RESUMEN
We established a mixed-effects model incorporating climatic factors for the base diameter and length of the primary branches of Larix kaempferi using stepwise regression, based on climatic data from a total of 40 standard plots located in Xiaolongshan, Gansu Province, Changlinggang Forest Farm in Jianshi County, Hubei Province, and Dagujia Forest Farm in Qingyuan County, Liaoning Province, as well as the data from 120 L. kaempferi sample trees. Additionally, we created prediction charts for the fixed effects portion of the optimal mixed model to determine the relationship between climatic factors and base diameter and branch length, to explore the differential response of L. kaempferi branches to climatic variables. The results showed that the base diameter mixing model with annual mean temperature and water vapor deficit and the branch length mixing model with annual mean temperature had the best fitting effect, with R2 of 0.6152 and 0.6823, respectively. Based on the fixed effects prediction chart of the mixed model, the overall basal diameter showed an increasing trend with the increases of relative branch depth. The average basal diameter size was in an order of young-aged plantationAsunto(s)
Clima
, Larix
, Larix/crecimiento & desarrollo
, China
, Temperatura
, Tallos de la Planta/crecimiento & desarrollo
, Modelos Teóricos
, Ecosistema
RESUMEN
Based on the biomass data of 516 sampling trees, and by using non-linear error-in-variable modeling approach, the compatible models for the total biomass and the biomass of six components including aboveground part, underground part, stem, crown, branch, and foliage of 15 major tree species (or groups) in Heilongjiang Province were established, and the best models for the total biomass and components biomass were selected. The compatible models based on total biomass were developed by adopting the method of joint control different level ratio function. The heteroscedasticity of the models for total biomass was eliminated with log transformation, and the weighted regression was applied to the models for each individual component. Among the compatible biomass models established for the 15 major species (or groups) , the model for total biomass had the highest prediction precision (90% or more), followed by the models for aboveground part and stem biomass, with a precision of 87.5% or more. The prediction precision of the biomass models for other components was relatively low, but it was still greater than 80% for most test tree species. The modeling efficiency (EF) values of the total, aboveground part, and stem biomass models for all the tree species (or groups) were over 0.9, and the EF values of the underground part, crown, branch, and foliage biomass models were over 0.8.