Riverine conceptual models—Background

Introduction

There are two broad approaches to regionalisation. The first uses patterns of landscape drivers influencing aquatic ecosystems (e.g. stream flow, temperature, soil type) to define areas where systems would be expected to be similar. The second approach uses geographic patterns in ecosystem biota (e.g. fauna such as macroinvertebrates and fish) to regionalise large areas and has been successfully implemented in Victoria, Australia[3][2].

The riverine conceptual models presented in this section deal solely with naturally occurring processes and do not focus on anthropogenic (human) impacts. The major natural processes which drive the physical (e.g. flow regimes, temperature, habitat availability) and chemical (e.g. pH, Nutrient concentrations) composition of the river systems and consequently determine the biota of the province. The major subgroups are: Climate, Geology and Topography, Habitat, Hydrology, and Water Quality (see diagram).

This is a bottom-up classification that assumes local biota reflect the biophysical properties of a region. These conceptual models focus on natural ecosystems and the physical processes driving them.

Riverine conceptual models

The riverine conceptual models are aimed at assisting water management decision making by demonstrating our conceptual understanding of Queensland’s freshwater riverine ecosystems. It provides a useful spatial framework within which to assess ecological data and information required for natural resource management. The specific objectives of the riverine conceptual models are to identify the:

• key freshwater aquatic systems represented in the state
• key ecological issues and key stressors
• linkages between the key stressors (drivers) and each ecological consequence (conceptual model or quantitative ecological model) and from this provide information on which drivers are most sensitive to management or controls.

Queensland's freshwater ecosystems are highly variable and subject to diverse and system-unique natural and anthropogenic processes. Consequently, the conceptual frameworks presented here are broadscale and ecosystem-based and ultimately will contain issue-oriented information, thereby recognising that environmental processes and human activities operate at various temporal and spatial scales. The issue-based emphasis acknowledges both natural and human-induced influences/threats to aquatic ecosystems and the responses to those disturbances.

AECM (Aquatic Ecosystem Conceptual Models—now referred to as Riverine Conceptual Models) V 1.0 focuses on the natural or 'pre-development' condition of Queensland’s freshwater ecosystems. The provincial bioregionalisation of Queensland's riverine ecosystems used provides a robust framework for assessing and reporting on a wide range of natural resource-related activities including planning, management, monitoring and research. Future versions will consider the influence of flow modification and other anthropogenic stressors.

The Riverine Models are a descriptive, not a quantitative/numerical tool, although it may ultimately incorporate numerical models to address specific processes.

Regional Summary

The following table provides a summary of regional variation in ecological processes and how they are linked to physical processes.  Refer to the text in the relevant section for more detail about the system. 

References

1. ^ Marshall, J, Prior, A, Steward, A & McGregor, G (2006), Freshwater Bioregionalisation of Queensland's Riverine Ecosystems: developement of Interim Freshwater Biogeographic Provinces, Queensland Government.
2. ^ Newall, P.,Wells, F. (2000), 'Potential for delineating indicator-defined regions for streams in Victoria, Australia', Journal of the North American Benthological Society, vol. 19, pp. 557-571, The Society for Freshwater Science.
3. ^ Wells, F, Metzeling, L & Newall, P (2002), 'Macroinvertebrate Regionalisation for use in the Management of Aquatic Ecosystems in Victoria, Australia', Environmental Monitoring and Assessment, vol. 74, pp. 271-294, Springer.

Last updated: 22 March 2013