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Ecological dynamics imposes fundamental challenges in community-based microbial source tracking
Quantifying the contribution of possible environmental sources (“sources”) to a specific microbial community (“sink”) is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities are formed, but also have far-reaching applications in pollution control, public health, and forensics. Numerous computational methods, referred to as MST solvers hereafter, have been developed in the past and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider inter-species interactions and priority effects (i.e., the order or timing of species arrival) in microbial communities. Here, we revisit the performance of several representative MST solvers. We find that those MST solvers work well for simulated data generated by ecological models when inter-species interaction and priority effects are absent, but fail when they are present. We offer a mathematical explanation why solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solver fails to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solver fails to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in solving the MST problem using computational approaches.
Quantifying the contribution of possible environmental sources (“sources”) to a specific microbial community (“sink”) is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities are formed, but also have far-reaching applications in pollution control, public health, and forensics. Numerous computational methods, referred to as MST solvers hereafter, have been developed in the past and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider inter-species interactions and priority effects (i.e., the order or timing of species arrival) in microbial communities. Here, we revisit the performance of several representative MST solvers. We find that those MST solvers work well for simulated data generated by ecological models when inter-species interaction and priority effects are absent, but fail when they are present. We offer a mathematical explanation why solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solver fails to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solver fails to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in solving the MST problem using computational approaches.