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<p>Endemic and endangered bird species, such as Salvadori serin (<i>C. xantholaema</i>), are vulnerable to environmental and anthropogenic changes. Understanding the impact of climate change on ecological niches is essential for effective conservation. This study employed advanced ML algorithms to model the current and future suitability of <i>C. xantholaema</i> under two scenarios (SSP245 and SSP585) for the years 2050 and 2070. The four machine learning models, namely, Maximum Entropy (MaxEnt), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGBoost), predicted habitat suitability using 188 presence occurrence data and 15 environmental factors. Model performance was assessed using AUC-ROC, accuracy, precision, sensitivity, specificity, kappa, and F1 score, with ensemble modeling techniques enhancing reliability. The current analysis indicated high predictive accuracy, with XGBoost achieving the highest AUC (0.99), followed by RF (0.98), SVM (0.97), and MaxEnt (0.92). Regarding habitat suitability, 75.3% of Ethiopia’s land was unsuitable for <i>C. xantholaema</i>, with only 3.9% classified as highly suitable. By 2050, 61.82% and 57.14% of areas were projected to be unsuitable under SSP245 and SSP585, respectively. By 2070, unsuitable habitats may increase to 65.24% (SSP245) and 60.17% (SSP585), further decreasing habitat suitability. High-suitability habitats are expected to decline by 80.8% in 2050, covering approximately 8,259.95&#xa0;km<sup>2</sup>, and by 73.2% in 2070, covering about 11,584.6&#xa0;km<sup>2</sup>. Precipitation during the driest month (Bio14) was the most crucial predictor of habitat suitability, with importance values ranging from 32.5% (XGBoost) to 100% (SVM and RF), while temperature-related factors, particularly annual mean temperature (Bio1), contributed differently across ML models. According to this study, climate factors impact habitat changes. The findings emphasize the urgent need for conservation strategies to mitigate <i>C. xantholaema</i> habitat loss. Future research should include local data and other human-related factors to enhance the effectiveness of conservation efforts and improve predictions.</p>

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Machine learning predictions of climate change effects on nearly threatened bird species (Crithagra xantholaema) habitat in Ethiopia for conservation strategies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Tadele Bedo Gelete,
  • Diriba Tulu,
  • Kalid Hassen Yasin,
  • Erana Kebede

摘要

Endemic and endangered bird species, such as Salvadori serin (C. xantholaema), are vulnerable to environmental and anthropogenic changes. Understanding the impact of climate change on ecological niches is essential for effective conservation. This study employed advanced ML algorithms to model the current and future suitability of C. xantholaema under two scenarios (SSP245 and SSP585) for the years 2050 and 2070. The four machine learning models, namely, Maximum Entropy (MaxEnt), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGBoost), predicted habitat suitability using 188 presence occurrence data and 15 environmental factors. Model performance was assessed using AUC-ROC, accuracy, precision, sensitivity, specificity, kappa, and F1 score, with ensemble modeling techniques enhancing reliability. The current analysis indicated high predictive accuracy, with XGBoost achieving the highest AUC (0.99), followed by RF (0.98), SVM (0.97), and MaxEnt (0.92). Regarding habitat suitability, 75.3% of Ethiopia’s land was unsuitable for C. xantholaema, with only 3.9% classified as highly suitable. By 2050, 61.82% and 57.14% of areas were projected to be unsuitable under SSP245 and SSP585, respectively. By 2070, unsuitable habitats may increase to 65.24% (SSP245) and 60.17% (SSP585), further decreasing habitat suitability. High-suitability habitats are expected to decline by 80.8% in 2050, covering approximately 8,259.95 km2, and by 73.2% in 2070, covering about 11,584.6 km2. Precipitation during the driest month (Bio14) was the most crucial predictor of habitat suitability, with importance values ranging from 32.5% (XGBoost) to 100% (SVM and RF), while temperature-related factors, particularly annual mean temperature (Bio1), contributed differently across ML models. According to this study, climate factors impact habitat changes. The findings emphasize the urgent need for conservation strategies to mitigate C. xantholaema habitat loss. Future research should include local data and other human-related factors to enhance the effectiveness of conservation efforts and improve predictions.