Journal article

Detecting ecological responses to flow variation using Bayesian hierarchical models

J Angus Webb, Michael J Stewardson, Wayne M Koster

Freshwater Biology | WILEY-BLACKWELL | Published : 2010


Inferring effects of environmental flows is difficult with standard statistical approaches because flow-delivery programs are characterised by weak experimental design, and monitoring programs often have insufficient replication to detect ecologically significant effects. Bayesian hierarchical approaches may be more suited to the task, as they are more flexible and allow data from multiple non-replicate sampling units (e.g. rivers) to be combined, increasing inferential strength. 2. We assessed the utility of Bayesian hierarchical models for detecting ecological effects of flow variation by conducting both hierarchical and non-hierarchical analyses on two environmental endpoints. We analysed..

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Funding Acknowledgements

This research was supported by the Victorian Department of Sustainability and Environment, as part of VEFMAP. We acknowledge the support of VEFMAP team members Yung En Chee, Peter Cottingham, Sabine Schreiber, Michael Jensz and Andrew Sharpe as well as the scientific steering committee ( Gerry Quinn, Angela Arthington, Mark Kennard, Barbara Downes, Alison King, Wayne Tennant), and the CMA Environmental Water Requirement officers in this larger project. Several of the above named provided useful comments on an earlier version of this manuscript. We also thank Michael McClain and Ken Newman for their reviews of the submitted manuscript. We thank Simon Bonner and other members of the BUGS community for their assistance with model formulation. The map in this paper was produced by Chandra Jayasuriya of the Department of Resource Management and Geography. Data for the analyses in this paper were commissioned and provided by the Glenelg, Wimmera and West Gippsland catchment management authorities. We would particularly like to thank Matt O'Brien, Hugh Christie and Jodie Halliwell for their assistance with data.