The drawdown zone of the Three Gorges Reservoir Region was assumed to be completely formed in 2009 and the water level would range from *145 m in flood season (summer) to *175 m during non-flood season (winter). The soil seed bank is an important propagule source for vegetation restoration. In order to evaluate the potential of the soil seed bank to revegetate the drawdown zone of this region, we examined the quantitative relationships between the germinable soil seed bank and the established vertical and horizontal vegetation patterns. A total of 45 soil samples at four sites was collected to examine seed bank density, species richness, and composition using the seedling-emergence method. Forty-five species (from 20 families) germinated from the soil seed bank, and the average seed density was 4578 m -2 . The seed bank was dominated by annual plants, suggesting reestablishment of some above-ground species was plausible. However, most established woody plants and perennials were absent from the seed bank indicating a low probability of reestablishment for non-annuals through the seed bank. Thus, due to low species compositional similarity to extant vegetation and the dominance of annual plants, the soil seed bank had a low potential to restore pre-dam vegetation in the drawdown zone of the Three Gorges Reservoir Region, but its potential as a propagule source should be considered regarding the management of the drawdown zone for vegetation cover.
Niche and neutral theories emphasize different processes contributing to the maintenance of species diversity. In this study, we calculated the local contribution to beta diversity (LCBD) of every cell, using variation partitioning in combination with spatial distance and environmental variables of the 25-ha Badagongshan plot (BDGS), to determine the contribution of environmentally-related variation versus pure spatial variation. We used topography and soil characteristics as environmental variables, distance-based Moran’s eigenvectors maps (dbMEM) to describe spatial relationships among cells and redundancy analysis (RDA) to apportion the variation in beta diversity into three components: pure environmental, spatially-structured environmental, and pure spatial. Results showed LCBD values were negatively related to number of common species and positively related to number of rare species. Environment and space jointly explained ~60% of the variation in species composition; soil variables alone explained 21.6%, slightly more than the topographic variables that explained 15.7%; topography and soil together explained 27%, slightly inferior to spatial variables that explained 34%. The BDGS forest was controlled both by the spatial and environmental variables, and the results were consistent across different life forms and life stages.
3D shape retrieval has attracted much attention and become a hot topic in computer vision field recently.With the development of deep learning, 3D shape retrieval has also made great progress and many view-based methods have been introduced in recent years. However, how to represent 3D shapes better is still a challenging problem. At the same time, the intrinsic hierarchical associations among views still have not been well utilized. In order to tackle these problems, in this paper, we propose a multi-loop-view convolutional neural network (MLVCNN) framework for 3D shape retrieval. In this method, multiple groups of views are extracted from different loop directions first. Given these multiple loop views, the proposed MLVCNN framework introduces a hierarchical view-loop-shape architecture, i.e., the view level, the loop level, and the shape level, to conduct 3D shape representation from different scales. In the view-level, a convolutional neural network is first trained to extract view features. Then, the proposed Loop Normalization and LSTM are utilized for each loop of view to generate the loop-level features, which considering the intrinsic associations of the different views in the same loop. Finally, all the loop-level descriptors are combined into a shape-level descriptor for 3D shape representation, which is used for 3D shape retrieval. Our proposed method has been evaluated on the public 3D shape benchmark, i.e., ModelNet40. Experiments and comparisons with the state-of-the-art methods show that the proposed MLVCNN method can achieve significant performance improvement on 3D shape retrieval tasks. Our MLVCNN outperforms the state-of-the-art methods by the mAP of 4.84% in 3D shape retrieval task. We have also evaluated the performance of the proposed method on the 3D shape classification task where MLVCNN also achieves superior performance compared with recent methods.
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