林业科学 ›› 2024, Vol. 60 ›› Issue (4): 109-118.doi: 10.11707/j.1001-7488.LYKX20220436
收稿日期:
2022-06-27
出版日期:
2024-04-25
发布日期:
2024-05-23
通讯作者:
郭素娟
E-mail:2253601192@qq.com
基金资助:
Received:
2022-06-27
Online:
2024-04-25
Published:
2024-05-23
Contact:
Sujuan Guo
E-mail:2253601192@qq.com
摘要:
目的: 探究影响板栗分布的主导环境因子,模拟不同气候条件下板栗潜在分布区,为板栗种质资源保护和引种提供理论依据。方法: 基于472个板栗现有种群分布地理信息与21个环境变量,采用物种分布组合模型模拟板栗潜在适宜分布区,通过AUC与TSS值评价模型准确性。利用组合模型推测末次冰盛期、全新世中期、当前、2030年和2050年板栗适宜区分布及其变化趋势。结果: 1) 10种模型中,RF、GBM、MARES和GAM预测精度最好且稳定,最终组合模型AUC和TSS值分别为0.974和0.816,精度较高。2) 影响板栗地理分布的主要环境因子为最冷月极端低温、海拔、最湿季降水量、年温变化范围和最湿月降水量,累计贡献率达89.79%。3) 当前气候条件下中国潜在板栗适宜区分布广泛,自末次冰盛期以来,板栗适宜区不断向高纬度地区扩张,气候越暖扩张越快。但板栗整体适宜性不断下降,南方较为突出,北方则降幅较小,高适宜区总面积逐步减少。结论: 气候和地形因子共同影响板栗的地理分布,其中气候因子中温度因子贡献最大,地形因子中海拔因子贡献最大。当前气候条件下板栗在中国适宜区分布广泛,建议在高适宜区进行引种栽植及种质资源保护,当未来气候不利于板栗生长时向高纬度适宜区进行迁移引种,减少气候变化带来的损失。
中图分类号:
刘成林,郭素娟. 气候变化下板栗适宜性分析及分布预测[J]. 林业科学, 2024, 60(4): 109-118.
Chenglin Liu,Sujuan Guo. Suitability Analysis and Distribution Prediction of Castanea mollissima under Climate Change[J]. Scientia Silvae Sinicae, 2024, 60(4): 109-118.
表1
环境变量数据①"
环境因子类型 Environmental factor type | 环境因子 Environmental factor | 单位 Unit | 变量代称 Code name |
生物气候因子 Bioclimatic factor | 年均温度 Annual mean temperature | ℃ | Bio1 |
平均温度日较差 Mean diurnal range | ℃ | Bio2 | |
等温性 Isothermality | % | Bio3 | |
温度变化方差 Temperature seasonality | % | Bio4 | |
最热月极端高温Max temperature of warmest month | ℃ | Bio5※ | |
最冷月极端低温Min temperature of coldest month | ℃ | Bio6※ | |
年温变化范围Temperature annual range | ℃ | Bio7※ | |
最湿季平均温 Mean temperature of wettest quarter | ℃ | Bio8 | |
最干季平均温 Mean temperature of driest quarter | ℃ | Bio9 | |
最暖季平均温 Mean temperature of warmest quarter | ℃ | Bio10 | |
最冷季平均温 Mean temperature of coldest quarter | ℃ | Bio11 | |
年均降水量Annual precipitation | mm | Bio12※ | |
最湿月降水量Precipitation of wettest month | mm | Bio13※ | |
最干月降水量Precipitation of driest month | mm | Bio14※ | |
降水量变化方差 Precipitation seasonality | % | Bio15 | |
最湿季降水量Precipitation of wettest quarter | mm | Bio16※ | |
最干季降水量 Precipitation of driest quarter | mm | Bio17 | |
最暖季平均降水量 Precipitation of warmest quarter | mm | Bio18 | |
最冷季平均降水量 Precipitation of coldest quarterm | mm | Bio19 | |
地形因子 Terrrain factor | 海拔Elevation | m | Elevation※ |
坡度Slope | ° | Slope※ |
表2
biomod2物种分布模型"
物种分布模型Species distribution models | 简称Model code |
最大熵模型 Maxiumu entropy | MaxEnt |
广义线性模型 Generalized linear model | GLM |
广义相加模型 Generalized add model | GAM |
分类回归树分析 Classification tree analysis | CTA |
人工神经网络 Artificial neural network | ANN |
柔性判别分析 Flexible discriminant analysis | FDA |
助推法 Generalized bossting model | GBM |
表面分室模型 Surface range envelope | SRE |
多元自适应回归样条模型 Multiple adaptive regression splines | MARS |
随机森林 Random forest | RF |
表4
不同气候条件下板栗适宜性分区面积占比"
时期 Period | 区域面积占比Proportion of regional area | |||
总适宜区 Plantable area | 低适宜区 Low suitable area | 中适宜区 Median suitable area | 高适宜区 High suitable area | |
末次冰盛期 Last glacial maximum | 23.22 | 2.82 | 7.28 | 13.10 |
全新世中期 Mid-holocene | 26.58 | 5.43 | 11.22 | 9.94 |
当前 Comtemporary | 25.34 | 4.57 | 9.73 | 11.03 |
SSP1-2.6 2030s | 24.36 | 6.68 | 12.94 | 4.74 |
SSP1-2.6 2050s | 25.49 | 7.16 | 14.11 | 4.23 |
SSP5-8.5 2030s | 24.48 | 6.77 | 11.90 | 5.81 |
SSP5-8.5 2050s | 27.01 | 7.63 | 15.72 | 3.66 |
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