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Spatial autocorrelation shapes liana distribution better than topography and host tree properties in a subtropical evergreen broadleaved forest in SW China
Author(s) -
Bai XiaoLong,
Liu Qi,
Mohandass Dharmalingam,
Cao Min,
Wen HanDong,
Chen YaJun,
Gupta Sunil Kumar,
Lin LuXiang,
Zhang JiaoLin
Publication year - 2022
Publication title -
biotropica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.813
H-Index - 96
eISSN - 1744-7429
pISSN - 0006-3606
DOI - 10.1111/btp.13043
Subject(s) - liana , spatial analysis , spatial distribution , elevation (ballistics) , evergreen , geography , subtropics , common spatial pattern , spatial heterogeneity , spatial variability , ecology , physical geography , biology , mathematics , statistics , remote sensing , geometry
A bstract 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.