生物多样性 ›› 2018, Vol. 26 ›› Issue (9): 931-940.DOI: 10.17520/biods.2018059

• 研究报告 • 上一篇    下一篇

基于最大熵模型的不同尺度物种分布概率优化热点分析: 以红色木莲为例

庄鸿飞1,2, 张殷波3, 王伟1,4,*(), 任月恒1, 刘方正1, 杜金鸿1, 周越1   

  1. 1 (国家环境保护区域生态过程与功能评估重点实验室, 中国环境科学研究院, 北京 100012)
    2 (山西大学黄土高原研究所, 太原 030006)
    3 (山西大学环境与资源学院, 太原 030006)
    4 (中国三江并流区域生物多样性协同创新中心, 云南大理 671003);
  • 收稿日期:2018-02-13 接受日期:2018-05-15 出版日期:2018-09-20 发布日期:2019-01-05
  • 通讯作者: 王伟
  • 作者简介:# 共同第一作者
  • 基金资助:
    国家重点研发计划(2016YFC0503304)

Optimized hot spot analysis for probability of species distribution under different spatial scales based on MaxEnt model: Manglietia insignis case

Hongfei Zhuang1,2, Yinbo Zhang3, Wei Wang1,4,*(), Yueheng Ren1, Fangzheng Liu1, Jinhong Du1, Yue Zhou1   

  1. 1 State Environmental Protection Key Laboratory of Regional Eco-process and Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012
    2 Institute of Loess Plateau, Shanxi University, Taiyuan 030006
    3 School of Environmental and Resource Sciences, Shanxi University, Taiyuan 030006
    4 Collaborative Innovation Center for Biodiversity and Conservation in the Three Parallel Rivers Region of China, Dali, Yunnan 671003
  • Received:2018-02-13 Accepted:2018-05-15 Online:2018-09-20 Published:2019-01-05
  • Contact: Wang Wei
  • About author:# Co-first authors

摘要:

单一空间尺度构建的最大熵(maximum entropy, MaxEnt)模型是否具有代表性, 是MaxEnt模型应用与发展中面临的重要问题。本研究基于有效的地理分布位点数据, 利用最小凸多边形法(the minimum convex polygon method)在三江并流、云南省及全国3个空间尺度下分别识别了红色木莲(Manglietia insignis)的建模区域, 并进一步建立MaxEnt模型: 使用ROC曲线分析法与遗漏率(omission rate, OR)检验评估MaxEnt模型预测精度; 基于ArcGIS分析分布概率及其热点区域的分布趋势, 并通过分区统计工具Zonal识别潜在适宜分布区域的质心位置; 采用刀切法检验环境因子贡献率。结果表明: (1)不同尺度下红色木莲的MaxEnt模型都有良好的预测效果, 三江并流、云南省及全国尺度下的AUC值分别为0.936、0.887和0.930, OR值分别为0.18、0.15和0.20; (2)各尺度红色木莲的适生区格局呈现一致性分布趋势, 集中在独龙江、怒江和澜沧江3个流域; (3) 3个空间尺度下红色木莲的地理分布受不同环境因子影响, 存在着尺度依赖效应。由此可见, 红色木莲在不同空间尺度下的预测模型有着稳定的性能表现与良好的预测效果。此外, 我们建议在野外实地调查与野生生物资源保护中加强对普通物种的关注, 在预测物种地理分布的研究中将MaxEnt模型与热点分析结合使用。

关键词: MaxEnt模型, 空间尺度, 红色木莲, 最小凸多边形, 热点分析, 普通物种

Abstract:

Whether a maximum entropy (MaxEnt) model constructed at one spatial scale is representative of species distributions at other scales is an important issue in the application and development of these models. Using distribution data for Manglietia insignis, we used the minimum convex polygon (MCP) method to model species distribution for three spatial scales—Three Parallel Rivers, Yunnan Province and China—with a 20 km buffer outside the distribution region. We built the MaxEnt model for Three Parallel Rivers, Yunnan Province and China using 19, 67, and 88 presence-only records respectively and combined these with data on environmental factors at the point locations. We estimated the prediction accuracy of the MaxEnt model using receiver operating characteristic (ROC) curve and omission rate (OR). Next, we used ArcGIS to analyze distribution trends for habitat suitability and potential hotspots. We identified the location of geometric centroid of potentially suitable areas using Zonal and used the Jackknife method to test the dominant environmental factors affecting the distribution of M. insignis. We found that the area under ROC curve (AUC value) for Three Parallel Rivers, Yunnan Province and China were 0.936, 0.887, and 0.930 respectively and OR values were 0.18, 0.15, and 0.20, indicating that MaxEnt model for all three spatial scales could successfully predict the distribution of M. insignis. Distribution trends of potential habitat suitability and habitat hotspots were consistent between different scales and were concentrated in the river basins of Dulong River, Nujiang River and Lancang River, with no significant zonal transfer for the location of geometric centroid. Different environmental factors affected the geographical distribution of M. insignis at the three spatial scales, suggesting scale dependence in the distribution patterns of M. insignis. In summary, this study indicates that MaxEnt model of M. insignis performs stably and successfully for different spatial scales. In addition, the consistency of results across spatial scales became more obvious for hotspots, indicating that hotspot analysis greatly reduced the effect of spatial scale for the MaxEnt model. Thus, we propose integrating MaxEnt model and hotspot analysis to simulate the geographical distributions of species.

Key words: MaxEnt model, spatial scale, Manglietia insignis, minimum convex polygon, hotspots, common species