Phylogenetic Insights Into Canidae Trait Variation Across Continents

系统发育树 特质 生物 分类单元 系统发育学 进化生物学 系统发育比较方法 生态学 克莱德 航程(航空) 系统发育多样性 地理 遗传学 基因 程序设计语言 材料科学 复合材料 计算机科学
作者
Lucas M. V. Porto,Arielli Fabrício Machado
出处
期刊:Journal of Biogeography [Wiley]
卷期号:52 (2): 304-312
标识
DOI:10.1111/jbi.15035
摘要

ABSTRACT Aim Understanding the spatial structuring of ecological communities involves considering the interplay between evolutionary history and environmental factors. This study investigates how the phylogenetic structure of Canidae influences the geographical distribution and trait patterns of lineages globally, and how these patterns relate to Bergmann's and Rapoport's rules. Location Americas, Africa, Eurasia. Time Period 12 million years ago—present. Major Taxa Studied Canidae. Methods Using distribution data and phylogenetic information for 37 Canidae species, we analysed key ecological, functional and evolutionary variables. We applied phylogenetic fuzzy‐weighting via principal coordinates of phylogenetic structure (PCPS) and variance partitioning analysis (VPA) to assess the contributions of phylogenetic structure and environmental factors to trait variation among species. Results Our results revealed distinct global patterns in body size, body weight, range size, habitat use and evolutionary distinctiveness among lineages. We also identified the shared contributions of phylogenetic structure and temperature to trait variation using variance partitioning analysis. The PCPS axes highlighted the influence of phylogenetic relationships on Canidae assemblages, particularly in South America. Main Conclusions Importantly, the study challenges the applicability of Bergmann's and Rapoport's rules across continents. The unique diversification history of Canidae in South America and Africa and their diverse environmental conditions likely contribute to the observed trait patterns that make both continents so distinguished when compared to North America and Eurasia. Our findings underscore the need to incorporate phylogenetic information in models assessing trait variation across geographic scales for unbiased estimates.
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