Abstract: Globalization has driven an increase in marine transportation and subsequent translocation of marine organisms within and between biogeographical regions. Marine transportation is the principal vector for the spread of nonindigenous species (NIS) in the marine environment via ship ballast water and hull fouling. NIS may establish at ports and become invasive, potentially threatening the health and productivity of nearby marine ecosystems. Globally representative, historical, and comparable data regarding the detection and monitoring of introduced and invasive species is scarce, particularly within the Red Sea, which serves as a major shipping corridor. This highlights the importance of establishing a robust and rapid baseline monitoring program to assess species distributions and facilitate early detection of NIS. Recently, DNA-based molecular techniques have been increasingly used in biosecurity applications as an alternative to the morphological taxonomic identification of invasive species. Yet, these techniques require further validation in the context of standardized port surveillance to facilitate routine biogeographical comparisons. Here, we apply DNA metabarcoding (targeting a 313 bp fragment of the cytochrome c oxidase subunit I (COI) gene and a 450 bp fragment of the V4 region of the nuclear small subunit ribosomal DNA (18S rRNA)), to identify potential NIS in Saudi Arabian Red Sea marinas and coral reefs. In this experimental study, we deploy short-term and long-term settlement tiles at 1-2 m depth at both natural reef sites and artificial marina sites. Short-term tiles are deployed seasonally (i.e. winter and summer) for 1 week and long-term tiles are deployed for 1, 3, 6, and 12 months. The preliminary data derived from this study will provide up-to-date information on the relative occurrence and diversity of NIS in Red Sea natural and artificial habitats, help to build the capacity to distinguish spatiotemporal patterns in pioneering community composition and seasonal recruitment, and may assist in improved risk assessment and management strategies against biological invasions in data-poor regions.