生物多样性 ›› 2023, Vol. 31 ›› Issue (1): 22272.  DOI: 10.17520/biods.2022272

所属专题: 昆虫多样性与生态功能

• 研究报告: 动物多样性 • 上一篇    下一篇

秦岭西段地区蝴蝶群落多样性与环境因子相关性

张超, 李娟, 程海云, 段家充, 潘昭*()   

  1. 河北大学生命科学与绿色发展研究院生命科学学院河北省动物系统学与应用重点实验室, 河北保定 071002
  • 收稿日期:2022-05-17 接受日期:2022-07-11 出版日期:2023-01-20 发布日期:2022-09-19
  • 通讯作者: *潘昭, E-mail: panzhao86@yeah.net
  • 基金资助:
    生态环境部生物多样性调查与评估项目(2019HJ2096001006)

Patterns and environmental drivers of the butterfly diversity in the western region of Qinling Mountains

Chao Zhang, Juan Li, Haiyun Cheng, Jiachong Duan, Zhao Pan*()   

  1. Key Laboratory of Zoological Systematics and Application, School of Life Sciences, Institute of Life Science and Green Development,Hebei University, Baoding, Hebei 071002
  • Received:2022-05-17 Accepted:2022-07-11 Online:2023-01-20 Published:2022-09-19
  • Contact: *Zhao Pan, E-mail: panzhao86@yeah.net

摘要:

为了探讨秦岭西段地区蝴蝶群落多样性与生境类型、季节和环境因子之间的关系, 本文于2020-2021年对该地区不同季节不同生境的蝴蝶群落进行了系统调查, 基于调查结果, 对α多样性进行了趋势和外推分析, 对β多样性进行了非度量多维尺度分析(non-metric multidimensional scaling, NMDS)和聚类分析, 运用广义加性模型(generalized additive model, GAM)对多样性指数与主要环境因子的关系进行了拟合分析。结果表明, 本次调查共观测到蝴蝶8,898头, 隶属于5科84属169种, 其中个体数量最多的是粉蝶科, 有3,671头, 物种数最多的是蛱蝶科, 有80种。α多样性分析结果显示, 在不同生境类型中, 针阔混交林的物种多样性指数最高; 在不同季节中, 夏季的物种多样性指数最高。β多样性分析结果显示, 针阔混交林和落叶阔叶林的蝴蝶群落组成相似性最高, 不同季节间蝴蝶群落物种组成相似性较低, 春季和夏季蝴蝶群落明显聚集, 秋季蝴蝶群落更为分散。广义加性模型拟合曲线表明, 较高的植被异质性可维持蝴蝶群落的多样性; 环境温度处于24-30℃之间时, Pielou均匀度指数较高, 蝴蝶群落结构较为稳定; 环境湿度处于70%-85%之间时, Simpson指数较高。综上, 秦岭西段地区蝴蝶群落的组成和多样性与生境类型有着密切联系, 随季节的改变蝴蝶群落结构变化明显; 植被盖度、植物丰富度、湿度和温度是维持蝴蝶群落多样性的重要因素。

关键词: 蝴蝶, 物种多样性, 群落组成, 生境, 季节, 秦岭

Abstract

Aims: The present work aims to analyze the environmental drivers of diversity in the butterfly community in the western Qinling Mountains.

Methods: In the autumn of 2020 and spring and summer of 2021, we investigated butterfly diversity in the western region of Qinling Mountains using line transects across multiple habitat types in 15 sampling areas. We used trend and extrapolation analyses for estimating α diversity, and non-metric multidimensional scaling (NMDS) and cluster analyses for β diversity. For determining drivers of butterfly diversity, we fit environmental factors to diversity indices using a generalized additive model (GAM).

Results: We observed a total of 8,898 individuals representing 169 species, 84 genera, and 5 families. Of these families, the highest number of individuals were from Pieridae (N = 3,671), and the most number of species were from Nymphalidae (N = 80). We found that α diversity was highest during the summer and in coniferous and broad-leaved forests. For β diversity, we found the highest degree of similarity between coniferous and broad-leaved forest and deciduous broad-leaved forest, the low similarity between seasons, and that species are concentrated in spring and summer but relatively dispersed in autumn. The GAM fitted curves demonstrated several key relationships between environmental factors and butterfly diversity, including: (1) plant heterogeneity was correlated with butterfly community diversity; (2) an ambient temperature between 24℃ and 30℃ underlined a higher Pielou evenness index and a more stable butterfly community structure; and (3) humidity between 70% and 85% was associated with a higher Simpson index.

Conclusion: Butterfly community composition and diversity in the western region of Qinling Mountains were closely related to habitat type and have a distinct chronological relationship with seasons. Plant cover, abundance, humidity, and temperature are important factors in maintaining the diversity of butterfly species on a regional scale.

Key words: butterfly, species diversity, community composition, habitat, season, Qinling Mountains