Prediction Model of Selected Rhizophoraceae Family Occurred along the East Coast of Peninsular Malaysia using MaxEnt

Abstract

Rhizophoraceae play a crucial role in coastal ecosystems by providing shoreline protection, storing carbon, and serving as habitats for a diverse array of biota. However, anthropogenic disturbances and climate change have disrupted their natural distribution, necessitating the development of predictive tools to support conservation planning. The research uses Ecological Niche Modelling (ENM) and the Maximum Entropy (MaxEnt) algorithm to predict potential habitat suitability for the six species of Rhizophoraceae along the East Coast of Peninsular Malaysia. Data for the environmental layers were obtained from various sources and then processed, standardised, and converted into formats suitable for modelling. This was followed by model training using cleaned occurrence data. The accuracy of model predictions for all species, as measured by Area Under the Curve (AUC), was very high, with values reaching above 0.89. The main environmental factors were the amount of rain, temperature, and how close to the coast the area was. The maps showing areas deemed suitable indicated a very close match with existing mangrove areas, especially in Terengganu, Kelantan, and Pahang estuarine and coastal areas. This means that the area is potentially significant in terms of restoration and conservation. The study overall illustrates MaxEnt’s capability of reflecting the Rhizophoraceae ecological niche, and it supplies the foundations for mangrove ecosystems’ management, biodiversity conservation strategies, and future restoration along the East Coast of Peninsular Malaysia.