Prediction of Potential Distribution and Response of Changium smyrnioides to Climate Change Based on Optimized MaxEnt Model

Plants, Journal Year: 2025, Volume and Issue: 14(5), P. 743 - 743

Published: Feb. 28, 2025

Changium smyrnioides, an endangered herb known for its medicinal roots, contains essential amino acids that are vital human health but cannot be synthesized by the body. However, wild populations of this species have been steadily declining due to combined impacts climate change and anthropogenic activities. In study, we employed optimized MaxEnt model predict potential distribution C. smyrnioides under different scenarios evaluate responses change. Our findings demonstrated achieved optimal performance with a regularization multiplier 0.5 feature combination linear quadratic terms. Among environmental variables, three emerged as most critical factors shaping species’ distribution: elevation, precipitation driest month (bio14), isothermality (bio2/bio7 × 100, bio3). Currently, primary suitable habitats concentrated in Jiangsu Province, estimated 21,135 km² classified highly suitable. The analysis further indicated that, response rising temperatures, is likely shift northeastward across China. Notably, results suggested total area would increase over time projected scenarios. Based on predicted centroid migration habitats, Anhui Province was identified future conservation zone smyrnioides. This region could serve refuge, ensuring long-term survival changing climatic conditions. Overall, study provides key insights into ecological change, offering evidence-based guidance development effective strategies aimed at safeguarding herb.

Language: Английский

Predicting Suitable Spatial Distribution Areas for Urban Trees Under Climate Change Scenarios Using Species Distribution Models: A Case Study of Michelia chapensis

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 638 - 638

Published: March 18, 2025

Climate change has presented considerable challenges in the management of urban forests and trees. Varieties studies have predicted potential changes species distribution by employing single-algorithm models (SDMs) to investigate impacts climate on plant species. However, there is still limited quantitative research suitable ranges commonly used tree Therefore, our study aims optimize traditional SDMs integrating multiple machine learning algorithms propose a framework for identifying trees under change. We took Michelia chapensis, particular significance southern China, as pilot evolution its range context two future scenarios (SSP126 SSP585) across four periods (2030s, 2050s, 2070s, 2090s). The findings indicated that ensemble SDM showed strong predictive capacity, with an area curve (AUC) value 0.95. chapensis estimated at 15.9 × 105 km2 currently it will expand most areas according projection. contract southeastern Yunnan, central Guangdong, Sichuan Basin, northern Hubei, Jiangxi, etc. location current located Hengyang, Hunan (27.36° N, 112.34° E), projected shift westward future. migration magnitude positively correlated intensity These provide scientific basis landscape planning chapensis. Furthermore, proposed can be seen valuable tool predicting response change, providing insights proactive adaptation management.

Language: Английский