Dealing with uncertainty: an innovative method to address climate change adaptation in the whale watch industry

• Main Authors: Meynecke, Jan-Olaf, Richards, Russell, Sahin, Oz
• Format: Conference Object
• Language: English
• Published: iEMSs 2016
• Subjects: Climate change adaptation; Whale watching; Systems thinking; Bayesian network models; Participatory modelling; Australia; 1712 Software; 2305 Environmental Engineering; 2611 Modelling and Simulation; Humpback Whale
• Online Access: https://espace.library.uq.edu.au/view/UQ:706857

Impacts of climate change on natural and human ‘systems’ are often difficult to assess due to high uncertainty and the need to integrate trans-disciplinary knowledge. This includes the worldwide, billion-dollar whale watching industry that depends on some key species such as the humpback whale. The migratory corridors, feeding, resting and calving sites, which are used for whale watching may be influenced by changing ocean currents and water temperatures. Whales are responding through a shift in migration time, behavior, abundance and distribution impacting on whale watching. To address these challenges, the authors developed a participatory model to understand and evaluate the potential effects of climate change (and other determinants) on the whale watching industry using the east coast of Australia as a case study. Using systems thinking and engaging with participants from the whale watching industry, a system structure for whale watching was developed. Elements encompassing climate change (e.g. length of season, temperature), policy (e.g. number of boats), ecology (e.g. number of whales age structure) and socioeconomics (e.g. number of tourists, fuel price) were integrated into this model with the interdependencies and feedback pathways investigated. Using the systems thinking model to help the participants contextualise and visualise their whale watching system, a Bayesian network (BN) model focusing on the determinants of ‘Whale Watching Profitability’ was then developed. The participants defined the structure and conditional probabilities for the BN. A sensitivity analysis on the BN helped identify important intervention points for the industry. This innovative methodology can be applied to other fields and can assist businesses and authorities in making rational management decisions even when data is very limited.