Developing an improved biomonitoring tool for fine sediment: Combining expert knowledge and empirical data
Developing an improved biomonitoring tool for fine sediment: Combining expert knowledge and empirical data
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开发改进的细沉积物生物监测工具：结合专家知识和经验数据
DOI：
10.1016/j.ecolind.2015.02.011
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发表时间：
2015
影响因子：
6.9
通讯作者：
Turley M
中科院分区：
文献类型：
--
作者：
Turley M
沉积物敏感无脊椎动物比例指数(PSI)是一种生物监测工具，旨在确定河流和溪流的沉积程度。尽管有良好的生物学基础，但该工具已被证明与细沉积物只有中等程度的相关性，尽管与其他压力特定指数相当，但限制了其应用的信心。这项研究的目的是调查是否可以通过使用经验数据来补充用于确定最初的四个细颗粒沉积物敏感性等级的专家知识和文献来提高PSI指数的性能。所使用的经验数据包括对各种参考条件下的温带溪流和河流生态系统收集的无脊椎动物丰度和细小沉积物百分比的观测(模型训练数据集n=2252)。根据先前确定的敏感度等级，在一定范围内为物种分配敏感度权重。使用一系列权重承认无脊椎动物占据的生态位的广度，以及它们作为指示器的不同潜力。通过非线性优化确定了最佳物种特定敏感性权重，因为这些权重导致经验加权PSI(E-PSI)得分与模型训练数据集中沉积的细颗粒沉积物之间的Spearman等级相关系数最高。在试验数据集中，细颗粒沉积物百分比与E-PSI分数之间的相关性(n=2252)比细颗粒沉积物百分比与原始PSI分数之间的相关性(E-PSIRS=−0.74，p<；)高出8个百分点(与PSIRS=0.66，p<；0.01)。这项研究证明了将合理的生物学基础与来自大型经验数据集的证据相结合的价值，以测试和增强生物监测工具的性能，以增加对其应用的信心。
The Proportion of Sediment-sensitive Invertebrates (PSI) index is a biomonitoring tool that is designed to identify the degree of sedimentation in rivers and streams. Despite having a sound biological basis, the tool has been shown to have only a moderate correlation with fine sediment, which although comparable to other pressure specific indices, limits confidence in its application. The aim of this study was to investigate if the performance of the PSI index could be enhanced through the use of empirical data to supplement the expert knowledge and literature which were used to determine the original four fine sediment sensitivity ratings. The empirical data used, comprised observations of invertebrate abundance and percentage fine sediment, collected across a wide range of reference condition temperate stream and river ecosystems (model training datasetn= 2252). Species were assigned sensitivity weights within a range based on their previously determined sensitivity rating. Using a range of weights acknowledges the breadth of ecological niches that invertebrates occupy and also their differing potential as indicators. The optimum species-specific sensitivity weights were identified using non-linear optimisation, as those that resulted in the highest Spearman's rank correlation coefficient between the Empirically-weighted PSI (E-PSI) scores and deposited fine sediment in the model training dataset. The correlation between percentage fine sediment and E-PSI scores in the test dataset (n= 252) was eight percentage points higher than the correlation between percentage fine sediment and the original PSI scores (E-PSIrs= −0.74,p< 0.01 compared to PSIrs= −0.66,p< 0.01). This study demonstrates the value of combining a sound biological basis with evidence from large empirical datasets, to test and enhance the performance of biomonitoring tools to increase confidence in their application.
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