Implications for BEFs: BEFs across biodiversity and spatial scales
BEF relationships have historically used species richness to quantify the diversity of communities (Hooper et al.2005). However, alternative approaches have emerged and improved our understanding of BEFs. In particular, phylogenetic diversity and/or functional traits diversity have been used as alternative measures of community diversity (e.g., Cadotteet al. 2012; Le Bagousse-Pinguet et al. 2019). Functional (trait) diversity has improved mechanistic inferences and revealed the causal mechanisms underlying BEFs (Norberg et al.2001; Cadotte et al. 2011). The use of phylogenetic diversity metrics has permitted capturing macro-evolutionary processes shaping community assemblages, and therefore the evolution of niche complementarity among species (Cadotte et al.2012; Mouquet et al. 2012). The use of PCCGs to estimate community diversity has the potential to encompass most aspects of the phylogenetic and functional approaches because PCCGs are intrinsically related to functional traits, and they are directly influenced by evolutionary processes. By aggregating both the functional and evolutionary components of diversity, we anticipate using PCCGs for studying BEF relationships may reveal novel causal processes and may improve the general fit of BEF relationships.
Most studies having used functional and phylogenetic diversity failed to integrate the intraspecific component of diversity (Mouquet et al. 2012). The approach we propose here intrinsically includes both the intra- and interspecific facets of biodiversity, which is -in our opinion- an important step forward given that intraspecific diversity can affect ecosystem functions as much as interspecific diversity (Raffard et al.2019; BOX 1). The few experimental works having simultaneously manipulated the two facets of diversity revealed relevant insights (e.g., Fridley & Grime 2010; Hargrave et al. 2011). In particular, they demonstrated that the relative effect of intra- vs . interspecific diversity was dependent upon the considered function. For instance, intraspecific diversity improved the temporal stability of biomass production in plant populations, whereas species richness improved the mean biomass production of the same community (Prieto et al.2015). This suggests an ecological complementarity between intra- and interspecific diversity that can not be revealed if only one of them is considered. While valuable, these studies failed to reproduce the continuum naturally occuring in nature, which would be overcome using the PCCGs approach that quantifies the two facets of biodiversity. This is an essential step for better understanding this potential complementarity along the intra-interspecific biodiversity continuum.
In addition, more specific -yet unresolved- questions might be addressed using the PCCGs approach. For instance, ecosystem functions generally display high variability among monocultures, which has often been explained by the intrinsic efficiency of a species to perform a specific function (Huston 1997). The performance of a species in monoculture is likely determined -amongst others- by its intraspecific diversity that can be revealed using PCCGs (Figure 4b). Species with higher performance should be more diversified, as expected if genetic complementarity (or the presence of particular genetic variants in that species) is linked to species performance (Hugheset al. 2008). Moreover, as a consequence of these differences in monocultures’ productions, species-rich communities might show high performances solely because of the presence of the most performant species (sampling effect, see BOX 1). Although this sampling effect has long been debated (Loreau 1998), assessing BEF relationships using PCCGs diversity -rather than diversity metrics at the species level- might reveal underlying mechanisms. For instance, understanding whether communities containing a high-performance species increase the rate of the target function because they contain the species per se , or because containing this species increases substantially PCCGs diversity (Figure 4c). By accounting for intra- and inter-specific diversity, PCCGs quantifies the “true” diversity present in the community, and allows forecasting ecosystem functions based on biodiversity with finer precision.
More generally, PCCGs diversity can reveal different patterns of biodiversity, and for instance communities with the same species richness might actually encompass different levels of PCCGs diversity (Figure 4c), and an apparently poor community might be as diverse as a community with many species if the former has a high intraspecific diversity for each species (compensation effect). Therefore, important questions regarding the spatial and the temporal heterogeneity of biodiversity can be addressed using PCCGs diversity as a continuous and realistic metric. This is particularly interesting when comparing, for example, the ecological efficiency (in terms of functions) of communities from different biomes. For instance, communities in tropical areas exhibit higher species diversity than communities at higher latitude, whereas they may exhibit lower intraspecific diversity than communities at higher latitude (although not necessarily true, De Kort et al. 2021), and vice versa . We can hypothesise that communities at higher latitudes mainly rely on intraspecific diversity and complementarity among individuals within populations -rather than on complementarity among species- to use and transform energy efficiently (Hughes et al. 2008). Comparing the strength and form of BEFs among contrasted biomes of this type is complicated using traditional approaches, whereas it becomes possible using the PCCGs approach because it relies on a single universal metric. This is essential for scaling-up BEF relationships to local from global scales (Gonzalezet al. 2020).
To sum up, PCCGs have the potential to be an inclusive measure of biodiversity tackling pending questions on BEF relationships. Assessing the ecological effects of diversity of communities through genes underlying ecologically-important traits, also permits rooting BEF relationships into an (eco-)evolutionary framework, which we discuss in the next section.