interactions in rivers using Structural Equation Modelling

Abstract: Effective management of river ecosystems requires knowledge of the interrelationships between biological, chemical and geomorphological processes and patterns. This is a complex challenge, and there are significant gaps in our understanding of these interrelationships. For example, the response of biological communities to geomorphological changes in rivers is particularly poorly understood. These knowledge gaps are compounded by the lack of coherent biological, chemical and geomorphological datasets for many rivers, limiting the extent to which traditional data analysis and modelling techniques can be applied. Here we describe the application of a new technique, Structural Equation Modelling (SEM), to the investigation of biological, chemical and geomorphological data collected from rivers across England and Wales. The SEM approach is a multivariate statistical technique enabling simultaneous examination of direct and indirect relationships across a network of variables. Further, SEM allows a-priori conceptual or theoretical models to be tested against available data. For example, a-priori models can be developed in collaboration with river managers and then evaluated using SEM as part of participatory modelling projects. This is a significant departure from the solely exploratory analyses which characterise other multivariate techniques. Bayesian approaches can also be applied within the SEM framework, offering the opportunity to address challenges such as incomplete datasets and non-normal data distributions. Such challenges are common in the analysis of spatia