Using acoustics and artificial intelligence to monitor pollination by insects and tree use by woodpeckers.

The collection and interpretation of field data is a prerequisite for informed conservation in protected environments. Although several techniques, including camera trapping and passive acoustic monitoring, have been developed to estimate the presence of animal species, very few attempts have been made to monitor ecological functions. Pollination by insects and wood use, including tree related foraging and intraspecific communication, by woodpeckers are key functions that need to be assessed in order to better understand and preserve forest ecosystems within the context of climate change. Here, we developed and applied for the first time an acoustic survey to monitor pollination by insects and wood use by woodpeckers in a protected Alpine forest in France. We deployed four autonomous recorders over a year, resulting in 2285 h of recordings. We trained a convolutional neural network (CNN) on spectrographic images to automatically detect the sounds of flying insects' buzzing and woodpeckers' drumming as they forage and call. We used the output of the CNN to estimate the seasonality, diel pattern, climatic breadth and distribution of both functions and their relationships with weather parameters. Our method showed that insects were flying (therefore potentially pollinating flowers) in bright, warm and dry conditions, after dawn and before dusk during spring and summer. Woodpeckers were mainly drumming around March at the time of pair formation in cool and wet conditions. Having considered the role of weather parameters, climate change might have contrasting effects on insect buzzing and woodpecker drumming, with an increase in temperature being favorable to pollination by insects but not to wood use by woodpeckers, and a concomitant increase in relative humidity being favorable to wood use but not to pollination. This study reveals that a systemic facet of biodiversity can be tracked using sound, and that acoustics provide valuable information for the environment description.