A preliminary classification of running-water sites in Great Britain based on macro-invertebrate species and the prediction of community type using environmental data

SUMMARY. 1. Macro-invertebrate species lists were obtained for 268 sites on forty-one river systems throughout Great Britain by qualitative sampling in spring, summer and autumn. Information on twenty-eight environmental variables was also collated for each site. The sites were ordinated on the basis of their species content using detrended correspondence analysis (DCA) and classified by two-way indicator species analysis (TWINSPAN). Correlation coefficients between ordination scores and single environmental variables indicated that Axis 1 distinguished between types of rivers and Axis 2 reflected variation along the length of rivers. A preliminary classification of sites into sixteen groups has been proposed, together with a key which allows new sites to be classified. Information on the species and environmental features which characterize each group is also presented.
2. Multiple discriminant analysis (MDA) was employed to predict the group membership of the 268 sites using the twenty-eight environmental variables. 76.1% of sites were classified correctly. An independent assessment of predictive ability using forty test sites yielded a 50% success rate. Predictive ability was higher for the classification presented in this paper than in fifteen additional classifications produced using data from single seasons and/or different taxonomic treatments.
3. TWINSPAN and MDA were found to be useful approaches to the classification of running-water sites by their macro-invertebrate fauna and the prediction of community type (as indicated by the occurrence of species in the sites comprising the group) using environmental variables. Extension of the scope of the classification, coupled with the use of additional environmental variables to increase predictive ability, is now in progress.     

Development and use of a system for predicting the macroinvertebrate fauna in flowing waters

Abstract R iver I nV ertebrate P rediction A nd C lassification S ystem (rivpacs ) is a software package developed by the Institute of Freshwater Ecology (IFE) for assessing the biological quality of rivers in the United Kingdom. The system can be used to generate site-specific predictions of the macroinvertebrate fauna to be expected in the absence of major environmental stress. Each prediction is based on a small number of environmental features that are used to characterize the site. The fauna predicted can then be compared with the fauna observed at the same site. This offers a procedure for evaluating biological quality with application in river management both at the local level and for national surveys. Close collaboration between the IFE team and biologists in the water industry during the project had a beneficial influence on the operational development of the system. A second feature of RIVPACS is the national classification of sites, based on the macro-invertebrate fauna. Although the classification is currently a pre-requisite for the prediction system, it also has intrinsic value because newly sampled sites of high biological quality can be placed within the national framework, based on their macroinvertebrate fauna. This facility is of interest to the statutory nature conservation bodies as an element in their site appraisal procedures. The predictive component of the current version of the system (RIVPACS n) was used in the 1990 River Quality Survey to assess the biological quality of almost 9000 sites throughout the United Kingdom. Further developmental work is now under way to provide a more comprehensive version of the system for the 1995 survey.     

Predicting the macroinvertebrate faunas of rivers by multiple regression of biological and environmental differences

1.   The prediction of macroinvertebrate community composition in flowing waters from environmental data has enabled pollution assessments that take account of natural variability. Polluted sites are identified by discrepancies between the observed fauna and the fauna expected at an unpolluted site on the same type of river.
2.   The usual method of prediction involves a sequence of (a) classification of unpolluted reference sites by cluster analysis of macroinvertebrate community data (b) multiple discriminant analysis to relate site clusters to environmental variables, and (c) use of site clusters, discriminant functions and environmental data to estimate the probability of collection of each macroinvertebrate taxon at sites that are to be assessed (test sites).
3.   This paper describes an alternative method that does not require classification and predicts abundance rather than probability of occurrence. The main steps are (a) multiple regression of biological differences between pairs of reference sites on differences in physical variables (b) use of the multiple regression relationship to predict the biological similarity of a test site to each reference site, and (c) estimation of the expected fauna at the test site as a weighted mean of the faunas at the reference sites. The predicted similarities of the test site to each reference site are used to derive the weightings.
4.   The method is illustrated using macroinvertebrate and environmental data collected in the upper Murrumbidgee River catchment as part of Australia's Monitoring River Health Initiative. In comparison with a classification-based analysis of these data, macroinvertebrate indices generated by the new method showed a greater distinction between human-disturbed and undisturbed test sites, and a similar or higher degree of correlation with physical and chemical indicators of human disturbance.     

Predictive modelling of freshwater fish as a biomonitoring tool in New Zealand

SUMMARY 1. A challenge has been issued to ecologists to find quantitative ecological relationships that have predictive power. A predictive approach has been successful when applied to biomonitoring using stream invertebrates with the River Invertebrate Prediction and Classification System (RIVPACS). This approach, to our knowledge, has not been applied to freshwater fish assemblages.
2. This paper describes the initial results of the application of a regional predictive model of freshwater fish occurrence using 200 reference sites sampled in the Manawatu–Wanganui region of New Zealand over late summer/autumn 2000. In brief (i) sites were classified into biotic groups (ii) the physical and chemical characteristics that best describe variation among these groups were determined and (iii) the relationship between these environmental variables and fish communities was used to predict the fauna expected at a site.
3. Reference sites clustered into six groups based on fish density and community composition. Using 14 physical variables least influenced by human activities, a discriminant model allocated 70% of sites to the correct biological classification group. The variables that best separated the site groups were mainly large-scale variables including altitude, distance from the coast, lotic ecoregion and map co-ordinates.
4. The model was further validated by randomly removing 20% of the sites, rebuilding the model and then determining the number of removed sites correctly allocated to their original biotic groups using environmental variables. Using this process 67% of the removed sites were correctly reassigned to the six predetermined groups.
5. A further 30 sites were used to determine the ability of the model to detect anthropogenic impact. The observed over expected taxa ( O / E ) ratios were significantly lower than the reference site O / E ratios, indicating a response of the fish assemblages to the known stressors.     

AusRivAS: using macroinvertebrates to assess ecological condition of rivers in Western Australia

1. AusRivAS (Australian River Assessment Scheme) models were developed, using macroinvertebrates as indicators, to assess the ecological condition of rivers in Western Australia as part of an Australia-wide program. The models were based on data from 188 minimally disturbed reference sites and are similar to RIVPACS models used in Britain. The major habitats in the rivers (macrophyte, channel) were sampled separately and macroinvertebrates collected were identified to family level. 2. Laboratory sorting of preserved macroinvertebrate samples recovered about 90% of families present when 150 animals were collected, whereas live picking in the field recovered only 76%. 3. Reference sites clustered into five groups on the basis of macroinvertebrate families present. Using seven physical variables, a discriminant function allocated 73% of sites to the correct classification group. A discriminant function based on seven physical and two chemical variables allocated 81% of sites to the correct group. However, when the same reference sites were re-sampled the following year, the nine variable discriminant function misallocated more sites than the seven variable function, owing to annual fluctuations in water chemistry that were not accompanied by changes in fauna. 4. In preliminary testing, the wet season channel model correctly assessed 80% of reference sites as undisturbed in the year subsequent to model building (10% of sites were expected to rate as disturbed because the 10th percentile was used as the threshold for disturbance). Nine sites from an independent data set, all thought to be disturbed, were assessed as such by the model. Results from twenty test sites, chosen because they represented a wide range of ecological condition, were less clear-cut. In its current state the model reliably distinguishes undisturbed and severely disturbed sites. Subtle impacts are either detected inconsistently or do not affect ecological condition.     

Sampling variation in macroinvertebrate data and implications for river quality indices

SUMMARY 1. The EC Water Framework Directive (2000/60/EC) recognises the need for biological monitoring. Indices derived from standard samples of macroinvertebrates are frequently used for the appraisal of the ecological quality of rivers. However, information on the errors or chance variation that can influence the value of an index is also important. 2. This paper describes a study to quantify the observed sampling variation in three ecological indices based on the Biological Monitoring Working Party (BMWP) score system across a wide range of river types and qualities. The indices are number of BMWP taxa, BMWP score and Average Score Per Taxon (ASPT). 3. The study sites were selected to encompass the four major groups within the River InVertebrate Prediction And Classification System (RIVPACS) site classification for Britain. Within each group, four sites which differed in ecological quality grade were chosen (total of 16 sites). At each site three standard RIVPACS samples were taken in each of spring, summer and autumn by trained staff. In each season, two samples were taken by one biologist and the third by a different individual to allow for within and between‐operator variation. 4. The effects of sampling variation within a season on the number of taxa, BMWP score and ASPT across all sites, irrespective of operator, could be represented by some simple parameters. We found that the sampling SD of the square root of the number of taxa, square root of BMWP score and the untransformed ASPT were roughly constant in each case, irrespective of site type or quality. For each index, SD for two and three seasons combined samples were smaller than for single season samples. 5. Inter‐operator influences on sample values were negligible (4–12% of total sampling SD) in this study. This underlines the importance of adequate training for all staff involved in extensive monitoring programmes which use standard procedures from one year to the next, but may involve different staff. 6. Indices for number of taxa, BMWP score and ASPT were all estimated with greater precision from combined season samples than from the averages of two or three seasons' samples. 7. This study enables us to estimate confidence intervals for the values of the number of taxa, BMWP score and ASPT based on single season, two or three season combined samples collected using standard RIVPACS procedures for any river site in Britain. The results can also be used in simulation models which incorporate the effects of sampling variation into assessments of the ecological quality of river sites based on the ratio of observed to RIVPACS expected values of these BMWP indices.     

Temporal and Spatial Distribution Patterns of Larval Trichoptera in Madeiran Streams

Temporal and spatial distribution patterns of lotic larval trichopteran assemblages in relation to environmental variables were investigated in Madeiran streams using multivariate analyses. TWINSPAN classification detected distinct faunal assemblages related to spatial factors between non-polluted high altitude sites and lower lying enriched sites where tolerant taxa were predominant but showed strong seasonal shifts in species composition and abundance. The 15 TWINSPAN end groups were grouped into five arbitrary clusters based upon the seasonal and spatial changes in the trichopteran assemblages detected by the analysis. Significant differences between environmental variables (distance from source, altitude, temperature, conductivity, alkalinity and nitrate) and the trichopteran assemblages (using trichopteran based metrics) of these clusters were confirmed by the Kruskal-Wallis test (H) and Dunn’s test. Chemical classification of samples within the clusters revealed a strong association between trichopteran assemblages and water quality. Canonical Correspondence Analysis and Monte Carlo global permutation tests also identified significant associations between the larval assemblages and physicochemical variables such as temperature and conductivity along a strong physical gradient (altitude, slope) and nitrate along a weaker seasonal gradient. Analysis of functional feeding group distribution patterns clearly showed that mid to high altitude indigenous woodland sites were trophically diverse whilst the lower reaches of the islands streams are trophically impoverished with strong seasonal shifts between two feeding groups of enrichment tolerant taxa. Trichopteran shredders are exclusive to indigenous woodland sites, indicating a limited distribution associated with land use, allochthonous input and habitat destruction. The results indicate that several ‘environmental filters’ operate at different levels upon the islands trichopteran fauna, producing temporally and spatially distinct ‘subsets’ of species best able to exploit conditions and resources at a given site or time, confounding the direct comparison of these insular systems with the findings of the River Continuum Concept, traditionally associated with unaffected continental lotic systems.     

ANNA: A new prediction method for bioassessment programs

1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial. 2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS. 3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r², intercept and slope. Second, the two models’ assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites. 4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS‐ and RIVPACS‐type models, if not to replace them.     

Development of a RIVPACS-type predictive model for bioassessment of wadeable streams in Wyoming

RIVPACS models produce a community-level measure of biological condition known as O/E, which is derived from a comparison of the observed (O) biota with those expected (E) to occur in the absence of anthropogenic stress. We used benthic macroinvertebrate and environmental data collected at 925 stream monitoring stations, from 1993 to 2001, to develop, validate, and apply a RIVPACS model to assess the biological condition of wadeable streams in Wyoming. From this dataset, 296 samples were identified as reference, 157 of which were used to calibrate the model, 46 to validate it, and 93 to examine temporal variability in reference site O/E-values. We used cluster analyses to group the model development reference sites into biologically similar classes of streams and multiple discriminant function analysis to determine which environmental variables best discriminated among reference groups. A suite of 14 categorical and continuous environmental variables best discriminated among 15 reference groups and explained a large proportion of the natural variability in biota within the reference dataset. Eleven of the predictor variables were derived from GIS. As expected, mean O/E-values for reference sites used in model development and validation were near unity and statistically similar. Temporal variability in O/E-values for reference sites was low. Test site values ranged from 0 to 1.45 (mean = 0.73). The model was accurate in both space and time and precise enough (S.D. of O/E-values for calibration data = 0.17) to detect modest alteration in biota associated with anthropogenic stressors. Our model was comparable in performance to other RIVPACS models developed in the United States and can produce effective assessments of biological condition over a broad, ecologically diverse region. We also provide convincing evidence that RIVPACS models can be developed primarily with GIS-based predictor variables. This framework not only simplifies the extraction of predictor variable information while potentially reducing expenditures of time and money in the collection of predictor variable information, but opens the door for development and/or application of RIVPACS models in regions where there is a paucity of local-scale, abiotic information.     

Classification of the Tatra Mountain lakes (Slovakia) using chironomids (Diptera,Chironomidae)

Chironomid assemblages in thirty-three mountain lakes situated above tree line in the Slovakian part of the Tatra Mountains were studied during 2000–2002. Chironomid species/taxa, collected as pupal exuviae, were correlated with physical, chemical, and lake morphometry variables of 22 lakes. Two-way indicator species analysis (TWINSPAN) was used to classify the lakes into four distinct groups: higher situated alpine lakes, lower situated alpine lakes, subalpine lakes and acidified lakes. Presence/absence of eight taxa was identified as indicative for this classification. In discriminant function analysis, pH, dissolved organic carbon, altitude and lake area were the most significant variables reflecting differences among groups of lakes. This model of four variables allowed 77% success in the prediction of group membership. A multiple regression model with lake area, concentration of magnesium and total phosphorus accounted for 37% of the variance in taxa richness. Lakes with greater area contained more chironomid taxa than smaller ones. Lakes with higher alkalinity and higher trophic status tend to support more taxa. Canonical correspondence analysis (CCA) indicated that most variation in the composition of chironomid assemblages was related to pH and to altitude. The results can be used as reference data for long-term monitoring of the Tatra lakes, especially in connection with a recovery from acidification and global climatic change.     

The PERLA system in the Czech Republic: a multivariate approach for assessing the ecological status of running waters

The assessment of running water quality has a long tradition in the Czech Republic, but in the past it focused on the evaluation of organic pollution using the saprobic system. Considering the modern trends of stream ecological status evaluation in water management a new assessment system named PERLA was developed. The system is a complex of biological methods of ecological status assessment of running waters and connected activities in the Czech Republic. It involves 300 reference sites with respective biotic and abiotic data and a prediction model using a newly developed software HOBENT. The model generally follows the published mathematical principles of RIVPACS and represents the site specific and stressor non-specific approaches. The HOBENT software allows the prediction of the target assemblage of benthic macroinvertebrates for any site based on a set of environmental variables (latitude, longitude, distance from source, altitude, slope, catchment area, and stream order) which characterise the site. The predicted assemblage can be compared with the fauna observed at the same site. The comparison makes it possible to evaluate the extent of disturbance, expressed by index B. The model allows to evaluate spring, summer, and autumn seasonal data of the majority of wadable streams in the Czech Republic. The practical application of the PERLA system has started in 2001.     

Benthic macroinvertebrate assemblages in north Swedish streams: environmental relationships

An analysis of the relationships between lotic macroinvertebrates and environmental variables was earned out on material from 60 riffle sites in streams in northern Sweden The approach involved the use of TWINSPAN classification and canonical correspondence analysis on presence/absence data from two seasons (spring and autumn) Variables most strongly associated with distribution patterns of assemblages were drainage area, elevation, water quality in terms of alkalinity, colour and phosphate and the presence of macrophytes The significance of affinities of different species to these variables are discussed The eight clusters resulting from the TWINSPAN analysis could biologically be interpreted as classes of taxa related to stream size, chemical conditions and algae A multiple regression analysis for predicting species nchness using three independent variables, viz drainage area amount or organic matter, and discharge was constructed The results of the study could be used as a starting point for further work on the community organization of benthie stream assemblages     

Choosing the best method for stream bioassessment using macrophyte communities: Indices and predictive models

The bioassessment and monitoring of the ecological status of rivers using macrophytes has gained new momentum since macrophytes were recognised as biological quality elements for the implementation of the European Water Framework Directive (WFD; EU/2000/60).Our objectives were to test the suitability of two predictive modelling approaches to macrophyte communities as a tool for water quality assessment, and to compare their performance with other more common approaches—the use of macrophytes as indicators of the trophic status of rivers and multimetric indices. We used floristic and environmental data that were collected in the spring of 2004 and 2005 from around 400 sites on rivers across mainland Portugal, western Iberia.We build two predictive models: MACPACS (MACrophyte Prediction And Classification System) and MAC (Macrophyte Assessment and Classification) based on RIVPACS and the BEAST methods, respectively. Whereas MACPACS is derived from taxa occurrence data, MAC uses a quantitative measure of taxa abundance. Both models showed good performance in predicting reference sites to the correct group and low rate of misclassification errors. However, they performed differently. MAC depicts a reliable response to the overall human-mediated degradation of fluvial systems, as does the multimetric index (RVI, Riparian Vegetation Index), but MACPACS presented only a poor correlation with the Global Human Disturbance Index and with the nutrients input. The incorporation of abundance data in vegetation predictive models appears to be particularly important to the detection of high levels of degradation. The values for correlations with physical–chemical pressure variables were lower than expected for MTR (Mean Trophic Rank) due to an insufficient number of scoring species found in Portuguese fluvial systems. Our results suggest that the most effective methods for bioassessment in Mediterranean-type rivers are either the RVI or the MAC predictive model.     

Selecting discriminant function models for predicting the expected richness of aquatic macroinvertebrates

1. The predictive modelling approach to bioassessment estimates the macroinvertebrate assemblage expected at a stream site if it were in a minimally disturbed reference condition. The difference between expected and observed assemblages then measures the departure of the site from reference condition. 2. Most predictive models employ site classification, followed by discriminant function (DF) modelling, to predict the expected assemblage from a suite of environmental variables. Stepwise DF analysis is normally used to choose a single subset of DF predictor variables with a high accuracy for classifying sites. An alternative is to screen all possible combinations of predictor variables, in order to identify several ‘best’ subsets that yield good overall performance of the predictive model. 3. We applied best‐subsets DF analysis to assemblage and environmental data from 199 reference sites in Oregon, U.S.A. Two sets of 66 best DF models containing between one and 14 predictor variables (that is, having model orders from one to 14) were developed, for five‐group and 11‐group site classifications. 4. Resubstitution classification accuracy of the DF models increased consistently with model order, but cross‐validated classification accuracy did not improve beyond seventh or eighth‐order models, suggesting that the larger models were overfitted. 5. Overall predictive model performance at model training sites, measured by the root‐mean‐squared error of the observed/expected species richness ratio, also improved steadily with DF model order. But high‐order DF models usually performed poorly at an independent set of validation sites, another sign of model overfitting. 6. Models selected by stepwise DF analysis showed evidence of overfitting and were outperformed by several of the best‐subsets models. 7. The group separation strength of a DF model, as measured by Wilks’Λ, was more strongly correlated with overall predictive model performance at training sites than was DF classification accuracy. 8. Our results suggest improved strategies for developing reliable, parsimonious predictive models. We emphasise the value of independent validation data for obtaining a realistic picture of model performance. We also recommend assessing not just one or two, but several, candidate models based on their overall performance as well as the performance of their DF component. 9. We provide links to our free software for stepwise and best‐subsets DF analysis.     

Distributional patterns of immature Simuliidae (Diptera) in northwestern North America

SUMMARY. 1. Major landscape features and hydrological parameters indicative of black fly species assemblages were examined at 101 stream sites in Alberta, northern British Columbia, the Yukon and Alaska during the summer. Forty-one black fly taxa were recorded at seventy- nine sites using qualitative sampling procedures. River sites lacking black flies had significantly higher conductivity, greater depth, shallower slope and were farther from the Pacific Ocean than sites with black flies.
2. Classification of sites by taxon occurrence using hierarchical cluster analysis suggested five groupings: A. Simulium tuberosum (Lundstrëm) complex + several taxa. B, S. venustum Saylverecundum Stone and Jamnback complexes + S, tuberosum complex; C. 5. arcticum Malloch complex + S. corbis Twinn complex; D, Gymnopais Stone/ Prosimulium Roubaud; and E. P. onychodactytum Dyar and Shannon complex + several taxa.
3. Multiple discriminant analysis (MDA) was used to predict group membership of the seventy-nine sites using nineteen environmental variables; 71% of the sites were classified correctly. MDA identified latitude and distance from stream source as important factors separating group D from other groups. Stream width and drainage basins entering the Arctic Oeean and Hudson Bay delineated group B. There was no clear separation among groups A. C or E. The presence of sibling species probably accounts for the overlap of black fly assemblages.
4. Our findings are briefly discussed in the context of stream classification systems, notably the river continuum concept.     

Influence of geographical extent on typology- and model-based assessments of taxonomic completeness of river macroinvertebrates

1. River InVertebrate Prediction and Classification System (RIVPACS)‐type predictive models are increasingly used to assess the biological condition of freshwaters, but management schemes may also be based on a priori groupings of similar water bodies (typologies) to control for natural variation in biota. The two approaches may lead to disagreements of the biological status of a site, depending on, for example, the spatial scale at which assessments are conducted. 2. We used data from 96 reference and 134 potentially impacted sites from Western and Central Finland to compare RIVPACS‐type models and a simple size‐based typology of rivers for the assessment of taxonomic completeness (the quotient of the Observed‐to‐Expected number of predicted taxa, O / E) of riffle macroinvertebrates. We specifically examined how geographical extent influences bioassessment performance (accuracy, precision and sensitivity to detect impact) of the two approaches. To fully examine the behaviour of the O / E‐index with the two approaches at differing spatial scales, we performed all assessments with a full range of thresholds for predicted taxa occurrence probabilities (p_t from 0+ to 0.9). 3. Both approaches performed consistently better than the corresponding null models. At the larger extent (i.e. assessment encompassing the whole study area), the RIVPACS‐approach performed in all aspects better than the typology‐approach. However, at the smaller extent (i.e. regional assessments) the RIVPACS‐type models and the typologies showed similar accuracy to predict the actual fauna (mean E), similar precision (SD) of cross‐validated O / E and similar sensitivity to detect sites with human impairment. 4. SD(O / E) decreased (i.e. precision increased) consistently with increasing p_t. However, both approaches were most sensitive at intermediate p_t:s (c. 0.2–0.6) when taxa with low predicted occurrence probabilities were excluded. 5. Our results show that RIVPACS‐type predictive models are less susceptible to variations in spatial scale, whereas the performance of a priori typologies increases with decreasing spatial extent. Thus, RIVPACS‐type models are more efficient for large‐scale bioassessments, but at restricted spatial scales, or with an otherwise biologically meaningful stratification, simple a priori classifications can be equally useful for the assessment of taxonomic completeness of river macroinvertebrates.     

Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state

Abstract This paper describes the first results for an alternative approach to the development of sediment quality criteria in the nearshore areas of the Laurentian Great Lakes. The approach is derived from methods developed in the United Kingdom for establishing predictive relationships between macroinvertebrate fauna and the physico-chemistry of riverine environments. The technique involves a multivariate statistical approach using (i) data on the structure of benthic invertebrate communities, (ii) functional responses (survival, growth and reproduction) in four sediment toxicity tests (bioassays) with benthic invertebrates; and (iii) selected environmental variables at 96 reference (‘clean’) sites in the nearshore areas of all five Great Lakes (Lakes Superior, Huron, Erie, Ontario and Michigan). Two pattern recognition techniques (using the computer software package PATN) are employed in the analysis: cluster analysis and ordination. The ordination vector scores from the original axes of the pattern analysis are correlated (using CORR in SAS) with environmental variables which are anticipated to be least affected by anthropogenic activities (e. g. alkalinity, depth, silt, sodium etc.). Multiple discriminant analysis (MDA) is used to relate the site groupings from the pattern analysis to the environmental variables and to generate a model that can be used to predict community assemblages and functional responses at new sites with unknown but potential contamination. The predicted community assemblages and functional responses are then compared with the actual benthic communities and responses at a site, and the need for remedial action is determined. The predictive capability of the discriminant model was confirmed by performing several validation runs on subsets of the data. An example of the use of the model for sediment in Collingwood Bay (an area of concern designated by the IJC in Georgian Bay, Lake Huron) is presented and the technique is shown to be more precise in determining the need for remediation than the currently used provincial sediment quality criteria based on Screening Level Concentration (SLC) and laboratory toxicity tests. The ultimate goal of the study is the development of a method to determine the need for, and the success of, remedial action and to predict what benthic communities should look like at a site if it were clean and what responses of organisms in sediment toxicity tests constitute an acceptable end-point.     

Method of predicting reference condition biota affects the performance and interpretation of ecological indices

1. The statistical rigour and interpretability of ecological assessments is strongly affected by how well we predict the biological assemblages expected to occur in the absence of human‐caused stress, i.e. the reference condition. In this study, we examined how the specific method used to predict the reference condition affected the performance of two commonly used types of ecological index: RIVPACS‐based O/E indices and multimetric indices (MMIs). 2. These two types of index have generally relied on different approaches to predicting the reference condition. For MMIs, some type of regionalisation is typically used to describe the range of metric values among reference sites and hence the expected range at assessed sites. For O/E indices, continuous modelling is used to predict how the biota varies among sites both among and within regions. Because the prediction method differs for these two types of index, it has been impossible to judge if differences in index performance (accuracy, precision, responsiveness and sensitivity) are caused by differences in the way reference condition biota are predicted or by differences in what the indices measure. 3. We used a common data set of 94 reference sites and 255 managed sites and the same potential set of predictor variables to compare the performance of five different MMIs and three O/E indices that were derived from different prediction methods: null models, multiple linear regression (MLR), classification and regression trees, Random Forests (RF) and linear discriminant functions models (LDM). We then calculated values of these indices for samples collected from the managed catchments as well as samples collected from 13 reference sites that were progressively altered in known ways by a simulation programme. 4. Both the type of predictor used and the type of index affected overall index performance. Modelled indices generally had the greatest sensitivity in assessing managed sites as biologically different from reference. Index sensitivity was determined by both an aspect of index precision (10th percentile of reference condition values) and responsiveness. The O/E indices showed the best scope of response to known biological alteration. All three O/E indices decreased linearly in response to simulated alteration in both overall assemblage structure (Bray‐Curtis dissimilarity) and taxa loss. The MMIs declined linearly from low to intermediate levels of assemblage alteration but were less responsive between intermediate and high levels of biological alteration. 5. Insights gained from simulations can aid in testing assumptions regarding index response to stress and help ensure that we select indices that are ecologically interpretable and most useful to resource managers.     

Natural flow regime,temperature and the composition and richness of invertebrate assemblages in streams of the western United States

1. We tested how strongly aquatic macroinvertebrate taxa richness and composition were associated with natural variation in both flow regime and stream temperatures across streams of the western United States. 2. We used long‐term flow records from 543 minimally impacted gauged streams to quantify 12 streamflow variables thought to be ecologically important. A principal component analysis reduced the dimensionality of the data from 12 variables to seven principal component (PC) factors that characterised statistically independent aspects of streamflow: (1) zero flow days, (2) flow magnitude, (3) predictability, (4) flood duration, (5) seasonality, (6) flashiness and (7) base flow. K‐means clustering was used to group streams into 4–8 hydrologically different classes based on these seven factors. 3. We also used empirical models to estimate mean annual, mean summer and mean winter stream temperatures at each stream site. We then used invertebrate data from 63 sites to develop Random Forest models to predict taxa richness and taxon‐specific probabilities of capture at a site from flow and temperature. We used the predicted taxon‐specific probabilities of capture to estimate how well predicted assemblages matched observed assemblages as measured by RIVPACS‐type observed/expected (O/E) indices and Bray–Curtis dissimilarities. 4. Macroinvertebrate taxon richness was only weakly associated with streamflow and temperature variables, implying that other factors more strongly influenced taxa richness. 5. In contrast to taxa richness, taxa composition was strongly associated with streamflow and temperature. Predictions of taxa composition (O/E and Bray–Curtis) were most precise when both temperature and streamflow PC factors were used, although predictions based on either streamflow PC factors or temperature alone were also better than null model predictions. Of the seven aspects of the streamflow regime we examined, variation in baseflow conditions appeared to be most directly associated with invertebrate biotic composition. We were also able to predict assemblage composition from the conditional probabilities of hydrological class membership nearly as well as Random Forests models that were based directly on continuous PC factors. 6. Our results have direct implication for understanding the relative importance of streamflow and temperature in regulating the structure and composition of stream assemblages and for improving the accuracy and precision of biological assessments.