Abstract
Digital camera monitoring data on recreational boating traffic are often manually interpreted and the reading cost can be expensive. Typically, these data are used along with other periodic survey information and camera data between these surveys may not be read, creating gaps in the time series. We predicted recreational boating traffic during these ‘gap’ periods using historical camera data and covariates to complete the time series data. Predictive models were built in a Bayesian regression modelling framework to determine the daily distribution of recreational boating traffic at two ramps in Western Australia based on climatic variables (temperature, humidity, wind speed, direction and gust, and sea level pressure) and some temporal classifications (month and day type). Two observed year-long datasets of boating traffic were used, with a year-long gap between them. One set was used to build models, and the other set was used for validation purposes. Models were developed using leave-one-out cross-validation, and ensemble prediction. Fitted models explained 50% [95% credible interval (CI) of R2: 0.40–0.58] and 62% [95% CI of R2: 0.58–0.66] of the variabilities in the daily number of boat launches at the two ramps. Subsequently, using data for the preceding period where camera data were read, we imputed plausible estimates for the period between readings. Imputed values generally aligned well with the observed data, with some temporal biases at the bulk and upper tail of the distributions. The 95% credible intervals adequately reflected the observed data at both ramps. Data for the constructed periods depicted the general trends for the observed periods. Our results provide useful insights into using climatic factors to predict boating traffic to ‘fill in the gaps’ between survey years which could assist in the ongoing monitoring to promote sustainable management of recreational fisheries.
Highlights
To achieve sustainable recreational fisheries, it is important for resource managers and researchers to anticipate current and future management needs
Predictive models were built in a Bayesian regression modelling framework to determine the daily distribution of recreational boating traffic at two ramps in Western Australia based on climatic variables and some temporal classifications
Recreational boat launches are highly correlated with retrievals in WA, because most boaters retrieve their boats at launching sites such as boat ramps (Afrifa-Yamoah, 2021)
Summary
To achieve sustainable recreational fisheries, it is important for resource managers and researchers to anticipate current and future management needs. Boat-based fishing originates from public boat ramps where launches and retrievals can be monitored in surveys, which serves as an important component in informing regula tory policies. Both on-site (e.g. access point, roving survey) and off-site (e.g. phone diary, mail) surveys have been used to collect recreational fishing data (Lai et al, 2019; Ryan et al, 2019; Smallwood et al, 2012; Viega et al, 2010). Because of the logistical challenges, time constraints and relatively high cost associated with these survey methods, data collections are not continuous, which is in contrast to most commercial fisheries, where there is typically a time series of catch and effort information
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