five

Sexual size dimorphism as a determinant of fighting performance dimorphism in Anolis lizards

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.bcc2fqzpc
下载链接
链接失效反馈
官方服务:
资源简介:
Rensch’s Rule describes a pattern of interspecific allometry in which sexual size dimorphism (SSD) increases with size among closely related species (i.e., among a group of related species, the largest ones tend to show more male-biased SSD). Sexual selection is often invoked to explain Rensch’s Rule, as larger male sizes are assumed to be favoured by sexual selection due to increased competitive ability they confer. Often the correlation between size and the trait under sexual selection is not well described. For example, a link between size and male fighting performance is often assumed, but rarely measured in studies of Rensch’s Rule. We studied a sexually selected performance trait, bite force in Anolis lizards, to determine whether patterns of SSD are linked to the allometry of performance dimorphism at the macroevolutionary level. Additionally, we tested whether allometric patterns of performance dimorphism differ between mainland and island species, as the latter have likely evolved under a stronger sexual selection regime. We found that SSD overwhelmingly explains the relationship between performance dimorphism and size in anoles, as expected under a sexual selection model for Rensch's Rule. However, the average value of performance dimorphism was higher in island than in mainland species, suggesting that these groups differ in performance dimorphism for reasons unrelated to size. Head size dimorphism was associated with residual performance dimorphism across all anoles, but did not fully explain the difference in performance dimorphism between island and mainland species. Together, these findings highlight the need to interpret Rensch’s Rule patterns of body size evolution cautiously, as allometric patterns of performance dimorphism and size dimorphism might not be equivalent.
创建时间:
2024-11-21
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作