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<p>The African savannah elephant (Loxodonta <i>africana</i>) and the forest elephant (Loxodonta <i>cyclotis</i>) were recently declared endangered elephant species in Africa. In addition to the direct human conflicts, climate change has made significant impacts on their natural habitats, causing physiological stress and making them lose life on land. Though there are several studies on the prediction of habitat suitability (HS) of L. <i>africana</i>, little is known about the HS of L. <i>cyclotis</i> under future climatic conditions. The present study aims to evaluate the HS of both the elephant species in view of the reported trends in their declining population and changes in migratory pathways, using various machine learning models under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) for 2070. A realistic prediction of the species’ habitat was provided by support vector machine (SVM) for L. <i>africana</i> and random forest (RF) for L. <i>cyclotis</i> for the selected bioclimatic variables (BV). Based on the significance of relative variabilities for the selected BVs as well as their relative contribution to HS predictions, the occurrence of high temperature, high rainfall, and drought is found to be the most critical habitat conditions for L. <i>africana</i>, while the persistence of extreme temperature (without precipitation) was observed to have a net negative impact on HS for L. <i>cyclotis</i>. Considering the predicted impacts on HS, a few critical adaptation strategies are proposed, focussing on microclimatic protection, habitat suitability measures, and community involvement for reducing conflicts and ensuring protection for these charismatic mammals on the planet.</p>

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Climate-driven habitat suitability and adaptation planning for endangered elephants (Loxodonta africana and Loxodonta cyclotis)

  • 期刊论文

  • 发表日期：

    2025年12月6日

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  • Narayanan Natarajan,
  • Senthil Kumar Dineshkumar,
  • S. Syed Masoodhu,
  • Mangottiri Vasudevan

摘要

The African savannah elephant (Loxodonta africana) and the forest elephant (Loxodonta cyclotis) were recently declared endangered elephant species in Africa. In addition to the direct human conflicts, climate change has made significant impacts on their natural habitats, causing physiological stress and making them lose life on land. Though there are several studies on the prediction of habitat suitability (HS) of L. africana, little is known about the HS of L. cyclotis under future climatic conditions. The present study aims to evaluate the HS of both the elephant species in view of the reported trends in their declining population and changes in migratory pathways, using various machine learning models under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) for 2070. A realistic prediction of the species’ habitat was provided by support vector machine (SVM) for L. africana and random forest (RF) for L. cyclotis for the selected bioclimatic variables (BV). Based on the significance of relative variabilities for the selected BVs as well as their relative contribution to HS predictions, the occurrence of high temperature, high rainfall, and drought is found to be the most critical habitat conditions for L. africana, while the persistence of extreme temperature (without precipitation) was observed to have a net negative impact on HS for L. cyclotis. Considering the predicted impacts on HS, a few critical adaptation strategies are proposed, focussing on microclimatic protection, habitat suitability measures, and community involvement for reducing conflicts and ensuring protection for these charismatic mammals on the planet.