Last week we published our latest Bat Detective research update, explaining how we’ve used the data you’ve labelled to improve our algorithms for detecting bat calls in audio data. The next step is to assess how well they perform in real-world bat survey situations – and the best way to do this is to put them into practice on new data. So following last week’s update, this post will focus on examples of where we’ve road-tested our new tools this year.
The first was a two-week garden bat survey we carried out over two weeks in July, in a row of suburban gardens in southeast England. We were testing our algorithms on data collected by two different bat detector types, both of which were deployed outside to collect data autonomously over multiple nights. Alongside some specialised full-spectrum ultrasonic detectors, which are commonly used in acoustic wildlife monitoring research and citizen science projects including the Norfolk Bat Survey, we were also also using some brand new, low-cost acoustic sensors developed by our collaborators in Oxford for a variety of biodiversity monitoring applications (including citizen science). This offered a great chance to test our call detection tools on data from two different types of acoustic sensor.
The videos below show examples of how the algorithms are used on the UK garden survey data. The first shows the distinctive calls of a common pipistrelle passing by the detector, and the second are calls from a noctule, the UK’s biggest bat. Each red line on the spectrogram shows where the algorithms predict a bat call – so you can see in these cases they’re performing well, successfully detecting every echolocation call in these recordings. For each predicted call, the algorithms also calculate a probability of detection – a measure of how likely the sound is to be a bat call, with reference to our training data from Bat Detective. This enables us to set a threshold for call detection – by setting a high probability threshold, this then makes it possible to only detect sounds that are very likely to be a bat call. As we discussed in the last post, this can help to reduce the risk of false positive detections (i.e. falsely thinking there’s a bat call, when actually there isn’t one).
Once the calls have been detected, the next step in the analysis is to classify those calls to species – what bat is that? Doing this requires a second set of algorithms, trained to distinguish between the calls of different species. Our group are currently working on developing these, and incorporating them into our tools, in order to more fully automate the analysis process (and therefore make it faster and more reliable for researchers monitoring bats).
As well as the UK data we collected in July, we’re also now using our call detection tools to analyse recordings collected during a huge bat survey on the Atlantic island of Madeira. There are thought to be three bat species on Madeira, including the endemic Madeira pipistrelle (Pipistrellus maderensis), which is listed as vulnerable on the IUCN Red List. However, little is known about the distribution and abundance of bats on the island, and its relatively small size makes it an ideal study system for an island-wide bat survey.
So a member of our research group (in collaboration with M-ITI in Madeira), has been busy out in the field throughout August and September, deploying full-spectrum bat detectors in locations across the island, which were selected to provide a randomised sample of its full range of habitats and altitudes. A map of the sample sites is shown below, with each blue marker showing where bat detectors have been placed. Now the data have all been collected, we’re starting to use our automated tools to detect bat calls in all the recordings. From there we can start to ask questions about the distribution of bats on the island, and to assess what habitats and locations might be particularly important for conservation.
This is a great example of how the tools we’ve developed using the Bat Detective data can now be applied to understand bat ecology and assist in conservation efforts. Without tools like these, the sheer quantity of audio data collected during a summer-long survey at this level of detail – which clocks up to hundreds of hours of survey-time in total – would be almost impossible to analyse by hand. Keep an eye on the Bat Detective blog in the coming months, as we’ll keep you informed on the last few steps in developing our bat call detector tools for open-source release, as well as letting you know about this and other test-case projects.