ReefState model predictions

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、珊瑚适应性与藻类植食作用等底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指标，即珊瑚覆盖度与群落组成。ReefState模型的研发目标为探究珊瑚白化事件（coral bleaching events）的发生频率与强度预计提升后，对珊瑚礁恢复力（coral reef resilience）造成的长期影响；更为具体地，即在未来数十年预期的气候增温阶段，各类有效管理措施（即水质管控、捕捞压力调控与禁捕区）如何协同作用以助力珊瑚礁保护。有关各子模型与整合框架的理论基础、开发流程与应用细节，可参阅以下同行评议期刊论文：Wooldridge S、Berkelmans R、Done TJ、Jones RN、Marshall P（2005）. 增温气候下珊瑚群落恢复力的前兆因子：基于信念网络的研究方法. 海洋生态学进展系列（Marine Ecology Progress Series）295:157-169。Wooldridge S、Done TJ（2004）. 从历史事件中学习预测大规模珊瑚白化：融合遥感数据、原位观测数据与环境代用指标的贝叶斯方法. 珊瑚礁（Coral Reefs）23:96-108。