Potential for coupling the monitoring of bush-crickets with established large-scale acoustic monitoring of bats

Monitoring biodiversity over large spatial and temporal scales is crucial for assessing the impact of global changes and environmental mitigation measures. However, large-scale monitoring of invertebrates remains poorly developed despite the importance of these organisms in ecosystem functioning. Exciting possibilities applicable to professional and citizen science are offered by new recording techniques and methods of semi-automated species recognition based on sound detection.

Static broad-spectrum detectors deployed to record throughout whole nights have been recommended for standardised acoustic monitoring of bats, but they have the potential to also collect acoustic data for other species groups. Large-scale deployment of such systems is only viable when combined with robust automated species identification algorithms. Here we examine the potential of such a system for detecting, identifying and monitoring bush-crickets (Orthoptera of the family Tettigoniidae). We use incidental sound recordings generated by an extensive citizen science bat survey and recordings from intensive site surveys to test a semi-automated step-wise method with a classifier for assigning species identities. We assess species’ diel activity patterns to make recommendations for survey timing and interpretation of existing nocturnal data sets and consider the feasibility of determining site occupancy.

Of six species of bush-crickets, the species classifier achieved over 85% accuracy for three, speckled bush-cricket, dark bush-cricket and Roesel's bush-cricket. It should be possible to automatically scan recordings for these species with minimal manual validation. Further refinement of the classifier is required for the three remaining species, in particular for the acoustically similar short-winged conehead and long-winged conehead. Diel activity patterns are species specific and it may be necessary to adjust the hours over which the detectors record to increase detection of key species, but this must be weighed against the costs in terms of increased memory and battery use and equipment security during daytime.

We conclude that with logistical support and centralised semi-automated species identification it is now possible for the public to contribute to large-scale acoustic monitoring of Orthoptera while recording bats. Further innovation of sound classifier algorithms is needed and would be aided by improved reference sound libraries from multiple locations spanning species’ ranges.