Writing statistical methods for ecologists

Abstract

The Methods section is a key component of any ecology research publication containing detailed information on how the data were collected and analyzed. However, descriptions of which statistical methods were used and how they were applied can substantially vary and may not provide enough information for the analyses to be reproducible. Computational and statistical programming advances have allowed ecological researchers without a strong statistical or mathematical background to access and use increasingly complex statistical methods. Thus, statistical methods are written by and need to be accessible to researchers across a range of quantitative expertise. Poorly written Methods sections can incorrectly inflate the strength of or call into question the results of an analysis. Although there are resources available, we have not found one that is specific to writing statistical methods, includes all the elements we discuss, and is targeted for ecologists. Here we provide guidelines for ecological researchers when writing statistical methods and review frequent errors made in Statistical Methods sections. We highlight some common dos and don'ts when writing Statistical Methods sections and present a simple checklist to help guide authors with their writing to ensure reproducibility. We illustrate the use of this guidance with two examples.

Seasonal Variation in Gut Microbiota of the Wild Daurian Ground Squirrel (Spermophilus dauricus): Metagenomic Insights into Seasonal Breeding

“…Species composition histograms, functional statistics plots, and bubble plots for differential enrichment analysis were created using the R package ggplot2 V3.1.0 [52], and species diversity analysis plots were created using the online website ImageGP [53]. This section is written with reference to an article on writing methods [54].…”

The Use of R and R Packages in Biodiversity Conservation Research

“…Therefore, it is also included in categories of both "Biodiversity Conservation" and "Ecology" on the Web of Science. The higher use of R in this journal stems primarily from the fact that the field of ecology represents a data-intensive area of study, often demanding advanced computational skills [25][26][27].…”