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Frequently Asked Questions

Have questions? We’ve got answers!

  • What is WildTrax?

    WildTrax is an online platform for storing, managing, processing, and sharing biological data collected by environmental sensors. WildTrax provides tools for managing large data sets, and also creates opportunities to address broad-scale questions using novel analytical approaches. Supported environmental sensors currently include autonomous recording units (ARUs) and remote wildlife cameras.

    WildTrax users have access to the latest techniques and technology for using sensors in biological monitoring. 

  • How can I get involved in WildTrax?

    WildTrax is available for organizations using cameras and/or ARUs⁠ -- simply create an online account to begin. Contact info@wildtrax.ca or see Get Started for details.

  • Why should I use WildTrax?

    WildTrax offers several benefits for users of environmental sensors, including:

    • Up to a 45% improvement in processing speed
    • More accurate, and higher-quality data via automated QAQC
    • Unlimited, online access to your data
    • Seamless and flexible data-sharing options with teams and collaborators
    • Standardized approaches to data collection across networks of organizations and individuals
    • Opportunities to discover data in your region of interest, coordinate with other groups, and address broad-scale ecological questions 
    • Centralized repository for long-term data archiving
  • How can I set up a remote camera or autonomous recording unit (ARU)?

    Sensors can be deployed in many ways, depending on the monitoring objective. Examples of methods and protocols are found here.  

    ABMI set-up methods are summarized  below:


    For cameras, choose a camera view that is not blocked by vegetation or other impediments for at least 10 m (try to anticipate vegetation growth). Set the camera (lens) height at 1 m and then focus the camera view on the reference stake at 80 cm above the ground. Your target detection zone should be approximately 3–5 m from the camera. Face the camera north (ideally) or south if possible to avoid visibility issues from direct sunlight.


    The ARU should be at a height of 1.5 m above ground, facing north with the microphones unobstructed by leaves, branches, or (if applicable) the trunk of the tree to which it’s affixed. Choose a sturdy tree or support, such as a stake, so that the unit won’t topple over in high winds or if disturbed by a large mammal.

  • What data is made publicly available through WildTrax?

    All data uploaded to WildTrax is by default private and only viewable by the account holder or data owner. Data owners maintain ownership and privacy rights over uploaded data, regardless of whether the data is private or publicly available. When you upload data to WildTrax, you have the option of releasing your data publicly or not. Options such as only publicly releasing metadata and buffering data spatially or temporally are being explored.

  • What brand of camera and memory card should I use to be consistent with WildTrax?

    The ABMI uses Reconyx PC900 and HF2 cameras. These cameras are great for first-time users as they are user-friendly and intuitive. For memory cards, we often use high-quality SanDisk SD cards. We also occasionally use the Kingstone Class 4 and 10 SD cards. It is not required that you use this type of remote camera. Currently, the system also natively supports the Reconyx HF2 and HC600 models. If another model is used, please contact support@wildtrax.ca so we add it to the list of supported units, and can extract the appropriate metadata for you from images taken by those units.

    For more information on camera brands, please click here.

  • Does WildTrax have tools to help process images more efficiently?

    Cameras can sometimes capture images that do not contain wildlife—‘false fires’—due to movement in vegetation or changes in sunlight. These false fires can increase processing cost and time. To aid in processing these images, WildTrax contains a model to automatically identify false fires, allowing them to be removed before further processing. The model uses training data from 1,325 camera deployments as well as a trained network, CaffeNet, specifically modified for WildTrax. This tool results in less human time spent sifting through images of vegetation movement. In addition, WildTrax contains an “Auto-Cow” model that similarly identifies images of cows so that a human tagger doesn’t need to look through them. Finally, WildTrax also contains a tool called a “Context Tagger”, which identifies series of images of the same animal and allows for batch tagging of the entire series.

  • How accurate are the false-fire models?

    The model was validated with an additional 121 camera deployments with 79,451 false-fire images. The model identified 34,456 (43.6%) of false fires with a 0.2% error (false positive) rate. That is, more than 40% of false fires can be reliably (0.2% error) removed before processing. Depending on the camera unit used, image quality and habitat type results may vary.

  • What is wildlife bioacoustics and why is it important?

    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.

  • Where can I learn more about wildlife bioacoustics?

    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.

  • How does the Bioacoustic Unit record sound?

    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. 

  • What brands of Autonomous Recording Units and memory cards does the Bioacoustic Unit use?

    The Bioacoustic Unit uses Song Meter Autonomous Recording Units (ARUs) 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.

    For more information on ARU brands, please click here.

  • Is there an optimal time of day or week of the year when I should deploy an Autonomous Recording Unit?

    Deciding on a recording schedule for an ARU-based program will depend somewhat on the taxa of interest and the objectives of the program; however, there are some general recommendations and standards based on BU research. Firstly, to save storage space, there is often little value in collecting afternoon data. Secondly, recording from the last week of May to the first week of July (in Alberta) yields the highest detection rates for the most species, with the exception of some amphibians and owls which are more likely to be detected in April and May. For more information click here.

    If you have a particular species of interest, please click here to view species-specific sampling times (Figure 28) and listening schedule (Table 5).

  • Should I sample repeatedly at the same location or new locations within my area of interest?

    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).

  • How long should I leave an Autonomous Recording Unit out?

    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 those of 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. 

  • With equal effort, should I sample for a day, a week, or a month?

    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.

  • How many repeated samples will I need to be 95% certain that a species is truly absent?

    This is entirely dependent on the frequency with which a species sings. We have estimates for all species, however, so you can assess the effort required to ensure you detect a species if it is present.

  • Are there ecological attributes that influence how I should sample?

    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.

  • I am interested in trend estimates of a particular species; should I sample repeatedly at the same station?

    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.

  • How long of a point count should I listen to?

    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.

  • Should I use more, shorter point counts or fewer, longer point counts?

    Using more point counts with shorter duration detected a larger proportion of all species compared to fewer, longer duration point counts.  See 'How many repeated samples will I need to be 95% certain that a species is truly absent?'.

  • If I have a limited listening budget, what should my listening schedule be?

    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.

  • I am only interested in a few specific species; is there a way to further increase processing efficiency?

    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.