Abstract
Tree mortality models play an important role in predicting tree growth and yield, but existing mortality models for Larix gmelinii subsp. principis-rupprechtii, an important species used for regeneration and afforestation in northern China, have overlooked potential regional influences on tree mortality. This study used data acquired from 102 temporary sample plots (TSPs) in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest (n = 67) and state-owned Boqiang Forest (n = 35) in northern China. To model stand-level tree mortality, we compared seven model forms of county data. Three continuous (dominant height, plot mean diameter, and basal area per hectare) and one dummy variable with two levels (region) were used as fixed effects variables. Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models. Results showed that tree mortality significantly positively correlated with stand basal area and dominant height, but negatively correlated with stand mean diameter. Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements, and Hurdle Poisson mixed-effects model showed the most attractive fit statistics (largest R2 and smallest RMSE) when employing leave-one-out cross-validation. These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.
Highlights
We considered seven commonly used versatile functions to develop the tree mortality models, such as Poisson model and negative binomial model (NB), which refer to as the standard function, zero-inflated Poisson model (ZIP), zeroinflated negative binomial model (ZINB), Hurdle Poisson model (HP), Hurdle negative binomial model (HNB), and logistic regression model
The parameter estimates of the dummy variable and all other parameters of each tree mortality model were significant (p < 0.05), except for β4 and β5 in the NB, ZINB and HNB models (Table 6)
Fitting and comparison of the seven basic models through incorporation of the dummy variable describing regional effects and random components describing the forest block effects on the tree mortality led us to the following conclusions: (1) The models fitted with the dummy variable and random effects significantly improved fit statistics and prediction statistics compared with the basic models
Summary
E. Murray (Pinaceae) is the main afforestation tree species in the mountains of northern China (Fu 2017) because of its faster growth, excellent wood materials, stronger resistance to bad weather and wind, and contributions to soil conservation. Murray (Pinaceae) is the main afforestation tree species in the mountains of northern China (Fu 2017) because of its faster growth, excellent wood materials, stronger resistance to bad weather and wind, and contributions to soil conservation Predicting tree mortality is one of the important parts of forest growth and yield models (Clutter and Jones 1980; Knoebel and Burkhart 1986).
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