基于MaxEnt模拟欧亚大陆气候变化下叉子圆柏的潜在分布

doi:10.11707/j.1001-7488.20210805

摘要/Abstract

摘要：

目的: 叉子圆柏是欧洲、亚洲和美洲石质山坡、河谷及覆沙丘陵地区的重要防沙固土树种，对维持生态环境稳定具有重要意义。探讨限制叉子圆柏分布的主导环境变量，模拟气候变化下叉子圆柏潜在适宜分布区，可为叉子圆柏资源管理与恢复提供理论依据。方法: 基于欧亚大陆叉子圆柏267个现有种群分布地理信息以及环境变量（气候、海拔），采用MaxEnt、BioClim、DoMain 3种模型，模拟叉子圆柏潜在适宜分布区。通过受试者工作特征（ROC）曲线下方面积（AUC值）和Kappa值对3种模型进行比较分析与筛选。基于MaxEnt模型比较末次盛冰期、全新世中期、当前及未来（2070年）的潜在地理分布格局，探讨制约叉子圆柏地理分布的环境变量。结果: 1）基于MaxEnt模型综合环境变量贡献率、置换重要值以及刀切法检验的结果表明，叉子圆柏地理分布主要受年均温、海拔、温度季节性变化3个环境变量影响。2）基于MaxEnt模型气候变量模拟的欧亚大陆叉子圆柏当前适宜生境面积为663.115×10³ km²，集中在30°~50° N之间，山地是叉子圆柏主适生区。3）基于MaxEnt模型不同地质历史时期预测的叉子圆柏适宜生境面积表明，亚洲是叉子圆柏的主分布区。亚洲的适生区面积在末次盛冰期占86.9%、全新世中期占87.0%、当前时期占57.8%、未来2070（RCP2.6）和2070（RCP8.5）时期分别占84.1%和79.2%。从末次盛冰期到当前至未来叉子圆柏适宜生境面积呈现先增加后减少的变化特征、分布中心具有从北到南再到北的迁移趋势。结论: 叉子圆柏地理分布不仅受气候环境变量（温度、降水）影响，也与海拔相关。分布区范围符合柏科分布带特征。本研究结果可为叉子圆柏种质资源管理、修复与重建提供重要参考。

关键词: 叉子圆柏, 最大熵模型, 气候变化, 潜在地理分布区

Abstract:

Objective: Juniperus sabina is an important tree species for sand-fixing and soil-holding in rocky mountain slopes, river valleys and sand-covered hilly areas in Europe, Asia and America, which is of great significance for maintaining the stability of ecological environment. The paper tries to identify the dominant environmental variables that limit the distribution of J. sabina and to predict its suitable distribution area, in order to provide a theoretical basis for the administration and restoration of the species. Method: The geographic information of 267 existing populations and environmental variables(climate, elevation), were analyzed using three models of MaxEnt, BioClim and DoMain to simulate the potential suitable distribution areas for the species. The three models were compared and analyzed with the area under curve(AUC value) of receiver operating characteristic(ROC) and Kappa value. The potential geographical distribution patterns of the Last Glacial Maximum(LGM), the Mid-Holocene(MID), Current and Future(2070) based on the MaxEnt modelling are compared and the environmental variables that restrict the geographical distribution of J. sabina are discussed. Result: 1) The percent contribution of the environmental variables, permutation importance and jackknife method showed that the geographical distribution of J. sabina was mainly affected by annual mean temperature, elevation and temperature seasonality. 2) The present suitable habitat area of J. sabina in Eurasia based on the MaxEnt model with climate variable simulation is 663.115×10³ km², concentrated in the range of 30°—50°N, with mountainous sites as the main suitable areas. 3) The suitable habitat areas of J. sabina in different geological historical periods indicated that Asia is the main distribution area, the suitable areas of Asia in the LGM, MID and Current period accounted for 86.9%, 87.0%, and 57.8%, respectively. In the Future periods, the suitable areas of Asia occupied with 84.1% under 2070(RCP2.6) and 79.2% under 2070(RCP8.5). From the Last Glacial Maximum, Current to the Future, the suitable habitat area increased first and followed by a decrease, and the distribution center tended to migration from north to south and then to further north. Conclusion: The geographical distribution of J. sabina was not only affected by climate and environmental variables(including temperature and precipitation), but also related to elevation. The distribution area is in accordance with the characteristics of Cupressaceae distribution zone. This study provides an important basis for the management and restoration, rehabilitation of the germplasm resources of J. sabina.

Key words: Juniperus sabina, Maximum Entropy model, climate change, potential geographical distribution area

中图分类号:

S717

王爱君,路东晔,张国盛,黄海广,王颖,呼斯楞,敖民. 基于MaxEnt模拟欧亚大陆气候变化下叉子圆柏的潜在分布[J]. 林业科学, 2021, 57(8): 43-55.

Aijun Wang,Dongye Lu,Guosheng Zhang,Haiguang Huang,Ying Wang,Sileng Hu,Min Ao. Potential Distribution of Juniperus sabina under Climate Change in Eurasia Continent Based on MaxEnt Model[J]. Scientia Silvae Sinicae, 2021, 57(8): 43-55.

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	环境变量Environmental variable
Bio1	年均气温Annual mean temperature/℃
Bio2	昼夜温差月均值Monthly mean diurnal range/℃
Bio4	温度季节性变化Temperature seasonality/℃
Bio7	年温度变化范围Temperature annual range/℃
Bio8	最湿季平均温度Mean temperature of wettest quarter/℃
Bio9	最干季平均温度Mean temperature of driest quarter/℃
Bio12	年降水量Annual precipitation/mm
Bio14	最干月降水量Precipitation of driest month/mm
Bio15	降水量季节性变化Precipitation seasonality(%)
Bio17	最干季降水量Precipitation of driest quarter/mm
Bio18	最热季降水量Precipitation of warmest quarter/mm
Bio19	最冷季降水量Precipitation of coldest quarter/mm
Elev	海拔Elevation/m

模型Models	AUC	Kappa
MaxEnt	0.926±0.009	0.811±0.021
BioClim	0.777±0.026	0.622±0.079
DoMain	0.977±0.065	0.852±0.124

环境变量 Environmental variable	贡献率 Percent contribution(%)	置换重要值 Permutation importance(%)	环境变量 Environmental variable	贡献率 Percent contribution(%)	置换重要值 Permutation importance(%)
Bio1	28.80±0.86	18.70±2.12	Bio15	2.90±0.98	7.70±1.50
Elev	29.00±0.36	45.70±5.67	Bio9	2.50±0.43	6.20±1.08
Bio4	13.00±0.98	8.00±7.08	Bio12	2.10±0.59	5.20±1.51
Bio14	7.10±2.14	0.30±1.25	Bio7	0.70±2.42	2.30±0.65
Bio18	5.70±0.49	2.10±0.24	Bio2	0.70±0.28	0.80±0.40
Bio17	3.60±1.63	0.10±0.17	Bio19	0.60±0.19	0.30±0.05
Bio8	3.50±0.12	2.70±0.62

适生区Suitable growing area	LGM	MID	当前Current	未来Future RCP2.6	未来Future RCP8.5
亚洲Asia	63.37×10³	62.85×10³	383.03×10³	56.47×10³	43.87×10³
欧洲Europe	9.52×10³	9.40×10³	280.09×10³	10.65×10³	11.49×10³
总面积Total area	72.89×10³	72.25×10³	663.12×10³	67.12×10³	55.36×10³

气候时期 Climatic periods	面积Area/(×10³ km²)
气候时期 Climatic periods	增加 Increase	不变 Unchanged	减少 Decrease
LGM—MID	27.32	39.99	31.08
MID—当前Current	559.71	49.92	17.39
当前Current—未来Future RCP2.6	22.80	39.91	569.72
当前Current—未来Future RCP8.5	22.75	26.81	582.81