Catching the future:applying Bayesian belief networks to exploratory scenario storylines to assess long‐term changes in Baltic herring (Clupea harengus membras, Clupeidae) and salmon (Salmo salar, Salmonidae) fisheries

• Main Authors: Pihlajamäki, M. (Mia), Helle, I. (Inari), Haapasaari, P. (Päivi), Sarkki, S. (Simo), Kuikka, S. (Sakari), Lehikoinen, A. (Annukka)
• Format: Article in Journal/Newspaper
• Language: English
• Published: John Wiley & Sons 2020
• Subjects: interdisciplinary assessment; long-term sustainability; management objectives; probabilistic scenario synthesis; strategic decision-making; uncertainty; Salmo salar
• Online Access: http://urn.fi/urn:nbn:fi-fe2020091169296

Abstract Fisheries management aims to ensure that the fishing activities are environmentally sustainable in the long term, while also achieving the economic, social and food security related management objectives. To facilitate this, both the ecological and human dimensions of sustainability need to be included in fisheries assessment. In addition, assessing long‐term sustainability calls for taking into account plausible changes in the surrounding societal conditions that shape the characteristics of the fisheries governance system, as well as the ecological conditions. The paper uses a combination of qualitative exploratory scenario storylines (ESS) and Bayesian belief networks (BBN) to integrate the environmental, economic, social and food security dimensions in an interdisciplinary assessment of the future sustainability of Baltic herring (Clupea harengus membras, Clupeidae) and salmon (Salmo salar, Salmonidae) fisheries. First, four alternative ESS were created based on plausible changes in societal drivers. The ESS were then formulated into a BBN to (a) visualize the assumed causalities, and (b) examine quantitatively how changes in the societal drivers affect the social‐ecological fisheries system and ultimately the fisheries management objectives. This type of probabilistic scenario synthesis can help in thinking qualitative scenarios in a quantitative way. Moreover, it can increase understanding on the causal links between societal driving forces and the complex fisheries system and on how the management objectives can be achieved, thereby providing valuable information for strategic decision‐making under uncertainty.