β-diversity, coined as species turnover among communities, has been used for many years as a successful way to investigate biodiversity patterns and species assembly rules. However, most of previously studies have only focused on species composition without considering differences in species biology and/or abundances. We aim going one step further by measuring the functional turnover among communities, through the use of the Rao’s quadratic entropy index. Indeed, this index allows partitioning functional diversity into α-(within-site) and β-(among-site) components while including species relative abundances and functional distances among species. We applied this functional diversity partitioning on a large dataset of tropical estuarine fish communities from the Terminos lagoon region (south of
Results/Conclusions
Species turnover was high both on spatial and temporal scales (around 0.8 for the taxonomic turn-over according to Lande’s index) whereas functional turnover was low on both scales and for the two functions (around 0.3). Moreover, when compared to a null model, which assumes a random assignation of traits values to observed abundances, functional turnover was lower than expected by chance. Besides, species clustering into functional groups for each function revealed that a couple of groups strongly dominated fish assemblages. Consequently, when species belonging to these two dominant functional groups were removed, functional β-diversity values were no longer significantly different than expected by chance. These results clearly indicate that despite a high species turnover, some core functional groups stabilize the functional structure of local communities, thus maintaining a low functional β-diversity through space and time in a highly variable environment. We believe that this new framework will be helpful in community ecology to go further than exploring only turnover in species composition. For instance, functional β-diversity used in parallel with taxonomic and phylogenetic β-diversities will help to analyze macroecological patterns and may contribute to disentangling the effects of dispersal, niche filtering and competitive interactions in communities.