As human impacts on the world accelerate, so does the need for tools to monitor the effects we have on species and ecosystems. Alongside technologies like camera traps and satellite remote sensing, passive acoustic monitoring (PAM) has emerged as an increasingly valuable and flexible tool in ecology. The idea behind PAM is straightforward: autonomous acoustic sensors are placed in the field to collect audio recordings. The wildlife sounds within those recordings are then used to calculate important ecological metrics – such as species occupancy and relative abundance, behaviour and phenology, or community richness and diversity.
The Pros and Cons of Passive Acoustic Monitoring
Using sound to monitor ecosystems, rather than traditional survey methods or visual media, has many advantages. For example, it’s much easier to survey vocalising animals that are nocturnal, underwater or otherwise difficult to see. Also, because acoustic sensors capture the entire soundscape, it’s possible to calculate acoustic biodiversity metrics that aim to describe the entire vocalising animal community, as well as abiotic elements in the environment.
The use of PAM in ecology has been steadily growing for a couple of decades, mainly in bat and cetacean studies. But with sensor costs dropping and audio processing tools improving, there’s currently a massive growth in interest in applying acoustic methods to large-scale or long-term monitoring projects. As very low-cost sensors such as AudioMoth start to emerge, it’s becoming easier to deploy large numbers of sensors in the field and start collecting data. Continue reading →
Heard but not seen, populations of forest elephants (Loxodonta cyclotis) are rapidly declining due to ivory poaching. As one of the largest land mammals in the world, this species is surprisingly difficult to observe in the dense forests of Central Africa, but their low frequency rumbles can be recorded. With the autonomous recording afforded by passive acoustic monitoring (PAM) though, we have a window onto forest elephant ecology and behaviour that’s providing data critical to their conservation and survival.
The diverse ways that PAM can contribute to conservation outcomes is growing and while still underappreciated, the availability of relatively inexpensive recorders, increased power efficiency, and powerful techniques to automate the detection of signals have led to an explosion in use. In 2007 there were only about 20 published papers using PAM techniques, but since then over 400 papers have appeared in peer-reviewed journals.
Spectrogram of two forest elephant rumbles. Horizontal line shows the limit of human hearing.
Essentially, PAM is the automatic recording of sounds in a given environment, often for long periods. The trick, and often greatest challenge, is to find the signals of interest (bird calls, elephant rumbles, gunshots) within the recordings. With these signals we can quantify abundance, occupancy and spatial or temporal patterns of activity. Particularly in landscapes or ecosystems where visual observation is difficult (e.g. oceans, rainforests, nocturnal environments) PAM may be uniquely capable of delivering informative and unbiased data. Because PAM is a relatively new method but of considerable interest across the disciplines of ecology, behaviour and conservation, there is huge interest in refining the sampling and statistical methods needed to deal with the peculiarities of acoustic data. Continue reading →
Kim led the work on this article and had an international team of co-authors. They have developed a way to harness laser technology for use in measurements of vegetation structure of forests. The study is an important development in the monitoring of carbon stocks for worldwide climate policy-making. Continue reading →
By now we’re all familiar with the global biodiversity crisis: increasing numbers of species extinct or at risk of extinction; widespread habitat loss and a seemingly endless set of political, logistical and financial obstacles hampering swift action for conservation. The international Convention on Biological Diversity (CBD) has set twenty global diversity targets, many of which require participating nations to conduct accurate and efficient monitoring to assess their progress and inform policy decisions. Governing bodies and organizations worldwide have agreed that immediate, efficient action is essential to preserving our planet’s increasingly threatened ecosystems.
But how? Diversity measurement techniques are a tricky business. Accurately recording diversity can be time-consuming, labor-intensive, expensive, invasive and highly susceptible to human error. Often these methods involve the employment of trained specialists to individually identify hundreds or even thousands of species, a process that can take many months to complete.
Marine habitats are particularly difficult to access because of the physical limitations of humans underwater, and are often flawed due to the influence of our presence on marine organisms. However, the oceans contain many of the world’s most diverse systems, and, despite the limitations of current methods, the need to monitor marine diversity is a top priority for the global conservation movement. 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 →