Reducing electronic monitoring review effort through probabilistic partial video review
Electronic monitoring (EM) often relies on human review to quantify discards, yet full review of sorting activity is time-consuming and resource-intensive. This study presents a framework for probabilistic partial review of EM video, combining stratified random sampling, mixed-effects modelling, and effort–precision simulation to support adaptive allocation of review effort. The framework is demonstrated using EM data of plaice ( Pleuronectes platessa ) from eight bottom beam trawl vessels. For each haul, five one-minute video segments were randomly selected across the sorting duration, and below-minimum-size individuals per minute were estimated using mixed-effects models with vessel and haul effects. Total discards were estimated by scaling predicted discard rates by observed sorting time. A simulation was conducted on the trial data to quantify the relationship between review effort and estimation precision. This application demonstrates a potential framework of estimating discards using partial EM review and how EM review effort can be linked to statistical precision, providing a practical basis for adaptive EM review strategies in discard monitoring programmes.