应用O/E模型评价淮河流域典型水体底栖动物完整性健康的研究

本研究尝试采用我国已有的季风气候区底栖动物观测值(O)/期望值(E)比值模型,在无足够数量参照样点的情况下,建立淮河流域O/E指数健康评价指标体系,评价淮河流域典型水体底栖动物物种组成完整性现状.于2014年夏季(8月)和秋季(11月)分别调查了淮河流域20和25个典型水体的底栖动物和水质指标.O/E模型评价结果表明,监测点位在PC(Probabilities of Capture)0和PC≥0.5条件下,夏季和秋季的物种期望丰富度分别约为25和9.所有25个点位中,仅1个点位为健康,其余为一般或较差;模型控制自然梯度后O/E0和O/E50值均没有显著的季节性差异(p=0.565和0.229).环境胁迫因子(TN、EC、CODCr和p H)和土地覆盖数据(水体比例、湿地比例、裸地比例、森林比例和草地比例)对秋季O/E50和O/E50-null的解释量高于夏季,TN是能够解释淮河流域典型水体夏季O/E指数变异最多的环境因子,p H和CODCr是能够解释秋季O/E指数变异最多的环境因子.研究表明,在缺少有效参照点位构建评价指标体系的情况下,在淮河流域应用已经构建的季风气候区底栖动物O/E指数模型进行健康评价是比较可靠的方法.     

Region of influence method improves macroinvertebrate predictive models in Maryland

Stream biological assessment reflects not just conventional water quality, but an environmental quality that represents the integrity of the stream ecosystem. In Britain, Australia and the United States, macroinvertebrate predictive models were built and applied to stream assessment by employing multivariate analysis. There were variations in these models, where adaptations were made for different regions, but the philosophy underlying the models was similar: employ site classification to predict expected assemblage. Taxon assemblage is predicted from reference groups with similar stream features; these resulting models are RIVPACS-style models. Because every site has to belong to one group in the classification process, each reference group might include some dissimilar sites, and their dissimilarity in taxon assemblage impaired the results of taxon predictions from these models. To avoid this limitation, this study employed a Region-of-Influence-style (ROI-style) modeling method, selecting only similar reference sites and allowing each site to build its own reference group.Three different Region-of-Influence selection schemes were applied to improve the macroinvertebrate predictive model in Maryland: the Assessment by Nearest Neighbour Analysis (ANNA), the Burn's Region of Influence (BROI), and the New Datum Region of Influence (NROI) predictive schemes. The prediction results from ANNA, BROI, and NROI were compared, and the reference selections of each predictive scheme were examined. The comparison showed no preference for the total number of reference sites used by either predictive scheme. The number of reference sites did not correlate to the quality of reference sites used and thus does not control the predictability. The ROI-style model in Maryland had better prediction performance than the RIVPACS-style models, and could improve the bioassessment of streams.     

A Priori Typology-Based Prediction of Benthic Macroinvertebrate Fauna for Ecological Classification of Rivers

We evaluated a simple bioassessment method based on a priori river typology to predict benthic macroinvertebrate fauna in riffle sites of rivers in the absence of human influence. Our approach predicted taxon lists specific to four river types differing in catchment area with a method analogous to the site-specific RIVPACS-type models. The reference sites grouped in accordance with their type in NMS ordination, indicating that the typology efficiently accounted for natural variation in macroinvertebrate assemblages. Compared with a null model, typology greatly increased the precision of prediction and sensitivity to detect human impairment and strengthened the correlation of the ratio of observed-to-expected number of predicted taxa (O/E) with the measured stressor variables. The performance of the typology-based approach was equal to that of a RIVPACS-type predictive model that we developed. Exclusion of rarest taxa with low occurrence probabilities improved the performance of both approaches by all criteria. With an increasing inclusion threshold of occurrence probability, especially the predictive model sensitivity first increased but then decreased. Many common taxa with intermediate type-specific occurrence probabilities were consistently missing from impacted sites, a result suggesting that these taxa may be especially important in detecting human disturbances. We conclude that if a typology-based approach such as that suggested by the European Union’s Water Framework Directive is required, the O/E ratio of type-specific taxa can be a useful metric for assessment of the status of riffle macroinvertebrate communities. Successful application of the approach, however, requires biologically meaningful river types with a sufficient pool of reference sites for each type.     

RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers

The European Union Water Framework Directive recognises the need for and value of biological monitoring. This paper reviews the modelling approach known as River Invertebrate Prediction and Classification System (RIVPACS for assessing the ecological quality of river sites using macroinvertebrate sampling. The RIVPACS philosophy is to develop statistical relationships between the fauna and the environmental characteristics of a large set of high quality reference sites which can be used to predict the macroinvertebrate fauna to be expected at any site in the absence of pollution or other environmental stress. The observed fauna at new test sites can then be compared with their site-specific expected fauna to derive indices of ecological quality. All methodological decisions in any such model development have implications for the reliability, precision and robustness of any resulting indices for assessing the ecological quality and ecological grade (‘status’) of individual river stretches. The choice of reference sites and environmental predictor variables, the site classification and discrimination methods, the estimation of the expected fauna, and indices for comparing the agreement, or lack of it, between the observed and expected fauna, are all discussed. The indices are assessed on the reference sites and on a separate test set of 340 sites, which are subject to a wide range of types and degrees of impairment.