Figure captions
Figure 1 . Conceptual diagram showing how an inclusive
quantification of biodiversity from phylogenetically-conserved candidate
genes (PCCGs) allows to move to an integrative view of
biodiversity-function (BEF) relationships (i), and to embrace a
community-based perspective of eco-evolutionary dynamics (ii). Our
concept is based on the idea to merge the fields of functional ecology
(a) and functional biology and genetics (b) to simultaneously quantify
the intra- and interspecific diversity components of focal communities
through PCCGs diversity. PCCGs should be selected so as to be variables
both intra- and interspecifically and to sustain for ecologically
important traits.
Figure 2. General framework describing the main steps to reveal
phylogenetically-conserved candidate genes (PCCGs) diversity within
focal communities. (a) This starts by defining appropriate focal
communities (two examples here within a river ecosystem; leaves from
riparian trees and crustaceans decomposing these leaves) and sampling
the biological diversity of the focal communities, both within and
between species (here two specie per community and two genotypes, large
and small, per species). The total DNA of this focal community is
extracted so as to represent both intra- and interspecific diversity.
(b) PCCGs are identified bioinformatically from existing literature (on
functional genes) and available genomic resources. The selected genes (a
hundred to a thousand of sequences) are sequenced for each focal
community separately. (c-e) Once the raw sequence data are obtained,
inclusive biodiversity can be quantified from PCCGs for each focal
community, it can be analysed spatially and/or temporally to search for
underlying eco-evo processes, and it can be linked (either
experimentally or empirically) to ecological processes so as to reveal
feedbacks between ecological and evolutionary dynamics occurring at the
community level.
Figure 3. Diagram illustrating the four main steps to select
phylogenetically conserved candidate genes (PCCGs) from a focal
community. The diagram builds on a concrete example involving the search
of PCCGs for a community of detritivorous freshwater crustaceans. (a) A
first step consists in defining the ecological process and the focal
community to target, as well as identifying the closest reference genome
to the focal community, and the traits associated to the ecological
process and focal community. (b) A second step aims at finding the
appropriate genes associated with the selected traits from the available
literature. (c) In a third step, the sequences associated with these
genes are acquired directly from articles or from the National Center
for Biotechnology Information (NCBI) database. Here, the gene sequences
were identified from NCBI by focusing on annotated genomes of Amphipoda.
(d) A final step uses a local base alignment search tool (BLAST) to
retrieve the sequence on the genome of reference(s) species. As genes
can not be targeted over their entire sequences, exons and/or promoter
regions are generally selected for the final panel of PCCGs. This final
PCCGs panel will serve as the basis for the design of the probes and for
the hybridization-based capture sequencing. DNA strand vector come fromwww.svgrepo.com.
Figure 4. The relationship between biodiversity (measured as
the number of species per local community) and ecosystem functioning
classically follows a saturating shape (a). The high variability
observed among monoculture (grey area in (a)) may be attributed to
variation in (intraspecific) PCCGs diversity within species (b). A PCCGs
approach may allow illuminating variation that is generally overlooked
in classical BEF relationships. Similarly, pluricultures (blue area in
(a)) may differ in their PCCGs diversity regardless of the number of
species (c). Eventually, this might allow forecasting ecosystem
functions more accurately, which might for instance change in the shape
of the BEF relationship and/or a higher predictive power (d).