Strong Influence of Variable Treatment on the Performance of Numerically Defined Ecological Regions

Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale “sub-domains” defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.     

Effect of Classification Procedure on the Performance of Numerically Defined Ecological Regions

Ecological regionalizations define geographic regions exhibiting relative homogeneity in ecological (i.e., environmental and biotic) characteristics. Multivariate clustering methods have been used to define ecological regions based on subjectively chosen environmental variables. We developed and tested three procedures for defining ecological regions based on spatial modeling of a multivariate target pattern that is represented by compositional dissimilarities between locations (e.g., taxonomic dissimilarities). The procedures use a “training dataset” representing the target pattern and models this as a function of environmental variables. The model is then extrapolated to the entire domain of interest. Environmental data for our analysis were drawn from a 400 m grid covering all of Switzerland and consisted of 12 variables describing climate, topography and lithology. Our target patterns comprised land cover composition of each grid cell that was derived from interpretation of aerial photographs. For Regionalization 1 we used conventional cluster analysis of the environmental variables to define 60 hierarchically organized levels comprising from 5 to 300 regions. Regionalization 1 provided a base-case for comparison with the model-based regionalizations. Regionalization 2, 3 and 4 also comprised 60 hierarchically organized levels and were derived by modeling land cover composition for 4000 randomly selected “training” cells. Regionalization 2 was based on cluster analysis of environmental variables that were transformed based on a Generalized Dissimilarity Model (GDM). Regionalization 3 and 4 were defined by clustering the training cells based on their land cover composition followed by predictive modeling of the distribution of the land cover clusters using Classification and Regression Tree (CART) and Random Forest (RF) models. Independent test data (i.e. not used to train the models) were used to test the discrimination of land cover composition at all hierarchical levels of the regionalizations using the classification strength (CS) statistic. CS for all the model-based regionalizations was significantly higher than for Regionalization 1. Regionalization 3 and 4 performed significantly better than Regionalization 2 at finer hierarchical levels (many regions) and Regionalization 4 performed significantly better than Regionalization 3 for coarse levels of detail (few regions). Compositional modeling can significantly increase the performance of numerically defined ecological regionalizations. CART and RF-based models appear to produce stronger regionalizations because discriminating variables are able to change at each hierarchic level.     

Variability of Lotic Macroinvertebrate Assemblages and Stream Habitat Characteristics Across Hierarchical Landscape Classifications

Streams are naturally hierarchical systems, and their biota are affected by factors effective at regional to local scales. However, there have been only a few attempts to quantify variation in ecological attributes across multiple spatial scales. We examined the variation in several macroinvertebrate metrics and environmental variables at three hierarchical scales (ecoregions, drainage systems, streams) in boreal headwater streams. In nested analyses of variance, significant spatial variability was observed for most of the macroinvertebrate metrics and environmental variables examined. For most metrics, ecoregions explained more variation than did drainage systems. There was, however, much variation attributable to residuals, suggesting high among-stream variation in macroinvertebrate assemblage characteristics. Nonmetric multidimensional scaling (NMDS) and multiresponse permutation procedure (MRPP) showed that assemblage composition differed significantly among both drainage systems and ecoregions. The associated R-statistics were, however, very low, indicating wide variation among sites within the defined landscape classifications. Regional delineations explained most of the variation in stream water chemistry, ecoregions being clearly more influential than drainage systems. For physical habitat characteristics, by contrast, the among-stream component was the major source of variation. Distinct differences attributable to stream size were observed for several metrics, especially total number of taxa and abundance of algae-scraping invertebrates. Although ecoregions clearly account for a considerable amount of variation in macroinvertebrate assemblage characteristics, we suggest that a three-tiered classification system (stratification through ecoregion and habitat type, followed by assemblage prediction within these ecologically meaningful units) will be needed for effective bioassessment of boreal running waters.     

A comparison of different biogeographical classifications of Europe,Great Britain and Spain

Various biogeographical and bioclimatic classifications of a number of regions, countries and continents have been created to meet different objectives. A policy maker might ask the question 'why is there no single accepted classification and how do the different classifications compare with one another?' In order to answer these two questions three classifications created by different methods for Great Britain and two for Spain are compared using the Kappa statistic. All of the classifications were created from data on cellular grids with a set window size. Further non-statistical comparisons are made with other classifications. The biogeographic classifications studied in this paper produced three different types of zone: those that were always identified whatever the method; those that were broadly similar but where the boundaries differed; and those that were unique to a particular classification. These different types of zone are likely to exist for any comparison between classifications of a particular region. The extent of the geographic window from which data were obtained had a major effect on the classification of grid cells at the edges of the window. For example, the few grid cells in the south of England, with characteristics of continental Europe, are not detected if data from Great Britain alone are used for the classification. We conclude that the data window should always be larger than the area for which the classification is being made. The objective Kappa statistic, although useful, was not capable of discerning similarities and dissimilarities that appear obvious to the subjective human eye. Although the details of the classifications differed there were broad similarities between the classifications and these differences reflect important divisions along major environmental axes that have been inferred by earlier biogeographers. As the divisions are real there is a sound basis for their use in future land use or environmental policy.     

Development of an Ecologic Marine Classification in the New Zealand Region

We describe here the development of an ecosystem classification designed to underpin the conservation management of marine environments in the New Zealand region. The classification was defined using multivariate classification using explicit environmental layers chosen for their role in driving spatial variation in biologic patterns: depth, mean annual solar radiation, winter sea surface temperature, annual amplitude of sea surface temperature, spatial gradient of sea surface temperature, summer sea surface temperature anomaly, mean wave-induced orbital velocity at the seabed, tidal current velocity, and seabed slope. All variables were derived as gridded data layers at a resolution of 1 km. Variables were selected by assessing their degree of correlation with biologic distributions using separate data sets for demersal fish, benthic invertebrates, and chlorophyll-a. We developed a tuning procedure based on the Mantel test to refine the classification's discrimination of variation in biologic character. This was achieved by increasing the weighting of variables that play a dominant role and/or by transforming variables where this increased their correlation with biologic differences. We assessed the classification's ability to discriminate biologic variation using analysis of similarity. This indicated that the discrimination of biologic differences generally increased with increasing classification detail and varied for different taxonomic groups. Advantages of using a numeric approach compared with geographic-based (regionalisation) approaches include better representation of spatial patterns of variation and the ability to apply the classification at widely varying levels of detail. We expect this classification to provide a useful framework for a range of management applications, including providing frameworks for environmental monitoring and reporting and identifying representative areas for conservation.     

Hydrological classification of natural flow regimes to support environmental flow assessments in intensively regulated Mediterranean rivers, Segura River Basin (Spain)

Hydrological classification constitutes the first step of a new holistic framework for developing regional environmental flow criteria: the “Ecological Limits of Hydrologic Alteration (ELOHA)”. The aim of this study was to develop a classification for 390 stream sections of the Segura River Basin based on 73 hydrological indices that characterize their natural flow regimes. The hydrological indices were calculated with 25 years of natural monthly flows (1980/81–2005/06) derived from a rainfall-runoff model developed by the Spanish Ministry of Environment and Public Works. These indices included, at a monthly or annual basis, measures of duration of droughts and central tendency and dispersion of flow magnitude (average, low and high flow conditions). Principal Component Analysis (PCA) indicated high redundancy among most hydrological indices, as well as two gradients: flow magnitude for mainstream rivers and temporal variability for tributary streams. A classification with eight flow-regime classes was chosen as the most easily interpretable in the Segura River Basin, which was supported by ANOSIM analyses. These classes can be simplified in 4 broader groups, with different seasonal discharge pattern: large rivers, perennial stable streams, perennial seasonal streams and intermittent and ephemeral streams. They showed a high degree of spatial cohesion, following a gradient associated with climatic aridity from NW to SE, and were well defined in terms of the fundamental variables in Mediterranean streams: magnitude and temporal variability of flows. Therefore, this classification is a fundamental tool to support water management and planning in the Segura River Basin. Future research will allow us to study the flow alteration-ecological response relationship for each river type, and set the basis to design scientifically credible environmental flows following the ELOHA framework.     

A Quantitative Method for Delineating Regions: An Example for the Western Corn Belt Plains Ecoregion of the USA

/ A method was developed to systematically delineate boundaries forecological classification of regions. The process entailed the use ofsmall-scale digital data to quantify spatial concordance among environmentalattribute data sets. The data sets were grouped into spatially related themesusing cluster analysis and multidimensional scaling. Selected data sets werethen used either individually or collectively to divide the study area intosubregions that exhibited different environmental attributes. The method wasapplied to a previously defined ecological unit, the western Corn Belt of thecentral United States. The results showed that the portion of the study areawith intensive corn and soybean production was identifiable using each of thethree input data sets selected for partitioning (soil associations; AVHRRremote-sensing imagery; and a combined data set of landform, forest, andsoils data). The classification of other portions of the study area washighly dependent on the type and scale of the input data. The systematicmethodology used here offers advantages over other methods for identifyingecological regions in that the results from the systematic approach can bereproduced, the boundaries between ecological units can be revised based onnew or more accurate data, important ecological processes are explicitlychosen to delineate boundaries, and transition zones between regions can bequantified.KEY WORDS: Ecoregions; Spatial analysis; Corn Belt; Iowa; GIS;Regionalization     

A Framework for Analyzing Longitudinal and Temporal Variation in River Flow and Developing Flow-Ecology Relationships1

Larned, Scott T., David B. Arscott, Jochen Schmidt, and Jan C. Diettrich, 2010. A Framework for Analyzing Longitudinal and Temporal Variation in River Flow and Developing Flow-Ecology Relationships. Journal of the American Water Resources Association (JAWRA) 46(3):541-553. DOI: 10.1111/j.1752-1688.2010.00433.x Abstract: We propose a framework for analyzing longitudinal flow variation and exploring its ecological consequences in four steps: (1) generating longitudinally continuous flow estimates; (2) computing indices that describe site-specific and longitudinal flow variation, including intermittence; (3) quantifying and visualizing longitudinal dynamics; (4) developing quantitative relationships between hydrological indices and ecological variables (flow-ecology relationships). We give examples of each step, using data from a New Zealand river and an empirical longitudinal flow model, ELFMOD. ELFMOD uses spot-gauging data and flow or proxy variable time series to estimate flow magnitude and state (flowing or dry) at user-defined intervals along river sections. Analyses of flow-ecology relationships for the New Zealand river indicated that fish and benthic and hyporheic invertebrate communities responded strongly to variation in mean annual flow permanence, flow duration, dry duration, drying frequency, inter-flood duration, and distances to flowing reaches. To put longitudinal flow variation into a broader context and guide future research, we propose a conceptual model that combines elements of two contrasting perspectives: rivers as longitudinal continua, and rivers as patch mosaics. In this conceptual model, hydrologically complex rivers are composed of linear sequences of nested hydrological gradients, which are bordered by hydrogeomorphic discontinuities, and which collectively generate hydrological dynamics at river-section scales.     

Ecoregions and ecodistricts: Ecological regionalizations for the Netherlands' environmental policy

For communicating data on the state of the environment to policy makers, various integrative frameworks are used, including regional integration. For this kind of integration we have developed two related ecological regionalizations, ecoregions and ecodistricts, which are two levels in a series of classifications for hierarchically nested ecosystems at different spatial scale levels. We explain the compilation of the maps from existing geographical data, demonstrating the relatively holistic, a priori integrated approach. The resulting maps are submitted to discriminant analysis to test the consistancy of the use of mapping characteristics, using data on individual abiotic ecosystem components from a national database on a 1-km² grid. This reveals that the spatial patterns of soil, groundwater, and geomorphology correspond with the ecoregion and ecodistrict maps. Differences between the original maps and maps formed by automatically reclassifying 1-km² cells with these discriminant components are found to be few. These differences are discussed against the background of the principal dilemma between deductive, a priori integrated, and inductive, a posteriori, classification.     

WATERSHED CLASSIFICATION USING CANONICAL CORRESPONDENCE ANALYSIS AND CLUSTERING TECHNIQUES: A CAUTIONARY NOTE1

ABSTRACT: Watershed classification using multivariate techniques requires the incorporation of continuous datasets representing controlling environmental variables. Often, out of convenience and availability rather than importance to the structure of the system being modeled, the environmental data used originate from a variety of sources and scales. To demonstrate the importance of appropriate environmental data selection, classifications of six‐digit hydrologic units (1:24,000) across selected geographic areas within the Interior Columbia River Basin were produced. Canonical correspondence analysis was used to select and test environmental variables important in predicting Rosgen stream types and valley bottom classes. Then, hierarchical agglomerative clustering was used to group (classify) watersheds based on these variables. Statistically significant results were derived from the use of organized classification data with presumed predictive relationships to watershed properties, and a random distribution of environmental variables from the same datasets provided similar results. The results contained herein demonstrate that these analysis techniques do not necessarily select meaningful variables from a broad spectrum of data and that significant results are easily generated from randomly associated data. It is suggested that classifications produced using these multivariate techniques, especially when using multi‐scale data or data of unknown significance, are subject to invalid inferences and should be used with caution.     

Differences in bioregional classifications among four aquatic biotic groups: Implications for conservation reserve design and monitoring programs

Bioregional classifications are used extensively for conservation management and monitoring programs. This study used generalised dissimilarity modelling (GDM) to test the ability of different regional classifications of four groups of aquatic biota to be used as surrogates for each other. Classifications were derived for aquatic macrophytes, macroinvertebrates, freshwater fish and frogs using community-level modelling, or GDM, which relates the biotic assemblage structure with environmental variables. Six regions were defined for each biotic group for the State of New South Wales. Regional classifications differed markedly between the different biotic groups because the environmental drivers that were related to species turnover throughout the region differed among groups. Altitude and rainfall were the strongest drivers of species turnover among the groups. Results suggest that physiographic variables should be incorporated in reserve design and monitoring programs to explicitly address differences in classifications between similar biotic groups.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

A WATERSHED APPROACH TO ECOSYSTEM MONITORING IN DENALI NATIONAL PARK AND PRESERVE,ALASKA1

ABSTRACT: The National Park Service and the National Biological Service initiated research in Denali National Park and Preserve, a 2.4 million-hectare park in southcentral Alaska, to develop ecological monitoring protocols for national parks in the Arctic/Subarctic biogeographic area. We are focusing pilot studies on design questions, on scaling issues and regionalization, ecosystem structure and function, indicator selection and evaluation, and monitoring technologies. Rock Creek, a headwater stream near Denali headquarters, is the ecological scale for initial testing of a watershed ecosystem approach. Our conceptual model embraces principles of the hydrological cycle, hypotheses of global climate change, and biological interactions of organisms occupying intermediate, but poorly studied, positions in Alaskan food webs. The field approach includes hydrological and depositional considerations and a suite of integrated measures linking key aquatic and terrestrial biota, environmental variables, or defined ecological processes, in order to establish ecological conditions and detect, track, and understand mechanisms of environmental change. Our sampling activities include corresponding measures of physical, chemical, and biological attributes in four Rock Creek habitats believed characteristic of the greater system diversity of Denali. This paper gives examples of data sets, program integration and scaling, and research needs.     

Grouping Lakes for Water Quality Assessment and Monitoring: The Roles of Regionalization and Spatial Scale

Regionalization frameworks cluster geographic data to create contiguous regions of similar climate, geology and hydrology by delineating land into discrete regions, such as ecoregions or watersheds, often at several spatial scales. Although most regionalization schemes were not originally designed for aquatic ecosystem classification or management, they are often used for such purposes, with surprisingly few explicit tests of the relative ability of different regionalization frameworks to group lakes for water quality monitoring and assessment. We examined which of 11 different lake grouping schemes at two spatial scales best captures the maximum amount of variation in water quality among regions for total nutrients, water clarity, chlorophyll, overall trophic state, and alkalinity in 479 lakes in Michigan (USA). We conducted analyses on two data sets: one that included all lakes and one that included only minimally disturbed lakes. Using hierarchical linear models that partitioned total variance into within-region and among-region components, we found that ecological drainage units and 8-digit hydrologic units most consistently captured among-region heterogeneity at their respective spatial scales using all lakes (variation among lake groups = 3% to 50% and 12% to 52%, respectively). However, regionalization schemes capture less among-region variance for minimally disturbed lakes. Diagnostics of spatial autocorrelation provided insight into the relative performance of regionalization frameworks but also demonstrated that region size is only partly responsible for capturing variation among lakes. These results suggest that regionalization schemes can provide useful frameworks for lake water quality assessment and monitoring but that we must identify the appropriate spatial scale for the questions being asked, the type of management applied, and the metrics being assessed.     

Headwater Influences on Downstream Water Quality

We investigated the influence of riparian and whole watershed land use as a function of stream size on surface water chemistry and assessed regional variation in these relationships. Sixty-eight watersheds in four level III U.S. EPA ecoregions in eastern Kansas were selected as study sites. Riparian land cover and watershed land use were quantified for the entire watershed, and by Strahler order. Multiple regression analyses using riparian land cover classifications as independent variables explained among-site variation in water chemistry parameters, particularly total nitrogen (41%), nitrate (61%), and total phosphorus (63%) concentrations. Whole watershed land use explained slightly less variance, but riparian and whole watershed land use were so tightly correlated that it was difficult to separate their effects. Water chemistry parameters sampled in downstream reaches were most closely correlated with riparian land cover adjacent to the smallest (first-order) streams of watersheds or land use in the entire watershed, with riparian zones immediately upstream of sampling sites offering less explanatory power as stream size increased. Interestingly, headwater effects were evident even at times when these small streams were unlikely to be flowing. Relationships were similar among ecoregions, indicating that land use characteristics were most responsible for water quality variation among watersheds. These findings suggest that nonpoint pollution control strategies should consider the influence of small upland streams and protection of downstream riparian zones alone is not sufficient to protect water quality.     

环境化学进展动态

本文概述了近年来环境化学包括环境分析化学、环境污染化学、环境污染控制化学、生态化学等方面研究的一些新进展     

Biomonitors of stream quality in agricultural areas: Fish versus invertebrates

Although the utility of using either fish or benthic invertebrates as biomonitors of stream quality has been clearly shown, there is little comparative information on the usefulness of the groups in any particular situation. We compared fish to invertebrate assemblages in their ability to reflect habitat quality of sediment-impacted streams in agricultural regions of northeast Missouri, USA. Habitat quality was measured by a combination of substrate composition, riparian type, buffer strip width, and land use. Invertebrates were more sensitive to habitat differences when structural measurements, species diversity and ordination, were used. Incorporating ecological measurements, by using the Index of Biological Integrity, increased the information obtained from the fish assemblage. The differential response of the two groups was attributed to the more direct impact of sediments on invertebrate life requisites; the impact of sedimentation on fish is considered more indirect and complex, affecting feeding and reproductive mechanisms.The Unit is sponsored by the US Fish and Wildlife Service, the Missouri Department of Conservation and the University of Missouri.     

Long-Term Biological Monitoring of an Impaired Stream: Synthesis and Environmental Management Implications

The long-term ecological recovery of an impaired stream in response to an industrial facility’s pollution abatement actions and the implications of the biological monitoring effort to environmental management is the subject of this special issue of Environmental Management. This final article focuses on the synthesis of the biological monitoring program’s components and methods, the efficacy of various biological monitoring techniques to environmental management, and the lessons learned from the program that might be applicable to the design and application of other programs. The focus of the 25-year program has been on East Fork Poplar Creek, an ecologically impaired stream in Oak Ridge, Tennessee with varied and complex stressors from a Department of Energy facility in its headwaters. Major components of the long-term program included testing and monitoring of invertebrate and fish toxicity, bioindicators of fish health, fish contaminant accumulation, and instream communities (including periphyton, benthic macroinvertebrate, and fish). Key parallel components of the program include water chemistry sampling and data management. Multiple lines of evidence suggested positive ecological responses during three major pollution abatement periods. Based on this case study and the related literature, effective environmental management of impaired streams starts with program design that is consistent across space and time, but also adaptable to changing conditions. The biological monitoring approaches used for the program provided a strong basis for assessments of recovery from remedial actions, and the likely causes of impairment. This case study provides a unique application of multidisciplinary and quantitative techniques to address multiple and complex regulatory and programmatic goals, environmental stressors, and remedial actions.     

Hydrogeomorphic Reference Condition and Its Relationship with Macroinvertebrate Assemblages in Southeastern U.S. Sand Hills Streams

Defining stream reference conditions is integral to providing benchmarks to ecological perturbation. We quantified channel geometry, hydrologic and environmental variables, and macroinvertebrates in 62 low‐gradient, SE United States (U.S.) Sand Hills (Level IV ecoregion) sand‐bed streams. To identify hydrogeomorphic reference condition (HGM), we clustered channel geometry deviation from expectations given watershed area (Aws), resulting in two HGM groups discriminated by area at the top of bank (Atob) residuals <0.6 m² and >0.6 m² predicted to be HGM reference/nonreference streams, respectively. Two independent partial least squares discriminate analyses used (1) hydrologic/environmental variables and (2) macroinvertebrate mean trait values (mT) on 10 reference/nonreference stream pairs of similar Aws for classification validation. Nonreference streams had flashier hydrographs and altered flow magnitudes, lower organic matter, coarser substrate, higher pH/specific conductivity compared with reference streams. Macroinvertebrate assemblages corresponded to HGM groupings, with mT indicative of multivoltinism, collector‐gatherer functional feeding groups, fast current‐preference taxa, and lower Ephemeroptera, Plecoptera, and Trichoptera richness and biotic integrity in nonreference streams. HGM classifications in Sand Hills, sand‐bed streams were determined from channel geometry. This easily implemented classification is indicative of contemporary hydrologic disturbance resulting in contrasting macroinvertebrate assemblages.