However, a critical step towards a quantitative understanding is to also characterize interactions, i.e., how mass moves through these ecological networks. Components of these complex, diverse and dynamic systems, e.g., microbes and substrates, can be observed at high resolution using modern technologies.
Microbes are members and affect the functioning of many ecosystems, from the human gut to the global ocean, with important implications for health and climate. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). We apply the method to characterize phytoplankton-heterotrophic bacteria interactions via dissolved organic matter in a marine system. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species.
#FERDI HELLWEGER SERIES#
We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive.
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