Abstract
Angling effort is often estimated from data captured by creel survey (prohibitively expensive to do on more than a few lakes), or aerial surveys (limited to summer effort estimates). A recent alternative method uses remote cameras to capture images of lakes at hourly intervals over long time periods. Technicians then visually analyze the thousands of generated images for features of interest (e.g., angler counts and environmental conditions), and use that data to estimate angling effort. The problem is that the visual analysis step is time-consuming, expensive, error-prone, and difficult to validate. Consequently, we elicited the strategies and best practices technicians used when analyzing images, and identified bottlenecks. We then designed software – called TIMELAPSE – to better support image analysis. In use for several years, TIMELAPSE has proven cost-effective: it significantly eases a technician’s workflow while reducing errors. TIMELAPSE is now an effective part of estimating angling effort in BC’s small lakes fisheries.
Refereed
No
