Microbial communities are held together by an intricate web of metabolic and antagonistic links at the single‐cell level. A major discovery from our lab revealed that certain Vibrio strains, when nutrients are scarce, deploy their Type VI Secretion System as a nanoscale spear to lyse neighbors and directly forage on released nutrients—turning a competitive weapon into a feeding strategy when resources are limited. In polysaccharide‐degrading consortia, downstream “cross‐feeders” consume breakdown products and, in turn, modulate enzyme secretion by primary degraders—reshaping aggregate structure and communal function. By combining microfluidic time‐lapse imaging with machine‐learning analysis, we map these cooperative and antagonistic interactions at single‐cell resolution to understand who interacts with whom, and with what functional consequence.

The same microscale interaction networks that drive community performance also underpin resilience to environmental fluctuations. We subject multi-species microbiomes to shifts in glycan type (e.g., algal blooms vs. host-diet polysaccharides), pulses of antimicrobials, and rising temperatures, then quantify carbon remineralization rates, enzyme activities, and spatial reorganization. Cell–cell proximity emerges as critical determinants of how quickly and efficiently communities recover function after perturbation. This multi-stress approach reveals which interaction motifs—cross-feeding loops or antagonistic checks—safeguard ecosystem processes under realistic, fluctuating conditions.

Interactions shape not only immediate community behavior but also long-term evolutionary trajectories. By constructing consortia with defined interaction topologies and subjecting them to selection for enhanced carbon remineralization or sequestration, we drive experimental evolution of community‐level traits. Genomic resequencing and transcriptomics then pinpoint adaptive changes—in secretion systems, adhesion factors, or metabolic gene content—that rewire interaction networks for improved function. This approach builds on the “less is more” paradigm, where selective advantages emerge from gene loss or regulatory shifts, to inform how we might rationally engineer microbiomes with tailored biochemical outputs.