Bioassessment of Freshwater Ecosystems using the Reference Condition Approach: Comparing Established and New Methods with Common Data Sets

• Published in: Freshwater Science
• Main Authors: Robert C. Bailey, Simon Linke, Adam G. Yates
• Format: Text
• Language: English
• Published: Society for Freshwater Science 2014
• Subjects: Canada; Yukon; Yukon river
• Online Access: https://doi.org/10.1086/678771

Although used in many jurisdictions around the world, analytical approaches of the Reference Condition Approach (RCA) to bioassessment of freshwater ecosystems have evolved quite slowly over the past 2 decades. For this special series of papers in Freshwater Science, researchers analyzed 3 data sets that included both benthic macroinvertebrate and environmental data from a number of reference sites. Australian Capital Territory (ACT) reference sites (ntotal = 107) were wadeable streams in the upper Murrumbidgee River catchment, Australian Capital Territory, Australia. Yukon Territory (YT) reference sites were wadeable streams (ntotal = 158) in the Yukon Territory, Canada, part of the Yukon River basin. Great Lakes (GL) sites (ntotal = 164) were all nearshore (<20 m) lentic sites in the North American Great Lakes. For each data set, sites were divided into model-building (training) and model-testing (validation) groups. Each validation site was further subjected to 3 levels of simulated degradation based on the sensitivity of the biota to eutrophication. The analytical approaches ranged from standard or slight modifications of methods used in national programs (Australian River Assessment [AUSRIVAS], Canadian Aquatic Biomonitoring Network [CABIN]), to improved matching of sites to be assessed and appropriate reference sites, and Bayesian and machine-learning modeling. In comparing Type 1 error rates (proportion of validation sites deemed not in reference condition) and power (proportion of simulated impairment sites deemed not in reference condition), we found no obvious pattern among the 3 data sets or approaches. Approaches commonly used in RCA programs would benefit from incorporating newer methods that better match reference and test-site environments and build better predictive models.