arXiv:2611.13323v1 Announce Type: new Abstract: Discrete random probability measures are central to Bayesian nonparametric mixture modeling and clustering. We show that every proper species sampling process (SSP) admits an exact two-stage finite representation of its sampling law. An almost surely finite index selects an initial set of atoms, and a draw is then made with explicitly reweighted probabilities. A decreasing visibility schedule determines both the reweighting and the index law. Averaging the finite conditional measures over this law recovers each realized SSP on every measurable set. Reversing this identity yields a novel and general method for constructing SSP priors from an index distribution and finite allocation probabilities, rather than specifying the infinite weights directly. We characterize compatibility between prescribed index probabilities, schedules and induced weights, and develop new SSP priors with full weak support using light-tailed, heavy-tailed and multimodal index distributions with suitable priors on their parameters. Within this framework, we couple marginal index laws with conditional weight distributions to construct SSP priors whose weights are invariant under size-biased permutation in their defining order. An example of such family is identified as the normalization of the sum of a gamma completely random measure and an independent compound-Poisson measure with gamma jumps, naturally leading to explicit partition and predictive laws. The joint distribution of the selected label and the finite index also gives exact simulation and recursive reweighting for SSPs. Total-variation bounds and numerical illustrations distinguish prior specification, exact averaging and fixed-level approximation.