J Plant Ecol ›› 2017, Vol. 10 ›› Issue (2): 386-396.doi: 10.1093/jpe/rtw033

• Research Articles • Previous Articles     Next Articles

Ecogeographical variation of 12 morphological traits within Pinus tabulaeformis: the effects of environmental factors and demographic histories

Mingfei Ji1,2, Jianming Deng1,*, Buqing Yao1, Renfei Chen1, Zhexuan Fan1, Jiawei Guan1, Xiaowei Li1, Fan Wu1 and Karl J. Niklas3   

  1. 1 State Key Laboratory of Grassland and Agro-Ecosystems, School of Life Science, Lanzhou University, 222 Tianshui Road, Lanzhou, Gansu 730000, China; 2 School of life Science and Technology, Nanyang Normal University, 1638 Wolong Road, Nanyang, Henan 473061, China; 3 Department of Plant Biology, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
  • Received:2015-06-07 Accepted:2016-04-10 Online:2016-04-15 Published:2017-03-25
  • Contact: Ji, Mingfei E-mail:dengjm@lzu.edu.cn

Abstract: Aims More data are needed about how genetic variation (GV) and environmental factors influence phenotypic variation within the natural populations of long-lived species with broad geographic distributions. To fill this gap, we examined the correlations among environmental factors and phenotypic variation within and among 13 natural populations of Pinus tabulaeformis consisting of four demographically distinct groups within the entire distributional range.
Methods Using the Akaike's Information Criterion (AIC) model, we measured 12 morphological traits and constructed alternative candidate models for the relationships between each morphological trait and key climatic variables and genetic groups. We then compared the AIC weight for each candidate model to identify the best approximating model for ecogeographical variation of P. tabulaeformis. The partitioning of variance was assessed subsequently by evaluating the independent variables of the selected best models using partial redundancy analysis.
Important findings Significant phenotypic variation of the morphological traits was observed both within individual populations and among populations. Variation partition analyses showed that most of the phenotypic variation was co-determined by both GV and climatic factors. GV accounted for the largest proportion of reproductive trait variation, whereas local key climatic factors (i.e. actual evapotranspiration, AET) accounted for the largest proportion of phenotypic variation in the remaining investigated traits. Our results indicate that both genetic divergence and key environmental factors affect the phenotypic variation observed among populations of this species, and that reproductive and vegetative traits adaptively respond differently with respect to local environmental conditions. This partitioning of factors can inform those making predictions about phenotypic variation in response to future changes in climatic conditions (particularly those affecting AET).

Key words: Akaike's Information Criterion, environmental factors, genetic variation, information-theoretic approach, phenotypic variation

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