Comparison between graph theory connectivity indices and landscape connectivity metrics for modeling river water quality in the southern Caspian sea basin

The maintenance of connectivity is critical to the proper functioning of an ecosystem. The present study was conducted with the aim of comparing graph theory connectivity indices and landscape connectivity metrics for the purpose of modeling river water quality. To conduct this study, a forest layer was extracted from land cover map and 25 large watersheds were selected. River water quality was then assessed from the perspective of 8 landscape connectivity metrics and 12 graph theory indices. We developed predictive models using stepwise linear regression, power, exponential, and logarithmic models to locate the best model form for each water quality parameter (dependent variable) we examined. The results indicated that models developed using graph theory connectivity indices resulted in higher coefficients of determination (R2) than models developed using landscape metrics. Only 5 independent variables from a potential set of 13 were significant in explaining the variation in water quality parameters. Also, the models with the highest R2 attempted to explain variations in CO3 (0.818), water discharge (0.733), and Ca levels (0.702). Therefore, the results of this study showed that graph theory connectivity indices had more significant correlation with water quality parameters compared to landscape connectivity metrics. This work also indicates that there exist nonlinear relationships among connectivity indices and water quality parameters.