Spicoid morphology of Mapanioideae (Cyperaceae): an evolutionary perspective

Ancestral character reconstruction was performed using<b> </b>the Bayesian Inference molecular phylogenetic hypothesis of Cyperaceae from<b> </b>Semmouri et al. (2019) restricted to Mapanioideae species. Cyperaceae inflorescences were previously classified based on the typology of the inflorescence architecture found in Cyperaceae. This resulted in the “scirpoid”, “cyperoid”, “rhynchosporoid” and “caricoid” type of spikelets proposed by Dahlgren et al. (1985). However, we believe that considering a typological classification for the spicoid morphology may hamper elucidating the evolution of its morphological variation, since typologies represent a combination of several characters that can be shared by different types. Therefore, instead of evaluating it as a single unit, the spicoid morphology was tackled in a reductive coding through an iterative process of decomposition. A similar approach was successfully used by Semmouri et al. (2019) to study the evolution of embryo morphology in Cyperaceae. Spicoid morphology was initially partitioned into eight characters: 1. presence/absence of subtending bract; 2. number of lateral scales; 3. presence/absence of keel in the lateral scales; 4. number of lateral stamens, 5. number of inner scales, 6. number of inner stamens; 7. position of the pistil; 8. Pistil merosity (related to the number of stigmas branches). From these characters, five were shown to be variable, potentially phylogenetically informative, and therefore selected for downstream comparative analyses. The variable characters are: 1. The presence of keels on the lateral scales (a: present, b: absent); 2. Number of lateral stamens (a: 0, b: 2); 3. Number of inner scales (a: 0, b: 1-6, c: 7-12, d: 13-19, e: &gt;19); 4. Number of inner stamens (a: 0, b: 1-6, c: 7-14, d: &gt;14); 5. Pistil merosity (a: dimerous, b: trimerous). The coding arrangements were performed to optimize the character reconstruction analyses. The parameter “type=discrete” was used since the morphological features were coded as discrete. Bayesian stochastic character (BSC) mapping (Huelsenbeck et al., 2018) was performed using the ‘make.simmap’ function available in phytools v0.5–64 (Revell, 2012). The analyses were implemented in R (R Core Team, 2018) using the model that best fits to the data (“equal rates” or “all rates different”) to recover the evolutionary history of the morphological characters under investigation and recover ancestral character state probabilities at the nodes. Outgroups, terminals with more than one accession per taxon and polytomies were removed from the analysis using the function ‘drop.tip’ in ape (Paradis et al., 2004) to prevent bias in character state reconstruction. Stochastic character mapping was performed using 1000 simulations. BSC provided transition rates which were used to build a transition matrix among features as input to the heatmaps depicted using pheatmap v1.0.12.