

Spatial autocorrelation is an intrinsic characteristic in freshwater stream environments where nested watersheds and flow connectivity may produce patterns that are not captured by Euclidean distance. Yet, many common autocovariance functions used in geostatistical models are statistically invalid when Euclidean distance is replaced with hydrologic distance. We use simple worked examples to illustrate a recently developed moving-average approach used to construct two types of valid autocovariance models that are based on hydrologic distances. These models were designed to represent the spatial configuration, longitudinal connectivity, discharge, and flow direction in a stream network. They also exhibit a different covariance structure than Euclidean models and represent a true difference in the way that spatial relationships are represented. Nevertheless, the multi-scale complexities of stream environments may not be fully captured using a model based on one covariance structure. We advocate using a variance component approach, which allows a mixture of autocovariance models (Euclidean and stream models) to be incorporated into a single geostatistical model. As an example, we fit and compare "mixed models," based on multiple covariance structures, for a biological indicator. The mixed model proves to be a flexible approach because many sources of information can be incorporated into a single model.