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Frequently Asked Questions
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What is WildTrax?
WildTrax is an online network for storing, managing, processing, and sharing biological data collected using sensor technology. Effective use of these technologies requires tools and systems for managing large datasets, and also provides opportunities to address broad-scale questions using novel analytical approaches.
WildTrax provides the latest techniques and technology to organizations and individuals interested in using sensors for biological monitoring. It is a platform for archiving, processing, and sharing biological sensor data.
How can I get involved in WildTrax?
WildTrax is available for organizations and citizens using cameras and ARUs for archiving, managing, processing, and sharing images and recordings. Contact email@example.com or see (REFERENCE DOCUMENT/LINK) for details on getting set up.
Why should I get involved in WildTrax?
WildTrax exists to support a standardized and centralized approach to sensor data collection across a network of organizations and individuals. By coordinating and centralizing activity, WildTrax will encourage data transparency, sharing, and collaboration on environmental monitoring and management activities.
The value of data is maximized through the integration of datasets in order to leverage investment in individual projects, avoid overlap and duplication, and create opportunities to answer broad-scale ecological questions.
How can I set up a camera or Autonomous Recording Unit?
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 focussed on the reference stake (see diagram) at 80 cm above the ground. Your target detection zone should be approximately 3–5 m from a camera. Face the camera North (ideally) or South if possible to avoid visibility issues from direct sunlight.
The Autonomous Recording Unit 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?
Data contributors maintain ownership and privacy rights of contributed data. When you contribute 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. Data owners can also transfer ownership of the data to the ABMI and Bioacoustic Unit at the owner’s discretion.
What brand of camera and memory card should I use to be consistent with the WildTrax?
WildTrax uses Reconyx PC900 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.
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 the 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.
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 removed before processing. Depending on 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 and use of vocalizations that birds, amphibians, and bats produce. Sounds are identified to the species or even individual level using unique patterns known as spectral signatures. These data are used to monitor the status of a species across a given area and through time.
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 Bioacoustic Unit uses robust environmental sensors, called Autonomous Recording Units (ARUs), to record sounds produced by vocalizing animals. There are recommend 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 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.
For more information on Autonomous Recording Unit brands, please click here.
Is there an optimal time of day or week of the year when I should deploy an Autonomous Recording Unit?
Firstly, to save storage space, there is little value in collecting afternoon data. Secondly, recording from the last week of May to the first week of July yields the highest detection rates for the most species. For more informnation 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.
How long should I leave an Automated Recording Unit out?
For migratory passerines, leaving an Automated Recording Unit 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. However, there is evidence that, overall, more species will be detected with more sampling effort.
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 detections 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.
How many repeated samples will I need to be 95% certain that a species is truly absent?
To be 95% certain that a species is truly absent, on average you will have to repeatedly sample with 15, 8, 6, and 5 visits for season, month, week, and day revisit designs, respectively.
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, 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 of repeatedly sampling at the same station when estimating trends of an individual species. However, it is unknown if these repeated samples should come from multiple samples within a day or throughout the entire breeding period.
How long 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. The shorter the point count, the lower the proportion of total species detected. Listening to the entire 10-minute point count will result in 100% of all vocalizing species present in a recording to be detected.
Should I listen to data sequentially or non-sequentially?
A 10-minute recording that is listened to non-sequentially will result in a greater proportion of the cumulative number of species being detected than will listening to data sequentially.
Should I use more, short point counts or few, long point counts?
Using more point counts with shorter duration detected a larger proportion of all the species compared to fewer, longer duration point counts.
How is sampling effort related to the proportion of species observed?
When 10%, 25%, 50%, 75%, and 100% of all point counts at a station are used to detect species, approximately 36%, 59%, 78%, 91%, and 100% of species will be detected, respectively.
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.
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, 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.