A Bayesian Method for Deriving Species-Sensitivity Distributions: Selecting the Best-Fit Tolerance Distributions of Taxonomic Groups
A Bayesian Method for Deriving Species-Sensitivity Distributions: Selecting the Best-Fit Tolerance Distributions of Taxonomic Groups
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DOI：
10.1080/10807031003670279
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发表时间：
2010-01-01
影响因子：
4.3
通讯作者：
Kashiwagi, Nobuhisa
中科院分区：
文献类型：
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作者：
Hayashi, Takehiko I.;Kashiwagi, Nobuhisa
我们提出了一种获得物种敏感性分布(SSD)的贝叶斯方法。我们使用了四个贝叶斯统计模型来考虑不同分类组之间对有毒物质耐受性的差异。我们首先使用基于这些模型的马尔科夫链蒙特卡罗模拟来估计SSD参数。然后计算模型的偏差信息标准值，并进行比较，以选择预测能力最好的模型。我们将这种方法应用于七种物质(锌、铅、六价铬、镉、镍、短链氯化石蜡和氯仿)作为案例，然后比较从选定的模型和假设分类组之间没有耐受性差异的模型得出的SSD。我们根据我们的结果讨论了我们的方法的优点和局限性。
We present a Bayesian method for deriving species-sensitivity distributions (SSDs). We employed four Bayesian statistical models to consider differences in tolerance to toxic substances among different taxonomic groups. We first used a Malkov chain Monte Carlo simulation based on these models to estimate the SSD parameters. We then computed deviance information criterion values of the models and compared them in order to select the model with the best predictive ability. We applied this approach to seven substances (zinc, lead, hexavalent chromium, cadmium, nickel, short-chain chloride paraffin, and chloroform) as case examples, and then compared the derived SSDs from the selected models and a model that assumed no tolerance differences among taxonomic groups. We discuss the advantages and limitations of our approach on the basis of our results.