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Robust decision support tools are critical to assist stakeholders to maximise the ecological benefit of their management strategies. Unfortunately, the development phase for decision support tools can often be considered an endpoint by stakeholders and insufficient testing and validation is a common trait for applied ecological models. The Murray Flow Assessment Tool (MFAT) divided a range of water-dependent taxa, including fish, into functional groups (e.g. flood spawners, wetland specialists) and used available literature and expert opinion to derive a range of response curves for relevant flow-related habitat conditions. Response curves were developed for variables such as flow and spawning timing and flow duration for fish, and were spatially differentiated where considered appropriate. While the development used best-available science, the level of confidence reported for many of these response curves was moderate or low, the sensitivity of the model to the method used to combine response curves could not be tested in the development timeframe and subsequent validation of model predictions has been minimal, to our knowledge. Weightings applied to the various component parts of a habitat condition score have also not been assessed. Here, we use fish data collected as a part of the Murray-Darling Basin Authority's Sustainable Rivers Audit (SRA) to begin to understand how well MFAT habitat condition scores relate to measured biological data. We compare whether fish species within functional groups respond similarly, whether the spatial differences captured by MFAT for fish are reflected in the SRA data and make a first assessment of how the method of combining individual response curves and then the weightings applied to each affect the final prediction, and then how that compares with measured fish data. Overall, we detected a low level of correlation between MFAT habitat condition scores and fish assemblages as measured by SRA. Individual model metrics for the various fish functional groups calculated by MFAT were not better correlated with measured fish population metrics for the corresponding functional groups calculated from SRA data. Main channel generalists were the best correlated individual group, and they were also the most abundant within the SRA data. However, little improvement in correlation was observed for the overall groups of total, native and invasive fish richness, abundance or biomass and the overall MFAT score was negatively correlated with those fish metrics in all years except for 2002. Overall measures of fish assemblage were better correlated with individual components of the MFAT score, including native richness and abundance which were moderately correlated with main channel generalist adult habitat condition, an index largely based on non-flow related parameters such as availability of woody debris. Introducing lags in fish response time did not improve the relatively-weak correlations. Methods of combining response curves resulted in large changes in the scores derived from MFAT, and the variability of combination methods across fish functional groups and locations is having an impact on the simulated habitat condition. The implications of this are unclear. Furthermore, the default weightings applied to each component did not influence the strength of the correlations observed with SRA data, with constant equal weightings and, for at least one zone, randomly-generated weightings, resulting in similar or better correlations. Thus, we recommend the use of constant equal weightings as a simplification to the MFAT model, and that the method of combining components be revisited with a view to increasing the consistency across the zones. We also recommend additional validation of the model using data encompassing a wider range of flows than were available for this assessment.

Improving ecological outcomes by refining decision support tools: A case study using the Murray Flow Assessment Tool and the Sustainable Rivers Audit