食草动物
古气候学
高原(数学)
古植物学
古生物学
生态学
地质学
古生态学
壳斗科
生物
真双子叶植物
气候变化
分类学(生物学)
基因
数学分析
生物化学
植物发育
数学
作者
Wei‐Yu‐Dong Deng,Tao Su,Torsten Wappler,Jia Liu,Shufeng Li,Jian Huang,He Tang,Shook Ling Low,Teng‐Xiang Wang,He Xu,Xiaoting Xu,Ping Liu,Zhe‐Kun Zhou
标识
DOI:10.1016/j.gloplacha.2020.103293
摘要
Herbivore damage patterns on fossil leaves are essential to explore the evolution of plant-herbivore interactions under paleoenvironmental changes and to better understand the evolutionary history of terrestrial ecosystems. The Eocene–Oligocene transition (EOT) is a period of dramatic paleoclimate changes that significantly impacted global ecosystems; however, the influences on plant-herbivore interactions during this period are largely unknown. We identified taxonomic composition of the flora, and investigated well-preserved herbivore damage on fossil leaves from two layers of the Lawula Formation in Markam County, southeastern Qinghai-Tibetan Plateau (QTP), China. Besides, paleoclimate conditions were reconstructed using fossil plant assemblages. The plant assemblage from the latest Eocene layer (MK-3, ~34.6 Ma) was dominated by Fagaceae and Betulaceae, whereas Rosaceae and Salicaceae were the most abundant in the earliest Oligocene layer (MK-1, ~33.4 Ma). In MK-3, 932 out of 2428 fossil leaves were damaged and presented 41 damage types (DTs). The richest functional feeding groups (FFGs) in this layer were hole feeding, margin feeding, and galling. In MK-1, 144 out of 599 leaves were damaged and presented 20 DTs, with the major FFGs being hole feeding, margin feeding, and skeletonization. Generally, MK-3 had a significantly higher damage frequency (DF) and more DTs compared to MK-1. The decline in temperature, accompanied by the mountain uplift during the EOT on the QTP margin, led to changes in plant composition, with a consequent decrease in herbivory quantity and diversity. Our results shed new light on the influence of paleoenvironmental changes in shaping the evolution of biodiversity as well as the ecosystem on the plateau.
科研通智能强力驱动
Strongly Powered by AbleSci AI