A practitioner's guide for exploring water quality patterns using principal components analysis and Procrustes

Area of Science:

• Environmental Science
• Ecology
• Data Science

Background:

• Sustainable water quality monitoring requires careful selection of variables and measurement scope.
• Multivariate statistical methods like ordination are widely used but lack standardization.
• Practitioners often face challenges with complex data analysis and lack accessible tools.

Purpose of the Study:

• To present a standardized seven-step framework for principal components analysis (PCA).
• To introduce and demonstrate the application of Procrustes analysis for comparing multivariate data matrices in water quality studies.
• To provide open-source R code and case studies for efficient water quality data exploration.

Main Methods:

• Development of a seven-step framework for principal components analysis (PCA).
• Application of Procrustes analysis to assess concordance between multivariate water quality data matrices.
• Utilizing three water quality case studies from US parklands to illustrate the methods.
• Providing annotated R code and datasets for reproducibility and adaptation.

Main Results:

• The framework facilitates efficient exploration of water quality patterns using PCA.
• Procrustes analysis effectively quantifies the similarity between different water quality datasets.
• Case studies demonstrate the ability to answer key monitoring questions regarding spatial and temporal variability.
• The provided R code enables users to replicate analyses and apply them to new datasets.

Conclusions:

• The proposed PCA and Procrustes analysis framework enhances the standardization and efficiency of water quality monitoring data analysis.
• Open-source tools and case studies lower the barrier for practitioners to adopt advanced multivariate techniques.
• This approach supports more robust interpretations of water quality regimes and variability across sites and time.