Angler Effort Estimates from Instantaneous Aerial Counts: Use of High‐Frequency Time‐Lapse Camera Data to Inform Model‐Based Estimators

Abstract

Due to the logistics of monitoring remote and diffuse fisheries, typically only a small portion of the annual fishing effort can be observed, and a random sample is often impractical. We used a large data set (over 250,000 observations) from time-lapse cameras placed at 53 lakes across the province of British Columbia, Canada, to better understand the relative influence of different temporal strata (time of day, day type, and month) on the relative number of fishing boats observed on lakes. The high temporal frequency data available from cameras were used to evaluate alternative effort monitoring and estimation strategies for aerial surveys that are commonly used in recreational fisheries. We tested the predictive performance of simple mean count expansion factors versus generalized linear mixed models (GLMMs) under random and stratified (targeting high-effort strata) sampling regimes. Out-of-sample cross validation showed that the current sampling protocol and estimation method (mean count expansion) used for aerial surveys in British Columbia have low predictive power and are biased. A stratified sampling design combined with the GLMM estimator was an efficient strategy that could easily be applied to effort monitoring programs using aircraft. Moreover, the GLMM approach is much more flexible to interpret data obtained from nonstandard sampling regimes and can estimate angler effort over any temporal scale. We outline several summary statistics on accuracy, precision, and bias of estimates at different sample sizes so that managers can evaluate expected precision versus sampling effort in their management jurisdictions. Furthermore, we suggest that our general approach of using camera data to parameterize predictive models applied to data from other low-frequency effort monitoring methods (such as aircraft) can easily be replicated elsewhere.

Recreational fishery discard practices influence use of tidal estuary by a large marine mesopredator

“…Previous research at this site highlighted that fish-cleaning intensity at the Woollamia Regional Boat Ramp is higher in the afternoons (Pini-Fitzsimmons et al 2018). This matches patterns in recreational fishing effort documented in the literature; specifically, effort is skewed to daylight hours, with peaks in fishing effort at approximately midday (Askey et al 2018) and returns to fish-cleaning facilities in the afternoon (Pini-Fitzsimmons et al 2018;Lynch et al 2020). Similarly, recreational fishing effort is increased on nonbusiness days (i.e.…”

Estimating Recreational Fishing Effort Using Autonomous Cameras at Boat Ramps versus Creel Surveys

“…For example, Askey et al (2018) used a large network of cameras at lakes in British Columbia to verify the proportion of fishing effort that occurred across multiple timescales. They used hourly, weekly, and seasonal patterns in effort to correct for biases in long‐term, broad‐scale aerial surveys (Askey et al 2018). Tracking fishing effort is perhaps most valuable in evaluating the effects of fishery management actions such as habitat restoration or changes in harvest regulations (Gent et al 1995; Fayram et al 2006; Powers and Anson 2016).…”

“…Even if detection biases exist, such as missing some anglers, if the biases can be kept constant through time, camera data can be treated as an index of fishing effort. For example, Askey et al (2018) used a large network of cameras at lakes in British Columbia to verify the proportion of fishing effort that occurred across multiple timescales. They used hourly, weekly, and seasonal patterns in effort to correct for biases in long-term, broad-scale aerial surveys (Askey et al 2018).…”

“…The use of these cameras began only in the late 2000s (as evidenced by their absence in a 2008 comprehensive review of techniques that are used to estimate fishery effort [McCluskey and Lewison 2008]) but has increased substantially over the past decade. In quantifying fishing effort, cameras can be used to count vessels (Powers and Anson 2016), count anglers (Hining and Rash 2016), measure trip duration (Stahr and Knudsen 2018), verify temporal fishing patterns (Askey et al 2018), and supplement other techniques (Patterson and Sullivan 2013; Askey et al 2018). Thus, autonomous cameras have diverse utility in estimating fishing effort and have already been widely adopted in some fisheries research and management programs (Blight and Smallwood 2015).…”