生物多样性 ›› 2016, Vol. 24 ›› Issue (4): 453-461.doi: 10.17520/biods.2015246

所属专题: 中国西南干旱河谷的植物多样性

• 研究报告 • 上一篇    下一篇

气候变化情景下基于最大熵模型的中国西南地区清香木潜在分布格局模拟

应凌霄1, 刘晔2, 陈绍田3, 沈泽昊1, *()   

  1. 1 北京大学城市与环境学院生态学系, 地表过程分析与模拟教育部重点实验室, 北京 100871。
    2 北京大学深圳研究生院城市规划与设计学院, 广东深圳 518055。
    3 中国科学院昆明植物研究所, 昆明 650204。
  • 收稿日期:2015-09-14 接受日期:2016-01-15 出版日期:2016-04-20
  • 通讯作者: 沈泽昊 E-mail:shzh@urban.pku.edu.cn
  • 基金项目:
    国家自然科学基金(41371190)和交通运输部西部计划项目(2008 318 799 17)

Simulation of the potential range of Pistacia weinmannifolia in Southwest China with climate change based on the maximum-entropy (Maxent) model

Lingxiao Ying1, Ye Liu2, Shaotian Chen3, Zehao Shen1, *()   

  1. 1 Department of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871.
    2 School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, Guangdong 518055.
    3 Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650204
  • Received:2015-09-14 Accepted:2016-01-15 Online:2016-04-20
  • Contact: Shen Zehao E-mail:shzh@urban.pku.edu.cn

清香木(Pistacia weinmannifolia)是中国西南干旱河谷植被的特征种。本文利用野外调查的165个清香木分布点信息以及22个环境变量数据, 基于最大熵(Maxent)算法构建清香木分布的适宜生境预测模型, 并据此模拟清香木在我国西南地区的适宜分布区, 以及历史和未来不同气候情景下的分布格局变化。结果表明: 清香木生境预测的Maxent模型准确性非常高(AUC = 0.974), 温度季节性变化、极端低温和降水量是限制其分布的主要气候因子。清香木当前的潜在分布区集中在我国西南干旱河谷区, 其适宜生境的气候特征是降水少、温度季节性变化小且无极端低温。对清香木在末次间冰期和末次冰盛期分布的模拟结果表明, 其分布区范围均以诸大江河的河谷为中心, 随气候变化在我国西南地区主要呈现先向东扩张, 然后向西退缩的趋势, 并印证了“冰期走出横断山(glacial out-of-Hengduan Mts.)”的观点。在未来(2061-2080年) 3种典型浓度路径(representative concentration pathway, RCP)的气候情景下, 清香木在我国西南地区的分布都向东扩张, 主要分布在云贵高原与四川盆地结合地带的河谷, 以及云贵高原与广西西部交界地带的河谷中, 这也反映了这些地区河谷地段干旱化的可能, 而当前的潜在分布区趋于消失; 清香木的潜在适宜分布面积在中低浓度路径情景下均将减少约33%, 而在高浓度路径情景下有所增加。

关键词: 最大熵(Maxent)模型, 清香木, 物种分布区, 适宜生境, 气候变化

Pistacia weinmannifolia is a characteristic species of dry valleys in Southwest China. In this study, 165 presence points of P. weinmannifolia were identified through field surveys, along with point data of 22 environmental factors. The suitable habitat model was formulated using the maximum-entropy (Maxent) algorithm and applied to simulate the potential range of the species in Southwest China, and to project the change of species range in past and future climate scenarios. The results indicate that the Maxent model gave a high accuracy in habitat predictions for P. weinmannifolia. Temperature seasonality, minimum temperature and precipitation were the major constraining climatic factors. Contemporarily, the environment suitable for P. weinmannifolia was located in the dry valleys of major rivers in Southwest China, and the regions was characterized by decreased temperature variability, no temperatures below 0°C, and low precipitation. Simulations using climate scenarios of the Last Inter-Glacial (LIG) and Last Glacial Maximum (LGM) periods indicated that the distribution of P. weinmannifolia was centered around the valleys of major rivers in Southwest China, substantially expanded eastward first, and retreated westward following climate change during glacial and inter-glacial periods, supporting the hypothesis of “glacial out-of-Hengduan Mts.”. Under the future climate scenario (2061-2080) with three representative concentration pathways (RCPs), the potential distribution of P. weinmannifolia was projected to migrate eastward to the valleys in the adjacent region of the Yunnan-Guizhou Plateau and Sichuan Basin, and the adjacent region of the Plateau and western Guangxi, reflecting a high possibility of increasing dryness in the river valleys in the future, while its current distribution might disappear. The potential distribution of P. weinmannifolia would decrease by 33% in Southwest China under the future scenario with both RCP2.6 and 4.5, but would increase with RCP8.5.

Key words: Maxent model, Pistacia weinmannifolia, species range, suitable habitat, climate change

图1

研究区165个采样点和133个气象站的位置"

表1

22个用于清香木潜在适宜分布模拟的环境变量"

环境变量 Environmental variables 代号 Code
年均温 Mean annual temperature Bio1
昼夜温差月均值 Mean diurnal range (Mean of monthly (max temperature - min temperature)) Bio2
等温性 Isothermality (Bio2/Bio7 × 100) Bio3
温度季节性变化标准差 Standard deviation of temperature seasonality Bio4
最暖月最高温 Max. temperature of warmest month Bio5
最冷月最低温 Min. temperature of coldest month Bio6
温度年较差 Temperature annual range (Bio5 - Bio6) Bio7
最湿季均温 Mean temperature of wettest quarter Bio8
最干季均温 Mean temperature of driest quarter Bio9
最暖季均温 Mean temperature of warmest quarter Bio10
最冷季均温 Mean temperature of coldest quarter Bio11
年降水量 Annual precipitation Bio12
最湿月降水量 Precipitation of wettest month Bio13
最干月降水量 Precipitation of driest month Bio14
降水量季节性变异系数 Coefficient of variation of precipitation seasonality Bio15
最湿季降水量 Precipitation of wettest quarter Bio16
最干季降水量 Precipitation of driest quarter Bio17
最暖季降水量 Precipitation of warmest quarter Bio18
最冷季降水量 Precipitation of coldest quarter Bio19
海拔 Elevation -
坡度 Slope -
坡向 Aspect -

图2

清香木Maxent模型的接受者操作特性曲线"

表2

影响清香木潜在适宜分布的主要环境变量"

环境变量
Environmental variables
贡献率
Contribution (%)
相关性
Correlation*
温度季节变化标准差
SD of temperature seasonality
24.2 -
等温性 Isothermality 15.3 +
最湿月降水量
Precipitation of wettest month
11.2 -
温度年较差
Temperature annual range
9.8 -
最湿季均温
Mean temperature of wettest quarter
7.6 -
年均温 Mean annual temperature 6.7 +
年降水量 Annual precipitation 4.5 -
最冷季均温
Mean temperature of coldest quarter
4.2 +

图3

气候变化下清香木在中国西南地区的潜在分布格局。为了突出显示适宜分布区, 只显示了研究区域内东部(95° E以东)的结果, 95° E以西的区域无适宜分布。(a)末次间冰期; (b)末次冰盛期; (c)当前历史时期; (d)未来时期(RCP2.6); (e)未来时期(RCP4.5); (f)未来时期(RCP8.5)。"

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