It’s very hard to make sensible choices without sensible information. When it comes to actions around changing land use and its ecological impact though, this is often what we are forced to do. If we want to reduce the impact of human activities on natural ecosystems, we need to know how much change has already occurred and how altered an ecosystem might be from its “natural” state.
Working out which parts of the landscape have been changed and mapping the absence of natural vegetation is an achievable (though onerous) task. However, moving beyond this binary view of the world is a huge challenge. Pretty much all habitat has been modified by human influences to some extent – by, for example, wood extraction, the introduction of invasive species or livestock grazing. This means that a lot of the apparently native habitat is no longer capable of supporting its full complement of native biodiversity. Continue reading →
Finding a call of a particular primate species within hours and hours of audio recordings of a forest is no easy task; like finding a ‘needle in a haystack’ so to speak. Automated acoustic monitoring relies on the ability of researchers to easily locate and isolate acoustic signals produced by species of interest from all other sources of noise in the forest, i.e. the background noise. This can be much harder than it sounds. Think about whenever you have to use any kind of voice recognition system: seeking out a quiet room will greatly improve the chances you are understood by the robot-like voice on the other end of the phone. If you ever set foot in a rainforest the first thing you’ll notice is that it is anything but quiet. In fact characterizing and quantifying soundscapes has become a marker for the complexity of the biodiversity present in a given environment.
Primate monitoring programmes can learn a great deal from cetacean research where Passive Acoustic Monitoring (PAM) is the norm (since individuals are rarely observable visually). Research on bats and birds can provide excellent examples to follow as well. Automated algorithm approaches including machine learning techniques, spectral cross-correlation, Gaussian mixture models, and random forests have been used in these fields to be able to detect and classify audio recordings using a trained automated system. Such automated approaches are often investigated for a single species but impressive across-taxa efforts have also been initiated within a framework of real-time acoustic monitoring. Implementing these in other research fields could lead to significant advances. Continue reading →