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

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Abstract

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.

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Acknowledgments

This article was conceived during a workshop funded by the National Park Service Inventory and Monitoring Program. A. Larsen contributed initial article ideas. K. Sherrill and J. Best, Jr. provided valuable preliminary reviews of the manuscript. P. Lisi shared R code to create color ramp biplots for Fig. 5. The views expressed in this article do not necessarily represent the views of the US National Park Service. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Author information

Authors and Affiliations

  1. National Park Service, Inventory and Monitoring Program, Southeast Alaska Network, 3100 National Park Road, Juneau, AK, USA

    C. J. Sergeant

  2. National Park Service, Inventory and Monitoring Program, Upper Columbia Basin Network, 105 E Second St, Suite 7, Moscow, ID, USA

    E. N. Starkey

  3. National Park Service, Inventory and Monitoring Program, Southwest Alaska Network, 240 W 5th Ave, #114, Anchorage, AK, USA

    K. K. Bartz

  4. National Park Service, Inventory and Monitoring Program, Chihuahuan Desert Network, New Mexico State University, MSC 3ARP 3655, Research Drive, Genesis Building D, Las Cruces, NM, USA

    M. H. Wilson

  5. School of Fisheries and Ocean Sciences, University of Alaska Fairbanks, 17101 Point Lena Loop Rd, Juneau, AK, USA

    F. J. Mueter

Authors
  1. C. J. Sergeant
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  2. E. N. Starkey
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  3. K. K. Bartz
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  4. M. H. Wilson
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  5. F. J. Mueter
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Corresponding author

Correspondence to C. J. Sergeant.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource 1

(1_CheckNorm_RCode.R): R script assessing data normality (“check.norm” function) (TXT 3 kb)

Online Resource 2

(2_BrokenStick.xlsx): Spreadsheet calculating the broken-stick model (XLSX 15 kb)

Online Resource 3

(3_CaseStudyA.xlsx): Case study A data (Northern Great Plains Network) (XLSX 10 kb)

Online Resource 4

(4_CaseStudyA_RCode.R): Case study A. R script with detailed annotations (TXT 3 kb)

Online Resource 5

(5_CaseStudyB.xlsx). Case study B data (Upper Columbia Basin Network) (XLSX 12 kb)

Online Resource 6

(6_CaseStudyB_RCode.R): Case study B. R script with detailed annotations (TXT 9 kb)

Online Resource 7

(7_CaseStudyC.xlsx): Case study C data (Southwest Alaska Network) (XLSX 13 kb)

Online Resource 8

(8_CaseStudyC_Env.xlsx): Case study C environmental variables data matrix (XLSX 12 kb)

Online Resource 9

(9_CaseStudyC_RCode.R): Case study C. R script with detailed annotation (TXT 18 kb)

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Sergeant, C.J., Starkey, E.N., Bartz, K.K. et al. A practitioner’s guide for exploring water quality patterns using principal components analysis and Procrustes. Environ Monit Assess 188, 249 (2016). https://doi.org/10.1007/s10661-016-5253-z

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  • DOI: https://doi.org/10.1007/s10661-016-5253-z

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