Abstract
Early detection of marine invasive species is key for mitigating and managing their impacts to marine ecosystems and industries. Human divers are considered the gold standard tool for detecting marine invasive species, especially when dive teams are familiar with the local biodiversity. However, diver operations can be expensive and dangerous, and are not always practical. Remote operated vehicles (ROVs) can potentially overcome these limitations, but it is unclear how sensitive they are compared to trained divers for detecting pests. We assessed the sensitivity and efficiency of ROVs and divers for detecting marine non-indigenous species (NIS), including the potential for automated detection algorithms to reduce post-processing costs of ROV methods. We show that ROVs can detect comparable assemblages of invasive species as divers, but with lower detection rates (0.2 NIS min -1 ) than divers (0.5 NIS min -1 ) and covered less seafloor than divers per unit time. While small invertebrates (e.g., skeleton shrimp Caprella mutica ) were more easily detected by divers, the invasive goby Acentrogobius pflaumii was only detected by the ROV. We show that implementation of computer vision algorithms can provide accurate identification of larger biofouling organisms and reduce overall survey costs, yet the relative costs of ROV surveys remain almost twice that of diver surveys. We expect that as ROV technologies improve and investment in autonomous and semi-autonomous underwater vehicles increases, much of the current inefficiencies of ROVs will be mitigated, yet practitioners should be aware of limitations in taxonomic resolution and the strengths of specialist diver teams.