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An algal model for predicting attainment of tiered biologicalcriteria of Maine's streams and rivers

机译：预测缅因州河流和河流的分层生物标准达到的藻类模型

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State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1-6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant model will broaden detection of biological impairment and further diagnose sources of impairment. The algal discriminant model is specific to Maine, but our approach of explicitly tying an assessment tool to tiered aquatic-life goals is widely transferrable to other regions, taxonomic groups, and waterbody types.
机译：开发新的生物评估方法的州水质专业人员通常很难将评估结果与水质标准中的叙述标准联系起来。任意选择指标阈值的一种替代方法是在评估方法的开发中包括生物条件梯度（BCG）。 BCG描述了生物群落条件的各个层次，以帮助沿着从自然到退化的水质梯度识别和传达水体的位置。虽然最初是为鱼类和大型无脊椎动物社区的河流和河流开发的，但BCG却很容易适应其他栖息地和生物分类群。我们使用流藻数据开发了判别分析模型，以预测缅因州水质标准中对水生生物的分层使用情况。我们修改了缅因州流藻的BCG框架，将BCG层与缅因州的水生生物分层用途相关联，并确定了用于描述BCG层的适当藻类指标。 5名水生生物学家使用改良的Delphi方法，独立评估了全州溪流和河流中230个样本的藻类群落指标，并将其BCG等级（1-6）和缅因州水质等级（AA / A，B，C，未达到任何课程）。我们使用干扰最小的参考点来近似自然条件（方法1）。对于53％的样本，生物学家的课堂分配是一致的，而42％的样本之间的差异为1级。生物学家辩论并制定了共识课程。建立用于复制先验类分配的线性判别模型可以对模型训练集中的150个样本中的95％和模型验证集中的80个样本中的91％进行正确分类。基于BCG分类群容忍度分组的本地派生指标（例如，敏感，中等，宽容）比其他地区制定的指标更有效。将藻类判别模型添加到缅因州现有的大型无脊椎动物判别模型中，将拓宽生物损伤的检测范围，并进一步诊断损伤的来源。藻类判别模型是缅因州特有的，但是我们明确将评估工具与分层的水生生物目标联系起来的方法可以广泛地转移到其他地区，生物分类群和水体类型。

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