Since the beginning of the 21st century, electronic monitoring (EM) has emerged as a cost‐efficient supplement to existing catch monitoring programmes in fisheries. An EM system consists of various activity sensors and cameras positioned on vessels to remotely record fishing activity and catches. The first objective of this review was to describe the state of play of EM in fisheries worldwide and to present the insights gained on this technology based on 100 EM trials and 12 fully implemented programmes. Despite its advantages, and its global use for monitoring, progresses in implementation in some important fishing regions are slow. Within this context, the second objective was to discuss more specifically the European experiences gained through 16 trials. Findings show that the three major benefits of EM were as follows: (a) cost‐efficiency, (b) the potential to provide more representative coverage of the fleet than any observer programme and (c) the enhanced registration of fishing activity and location. Electronic monitoring can incentivize better compliance and discard reduction, but the fishing managers and industry are often reluctant to its uptake. Improved understanding of the fisher's concerns, for example intrusion of privacy, liability and costs, and better exploration of EM benefits, for example increased traceability, sustainability claims and market access, may enhance implementation on a larger scale. In conclusion, EM as a monitoring tool embodies various solid strengths that are not diminished by its weaknesses. Electronic monitoring has the opportunity to be a powerful tool in the future monitoring of fisheries, particularly when integrated within existing monitoring programmes.
Anticipating fisher behaviour is necessary for successful fisheries management. Of the different concepts that have been developed to understand individual fisher behaviour, random utility models (RUMs) have attracted considerable attention in the past three decades, and more particularly so since the 2000s. This study aimed at summarizing and analysing the information gathered from RUMs used during the last three decades around the globe. A methodology has been developed to standardize information across different studies and compare RUM results. The studies selected focused on fishing effort allocation. Six types of fisher behaviour drivers were considered: the presence of other vessels in the same fishing area, tradition, expected revenue, species targeting, costs, and risk‐taking. Analyses were performed using three separate linear modelling approaches to assess the extent to which these different drivers impacted fisher behaviour in three fleet types: fleets fishing for demersal species using active gears, fleets fishing for demersal species using passive gears and fleets fishing for pelagic species. Fishers are attracted by higher expected revenue, tradition, species targeting and presence of others, but avoid choices involving large costs. Results also suggest that fishers fishing for demersal species using active gears are generally more influenced by past seasonal (long‐term) patterns than by the most recent (short‐term) information. Finally, the comparison of expected revenue with other fisher behaviour drivers highlights that demersal fishing vessels are risk‐averse and that tradition and species targeting influence fisher decisions more than expected revenue.
Poos, J. J., Turenhout, M. N. J., van Oostenbrugge, H., and Rijnsdorp, A. D. 2013. Adaptive response of beam trawl fishers to rising fuel cost – ICES Journal of Marine Science, 70: 675–684. In this paper, we develop models to test different hypotheses on the optimal towing speed at which fuel savings are traded off against the reduction in catch due to the decrease in swept area. The model predicts that optimal towing speed is a decreasing function of fuel price and an increasing function of fish abundance and price. The model was fitted to vessel monitoring system (VMS) data. By means of mixture analysis, these VMS data were attributed to one of three behavioural modes: floating, towing, or navigating. Data attributed to the towing mode were used to determine the model that best fit the data. The preferred model includes a maximum towing speed and a component describing the decline in catch efficiency with decreasing towing speed. Towing speed is reduced by up to 14%. The savings obtained by reducing towing speed were estimated for each month and showed that vessels reduced their fuel consumption by between 0 and 40%.
Historic hunting has led to severe reductions of many marine mammal species across the
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