A climate sensitive mixed-effects diameter class mortality model for Prince Rupprecht larch (Larix gmelinii var. principis-rupprechtii) in northern China

Zhou, Xiao Hua; Chen, Qiao; Sharma, Ram P.; Wang, Yihao; He, Peng; Guo, Jun; Lei, Yinru; Fu, Liyong

doi:10.1016/j.foreco.2021.119091

Cited by 18 publications

(33 citation statements)

References 49 publications

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“…12) with and without using the variance functions. the sample plot and is widely used in various forest models (Zhang et al, 2017;Yang et al, 2020;Zhou et al, 2021a;Zhou et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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Height to crown base (HCB) is an important predictor variable for forest growth and yield models and is of great significance for bamboo stem utilization. However, existing HCB models built so far on the hierarchically structured data are for arbor forests, and not applied to bamboo forests. Based on the fitting of data acquired from 38 temporary sample plots of Phyllostachys edulis forests in Yixing, Jiangsu Province, we selected the best HCB model (logistic model) from among six basic models and extended it by integrating predictor variables, which involved evaluating the impact of 13 variables on HCB. Block- and sample plot-level random effects were introduced to the extended model to account for nested data structures through mixed-effects modeling. The results showed that bamboo height, diameter at breast height, total basal area of all bamboo individuals with a diameter larger than that of the subject bamboo, and canopy density contributed significantly more to variation in HCB than other variables did. Introducing two-level random effects resulted in a significant improvement in the accuracy of the model. Different sampling strategies were evaluated for response calibration (model localization), and the optimal strategy was identified. The prediction accuracy of the HCB model was substantially improved, with an increase in the number of bamboo samples in the calibration. Based on our findings, we recommend the use of four randomly selected bamboo individuals per sample to provide a compromise between measurement cost, model use efficiency, and prediction accuracy.

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“…12) with and without using the variance functions. the sample plot and is widely used in various forest models (Zhang et al, 2017;Yang et al, 2020;Zhou et al, 2021a;Zhou et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…A small number of samples may capture the unobserved influence of stand variables on CW. Researchers have discussed the sample size necessary to predict random effects with reasonable accuracy (Fu et al, 2017b;Yang et al, 2020;Zhou et al, 2021a;Ma et al, 2022;Zhou et al, 2022). In this study, we evaluated the impact of four sampling methods on the prediction accuracy of the NLME CW model.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…Mixed-effects modeling accounts for most of the heterogeneity and randomness caused by known or unknown factors. Mixedeffects models demonstrate a promising flexibility because only the fixed effect part [M response model (without random effects)] or both fixed and random effects parts following response calibration can be applied (Fu et al, 2017c;Yang et al, 2020;Zhou et al, 2021a). Most CW models have been based on data from tree species Fu et al, 2017c;Lei et al, 2018;Ma et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Li²,

Liu³

et al. 2023

Front. Plant Sci.

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Bamboo crown width (CW) is a reliable index for evaluating growth, yield, health and vitality of bamboo, and light capture ability and carbon fixation efficiency of bamboo forests. Based on statistical results produced from fitting the eight basic growth functions using data from 1374 Phyllostachys pubescens in Yixing, Jiangsu Province, China, this study identified the most suitable function (logistic function) to construct a two-level mixed effects (NLME) CW model with the forest block and sample plot-level effects included as random effects in the model. Four methods for selecting sample bamboos per sample plot (largest bamboo, medium-sized bamboo, smallest bamboo, and randomly selected bamboos) and eight sample sizes (1–8 selected bamboos per sample plot) were evaluated to calibrate our NLME CW model. Using diameter at breast height (DBH), height to crown base (HCB), arithmetic mean diameter at breast height (MDBH), and height (H) as predictor variables, the model produced the best fit statistics (Max R2, min RMSE, and TRE). This model was further improved by introducing random effects at two levels. The results showed a positive correlation of CW with HCB and DBH and a negative correlation with H. The smallest two bamboo poles per sample plot used to estimate the random effects of the NLME model provided a satisfactory compromise regarding measurement cost, model efficiency, and prediction accuracy. The presented NLME CW model may guide effective management and carbon estimation of bamboo forests.

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A climate sensitive nonlinear mixed-effects height to crown base model: a study focuses on Phyllostachys pubescens

Zhou,

Zhang,

et al. 2024

Trees

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A climate sensitive mixed-effects diameter class mortality model for Prince Rupprecht larch (Larix gmelinii var. principis-rupprechtii) in northern China

Cited by 18 publications

References 49 publications

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

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

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

A climate sensitive nonlinear mixed-effects height to crown base model: a study focuses on Phyllostachys pubescens

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