Estimating fishing effort from remote traffic counters: Opportunities and challenges

This paper introduces traffic counters paired with infrequent observations of total fishing effort and relative fishing and non-fishing traffic to estimate daily and seasonal fishing effort. Fishing effort is an important metric in recreational fisheries, used as an index of fishing attractiveness and fishing mortality. There are several options for monitoring fishing effort, but for high-precision effort on a particular fishery, creel surveys and time-lapse cameras have been the only options. However, time-lapse cameras have high costs associated with reviewing images and technology failure requires imputing missing observations. To translate this index of counts of boats or shore anglers derived from time-lapse images into absolute fishing effort, this method requires independent measures of total fishing effort as well as observations of fishing and non-fishing traffic. We use remote traffic counters coupled with periodic on-site creel surveys to estimate daily and seasonal fishing effort. Fishing effort is estimated using a state-space Bayesian hierarchical model, which incorporates all of these data to provide a measure of daily or seasonal fishing effort. We further show that for our case study, fishing effort estimates require a high number of independent observations of effort and proportional distribution of fishing and non-fishing traffic. Comparing effort estimates from traffic counters with estimates derived from just a stratified random creel survey shows traffic counters provide more precise estimates of effort, though an absolute comparison is not possible with the data available. Our mixed-use lake necessitates a high number of independent observations of traffic and angling to produce reliable fishing effort estimates; we recommend traffic counters for lakes where most traffic is devoted to angling. We conclude traffic counters are a useful tool for estimating fishing effort, but should be used concurrently with other methods such as creel surveys or motion-detecting cameras that can estimate fishing and non-fishing traffic.