A guide to the application of Hill numbers to DNA‐based diversity analyses

Abstract

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.

DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats

“…High-throughput DNA sequencing-based (DNA metabarcoding) and species distribution modelling (SDM) tools now enable more nuanced analysis of dietary and spatial patterns. DNA metabarcoding allows comprehensive analysis of dietary variation, by considering different components of dietary diversity, such as richness (how many prey types are consumed), evenness (the balance of the relative consumption of each prey), and regularity (the degree of similarity across consumed prey) 14,15 . SDMs predict how presence probability is distributed across the geographic range of a species, hence enabling the estimation of homogeneity of the spatial distribution.…”

hilldiv: an R package for the integral analysis of diversity based on Hill numbers

“…One such general statistical framework that can enable diversity analysis is that developed around the so-called 'Hill numbers' (Hill, 1973;Jost, 2006). This framework provides a robust toolset with which to perform the most common operations researchers routinely use when analysing the diversity of biological systems, and includes among others, diversity measurement and estimation, diversity partitioning, diversity decomposition and (dis)similarity computation (Alberdi & Gilbert, 2019). R packages containing functions to perform basic diversity analyses based on Hill numbers already exist, including entropart (Marcon & Hérault, 2015), vegan (Oksanen et al, 2013), vegetarian (Charney & Record, 2012), hillR and simba (Jurasinski, 2007).…”

Decision making and best practices for taxonomy-free eDNA metabarcoding in biomonitoring using Hill numbers

“…We calculated site-and coverage-based rarefaction and extrapolation curves 225 (Chao & Jost, 2012;Colwell et al, 2012) for the catchment diversity with the 'iNEXT' R package (Hsieh,226 Ma, & Chao, 2016, version 2.0.20). To do so, we utilized incidence data, which are based on relative 227 detection (presence/absence) over the whole catchment rather than relative abundances at the individual 228 sites (Alberdi & Gilbert, 2019a;Colwell & Coddington, 1994). To assess in how the different stringency treatments are affecting the diversity measures at the individual 231 sampling sites, we calculated Hill numbers for each of the five treatments using presence/absence data 232 (order 0) and abundance based data (order 1 and 2).…”

“…Thus, a coherent understanding of 322 diversity measures needs to consider also abundance-based estimates, and do so in ways that are com-323 parable across and within data sets. This is possible in the mathematical unifying framework of Hill 324 numbers when calculating diversity measures (Alberdi & Gilbert, 2019a;Chao et al, 2014;Jost, 2007).…”