Tecomella undulata under threat: The impact of climate change on the distribution of a valuable tree species using a machine learning model

TL;DR

This study uses a machine learning model to predict how climate change will reduce the suitable habitat for Tecomella undulata, a valuable tree species in Iran.

Contribution

The study introduces a high-performance machine learning model to predict future habitat suitability of Tecomella undulata under climate change scenarios.

Findings

The MaxEnt model achieved high accuracy (AUC 0.91, TSS 0.79) in predicting Tecomella undulata's habitat.

Temperature seasonality was the most important factor influencing habitat suitability.

Future projections show a drastic reduction in suitable habitats, up to 98% by 2090 under SSP scenarios.

Abstract

Climate change has emerged as a significant driver of biodiversity loss, with profound implications for species distribution. This study assessed the current and future distribution of Tecomella undulata (Desert teak), an economically and medicinally valuable species facing threats from climate change. MaxEnt model, built using 44 occurrence points and environmental data including bioclimatic factors and Digital Elevation Model (DEM), demonstrated an impressive Area Under the Curve (AUC) value of around 0.91 and a True Skill Statistic (TSS) value of 0.79, indicating excellent predictive performance. Temperature seasonality (Bio4) emerged as the most crucial variable, contributing 35.9% to the modeling, followed by the mean temperature of the wettest quarter (Bio8) and precipitation seasonality (Bio15). The habitat suitability maps showed a strong presence of T. undulata in the southern regions of Iran, with Fars and Bushehr provinces being particularly conducive to its growth. Future projections under Shared Socioeconomic Pathways (SSP) scenarios SSP245 and SSP585 for 2030, 2050, 2070, and 2090 suggested a decline in suitable habitats for T. undulata, with high-suitability areas projected to decrease by up to 98% and unsuitable habitats predicted to increase. The study underscores the urgency for tailored conservation measures to mitigate the impacts of climate change on this valuable species.

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