AtlantECO-KER-IM-2

Understanding the drivers and consequences of horizontal gene transfer (HGT) is a key goal of microbial evolution research. Although co-occurring taxa have long been appreciated to undergo HGT more often, this association is confounded with other factors, most notably their phylogenetic relatedness. To disentangle these factors, we analyzed 15 339 marine prokaryotic genomes (mainly bacteria) and their distribution in the global ocean. We identified HGT events across these genomes and enrichments for functions previously shown to be prone to HGT. By mapping metagenomic reads from 1862 ocean samples to these genomes, we also identified co-occurrence patterns and environmental associations. Although we observed an expected negative association between HGT rates and phylogenetic distance, we only detected an association between co-occurrence and phylogenetic distance for closely related taxa. This observation refines the previously reported trend to closely related taxa, rather than a consistent pattern across all taxonomic levels, at least here within marine environments. In addition, we identified a significant association between co-occurrence and HGT, which remains even after controlling for phylogenetic distance and measured environmental variables. In a subset of samples with extended environmental data, we identified higher HGT levels associated with particle-attached prokaryotes and associations of varying directions with specific environmental variables, such as chlorophyll a and photosynthetically available radiation. Overall, our findings demonstrate the significant influence of ecological associations in shaping marine prokaryotic evolution through HGT.

Understanding the complexity of food webs is crucial for assessing ecosystem dynamics. However, the high number of trophic interactions in typical food webs complicates the identification of clear top-down or bottom-up regulatory effects. In this study, we applied interaction asymmetry analysis to examine causality within food webs, allowing for the identification of critical interactions based on topological importance (TI) metrics, which incorporate indirect interactions. We evaluated this method using 34 food web models from the Ecobase database and compared its performance with widely used network metrics. By constructing asymmetry graphs, we transformed original, undirected binary food webs into directed networks highlighting strong causal interactions. Pairwise correlation analyses revealed that ecosystems with higher total biomass indicated stronger bottom-up causal links and greater consumer diversity. Our findings suggest that asymmetry-based metrics provide robust quantitative indicators of causality, offering a straightforward yet powerful tool for assessing ecosystem functioning and health.