Objective: Improving forest quality is one of the main tasks of China's forestry construction in the new period, and biomass and productivity are the two important indicators of forest quality. Poplar(Populus spp.)is the most planted broad-leaved tree species in China. Developing biomass models and analyzing impact of climate factors to productivity of planted poplar trees has an important practical significance. Method: Based on the mensuration data of above- and below-ground biomass from 450 and 147 destructive sample trees of planted poplar, respectively, collected from 15 provinces in China, one- and two-variable simultaneous biomass equations were developed using dummy variable modeling approach and error-in-variable simultaneous equation approach; and based on the paired data of diameter, height, and age of sample trees, individual tree growth models with climate factors were established. According to the individual tree diameter and height growth models and two-variable biomass equations, effects of climate factors on productivity of planted poplar trees were analyzed. In addition, based on the data of planted poplar plots of national forest inventory(NFI), the biomass and productivity of each plot were calculated, and linear regression model between productivity of planted poplar forests and climate factors was developed, which would verify the effects of climate factors on productivity of trees. Result: The coefficients of determination(R²)of one- and two-variable aboveground biomass equations for planted poplar trees developed in this study were above 0.90, and the mean prediction errors(MPEs)were within 5%; whereas the R² of belowground biomass equations were above 0.83, and MPEs were within 10%. The R² of individual tree diameter and height growth models with climate factors were above 0.70, and MPEs were within 5% and 3%, respectively. The diameter and height growth of planted poplar trees were significantly related with mean annual temperature(T). The diameter at breast height, tree height, and total biomass of a 20-years-old planted poplar tree on site for T=20℃ are 2.4, 2.4, and 9.5 times of those on site for T=0℃, respectively. The verification result using the data of NFI plots show that average productivity of poplar plantations can increase 2.5 t·hm^-2a^-1 with an increase of 10℃ for mean annual temperature, and the productivity of poplar plantation on site for T=20℃ is more than 7 times of that on site for T=0℃, which is consistent with the comparable results from developed growth models. Conclusion: The above- and below-ground biomass equations and the compatible biomass conversion factor and root-to-shoot ratio models developed for planted poplar trees in this study could meet the needs of precision requirements to relevant regulation, and could be used in application. Temperature is an important factor affecting the productivity of planted poplar trees. With the increase of mean annual temperature, the productivity of planted poplar trees increases accordingly.

Objective: This paper was carried out to analyze the trade-offs and synergies among forest supply(productivity), regulation(carbon sequestration and oxygen release, water conservation)and support(biodiversity)functions in order to provide a theoretical basis for multi-functional forest management. Method: The study was implemented on Picea schrenkiana forests in the central Tianshan Mountains region of Xinjiang, based on the data from 42 sample plots and 1 170 sub-compartments in forest resources class Ⅱ survey of Banfanggou forest farm. Four functions, including stand productivity, carbon sequestration and oxygen release, water conservation and biodiversity, were predicted by using random forest method. The correlation analysis was made among the four functions of the stand, and the correlation of stand average age, stand volume, average tree height, average DBH, canopy density, slope, altitude, and vegetation coverage with these functions. Result: The main factors those have great influences on stand productivity, water conservation, carbon sequestration, oxygen release and biodiversity are stand average DBH, volume, vegetation coverage, average forest age and canopy density. The correlation coefficient is 0.225-0.917(P < 0.01)between stand average age, tree height, DBH, canopy density, stand volume and four functions, comprehensive function. The correlation coefficient is 0.167-0.393(P < 0.01)between slope and water conservation, carbon sequestration and oxygen release, biodiversity. The correlation coefficient between altitude and productivity, water conservation, comprehensive function is -0.264—-0.143(P < 0.01).There is a positive correlation between vegetation coverage and water conservation and biodiversity, the correlation coefficients are 0.237 and 0.081(P < 0.01), respectively. The correlation coefficient is 0.228-0.645(P < 0.01)with four functions, among which, the correlation between biodiversity and carbon sequestration and oxygen release is the highest. There is a synergistic relationship between the functions of the stands. Conclusion: There is a synergistic relationship among the four functions of Picea schrenkiana stands, harmonization of multiple functions could be achieved through rational forest management.

Objective: The paper was intended to reveal the species composition and diversity characteristics of semi-natural mixed forest of Pinus massoniana and broad-leaved trees,especially the characteristics of the naturally regenerated broad-leaved trees,aimed to provide a scientific basis for the management and utilization of this type of forest. Method: A 50 m×160 m plot was set up in semi-natural mixed forest of Pinus massoniana and broad-leaved trees in Lingtou village,Yong'an municipality,Fujian Province. The plot was divided into 80 10 m×10 m quadrats,then the arbor and shrub layer of each quadrat were investigated. 128 small quadrats of 2 m×2 m were set up in the plot for the investigation of the herb layer. By using maximum light receiving plane (MLRP) method,the arbor layer was divided into two sub-layers,called sub-layer Ⅰ (light receiving layer) and sub-layer Ⅱ (non-light receiving layer). Then,the tree species composition and diversity of arbor layer (including the sub-layer Ⅰ and Ⅱ) and shrub layer were analyzed by importance value and several commonly used diversity indices. Result: The species richness of arbor layer,shrub layer and herb layer were 47,109,and 56. The Shannon-Wiener index,evenness index and dominance index of arbor layer were 2.93,0.53,and 0.26,respectively. The density of arbor layer was 2 686 per hectare,and there were only 9 shrub species,whose totaled density was 28 per hectare and importance value was 1.58%. Among all the species in the arbor layer,the top five of importance values were as follows:Pinus massoniana (37.87%),Cunninghamia lanceolata (16.85%),Alniphyllum fortunei (10.40%),Sassafras tzumu (7.01%) and Schima superba (4.91%). The height of maximum light receiving plane was 12.6 m. The species richness,Shannon-Wiener index,evenness index,and dominance index of sub-layer Ⅰ were 23,1.80,0.40,and 0.47,respectively. In sub-layer Ⅰ,the top five of importance values were P. massoniana (55.49%),A. fortunei (13.38%),S. tzumu (10.69%),C. lanceolata (9.04%),and C. fargesii (2.00%). The species richness,Shannon-Wiener index,evenness index and dominance index of sub-layer Ⅱ were 45,3.17,0.77and 0.23,respectively,and the top five of importance values in this sub-layer were C. lanceolata (38.84%),A. fortunei (10.73%),S. superba (9.21%),Styrax confusus (8.29%) and C. fargesii(4.26%).The Shannon-Wiener index,evenness index and dominance index of shrub layer were 4.44,0.65,and 0.08,respectively. The density of shrub layer was as high as 9 651 per hectare,and the density and the total importance values of shrubs were only 3 143 per hectare and 28.23%,respectively. Among all the species in shrub layer,the top five of importance values were C. lanceolate (15.47%),S. confuses (11.10%),C. fargesii (5.75%),Eurya loquaiana (7.84%) and A. fortunei (5.61%); the number of herbaceous plants counts 37.90% in the herb layer. Conclusion: The semi-natural mixed forest of P. massoniana and broad-leaved trees have formed a multi-stories mixed uneven-aged structure,the arbor layer,shrub layer and herb layer all have high species richness. The arbor layer was dominated by P. massoniana; and the majority of the naturally-regenerated trees were A. fortunei,S. tzumu,S. superba and S. confusus. The species composition and diversity of the naturally regenerated broad-leaved trees in the arbor layer were close to the broad-leaved forest with regeneration promoted by artificial measures at similar age. The species richness of sub-layer Ⅰ and Ⅱ were 23 and 45,respectively,P. massoniana had an absolute advantage in the sub-layer Ⅰ,and C. lanceolata had the largest importance value in the sub-layer Ⅱ. The majority of the shrub layer were saplings,and there were few herbaceous plants in the herb layer.

Objective: As an important component of forest growth yield systems,it is necessary to make accurate prediction for stand mortality. Method: About 295 permanent sample plots were established across the natural range of Mongolian oak in the Jilin Province in 1994. All plots were measured every 5 years,and the data were measured three times. 236 plots were used as simulation data and the other 59 plots as validation data. The main objective of this study was to construct stand-level mortality model of Quercus mongolica in relation to stand factor,site factor and climate factor. The basic forms of the model include Poisson distribution model and negative binomial distribution model. Considering the existence of a large number of zero values in the sample plots,the zero-inflated and zero-altered models were added to these basic models. In order to solve the problem of nesting and longitudinal data,the random effects of sample plot were taken into account in the construction of the model. In the end,the validation data was used to verify. Result: The results showed that the basal area of hectare,the number per hectare and the mean warmest month temperature are the most important factors influencing the probability and quantity of mortality. The simulation precision of the model was improved obviously after considering the plot random effects. Due to the over-dispersed of the data the accuracy of the negative binomial distribution model was higher than that of the Poisson distribution. Conclusion: The simulation effects of the model were the best when considering the random effects and the zero-inflated negative binomial distribution model simultaneously. The validation result also supported this conclusion.

Objective: Simulating the potential NPP spatial pattern and classifying the appropriate growth level of Larix principis-rupprechtii trees,are essential steps to assess the impact of climate change on the species and to provide a theoretical basis for formulating adaptive forest management strategies and for improving forest quality. Method: Based on the forest resource inventory data and the yearly accumulative NDVI data from 2001 to 2010 in Hebei Province,the climate datasets of current and two future periods (2040-2069 and 2070-2099)were generated using the Climate AP model. The CASA model was applied to predict potential NPP of the L. principis-rupprechtii and to analyze its growth suitability in the current and the two future periods. Pearson correlation analysis was used to explore influence of temperature and precipitation on the spatial pattern of potential NPP at the pixel scale. Result: The current average annual potential NPP of L. principis-rupprechtii was 342.7 gC·m^-2a^-1in Hebei Province. More than 80% areas of Hebei Province,including plains and northwest region with high elevation,had low suitability for the L. principis-rupprechtii growth. Whereas areas with high growth suitability of L. principis-rupprechtii might tend to potentially occur in the subalpine region,only accounting for less than 20% of the whole region. From 2040 to 2069, the average annual potential NPP of L. principis-rupprechtii will be increased to 392.9 gC·m^-2a^-1,the overall suitability will be significantly improved,and the medium-suitable area will be expanded significantly to 59.4% of the whole region. From 2070 to 2099, the average annual potential NPP of L. principis-rupprechtii will be decreased slightly to 375.1 gC·m^-2a^-1,the growth suitability will be decreased,and the proportion of high suitable area will be reduced. The potential NPP of L. principis-rupprechtii in Hebei Province was mainly affected by precipitation and it was significantly positively correlated with precipitation in most areas(P < 0.05). The current precipitation conditions limit the productivity of L. principis-rupprechtii. Conclusion: The results indicated that the growth suitability of L. principis-rupprechtii change under changing climate in the future. The change mainly occurred in the region with low and medium-growth suitability and the overall suitability will be improved. Enlarging afforestation area of L. principis-rupprechtii in high growth suitable region for current and future could be an option for preventing the adverse effects of climate change.

Objective: Aiming at the complex relationships between neighborhood trees characteristics and subject tree crown width,a method of estimating crown width based on adaptive neuro-fuzzy inference system was proposed to improve the intelligent level of estimating crown width. Method: Chinese fir was taken as the research object. According to the distance and azimuth of the neighborhood trees relative to the subject tree,a method of adding trees in quadrants was presented to build spatial structural units. One hundred sets of data which contained crown width in four aspects,distances and azimuths were measured,computing method of two independent variables,including neighborhood crown width and distance from the subject tree,were proposed,and then the ratio of the subject tree crown width to the neighborhood crown width was defined as the dependent variable. According to the sample data,the nonlinear mapping relations among variables were analyzed,and twenty-five fuzzy logic inference rules were established. Azero-order Takagi-Sugeno model,which was composed of two inputs and one output,was designed. The adaptive neuro-fuzzy inference system was trained by seventy sets of data,and tested by thirty sets of data. It was contrasted with multi-element linear regression and back propagation neural network. Result: The linear relationships of crown width estimated by three method and the true value all reached significant levels. On inspection,determination coefficients of this method,back propagation neural network and multi-element linear regression were 0.71,0.67,and 0.66,respectively. Conclusion: According to the neighborhood trees characteristics in the spatial structural unit,this method could directly,effectively and intelligently estimate the subject tree crown width without the independent variables those contain attributes of the subject tree.

Objective: The effects of reversion search strategy on improving the performance of simulated annealing algorithm were quantificationally evaluated,which could provide some new insights into solving the complex forest planning problems. Method: The reversion search process were estimated based on the 1-opt and 2-opt moves of simulated annealing,and then were applied into a large and real forest planning in Pangu forest farm in the northeastern part of China. The objective function of the planning problem focused on the needs for an even flow of harvest volume. A special strategy was employed to deal with the spatial and temporal distribution of forest management activities,in which the forest tending prescriptions complied with the unit restriction model of adjacency constraints,however the clear-cutting prescription complied with the area restriction model of adjacency constraints. In addition,both adjacency models should meet 3-yrs green-up constraints. Result: The results indicated that the number of reversions between 1-opt and 2-opt moves usually had no significant effects on the planning results. For a minimization planning problem,the mean objective function values of reversion search decreased significantly when compared that with 1-opt (P < 0.01) and 2-opt (P < 0.01) moves,respectively,however,the mean computation time of reversion search was only as large as five and two times than that of 1-opt and 2-opt moves,respectively. The optimal solution indicated that the amount of assigned harvest timber during an entire planning horizon were approximately 5.00×10⁵ m³,in which the levels of harvest timber using different forest tending prescriptions were 3.12×10⁵ m³,and the levels of harvest timber using clear-cutting prescription were 1.88×10⁵ m³,the assigned harvest area of forest tending and clear-cutting only accounted for approximately 10.94% and 1.02% of the total forest area,respectively. Therefore,the optimal solution from this study could meet the requirements of sustainable forest management. Conclusion: Reversion search is one of the most efficient and stable strategy to improve the search ability of heuristics,which could adapt to the complex forest management planning,and then provide some technical supports for making approximate forest management plans in future.

Objective: Illustrating size inequality dynamic with stand growth trajectory, will improve our knowledge about relationships between size inequality and stand productivity. Method: We examined the effects of stand age, planting density and self-thinning on size inequality and relationships between stand productivity and size inequality using Chinese fir plantations with five planting density levels(2.0 m×3.0 m, 2.0 m×1.5 m, 2.0 m×1.0 m, 1.0 m×1.5 m and 1.0 m×1.0 m)with three replications, respectively. The experimental stands were measured in winter every other year for 26 years, from 1981 to 2006. Generalized linear mixed effect models were used to examine the relationships between stand age, planting density, self-thinning, size inequality(assessed using the Gini coefficient)and stand productivity. Result: Gini coefficients, and hence size inequality, increased with stand age and planting density, and decreased with self-thinning trajectory. The onset of self-thinning reduced the rate at which size inequality increased, which resulted in a slower rate than the increase prior to the onset of self-thinning, and then increased size inequality following self-thinning trajectory. Stand productivity increased with planting density, but stand productivity declined at a greater rate(with increasing size inequality)after self-thinning had begun to occur. Stand productivity was negatively correlated with size inequality. Conclusion: Both Gini and stand productivity increased with stand age and planting density, the decreasing Gini and declining stand productivity occurred at the onset of self-thinning, both Gini and productivity increased again after the beginning of self-thinning. The stand self-thinning could modify the intra-specific competition intensity, and further changed the trajectory of size inequality and stand productivity. An understanding of the mechanisms driving the effects of self-thinning and growth on the relationships between stand productivity and size inequality may facilitate the further development of stand productivity. Meanwhile, the forest plantation silvicultural regimes of the development and enhancement of stand productivity may imitate the accurate improvement of forest quality.

Objective: Cunninghamia lanceolata is widely distributed in China, and the growth speed is quite different in different areas. The spatial clustering method was used to group the distribution area of C. lanceolata, and the growth model of C. lanceolata was established in different groups, which provided a method for the prediction of nationwide C. lanceolata high-precision growth. Method: 16 provinces(autonomous regions)where C. lanceolata was distributed were selected as study areas. Based on seventh and eighth continuous inventory of forest resources in China for the retest data of fixed plots of C. lanceolata and the topography, soil, meteorology and other environmental data of the study area, the importance of environmental factors affecting the growth of C. lanceolata were analyzed by the random forest model. Five environmental factors with great influences on the growth of C. lanceolata were selected. Using the group analysis function of ArcGIS 10.2, the C. lanceolata in the study area was grouped according to the environmental similarity and spatial proximity. The accumulation growth rate model of grouped and ungrouped was respectively established. Taking the national ungrouped model as a reference, the five indicators including coefficient of determination(R²), root mean square error(RMSE), mean relative error(MRE), systematic error(SE)and residual standard deviation(S)were used as the evaluation indexes of the model to analyze the modeling results. Result: The top eight environmental factors those have great influences on the growth of C. lanceolata by random forest model analysis were bio4(standard deviation of seasonal variation of temperature), elevation, bio3(isothermality), bio8(the wettest quarterly mean temperature), bio1(annual mean temperature), bio14(the most dry month precipitation), bio12(annual mean precipitation), and bio2(monthly mean diurnal temperature variation). The results of the group analysis showed that when the research area were divided into 7 groups, the internal environment similarity within the group could be maximized, and the environmental similarity between groups was the smallest. As only four of the 7 groups had data of C. lanceolata plot, the growth rate model of C. lanceolata stands was established using two model formulas in these four groups. Compared with the ungrouped models, the determination coefficients of the grouping models were all increased by more than 0.1, indicating that the fitting degree of the grouping model was better. At the same time, the accuracy of grouping modeling was also significantly improved:RMSE was reduced by about 0.5, MRE was reduced by about 6%, SE was reduced by about 3%, and S was reduced by about 1. Conclusion: The growth model based on the grouping of C. lanceolata study areas according to the environmental similarity is a high-precision forecasting method for C. lanceolata growth nationwide. The model could be used to estimate the growth of C. lanceolata stand and to update the data of forest subcompartment. The proposed method might provide a new method for realizing large area growth prediction of main plantation species.

Objective: In this paper, the total phosphorus content of young Dalbergia odorifera leaves in per unit-mass was estimated by using the image understanding method with the tree image as the data source. Method: Firstly, the algorithm which was used to extract the canopy image of Dalbergia odorifera from the image is provided. Then the statistical model form and the effective image parameters used to estimate the total phosphorus content of leaves are constructed. Finally, the plant leaf total phosphorus content prediction model with image parameters as independent variables is established by using nonlinear mixed-effects model. Result: Based on the color difference between foreground and background, this paper proposes a simple method to extract canopy image with green rate. Through a large number of image tests, we know that it can effectively erase the background when the green rate is set between 0.35 and 0.42. Furthermore, we combined and analyzed the image parameters, and built the nutrient content estimation model. The leaf total phosphorus content prediction model is established, which takes the standardized gray value as indicators and adjusts them with the the warm data. The model can achieve a high precision estimation of the phosphorus content per unit weight of canopy leaves. At the same time, the random effects are introduced to the model parameter estimation, and the results show a good adaptability to the prediction of total phosphorus content of Dalbergia odorifera with different soil c onditions in different regions. Conclusion: The result indicate that the green rate is a good method for tree crown image segmentation and extraction when there exist a certain difference between the background and foreground. The two-image parameter model could effectively improve the estimation accuracy of total phosphorus content prediction. For the prediction of total phosphorus content in canopy leaves of Dalbergia odorata with different soils or environments in different regions, the mixed effect model integrates these differences into one model, and shows a strong adaptability.

Objective: The effects of thinning and mixed transformation of Larix principis-rupprechtii plantations in Saihanba on the communities and biodiversities of soil macro-fauna were comparatively investigated to provide a theoretical basis for the sustainable management of the regional plantations. Method: In this experiment, three types of forests, including a unmanaged larch pure forest (CK), a thinning larch forest (TH) and a larch-birch mixed forest (M), were targeted. From May to September, 2014, the soil macro-faunal community was investigated by hand picking method to study the differences of structure composition, spatial pattern, biodiversity and functional groups composition of soil fauna in different managed forests and their influencing factors. Result: A total of 1 405 soil macro-fauna were obtained and identified, and they belong to 2 phyla, 4 classes, 12 orders, 55 groups. The dominant groups were Lumbricina, Curculionidae larvae and Chrysomelidae larvae. In general, the group number of soil macro-fauna in TH and M was significantly higher than that in CK (P < 0.05), and the density of soil fauna in TH was also significantly higher than that in CK (P < 0.05). The group number of soil macro-fauna showed a gradually increasing trend along the soil profile in the three forests. The group number of soil fauna in litter layers and the mean density in 0-10 cm soil layer in M and TH were significantly higher than those in CK. In addition, thinning and mixed transformation improved the diversity indices of soil macro-fauna, and the Shannon-Wiener diversity index of M was significantly higher than that of CK (P < 0.05). Among functional groups, phytophagous had the highest density (47.19%), followed by saprophagous (33.31%). Predatory (19) and phytophagous (18) had more group numbers. The results of multivariate analysis (MANOVA) showed that the management measures, soil layers and seasons significantly influenced the four functional groups composition (P < 0.05); in particular, thinning and mixed transformation significantly increased the group numbers of phytophagous. The results of redundancy analysis (RDA) indicated that the distribution of soil macro-fauna in litter layer was significantly correlated with the organic carbon (P=0. 03) and C/N (P < =0.08). The soil macro-fauna in soil layers were significantly correlated with soil water content (P < =0.008), pH (P=0. 012) and soil bulk density (P=0. 062). Conclusion: After thinning and mixed management, the changes in stand structure and microenvironments in the forest enhanced soil macro-faunal community structure and biodiversity, especially the increase of group numbers of functional groups may be more conducive to the early pulverization and decomposition of litter.

Objective: Based on actual measurement biomass data and weighted average carbon content of the above-ground and below-ground components of Schima superba in Guangdong Province, this study established the above-ground and below-ground biomass models for individual tree. The carbon density and its uncertainty for S. superba were estimated at a regional-scale, which could provide a reference for the estimation of tree species carbon sinks at other regional-scale. Method: According to the inventory data of distribution of S. superba in Guangdong Province, the number of 90 trees of S. superba were cut down, the carbon content and biomass of the above-ground part were measured and the number of 40 trees among were selected to measure the carbon content and biomass of the below-ground part. The above-ground and below-ground biomass relative growth models were constructed respectively based on diameter at breast height (DBH). The model parameters were obtained by non-linear regression. Based on the 8^th National Forest Inventory data of Guangdong Province, Monte Carlo method was used to simulate the process of estimating the carbon density of S. superba components at the regional-scale. Used R-square, root-mean-square error and mean predicted error to evaluate the fitting individual tree biomass model effect. Regional-scale carbon density uncertainty were calculated by root-mean-square error and relative-root-mean-square error. Result: 1) The above-ground carbon content is 0.554 9 and the below-ground carbon content is 0.548 7 for S. superba in Guangdong Province; 2) The individual tree above-ground biomass model's R² is 0.909 8 and the below-ground biomass model's R² is 0.793 1, which showed that the biomass models fitted well and predicted accurately; 3) In the 8^th National Forest Inventory in Guangdong Province, the above-ground carbon density of S. superba was 5.80±0.44 t·hm^-2, uncertainty was 7.62%; the below-ground carbon density was 1.73±0.17 t·hm^-2, uncertainty was 9.76%; the total carbon density was 7.53±0.54 t·hm^-2, uncertainty was 7.23%. Conclusion: The above-ground part and the below-ground part carbon content of S. superba in Guangdong Province is higher than the average level in southern China, and it obviously has regional characteristics. The stable and reliable regional-scale estimates of carbon density and their uncertainty could be obtained by using Monte Carlo method.

Objective: This study aimed to explore the relative importance of negative density dependence and habitat filtering on tree seedling survivalin community, age class and species, so as to provide theoretical basis for the mechanisms of seedling regeneration and species diversity maintenance in a temperate forest. Method: During 2016 to 2018, 209 seedling census stations were monitored in the secondary mixed conifer and broad-leaved forest plot in Jiaohe, Jilin Province, northeast China. We used generalized linear mixed models including zero, biotic, habitat, and full model to assess the relative importance of the habitat factors and biotic neighborhood variables on tree seedling survival at the levels of community, age classes and species. Result: From 2016 to 2018, a total of 3 970 tree seedlings belonged to 13 genera, 10 families and 19 species were recorded. In order of the important values, the top five seedling species were Fraxinus mandschurica, Tilia amurensis, Acer mono, Pinus koraiensis, and Acer mandshuricum. At the end of the census year, 2 644 seedlings had died, with a survival rate of 33.4%. At the community level, the survival of seedlings was significantly correlated with both biotic neighborhoods and habitat factors. Specifically, the survival of seedlings was positively correlated with the number of heterospecific seedling neighbors, the soil available potassium and total phosphorus(P < 0.05). The response of seedling survival to biotic neighborhoods and habitat factors varied among different age groups. The survival of annual seedlings was positively correlated with their conspecificand heterospecific seedling neighbors(P < 0.05, P < 0.01, respectively), but negatively correlated with heterospecific adult neighbors(P < 0.05). For perennial seedlings, however, there is no significant correlation between the seedling survival and the biotic neighborhoods. Nevertheless, we found a significant positive correlation between perennial seedling survival and habitat factors(P < 0.05), such as soil available potassium and total phosphorusty. Individual species-level analyses indicated that the optimal seedling survival models varied among species. We also found that the response of seedling survival to biotic neighborhoods and habitat factors varied widely among species. Specifically, the Fraxinus mandshurica seedlings survival was positively correlated with heterospecific seedling neighbors(P < 0.05). The survival of Acer mono seedlings was negatively correlated with conspecific adult neighbors(P < 0.05). Tilia amurensis had a higher survival rate in habitats with higher soil available potassium. Pinus koraiensis seedlings, however, preferred to soil with lower available nitrogen and phosphorus. Conclusion: We provided strong evidence that both density dependence and habitat filtering affect tree seedling survival and their relative importance varied with age classes and seedling species. The survival of annual seedlings was mainly affected by biotic neighbors, however the effect of habitat factors was more significant on the survival of perennial seedlings. Our result indicated that the effect of negative density dependence was verified in our forest plot. Among the habitat factors, soil nutrient had a significant effect on seedling survival.According to the differences of biotic neighborhood and habitat effects on seedling survival among species and age classes, corresponding forest managements are needed in seedling raising and replantation of the main tree species.

Objective: The stem biomass of moso bamboo (Phyllostachys edulis) were accurately measured in sample plots. Proper prediction variables and models were determined on the basis of establishment and comparison among different biomass models with different variables. And the research is carried out to accurately estimate the stem biomass and provide a theoretical basis for the site quality assessment and efficient cultivation for bamboo forest in Zhejiang Province. Method: Firstly, mensuration of 216 sample bamboos harvested from 10 counties that distributed in eastern, southern, western, northern, and central part of Zhejiang Province was carried out. Secondly, the diameter at breast height (D), bamboo age(A), and internode length of bamboo at breast height (L) were introduced. Three different stem biomass models were fitted based on the allometric growth equations and all the sample information. Then, the model fitting method was selected by error structure that decided by the likelihood analysis. Finally, the most suitable stem biomass model was determined on the basis and analysis of the fitting goodness and prediction accuracy of the three different bamboo stem biomass models. Result: The moisture content of bamboo stem decreased with years and the mean water content at the age of degree Ⅴ was 24% lower than that at degree Ⅰ. While bamboo stem biomass accounted for an increase in the proportion of above-ground biomass year by year and that at degree Ⅴ was more than 80%. The error structure of biomass models was determined to be multiplicative based on the likelihood analysis, thus the log-transformed model for fitting was required. 3) Upon accuracy inspection, the coefficient of determination (R_a²) for model (M₁) W=0.104 6D^{2.257 8} was 0.774 2, lower than that of the binary model (M₂)W=0.052 0D^{2.205 2}A^0.4457 based on D-A and the trigram model (M₃) W=0.026 5D^{2.143 9}A^{0.449 5}L^{0.262 9} based on D-A-L, whose value were up to 0.89. Meanwhile, the standard error of the estimate (SEE) and the mean absolute error (MAE) of model (M₃)were the minimum. The three log-transformed models predicted well among different diameter classes as the prediction error were all close to 0. Over all the model M₃ performed optimally among different classes. Conclusion: This study conducts the anti-log transformation of the log-transformed model without correction, for it can reduce the prediction accuracy. The binary and trigram models perform better than the unary model in the fitting goodness and prediction accuracy. Thus the optimum model is W=0.026 5D^{2.143 9}A^{0.449 5}L^{0.262 9}, based on the variable D-A-L.