Coenocline Simulation of Microbiome Samples: A Biologically Mechanistic Framework for Generating Ecologically...

Machine learning and statistical classification methods are widely applied to microbiome data for diagnostic, prognostic, and phenotypic insights. However, the complex, multivariate nature of microbiome communities makes it difficult to assess the relative performance of these methods. Most comparisons rely on a small number of published datasets, without considering their underlying ecological properties or how these properties may, in turn, influence classification performance. We introduced a coenocline-based simulation framework to generate synthetic microbiome datasets that incorporate realistic ecological variation arising from species' responses to host-associated gradients such as disease severity. To evaluate the ecological fidelity of these simulations, we compared synthetic datasets to five widely used real-world microbiome datasets: Cirrhosis, Colorectal Cancer (CRC), Type 2 Diabetes (Chinese and Women cohorts), and the Human Microbiome Project (HMP). Comparisons across α-diversity (species richness), β-diversity (species composition and turnover), and abundance distributions demonstrated that coenocline simulations closely recapitulate the key ecological structures of empirical data. Synthetic datasets exhibited similar richness and abundance patterns to disease-associated microbiomes, with realistic distributions of few dominant and many rare taxa. Moreover, community composition analyses (Bray-Curtis index) revealed that the simulated datasets captured natural levels of compositional dissimilarity among samples, spanning the same variability range observed in real data. When compared against 100 independently simulated datasets, the coenocline model consistently reproduced empirical ranges of species diversity, relative abundance, and between-group compositional differences (ANOSIM-R values), confirming the model's robustness and reproducibility. This coenocline-based simulation framework provides a novel, flexible, and ecologically grounded approach for generating synthetic microbiome data with controlled complexity. By reproducing realistic ecological gradients and community structures, the framework supplies the controlled test beds needed for systematic future benchmarking of machine learning and statistical classification methods across diverse and biologically meaningful scenarios. In doing so, it will help bridge the gap between ecological realism and computational modeling, thereby supporting more reliable and generalizable inference from microbiome data.