Özdel et al. (2025) Climate-Driven Futures of Olive (Olea europaea L.): Machine Learning-Based Ensemble Species Distribution Modelling of Northward Shifts Under Aridity Stress

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Identification

• Journal: Plants
• Year: 2025
• Date: 2025-12-11
• Authors: Muhammed Mustafa Özdel, Beyza Ustaoğlu, İsa Cürebal
• DOI: 10.3390/plants14243774

Research Groups

Not specified in the provided text.

Short Summary

This study assessed the impact of climate change on olive (Olea europaea L.) distribution in Türkiye using machine learning-based species distribution models, predicting a significant northward and upward shift in suitable areas, a drastic decline in highly suitable land, and increased aridity pressure by the end of the century.

Objective

• To assess the effects of climate change on the potential distribution of olive (Olea europaea L.) in Türkiye.

Study Configuration

• Spatial Scale: Türkiye's land area.
• Temporal Scale: Reference period (1970-2000) and future projections for 2041-2060 and 2081-2100.

Methodology and Data

• Models used: Machine learning-based Species Distribution Models (SDMs).
• Data sources: Bioclimatic variables (e.g., winter precipitation (Bio19), mean temperature of driest quarter (Bio9)), topographic factors (elevation, slope, aspect), UNEP Aridity Index, and climate change scenarios (SSP2-4.5, SSP5-8.5).

Main Results

• The model exhibited strong predictive performance (AUC = 0.93; TSS = 0.77).
• Primary variables influencing olive distribution were identified as elevation, winter precipitation (Bio19), and mean temperature of driest quarter (Bio9).
• Significant shifts in suitable areas are projected: northward (from the traditional Aegean and Mediterranean coastal belt toward the Marmara and Black Sea regions) and upward (into higher-altitude inland areas).
• High-suitability areas are projected to decline from 4.4% of Türkiye's land area during the reference period to 0.2% by 2081-2100 under the SSP5-8.5 scenario.
• Aridity pressure is projected to increase: 87.2% of suitable habitats were classified as sub-humid in the reference period, but by 2081-2100 under SSP5-8.5, 40.1% will shift to dry sub-humid and 26.4% to semi-arid conditions.

Contributions

• This study provides a comprehensive assessment of climate change impacts on olive distribution in Türkiye, identifying key environmental drivers and projecting significant spatial shifts and habitat loss, which is crucial for strategic agricultural planning in the region.

Funding

Not specified in the provided text.

Citation

@article{Özdel2025ClimateDriven,
  author = {Özdel, Muhammed Mustafa and Ustaoğlu, Beyza and Cürebal, İsa},
  title = {Climate-Driven Futures of Olive (Olea europaea L.): Machine Learning-Based Ensemble Species Distribution Modelling of Northward Shifts Under Aridity Stress},
  journal = {Plants},
  year = {2025},
  doi = {10.3390/plants14243774},
  url = {https://doi.org/10.3390/plants14243774}
}