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).