David Warton (University of New South Wales) interviews interviews Ben Stevenson (University of St Andrews) about his 2015 Methods in Ecology and Evolution paper ‘A general framework for animal density estimation from acoustic detections across a fixed microphone array’. They also discuss what Ben is currently up to, including an interesting new method for dealing with uncertain identification in capture-recapture, published in Statistical Science as ‘Trace-Contrast Models for Capture–Recapture Without Capture Histories’.
– ctmm: An R package which implements all of the continuous-time stochastic processes currently in use in the ecological literature and couples them with powerful statistical methods for autocorrelated data adapted from geostatistics and signal processing.
The biggest library of bat sounds has been compiled to detect bats in Mexico – a country which harbours many of the Earth’s species and has one of the highest rates of species extinction and habitat loss.
Fish and invertebrates predominantly or exclusively detect particle motion.
A growing number of studies on the behaviour of aquatic animals are revealing the importance of underwater sound, yet these studies typically overlook the component of sound sensed by most species: particle motion. In response, researchers from the Universities of Bristol, Exeter and Leiden and CEFAS have developed a user-friendly introduction to particle motion, explaining how and when it ought to be measured, and provide open-access analytical tools to maximise its uptake. 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 →