Mapping Potential Mangrove Forest Restoration Areas in Coastal Ghana Using Multi-Model Habitat Suitability Analysis

Abstract

Extensive ecosystem degradation along the coastal areas of Ghana highlights the need for targeted landscape restoration. This study identified areas with high restoration potential by evaluating environmental, climatic, and anthropogenic determinants of mangrove distribution. We tested multiple habitat suitability models, including Random Forest (RF), Generalized Additive Model (GAM), Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), and an ensemble approach. Mangrove occurrence records were compiled from field observations, drone-derived data, and publicly available biodiversity and mapped datasets. The ensemble GAM-RF model emerged as the most robust model, with the highest predictive performance. Hydrological and topographic factors were the strongest predictors of mangrove habitat suitability, particularly proximity to rivers and the coastline, low-elevation terrain (<5 m asl), and temperature-related bioclimatic variables, while precipitation metrics and anthropogenic proxies played secondary roles. Using the ensemble approach, the estimated potentially suitable habitat ranged from 417 to 1313 km2, indicating that substantial areas remain available for mangrove restoration. These findings align with ongoing national and international restoration initiatives, including the coastal restoration plan, and have important implications for coastal protection, carbon sequestration, fisheries habitat, and local livelihoods. Our findings demonstrate that multi-model habitat suitability analyses can guide spatially targeted, and evidence-based restoration planning.