Modelling tree height-diameter allometry of Chinese fir in relation to stand and climate variables through Bayesian model averaging approach

Tree height-diameter allometry reflects the response of specific species to above and belowground resource allocation patterns. However, traditional methods (e.g. stepwise regression (SR)) may ignore model uncertainty during the variable selection process. In this study, 450 trees of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) grown at five spacings were used. We explored the height-diameter allometry in relation to stand and climate variables through Bayesian model averaging (BMA) and identifying the contributions of these variables to the allometry, as well as comparing with the SR method. Results showed the SR model was equal to the model with the third highest posterior probability of the BMA models. Although parameter estimates from the SR method were similar to BMA, BMA produced estimates with slightly narrower 95% intervals. Heights increased with increasing planting density, dominant height, and mean annual temperature, but decreased with increasing stand basal area and summer mean maximum temperature. The results indicated that temperature was the dominant climate variable shaping the height-diameter allometry for Chinese fir plantations. While the SR model included the mean coldest month temperature and winter mean minimum temperature, these variables were excluded in BMA, which indicated that redundant variables can be removed through BMA.

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