Extracting abundance information from DNA‐based data. (30th August 2022)

• Record Type: Journal Article
• Authors: Luo, Mingjie; Ji, Yinqiu; Warton, David; Yu, Douglas W.
• Is Part Of: Molecular ecology resources. Volume 23:Number 1(2023)
• Page Start: 174
• Page End: 189
• Publication Date: 2022-08-30
• Subjects: Arthropoda -- biomonitoring -- community composition -- DNA barcoding -- environmental DNA -- Insecta -- internal standard -- polymerase chain reaction -- taxonomic bias; Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13703
• Languages: English
• ISSNs: 1755-098X

Abstract

Abstract: The accurate extraction of species‐abundance information from DNA‐based data (metabarcoding, metagenomics) could contribute usefully to diet analysis and food‐web reconstruction, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, multiple sources of bias and noise in sampling and processing combine to inject error into DNA‐based data sets. To understand how to extract abundance information, it is useful to distinguish two concepts. (i) Within‐sample across‐species quantification describes relative species abundances in one sample. (ii) Across‐sample within‐species quantification describes how the abundance of each individual species varies from sample to sample, such as over a time series, an environmental gradient or different experimental treatments. First, we review the literature on methods to recover across‐species abundance information (by removing what we call "species pipeline biases") and within‐species abundance information (by removing what we call "pipeline noise"). We argue that many ecological questions can be answered with just within‐species quantification, and we therefore demonstrate how to use a "DNA spike‐in" to correct for pipeline noise and recover within‐species abundance information. We also introduce a model‐based estimator that can be used on data sets without a physical spike‐in to approximate and correct for pipeline noise.