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

C. Y. Shen,

Xi Chen, Chao Zhou

et al.

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

The nonlinear climatological impacts of urban morphology on extreme heats in urban functional zones: An interpretable ensemble learning-based approach DOI
Xiaochang Liu, Tao Wu, Qingrui Jiang

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: 273, P. 112728 - 112728

Published: Feb. 18, 2025

Language: Английский

Citations

0
Predicting Suitable Spatial Distribution Areas for Urban Trees Under Climate Change Scenarios Using Species Distribution Models: A Case Study of Michelia chapensis DOI Creative Commons

C. Y. Shen,

Xi Chen, Chao Zhou

et al.

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

Citations

0