Linking trait network parameters with plant growth across light gradients and seasons

Abstract

Abstract Reduced light availability induced by eutrophication has dramatically affected the growth of submerged macrophytes and caused their rapid decline globally in lakes. Functional traits have usually been used to predict ecological processes and explain plant adaptation. Trait networks, which are constructed from a series of nodes (traits) and edges (trait–trait correlations), can reveal complex relationships among traits. Plant traits belonging to different organs are considered relevant for overall plant performance. Therefore, variation in trait network topology at the whole plant level can better reflect plant adaptation and response to environments than traditional methods, but the mechanisms underlying the decline of plants from a trait network perspective are not well understood. In this study, based on a 1‐year manipulation experiment for Potamogeton maackianus cultured with four levels of light intensity, we constructed trait networks from 20 traits belonging to different organs. Our results showed that trait network connectivity decreases in harsh environments, probably due to increased trait modules responding independently to stress. Network connectivity was positively related to the plant relative growth rate (RGR), as high trait connectivity and coordination should be beneficial for plants to acquire and transport resources efficiently across the whole plant. Additionally, we found that specific stem length, leaf: root mass ratios and leaf total non‐structural carbohydrates were hub traits with high connectivity. Hub traits expressed high phenotypic plasticity, had close links with plant growth and consistently held their higher importance within the network across light gradients or seasons. We found that low phenotypic integration in stressful environments may constrain plant growth, which can provide important implications for understanding plant adaptation strategies to low‐light stress and even predicting community dynamics in the context of global environmental change. Read the free Plain Language Summary for this article on the Journal blog.