Sergeant C J, Starkey E N, Bartz K K, Wilson M H, Mueter F J
National Park Service, Inventory and Monitoring Program, Southeast Alaska Network, 3100 National Park Road, Juneau, AK, USA.
National Park Service, Inventory and Monitoring Program, Upper Columbia Basin Network, 105 E Second St, Suite 7, Moscow, ID, USA.
Environ Monit Assess. 2016 Apr;188(4):249. doi: 10.1007/s10661-016-5253-z. Epub 2016 Mar 28.
To design sustainable water quality monitoring programs, practitioners must choose meaningful variables, justify the temporal and spatial extent of measurements, and demonstrate that program objectives are successfully achieved after implementation. Consequently, data must be analyzed across several variables and often from multiple sites and seasons. Multivariate techniques such as ordination are common throughout the water quality literature, but methods vary widely and could benefit from greater standardization. We have found little clear guidance and open source code for efficiently conducting ordination to explore water quality patterns. Practitioners unfamiliar with techniques such as principal components analysis (PCA) are faced with a steep learning curve to summarize expansive data sets in periodic reports and manuscripts. Here, we present a seven-step framework for conducting PCA and associated tests. The last step is dedicated to conducting Procrustes analysis, a valuable but rarely used test within the water quality field that describes the degree of concordance between separate multivariate data matrices and provides residual values for similar points across each matrix. We illustrate the utility of these tools using three increasingly complex water quality case studies in US parklands. The case studies demonstrate how PCA and Procrustes analysis answer common applied monitoring questions such as (1) do data from separate monitoring locations describe similar water quality regimes, and (2) what time periods exhibit the greatest water quality regime variability? We provide data sets and annotated R code for recreating case study results and as a base for crafting new code for similar monitoring applications.
为了设计可持续的水质监测计划，从业者必须选择有意义的变量，论证测量的时间和空间范围，并证明计划目标在实施后得以成功实现。因此，必须对多个变量的数据进行分析，而且这些数据通常来自多个地点和不同季节。诸如排序分析等多元技术在水质文献中很常见，但方法差异很大，若能实现更大程度的标准化则会大有裨益。我们发现，关于有效开展排序分析以探究水质模式，几乎没有明确的指导和开源代码。不熟悉主成分分析（PCA）等技术的从业者，在定期报告和稿件中总结大量数据集时面临着陡峭的学习曲线。在此，我们提出一个用于进行主成分分析及相关测试的七步框架。最后一步专门用于进行普罗克汝斯分析，这是水质领域一项有价值但很少使用的测试，它描述了不同多元数据矩阵之间的一致程度，并为每个矩阵中的相似点提供残差值。我们通过美国公园绿地中三个日益复杂的水质案例研究来说明这些工具的效用。这些案例研究展示了主成分分析和普罗克汝斯分析如何回答常见的应用监测问题，例如：（1）来自不同监测地点的数据是否描述了相似的水质状况？（2）哪些时间段的水质状况变化最大？我们提供数据集和带注释的R代码，用于重现案例研究结果，并作为编写类似监测应用新代码的基础。
