Environmental surveillance of Legionella bacteria relies on standardized culture-based methods that enable reliable detection; however, accurate species-level identification remains challenging. Phenotypic, serological, and proteomic methods, including latex agglutination and MALDI-TOF mass spectrometry, enable rapid screening but often lack sufficient resolution to distinguish closely related species. This study proposes a structured framework for the identification and taxonomic characterization of environmental Legionella isolates. Environmental isolates were obtained using standard culture methods and initially identified by MALDI-TOF MS, followed by molecular characterization using PCR, 16S rRNA gene sequencing, and whole-genome sequencing. Phylogenomic analyses were conducted using overall genomic relatedness indices, including Average Amino Acid Identity (AAI) and Percentage of Conserved Proteins (POCP), and a phylogenomic tree was constructed based on 400 universal marker genes. The framework was applied to four environmental isolates (PATHC032, PATHC035, PATHC038, and PATHC039), including one previously described species, Legionella sheltonii. Results showed AAI values ranging from 94.3% to 96.6% and POCP values from 86.5% to 91.0%, supporting their genomic distinctiveness from any validly described Legionella species, which was confirmed by phylogenomic analysis. These findings demonstrate that the proposed framework enables consistent species-level identification and provides guidelines for recognizing candidate novel Legionella species through the integration of environmental surveillance and genome-based taxonomy.

Accurate species-level identification of Legionella during routine environmental surveillance remains challenging, as standard proteomic screening methods like MALDI-TOF MS often lack the resolution to distinguish closely related taxa. This study introduces a novel, cost-effective, and stepwise taxonomic framework that bridges routine environmental monitoring with multi-layered, genome-resolved analysis. We expanded upon our previous screening of four novel candidate lineages by focusing on the three remaining, uncharacterized environmental isolates, while using the recently validly described Legionella sheltonii (strain PATHC038) as a reference framework control. By applying nucleotide- and protein-based overall genomic relatedness indices (ANI, dDDH, AAI, and POCP) alongside core-genome phylogenomics, our framework independently confirmed the genomic distinctiveness of the three isolates, which exhibited AAI values of 94.3–96.6% and POCP values of 86.5–91.0%, with POCP values lying close to the proposed species delineation threshold. By defining clear criteria for escalating isolates from culture to targeted sequencing, this workflow prevents diagnostic misidentification during epidemiological outbreak investigations and provides public health agencies with a scalable tool to monitor hidden Legionella diversity, ultimately enhancing proactive water safety and risk assessment.