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.