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Abstract
Interactions occur between two or more organisms affecting each other. Interactions are decisive for the ecology of the organisms. Without direct experimental evidence the analysis of interactions is difficult. Correlation analyses that are based on co-occurrences are often used to approximate interaction. Here, we present a new mathematical model to estimate the interaction strengths between taxa, based on changes in their relative abundances across environmental gradients.
Highlights
The composition of microbial communities is a key driver of ecological processes [1,2,3]
Our first application of the methods developed in the present study to real-world data allowed us to identify several biotic interactions that are likely to shape soil microbial communities but were previously not recognized
Inferring interactions in complex microbial communities spectrum from intra-specific, over intra-phylum, to inter-domain interactions in complex microbial communities
Summary
The composition of microbial communities is a key driver of ecological processes [1,2,3]. A major step forward in analyzing the composition of microbial communities in the environment has been the advent of high-throughput next-generation sequencing technology for microbial DNA or RNA extracted from the environment This enables the determination of the relative abundance of many taxa per sample and the analysis of co-occurrence and correlation patterns, which have been suggested as proxies for species interactions [7, 11]. Here, we present a novel method which is based on generalized Lotka-Volterra models and allows to quantify interactions from high-throughput microbial sequence data derived from cross-sectional samples for which larger datasets on environmental parameters are available This method does not rely on data obtained during different time points, since the strength and direction of interactions between partners are a function of gradients in abiotic environmental parameters, for example of temperature [13], nutrient conditions [14] or.
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