物种丰富度
干旱化
生物
生物群落
雨林
多元化(营销策略)
热带
生态学
生物多样性
气候变化
生态系统
营销
业务
作者
Jian Liu,Anders J. Lindström,Yiqing Gong,Shanshan Dong,Yu‐Sheng Liu,Shouzhou Zhang,Xun Gong
摘要
ABSTRACT The evolution of the latitudinal diversity gradient (LDG), characterized by a peak in diversity toward the tropics, has captured significant attention in evolutionary biology and ecology. However, the inverse LDG (i‐LDG) mechanism, wherein species richness increases toward the poles, remains inadequately explored. Cycads are among one of the oldest lineages of extant seed plants and have undergone extensive diversification in the tropics. Intriguingly, the extant cycad abundance exhibits an i‐LDG pattern, and the underlying causes for this phenomenon remain largely elusive. Here, using 1,843 nuclear genes from a nearly complete sampling, we conducted comprehensive phylogenomic analyses to establish a robust species‐level phylogeny for Cycas , the largest genus within cycads. We then reconstructed the spatial‐temporal dynamics and integrated global environmental data to evaluate the roles of species ages, diversification rates, contemporary environment, and conservatism to ancestral niches in shaping the i‐LDG pattern. We found Cycas experienced decreased diversification rates, coupled with the cooling temperature since its origin in the Eocene from continental Asia. Different regions have distinctively contributed to the formation of i‐LDG for Cycas , with the northern hemisphere acting as evolutionary museums and the southern hemisphere serving as cradles. Moreover, water‐related climate variables, specifically precipitation seasonality and potential evapotranspiration, were identified as paramount factors constraining Cycas species richness in the rainforest biome near the equator. Notably, the adherence to ancestral monsoonal climates emerges as a critical factor in sustaining the diversity pattern. This study underscores the imperative of integrating both evolutionary and ecological approaches to comprehensively unravel the mechanisms underpinning global biodiversity patterns.
科研通智能强力驱动
Strongly Powered by AbleSci AI