Abstract Submerged macrophytes play a crucial role as primary producers in freshwater ecosystems, with their growth strategies being significantly shaped by environmental factors. Plant trait networks (PTNs) reveal the complex associations among plant traits, offering deeper insights into plant environmental adaptation than traditional methods. However, the response mechanism of plants to internal sediment nutrients, especially within the framework of PTNs, is not well understood. In this study, we conducted a three-month cultivation experiment with 12 common submerged macrophyte species across 11 sediment nutrient gradients, measuring 30 functional traits per species to elucidate their adaptive strategies to nutrient variations. Our results show that as sediment nutrient levels increase, the number of edges and graph density within the PTNs increase, while the average path length decreases, which suggests that plant traits are more likely to interact cooperatively under nutrient-sufficient conditions. Co-occurrence network analysis also identifies the N:P ratio as a hub trait with strong links to other traits across most sediment nutrient gradients. Additionally, we find a positive correlation between network connectivity and the relative growth rate (RGR) of plants. Partial least squares structural equation modeling (PLS-SEM) indicates that enhanced network connectivity directly improves plant nutrient use efficiency (NUE), which in turn boosts the RGR. These results suggest that high plant trait connectivity and coordination are beneficial for the efficient acquisition and utilization of resources essential for plant growth. Our study explores the adaptive strategies of submerged macrophytes to increasing internal sediment nutrients from the perspective of PTNs, uncovering the mechanisms of plant adaptation under varying nutrient conditions. These findings can enhance our understanding of plant responses to environmental changes and guide the development of ecological conservation strategies on a scientific basis.
Zhao et al. (Tue,) studied this question.