Spatial autocorrelation shapes liana distribution better than topography and host tree properties in a subtropical evergreen broadleaved forest in SW China

Xiao Long Bai, Qi Liu, Dharmalingam Mohandass, Min Cao, Han Dong Wen, Ya Jun Chen, Sunil Kumar Gupta, Lu Xiang Lin, Jiao Lin Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Lianas are an important component of subtropical forests, but the mechanisms underlying their spatial distribution patterns have received relatively little attention. Here, we selected 12 most abundant liana species, constituting up to 96.9% of the total liana stems, in a 20-ha plot in a subtropical evergreen broadleaved forest at 2472–2628 m elevation in SW China. Combining data on topography (convexity, slope, aspect, and elevation) and host trees (density and size) of the plot, we addressed how liana distribution is shaped by host tree properties, topography and spatial autocorrelation by using principal coordinates of neighbor matrices (PCNM) analysis. We found that lianas had an aggregated distribution based on the Ripley's K function. At the community level, PCNM analysis showed that spatial autocorrelation explained 43% variance in liana spatial distribution. Host trees and topography explained 4% and 18% of the variance, but less than 1% variance after taking spatial autocorrelation into consideration. A similar trend was found at the species level. These results indicate that spatial autocorrelation might be the most important factor shaping liana spatial distribution in subtropical forest at high elevation.

Original languageEnglish
Pages (from-to)301-308
JournalBiotropica
Volume54
Issue number2
Early online date12 Dec 2021
DOIs
Publication statusPublished - Mar 2022

Keywords

  • climbing mechanism
  • dispersal limitation
  • habitat preference
  • spatial process
  • variation partitioning

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