Phylogenetic Comparative Approach Reveals Evolutionary Conservatism, Ancestral Composition, and Integration of Vertebrate Gut Microbiota.

How host-associated microbial communities evolve as their hosts diversify remains equivocal: how conserved is their composition? What was the composition of ancestral microbiota? Do microbial taxa covary in abundance over millions of years? Multivariate phylogenetic models of trait evolution are key to answering similar questions for complex host phenotypes, yet they are not directly applicable to relative abundances, which usually characterize microbiota. Here, we extend these models in this context, thereby providing a powerful approach for estimating phylosymbiosis (the extent to which closely related host species harbor similar microbiota), ancestral microbiota composition, and integration (evolutionary covariations in bacterial abundances). We apply our model to the gut microbiota of mammals and birds. We find significant phylosymbiosis that is not entirely explained by diet and geographic location, indicating that other evolutionary-conserved traits shape microbiota composition. We identify main shifts in microbiota composition during the evolution of the two groups and infer an ancestral mammalian microbiota consistent with an insectivorous diet. We also find remarkably consistent evolutionary covariations among bacterial orders in mammals and birds. Surprisingly, despite the substantial variability of present-day gut microbiota, some aspects of their composition are conserved over millions of years of host evolutionary history.

Quantifying the impact of ecological memory on the dynamics of interacting communities.

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

Modelling spatial patterns in host-associated microbial communities.

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.

Global drivers of eukaryotic plankton biogeography in the sunlit ocean.

Eukaryotic plankton are a core component of marine ecosystems with exceptional taxonomic and ecological diversity, yet how their ecology interacts with the environment to drive global distribution patterns is poorly understood. In this work, we use Tara Oceans metabarcoding data, which cover all major ocean basins, combined with a probabilistic model of taxon co-occurrence to compare the biogeography of 70 major groups of eukaryotic plankton. We uncover two main axes of biogeographic variation. First, more-diverse groups display clearer biogeographic patterns. Second, large-bodied consumers are structured by oceanic basins, mostly through the main current systems, whereas small-bodied phototrophs are structured by latitude and follow local environmental conditions. Our study highlights notable differences in biogeographies across plankton groups and investigates their determinants at the global scale.

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

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.

Inferring neutral biodiversity parameters using environmental DNA data sets.

The DNA present in the environment is a unique and increasingly exploited source of information for conducting fast and standardized biodiversity assessments for any type of organisms. The datasets resulting from these surveys are however rarely compared to the quantitative predictions of biodiversity models. In this study, we simulate neutral taxa-abundance datasets, and artificially noise them by simulating noise terms typical of DNA-based biodiversity surveys. The resulting noised taxa abundances are used to assess whether the two parameters of Hubbell's neutral theory of biodiversity can still be estimated. We find that parameters can be inferred provided that PCR noise on taxa abundances does not exceed a certain threshold. However, inference is seriously biased by the presence of artifactual taxa. The uneven contribution of organisms to environmental DNA owing to size differences and barcode copy number variability does not impede neutral parameter inference, provided that the number of sequence reads used for inference is smaller than the number of effectively sampled individuals. Hence, estimating neutral parameters from DNA-based taxa abundance patterns is possible but requires some caution. In studies that include empirical noise assessments, our comprehensive simulation benchmark provides objective criteria to evaluate the robustness of neutral parameter inference.