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Peer-reviewedCC BY 4.0PLOS One

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

  • Pavan Kumar1
  • Megha Paul2
  • Preeti Kumari3
  • Manmohan Dobriyal4
  • Pankaj Lavania1
  • Manish Srivastav4
  • Prem Chandra Pandey5
  • Benson Turyasingura6
6 affiliations
  1. 1Department of Forest Resource Management, College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India
  2. 2Centre of Geospatial Science and Technology, College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India
  3. 3Department of Environmental Science, Amity University, Ranchi, India
  4. 4College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India
  5. 5Department of Life Sciences, School of Natural Sciences (SoNS), Shiv Nadar Institution of Eminence (Deemed to be University), Greater Noida, India
  6. 6Faculty of Agriculture and Environmental Sciences, Kabale University, Kampala City, Uganda
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{kumar2026integrating,
  title = {{Integrating MaxEnt and MaxNet for climate-resilient habitat restoration: Predicting the ecological niche of Eucalyptus tereticornis in semi-arid landscapes}},
  author = {Pavan Kumar and Megha Paul and Preeti Kumari and Manmohan Dobriyal and Pankaj Lavania and Manish Srivastav and Prem Chandra Pandey and Benson Turyasingura},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0360385},
  url = {https://doi.org/10.1371/journal.pone.0360385},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Integrating MaxEnt and MaxNet for climate-resilient habitat restoration: Predicting the ecological niche of Eucalyptus tereticornis in semi-arid landscapes
AU  - Pavan Kumar
AU  - Megha Paul
AU  - Preeti Kumari
AU  - Manmohan Dobriyal
AU  - Pankaj Lavania
AU  - Manish Srivastav
AU  - Prem Chandra Pandey
AU  - Benson Turyasingura
PY  - 2026
DA  - 2026/10/13
JO  - PLOS One
DO  - 10.1371/journal.pone.0360385
UR  - https://doi.org/10.1371/journal.pone.0360385
AB  - 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.
SN  - 1932-6203
ER  -