ReefState model predictions
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ReefState (version 3.0) utilises a Bayesian Network modelling framework to integrate lower-level submodels of future warming, coral damage, coral recovery, coral adaptation, and algal herbivory, into a continuous causal chain. The integrated model allows prediction of ecological endpoints that reflect important management concerns, namely coral cover and composition. The purpose of the ReefState model is to investigate the long-term implications on coral reef resilience of projected increases in the frequency and intensity of coral bleaching events. And more specifically, how successful management outcomes (viz. water quality, fishing pressure, and no take zones) might interact to benefit coral reefs during the period of climate warming that is expected in the coming decades. Details pertaining to the rationale, development and application of the individual submodels and integrating framework can be found within the refereed journal articles:Wooldridge S, Berkelmans R, Done TJ, Jones RN, Marshall P (2005). Precursors for resilience in coral communities in a warming climate: a belief network approach. Marine Ecology Progress Series 295:157-169.Wooldridge S, Done TJ (2004). Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies. Coral Reefs 23: 96-108.
ReefState（版本3.0）采用贝叶斯网络（Bayesian Network）建模框架，将未来增温、珊瑚损伤、珊瑚恢复、珊瑚适应及藻类植食作用的低层子模型整合为一条连续的因果链。该整合模型可预测反映核心管理关切的生态终点变量，即珊瑚覆盖度与群落组成。本ReefState模型的研究目标为，探究珊瑚白化事件（coral bleaching events）的预测发生频率与强度上升时，对珊瑚礁韧性（coral reef resilience）的长期影响；更具体而言，其旨在明确未来数十年预期气候变暖时段内，各类有效管理措施（即水质管控、捕捞压力调控与禁捕区）如何通过协同作用惠及珊瑚礁生态系统。有关各子模型与整合框架的理论基础、开发流程及应用细节，可参阅以下两篇同行评议期刊论文： Wooldridge S, Berkelmans R, Done TJ, Jones RN, Marshall P (2005). Precursors for resilience in coral communities in a warming climate: a belief network approach. Marine Ecology Progress Series 295:157-169. Wooldridge S, Done TJ (2004). Learning to predict large-scale coral bleaching from past events: A Bayesian approach using remotely sensed data, in-situ data, and environmental proxies. Coral Reefs 23: 96-108.
