Integrative systems approach reveals dynamics of microbiome-metal-ion axis in mesocosms representing tropical urban freshwater canal ecosystem

2020 
Freshwater ecosystems of tropical urban canals systems (TrUCS), are highly dynamic and experience constant pressures from interspersed effects of land-use and rain. The dynamic nature of TrUCS ecosystems presents a unique opportunity to unravel the signature interactions between the macro-organisms (top-down), sedimentary microbial communities (SedMICs), their functioning, and the geochemical environment (bottom-up). A systems-level understanding of the molecular and mechanistic basis of the highly dynamic behavior that leads to specific ecosystem outcomes is currently lacking. Therefore, a research framework to identify the direct link between top-down and bottom-up ecological effects on SedMICs in a highly dynamic urban canal sedimentary system is needed. Here, we present a framework of integrated multi-dimensional data across system-level biotic and abiotic ecological descriptors, such as environmental variables and active SedMICs. We followed the ecosystem shifts after a natural disturbance (rain) in two different anthropogenic disturbance (land-use) regimes. Shifts in profiles of the metabolically active communities were conserved across different land-use types, indicating resilience to perturbation is an intrinsic property of the TrUCs ecosystem. Three distinct phases, which were dominated sequentially by autotrophy, anoxic-heterotrophy, and oxic-heterotrophy, were identified within these shifts. The first two phases were influenced by the bottom-up effects of specific metal-ion combinations of nitrates and sulfates with magnesium, aluminum, and iron, and the third phase was triggered by top-down influences of bioturbation. This generalized systems-level approach, which provides an ecosystem-centric understanding of TrUCS and integrates them in sustainable management practices, can also be extended to other freshwater ecosystems.
    • Correction
    • Source
    • Cite
    • Save
    54
    References
    0
    Citations
    NaN
    KQI
    []
    Baidu
    map