Species co‐occurrence networks: Can they reveal trophic and non‐trophic interactions in ecological communities? (original) (raw)

Species abundance correlations carry limited information about microbial network interactions

PLOS Computational Biology

Unraveling the network of interactions in ecological communities is a daunting task. Common methods to infer interspecific interactions from cross-sectional data are based on co-occurrence measures. For instance, interactions in the human microbiome are often inferred from correlations between the abundances of bacterial phylogenetic groups across subjects. We tested whether such correlation-based methods are indeed reliable for inferring interaction networks. For this purpose, we simulated bacterial communities by means of the generalized Lotka-Volterra model, with variation in model parameters representing variability among hosts. Our results show that correlations can be indicative for presence of bacterial interactions, but only when measurement noise is low relative to the variation in interaction strengths between hosts. Indication of interaction was affected by type of interaction network, process noise and sampling under non-equilibrium conditions. The sign of a correlation ...

Molecular ecological network analyses

2012

Background: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

Inferring species roles in metacommunity structure from species co-occurrence networks

Proceedings of the Royal Society B: Biological Sciences, 2014

A long-standing question in community ecology is what determines the identity of species that coexist across local communities or metacommunity assembly. To shed light upon this question, we used a network approach to analyse the drivers of species co-occurrence patterns. In particular, we focus on the potential roles of body size and trophic status as determinants of metacommunity cohesion because of their link to resource use and dispersal ability. Small-sized individuals at low-trophic levels, and with limited dispersal potential, are expected to form highly linked subgroups, whereas large-size individuals at higher trophic positions, and with good dispersal potential, will foster the spatial coupling of subgroups and the cohesion of the whole metacommunity. By using modularity analysis, we identified six modules of species with similar responses to ecological conditions and high co-occurrence across local communities. Most species either co-occur with species from a single modul...

Cross-biome comparison of microbial association networks

Frontiers in Microbiology, 2015

Clinical and environmental meta-omics studies are accumulating an ever-growing amount of microbial abundance data over a wide range of ecosystems. With a sufficiently large sample number, these microbial communities can be explored by constructing and analyzing co-occurrence networks, which detect taxon associations from abundance data and can give insights into community structure. Here, we investigate how co-occurrence networks differ across biomes and which other factors influence their properties. For this, we inferred microbial association networks from 20 different 16S rDNA sequencing data sets and observed that soil microbial networks harbor proportionally fewer positive associations and are less densely interconnected than host-associated networks. After excluding sample number, sequencing depth and beta-diversity as possible drivers, we found a negative correlation between community evenness and positive edge percentage. This correlation likely results from a skewed distribution of negative interactions, which take place preferentially between less prevalent taxa. Overall, our results suggest an under-appreciated role of evenness in shaping microbial association networks.

A network approach for inferring species associations from co-occurrence data

Ecography, 2016

by broad-scale models, then stacking independent species distribution models to predict species assemblages (sensu Guisan and Rahbek 2011, Calabrese et al. 2014) will provide misleading predictions of fine-scale community assembly. Thus, a better understanding of species associations across scales could improve predictions of the dynamics of local community composition in changing environments. The goal of this paper is to improve the tools needed to detect interspecific associations from co-occurrence data. We first briefly describe the development of co-occurrence methods and then draw from different lines of research to present a more complete and flexible general framework for inferring species associations that overcomes multiple challenges faced by previous approaches. From experiments to co-occurrence methods Efforts to infer species associations and their role in structuring communities have a long history. Traditionally, associations have been derived from small-scale field observations

Microbial co-occurrence networks as a biomonitoring tool for aquatic environments: a review

Marine and Freshwater Research, 2022

Aquatic microbial ecosystems are increasingly under threat from human activities, highlighting the need to for the development and application of biomonitoring tools that can identify anthropogenically induced stress across a wide range of environments. To date, microbial biomonitoring has generally focussed on community composition and univariate endpoints, which do not provide discrete information about how species both interact with each other and as a collective. To address this, co-occurrence networks are being increasingly used to complement traditional community metrics. Co-occurrence network analysis is a quantitative analytical tool that examines the interactions between nodes (e.g. taxa) and their strengths. This information can be integrated and visualised as a network, whose characteristics and topological structures can be quantified. To date, co-occurrence network analysis has rarely been applied to aquatic systems. Here we explore the potential of co-occurrence networks as a biomonitoring tool in aquatic environments, demonstrating its capacity to provide a more comprehensive view of how microbial, notably bacterial, communities may be altered by human activities. We examine the key attributes of networks and providence evidence of how these may change as a response to disturbances while also highlighting some of the challenges associated with making the approach routine.

Significant Pairwise Co-occurrence Patterns Are Not the Rule in the Majority of Biotic Communities

Diversity, 2012

Our aim was to investigate species co-occurrence patterns in a large number of published biotic communities, in order to document to what extent species associations can be found in presence-absence matrices. We also aim to compare and evaluate two metrics that focus on species pairs (the 'natural' and the 'checkerboard' metric) using also artificial matrices. We applied the two metrics to many data sets from a huge variety of insular systems around the world. Both metrics reliably recover deviating species pairs and provide similar, albeit not identical, results. Nevertheless, only a few matrices exhibit significant deviations from random patterns, mostly vertebrates and higher plants. The benchmark cases cited in literature in favor of such assembly rules are indeed included in these exceptional cases. In conclusion, competitive or cooperative species interactions shaping communities cannot be inferred from patterns exhibited by presence-absence matrices. When such an analysis is attempted though, both the 'natural' and the 'checkerboard' metric should be set in a proper framework in order to provide useful insights regarding species associations. A large part of the discussion on species co-occurrence had originally been based on a few exceptional data sets that are not indicative of general patterns.

Investigating microbial co-occurrence patterns based on metagenomic compositional data

Bioinformatics (Oxford, England), 2015

The high-throughput sequencing technologies have provided a powerful tool to study the microbial organisms living in various environments. Characterizing microbial interactions can give us insights into how they live and work together as a community. Metagonomic data are usually summarized in a compositional fashion due to varying sampling/sequencing depths from one sample to another. We study the co-occurrence patterns of microbial organisms using their relative abundance information. Analyzing compositional data using conventional correlation methods has been shown prone to bias that leads to artifactual correlations. We propose a novel method, REBACCA, to identify significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank. To be specific, we construct the system using log ratios of count data and solve the system using the l1-norm shrinkage method. Our comprehensive simulation studies show that REBACCA 1) achieves higher accuracy in general t...

Inferring biotic interactions from proxies

Trends in Ecology & Evolution, 2015

Inferring biotic interactions from functional, phylogenetic and geographical proxies remains one great challenge in ecology. We propose a conceptual framework to infer the backbone of biotic interaction networks within regional species pools. First, interacting groups are identified to order links and remove forbidden interactions between species. Second, additional links are removed by examination of the geographical context in which species co-occur. Third, hypotheses are proposed to establish interaction probabilities between species. We illustrate the framework using published food-webs in terrestrial and marine systems. We conclude that preliminary descriptions of the web of life can be made by careful integration of data with theory.

Resistant microbial co-occurrence patterns inferred by network topology

Applied and environmental microbiology, 2015

Although complex co-occurrence patterns have been described among microbes in natural communities, these patterns have scarcely been interpreted in the context of ecosystem functioning and stability. Here, we constructed networks from species co-occurrences between pairs of microorganisms, which were extracted from five individual aquatic time-series, including a dystrophic and a eutrophic lake, as well as an open ocean site. The resulting networks exhibited higher clustering coefficient, shorter path lengths, higher average node degree and betweenness when compared to random networks. Moreover, computer simulations demonstrated that taxa with a high number of co-occurrences and placement at convergence positions in the network, so called 'hubs' and 'bottlenecks', confer resistance against random removal of 'taxa'. Accordingly, we refer to co-occurrences at convergence positions as system-relevant interdependencies, as they, like 'hubs' and 'bottl...