Functional diversity has become an important descriptor of species assemblages in two research areas. Firstly, it has been seen as an indicator of processes governing community assembly (Cornwell, Schwilk & Ackerly 2006), and one that can show the impact of perturbations (Villéger, Mason & Mouillot 2008) or identify ecological gradients (Mouillot, Dumay & Tomasini 2007). It has been predicted that increased productivity will lead to convergence in traits, whilst disturbance may increase functional diversity (Grime 2006). There is evidence for convergence of some traits, especially at high productivities (Cornwell & Ackerly 2009; Pakeman, Lennon & Brooker in press), but there is also limited evidence for divergence in others (Cornwell & Ackerly 2009) and no evidence of either convergence or divergence in other studies (Schamp, Chau & Aarssen 2008).
Secondly, functional diversity has also been suggested as an indicator of ecosystem function (Díaz & Cabido 2001; Petchey, Hector & Gaston 2004). For instance, it has been shown to be correlated to productivity, though it was outperformed by other parameters (Cadotte et al. 2009). Also, in a study of the relationship between ecosystem processes and functional diversity, community-weighted mean traits and abiotic factors, functional diversity was often well correlated to the processes examined (Díaz et al. 2007). However, in no case was functional diversity kept in the best predictive model, indicating that variation in this metric often covaries with variation in other metrics or the environment.
However, there have been many different metrics of functional diversity that have been developed. The earliest were simple measures of functional group number, but these have progressively been replaced by the ones that take into account functional traits and species abundance together in a robust theoretical framework (e.g. Mason et al. 2005; Cornwell, Schwilk & Ackerly 2006; Villéger, Mason & Mouillot 2008). This is because ideal measures of functional diversity take into account abundance and use multiple traits to acknowledge linkages between them. They are also not trivially related to species richness and rely on original data rather than data transformed through classification or ordination processes (Villéger, Mason & Mouillot 2008). Functional diversity has now been decomposed into three parts, Functional Richness (FRic), Functional Evenness (FEve) and Functional Divergence (FDiv), that each measure different aspects of the diversity of functional traits within a community (Mason et al. 2005; Villéger, Mason & Mouillot 2008). The utility of these measures has been assessed by Mouchet et al. (2010) against other candidate diversity indices and each performed best in measuring their individual aspects of functional diversity: richness, evenness and divergence.
Despite the theoretical basis behind these measures of functional diversity being sound, there has been little testing of them with field data and so there is little knowledge of how they may respond to different environmental gradients. This knowledge of how they relate to the environment is necessary to assess their utility as measures of both community assembly and ecosystem function. Theoretical expectations include negative departures from expectation revealing habitat filtering whilst positive ones reveal neutral assembly rules (Mouchet et al. 2010). Also, the dynamics of functional diversity against species diversity have been used to set up a framework to assess how land use change is impacting on plant communities (Mayfield et al. 2010). Field assessments of functional diversity reveal a number of patterns. Plots of tropical trees appear to be more functionally diverse (larger range in traits) than expected (Kraft, Valencia & Ackerly 2008), although measures of diversity were not tested against environmental gradients. Expectations covering FRic are more common than for the other measures; early work suggested it should increase linearly with species richness (Díaz & Cabido 2001), but other predictions include a monotonic increase with species richness (Mayfield et al. 2005). In a study of lake fish assemblages (Mason et al. 2008) there was, however, a ‘hump-backed’ relationship between both FRic and FDiv versus species richness, and a power relationship between FEve and species richness. A similar relationship was demonstrated for estuarine fishes (Villéger et al. 2010) – it appears that if conditions are conducive for high species richness then assemblages are characterised by many redundant species. Similarly, a study aimed specifically at analysing the response of the functional diversity of plants to disturbance showed peaks in richness at intermediate levels of disturbance (Biswas & Mallik 2010). The Mason et al. (2008) study of lake fish assemblages also showed hump-backed relationships between both FRic and FDiv with temperature, and a linear one between FEve and temperature. Their interpretation was that increased temperature may have permitted increased species richness by allowing increased niche specialization.
To further develop the utility of these metrics this paper tests the following questions for a set of plant assemblages monitored in the field: (i) Are the chosen measures of functional diversity independent of one another, as indicated by tests with artificial data, and of species richness? (ii) Can the functional diversity indices identify the operation of ecological processes, such as the operation of assembly rules, and whether these processes are modulated by environmental disturbance and productivity?