Replaying the tape of life is generally speculated to give rise to similar biological diversity as observed in present-day. I hypothesize that if indeed evolution tends to repeat itself (to a degree) then it should be possible to predict the nature of emerging specie, given only a description of the underlying biochemistry. I utilised chemical networks to analyse the emergence of "species" within an artificial life model of prebiotic evolution, based on the Graded Autocatalysis Replication Domain (GARD) simulator for molecular assemblies. The temporal evolution of these assemblies is stochastically determined by a rates matrix that governs the likelihood that a given molecule joins or leaves an assembly. I re-interpreted the rates matrix as a network and analysed the network's community structure. I asked whether communities are related (and how) to the emerging species under GARD's dynamic, and found that the derived communities correspond well to the species that emerge from the prebiotic evolution simulations. Importantly, it is possible to use the ensemble of communities to predict proto-species emergence without performing any simulations. Establishing such a link between the network to the emerging species is an important foundational step towards being able to engineer a complex network that would give give to desired and specific species.