School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding 071002, China; Department of Integrative Biology, Michigan State University, East Lansing, MI 48824, USA.
School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding 071002, China.
Sci Total Environ. 2021 Jun 1;771:145417. doi: 10.1016/j.scitotenv.2021.145417. Epub 2021 Jan 27.
Multimetric index (MMI) approach is a broadly used in ecological assessment because it can integrate information of various kinds of ecologically related metrics of freshwater ecosystems and provide an easily understandable score for purpose of further evaluation and managements. Accounting for natural variation and disentangling covariation between natural environmental factors and human disturbance factors are imperative for an accurate assessment. Lots of progress has been made recently on the aforementioned two aspects. Three approaches, a priori classification of sites by regions or typologies, site-specific modeling of expected reference condition and varying metrics in site groups, have been tested in lakes and streams to improve assessment accuracy. All existed studies support that site-specific modeling can efficiently account for natural variation and generate a MMI with good performance. However, until now, no strong evidence has shown that diatom/blue-algae typologies are better than regionalization frameworks on accounting for natural variation either in lakes or in streams. To separate the natural variation explained by site specific modeling from that of varying metrics is necessary for a thorough and accurate evaluation on the valuableness of site-grouping by typologies. Different performance of varying metrics among site groups of streams and lakes was most probably caused by the lack of representativeness of diatom metrics on biological condition rather than the complex multi-stressor gradients in streams and rivers. A recent study showed that blue-green algae enhanced performance of diatom-based MMI on defining lake condition under high level of human disturbance. On the other hand, with more and more extensive and intensive use of statistics techniques in developing MMI, we also discussed some statistical challenges faced by scientists in field of ecological assessment, especially on setting significance level of a statistical test and multiple comparison issue in MMI performance comparison.
多指标指数（MMI）方法在生态评估中被广泛应用，因为它可以整合各种与淡水生态系统相关的生态指标信息，并提供一个易于理解的分数，用于进一步的评估和管理。考虑到自然变异，并理清自然环境因素和人为干扰因素之间的共变关系，对于准确评估至关重要。最近在上述两个方面都取得了很多进展。已经在湖泊和溪流中测试了三种方法，即通过区域或类型学对站点进行先验分类、对预期参照条件和站点组中变化的指标进行特定站点建模，以提高评估的准确性。所有已有的研究都支持特定站点建模可以有效地考虑自然变异，并生成具有良好性能的 MMI。然而，到目前为止，没有强有力的证据表明藻类/蓝藻类型学在湖泊或溪流中都比区域化框架在考虑自然变异方面表现更好。为了对基于藻类/蓝藻类型学的站点分组的价值进行彻底和准确的评估，有必要将由特定站点建模解释的自然变异与变化指标的自然变异分开。溪流和湖泊中站点组之间变化指标的不同性能很可能是由于生物条件的藻类指标缺乏代表性，而不是溪流和河流中复杂的多胁迫梯度造成的。最近的一项研究表明，在人为干扰水平较高的情况下，蓝藻增强了基于藻类的 MMI 对湖泊条件的定义能力。另一方面，随着统计学技术在 MMI 开发中的应用越来越广泛和深入，我们还讨论了生态评估领域的科学家面临的一些统计挑战，特别是在统计学检验的显著性水平设置和 MMI 性能比较中的多重比较问题。
