Latent Dirichlet Allocation reveals spatial and taxonomic structure in a DNA‐based census of soil biodiversity from a tropical forest

Abstract

High‐throughput sequencing of amplicons from environmental DNA samples permits rapid, standardized and comprehensive biodiversity assessments. However, retrieving and interpreting the structure of such data sets requires efficient methods for dimensionality reduction. Latent Dirichlet Allocation (LDA) can be used to decompose environmental DNA samples into overlapping assemblages of co‐occurring taxa. It is a flexible model‐based method adapted to uneven sample sizes and to large and sparse data sets. Here, we compare LDA performance on abundance and occurrence data, and we quantify the robustness of the LDA decomposition by measuring its stability with respect to the algorithm's initialization. We then apply LDA to a survey of 1,131 soil DNA samples that were collected in a 12‐ha plot of primary tropical forest and amplified using standard primers for bacteria, protists, fungi and metazoans. The analysis reveals that bacteria, protists and fungi exhibit a strong spatial structure, which matches the topographical features of the plot, while metazoans do not, confirming that microbial diversity is primarily controlled by environmental variation at the studied scale. We conclude that LDA is a sensitive, robust and computationally efficient method to detect and interpret the structure of large DNA‐based biodiversity data sets. We finally discuss the possible future applications of this approach for the study of biodiversity.

Global drivers of eukaryotic plankton biogeography in the sunlit ocean

“…Once we had selected the K value, we ran 100 independent MCMC chains on the whole dataset from random initial conditions. To check for potential insufficient mixing along the chains, we measured the similarity in the spatial distribution of assemblages across the chains (Table S1), using the metric defined in Sommeria-Klein et al (2019). We picked the chain with posterior probability closest to the mean across chains for the final interpretation.…”

“…The data encompass 250,057 eukaryotic Operational Taxonomic Units (OTUs) sampled globally at the surface and at the Deep Chlorophyl Maximum (DCM) across 129 stations. We use a probabilistic model that allows identification of a number of ‘assemblages’, each of which represents a set of OTUs that tend to co-occur across samples (Sommeria-Klein et al, 2019; Valle, Baiser, Woodall, & Chazdon, 2014; Methods). Each local planktonic community can then be seen as a sample drawn in various proportions from the assemblages.…”

Revealing the microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation

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Stratification of the Gut Microbiota Composition Landscape Across the Alzheimer’s Disease Continuum in a Turkish Cohort

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