A new species delimitation method, Poisson Tree Processes (PTP), is introduced for phylogenetic analysis. PTP uses phylogenetic trees to infer species boundaries without requiring an ultrametric tree or sequence similarity thresholds. It integrates with the evolutionary placement algorithm (EPA) to count species in phylogenetic placements. PTP outperforms existing methods like GMYC and OTU-picking, especially when evolutionary distances between species are small. In open reference species delimitation, EPA-PTP provides more accurate results than de novo methods. EPA-PTP scales well on large datasets due to parallel implementations of EPA and RAxML, enabling species delimitation in high-throughput sequencing data. The method is implemented in Python and available for use. The study compares PTP with other methods on real and simulated datasets, showing that PTP performs well in species delimitation, particularly when the reference tree is incomplete. The results indicate that PTP is more robust and efficient than traditional methods, especially for large datasets. The method is applicable to metagenomic data and allows for the use of a widely accepted species concept in DNA barcoding. EPA-PTP provides more accurate species delimitation than traditional OTU-picking methods, especially when reference data are incomplete. The method is also faster than some other approaches, making it suitable for large-scale analyses. The study highlights the importance of using phylogenetic information in species delimitation and demonstrates the effectiveness of PTP in improving accuracy and efficiency.A new species delimitation method, Poisson Tree Processes (PTP), is introduced for phylogenetic analysis. PTP uses phylogenetic trees to infer species boundaries without requiring an ultrametric tree or sequence similarity thresholds. It integrates with the evolutionary placement algorithm (EPA) to count species in phylogenetic placements. PTP outperforms existing methods like GMYC and OTU-picking, especially when evolutionary distances between species are small. In open reference species delimitation, EPA-PTP provides more accurate results than de novo methods. EPA-PTP scales well on large datasets due to parallel implementations of EPA and RAxML, enabling species delimitation in high-throughput sequencing data. The method is implemented in Python and available for use. The study compares PTP with other methods on real and simulated datasets, showing that PTP performs well in species delimitation, particularly when the reference tree is incomplete. The results indicate that PTP is more robust and efficient than traditional methods, especially for large datasets. The method is applicable to metagenomic data and allows for the use of a widely accepted species concept in DNA barcoding. EPA-PTP provides more accurate species delimitation than traditional OTU-picking methods, especially when reference data are incomplete. The method is also faster than some other approaches, making it suitable for large-scale analyses. The study highlights the importance of using phylogenetic information in species delimitation and demonstrates the effectiveness of PTP in improving accuracy and efficiency.