Advances and limitations of individual-based models to analyze and predict dynamics of mangrove forests: A review

Mangrove ecosystems are considered vulnerable to climate change as coastal development limits the ecosystem services and adaptations important to their survival. Although they appear rather simple in terms of species diversity, their ecology is complex due to interacting geophysical forces of tides, surface runoff, river and groundwater discharge, waves, and constituents of sediment, nutrients and saltwater. These interactions limit developing a comprehensive framework for science-based sustainable management practices. A suite of models have been developed independently by various academic and government institutions worldwide to understand the dynamics of mangrove ecosystems and to provide ecological forecasting capabilities under different management scenarios and natural disturbance regimes. The models have progressed from statistical tables representing growth and yield to more sophisticated models describing various system components and processes. Among these models are three individual-based models (IBMs) (FORMAN, KIWI, and MANGRO). A comparison of models’ designs reveal differences in the details of process description, particularly, regarding neighbor competition among trees. Each model has thus its specific range of applications. Whereas FORMAN and KIWI are most suitable to address mangrove forest dynamics of stands, MANGRO focuses on landscape dynamics on larger spatial scale. A comparison of the models and a comparison of the models with empirical knowledge further reveal the general needs for further field and validation studies to advance our ecological understanding and management of mangrove wetlands.