Large-scale semi-automated acoustic monitoring allows to detect temporal decline of bush-crickets

Monitoring biodiversity over large spatial and temporal scales is crucial to assess 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. The development of new recording techniques and new methods of automatic species recognition based on sound detection and easily applicable within a citizen-science framework, offers interesting possibilities. However, the value of such protocols has not been tested for the study of temporal trends on a large spatial scale.

We used an acoustic region-wide citizen-monitoring program of Orthoptera, conducted along roads, to assess the relevance of automatic species recognition methods to detect temporal trends while taking into account spatial and seasonal patterns of two Orthoptera species activity (Tettigonia viridissima Linnaeus, 1758, and Ruspolia nitidula Scopoli, 1786) at a large scale. Additionally, we tested the effect of climate and land-use variables on spatio-temporal abundance patterns using generalized linear mixed models. We found negative temporal trends for the two species across the survey period (2006–2012). The spatial variations were largely explained by the geoclimatic conditions and, to a lesser extent, by land use (negative effects of urbanization). The temporal variations were highly correlated to the climatic conditions of the year, and of the previous year (nonlinear effect of temperature, precipitation).

To our knowledge, this paper describes the first successful attempt to calculate large-scale temporal trends of insect populations on the basis of an automatic identification process of acoustic data. We argue that acoustic monitoring along roads, coupled with the automatic recognition of species sounds, offers several advantages for assessing Orthoptera biodiversity response to global changes and environmental measures.