Imputation of missing data from time-lapse cameras used in recreational fishing surveys

Ebenezer Afrifa-Yamoah,Ute Mueller,Aiden Fisher,Stephen M Taylor,Kieran Hyder

doi:10.1093/icesjms/fsaa180

Ebenezer Afrifa-Yamoah, Ute Mueller + Show 3 more

Open Access

https://doi.org/10.1093/icesjms/fsaa180

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Abstract

Abstract While remote camera surveys have the potential to improve the accuracy of recreational fishing estimates, missing data are common and require robust analytical techniques to impute. Time-lapse cameras are being used in Western Australia to monitor recreational boating activities, but outages have occurred. Generalized linear mixed effect models formulated in a fully conditional specification multiple imputation framework were used to reconstruct missing data, with climatic and some temporal classifications as covariates. Using a complete 12-month camera record of hourly counts of recreational powerboat retrievals, data were simulated based on ten observed camera outage patterns, with a missing proportion of between 0.06 and 0.61. Nine models were evaluated, including Poisson and negative binomial models, and their associated zero-inflated variants. The imputed values were cross-validated against actual observations using percent bias, mean absolute error, root mean square error, and skill score as performance measures. In 90% of the cases, 95% confidence intervals for the total imputed estimates from at least one of the models contained the total actual counts. With no systematic trends in performance among the models, zero-inflated Poisson and its bootstrapping variant models consistently ranked among the top 3 models and possessed the narrowest confidence intervals. The robustness and generality of the imputation framework were demonstrated using other camera datasets with distinct characteristics. The results provide reliable estimates of the number of boat retrievals for subsequent estimates of fishing effort and provide time series data on boat-based activity.

Highlights

As many recreational fisheries are of large spatial extent, diverse, and not well defined, it can be challenging and costly to obtain accurate recreational fishing information for sustainable management (Smallwood et al, 2012; Hyder et al, 2018)
Study area and camera data description In Western Australia (WA), an estimated 26% of residents participate in recreational fishing at least once a year (Department of Primary Industries and Regional Development, 2019)
Subsequent analysis for this paper was restricted to powerboat retrievals, as this is the common vessel type used for boat-based recreational fishing activities in WA

Summary

Introduction

As many recreational fisheries are of large spatial extent, diverse, and not well defined, it can be challenging and costly to obtain accurate recreational fishing information for sustainable management (Smallwood et al, 2012; Hyder et al, 2018). Remote camera surveys ( referred to as digital camera monitoring) are increasingly being used throughout Europe, North America, and Australasia to monitor recreational fishing effort in marine and freshwater fisheries (Smallwood et al, 2012; van Poorten et al, 2015; Hartill et al, 2016, 2020; Lancaster et al, 2017; Askey et al, 2018). In comparison to onsite surveys (e.g. boat ramp surveys), remote cameras provide a cost-effective method of monitoring the movement of boats (Smallwood et al, 2012; Hartill et al, 2016) or fishers (van Poorten et al, 2015; Askey et al, 2018; Stahr and Knudsen, 2018), where results can form a basis for subsequent calculations of fishing effort (Hartill et al, 2020).

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