Comparing estimates of fishing effort and lake choice derived from aerial creel surveys and smartphone application data in Ontario, Canada (2017 MSc)

Anglers make decisions that have consequences for the fish stocks, ecosystems, and socio-economics with which they interact. Smartphone angling applications (apps) are a potentially less expensive and more comprehensive data source than conventional methods, but their utility has not been evaluated. In this study, I compared results from app and aerial creel survey data from Ontario, Canada. A standard major axis regression found low agreement between effort estimates (n=111, R2=0.20, p=8.2458e-07) and app-based effort was poorly explained by lake characteristics in a random forest analysis (7.66% vs. 29.52% for creels). Explained variation improved when I included more lakes, but province-wide effort prediction did not agree with those based on creel data. I attribute these inconsistent results to low app data volumes and inherent differences between collection and analyses. Until more app data are generated, I recommend using app data to supplement conventional surveys and gain novel insights into angler behavior.