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<p>Climate change poses a significant threat to global biodiversity, altering species ranges and ecological dynamics. This study investigates the impact of climate change on <i>Tabanus taeniola</i> (Diptera: Tabanidae), a widely distributed horsefly with ecological importance in Africa and South America. Our objective was to model its current habitat suitability and predict future distribution shifts under various climatic scenarios. Using occurrence data from GBIF and and bioclimatic variables from WorldClim (BIO1–BIO19), we screened predictors for multicollinearity and calibrated MaxEnt models using a final subset of five variables. The model showed high accuracy, with an AUC of 0.918. Our findings identify the Minimum Temperature of the Coldest Month (BIO6) and Mean Temperature of Coldest Quarter (BIO11) as the key climatic drivers, with the species thriving in temperatures from 16&#xa0;°C to 29&#xa0;°C. Future projections, using the BCC-CSM2-MR and MRI-ESM2-0 models under SSP370 and SSP585 scenarios for 2050–2070, predict significant distributional shifts. We forecast a decline in optimal habitats in lowland tropical regions, with an expansion into temperate zones and higher altitudes in East Africa, South America, and parts of southern Europe. These projections indicate substantial redistribution toward higher elevations and temperate regions under future warming scenarios.</p>

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Predicting global distribution shifts of Tabanus taeniola under different climate change scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Abdalrahman E. Afifi,
  • Refaat M. Gabre,
  • Areej A. Al-Khalaf,
  • Abeer M. Salem,
  • Fadila Al Salameen,
  • Mohamed G. Nasser

摘要

Climate change poses a significant threat to global biodiversity, altering species ranges and ecological dynamics. This study investigates the impact of climate change on Tabanus taeniola (Diptera: Tabanidae), a widely distributed horsefly with ecological importance in Africa and South America. Our objective was to model its current habitat suitability and predict future distribution shifts under various climatic scenarios. Using occurrence data from GBIF and and bioclimatic variables from WorldClim (BIO1–BIO19), we screened predictors for multicollinearity and calibrated MaxEnt models using a final subset of five variables. The model showed high accuracy, with an AUC of 0.918. Our findings identify the Minimum Temperature of the Coldest Month (BIO6) and Mean Temperature of Coldest Quarter (BIO11) as the key climatic drivers, with the species thriving in temperatures from 16 °C to 29 °C. Future projections, using the BCC-CSM2-MR and MRI-ESM2-0 models under SSP370 and SSP585 scenarios for 2050–2070, predict significant distributional shifts. We forecast a decline in optimal habitats in lowland tropical regions, with an expansion into temperate zones and higher altitudes in East Africa, South America, and parts of southern Europe. These projections indicate substantial redistribution toward higher elevations and temperate regions under future warming scenarios.