This paper introduces a new method for species delimitation, the Poisson Tree Processes (PTP) model, which is designed to infer putative species boundaries on a given phylogenetic input tree. The authors integrate PTP with their evolutionary placement algorithm (EPA-PTP) to count the number of species in phylogenetic placements. They compare PTP with popular OTU-picking methods and the General Mixed Yule Coalescent (GMYC) model. For de novo species delimitation, PTP generally outperforms GMYC and OTU-picking methods when evolutionary distances between species are small. PTP does not require an ultrametric input tree or a sequence similarity threshold. In the open reference species delimitation approach, EPA-PTP yields more accurate results than de novo species delimitation methods. Finally, EPA-PTP scales well on large datasets due to its reliance on parallel implementations of the EPA and RAxML. The code for PTP is freely available at www.exelixis-lab.org/software.html.This paper introduces a new method for species delimitation, the Poisson Tree Processes (PTP) model, which is designed to infer putative species boundaries on a given phylogenetic input tree. The authors integrate PTP with their evolutionary placement algorithm (EPA-PTP) to count the number of species in phylogenetic placements. They compare PTP with popular OTU-picking methods and the General Mixed Yule Coalescent (GMYC) model. For de novo species delimitation, PTP generally outperforms GMYC and OTU-picking methods when evolutionary distances between species are small. PTP does not require an ultrametric input tree or a sequence similarity threshold. In the open reference species delimitation approach, EPA-PTP yields more accurate results than de novo species delimitation methods. Finally, EPA-PTP scales well on large datasets due to its reliance on parallel implementations of the EPA and RAxML. The code for PTP is freely available at www.exelixis-lab.org/software.html.