Fish have been shown to be sensitive indicators of environmental quality in Great Lakes coastal wetlands. Fish composition also reflects aquatic macrophyte communities, which provide them with critical habitat. Although investigators have shown that the relationship between water quality and fish community structure can be used to indicate wetland health, we speculate that this relationship is a result of the stronger, more direct relationship between water quality and macrophytes, together with the ensuing interconnection between macrophyte and fish assemblages. In this study, we use data collected from 115 Great Lakes coastal marshes to test the hypothesis that plants are better predictors of fish species composition than is water quality. First we use canonical correspondence analysis (CCA) to conduct an ordination of the fish community constrained by water quality parameters. We then use co-correspondence analysis (COCA) to conduct a direct ordination of the fish community with the plant community data. By comparing the statistic ‘percent fit,’ which refers to the cumulative percentage variance of the species data, we show that plants are consistently better predictors of the fish community than are water quality variables in three separate trials: all wetlands in the Great Lakes basin (whole: 21.2% vs 14.0%; n = 60), all wetlands in Lakes Huron and Superior (Upper: 20.3% vs 18.8%; n = 32), and all wetlands in Georgian Bay and the North Channel (Georgian Bay: 18% vs 17%; n = 70). This is the largest study to directly examine plant-fish interactions in wetlands of the Great Lakes basin.