Concluding remarks
The framework we propose here provides a novel perspective to quantify biodiversity, which may allow breaking the historical boundary between the intra- and interspecific facets of diversity. We hope that we have been convincing enough in demonstrating that using this novel framework has the potential to substantially change our ability to understand the reciprocal links between environmental changes, biodiversity and ecosystem dynamics. There has been previous attempts to break this boundary (e.g., Vellend 2005; Gaggiotti et al. 2018; Start & Gilbert 2019), but our approach differs from previous ones in that it is rooted on the idea of a single biodiversity unit that goes beyond the species concept, that is directly affected by demographic and evolutionary processes and that putatively affects ecological processes. This approach is somewhat similar to that used by microbiologists (e.g., Konopka 2009; Burke et al. 2011; Morris et al. 2019) that uses molecular markers to characterise bacterial communities, mainly because specifically naming bacteria is an unresolvable and irrelevant issue. Our approach is also “agnostic” (sensu Morriset al. 2019) in that this is not species that matters anymore, but candidate gene frequencies at the community level (whatever the species that carry the genes), which in a certain sense join the neutral perspective developed by Hubbell (2001).
The PCCGs approach is based on the sum of data and knowledge acquired in the last decades from functional biologists and geneticists. Contrary to recent perspectives (Rudman et al.2018; Skovmand et al. 2018), we do not aim to search for “new” candidate genes with extremely strong ecological effects (Skovmand et al. 2018). Although this quest for keystone genes is valuable and necessary, we rather believe that novel insights can emerge by merging previous findings from research fields that are yet poorly connected. Moreover (and more pragmatically), we are in an Era in which all of us must be aware about our energy consumption. Looking for novel candidate genes in a few species is costly energetically given that this requires sequencing entire genomes, archiving these data, and long bioinformatic runs. The PCCGs approach is based on sequences that already exist and that represents relatively short sequence lengths to reveal (portions of 500 hundreds PCCGs represents ~200000 bp, which is a tiny portion of entire genomes that are often billions bp each, Figure 4), and hence much less energy consumption overall. This is even more evident when pool-seq approaches are used (see above) as tens of communities can be sequenced on a single lane. Of course, our a priori approach is not without limitations, and it is evident that important genes will be missed, whereas they would have been revealed from a keystone gene approach. Both approaches are therefore valuable and should be pursued. But we underline that controlling the energy we consume for Science should also be our collective responsibility.
Another limit of the PCCGs approach is that it only focuses on genes that code for important ecological traits, while ignoring functional trait variability observed in the wild. The main implication is that the environmental component of trait variability is missed. There has been some attempts to link traits measured at the community level (including or not intraspecific variability) and ecological processes and functions (e.g., Le Bagousse-Pinguet et al. 2019; Start & Gilbert 2019), and we fully acknowledge that this is certainly an excellent way to illuminate mechanistic pathways (Norberg et al.2001). Nonetheless, traits can be tricky to estimate, especially for animals when trait measurements need to be done under laboratory conditions (e.g. , behavioural traits), which can bias estimations. Moreover, some important traits may be missed while being captured by genetic diversity of populations of communities (“ghost” traits); for instance, Raffard et al . (2021) found that both traits diversity and genetic diversity in fish populations were complementary for explaining a series of ecological processes. Because the PCCGs approach is based on a large number of genes, this “missing” information may be limited. Finally, for eco-evolutionary dynamics, what matters is the information that is transmitted across generations (De Meester et al. 2019), and the environment is rarely (but see Danchinet al. 2011) transmitted across generations. Focusing directly on the genes that potentially sustain trait variation therefore allows for a better integration of biodiversity into the framework of eco-evolutionary dynamics.
To conclude, we suspect that the approach we described here has many implications that actually goes beyond BEF relationships and eco-evolutionary dynamics (e.g. , conservation biology), and that could be discussed elsewhere and after some proof-of-concepts have emerged. Reducing the complexity of natural communities to candidate gene frequencies will likely ease the links between theories and empirical observations, as the theory generally begins by simplifying premises (Loreau 1998; Norberg et al. 2001; Govaert et al. 2019). We now hope that empiricists and theoreticians will be convinced enough that future works integrating PCCGs will soon emerge.