BR-MANGUE

Overview

BR-MANGUE is a spatially explicit cellular automata model designed to simulate the impacts of sea-level rise (SLR) on mangrove ecosystems [1, 2]. Developed as a virtual laboratory, it helps to understand the patterns of resistance, migration, and decline of mangrove forests facing climate change scenarios, particularly in coastal zones with complex anthropic occupation [1, 3, 4].

Conceptual Model

The model stratifies the relevant aspects for mangrove persistence into four main components [5, 6]:

• Sea-Level Rise (NMRM): Simulates the advance of the water column over the continent and its impacts, such as permanent inundation and erosion [7, 8].
• Land Use and Occupation: Considers different land cover types as potential areas for mangrove colonization or as anthropic/natural barriers that prevent migration [9-11].
• Environmental Restrictions: Evaluates the suitability of the substrate, considering factors like soil type (e.g., indiscriminate mangrove soils) and sediment accretion processes [12, 13].
• Mangrove Dynamics: Integrates the interactions between SLR, tidal influence area (AIM), land cover, and environmental constraints to determine if the mangrove will resist, migrate, or decline [14, 15].

Technologies

• TerraME: A programming environment for spatial dynamical modeling, supporting cellular automata and integrated with 2D cellular spaces [16, 17].
• Lua: The model's source code is implemented using Lua, an open-source, lightweight, and robust programming language [18].
• TerraView / GIS: Used for geographic database organization and cellular space creation [19].

Input Data Requirements

To run simulations, the model requires a cellular space containing the following attributes for each cell:

• Land Cover / Use: Initial state of the cell (e.g., mangrove, water, anthropic area, terrestrial vegetation) [20].
• Altimetry: Minimum altitude values, often derived from Digital Elevation Models (DEM) [21, 22].
• Soil Classes: To determine if the soil is suitable for mangrove colonization [23].
• Tidal Influence Area (AIM): Defined by the local tide height [21].

Key Mechanisms

The model's transition rules incorporate complex biophysical interactions [24-28]:

• Water Flux: Determined by the water column height relative to adjacent cells [24, 29].
• Vertical & Longitudinal Accretion: Simulates the formation of new mud banks and increases in sediment height, which can counteract sea-level rise [30-32].
• Migration: Mangroves can colonize new areas if the tidal influence shifts and no anthropic or natural barriers exist [26, 27].

Case Studies

The model has been successfully applied to simulate sea-level rise impacts in vulnerable Brazilian coastal zones, such as Maranhão Island [1, 3, 33] and the Baixada Maranhense (a Ramsar site) [34, 35].

References

• Bezerra, D. S. (2014). Modelagem da dinâmica do manguezal frente à elevação do nível do mar. INPE [36, 37].
• Bezerra, D. S. et al. (2014). Simulating Sea-Level Rise Impacts on Mangrove Ecosystem adjacent to Anthropic Areas: the case of Maranhão Island, Brazilian Northeast. Pan-American Journal of Aquatic Sciences [33, 38].
• Bezerra, D. S. et al. (2025). *Spatially Explicit Modeling for Impacts of Sea-Leve