Automated identification system for ships data as a proxy for marine vessel related stressors.

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.

Modeling the distribution of illicit oily discharges detected by aerial surveillance in western Canadian marine waters.

Oily discharges from vessel operations have been documented in Canada's Pacific region by the National Aerial Surveillance Program (NASP) since the early 1990s. We explored a number of regression methods to explain the distribution and counts per grid cell of oily discharges detected from 1998 to 2007 using independent predictor variables, while trying to address the large number of zeros present in the data. Best-fit models indicate that discharges are generally concentrated close to shore typically in association with small harbours, and with major commercial and tourist centers. Oily discharges were also concentrated in Barkley Sound and at the entrance of Juan de Fuca Strait. The identification of important factors associated with discharge patterns, and predicting discharge rates in areas with surveillance effort can be used to inform future surveillance. Model output can also be used as inputs for risk models for existing conditions and as baseline for future scenarios.

Visualization of spatial patterns and temporal trends for aerial surveillance of illegal oil discharges in western Canadian marine waters.

This paper examines the use of exploratory spatial analysis for identifying hotspots of shipping-based oil pollution in the Pacific Region of Canada's Exclusive Economic Zone. It makes use of data collected from fiscal years 1997/1998 to 2005/2006 by the National Aerial Surveillance Program, the primary tool for monitoring and enforcing the provisions imposed by MARPOL 73/78. First, we present oil spill data as points in a "dot map" relative to coastlines, harbors and the aerial surveillance distribution. Then, we explore the intensity of oil spill events using the Quadrat Count method, and the Kernel Density Estimation methods with both fixed and adaptive bandwidths. We found that oil spill hotspots where more clearly defined using Kernel Density Estimation with an adaptive bandwidth, probably because of the "clustered" distribution of oil spill occurrences. Finally, we discuss the importance of standardizing oil spill data by controlling for surveillance effort to provide a better understanding of the distribution of illegal oil spills, and how these results can ultimately benefit a monitoring program.