Influences of biotic and abiotic factors on the relationship between tree productivity and biomass in China

The relationship between tree productivity and biomass not only reflects plant adaptation and the interaction of plants and the environment, but also has significant implications in global carbon cycling, climate change, and forest management. However, how biotic factors (e.g. tree age, diameter at breast height [DBH], height) and abiotic factors (e.g. elevation, latitude, and longitude) influence the relationship between tree productivity and biomass has not been well investigated. We analyzed a large database on tree productivity and biomass in China to derive the relationships between these two variables. The entire database was split into different groups by tree age, DBH, height, latitude, longitude and elevation. The relationship between productivity and biomass was developed using both a linear regression model and an allometric equation (i.e. power function) for each group. Differences in model parameters among groupings based on biotic or abiotic factors indicate the effect of each factor on the relationship between productivity and biomass. The slope of the linear regression model decreased with tree age, DBH, height, and elevation, but increased with tree density and longitude. The scaling exponent of the power function varied with tree age, height, and density following a quadratic pattern, but decreased linearly with elevation. Our results indicated that there is a significant relationship between tree productivity and biomass in China, but the relationship varies with some biotic and abiotic factors. To better predict tree productivity from biomass, tree age and size need to be considered.

► We study the relationship between tree productivity and biomass in China.
► Scaling exponents and slopes of tree productivity–biomass regression are not constant.
► Regression slope decreased with tree age, DBH, height, and elevation, but increased with tree density and longitude.
► Using tree size and age improves prediction of productivity from biomass estimates.