Comparison of two commonly used methods for identifying water quality thresholds in freshwater ecosystems using field and synthetic data

Defining ecological thresholds has become increasingly relevant for water resource management. Despite the fact that there has been a rapid expansion in methods to evaluate ecological threshold responses to environmental stressors, evaluation of the relative benefits of various methods has received less attention. This study compares the performance of Gradient Forest (GF) and Threshold Indicator Taxa Analysis (TITAN) for identifying water quality thresholds in both field and synthetic data. Analysis of 14 years of macroinvertebrates data from the Mediterranean catchments of the Torrens and Onkaparinga Rivers, South-Australia, identified electrical conductivity (EC) and total phosphorus (TP) as the most important water quality variables affecting macroinvertebrates. Water quality thresholds for macroinvertebrates identified by both methods largely corresponded at low EC (GF: 400–900 μS cm−1 vs. TITAN: 407–951 μScm−1), total phosphorus (TP) (GF: 0.02–0.18 mg L−1 vs. TITAN: 0.02–0.04 mg L−1) and total nitrogen (TN) (GF: 0.2 mg L−1 vs. TITAN: 0.28–0.67 mg L−1) concentrations. However, multiple GF-derived thresholds, particularly at high stressor concentrations, were representative of low data distribution, and thus need to be considered with caution. In another case study of South Australian diatom data, there were marked differences in GF and TITAN identified thresholds for EC (GF: 5000 μScm−1 vs. TITAN 1004–2440 μS cm−1) and TP (GF: 250–500 μg L−1 vs. TITAN: 11–329 μg L−1). These differences were due to the fact that while TITAN parsed species responses into negative and positive taxa, GF overestimated thresholds by aggregating the response of taxa that increase and decrease along environmental gradients. Given these findings, we also evaluated the methods' performance using different distributions of synthetic data i.e. with both skewed and uniform distribution of samples and species responses. Both methods identified similar change-points in the case of a uniform environmental gradient, except when species optima were simulated at centre of the gradient. Here GF detected the change-points but TITAN failed to do so. GF also outperformed TITAN when four simulated species change-points were present. Thus, the distribution of species responses and optima and the evenness of the environment gradient can affect the models' performance. This study has shown that both methods are robust in identifying change in species response but threshold identification differs depending both on the analysis used and the nature of ecological data. We recommend the careful application of GF and TITAN, noting these differences in performance, will improve their application for water resource management. © 2020 Elsevier B.V.