Beta diversity describes the variation in species composition among communities within a region and it is determined by two antithetic processes: species turnover (or species replacement), and nestedness (or difference in richness). Beta-diversity partitioning aims to separate these two processes when examining species composition among communities, and to reveal their underlying mechanisms. Since 2010, the partitioning methods were proposed following two dominant frameworks: the BAS method proposed by Andrés Baselga in 2010 (partitioning overall beta diversity into turnover and nestedness components) and the POD method proposed by János Podani and Dénes Schmera in 2011 and José C. Carvalho et al. in 2012 (partitioning overall beta diversity into species replacement and richness difference components). With the continuous debate on the nature of the BAS and POD methods, studies on beta-diversity partitioning have developed rapidly worldwide. We reviewed journal articles in the field of beta-diversity partitioning since 2010. Results showed that the number of publications and citations using the BAS method were greater than those using the POD method (75% vs. 20%). In those publications, most of study sites were located in Europe (45%) and research taxa were dominated by animals (64%). Here, we introduce the history and development of beta-diversity partitioning, potential applications in studying biodiversity distributions across spatial-temporal scales (latitudinal/altitudinal gradients, habitat fragmentation, seasonal and annual dynamics), multiple-faceted diversity (taxonomic, functional and phylogenetic diversity), and comparisons among various biological taxa. We point out the following directions in the field of beta-diversity partitioning in the future: (1) the synthesis and comparative analysis of the methods of beta-diversity partitioning; (2) examining patterns of overall beta diversity and its components by incorporating species abundance; and (3) testing the generality of results yielded from beta-diversity partitioning across large scales.