Hard substrata are limiting in the marine environment. Artificial structures can therefore provide direct habitat or structural complexity for marine organisms.
A recently established MPA in north Bali, has supplemented existing hard substrata with large cement and lime-based structures to attract fish and provide a foundation for colonisation from coral and other invertebrates.
We model this case-study using Bayesian belief networks (BBNs); intuitive and easy to produce models. BBNs have been previously criticised due to their inability to model feedback loops common in ecological systems. We have modified BBNs to include both positive and negative feedback. The BBN predictions show that, in isolation, and over short time-frames (< 1 year) they benefit the ecological community, especially in terms of small fish numbers – and results agree with initial monitoring results from the site.
However, by increasing the number of iterations of the BBN, we examine the development of the community over longer time periods, looking at the colonisation of sessile invertebrates and how this further enhances the habitats for fish (positive feedback), and how the fish remove algal growth (negative feedback). Equally predictions on incentives from tourism and money into the local economy improved over time (linking natural and social systems).
The study provides evidence for the long-term conservation effectiveness of artificial structures in the marine environment as well as the ability of BBNs to integrate data from a wide range of sources, and provide information based on ecological feedback processes.
Topics: Conservation and management of tropical marine ecosystems , Topics: Conservation engineering
