Predicting coastal eutrophication from land-use: an empirical approach to small non-stratified estuaries

Few models exist to directly and quantitatively predict the effect of land-use on coastal water quality even though it is recognized that land-use is a major determinant of coastal water quality. Such models are needed because (1) land-use is a major source of nutrients transported to estuaries, (2) land-use integrates multiple factors that may determine the mean algal biomass and (3) land-use is more easily managed than single nutrients when nutrient sources are nonpoint. Regression models accurately predict lake eutrophication as a function of land-use. Similar models do not exist for estuaries. A data set was compiled for 15 estuaries in Prince Edward Island (PEI), Canada, that includes phytoplankton biomass as chlorophyll a (chl), total phosphorus (TP), total nitrogen (TN), estuary morphometry and land-use characteristics. A regression model predicting chi as a function of estuary volume and area of agriculture was developed. This model accounts for 68% of the variance in chi, a level similar to that of models based on TN (r(2) = 0.72) or total phosphorus (r(2) = 0.66). The estuary models based on land-use and total nutrients demonstrate low yields of chi when compared to analogous lake models; this low yield is likely attributable to high levels of herbivory by suspension feeding mussels. Despite these low yields, the pattern between chi and land-use is sufficiently accurate such that environmental managers can predict the effects of changing land-use on estuary water quality in PEI with a known level of error.