Modeling Climate Change Effects on the Distribution of Oak Forests with Machine Learning

The present study models the effect of climate change on the distribution of Persian oak (Quercus brantii Lindl.) in the Zagros forests, located in the west of Iran. The modeling is conducted under the current and future climatic conditions by fitting the machine learning method of the Bayesian additive regression tree (BART). For the anticipation of the potential habitats for the Persian oak, two general circulation models (GCMs) of CCSM4 and HADGEM2-ES under the representative concentration pathways (RCPs) of 2.6 and 8.5 for 2050 and 2070 are used. The mean temperature (MT) of the wettest quarter (bio8), solar radiation, slope and precipitation of the wettest month (bio13) are respectively reported as the most important variables in the modeling. The results indicate that the suitable habitat of Persian oak will significantly decrease in the future under both climate change scenarios as much as 75.06% by 2070. The proposed study brings insight into the current condition and further projects the future conditions of the local forests for proper management and protection of endangered ecosystems.

Automated summary

The present study models the effect of climate change on the distribution of Persian oak (Quercus brantii Lindl.) in the Zagros forests of Iran. Using the machine learning method of Bayesian additive regression tree (BART), the study finds that the suitable habitat of Persian oak will decrease by 75.06% by 2070 under both climate change scenarios. This study provides insights into the current condition and future projections of the local forests for proper management and protection of endangered ecosystems.