Which randomizations detect convergence and divergence in trait-based community assembly? A test of commonly used null models

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/v3 ◽

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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Community assembly along a soil depth gradient: contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland

Journal of Ecology ◽

10.1111/1365-2745.12003 ◽

2012 ◽

Vol 100 (6) ◽

pp. 1422-1433 ◽

Cited By ~ 190

Author(s):

Maud Bernard-Verdier ◽

Marie-Laure Navas ◽

Mark Vellend ◽

Cyrille Violle ◽

Adeline Fayolle ◽

...

Keyword(s):

Community Assembly ◽

Soil Depth ◽

Depth Gradient ◽

Plant Trait ◽

Trait Convergence ◽

Convergence And Divergence

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Use of null models to compare the assembly of northeast Atlantic bacterial community in the presence of crude oil with either chemical dispersant or biosurfactant

10.1101/2020.12.23.424141 ◽

2020 ◽

Author(s):

Christina Nikolova ◽

Umer Zeeshan Ijaz ◽

Tony Gutierrez

Keyword(s):

Bacterial Community ◽

Crude Oil ◽

Community Assembly ◽

Beta Diversity ◽

Evolutionary Process ◽

Null Model ◽

Null Models ◽

Relative Importance ◽

Diversity Pattern ◽

Chemical Dispersant

AbstractThe compositions of marine microbial communities in response to crude oil in the presence of biosurfactant or synthetic dispersants have been extensively studied in the last decade. Assembly processes, however, in such communities are poorly understood. In this study, we used seven different but complementing null model approaches, such as elements of metacommunity structure, Raup-Crick beta-diversity, normalised stochasticity ratio, Tucker’s null model, quantitative process estimates, lottery assembly, and phylogenetic dispersion models, to quantify the relative importance of ecological process that drive the community assembly. We found that the presence of chemical dispersant in the oil-amended microcosms induced significant temporal changes in the assembly processes that were different from the oil-only or biogenic dispersant-amended microcosms. The assembly processes in all microcosms were neither purely deterministic nor stochastic, but increasingly deterministic in dispersant-amended microcosms. Furthermore, the relative importance of determinisms varied over time and was strongest during the middle phase of incubation. Tucker’s null model revealed that phylogenetically distinct taxa might have shaped the bacterial community assembly in the different microcosms towards more niche or neutral processes. Moreover, there was faster recruitment of phylogenetically distant species in the dispersant-amended community. Drift, homogenising selection and dispersal limitation were the dominant assembly processes in all microcosms, but variable selection was only important in dispersant-amended microcosms. In conclusion, our study highlights that the assembly processes in marine bacterial communities are not static but rather dynamic, and the chemical dispersant can cause significantly different patterns of community assembly compared to non-amended or biosurfactant-amended microcosms.ImportanceThe null model strategy is designed to intentionally exclude an ecological or evolutionary process of interest and create a beta diversity pattern that would be expected in the absence of this particular process – i.e. the community structure is random in respect to the process being tested. Recent advancements of bioinformatics and statistical tools have made it possible to apply theoretical macroecological concepts to microbial metagenomics in order to better understand and quantify the mechanisms and patterns controlling the complexity of microbial ecology. The conclusions from the null models can help predict the changes in microbial biodiversity and ecosystem services in oil polluted environments and therefore assist in making effective decisions with regards to what would be the best oil spill response option for similar environmental conditions.

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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.

Download Full-text