Collective behavior is ubiquitous in animal groups. In fish schools, global patterns emerge from simple local decision rules and flow-mediated interactions. But how stable are these states to increasingly large groups of fish? By combining a data-driven agent-based model with far-field hydrodynamics interactions, and leveraging high-performance parallel computing, we studied the emergent collective patterns in large schools of up to 50,000 individuals. We found that the stable structures that emerge in smaller groups, such as milling and schooling, break down with increasing school size. Instead, the school manifests dynamic reorganization among locally ordered groups. Mechanistically, in polarized schools this fragmentation is driven by hydrodynamic coupling, whereas in rotationally ordered schools it persists even when flow interactions are suppressed. Our findings reveal how visual and flow interactions set the limits of stability in very large schools of fish, offering new insight into the mechanisms that shape collective behavior in nature.
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