A guide to the application of Hill numbers to DNA‐based diversity analyses
Data Availability Statement:: The data and scripts needed to reproduce the analyses mentioned in the article are available as Supporting Information.
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.
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Number of times cited according to CrossRef: 7
- Caitlin I Looby, Patrick H Martin, Diversity and function of soil microbes on montane gradients: the state of knowledge in a changing world, FEMS Microbiology Ecology, 10.1093/femsec/fiaa122, 96, 9, (2020).
- Shawn Narum, Joanna Kelley, Ben Sibbett, Editorial 2020, Molecular Ecology Resources, 10.1111/1755-0998.13125, 20, 1, (1-7), (2020).
- Marian L Schmidt, Bopaiah A Biddanda, Anthony D Weinke, Edna Chiang, Fallon Januska, Ruben Props, Vincent J Denef, Microhabitats are associated with diversity–productivity relationships in freshwater bacterial communities, FEMS Microbiology Ecology, 10.1093/femsec/fiaa029, 96, 4, (2020).
- Antton Alberdi, Orly Razgour, Ostaizka Aizpurua, Roberto Novella-Fernandez, Joxerra Aihartza, Ivana Budinski, Inazio Garin, Carlos Ibáñez, Eñaut Izagirre, Hugo Rebelo, Danilo Russo, Anton Vlaschenko, Violeta Zhelyazkova, Vida Zrnčić, M. Thomas P. Gilbert, DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats, Nature Communications, 10.1038/s41467-020-14961-2, 11, 1, (2020).
- Oskar Modin, Raquel Liébana, Soroush Saheb-Alam, Britt-Marie Wilén, Carolina Suarez, Malte Hermansson, Frank Persson, Hill-based dissimilarity indices and null models for analysis of microbial community assembly, Microbiome, 10.1186/s40168-020-00909-7, 8, 1, (2020).
- Julia Tiede, Melanie Diepenbruck, Jürgen Gadau, Bernd Wemheuer, Rolf Daniel, Christoph Scherber, Seasonal variation in the diet of the serotine bat (Eptesicus serotinus): A high-resolution analysis using DNA metabarcoding, Basic and Applied Ecology, 10.1016/j.baae.2020.09.004, (2020).
- Leonie Suter, Andrea Maree Polanowski, Laurence John Clarke, John Andrew Kitchener, Bruce Emerson Deagle, Capturing open ocean biodiversity: Comparing environmental DNA metabarcoding to the continuous plankton recorder, Molecular Ecology, 10.1111/mec.15587, 0, 0, (2020).




