Maxent模型复杂度对物种潜在分布区预测的影响

朱耿平1 乔慧捷2

(1.天津市动植物抗性重点实验室天津师范大学生命科学学院, 天津 300387)
(2.中国科学院动物研究所, 北京 100101)
【知识点链接】分子系统学; 物种; 生境

【摘要】生态位模型在入侵生物学和保护生物学中具有广泛的应用, 其中Maxent模型最为流行, 被越来越多地应用在预测物种的现实分布和潜在分布的研究中。在Maxent模型中, 多数研究者采用默认参数来构建模型, 这些默认参数源自早期对266个物种的测试, 以预测物种的现实分布为目的。近期研究发现, Maxent模型采用复杂机械学习算法, 对采样偏差敏感, 易产生过度拟合, 模型转移能力仅在低阈值情况下较好。基于默认参数的Maxent模型不仅预测结果不可靠, 而且有时很难解释。在本研究中, 作者以入侵害虫茶翅蝽 (Halyomorpha halys) 为例, 采用经典模型构建方案 (即构建本土模型然后将其转移至入侵地来评估) , 利用ENMeval数据包来调整本土Maxent模型调控倍频和特征组合参数, 分析各种参数条件下模型的复杂度, 然后选取最低复杂度的模型参数 (即为最优模型) , 综合比较默认参数和调整参数后Maxent模型的响应曲线和预测结果, 探讨Maxent模型复杂度对预测结果的影响及Maxent模型构建时所需注意事项, 以期对物种潜在分布进行合理的预测, 促进Maxent模型在我国的合理运用和发展。作者认为, 环境变量的选择至关重要, 需要综合分析其对所模拟物种分布的限制作用和环境变量之间的空间相关性。构建Maxent模型前需对物种分布采样偏差及模型的构建区域进行合理地判断, 模型构建时需要比较不同参数下模型的预测结果和响应曲线, 选取复杂度较低的模型参数来最终建模。在茶翅蝽的分析中, Maxent模型的默认参数和最优模型参数不同, 与Maxent模型默认参数相比, 采用调整参数后所构建的模型预测效果较好, 响应曲线较为平滑, 模型转移能力较高, 能够较为合理反映物种对环境因子的响应和准确地模拟该物种的潜在分布。

【关键词】 生态位模型; Maxent模型; 模型复杂度; 转移能力; 现实分布; 潜在分布;

【DOI】

【基金资助】 国家自然科学基金 (31401962) 天津师范大学人才引进基金项目 (5RL127) 天津市131创新人才培养工程项目 (ZX110204) 天津市用三年时间引进千名以上高层次人才项目 (5KQM110030)

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This Article

ISSN:1005-0094

CN: 11-3247/Q

Vol 24, No. 10, Pages 1189-1196

October 2016

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摘要

  • 1 材料与方法
  • 2 结果
  • 3 讨论
  • 参考文献