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Evaluating the Use of an Angler Smartphone App for Fisheries Data Collection
Paul Venturelli1, Erin Dunlop2, Len Hunt3, and Tim Martin4
1 Ball State University, 121 Cooper Building, Muncie IN 47306
2Ontario Ministry of Natural Resources, 2140 East Bank Drive, Trent University, Peterborough ON K9J 8N8
3Centre for Northern Forest Ecosystem Research, Ontario Ministry of Natural Resources, 955 Oliver Road, Thunder Bay, ON P7B 5E1
4University of Minnesota, 135 Skok Hall, 2003 Upper Buford Circle, St. Paul MN 55108
Anglers make decisions that affect the fish stocks, ecosystems, and socio-economic systems 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, we compared results from app and aerial creel survey data from Ontario, Canada. We found a positive relationship between the logged number of trips to a lake in a year by app users, and the logged number of angler hours in a year estimated via aerial creel (n = 210 lakes, r2 = 0.34, p = 2.22e-16). Random forest models revealed that 10 lake characteristics explained 45.1% and 46.76% of variation in trip number (n = 1,056) and effort hours (n = 554), respectively. In general, effort was highest on large lakes with low natural cover and either convoluted shorelines (app data) or many access points (creel data). We used these models to predict effort for 9,400 lakes across Ontario, and then interpolated to generate province-wide effort maps. Both sources of data predicted high effort in many of the same areas, particularly in a large region of southern Ontario. A direct comparison of re-scaled data revealed a tendency for trips to underestimate effort, but also found that agreement between app- and creel-based predictions was highly heterogeneous. Our results suggest that the number of trips to a lake in a year by app users does not reliably predict angler hours per year, but that app data can be used to identify and characterize lake features that determine angler effort. App data show promise as a way to predict effort over large spatial scales, but more research is needed to refine this approach.