Ordination analysis in sedimentology, geochemistry and paleoenvironment - background, current trends and recommendations

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Or M. Bialik , Emilia Jarochowska, Michal Grossowicz

Abstract

Ordination is the name given to a group of methods used to analyze multiple variables without preceding hypotheses. Over the last few decades the use of these methods in Earth science in general, and notably in analyses of sedimentary sources, has dramatically increased. However, with limited resources oriented towards Earth scientists on the topic, the application of ordination analysis is at times suboptimal and misuse by authors can occur. This text was written for researchers with little to no experience with ordination with the aim of exposing them to the utility and the pitfalls of this branch of exploratory statistics. To do so, we offer a detailed review of three ordination methods: principal component analysis (PCA), non-metric multidimensional scaling (NMDS) and detrended correspondence analysis (DCA). We then present a survey of 163 publications in Earth science, in which these ordination methods were used. We summarize how, why and on what type of data ordination was used and outline common mistakes and misuses identified in those publications. Notably, we found issues with reproducibility, documentation, data set dimensions and transformations. Based on this survey, we offer a recommended workflow for Earth scientists who wish to apply ordination. Additionally, this article is accompanied by highly annotated R scripts for novice users to use these methods.

DOI

https://doi.org/10.31223/X5N31Q

Subjects

Biogeochemistry, Earth Sciences, Environmental Indicators and Impact Assessment, Environmental Monitoring, Geochemistry, Geology, Geomorphology, Geophysics and Seismology, Glaciology, Multivariate Analysis, Oil, Gas, and Energy, Other Earth Sciences, Paleobiology, Paleontology, Sedimentology

Keywords

clustering, multivariate statistics, PCA, NMDS, DCA, exploratory statistics, dimension reduction

Dates

Published: 2021-02-01 12:50

Last Updated: 2021-02-01 15:50

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None