Effects of forest age and season on soil microbial communities in Chinese fir plantations.

Understanding changes in the distribution patterns and diversity of soil microbial communities from the perspectives of age-related changes, seasonal variations, and the interaction between the two factors can facilitate the management of plantations. In Chinese fir plantations, we collected soils from different depths in over-mature forests, mature forests, near-mature forests, middle-aged forests, and young forests in summer, autumn, and winter in China's subtropical regions. As the forests developed, bacterial and fungal communities' diversity changed, reached a minimum value at near-mature forests, and then increased in mature forests or over-mature forests. Near-mature forests had the lowest topological properties. The Shannon index of microbial communities varied with seasonal changes (P < 0.05). Bacterial and fungal community composition at genus level was more closely related to temperature indicators (including daily average temperature, daily maximum temperature, and daily minimum temperature) (P < 0.01, 0.5554 < R2 <0.8185) than daily average precipitation (P > 0.05, 0.0321 < R2 <0.6773). Bacteria were clustered by season and fungi were clustered by forest age. We suggested that extending the tree cultivation time of plantations could promote microbial community recovery. In addition, we found some species worthy of attention, including Bacteroidetes in autumn in over-mature forests, and Firmicutes in summer in young forests.IMPORTANCEChinese fir [Cunninghamia lanceolata (Lamb.) Hook] is an important fast-growing species with the largest artificial forest area in China, with the outstanding problems of low quality in soil. Soil microorganisms play a crucial role in soil fertility by decomposing organic matter, optimizing soil structure, and releasing essential nutrients for plant growth. In order to maintain healthy soil quality and prevent nutrient depletion and land degradation, it is crucial to understand the changes of soil microbial composition and diversity. Our study determined to reveal the change of soil microbial community from stand age, season, and the interaction between the two aspects, which is helpful to understand how interannual changes in different years and seasonal changes in one year affect soil fertility restoration and sustainable forest plantation management. It is a meaningful exploration of soil microbial communities and provides new information for further research.

A framework for precisely thinning planning in a managed pure Chinese fir forest based on UAV remote sensing.

Where and how many trees should be thinned in a pure managed forest to improve forest quality and increase ecological benefits are important forest questions. In this study we address such challenges by providing a novel framework for planning thinning operations through Unmanned Aerial Vehicle (UAV) remote sensing techniques, which can not only obtain forest attributes of its entire stand with spatial properties, but also optimize the selection of thinning areas, thinning intensities and cut-trees. This study helps to reduce the costs of time-consuming and laborious ground investigations. The framework was demonstrated by applying it into a subtropical Chinese fir plantation in southeastern China. Results showed that RGB images acquired by a low-cost UAV have great potential in depicting forest structure. The overall accuracy of the individual tree detection in the case study was 85.19 % ± 0.48 %. The overall accuracy and the intersection over union of the non-crown area extraction were 94.94 % and 82.65 %, respectively. For the two determined thinning areas, 19.5 % and 14.3 % crown density were required to thin in the primary and secondary regions, respectively. In addition, the top-down perspective of UAV remote sensing makes up for the limitations of the bottom-up perspective of traditional forestry. The framework can act as a basic model for forest managers to modify and expand for customizing detailed thinning guidelines.

A Bimodal Pattern and Age-Related Growth of Intra-Annual Wood Cell Development of Chinese Fir in Subtropical China.

Age plays an important role in regulating the intra-annual changes in wood cell development. Investigating the effect of age on intra-annual wood cell development would help to understand cambial phenology and xylem formation dynamics of trees and predict the growth of trees accurately. Five intermediate trees in each stand (total of 5 stands) in five age groupings of Chinese fir (Cunninghamia lanceolata Hook.) plantations in subtropical China were monitored on micro-cores collected weekly or biweekly from January to December in 2019. We modeled the dynamics of wood cell development with a mixed effects model, analyzed the age effect on intra-annual wood cell development, and explored the contribution of rate and duration of wood cell development on intra-annual wood cell development. We found a bimodal pattern of wood cell development in all age classes, and no matter the date of peak or the maximal number of cells the bimodal patterns were similar in all age classes. In addition, compared with the older trees, the younger trees had the longest duration of wood cell development because of the later end of wood cell development and a larger number of wood cells. The younger trees had the faster growth rate than the older trees, but the date of the maximal growth rate in older trees was earlier than younger trees, which led to the production of more wood cells in the younger trees. Moreover, we found that the number of cells in wood cell formation was mostly affected by the rate (92%) rather than the duration (8%) of wood cell formation.

Development of a stem taper equation and modelling the effect of stand density on taper for Chinese fir plantations in Southern China.

Chinese fir (Cunninghamia lanceolata) is the most important commercial tree species in southern China. The objective of this study was to develop a variable taper equation for Chinese fir, and to quantify the effects of stand planting density on stem taper in Chinese fir. Five equations were fitted or evaluated using the diameter-height data from 293 Chinese fir trees sampled from stands with four different densities in Fenyi County, Jiangxi Province, in southern China. A total of 183 trees were randomly selected for the model development, with the remaining 110 trees used for model evaluation. The results show that the Kozak's, Sharma/Oderwald, Sharma/Zhang and modified Brink's equations are superior to the Pain/Boyer equation in terms of the fitting and validation statistics, and the modified Brink's and Sharma/Zhang equations should be recommended for use as taper equations for Chinese fir because of their high accuracy and variable exponent. The relationships between some parameters of the three selected equations and stand planting densities can be built by adopting some simple mathematical functions to examine the effects of stand planting density on tree taper. The modelling and prediction precision of the three taper equations were compared with or without incorporation of the stand density variable. The predictive accuracy of the model was improved by including the stand density variable and the mean absolute bias of the modified Brink's and Sharma/Zhang equations with a stand density variable were all below 1.0 cm in the study area. The modelling results showed that the trees have larger butt diameters and more taper when stand density was lower than at higher stand density.

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

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.

Relationship between Modelling Accuracy and Inflection Point Attributes of Several Equations while Modelling Stand Diameter Distributions.

In this study, seven popular equations, including 3-parameter Weibull, 2-parameter Weibull, Gompertz, Logistic, Mitscherlich, Korf and R distribution, were used to model stand diameter distributions for exploring the relationship between the equations' inflection point attributes and model accuracy. A database comprised of 146 diameter frequency distributions of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations was used to demonstrate model fitting and comparison. Results showed that the inflection points of the stand diameter cumulative percentage distribution ranged from 0.4 to 0.6, showing a 1/2 close rule. The equation's inflection point attribute was strongly related to its model accuracy. Equation with an inflection point showed much higher accuracy than that without an inflection point. The larger the effective inflection point interval of the fitting curve of the equation was, and the closer the inflection point was to 0.5 for the equations with fixed inflection points, the higher the equation's accuracy was. It could be found that the equation's inflection point had close relationship with skewness of diameter distribution and stand age, stand density, which provided a scientific basis for model selection of a stand diameter distribution for Chinese fir plantations and other tree species.

Modeling forest fire occurrences using count-data mixed models in Qiannan autonomous prefecture of Guizhou province in China.

Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence.

Insect-induced tree mortality of boreal forests in eastern Canada under a changing climate.

Forest insects are major disturbances that induce tree mortality in eastern coniferous (or fir-spruce) forests in eastern North America. The spruce budworm (SBW) (Choristoneura fumiferana [Clemens]) is the most devastating insect causing tree mortality. However, the relative importance of insect-caused mortality versus tree mortality caused by other agents and how this relationship will change with climate change is not known. Based on permanent sample plots across eastern Canada, we combined a logistic model with a negative model to estimate tree mortality. The results showed that tree mortality increased mainly due to forest insects. The mean difference in annual tree mortality between plots disturbed by insects and those without insect disturbance was 0.0680 per year (P < 0.0001, T-test), and the carbon sink loss was about 2.87t C ha(-1) year(-1) larger than in natural forests. We also found that annual tree mortality increased significantly with the annual climate moisture index (CMI) and decreased significantly with annual minimum temperature (T min), annual mean temperature (T mean) and the number of degree days below 0°C (DD0), which was inconsistent with previous studies (Adams et al. 2009; van Mantgem et al. 2009; Allen et al. 2010). Furthermore, the results for the trends in the magnitude of forest insect outbreaks were consistent with those of climate factors for annual tree mortality. Our results demonstrate that forest insects are the dominant cause of the tree mortality in eastern Canada but that tree mortality induced by insect outbreaks will decrease in eastern Canada under warming climate.

Estimating tree height-diameter models with the Bayesian method.

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the "best" model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.

Tree biomass estimation of Chinese fir (Cunninghamia lanceolata) based on Bayesian method.

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is the most important conifer species for timber production with huge distribution area in southern China. Accurate estimation of biomass is required for accounting and monitoring Chinese forest carbon stocking. In the study, allometric equation W = a(D2H)b was used to analyze tree biomass of Chinese fir. The common methods for estimating allometric model have taken the classical approach based on the frequency interpretation of probability. However, many different biotic and abiotic factors introduce variability in Chinese fir biomass model, suggesting that parameters of biomass model are better represented by probability distributions rather than fixed values as classical method. To deal with the problem, Bayesian method was used for estimating Chinese fir biomass model. In the Bayesian framework, two priors were introduced: non-informative priors and informative priors. For informative priors, 32 biomass equations of Chinese fir were collected from published literature in the paper. The parameter distributions from published literature were regarded as prior distributions in Bayesian model for estimating Chinese fir biomass. Therefore, the Bayesian method with informative priors was better than non-informative priors and classical method, which provides a reasonable method for estimating Chinese fir biomass.

Stand diameter distribution modelling and prediction based on Richards function.

The objective of this study was to introduce application of the Richards equation on modelling and prediction of stand diameter distribution. The long-term repeated measurement data sets, consisted of 309 diameter frequency distributions from Chinese fir (Cunninghamia lanceolata) plantations in the southern China, were used. Also, 150 stands were used as fitting data, the other 159 stands were used for testing. Nonlinear regression method (NRM) or maximum likelihood estimates method (MLEM) were applied to estimate the parameters of models, and the parameter prediction method (PPM) and parameter recovery method (PRM) were used to predict the diameter distributions of unknown stands. Four main conclusions were obtained: (1) R distribution presented a more accurate simulation than three-parametric Weibull function; (2) the parameters p, q and r of R distribution proved to be its scale, location and shape parameters, and have a deep relationship with stand characteristics, which means the parameters of R distribution have good theoretical interpretation; (3) the ordinate of inflection point of R distribution has significant relativity with its skewness and kurtosis, and the fitted main distribution range for the cumulative diameter distribution of Chinese fir plantations was 0.4∼0.6; (4) the goodness-of-fit test showed diameter distributions of unknown stands can be well estimated by applying R distribution based on PRM or the combination of PPM and PRM under the condition that only quadratic mean DBH or plus stand age are known, and the non-rejection rates were near 80%, which are higher than the 72.33% non-rejection rate of three-parametric Weibull function based on the combination of PPM and PRM.