Beyond métiers: social factors influence fisher behaviour. (1st April 2021)

• Record Type: Journal Article
• Authors: Schadeberg, Amanda; Kraan, Marloes; Hamon, Katell G
• Editors: Neis, Barbara
• Is Part Of: ICES journal of marine science. Volume 78:Number 4(2021)
• Page Start: 1530
• Page End: 1541
• Publication Date: 2021-04-01
• Subjects: Dutch demersal fishery -- fisher behaviour -- fisheries management -- métier -- mixed methods -- social factors -- social heterogeneity; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsab050
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Fisheries management is usually supported by technical and financial measurements (i.e. logbooks and market data), which are helpful for ecological or economic assessments. Yet this information is not able to address social heterogeneity and fisher motivations, which are key to understanding fisher behaviour. This case study of the demersal segment in the Netherlands shows that combining quantitative analysis of logbooks with qualitative data collected by engaging with fishers can capture both fishing activity and its motivations, generating a more social understanding of fisher behaviour. A métier analysis of logbook data describes five dominant fishing practices among the selected segment. Twenty-five in-depth interviews with fishers along with focus groups including other experts identify three social factors that influence fisher behaviour in the Dutch demersal fleet: business structure, working rhythm, and polyvalence. The results show that motivations for fisher behaviour are more complex than complying with regulations or seeking profit: social factors also influence fishing activity. Furthermore, these social factors have real implications for the impacts of management measures on both the fishing communities and the environment, especially in times of change. These results are useful for management strategy development or evaluation because they are feasibly observable through existing data collection protocols.