Prediction of the potential distribution pattern of Pinus sylvestris var. mongolica in China under climate change
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摘要:
目的 预测不同气候条件下樟子松Pinus sylvestris var. mongolica在中国的潜在分布及迁移，确定影响樟子松分布的主要环境变量，为樟子松合理引种与保护提供理论依据。 方法 根据200个樟子松分布点和20个环境变量，利用R语言中ENMeval数据包优化最大熵模型(MaxEnt)并利用ArcGIS空间分析技术对当前气候条件下樟子松在中国潜在分布进行模拟，通过Person相关分析和方差膨胀因子分析(VIF)结合预建模结果对环境因子的筛选，综合Jackknife检验和相关系数，分析樟子松主导限制因子，预测樟子松从当前到未来时期(2050s和2100s)的3种气候情景(SSP126、SSP245和SSP585)下适生区变化趋势。 结果 MaxEnt模型受训者工作特征曲线(AUC)都大于0.94，说明模型精度较高，能较好预测樟子松潜在分布；影响樟子松分布的主导因子为最冷季度平均气温、降水量季节性变化、最冷月最低气温、温度季节变动系数、最干季度平均气温和最热月最高气温，累计贡献率为92.9%；当前气候条件下，樟子松的适宜分布区主要位于中国大兴安岭地区，总适宜区面积占中国总面积的6.72%；未来时期不同气候条件下樟子松潜在分布区面积减少，质心向西北高纬度和西南降水量充沛地区迁移。 结论 以年为单位的温度和降水是樟子松分布的主要影响因子，当前樟子松的适生区主要在中国大兴安岭地区，未来樟子松分布区有向现有分布区的西北和西南地区迁移的趋势。图1表7参29 Abstract:Objective This study aims to predict the potential distribution and migration of Pinus sylvestris var. mongolica under different climate conditions in China and to determine the main environmental variables affecting its distribution, so as to provide theoretical basis for rational introduction and protection of P. sylvestris var. mongolica. Method Based on 200 distribution points and 20 environmental variables, the potential distribution of P. sylvestris var. mongolica under current climate conditions was simulated by using ENMeval packet optimization maximum entropy model (MaxEnt) in R language and ArcGIS spatial analysis technology. Through Person correlation analysis and variance inflation factor (VIF) analysis combined with the screening of environmental factors based on pre-modeling results, Jackknife test and correlation coefficient were integrated to analyze the dominant limiting factors of P. sylvestris var. mongolica, and predict the change trend of suitable habitat under three climate scenarios (SSP126, SSP245 and SSP585) from the current to the future (2050s and 2100s). Result The area under ROC curve (AUC) of the MaxEnt model was greater than 0.94, indicating that the model had high accuracy and could better predict the potential distribution of P. sylvestris var. mongolica. The main factors affecting the distribution were the average temperature in the coldest quarter, seasonal variation of precipitation, minimum temperature in the coldest month, seasonal variation coefficient of temperature, average temperature in the driest quarter and maximum temperature in the hottest month, with a cumulative contribution rate of 92.9%. Under the current climate conditions, the suitable distribution area of P. sylvestris var. mongolica was mainly located in the Greater Hinggan Mountains of China, and the total suitable area accounted for 6.72% of the total area of China. In the future, the potential distribution area of P. sylvestris var. mongolica would decrease under different climatic conditions, and the centroid would migrate to the northwest area at high latitude and southwest area with abundant precipitation. Conclusion The annual temperature and precipitation are the main factors affecting the distribution of P. sylvestris var. mongolica. At present, the suitable growing areas are mainly concentrated in the Greater Hinggan Mountains of China, and its distribution will migrate to the northwest and southwest of the existing distribution area in the future. [Ch, 1 fig. 7 tab. 29 ref.] -
表 1 全球气候数据库的环境因子
Table 1. Environmental factors in the Global Climate Database
代号 内容 单位 代号 内容 单位 Bio1 年平均气温 ℃ Bio11 最冷季度平均气温 ℃ Bio2 月平均昼夜温差 ℃ Bio12 年降水量 mm Bio3 等温性 Bio13 最湿月降水量 mm Bio4 气温季节性变化 Bio14 最干月降水量 mm Bio5 最热月最高气温 ℃ Bio15 降水量季节性变化 Bio6 最冷月最低气温 ℃ Bio16 最湿季度降水量 mm Bio7 气温年较差 ℃ Bio17 最干季度降水量 mm Bio8 最湿季度平均气温 ℃ Bio18 最暖季度降水量 mm Bio9 最干季度平均气温 ℃ Bio19 最冷季度降水量 mm Bio10 最暖季度平均气温 ℃ Elev 高程 m 
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表 2 不同参数MaxEnt数模型优化结果
Table 2. Environmental results of MaxEnt model under different parameter settings
模型评价 特征组合 调控倍频 ∆AICc 10%训练遗漏率 默认 LQHPT 1.0 29.77 0.169 75 最优 LQHPT 1.5 0 0.161 89
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表 3 中国不同气候情境下樟子松适生区面积
Table 3. Suitable growing area of P. sylvestris var. mongolica under different climates scenarios in China
气候变化情景 高度适生区/km2 一般适生区/km2 较不适生区/km2 不适生区/km2 总适生区/km2 当前 147 638.9 497 465.3 896 614.6 8 058 281.8 645 104.2 SSP126-2050s 9 809.0 405 069.4 618 090.3 8 567 031.4 414 878.5 SSP126-2100s 4 219.8 152 916.7 571 493.1 8 871 371.6 157 135.4 SSP245-2050s 4 433.2 302 221.6 848 215.5 8 445 129.9 306 654.8 SSP245-2100s 0 191 892.4 802 395.8 8 605 711.9 191 892.4 SSP585-2050s 0 112 222.2 437 517.4 9 050 260.5 112 222.2 SSP585-2100s 0 0 108 142.4 9 491 857.8 0
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表 4 不同时期樟子松适生区空间格局变化
Table 4. Dynamic changes in the suitable area for P. sylvestris var. mongolica under different combination of climates cenarios
气候变化情景 面积/km2 面积变化率/% 丧失区 增加区 保留区 丧失率 增加率 保留率 SSP126-2050s 300 586.5 782 87.1 337 725.7 46.59 12.13 52.35 SSP126-2100s 277 630.1 173 36.2 138 584.8 41.12 2.56 20.53 SSP245-2050s 378 636.3 446 69.6 258 933.5 57.79 6.81 39.53 SSP245-2100s 185 735.3 706 42.3 118 506.6 27.11 10.31 17.30 SSP585-2050s 549 974.5 200 00.8 889 97.1 82.69 3.00 13.38 SSP585-2100s 111 059.9 0 0 15.97 0 0
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表 5 不同气候情景下樟子松适生区质心变化
Table 5. Core distributional shifts under different climate scenario/periods for P. sylvestris var. mongolica
时期 气候情景 坐标/(°) 迁移距离/km N E 当前 47.09 120.25 2050s SSP126 47.11 120.04 16.04 SSP245 46.65 119.29 87.86 SSP585 47.88 119.06 126.36 2100s SSP126 48.41 120.17 161.47 SSP245 45.66 117.39 270.80 说明：SSP585气候情景下在2100s时期无樟子松适生区
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表 6 参与建模的环境因子贡献率及置换重要值
Table 6. Percentage contribution and permutation importance of environment variables for P. sylvestris var. mongolica in the MaxEnt model
因子 描述 贡献率/% 置换重要值 因子 描述 贡献率/% 置换重要值 bio11 最冷季度平均气温 22.1 2.7 bio5 最热月最高气温 6.1 6.0 bio15 降水量季节性变化 19.8 12.4 bio12 年降水量 4.9 9.0 bio6 最冷月最低气温 18.1 4.3 elev 海拔 1.7 48.2 bio4 气温季节变动系数 17.8 3.5 bio1 年平均气温 0.5 9.4 bio9 最干季度平均气温 9.0 4.3
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表 7 樟子松适生区环境因子结果分析
Table 7. Results analysis of major environmental variables in P. sylvestris var. mongolica suitable area
时期 气候
情景年平均
气温/℃气温季节
变动系数最热月最高
气温/℃最冷月最低
气温/℃最干季度平均
气温/℃最冷季度平均
气温/℃年降水
量/mm降水量季节性
变化海拔/
m物种生境
适宜度当前 1.60 1 524.06 24.35 −29.18 −18.79 −20.64 425.20 112.41 828.22 0.74 2050s SSP126 1.90 1 483.54 26.94 −25.97 −16.44 −17.75 430.87 113.05 828.22 0.36 SSP245 2.49 1 511.33 27.97 −25.37 −16.05 −17.45 445.52 110.16 828.22 0.28 SSP585 3.20 1 517.97 28.53 −25.13 −15.46 −17.14 469.78 114.43 828.22 0.19 2100s SSP126 2.20 1 479.34 26.75 −26.44 −16.12 −17.77 460.45 116.35 828.22 0.20 SSP245 3.68 1 519.68 28.91 −23.84 −14.88 −16.47 463.79 115.97 828.22 0.19 SSP585 6.11 1 494.04 30.98 −21.86 −12.59 −13.42 515.40 121.54 828.22 0.07
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