Non-equilibrium dynamics and floral trait interactions shape extant angiosperm diversity

Abstract

Why are some traits and trait combinations exceptionally common across the tree of life, whereas others are vanishingly rare? The distribution of trait diversity across a clade at any time depends on the ancestral state of the clade, the rate at which new phenotypes evolve, the differences in speciation and extinction rates across lineages, and whether an equilibrium has been reached. Here we examine the role of transition rates, differential diversification (speciation minus extinction) and non-equilibrium dynamics on the evolutionary history of angiosperms, a clade well known for the abundance of some trait combinations and the rarity of others. Our analysis reveals that three character states (corolla present, bilateral symmetry, reduced stamen number) act synergistically as a key innovation, doubling diversification rates for lineages in which this combination occurs. However, this combination is currently less common than predicted at equilibrium because the individual characters evolve infrequently. Simulations suggest that angiosperms will remain far from the equilibrium frequencies of character states well into the future. Such non-equilibrium dynamics may be common when major innovations evolve rarely, allowing lineages with ancestral forms to persist, and even outnumber those with diversification-enhancing states, for tens of millions of years.

Keywords: diversification; floral evolution; flower symmetry; macroevolution; non-equilibrium; pollination.

Figure 1.

Simplification of MuSSE model by partitioning into sets. The diagram depicts a network of eight states, which are combinations of three binary (0/1) characters. Thus, ‘000’ is the combination with state 0 for each of the three characters. Each combination is connected to three other combinations that differ by just one state. A full MuSSE analysis would involve estimating transition rates (black arrows) along each of the edges of this network as well as speciation and extinction rates (not shown) for each combination. We simplified this network by dividing it into two sets (e.g. the black ‘A’ set and the grey ‘B’ set in the middle). We can then consider rates of transition within the sets (q_AA, q_BB) and between the sets (here, q_AB = q_BA). This can be represented as a matrix (right), and if we assume a single rate within each set plus a rate of moving between sets, the model includes just three transition rate parameters. We could also assume a single speciation and an extinction rate for each set (not shown), which would bring the model to seven parameters. These assumptions can be relaxed to create additional more complex models, but which are still less parameter-rich than the full model.

Figure 2.

Non-equilibrium dynamics and the evolution of the floral trait combinations. (a) Maximum-likelihood reconstructions of the history of the three key character combination using model-averaged rates from the MuSSE analysis. Repeated origins of the key combination are shown in red. All other combinations are shown in black or grey lines, with the latter indicating uncertainty in the reconstruction of the subtending node. (b) The proportion of taxa with each possible combination of states for the three characters that comprise the key combination (corolla presence/absence, symmetry, and relative stamen number) in the observed (empirical) dataset (grey), in stochastic simulations to the present (blue), in simulations extending 15 Myr into the future (green), and at equilibrium (black). Ancestral states are shown in black and derived states in purple (see also the electronic supplementary material, figure S1), and the key combination is highlighted in yellow. (c) Mean (±95% CI) time of appearance of each of the eight trait combinations defined in (b) during the course of simulations. Flower cartoons show states of perianth (white, corolla; green, calyx), symmetry and stamen number.