Dataset Information

A Hierarchical Bayesian Model to Predict Self-Thinning Line for Chinese Fir in Southern China.

ABSTRACT: Self-thinning is a dynamic equilibrium between forest growth and mortality at full site occupancy. Parameters of the self-thinning lines are often confounded by differences across various stand and site conditions. For overcoming the problem of hierarchical and repeated measures, we used hierarchical Bayesian method to estimate the self-thinning line. The results showed that the self-thinning line for Chinese fir (Cunninghamia lanceolata (Lamb.)Hook.) plantations was not sensitive to the initial planting density. The uncertainty of model predictions was mostly due to within-subject variability. The simulation precision of hierarchical Bayesian method was better than that of stochastic frontier function (SFF). Hierarchical Bayesian method provided a reasonable explanation of the impact of other variables (site quality, soil type, aspect, etc.) on self-thinning line, which gave us the posterior distribution of parameters of self-thinning line. The research of self-thinning relationship could be benefit from the use of hierarchical Bayesian method.

SUBMITTER: Zhang X

PROVIDER: S-EPMC4594911 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A Hierarchical Bayesian Model to Predict Self-Thinning Line for Chinese Fir in Southern China.

Zhang Xiongqing X Zhang Jianguo J Duan Aiguo A

PloS one 20151006 10

Self-thinning is a dynamic equilibrium between forest growth and mortality at full site occupancy. Parameters of the self-thinning lines are often confounded by differences across various stand and site conditions. For overcoming the problem of hierarchical and repeated measures, we used hierarchical Bayesian method to estimate the self-thinning line. The results showed that the self-thinning line for Chinese fir (Cunninghamia lanceolata (Lamb.)Hook.) plantations was not sensitive to the initial ...[more]

PMID: 26440942

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Project description:BackgroundHigh-density conditions are global issues that threaten the sustainable management of plantations throughout the world. Monocultures and untimely management practices have identically resulted in the simplex of community structures, decreases in biodiversity, and long-term productivity losses in plantations China. The most popular measure which is commonly used to address these issues is thinning, which potentially results in increases in the development of understory plants in plantations. However, there is limited information currently available regarding the community composition of understory vegetation and the associated environmental factors, which has limited the sustainable management of China's fir plantation ecosystems.MethodIn the present study, a thinning experiment was implemented which included a control check (CK: no thinning), as well as low intensity thinning (LIT: 20%), moderate intensity thinning (MIT: 33%), and high intensity thinning (HIT: 50%) in Chinese fir plantations located in the Southeastern China. During the investigation process, the understory vegetation examined three years after thinning measures were completed, in order to analyze the impacts of different thinning intensities on the growth and community composition of the understory plants. At the same time, the associated environmental factors in the fir plantations were also investigated.ResultsThe species richness, total coverage, and biomass of the understory vegetation were observed to be apparently increased with increasing thinning intensity. In addition, it was found that the thinning measures had prominently influenced the soil nutrients. The community compositions of the understory vegetation were significantly different among the four thinning intensity levels, especially between the CK and the HIT. Furthermore, the development of the understory vegetation was found to be significantly correlated with the soil nutrient contents, and the community compositions of the understory vegetation were prominently driven by the tree densities, slope positions, and soil nutrient contents.

Project description:Soil multifunctionality is essential for the enhancement of soil carbon sequestration, but disturbances such as thinning practices can influence soil microbial activity and carbon cycling. Microbial residues, particularly microbial residue carbon (MRC), are important contributors to soil organic carbon (SOC), but the effects of thinning intensity on MRC accumulation remain poorly understood. This study evaluated the impact of four thinning treatments-control (CK, 0%), light-intensity thinning (LIT, 20%), medium-intensity thinning (MIT, 30%), and high-intensity thinning (HIT, 45%)-on soil multifunctionality in Chinese fir plantations five years after thinning. Soil nutrient provision, microbial biomass, enzyme activity, and microbial residue carbon were assessed. The results showed that thinning intensity significantly affected soil nutrient provision and microbial biomass, with MIT and HIT showing higher nutrient levels than CK and LIT. Specifically, MIT's and HIT's total nutrient provision increased by 0.04 and 0.15 compared to that of CK. Enzyme activity was highest in LIT (+0.89), followed by MIT (+0.07), with HIT showing a decline (-0.84). Microbial biomass, including bacterial PLFAs (B-PLFAs), fungal PLFAs (F-PLFAs), microbial biomass carbon (MBC), and nitrogen (MBN), was highest in CK and MIT, and lowest in HIT, with MIT showing a 0.13 increase compared to CK. Microbial residue carbon (MRC) accumulation was positively correlated with soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), and easily oxidized organic carbon (EOC). The highest MRC content in the 0-20 cm soil layer was observed in MIT and CK (10.46 and 11.66 g/kg, respectively), while the MRC in LIT and HIT was significantly lower, reduced by 24% and 12%, respectively. These findings highlight the significant role of thinning intensity in microbial activity and carbon cycling. Medium-intensity thinning (MIT, 30%) was identified as the most effective approach for promoting microbial biomass and enhancing carbon cycling in Chinese fir forest soils, making it an optimal approach for forest management aimed at increasing soil carbon sequestration.

Project description:BackgroundThinning practices are useful measures in forest management and play an essential role in maintaining ecological stability. However, the effects of thinning on the soil properties and microbial community in large Chinese fir timber plantations remain unknown. The purpose of this study was to investigate the changes in soil physicochemical properties and microbial community composition in topsoil (0-20 cm) under six different intensities (i.e., 300 (R300), 450 (R450), 600 (R600), 750 (R750) and 900 (R900) trees per hectare and 1650 (R1650) as a control) in a large Chinese fir timber plantation.ResultsCompared with the CK treatment, thinning significantly altered the contents of soil organic carbon (SOC) and its fractions but not in a linear fashion; these indicators were highest in R900. In addition, thinning did not significantly affect the soil microbial community diversity indices but significantly affected the relative abundance of the core microbial community. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant bacterial phyla; the relative abundances of Proteobacteria and Acidobacteria were highest in R900, and that of Actinobacteria was lowest in R900. The dominant fungal phyla were Ascomycota, Basidiomycota and Mucoromycota; the relative abundance of Ascomycota was lowest in R900, and that of Mucoromycota was highest in R900. The fungal microbial community composition was more sensitive than the bacterial community composition. The activity of the carbon-cycling genes was not linearly correlated with thinning, and the abundance of C-cycle genes was highest in R900.ConclusionsThese findings are important because they show that SOC and its fractions and the abundance of the soil microorganism community in large Chinese fir timber plantations can be significantly altered by thinning, thus affecting the capacity for carbon storage. These results may advance our understanding of how the density of large timber plantations could be modified to promote soil carbon storage.

Project description:BackgroundBipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days.ObjectiveThis study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial.MethodsWe applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast.ResultsThe best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R2=0.51 and root mean squared error of 0.32, for mood scores on a scale of -3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution.ConclusionsOur proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.

Dataset Information

A Hierarchical Bayesian Model to Predict Self-Thinning Line for Chinese Fir in Southern China.

Publications

A Hierarchical Bayesian Model to Predict Self-Thinning Line for Chinese Fir in Southern China.

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