Plant silicon content as a proxy for understanding plant community properties and ecosystem structure

Abstract

AbstractSilicon (Si) content in plant tissues is considered a functional trait that can provide multiple morpho‐physiological benefits to plant individuals. However, it is still unclear whether and how these individual benefits extend to plant community processes and ecosystem functioning. Here we investigated how plant Si content is associated with plant community properties and the ecosystem structure of herbaceous communities in Israel. We sampled 15 sites across the Mediterranean and desert ecosystems and built models to evaluate how plant silicon content (community‐weighted mean and standard variation) is associated with variables such as species richness, biomass production, plant cover, and functional diversity. Finally, we used model selection techniques to test whether models depicting plant Si content perform better than models using data on soil Si instead. Sites with lower susceptibility to drought had significantly more Si‐accumulating grass species and higher soils Si content. Models with plant Si content instead of soil Si, always performed better, although those considering Si content variation had overall stronger associations with community properties than only mean Si content. For instance, up to 51% of plant Si content variation was explained by climate, biomass production, and species richness, combined. Still, mean plant Si content and plant cover combined explained up to 42% of plant functional diversity. Our results suggest the that plant Si content serves as a proxy for understanding the ecological properties and functioning of arid and Mediterranean ecosystems. Nevertheless, the significance of Si has not been fully explored in other ecosystem types, where its influence may be less pronounced compared with the ecosystems examined in this study. In light of various global change scenarios, enhancing our understanding of Si as a plant functional trait could help bridge existing knowledge gaps and improve ecological modeling, thus enabling more accurate forecasts of changes in plant distributions.