A data-driven method for selecting candidate reference sites for stream bioassessment programs using generalised dissimilarity models

Abstract

Key issues with defining reference condition for stream bioassessment are (1) equivocal definitions of ‘minimally disturbed’ pressure criteria and wide-ranging approaches to site selection, (2) highly modified regions where near-pristine areas do not exist, leading to management decisions based on inconsistent and unquantified benchmarks and (3) costly field campaigns associated with ‘extensive spatial survey’ approaches. We used generalised dissimilarity modelling (GDM) to classify stream segments into ecotypes, and transparently and efficiently define candidate reference conditions for the Ecosystem Health Monitoring Program (EHMP) assessment area in south-eastern Queensland, a highly modified region with a complex biogeographic history. We modelled fish presence–absence data from 396 sites with GIS-based natural and anthropogenic predictors. Stream segments were classified into ecotypes using the GDM-transformed natural variables so that (1) reference-site selection adequately covered the β-diversity of the study area and (2) we could evaluate the validity of incorporating sites from neighbouring catchments outside of the EHMP assessment area. Relationships between selected anthropogenic variables (the river disturbance index and %stream connectivity) and fish assemblages were used to define pressure criteria and map candidate reference conditions. We conclude by describing a new framework that can be used to select indicator-specific reference sites by GDM and a stratified, probabilistic sampling design.