Prediction of the Potential Distribution ofin China Under Climate Change Scenarios Based on Its First Recorded Occurrences in Southern China
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
ABSTRACT (Asteraceae) is an invasive plant species whose distribution is expanding globally. It was recently recorded in Yunnan Province, China, far south of its previous known range in China, necessitating a timely invasion risk assessment. This study aimed to provide a more comprehensive assessment of its potential distribution in China under climate change scenarios. We used an optimizedmodel combined with a Multivariate Environmental Similarity Surface () analysis, based on current global occurrences and 52 newly collected field occurrence records from Yunnan Province. The model demonstrated robust predictive performance (= 0.971) and identified temperature seasonality (Bio4, contribution rate 42.8%) and precipitation seasonality (Bio15, 20.0%) as the two main influencing factors. The species exhibited a dual environmental response pattern that would allow it to adapt to mild, low‐seasonality settings of southwestern highlands, while also enduring the harsh, strongly continental climate of the northwestern inland. Thus, facilitating a multi‐center distribution pattern including Yunnan, Xinjiang and Taiwan. Under future climate change scenarios, the model showeddisplayed a rare inverse elevational shift. There was a significant centroid shifting northwestward and a sharp decline in elevation from the current high‐altitude mountainous region (4900 m) to the edge of the Qaidam Basin (3100 m). This suggests thatpopulations are potentially able to track high‐seasonality climat
Abstract
ABSTRACT (Asteraceae) is an invasive plant species whose distribution is expanding globally. It was recently recorded in Yunnan Province, China, far south of its previous known range in China, necessitating a timely invasion risk assessment. This study aimed to provide a more comprehensive assessment of its potential distribution in China under climate change scenarios. We used an optimizedmodel combined with a Multivariate Environmental Similarity Surface () analysis, based on current global occurrences and 52 newly collected field occurrence records from Yunnan Province. The model demonstrated robust predictive performance (= 0.971) and identified temperature seasonality (Bio4, contribution rate 42.8%) and precipitation seasonality (Bio15, 20.0%) as the two main influencing factors. The species exhibited a dual environmental response pattern that would allow it to adapt to mild, low‐seasonality settings of southwestern highlands, while also enduring the harsh, strongly continental climate of the northwestern inland. Thus, facilitating a multi‐center distribution pattern including Yunnan, Xinjiang and Taiwan. Under future climate change scenarios, the model showeddisplayed a rare inverse elevational shift. There was a significant centroid shifting northwestward and a sharp decline in elevation from the current high‐altitude mountainous region (4900 m) to the edge of the Qaidam Basin (3100 m). This suggests thatpopulations are potentially able to track high‐seasonality climates to offset warming impacts caused by climate change scenarios. Although the total suitable area ofin China is projected to contract in the future, the Yunnan–Guizhou Plateau and Xinjiang's Ili River Valley remain stable climate refugia. Therefore, this study recommends considering these core distribution areas as primary priorities for prevention and control ofand highlights the colonization risk to oasis agricultural regions via the northwestern dispersal corridors. This study aimed to determine its potential distribution under climate change scenarios using a MaxEnt model and Multivariate Environmental Similarity Surface (MESS) analysis based on the new Yunnan records and current global occurrences. This study elucidates the invasion pattern ofand provides a scientific basis for understanding the mechanisms of niche shifts in high‐altitude invasive plants in response to climate change. graphical