Modelling Multiscale Relationships in Riverine Landscapes: Putting the "Riverscape" into Statistical Models for River Ecology and Management

Abstract

Many questions in ecology involve exploring the environmental processes that influence species’ distributions and abundances in both space and time. Such environmental processes are rarely independent, and generally operate across many scales. This is particularly relevant to riverine systems, where the nested hierarchical structure of the riverscape means fine-scale processes are strongly influenced by processes operating across larger scales. Recent research has identified some key advantages in applying Bayesian hierarchical models to hierarchical ecological problems such as identifying relationships between species’ abundances and environmental predictor variables across multiple scales. This thesis focuses on applying Bayesian hierarchical models to multiscale datasets for freshwater fishes and aquatic macrophyte cover in South-East Queensland, Australia, to address two key aspects of applied river ecology and management. Firstly, it examines multiscale species-environment relationships for freshwater fishes. This involves developing Bayesian hierarchical models that reflect the structure of a conceptual model of fish species’ distribution and abundance. Secondly, this thesis examines methods to integrate such multiscale relationships into models for river management and restoration using Bayesian networks with an emphasis on the management of aquatic macrophytes (BNs). Novel statistical methods such as Bayesian hierarchical models and BNs have the potential to advance our understanding of multiscale abiotic drivers of ecosystem structure and function across the riverscape.