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Questions: Increasingly, studies have demonstrated that not only environmental factors but also socioeconomic factors are important in determining species richness patterns. However, few studies have explored their effects on beta diversity. Here, we aimed to answer two questions based on woody species composition in a number of geographical administrative divisions: (1) how does beta diversity change with socioeconomic and environmental factors; and (2) what is the relative importance of socioeconomic and environmental factors in driving beta diversity of woody species? Location: Eastern China. Methods: We used abundance‐based beta diversity (Bray–Curtis) and species accumulation rate to quantify beta diversity of woody species of 20 counties in eastern China. Linear regression models were used to test the relationships between beta diversity and socioeconomic and environmental factors. The relative importance of socioeconomic and environmental factors in driving beta diversity was investigated using the variation partitioning method. Results: We found that elevation, annual precipitation, and location significantly affected species composition. Species accumulation rate increased significantly with Gross Domestic Product (GDP), GDP per capita and human population density, and decreased with forest cover, topographic and climatic heterogeneity. In addition, abundance‐based beta diversity increased significantly with human population density. Moreover, socioeconomic factors, topographic and climatic heterogeneity independently explained 25%, 15%, and 1% of abundance‐based beta diversity separately. Conclusion: Socioeconomic factors may have significant effects on beta diversity patterns of woody plants. Our study highlights the urgent need to consider socioeconomic factors when exploring large‐scale biodiversity patterns in human‐dominated landscapes and formulating related forest management strategies. [ABSTRACT FROM AUTHOR]

Non-commercial forests represent important habitats for the maintenance of biodiversity and ecosystem function in China, yet no studies have explored the patterns and determinants of plant biodiversity in these human dominated landscapes. Here we test the influence of (1) forest type (pine, mixed, and broad-leaved), (2) disturbance history, and (3) environmental factors, on tree species richness and composition in 600 study plots in eastern China. In total, we found 143 species in 53 families of woody plants, with a number of species rare and endemic in the study region. Species richness in mixed forest and broad-leaved forest was higher than that in pine forest, and was higher in forests with less disturbance. Species composition was influenced by environment factors in different ways in different forest types, with important variables including elevation, soil depth and aspect. Surprisingly, we found little effect of forest age after disturbance on species composition. Most non-commercial forests in this region are dominated by species poor pine forests and mixed young forests. As such, our results highlight the importance of broad-leaved forests for regional plant biodiversity conservation. To increase the representation of broad-leaved non-commercial forests, specific management practices such as thinning of pine trees could be undertaken. [ABSTRACT FROM AUTHOR]

In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research. [ABSTRACT FROM AUTHOR]

Abstract: This study has analyzed TRIPLEX1.0 by applying the model to the subtropical forest regions in Zhejiang Province, southeastern China. The main objective was to test the process-based hybrid model TRIPLEX1.0 in simulating density, tree height (H), diameter at breast height (DBH), litter pool and biomass using forest growth and yield data collected from three forest types: subtropical evergreen broad-leaved, coniferous broad-leaved mixed and warm temperate pine (Pinus massoniana Lamb.) forests. The results show that simulated density, H, DBH, litter pool, aboveground and total biomass are consistent with observed data collected through Zhejiang Province, suggesting that the TRIPLEX1.0 model is capable in simulating forest growth and biomass dynamics of subtropical forest ecosystems. The coefficient of determination (r 2) between simulated values and yield measurements show a 0.91 variability for density, 0.86 for DBH, 0.83 for H, 0.89 for aboveground biomass and 0.91 for total biomass (except for litter pool that showed a 0.54 variability). The independent validations obtained by utilizing TRIPLEX1.0 demonstrate that the model offers competency while providing confidence when applying its ability to extrapolate outcomes at regional scales and its ability to withstand rigorous testing for simulating carbon storage in subtropical forest ecosystems. [Copyright &y& Elsevier]