A Monte Carlo method to account for sampling error in multi-species indicators
A Monte Carlo method to account for sampling error in multi-species indicators
复制标题
考虑多物种指标抽样误差的蒙特卡罗方法
DOI：
10.1016/j.ecolind.2017.05.033
复制
发表时间：
2017
影响因子：
6.9
通讯作者：
A. Strien
中科院分区：
文献类型：
--
作者：
L. Soldaat;J. Pannekoek;Richard J. T. Verweij;C. Turnhout;A. Strien
生物多样性指标的效用如果辅之以不确定性的衡量标准，就会大大增加。然而，在结合了联合收割机种群指数的指标中，列入物种指数的不确定性很难实现，这通常是由于监测方案不完善。缺失值和不同长度的时间序列排除了分析方法的使用，而跨站点的自助法需要站点一级的原始丰度数据，而这些数据可能并不总是可用的。有时选择跨物种而不是站点的自助方法，但这种方法忽略了物种指数的不确定性。基于对年物种指数的蒙特卡罗模拟，提出了一种计算多物种指标时考虑物种指数抽样误差的方法。置信区间的构建使得能够进行各种趋势评估，如检验线性或平滑趋势、检验两个时间点之间的变化、检验可疑变化点的显著性以及检验两个多物种指标之间的差异。在这里，我们将我们的方法与传统方法进行比较，并说明我们的方法使用荷兰种鸟指标的好处。
The usefulness of biodiversity indicators strongly increases if accompanied by measures of uncertainty. In the case of indicators that combine population indices of species, however, the inclusion of the uncertainty of the species indices has shown to be hard to realize, usually due to imperfections in monitoring programmes. Missing values and time series of different lengths preclude the use of analytical approaches, whereas bootstrapping across sites requires the raw abundance data on the site level, which may not always be available. Sometimes bootstrapping across species rather than sites is opted for, but this approach ignores the uncertainty attached to species indices. We developed a method to account for sampling error of species indices in the calculation of multi-species indicators based on Monte Carlo simulation of annual species indices. The construction of confidence intervals enables various trend assessments, like testing for linear or smooth trends, testing for changes between two time points, testing the significance of a suspected change-point and testing for differences between two multi-species indicators. Here, we compare our method with conventional methods and illustrate the benefits of our approach using Dutch breeding bird indicators.