Aroviita Jukka, Koskenniemi Esa, Kotanen Juho, Hämäläinen Heikki
Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä University, Finland.
Environ Manage. 2008 Nov;42(5):894-906. doi: 10.1007/s00267-008-9173-8. Epub 2008 Jun 20.
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.
我们评估了一种基于先验河流类型学的简单生物评估方法,以预测在无人类影响情况下河流浅滩处的底栖大型无脊椎动物群落。我们的方法采用类似于特定地点的RIVPACS型模型的方法,预测了特定于四种流域面积不同的河流类型的分类单元列表。在非度量多维标度排序中,参考站点根据其类型进行分组,这表明该类型学有效地解释了大型无脊椎动物群落的自然变异。与零模型相比,类型学大大提高了预测精度和检测人类影响的敏感性,并加强了预测分类单元的观测与预期数量之比(O/E)与实测压力变量之间的相关性。基于类型学的方法的性能与我们开发的RIVPACS型预测模型相当。排除出现概率低的最稀有分类单元,在所有标准下都提高了两种方法的性能。随着出现概率纳入阈值的增加,尤其是预测模型的敏感性先增加后降低。受影响站点始终缺少许多具有中等特定类型出现概率的常见分类单元,这一结果表明这些分类单元在检测人类干扰方面可能特别重要。我们得出结论,如果需要一种基于类型学的方法,如欧盟水框架指令所建议的方法,特定类型分类单元的O/E比可以作为评估浅滩大型无脊椎动物群落状况的有用指标。然而,该方法的成功应用需要具有生物学意义的河流类型,且每种类型都有足够的参考站点。