平行进化
生物
进化生物学
自然选择
适应(眼睛)
收敛演化
生态物种形成
渗入
基因组
选择(遗传算法)
生态遗传学
生态学
系统发育学
遗传学
遗传变异
基因
基因流
人口
计算机科学
社会学
人口学
人工智能
神经科学
作者
Samridhi Chaturvedi,Zachariah Gompert,Jeffrey L. Feder,Owen G. Osborne,Moritz Muschick,Rüdiger Riesch,Víctor Soria‐Carrasco,Patrik Nosil
标识
DOI:10.1038/s41559-022-01909-6
摘要
Evolution can repeat itself, resulting in parallel adaptations in independent lineages occupying similar environments. Moreover, parallel evolution sometimes, but not always, uses the same genes. Two main hypotheses have been put forth to explain the probability and extent of parallel evolution. First, parallel evolution is more likely when shared ecologies result in similar patterns of natural selection in different taxa. Second, parallelism is more likely when genomes are similar because of shared standing variation and similar mutational effects in closely related genomes. Here we combine ecological, genomic, experimental and phenotypic data with Bayesian modelling and randomization tests to quantify the degree of parallelism and its relationship with ecology and genetics. Our results show that the extent to which genomic regions associated with climate are parallel among species of Timema stick insects is shaped collectively by shared ecology and genomic background. Specifically, the extent of genomic parallelism decays with divergence in climatic conditions (that is, habitat or ecological similarity) and genomic similarity. Moreover, we find that climate-associated loci are likely subject to selection in a field experiment, overlap with genetic regions associated with cuticular hydrocarbon traits and are not strongly shaped by introgression between species. Our findings shed light on when evolution is most expected to repeat itself. Multiple factors contribute to the independent evolution of the same adaptations in nature. This study quantifies parallel evolution among species of Timema stick insects and shows that the degree of parallelism is determined by shared ecology and genomic background.
科研通智能强力驱动
Strongly Powered by AbleSci AI