An evaluation of approaches used to determine the distribution and biomass of emergent and submerged aquatic macrophytes over large spatial scales

We compared the performance of various approaches to determine the distribution and biomass of submerged and emergent aquatic plants in a large fluvial lake. Three empirical models linking local macrophyte biomass to single and multiple environmental variables were applied in a GIS-framework to estimate the spatial distribution and biomass of aquatic macrophytes in Lake St. Pierre, a large (300 km 2), shallow (mean depth: 3 m) and complex widening of the St. Lawrence River (Quebec, Canada). The resulting maps and emergent and submerged macrophyte distributions obtained independently by remote sensing and echo sounding techniques were compared to field data collected in 2000. Maps derived from echo sounding, from a biomass versus depth regression and from a four-variable model (i.e. exposure to wind and waves, plant growth form, water depth and transparency) were the most accurate (55–63% overall agreement with field data). Remote sensing techniques were the least accurate for determining underwater macrophyte distribution in Lake St. Pierre due to the limitations of image-based methods for detecting submerged aquatic vegetation in coloured, turbid waters. This study demonstrates that environmental models in combination with GIS can be used to estimate aquatic macrophyte distribution over larger spatial scales and to examine potential change in macrophyte growth form assemblages arising from different environmental conditions.