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.Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31870385Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31470470Funding provided by: CAS 135*Crossref Funder Registry ID: Award Number: 2017XTBG-F01Funding provided by: Light of West China*Crossref Funder Registry ID: Award Number: Funding provided by: China Scholarship CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004543Award Number: Funding provided by: CAS Key Laboratory of Tropical Forest Ecology*Crossref Funder Registry ID: Award Number:From October 2015 to January 2016, all rooted lianas with the diameter ≥ 1 cm were tagged, mapped, measured, and identified to species level following standard methods. Briefly, we labelled and mapped the largest "principal stem" and additional multiple stems, and then measured the diameter 130 cm from the rooting point at the soil surface. When lianas branched below 130 cm (but ≥ 40 cm from the roots), we measured the diameter 20 cm below the branching point. Because multiple stems seem to be functionally equivalent in their dynamics and effects on trees, we treated them as separate individuals in the subsequent statistical analyses. We classified climbing mechanisms into four categories: stem twiner, tendril, hook, and root climbers. In total, 3,725 liana stems, including multiple stems, were recorded in the 20-ha plot, representing 24 species and 15 families. We selected the 12 most abundant species for the study (abundance > 1 stem ha–1), constituting up to 96.9% (3,609 stems) of the total liana stems and 97.5% (8.83 m2) of the total basal area. We quantified the numbers of stems (including multiple stems) per species in each of the 500 quadrats. For each of the 500 20 × 20 m quadrat, we calculated elevation as the mean elevation of the four corners of the quadrat. We calculated slope as the average angular deviation of each of the four triangular planes formed by connecting three corners at a time with respect to the horizontal line. Convexity was the difference between the average elevation of the focused quadrat and the average elevation of the eight neighbor quadrats. For the edge quadrat, convexity was calculated as the difference between the elevation of the center of the focal quadrat and the average elevation of the four corners. Aspect was calculated as: , where fx and fy are the elevation difference from east to west, north to south in the 20 × 20 m quadrat, respectively