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

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 2,472 – 2,628 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.