Hill-based Dissimilarity Indices and Null Models for Analysis of Microbial Community Assembly

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

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Hill-based dissimilarity indices and null models for analysis of microbial community assembly

10.21203/rs.3.rs-33130/v2 ◽

2020 ◽

Author(s):

Oskar Modin ◽

Raquel Liébana ◽

Soroush Saheb-Alam ◽

Britt-Marie Wilén ◽

Carolina Suarez ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Community Assembly ◽

Null Model ◽

Amplicon Sequencing ◽

Null Models ◽

Marker Genes ◽

Experimental Systems ◽

Microbial Community Assembly ◽

The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

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Hill-based dissimilarity indices and null models for analysis of microbial community assembly

Microbiome ◽

10.1186/s40168-020-00909-7 ◽

2020 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Oskar Modin ◽

Raquel Liébana ◽

Soroush Saheb-Alam ◽

Britt-Marie Wilén ◽

Carolina Suarez ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Community Assembly ◽

Null Model ◽

Amplicon Sequencing ◽

Null Models ◽

Marker Genes ◽

Experimental Systems ◽

Microbial Community Assembly ◽

The Impact

Abstract Background High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases, and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models. Results Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems. Conclusions Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

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Hill-based dissimilarity indices and null models for analysis of microbial community assembly

10.21203/rs.2.24335/v2 ◽

2020 ◽

Author(s):

Oskar Modin ◽

Raquel Liébana ◽

Soroush Sabeh-Alam ◽

Britt-Marie Wilén ◽

Carolina Suarez ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Community Assembly ◽

Null Model ◽

Amplicon Sequencing ◽

Null Models ◽

Marker Genes ◽

Experimental Systems ◽

Microbial Community Assembly ◽

The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

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Hill-based dissimilarity indices and null models for analysis of microbial community assembly

10.21203/rs.2.24335/v1 ◽

2020 ◽

Author(s):

Oskar Modin ◽

Raquel Liébana ◽

Soroush Sabeh-Alam ◽

Britt-Marie Wilén ◽

Carolina Suarez ◽

...

Keyword(s):

Microbial Community ◽

Relative Abundance ◽

Community Assembly ◽

Null Model ◽

Amplicon Sequencing ◽

Marker Genes ◽

Rrna Gene ◽

Data Set ◽

Microbial Community Assembly ◽

The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choices of bioinformatics pipeline and dissimilarity index can have considerable impacts on results and conclusions. Analysis of the AGS data set showed that results are sensitive to bioinformatics choices when dissimilarities between sample groups are compared with incidence-based indices. Analysis of the MFC data set with a combination of Hill-based indices and a null model revealed that random dispersal could explain the distribution of both rare and highly abundant taxa within a glucose-fed MFC whereas the distribution of taxa of intermediate relative abundance was governed by heterogeneous selection.Conclusions: Hill-based indices provides a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic factors on taxa of low-, intermediate-, and high relative abundance during microbial community assembly can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv).

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A conceptual framework for the phylogenetically constrained assembly of microbial communities

Microbiome ◽

10.1186/s40168-019-0754-y ◽

2019 ◽

Vol 7 (1) ◽

Cited By ~ 5

Author(s):

Daniel Aguirre de Cárcer

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Community Assembly ◽

Phylogenetic Signal ◽

A Priori ◽

Amplicon Sequencing ◽

Bioinformatic Analysis ◽

Microbial Ecosystems ◽

And Function ◽

Microbial Community Assembly

Abstract Microbial communities play essential and preponderant roles in all ecosystems. Understanding the rules that govern microbial community assembly will have a major impact on our ability to manage microbial ecosystems, positively impacting, for instance, human health and agriculture. Here, I present a phylogenetically constrained community assembly principle grounded on the well-supported facts that deterministic processes have a significant impact on microbial community assembly, that microbial communities show significant phylogenetic signal, and that microbial traits and ecological coherence are, to some extent, phylogenetically conserved. From these facts, I derive a few predictions which form the basis of the framework. Chief among them is the existence, within most microbial ecosystems, of phylogenetic core groups (PCGs), defined as discrete portions of the phylogeny of varying depth present in all instances of the given ecosystem, and related to specific niches whose occupancy requires a specific phylogenetically conserved set of traits. The predictions are supported by the recent literature, as well as by dedicated analyses. Integrating the effect of ecosystem patchiness, microbial social interactions, and scale sampling pitfalls takes us to a comprehensive community assembly model that recapitulates the characteristics most commonly observed in microbial communities. PCGs’ identification is relatively straightforward using high-throughput 16S amplicon sequencing, and subsequent bioinformatic analysis of their phylogeny, estimated core pan-genome, and intra-group co-occurrence should provide valuable information on their ecophysiology and niche characteristics. Such a priori information for a significant portion of the community could be used to prime complementing analyses, boosting their usefulness. Thus, the use of the proposed framework could represent a leap forward in our understanding of microbial community assembly and function.

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A quantitative framework reveals the ecological drivers of grassland soil microbial community assembly in response to warming

10.1101/2020.02.22.960872 ◽

2020 ◽

Author(s):

Daliang Ning ◽

Mengting Yuan ◽

Linwei Wu ◽

Ya Zhang ◽

Xue Guo ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Community Assembly ◽

Soil Microbial Community ◽

Null Model ◽

Relative Importance ◽

Community Based ◽

Grassland Soil ◽

Soil Microbial ◽

Microbial Community Assembly

AbstractUnraveling the drivers controlling community assembly is a central issue in ecology. Selection, dispersal, diversification and drift are conceptually accepted as major community assembly processes. Defining their relative importance in governing biodiversity is compellingly needed, but very challenging. Here, we present a novel framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). Our results with simulated microbial communities showed that iCAMP had high accuracy (0.93 - 0.99), precision (0.80 - 0.94), sensitivity (0.82 - 0.94), and specificity (0.95 - 0.98), which were 10-160% higher than those from the entire community-based approach. Applying it to grassland microbial communities in response to experimental warming, our analysis showed that homogeneous selection (38%) and “drift” (59%) played dominant roles in controlling grassland soil microbial community assembly. Interestingly, warming enhanced homogeneous selection, but decreased “drift” over time. Warming-enhanced selection was primarily imposed on Bacillales in Firmicutes, which were strengthened by increased drought and reduced plant productivity. This general framework should also be useful for plant and animal ecology.

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Deterministic Selection Dominates Microbial Community Assembly in Termite Mounds Across a Large Spatial Area

10.21203/rs.3.rs-34782/v1 ◽

2020 ◽

Author(s):

Qing-Lin Chen ◽

Hang-Wei Hu ◽

Zhen-Zhen Yan ◽

Chao-Yu Li ◽

Bao-Anh Thi Nguyen ◽

...

Keyword(s):

Microbial Community ◽

Stochastic Processes ◽

Microbial Communities ◽

Fungal Community ◽

Community Assembly ◽

Distance Decay ◽

Termite Mounds ◽

Deterministic Processes ◽

Microbial Community Assembly

Abstract Background: Termites are ubiquitous insects in tropical and subtropical habitats, where they construct massive mounds from soil, their saliva and excreta. Termite mounds harbor an enormous amount of microbial inhabitants, which regulate multiple ecosystem functions such as mitigating methane emissions and increasing ecosystem resistance to climate change. However, we lack a mechanistic understanding about the role of termite mounds in modulating the microbial community assembly processes, which are essential to unravel the biological interactions of soil fauna and microorganisms, the major components of soil food webs. We conducted a large-scale survey across a >1500 km transect in northern Australia to investigate biogeographical patterns of bacterial and fungal community in 134 termite mounds and the relative importance of deterministic versus stochastic processes in microbial community assembly. Results: Microbial alpha (number of phylotypes) and beta (changes in bacterial and fungal community composition) significantly differed between termite mounds and surrounding soils. Microbial communities in termite mounds exhibited a significant distance-decay pattern, and fungal communities had a stronger distance-decay relationship (slope = -1.91) than bacteria (slope = -0.21). Based on the neutral community model (fitness < 0.7) and normalized stochasticity ratio index (NST) with a value below the 50% boundary point, deterministic selection, rather than stochastic forces, predominated the microbial community assembly in termite mounds. Deterministic processes exhibited significantly weaker impacts on bacteria (NST = 45.23%) than on fungi (NST = 33.72%), probably due to the wider habitat niche breadth and higher potential migration rate of bacteria. The abundance of antibiotic resistance genes (ARGs) was negatively correlated with bacterial/fungal biomass ratios, indicating that ARG content might be an important biotic factor that drove the biogeographic pattern of microbial communities in termite mounds. Conclusions: Deterministic processes play a more important role than stochastic processes in shaping the microbial community assembly in termite mounds, an unique habitat ubiquitously distributed in tropical and subtropical ecosystems. An improved understanding of the biogeographic patterns of microorganisms in termite mounds is crucial to decipher the role of soil faunal activities in shaping microbial community assembly, with implications for their mediated ecosystems functions and services.

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A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming

Nature Communications ◽

10.1038/s41467-020-18560-z ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Daliang Ning ◽

Mengting Yuan ◽

Linwei Wu ◽

Ya Zhang ◽

Xue Guo ◽

...

Keyword(s):

Animal Ecology ◽

Microbial Community ◽

Community Assembly ◽

Null Model ◽

High Accuracy ◽

Relative Importance ◽

Experimental Warming ◽

Community Based ◽

Microbial Community Assembly ◽

Over Time

Abstract Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93–0.99), precision (0.80–0.94), sensitivity (0.82–0.94), and specificity (0.95–0.98) on simulated communities, which are 10–160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and ‘drift’ (59%). Interestingly, warming decreases ‘drift’ over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology.

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