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A guide to the application of Hill numbers to DNA‐based diversity analyses
Author(s) -
Alberdi Antton,
Gilbert M. Thomas P.
Publication year - 2019
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.13014
Subject(s) - biology , biodiversity , diversity (politics) , scripting language , phylogenetic tree , phylogenetic diversity , abundance (ecology) , data science , computational biology , ecology , evolutionary biology , data mining , statistics , computer science , genetics , mathematics , sociology , anthropology , gene , operating system
With the advent of DNA sequencing‐based techniques, the way we detect and measure biodiversity is undergoing a radical shift. There is also an increasing awareness of the need to employ intuitively meaningful diversity measures based on unified statistical frameworks, so that different results can be easily interpreted and compared. This article aimed to serve as a guide to implementing biodiversity assessment using the general statistical framework developed around Hill numbers into the analysis of systems characterized using DNA sequencing‐based techniques (e.g., diet, microbiomes and ecosystem biodiversity). Specifically, we discuss (a) the DNA‐based approaches for defining the types upon which diversity is measured, (b) how to weight the importance of each type, (c) the differences between abundance‐based versus incidence‐based approaches, (d) the implementation of phylogenetic information into diversity measurement, (e) hierarchical diversity partitioning, (f) dissimilarity and overlap measurement and (g) how to deal with zero‐inflated, insufficient and biased data. All steps are reproduced with real data to also provide step‐by‐step bash and R scripts to enable straightforward implementation of the explained procedures.