Great Lakes water-level fluctuations drive coastal wetland plant species composition and abundance. Receding levels stimulate seed-bank germination and increase plant diversity, while increasing levels drown flood-intolerant species, reducing diversity. While short-term studies have quantified aspects of these dynamics, long-term studies of natural wetlands undergoing multiple water-level cycles are necessary to clarify plant responses and provide a baseline for predicting future conditions. Nearly every year since 1971, students and researchers based at the University of Michigan Biological Station have collected vegetation and physical data from Cecil Bay, a Great Lakes coastal wetland in the Straits of Mackinac. We examined this unique 50-year vegetation dataset that includes multiple periods of high and low water-levels. We found that the entire marsh maintained a surprisingly stable breadth throughout the 50 years, with consistent emergent plant composition defining the outer wetland. Notably, the conventional models of vegetation change with water-level fluctuation underestimate the tolerance of rhizomatous emergent plants to long-term flooding, a capacity critical for maintaining Great Lakes coastal wetlands. We incorporated our findings into a new conceptual model of Great Lakes coastal wetland vegetation dynamics. This long-term study enhances understanding of the resilience of these remarkably dynamic ecosystems.
