Variations on the maximum density-size lines to climate and site factors for Larix spp. plantations in northeast China

Abstract

The maximum density-size line (MDSL) is a valuable tool in sustainable forest management, as it shows the relationship between site occupancy measures and mean tree size on a log-log scale. However, the responses of MDSLs to different climate and site variables still need to be clarified. Thus, this study aimed to assess the potential effects of various climate- and site-related factors on the slopes and intercepts of MDSLs for Larix spp. plantations in northeast China. The parameters of MDSLs were estimated using stochastic frontier regression (SFR) with three different error distribution assumptions, namely half-normal distribution (HN), exponential distribution (ED), and truncated-normal distribution (TN). Spatial distributions of maximum stand density index (SDI_max) were mapped under different climate scenarios (RCP 8.5, RCP 4.5, and RCP2.6). The results revealed that the slopes on MDSLs without covariates were significantly shallower than Reineke's slope (−1.605), ranging from −1.2485 to −1.2026. Of the 22 covariates considered, 13 variables on SFR-HN and SFR-TN and 16 variables on SFR-ED had significant influences on MDSLs. The optimal MDSL model, including mean annual temperature (MAT) and soil pH as covariates using a HN assumption, decreased the Akaike's Information Criterion (AIC) by approximately 7.76%. The results also indicated that increasing MAT significantly reduced the maximum stand density for stands with a natural logarithm of quadratic mean diameter [ln(QMD)] below 2.6, while consistent increases were observed over the entire ln(QMD) range for soil pH. Moreover, the mean SDI_max within the whole region increased significantly from 15.04% under RCP4.5 to 27.78% under RCP8.5. These findings emphasize the significant influences of climate and site conditions on the MDSL, thereby calibrating on traditional density management strategies may contribute significantly on carbon sequestration capacity of forests in the face of climate change.