O'Hara, Patrick D.Serra-Sogas, NormaMcWhinnie, LaurenPearce, KimLe Baron, NicoleO'Hagan, GregoryNesdoly, AndreaMarques, TunaiCanessa, Rosaline2024-02-072024-02-0720232023O'Hara, P. D., Serra-Sogas, N., McWhinnie, L., Pearce, K., Le Baron, N., O'Hagan, G., ... Canessa, R. (2023). Automated identification system for ships data as a proxy for marine vessel related stressors. Science of the Total Environment, 865, 160987. https://doi.org/10.1016/j.scitotenv.2022.160987https://doi.org/10.1016/j.scitotenv.2022.160987http://hdl.handle.net/1828/15961The authors thank two anonymous reviewers as their suggestions and comments have helped improve this manuscript immensely. We thank the NASP team including: Louis Armstrong, Superintendent NASP Intelligence, Surveillance and Reconnaissance Division; Surveillance Officers: Owen Rusticus, Louis Henault, RobertWhitaker and Ian Horner; and pilots: John Heiler, Donald Kavalench, Simon Pearce, Joshua Kerr, Matt McMillan, Angela Tanton, and Brad Jorgenson. Zoe Crysler (Canadian Wildlife Service) helped O'Hara develop elegant r code for processing, analyzing, and graphically displaying analytical results based o the POS data. Barbara Sobota (CanadianWildlife Service) was very helpful developing earlier versions of the mapped output of the GAMs. Iain Duncan and Nev Gibson (XORNOT Studios)were critical for developing the python script used to set up and run POS, for developing a centralized webbased monitoring tool to check on system health for the various POS installations, and for troubleshooting. Without their help POS would never have existed. We would also like to thank SIMRES (Saturna Island Marine Research and Education Society) and a private citizen who hosted our POS installation on Saturna Island. In particular, we thank Tom Dakin and Jeff Bosma (Sea to Shore Systems)who made sure the infrastructure supporting the POS installed on Saturna Island wasworking properly. Sea to Shore Systems also facilitated virtual access to POS, allowing us to monitor our system remotely. Finally, but not the least by any means, we thank Ryan Flagg and his team at Oceans Network Canada for providing us with AIS data when requested (via a data-sharing agreement with the Canadian Coast Guard – the original AIS data source), which is no small feat.An increasing number of marine conservation initiatives rely on data from Automatic Identification System (AIS) to inform marine vessel traffic associated impact assessments and mitigation policy. However, a considerable proportion of vessel traffic is not captured by AIS in many regions of the world. Here we introduce two complementary techniques for collecting traffic data in the Canadian Salish Sea that rely on optical imagery. Vessel data pulled from imagery captured using a shore-based autonomous camera system (“Photobot”) were used for temporal analyses, and data from imagery collected by the National Aerial Surveillance Program (NASP) were used for spatial analyses. The photobot imagery captured vessel passages through Boundary Pass every minute (Jan–Dec 2017), and NASP data collection occurred opportunistically across most of the Canadian Salish Sea (2017–2018). Based on photobot imagery data, we found that up to 72 % of total vessel passages through Boundary Pass were not broadcasting AIS, and in some vessel categories this proportion was much higher (i.e., 96 %). We fit negative binomial General Linearized Models to our photobot data and found a strong seasonal variation in non-AIS, and a weekend/weekday component that also varied by season (interaction term p < 0.0001). Non-AIS traffic was much higher during the summer (Apr–Sep) and during the weekend (Sat-Sun), reflecting patterns in recreational vessel traffic not obligated to broadcast AIS. Negative binomial General Additive Models based on the NASP data revealed strong spatial associations with distance from shore (up to 10 km) and non-AIS vessel traffic for both summer and winter seasons. There were also associations between non-AIS vessels and marina and anchorage densities, particularly during the winter, which again reflect seasonal recreational vessel traffic patterns. Overall, our GAMs explained 20–37 % of all vessel traffic during the summer and winter, and highlighted subregions where vessel traffic is under represented by AIS.enAISNon-AIS vesselsRisk assessmentsSalish SeaAerial surveyAutonomous optical data collectionArticle