Evaluating and optimising performance of multi-species call recognisers for ecoacoustic restoration monitoring.

Monitoring the effect of ecosystem restoration can be difficult and time-consuming. Autonomous sensors, such as acoustic recorders, can aid monitoring across long time scales. This project successfully developed, tested and implemented call recognisers for eight species of frog in the Murray-Darling Basin. Recognisers for all but one species performed well and substantially better than many species recognisers reported in the literature. We achieved this through a comprehensive development phase, which carefully considered and refined the representativeness of training data, as well as the construction (amplitude cut-off) and the similarity thresholds (score cut-offs) of each call template used. Recogniser performance was high for almost all species examined. Recognisers for , , , and all performed well, with most templates having receiver operating characteristics values (the proportion of true positive and true negatives) over 0.7, and some much higher. Recognisers for , and performed particularly well in the training data set, which allowed for responses to environmental watering events, a restoration activity, to be clearly observed. While slightly more involved than building recognisers using commercial packages, the workflows ensure that a high-quality recogniser can be built and the performance fine-tuned using multiple parameters. Using the same framework, recognisers can be improved on in future iterations. We believe that multi-species recognisers are a highly effective and precise way to detect the effects of ecosystem restoration.