Integrating MaxEnt and MaxNet for climate-resilient habitat restoration: Predicting the ecological niche of Eucalyptus tereticornis in semi-arid landscapes.

Accurate modeling of species distribution is essential for effective biodiversity conservation, afforestation planning, and land-use management. This study presents a comparative analysis of two prominent presence-only species distribution models Maximum Entropy (MaxEnt) and MaxNet to evaluate their efficacy in predicting the ecological niche of Eucalyptus tereticornis in a semi-arid agro-climatic region. A total of 138 georeferenced occurrence records and 25 pseudo-absence points were used, incorporating key environmental variables such as wind speed, temperature range, precipitation, and root zone soil wetness. Both models were optimized and assessed using metrics including accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC). Results indicate that MaxEnt outperformed MaxNet in most metrics, achieving an accuracy of 87.5% and an AUC of 0.956, compared to MaxNet's accuracy of 75% and AUC of 0.926. Environmental variable importance varied by model, with MaxEnt highlighting surface pressure and MaxNet emphasizing wind speed. Probability density plots revealed that MaxEnt distinctly identified high suitability zones, whereas MaxNet offered a more balanced, conservative distribution across the landscape. These differences underscore the influence of algorithm design on ecological inference. This study emphasizes the importance of model selection and tuning in species distribution modeling and suggests that integrating outputs from both models can yield robust insights for habitat suitability assessment. The findings support data-driven afforestation strategies, especially under changing climatic conditions, and provide valuable guidance for sustainable plantation efforts involving Eucalyptus tereticornis in similar agro-ecological zones.