Round-the-clock acoustic recordings have been used to monitor the effects of logging and fires on biodiversity in tropical forests.
Danielle Rappaport at the University of Maryland and her colleagues used historical satellite data to select sites in the southern Brazilian Amazon that had experienced logging or forest fires over the past few decades. They placed sound recorders at the sites in September and October 2016 to try to capture the acoustic markers of ecosystem degradation.
The aim was to develop a way to measure ecosystem intactness through the overall acoustic patterns created by all the sound-generating animals in an environment.
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This would allow researchers to track variations in the composition of animal communities without having to identify individual species, says Rappaport.
Traditionally, biodiversity research in the Amazon has focused on dawn and dusk inventories of identifiable animals, most commonly birds, says Rappaport.
鈥淥ur understanding of biodiversity really has excluded the taxa we can鈥檛 identify, like insects, which makes up the bulk of biodiversity in the Amazon,鈥� she says.
After analysing thousands of hours of ecosystem audio, the researchers found that there was a difference in 24-hour acoustic activity patterns between logged and burned forests.
鈥淔ire has a major pronounced effect on animal communities,鈥� says Rappaport.
In particular, the researchers found that this fire effect was far more pronounced in forests that had experienced two or more fire events. In these forests, animal communication networks were quieter, less connected and more homogenous.
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鈥淚t really underscores the importance of protecting a burned forest from subsequent burns,鈥� says Rappaport.
Rappaport鈥檚 previous research using lidar technology has shown that forest degradation from fire and logging results in large and sustained losses of stored carbon.
鈥淲e haven鈥檛 known much about how those same processes 鈥� fire and logging 鈥� affect the animal communities that inhabit these human-modified Amazon forests,鈥� she says.
In future, the team plans to use artificial intelligence to help pick out the individual animal species behind the acoustic patterns, to see how they may be differently affected by logging or fire events.
Reference: bioRxiv,
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