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.