基于MaxEnt优化模型的气候变化背景下入侵植物喀西茄在中国的潜在风险区预测
Prediction of the Potentially Risk Areas for the Invasive Plant Solanum aculeatissimum Jacq. in China under the Background of Climate Change Based on MaxEnt Optimization Model
摘要: 气候是影响植物分布格局的关键生态因素之一。作为外来入侵植物，喀西茄(Solanum aculeatissimum Jacq.)已对中国的生态安全和粮食安全构成严重威胁。因此，深入研究气候变化与喀西茄分布格局之间的关系，将有助于制定全面的入侵植物的控制战略，保护当地的生态环境。本研究基于中国境内513条喀西茄分布记录和14个主要影响因子，运用R语言ENMeval包调整调控倍频(RM)和特征组合(FC)优化的MaxEnt模型，分析影响喀西茄分布的环境因子，并预测其在当前、未来2041~2060年(2050s)和2061~2080年(2070s) 3种不同气候变化情境(SSP126、SSP370和SSP585)下的潜在入侵风险区的分布格局及其质心迁移路径。结果表明：(1) 当参数设定为RM = 0.5和FC = LQ时，MaxEnt模型的结果表现出较高的预测精度(AUC = 0.96)且过拟合程度较低(Delta. AICc = 0)；(2) 最冷季平均气温(bio11)、人类足迹指数(Hf)、最暖季降水量(bio18)和海拔(elev)是影响喀西茄分布的主要因素，其中最冷季平均气温是最关键的环境因子；(3) 当前气候条件下，喀西茄在中国的潜在入侵风险面积为517.24 × 104 km2，约占全国总面积的53.88%，主要分布在中国西南部及台湾地区；(4) 在未来气候变化情境下，喀西茄的潜在风险区域面积将随着时间的推移逐步扩大；(5) 从当前到未来(2050s和2070s)，在不同气候变化情境下喀西茄入侵风险区的质心迁移路径基本一致，均向西北方向移动。这表明未来应特别关注我国西北地区的入侵风险，并提前制定和部署针对性的防控措施。
Abstract: Climate is a crucial ecological factor influencing plant distribution patterns. As an invasive species, Solanum aculeatissimum Jacq. poses a serious threat to both ecological and food security in China. Therefore, a comprehensive investigation into the relationship between climate change and the distribution of Solanum aculeatissimum Jacq. is essential for formulating effective strategies to control this invasive species and protect local ecosystems. This study analyses 513 distribution records of Solanum aculeatissimum Jacq. in China along with 14 key environmental variables. MaxEnt models were optimized using the R package ENMeval by tuning regularization multiplier (RM) and feature combination (FC) to analyse the environmental factors influencing the species’ distribution. It predicts the distribution pattern and centroid migration paths of potential invasion risk areas for Solanum aculeatissimum Jacq. under three different climate change scenarios (SSP126, SSP370, and SSP585) for the current period, the future 2041~2060 (2050s), and 2061~2080 (2070s). The results indicate that (1) when the parameters are set to RM = 0.5 and FC = LQ, the MaxEnt model demonstrates high predictive accuracy (AUC = 0.96) and minimal overfitting (Delta. AICc = 0). (2) The average temperature of the coldest season (bio11), human footprint (Hf), precipitation of the warmest season (bio18), and elevation (elev) are the primary factors influencing Solanum aculeatissimum Jacq. distribution, with the average temperature of the coldest season being the most significant. (3) Under current climate conditions, the total potential invasion risk area in China is 517.24 × 10⁴ km2, accounting for 53.88% of the country’s total area, primarily located in the southwestern regions and Taiwan Region. (4) In future climate scenarios, the potential risk area is expected to gradually increase. (5) From the present to the 2050s and 2070s, the centroid migration paths of invasion risk zones show a consistent northwestward shift across various climate change scenarios. This indicates that special attention should be paid to the invasion risk in northwest China in the future, and targeted prevention and control measures should be formulated and deployed in advance.
文章引用：韩鸿沛, 刘露露, 高俊雅, 吕明洋. 基于MaxEnt优化模型的气候变化背景下入侵植物喀西茄在中国的潜在风险区预测[J]. 自然科学, 2025, 13(2): 261-276. https://doi.org/10.12677/ojns.2025.132027

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