Emergence of New Explanatory Variables for 2D Habitat Modelling in Large Rivers: The St. Lawrence Experience

The St. Lawrence River is one of the most important large rivers in North America. This 600-km long watercourse is characterized by a high degree of physical heterogeneity, including fast moving narrow reaches separated by fluvial lakes reaching 10 km in width. The mean annual discharge from the outflow of Lake Ontario is 7500 m3/s and has been managed for hydropower and transportation since the 1960s. With the management plan currently under review an effort is being made to include criteria that take into account the impacts of regulation on the biotic components of the river ecosystem. High resolution 2D spatial modelling of river habitats and floodplains is a powerful tool to make quantitative impact assessments of the biota. Physical variables commonly used in habitat models include depth, velocity and substrate size. In addition, other abiotic variables such as wind-generated wave stress, light penetration, water temperature, sedimentation of fine particles, specific discharge and bottom slope, that define the local ’hydroperiod’ have been suggested. Our proposed approach integrates abiotic data obtained from numerical models, field measurements and biological information to overcome problems inherent in temporally and spatially heterogeneous river systems. This approach was tested with a habitat model applied to submerged aquatic vegetation, various categories of wetlands, benthic organisms and various life stages of a number offish species. Logistic regression is the statistical model currently used to synthesize the relationships between abiotic and biotic factors. The short-term objective of this modelling exercise in the St. Lawrence River is to understand the underlying links between fluvial physics and biota. A longer-term objective is to provide a real-time analysis of key variables and to quantify the links between trophic levels.