wildRtrax, pronounced ‘wilder tracks’, is an R package for ecologists and advanced users who work with environmental sensor data, mainly from autonomous recordings units (ARUs). It contains functions designed to meet most needs in order to organize, analyze and standardize data to the WildTrax infrastructure.
wildRtrax is self-contained and must be run under an R statistical environment, and it also depends on many other R packages.
wildRtrax is free software and distributed under MIT License (c) 2020.
WildTrax is a web-enabled portal designed to manage, store, process, and share this environmental sensor data and transform it into biological data. It was developed by the Alberta Biodiversity Monitoring Institute and the Bioacoustic Unit.
wildRtrax serves as a parallel design and indicator to analytics and functions that can be developed in WildTrax.
wildRtrax depends on a multitude of packages to provide flexible routines and work flows for data management.
tidyverse for piping functions, standard grammar and tidy data manipulation,
doParallel for parallel computing, and acoustic analysis packages:
seewave. Certain functions are indebted to the QUT Ecoacoustics Analysis Software software package as well.
If you think you have found a bug in
wildRtrax, you should report it to developers or maintainers. Please do not send bug reports to R mailing lists, since
wildRtrax is not a standard R package. The preferred forum to report bugs is GitHub. Here is what is required in order to report a bug - issues are welcome and encouraged and are the only way to make
wildRtrax is dependent on user contribution and all feedback is welcome. If you have problems with
wildRtrax, it may be as simple as incomplete documentation. Feature requests also are welcome, but they are not necessarily fulfilled. A new feature will be added if it is easy to do and it looks useful to the user base of the package, or if you submit fully annotated code.
See here for more information.
Autonomous recording units (ARUs) and remote camera traps collect data on the environment by means of capturing acoustic or visual data, respectively. ARUs are used to survey a variety of species such as birds, amphibians, and bats. But really anything that gives a vocalizing cue. Camera traps are best used to detect and monitor mammals. See
abmi.camera.extras if you’re interested in getting estimates of animal density collected from remote camera traps. Both environmental sensors are designed to record sound or images autonomously for long periods of time. This is turn can accrue a large amount of data.
There are two major audio file types used within the wildRtrax framework: wac and wav
There are also a couple other file types you might be working with:
file <- "/volumes/GoogleDrive/Shared drives/wildRtrax/data/example/ABMI-0754-SW_20170301_085900.wav" wave_t <- tuneR::readWave(file, header = T) #True header format wave_f <- tuneR::readWave(file, header = F) list(wave_t, wave_f) #> [] #> []$sample.rate #>  44100 #> #> []$channels #>  2 #> #> []$bits #>  16 #> #> []$samples #>  26459904 #> #> #> [] #> #> Wave Object #> Number of Samples: 26459904 #> Duration (seconds): 600 #> Samplingrate (Hertz): 44100 #> Channels (Mono/Stereo): Stereo #> PCM (integer format): TRUE #> Bit (8/16/24/32/64): 16
sound_length_S4 <- round((wave_f@left / email@example.com), 2) #Is equivalent to: sound_length_list <- wave_t$samples / wave_t$sample.rate
A spectrogram is a visual representation of the spectrum of frequencies of an audio signal as it varies with time (Wikipedia). Spectrograms can be used to identify animal vocalizations by their unique spectral signature. Generally speaking, there are three pieces of information you can extract from a spectrogram:
Let’s create a spectrogram to get a better look at some of those audio files. Here’s one way to do it using
SoX is also a very powerful command line tool that can build spectrograms as well. Processing time here is much faster given R doesn’t have to read in the file as an S4 wave object. More on this later.
Familiarity with processes, protocols, equipment and data is an important first step in understanding how to manage environmental sensor data. Check with your study design or monitoring plan to ensure that you are correctly managing your data prior to heading into the field. wildRtrax doesn’t focus on the field components of the data flows but is heavily dependent on it. Acoustic data has certain metadata dependencies that can be extracted from raw data. But robust field or visit metadata as it’s called in wildRtrax and WildTrax is important to support the quality control process of the incoming sensor data.
The wildRtrax prefers that the file name string from which the data is deriving is composed of two parts: a spatial component and a temporal component. We call these fields the location and the recording_date_time of the audio respectively. The location, date and time should be critical pieces of information that should be collected and checked when you are visiting environmental sensors in the field.
Collecting lots of data with environmental sensors is easy. Are there ways you can reduce what you collect and have to process?