Dataset Information

Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal, environmental filtering, and stochasticity on community assembly.

ABSTRACT: Identifying the influence of stochastic processes and of deterministic processes, such as dispersal of individuals of different species and trait-based environmental filtering, has long been a challenge in studies of community assembly. Here, we present the Univariate Community Assembly Analysis (UniCAA) and test its ability to address three hypotheses: species occurrences within communities are (a) limited by spatially restricted dispersal; (b) environmentally filtered; or (c) the outcome of stochasticity-so that as community size decreases-species that are common outside a local community have a disproportionately higher probability of occurrence than rare species. The comparison with a null model allows assessing if the influence of each of the three processes differs from what one would expect under a purely stochastic distribution of species. We tested the framework by simulating "empirical" metacommunities under 15 scenarios that differed with respect to the strengths of spatially restricted dispersal (restricted vs. not restricted); habitat isolation (low, intermediate, and high immigration rates); and environmental filtering (strong, intermediate, and no filtering). Through these tests, we found that UniCAA rarely produced false positives for the influence of the three processes, yielding a type-I error rate ?5%. The type-II error rate, that is, production of false negatives, was also acceptable and within the typical cutoff (20%). We demonstrate that the UniCAA provides a flexible framework for retrieving the processes behind community assembly and propose avenues for future developments of the framework.

SUBMITTER: Sydenham MAK

PROVIDER: S-EPMC6374725 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal, environmental filtering, and stochasticity on community assembly.

Sydenham Markus A K MAK Moe Stein R SR Steinert Mari M Eldegard Katrine K

Ecology and evolution 20190113 3

Identifying the influence of stochastic processes and of deterministic processes, such as dispersal of individuals of different species and trait-based environmental filtering, has long been a challenge in studies of community assembly. Here, we present the Univariate Community Assembly Analysis (UniCAA) and test its ability to address three hypotheses: species occurrences within communities are (a) limited by spatially restricted dispersal; (b) environmentally filtered; or (c) the outcome of st ...[more]

PMID: 30805175

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Project description:Understory vegetation accounts for the majority of plant species diversity and serves as a driver of overstory succession and nutrient cycling in boreal forest ecosystems. However, investigations of the underlying assembly processes of understory vegetation associated with stand development following a wildfire disturbance are rare, particularly in Eurasian boreal forests. In this study, we measured the phylogenetic and functional diversity and trait dispersions of understory communities and tested how these patterns changed with stand age in the Great Xing'an Mountains of Northeastern China. Contrary to our expectation, we found that understory functional traits were phylogenetically convergent. We found that random patterns of phylogenetic, functional, and trait dispersions were dominant for most of our surveyed plots, indicating that stochastic processes may play a crucial role in the determination of understory community assembly. Yet, there was an evidence that understory community assembly was also determined by competitive exclusion and environmental filtering to a certain degree, which was demonstrated by the observed clustered phylogenetic and functional patterns in some plots. Our results showed that phylogenetic diversity significantly decreased, while functional diversity increased with stand age. The observed shift trends in phylogenetic and functional patterns between random to clustering along with stand age, which suggested that understory community assembly shifted from stochasticity to competitive exclusion and environmental filtering. Our study presented a difference to community assembly and species coexistence theories insisted solely on deterministic processes. These findings indicated that Eurasian boreal understory communities may be primarily regulated by stochastic processes, providing complementary evidence that stochastic processes are crucial in the determination of community assembly both in tropical and boreal forests.

Project description:BackgroundHigh-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.ResultsUsing 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.ConclusionsHill-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. Video Abstract.

Dataset Information

Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal, environmental filtering, and stochasticity on community assembly.

Publications

Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal, environmental filtering, and stochasticity on community assembly.

Similar Datasets

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