Echo-dash: Keeping ecologists in the loop with an open source, online ecoacoustic dashboard for interactive exploration of spatiotemporal soundscape data

Passive acoustic monitoring (PAM) is being adopted in a range of contexts. Emerging methods facilitate analysis of large-scale data sets, but ecological interpretation of acoustic indices is not straightforward. In addition, the technical and logistical requirements of using emerging AI methods for big data mean that conservation actors increasingly adopt third-party analysis solutions. We argue that these compounding factors undermine robust ecological inference, clouding insight and decision making. To address this, we present echo-dash, an accessible, interactive dashboard that facilitates rapid, interactive exploration of spatiotemporal soundscape data by conservation actors. Developed through participatory design, echo-dash is built on the simple premise that the potential of PAM can best be realised by keeping human ecological knowledge in the analysis loop. Five key functions to facilitate analysis and interpretation of PAM data were identified and implemented: 1) Calculating soundscape descriptors and probability of species presence; 2) Checking data integrity; 3) Exploring data interactively and in spatiotemporal, environmental contexts; 4) Filtering data by extreme weather, outliers or clusters; 5) Exporting data subsets, plots and code to generate plots. By supporting integration of human, situated ecological knowledge with large scale spatiotemporal data sets, echo-dash bolsters PAM’s potential to transform ecological monitoring for applied and fundamental ecoacoustics.