U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Monitoring and Modelling, 26 Martin Luther King Drive West, Cincinnati, OH 45168, United States of America.
Sci Total Environ. 2023 Oct 15;895:165121. doi: 10.1016/j.scitotenv.2023.165121. Epub 2023 Jun 26.
Effective water quality management is based on associations between at least two pieces of information: a stressor and a response. However, assessments are hindered by the lack of pre-developed stressor-response associations. To remedy this, I developed genus stressor-specific sensitivity values (SVs) for up to 704 genera to estimate a sensitive genera ratio (SGR) metric for as many as 34 common stream stressors. The SVs were estimated from a large, paired macroinvertebrate and environmental data set for the contiguous United States. Environmental variables measuring potential stressors were selected that were generally uncorrelated and usually had several thousand station observations. I calculated relative abundance weighted averages (WA) for each genus and environmental variable meeting data requirements in a calibration data set. Each environmental variable was split into 10 intervals along each stressor gradient. Genera were assigned an SV from 1 to 10 based on the interval consistent with the WA for each environmental parameter. Using the calibration derived SVs, SGRs were calculated for the calibration and a validation subsets. SGRs are the number of genera with SV ≤ 5 divided by the total number of genera in a sample. In general, as stress increased, the SGR (range: 0-1) decreased for many environmental variables, but for five environmental variables, the decrease was not consistent. The 95 % confidence intervals of the mean of the SGRs were greater for least disturbed stations compared to all other stations for 23 of the remaining 29 environmental variables. Regional performance of SGRs was evaluated by subdividing the calibration data set into West, Central, and East subsets and recalculating SVs. SGR mean absolute errors were smallest in the East and Central regions. These stressor-specific SVs expand the available tools for assessing stream biological impairments from commonly encountered environmental stressors.
胁迫因子和响应。然而，评估受到缺乏预先开发的胁迫因子-响应关联的阻碍。为了解决这个问题，我为多达 704 个属开发了属特异性胁迫敏感性值 (SV)，以估算多达 34 种常见溪流胁迫因子的敏感属比 (SGR) 指标。SV 是根据美国大陆的大型配对大型无脊椎动物和环境数据集估计的。选择了测量潜在胁迫因子的环境变量，这些变量通常是不相关的，并且通常有几千个站点的观测值。我为每个属和环境变量计算了符合校准数据集数据要求的相对丰度加权平均值 (WA)。每个环境变量在每个胁迫梯度上分为 10 个间隔。根据与每个环境参数的 WA 一致的间隔，属被分配 1 到 10 的 SV。使用校准得出的 SV，计算了校准和验证子集的 SGR。SGR 是 SV≤5 的属数除以样本中属总数。一般来说，随着胁迫的增加，许多环境变量的 SGR(范围：0-1)下降，但对于五个环境变量，下降并不一致。对于其余 29 个环境变量中的 23 个，与所有其他站点相比，受干扰最小的站点的 SGR 均值的 95%置信区间更大。通过将校准数据集细分为西部、中部和东部子集并重新计算 SV，评估了 SGR 的区域性能。SGR 平均绝对误差在东部和中部地区最小。这些特定于胁迫的 SV 扩展了用于评估常见环境胁迫因子引起的溪流生物受损的可用工具。
