Applying community ecology principles to observational microbiome data presents substantial challenges, especially given the complexity of microbial interactions. Even in controlled experiments with only two microbial strains, interactions can be unexpectedly intricate (1, 2), underscoring the difficulty of inferring detailed community dynamics from observational data alone. Additionally, microbiome composition varies widely among individual hosts (3); for instance, only a small fraction of microbial species are consistently shared across human microbiomes (4, 5). This variability arises from factors such as diet (6), genetics (7), geography (8), gender (9), and medication (10), though random events (11) also play a substantial yet often underexplored role. Disentangling the deterministic and stochastic processes driving this variability requires comprehensive metadata collection. However, confounding variables often complicate analysis (12, 13), and it is impossible to fully capture all host-associated factors or confounders without controlled conditions. Consequently, observational (top-down) studies alone offer limited insight into the causes and consequences of microbiome variation. Integrating observational data with controlled (bottom-up) experiments under identical conditions holds great promise for revealing baseline stochasticity and the deterministic factors shaping microbial communities. Understanding these ecological processes in-depth is essential to the success of therapeutic microbiome interventions like fecal microbiota transplantation (FMT) and personalized healthcare, where context-dependent factors and historical contingencies contribute to variable outcomes (14–16). Leveraging these insights enables systematic examination of assembly dynamics, uncovering the ecological principles that govern these complex and variable communities.