One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.

In recent years, spatial applications have become more and more important in both scientific research and industry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the stateof-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a twotier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an "indexing + MapReduce" data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness.

In this paper, we present an approach to construct a built-in block-based hierarchical index structures, like Rtree, to organize data sets in one, two, or higher dimensional space and improve the query performance towards the common query types (e.g., point query, range query) on Hadoop distributed file system (HDFS). The query response time for data sets that are stored in HDFS can be significantly reduced by avoiding exhaustive search on the corresponding data sets in the presence of index structures. The basic idea is to adopt the conventional hierarchical structure to HDFS, and several issues, including index organization, index node size, buffer management, and data transfer protocol, are considered to reduce the query response time and data transfer overhead through network. Experimental evaluation demonstrates that the built-in index structure can efficiently improve query performance, and serve as cornerstones for structured or semistructured data management.