Abstract

Over the past two hundred years, anthropogenic activities have resulted in the substantial decline of the once extensive wetlands in both Hamilton Harbour and Cootes Paradise. Some of the key stressors for aquatic vegetation have been infilling, sustained high water levels and level regulation in Lake Ontario, and reduced water clarity because of eutrophication and suspended sediments. Designated an Area of Concern in 1985, remediation efforts have included upgrades to the sewage treatment plants discharging into the harbour to reduce eutrophication and the 1996 construction of a fish barrier to exclude large Carp from entering Cootes Paradise to reduce turbidity. Over the past ten years, Cootes Paradise has seen a 120% increase in the areal extent of emergent vegetation, but this still represents less than 20% of the circa 1900's marsh area. Despite substantial reductions in Carp density in Cootes Paradise, submerged aquatic vegetation has been sparse and typically found at depths of less than 0.5 m, likely because of impaired light penetration. Following a significant improvement in water clarity in Hamilton Harbour between 1987 and 1997, submerged aquatic vegetation in the harbour proper expanded, achieving a mean maximum depth of 2.9 m in 2012. Species richness was considerably lower in Hamilton Harbour when compared to Cootes Paradise; however, the species composition in both these areas indicated degraded conditions throughout the time period of our assessment. Using our recent dataset, we tested relationships that had been previously established in the literature between emergent extent and water levels for Cootes Paradise and also the connection between maximum depth of submergent colonization and Secchi depths but simple univariate tests were not significant. A combination of small sample size, simple tests, and a small range for the independent variable may be issues in establishing simplified response relationships and are likely oversimplifications of vegetation response in the area that require more complex modelling.

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