SEASONAL VARIATIONS RESULT IN SLIGHT CHANGES IN THE MACROPHYTE COMMUNITY OF AN ATLANTIC FOREST WETLAND

Seasonal variations in the macrophyte community of a wetland

Abstract

Wetlands are shallow water ecosystems typically dominated by macrophytes. These plant communities play a crucial role in maintaining biodiversity and supporting key ecosystem functions. Their structure is regulated by environmental factors that vary across space and time, such as water temperature and depth. In the Atlantic Forest, climate seasonality is characterized by cyclic, predictable changes throughout the year. Given the importance of understanding the structure and dynamics of macrophyte communities, we analyzed whether the structure and composition of the macrophyte community in an Atlantic Forest wetland changed between hydrological seasons. The expeditions were carried out monthly between May 2019 and February 2020. We estimated macrophyte coverage in 150 plots of 2 by 2 m distributed in six standardized transects. Then, we applied multivariate techniques to summarize the seasonal variation in environmental variables and macrophyte coverage species composition. The composition of macrophyte assemblages showed slight differences between the wet and dry seasons, thus exhibiting mostly similar taxonomic compositions. The seasonal variation had little influence on the dynamics of the macrophyte community in the studied wetland. The analysis shows that the composition of the wetland remained relatively stable between the dry and wet seasons, likely due to the dominance of abundant species that are not highly sensitive to seasonal variations. Additionally, the results suggest that factors beyond seasonal variations, such as water depth, biotic interactions, or anthropogenic impacts, may play a key role in shaping the structure of this macrophyte community. Understanding the structure and dynamics of macrophyte communities is essential for evaluating the functioning and resilience of wetlands. Identifying the drivers of community stability or change can improve our ability to predict ecosystem responses and inform more effective conservation and management strategies.