Phenotypic landscape inference reveals multiple evolutionary paths to C₄ photosynthesis

1) A strength of this work is that the conclusions emerge from an inference framework that is much more convincing than when traditional (qualitative and argumentative) approaches are used. Specifically, the methodology unveiled here is both quantitative and “objective”. However, the authors need to be more explicit about the level of “objectiveness” of their work. Indeed, they present a framework where choices had to be made and the reader cannot know whether attempts with other choices were less conclusive. To address this issue, the authors have to demonstrate that their conclusions are robust to choices made within their modeling framework. Two specific tests should be performed:

1A) First, use a structural change to your framework: instead of the 16 traits you have selected, remove say 2 of these traits, and if possible include 2 others.

We performed three additional analyses. In the first two, we removed two randomly selected independent pairs of traits (Figure 3—figure supplement 2A and B). Neither the predicted timing of the remaining 14 traits nor our main conclusions about C₄ evolution were affected. Thirdly, we repeated the analysis including data for two additional traits associated with C₄ evolution (Figure 3—figure supplement 3C). Removing these traits from the analysis did not alter the predicted order of trait acquisition compared with the initial analysis, or the conclusions that we draw from these predictions.

1B) Second, you treat quantitative traits using EM and represent these by a binary value (presence vs absence). Sometimes the assignment will not be clear-cut; could you then use the other assignment (which is nearly as justified)?

To test this, we repeated the analysis with presence and absence scores assigned by hierarchical clustering as opposed to EM. Hierarchical clustering generated alternative binary values to EM for all the traits where assignment was not clear-cut. Despite this, extremely similar predictions were generated and the conclusions we draw about C₄ evolution were the same. We include the results of this analysis in a new supplement (Figure 3—figure supplement 1). We present both the data from hierarchical-clustered data on its own, as well as a comparison with posterior probabilities obtained using data clustered by EM. These results suggest that the data points whose assignment is not clear-cut do not strongly affect our conclusions.

An even simpler approach would have been to simply put a threshold (common for all) bypassing any EM (you do this for some of your traits). Did you first try that but not succeed? It is okay to be honest about the potential weaknesses of the conclusions given that the data is limited.

We note that both the EM algorithm and hierarchical clustering assign thresholds based on the distribution of data available. We did not try assigning thresholds of our own definition to these quantitative traits at any stage, but rather preferred to use clustering by statistical methods such as EM so as to minimize bias in assigning presence/absence scores. We only assigned our own thresholds to data for which clustering by EM was not possible (i.e., when traits were measured qualitatively, or too few data points were available).

We have integrated these new supplements associated with points 1A&B above into the main article.

2) To be clear to a broad audience, please be explicit about how you define a plant ‘lineage’ as used in your analysis. Your text describes that you have analysed data from 18 lineages. Reference is made to ‘taxonomic lineage’ but in Table 1 there are 12 families and 22 genera, neither of which tallies with the number 18. Also, please double check the species numbers: Table 1 appears to list 73 species. In the text you refer to 18 C₃, 17 C₄ and 37 C₃-C₄ intermediates, which totals 72 species. So, is there an extra species listed in Table 1? Please check to make sure you (or the reviewers) didn't miscount. A graphical representation of the phylogeny for the species used in the analysis would be useful.

We define the number of C₃-C₄ lineages by evolutionary independent origins of C₃-C₄ intermediacy. Although our analysis included 15 genera possessing C₃-C₄ species, within two of these genera there are multiple independent lineages of intermediates. For example, in Flaveria and Mollugo there are three and two distinct clades of C₃-C₄ species respectively. This totals 18 independent C₃-C₄ lineages. We have annotated species in Table 1 as C₃, C₄, or C₃-C₄ to provide further clarity. We included the following at the beginning of the Results section to better clarify the definition of intermediates:

“To parameterise the phenotypic landscape underlying photosynthetic phenotypes, data was consolidated from 43 studies encompassing 18 C₃, 18 C₄, and 37 C₃-C₄ intermediate species from 22 genera (Table 1). These C₃-C₄ species are from 18 independent lineages likely representing 18 distinct evolutionary origins of C₃-C₄ intermediacy (Sage et al. 2011a) (Figure 1—figure supplement 2).”

Regarding the number of species, the 73 species listed in Table 1 is complete. The numbers presented in the text were a count of these. A recount confirms that the analysis included 18 C₃, 18 C₄, and 37 C₃-C₄ species. We are grateful for this anomaly being spotted.

We have included an angiosperm phylogeny with the distribution of independent C₃-C₄ and C₄ lineages annotated onto it (Figure 1—figure supplement 2).