Open AccessOrdination analysis in sedimentology, geochemistry and palaeoenvironment—Background, current trends and recommendations
Author(s) -
Bialik Or M.,
Jarochowska Emilia,
Grossowicz Michal
Publication year - 2021
Publication title -
the depositional record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.604
H-Index - 3
ISSN - 2055-4877
DOI - 10.1002/dep2.161
Subject(s) - ordination , data science , workflow , multidimensional scaling , detrended correspondence analysis , exploratory data analysis , computer science , documentation , set (abstract data type) , metric (unit) , data mining , engineering , machine learning , database , programming language , operations management
Ordination is the name given to a group of methods used to analyse 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, a detailed review of three ordination methods is offered: principal component analysis, non‐metric multidimensional scaling and detrended correspondence analysis. A survey of 163 publications in Earth science is presented, in which these ordination methods were used together with a summary of how, why and on what type of data ordination was used. With common mistakes outlined and misuses in those publications identified. Notably, issues were found with reproducibility, documentation, data set dimensions and transformations. Based on this survey, a recommended workflow is offered 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.