Quantifying spatial phylogenetic structures of fully stem-mapped plant communities

Authors

  • Guochun Shen,

    Corresponding author
    1. State Key Lab of Biological Control and School of Life Sciences, Guangdong Key Lab of Plant Resources, SYSU-Alberta Joint Lab for Biodiversity Conservation, Sun Yat-sen University, Guangzhou, Guangdong, China
    2. Department of Ecological Modelling, UFZ Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
    3. Tiantong National Field Observation Station for Forest Ecosystem, East China Normal University, Shanghai, China
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  • Thorsten Wiegand,

    1. Department of Ecological Modelling, UFZ Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
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  • Xiangcheng Mi,

    1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences, Beijing, China
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  • Fangliang He

    1. State Key Lab of Biological Control and School of Life Sciences, Guangdong Key Lab of Plant Resources, SYSU-Alberta Joint Lab for Biodiversity Conservation, Sun Yat-sen University, Guangzhou, Guangdong, China
    2. Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada
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Summary

  1. Analysis of the phylogenetic similarity of co-occurring species at different spatial scales is increasingly used for decoding community assembly rules. Here, we integrated the analysis of phylobetadiversity and marked point pattern analysis to yield a new metric, the phylogenetic mark correlation function, kd(r), to quantify spatial phylogenetic structure of fully stem-mapped communities.
  2. kd(r) is defined as the expected phylogenetic distance of two heterospecifics separated by spatial distance r, and normalized with the expected phylogenetic distance of two heterospecifics taken randomly from a study area. It measures spatial phylogenetic turnover relative to spatial species turnover and is closely related with the spatially explicit Simpson index.
  3. We used simulated fully stem-mapped plant communities with known spatial phylogenetic structures to assess type I and II errors of the phylogenetic mark correlation function kd(r) under a null model of random phylogenetic spatial structure, and to test the ability of the kd(r) to detect scale-dependent signals of phylogenetic spatial structure. We also compared the performance of the kd(r) with two existing measures of phylobetadiversity that have been previously used to analyse fully stem-mapped plots. Finally, we explored the spatial phylogenetic structure of a 24-ha fully stem-mapped subtropical forest in China.
  4. Simulation tests showed that the new metric yielded correct type I and type II errors and accurately detected the spatial scales at which various processes (e.g. habitat filtering and competition) were invoked to generate spatial phylogenetic structures. The power of the kd(r) was not affected by a phylogenetic signal in species abundance and different topologies of the phylogenetic tree.
  5. Replacing phylogenetic distance by functional distance allows for application of the kd(r) to estimate spatial correlations in functional community structure. Thus, the kd(r) allows trait and phylogenetic structure to be analysed in the same framework. The phylogenetic mark correlation function is a powerful and accurate tool for revealing scale-dependent phylogenetic/functional footprints in community assemblages and allows ecologists to keep up with the increasingly available data of fully stem-mapped plots, functional traits and community phylogenies.

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