Generalized or general mixed-effect modelling of tree morality of Larix gmelinii subsp. principis-rupprechtii in Northern China

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.

Constructing two-level nonlinear mixed-effects crown width models for Moso bamboo in China

“…Among the eight commonly used CW-DBH models evaluated in this study, the logistic form (i.e., Model 8) was used as the optimal basic model because it is superior to the other models, especially in flexibility. The logistic model is widely used to fit forest growth data, including DBH growth (Fu et al, 2018), survival (Buchman et al, 1983;Xie et al, 2022), mortality (Zhou et al, 2021b;Glover and Hool, 1979;Zhou et al, 2021a), tree CW Lei et al, 2018), and HCB (Yang et al, 2020;Pan et al, 2020;Zhou et al, 2022). These examples show that the logistic model is sufficiently flexible in detailing potential tree growth variations, including bamboo CW variations.…”

“…Using ordinary least squares (OLS) regression to estimate the CW models with the nested data structure will lead to a significant bias (West et al, 1984;Zhang et al, 2017). Mixed-effects modeling, which effectively addresses the aforementioned observation dependence [the data are hierarchically structured (a sample plot nested in the blocks)] problems, must be applied to reduce potential bias Yang et al, 2020;Pan et al, 2020;Zhou et al, 2021a;Zhou et al, 2021b;Ma et al, 2022). Mixed-effects modeling accounts for most of the heterogeneity and randomness caused by known or unknown factors.…”

Two-level mixed-effects height to crown base model for moso bamboo (Phyllostachys edulis) in Eastern China

“…Mixed-effects modeling for such a nested data structure (e.g., bamboo culms within a sample plot, sample plots within a block, multiple measurements on the same plot or bamboo plant) provides the most robust method to avoid the problems caused by a nested data structure, including problems with spatial correlations. Mixed-effects modeling has been widely used in forest modeling in recent years because of its high efficiency and robustness ( Fu et al., 2017 ; Sharma et al., 2017 ; Pan et al., 2020 ; Yang et al., 2020 ; Zhou et al., 2021a ; Zhou et al., 2021b ). Only a few HCB modeling studies exist, and they are based solely on arboreal species ( Fu et al., 2017 ; Pan et al., 2020 ; Yang et al., 2020 ) and use one-level mixed-effects modeling.…”

Modeling the Dominant Height of Larix principis-rupprechtii in Northern China—A Study for Guandi Mountain, Shanxi Province

“…Larch forests in this region are usually characterized by a large amount of biomass and high primary productivity. Many studies have demonstrated that larch forests play critical roles in regional carbon storage and carbon cycling [1][2][3][4][5].…”

“…Therefore, it is necessary to use the nonlinear mixed-effects (NLME) method to solve the above problems. In recent years, this approach has been increasingly used to develop various forest models, including forest growth models [1][2][3][24][25][26], to efficiently analyze hierarchically structured data, and increase prediction accuracy [1][2][3]23]. There are some research methods that can hierarchically structure data, and there are only a few studies on the correlation of time-series data of dominant height [1][2][3][23][24][25][26].…”