Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China

Abstract

Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently, it is necessary to quantitatively characterize the dynamic spatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree (BRT) model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region (Wuyishan National Nature Reserve, China). Long-term, stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass (t) increased from 4.62 × 10⁶ to 5.30 × 10⁶ t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha⁻¹ (mean ± standard error) to 94.33 ± 0.41 t ha⁻¹ in the study region. The major factors that controlled biomass (in decreasing order of importance) were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolation methods for mapping forest biomass increments at regional scales.

Keywords

Spatiotemporal variation / Stand-level biomass increment / Mid-subtropical forest / Boosted regression tree / Biotic factor / Abiotic factor

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