Spatial heterogeneity of leaf area index in a temperate old-growth forest: Spatial autocorrelation dominates over biotic and abiotic factors
Leaf area index (LAI) controls many eco-physiological processes and can be widely used to scale-up leaf processes to ecosystem, landscape and regional levels. However, the macro-scale spatial heterogeneity of LAI and its controlling factors are not fully understood. We estimated annual maximum LAI using an LAI-2200 plant canopy analyzer in a 9-ha, old-growth, mixed broadleaved-Korean pine (Pinus koraiensis) forest in China. We analyzed the spatial heterogeneity of LAI and mapped its distribution using geostatistical methods; then, through variance partitioning, we examined the influences of several biotic factors, abiotic factors and spatial autocorrelation on the LAI distribution. Variance partitioning showed that these factors altogether explained 59% of the variation in the distribution of LAI. Compared to biotic and abiotic factors, spatial autocorrelation controlled more spatial heterogeneity of LAI by explaining 51.4% of the total variation in LAI. For biotic and abiotic factors, the mean diameter at breast height (DBH) of large trees (DBH > 10 cm), elevation, soil temperature and soil mass moisture content significantly affected the LAI distribution (P < 0.01). Notably, spatial autocorrelation unexpectedly explained the most variation in the LAI values, and it also varies with cardinal direction and is a key descriptor of LAI spatial variability. These results suggest that the influence of spatial autocorrelation on LAI distribution should attract more attention and that both the relative importance of and interactions among different determining factors is helpful for better understanding the mechanistic determinants of LAI distributions in temperate mixed forests.