five

Shape coordinates and centroid size for adults and ontogenetic series analyzed in predictable complexity of evolutionary allometry

收藏
DataCite Commons2026-03-17 更新2026-04-25 收录
下载链接:
https://datadryad.org/dataset/doi:10.5061/dryad.t4b8gtj5j
下载链接
链接失效反馈
官方服务:
资源简介:
Allometry has been a paradigm of constraints, including intrinsic constraints on the evolvability of allometry, as a source of developmental and genetic constraints on the evolution of form, and of functional constraints, maintaining functional equivalence as body size evolves. Yet, allometry may be the simplest case of varied constraints, and of morphological integration, even though allometry itself is not simple. Evolutionary allometry may be especially complex because it depends not only on the developmental origins of allometry and determinants of allometric variation but also on the evolutionary dynamics of size and shape. It should also depend on the ecological opportunity for size-dependent ecomorphological specialization. We predict that lineages that converge in those would exhibit similar evolutionary allometries but otherwise, evolutionary allometries would be heterogeneous. Countering this expectation are familiar craniofacial evolutionary allometries, often ascribed to developmental bias. To test both those hypotheses, we compare evolutionary allometries of mandibles across lineages of squirrels and evolutionary to growth allometries. As expected, lineages that converge on size-dependent specializations exhibit similar evolutionary allometries, but otherwise, their allometries are no more similar than expected by chance. Growth allometries of squirrels (and a cricetid rodent) slightly resemble the evolutionary allometry of one lineage, but growth allometries of species from other lineages are orthogonal to their own lineages’ evolutionary allometry. We would expect that craniofacial allometries that are not brain-driven would, like mandibular evolutionary allometries, be predictable only from size-dependent ecological specializations.
提供机构:
Dryad
创建时间:
2022-11-16
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