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林业科学 ›› 2025, Vol. 61 ›› Issue (4): 104-116.doi: 10.11707/j.1001-7488.LYKX20230618

• 研究论文 • 上一篇    

基于优化的MaxEnt模型预测未来气候变化下刺槐在中国的空间分布

高婉婷1,2,3, 胡晓创1,2,3, 孙守家1,2,3, 张劲松1,2,3, 孟平1,2,3, 蔡金峰2   

  1. 1. 中国林业科学研究院林业研究所 国家林业和草原局林木培育重点实验室 北京100091;
    2. 南京林业大学南方现代林业协同创新中心 南京 210037;
    3. 河南小浪底森林生态系统国家野外科学观测研究站 济源 454650
  • 收稿日期:2023-12-15 修回日期:2024-05-28 发布日期:2025-04-21
  • 通讯作者: 孙守家为通信作者。E-mail:ssj1011@163.com。
  • 基金资助:
    国家重点研发计划项目( 2020YFA0608101);中央级公益性科研院所基本科研业务费专项资金( CAFYBB2022ZA00102);中央级公益性科研院所基本科研业务费专项资金(CAFYBB2023PA00101)。

Prediction of the Distribution of Robinia pseudoacacia in China under Future Climate Change Using an Optimized MaxEnt Model

Gao Wanting1,2,3, Hu Xiaochuang1,2,3, Sun Shoujia1,2,3, Zhang Jinsong1,2,3, Meng Ping1,2,3, Cai Jinfeng2   

  1. 1. Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration Research Instituteof Forestry, Chinese Academy of Forestry Beijing 100091;
    2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forest University Nanjing 210037;
    3. Henan Xiaolangdi Forest Ecosystem National Observation and Research Station Jiyuan 454650
  • Received:2023-12-15 Revised:2024-05-28 Published:2025-04-21

摘要: 目的 探究全国尺度下刺槐分布与环境变量的关系以及未来适生区域变化,为刺槐造林规划与管理提供数据支持。方法 应用经R语言Kuenm包优化的MaxEnt模型和ArcGIS软件,基于筛选后181条刺槐有效分布点数据和12个环境因子变量,探讨影响其地理分布的主要环境因子,并预测当代和未来2030s、2050s和2070s 3种气候变化情景(ssp126、ssp245、ssp585)下刺槐在中国的潜在适生区及其质心变化趋势。结果 选用特征组合FC=linear+product(线性特征+乘积型特征)且调控倍频RM=0.5时,模型复杂度最低,模型预测准确性较高,受试者工作特征曲线下面积(AUC)为0.880,可用来预测刺槐适生区范围。最冷季平均气温、最暖季降水量和海拔是影响刺槐潜在地理分布的主要环境因子,其适应范围分别为–5~6.5 ℃、335~1 825 mm和–155~1 725 m。当代气候条件下,刺槐在中国的总适生区面积为262.51 ×104 km2,高适生区面积为37.86 ×104 km2。未来3种气候变化情景下,刺槐总适生区面积与当代总体一致,高适生区面积减少,但2070s中ssp126情景的高适生区面积增加。质心分析结果表明,未来气候变化情景下,刺槐在中国的潜在总适生区向东北部偏移,高适生区向西南部偏移。结论 优化后的MaxEnt模型能够准确预测刺槐在中国的潜在适生区分布;温度、降水和海拔是影响刺槐地理分布的主要环境因子;气候变化会引起未来刺槐在中国的潜在高适生区面积减少,潜在适生区发生迁移。

关键词: 气候变化, 刺槐, MaxEnt模型, 潜在适生区

Abstract: Objective This study aims to explore the relationship between Robinia pseudoacacia (black locust) distribution and environmental variables at the national scale as well as the changes of future adaptation areas, so as to provide data support for afforestation planning and management of R. pseudoacacia.Method The MaxEnt model optimized by the Kuenm package in R language and ArcGIS software were applied to explore the main environmental factors affecting its geographical distribution. With the selected 181 distribution point records of black locust in China and 12 environmental factors, this optimized MaxEnt model was used to predict the potential habitat area and centroid changes of black locust in China under three different climate change scenarios (ssp126, ssp245, ssp585) in four periods, namely contemporary, future 2030s, 2050s, and 2070s. Result The results showed that when the feature combination (FC) = linear + product and the modulation frequency was 0.5 (RM = 0.5), the model had the lowest complexity and higher prediction accuracy. The area under curve (AUC) was 0.880, which was able to be used to predict the suitable growth range of black locust. The mean air temperature in the coldest quarter, precipitation in the warmest quarter, and the altitude were the main environmental factors affecting the potential geographical distribution of black locust, and their adaptation ranges were from –5 to 6.5 ℃, from 335 to 1 825 mm, and from –155 to 1 725 m, respectively. Under contemporary climate conditions, the total suitable area for black locust in China is 262.51 × 104 km2, and the highly suitable area is 37.86×104 km2. The total suitable area for black locust in all three future climate change scenarios would generally consistent compared with current situation, while the highly suitable area would decrease. However, the highly suitable area in the ssp126 scenario would decrease in 2070s. The centroid analysis results indicated that under future climate change scenarios, the potential total suitable area for black locust in China would shift towards the northeast, and the highly suitable area would shift towards the southwest.Conclusion The optimized MaxEnt model can accurately predict the potential suitable habitats of black locust in China. Temperature, precipitation, and altitude are identified as the dominant environmental variables influencing its distribution. Climate change is expected to reduce the highly suitable habitat area for black locust in the future and cause shifts in its potential distribution.

Key words: climate change, Robinia pseudoacacia, MaxEnt model, potential suitable areas

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