**4. Natural variation and heritability of stomatal conductance**

The analysis of evolution of stomata over species should depend on two strategies, i.e., fossil studies on ancestor plants and genetic studies on current plants. Fossil evidence shows that stomata have occurred in sporophytes and (briefly) gametophytes of embryophytes during the last 400 million years. Cladistic analyses with hornworts basal are consistent with a unique origin of stomata, although cladograms with hornworts as the deepest branching embryo‐ phytes require loss of stomata early in the evolution of liverworts (reviewed by [58]).

Genetic variation is a vital characteristic of every population that is required to adapt. Phenotypic trait variance within a population can be related to genetic variance as an estima‐ tion of broad-sense heritability (*H* <sup>2</sup> ). In theory, when a greater proportion of phenotypic variation is attributable to genetic variance, the corresponded trait is highly heritable. Explor‐ ing stomatal traits with high *H* <sup>2</sup> under multiple environments could provide strategy for artificial selection and improvements on stomatal traits. Although natural variation in photosynthetic capacity especially stomatal features is known to exist among different species [59–63], relatively few studies have examined natural variation among accessions of the same species [64–67]. Besides, studying the genetic variation of photosynthetic capacity of different rice accessions with diverse genetic background could be an effective way to improve the photosynthetic capacity of existing rice elite germplasm [67, 68].

In fact, mining natural variations of photosynthetic and stomatal parameters is regarded as a promising approach to identify new genes or alleles for crop improvement. Conventionally, the identification of genomic loci that govern complex traits has been extensively facilitated by the development of quantitative trait locus (QTL) mapping approaches. Recent advances in high-throughput and high-dimensional genotyping and phenotyping technologies enable us to reduce the gaps between genomics and phenomics using the principles of genome-wide association studies (GWAS). This biostatistic method has been widely used in food crops for identifying genes that underlie natural variation of various ecological and agricultural traits [69–71]. Consequently, a combination of GWAS and QTL mapping as well as co-expression network would be a better option to obtain additive, dominance, and epistasis effects of genes, for example, in Arabidopsis [72] and soybean [73].

Therefore, understanding the mechanisms that underlie efficient carbon gain driven by stomatal adjustments in fluctuating light can open doors for increasing plant yields and, more broadly, can reveal fundamental principles to optimize the water cycle system in the biosphere.
