Wildlife bioacoustics is the study of animals using the vocalizations that they produce. Sounds are identified to the species or even individual level using unique patterns known as spectral signatures. These data are used to answer research and monitoring questions about individual species or groups of species.

The Bioacoustic Unit is a collaboration between the Bayne Lab at the University of Alberta and the Alberta Biodiversity Monitoring Institute. Our research group develops tools, protocols, and recommendations for acoustic monitoring programs across the country.

To learn more about the Bioacoustic Unit, please click here.

The BU uses robust environmental sensors, called Autonomous Recording Units (ARUs)—essentially sophisticated battery-operated microphones⁠—to record sounds produced by vocalizing animals⁠. There are recommended settings that can be used to optimize recordings of birds, mammals, and other taxa.

The Bioacoustic Unit uses Song Meter Autonomous Recording Units made by Wildlife Acoustics. Most of our Song Meters are the SM2+ and SM4 models. Other less frequently used models include the SM3, the SM2 with GPS, and the SM2+BAT. The GPS-enabled units permit more precise localization of animals in space. For memory cards, we often use high-quality SanDisk SD cards. We also occasionally use the Kingstone Class 4 and 10 SD cards. Wildtrax can take data from any type of digital sound recorder.

Cumulatively, more species are observed by going to new stations within a study area than by listening to more recordings of the same locations; however, the difference is not that large. If sufficient funding exists to go to more locations, that will provide a better estimate of total species. However, when restrained by field costs, leaving ARUs in the same location and repeatedly sub-sampling is recommended, particularly if you are interested in multiple taxa (e.g., owls and songbirds).

For songbirds, leaving an ARU out for several days will yield higher occupancy rates and probability of detection than repeatedly sampling in a single day. The additional benefit of leaving an ARU out for a month is relatively small for songbirds. However, there is evidence that more species will be detected with more sampling effort and owls, amphibians, and mammals have very different calling behaviours from songbirds.

Minimum sampling effort recommended by the BU in order to maximize detection for most acoustic species is 3–7 days. Each sampling event is recommended to be at least 3 minutes long, either at dawn or dusk and at least one day apart.

The question here is whether you could achieve the same results by listening to the same total number of recordings from a single day vs. a week vs. a month. Sampling for approximately a week results in higher estimates of species richness at a station compared to sampling for a day. In our tests, there was no significant difference between leaving an ARU out for a week vs. a month but that was only for songbirds.

This is entirely dependent on the frequency with which a species sings. The Bioacoustic Unit and the Boreal Avian Modelling project have estimates for all species, however, so you can assess the effort required to ensure you detect a species if it is present.

Calling rate has the greatest effect on detection rate, explaining 49% of the variance in detection rate. Calling rate coupled with the abundance of a species, time period, and a species’ log body weight explained 69% of the variance in detection rate. When the abundance of a species is high, there is higher detectability. Species that call at night have lower detection rates than those that call during the day. Also, larger species generally have lower calling rates. In general, species that are less abundant, have a large body weight, and vocalize infrequently and/or more often during the night have a lower detection rate and will require more extensive sampling.

There are consistent benefits to repeatedly sampling at the same station when estimating trends for a species as you are more certain if the species is present or absent. However, the statistical power of trends is driven by the number of stations and the number of years observed.

Within the first minute of a 10-minute point count, 49.8% of all vocalizing species are detected. Within the first five minutes, 79.2% of all vocalizing species are detected. However, if you have the choice between 10 1-minute samples taken at different times of day or year and 1, 10-minute period you will detect far more species using 10 1-minute segments.

Using more point counts with shorter duration detected a larger proportion of all species compared to fewer, longer duration point counts.

If you sample only a few points from the total number of available recordings, there is strong evidence that afternoon sampling can be avoided altogether if you are relying on listening.

Recognizers can be used when you are targeting a specific species, and a manual scanning spectrogram can be very effective in processing data when vocalizations are visually distinctive and recognizable. In short, the training data is used to create a template (“recognizer”) and is then matched to a recording segment from the test data. More information can be found here.

BirdNET is a multi-species bird classifier developed by Cornell University. WildTrax utilizes the BirdNET API to allow users to obtain results from the classifier for their projects.

Example of BirdNet score for a tag

When using BirdNET as a part of species verification, you can identify whether or not the tag achieves a high BirdNET confidence level by hovering over the brain icon. This is usually useful when the time of first detection of the tag is not high quality; however, if the individual is occupying the space around the recorder with high enough signal amplitude and unobstructed calls, BirdNET’s confidence will be more valuable.

You can also use BirdNET as a guide to automatically return the species it thinks it found. BirdNET provides values in 3-second windows for each recording in a project. You can find BirdNET’s output in the _birdnet_report.csv in Data Downloads.

Evaluating the performance of BirdNET on a data set is also possible. In a binary classification task (BirdNET = predicted, human = observed), you can distinguish false positives (incorrect detections) and false negatives (missed detections) and subsequently create performance metrics. This is useful for questions like species presence, where the highest confidence value of BirdNET across many recordings, can yield a positive result, with minimal effort.