J Syst Evol

• Research Articles •    

Phenotypic traits evolution and morphological traits associated with echolocation calls in cryptic horseshoe bats (Rhinolophidae)

Ada Chornelia1,2, Alice Catherine Hughes3*   

  1. 1Landscape Ecology Group, Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden (XTBG), Chinese Academy of Sciences, Menglun, PR China
    2International College, University of Chinese Academy of Sciences, Huairou, Beijing PR China
    3School of Biological Sciences, University of Hong Kong, Pokfulam, Hong Kong SAR, China
  • Received:2022-07-09 Accepted:2022-10-15 Online:2022-10-21

Abstract: Bats provide an excellent case study for studying evolution due to their remarkable flight and echolocation capabilities. In this study, we sought to understand the phenotypic evolution of key traits in Rhinolophidae (horseshoe bats) using phylogenetic comparative methods. We aim to test the phylogenetic signals of traits, evaluated the best-fit evolutionary models given the data for each trait considering different traits may evolve under different models (i.e., Brownian Motion (BM), Ornstein-Uhlenbeck (OU) and Early Burst (EB)) and reconstruct ancestral character states. We examined how phenotypic characters are associated with echolocation calls and minimum detectable prey size. We measured 34 traits of 10 Asian rhinolophids species (187 individuals). We found that the majority of traits showed a high phylogenetic signal based on Blomberg’s K and Pagel’s λ, but each trait may evolve under different evolutionary models. Sella traits were shown to evolve under stabilizing selection based on OU models, indicating sella traits have the tendency to move forward along the branches toward some medial value in equilibrium. Our findings highlight the importance of sella characters in association with echolocation calls emissions in Rhinolophidae, as calls are important for spatial cognition and also influences dietary preferences. Minimum detectable prey size in Rhinolophidae was associated with call frequency, bandwidth, call duration, wingspan and wing surface area. Ultimately, understanding trait evolution requires sensitivity due to the differential selective pressures which may apply to different characteristics.

Key words: Brownian Motion, Early Burst, Echolocation, Phylogenetic signals, Traits evolution, Ornstein-Uhlenbeck