Using mobile device built-in microphones to monitor bats: A new opportunity for large-scale participatory science initiatives
Data files
Mar 13, 2024 version files 3.91 GB
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Appendix_S6_recs.zip
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mobile_device_recs.zip
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README.md
Abstract
Here we present audio data collected in the study titled "Using mobile device built-in microphones to monitor bats: a new opportunity for large-scale participatory science initiatives", now accepted for publication in Biodiversity and Conservation (DOI: 10.1007/s10531-024-02818-9).
Citizen science has become a crucial tool in biodiversity monitoring, often facilitated by the diffusion of mobile devices, such as smartphones and tablets. High costs of professional equipment often limit large-scale monitoring, particularly in bat monitoring programmes based on acoustic surveys. Here we present the potential of using mobile devices for bat monitoring, allowing for large-scale, volunteer-based monitoring programmes. We initially compared mobile devices’ performance with a professional bat detector for recording low-frequency bat calls. We then conducted a citizen science pilot study to test the method’s feasibility in a real-world setting, recording echolocation and social calls of nine European bat species. We found high similarity in spectrogram quality between calls recorded by mobile devices and professional bat detectors. However, differences in sound quality and effectiveness among mobile device brands and models were found. The citizen science pilot study tested 35 mobile device models, all of which effectively recorded bats. This study suggests that mobile devices could be an accessible, no-cost tool for large-scale bat monitoring. Incorporating mobile devices into existing monitoring networks or creating new dedicated programmes could not only enhance data collection, but also boost public knowledge and awareness about bats, ultimately promoting informed decision-making and better conservation strategies.
To facilitate a comprehensive evaluation of the acoustic data quality achievable through the described method, we are providing access to all bat recordings collected via mobile devices during the study. These have been compiled into the compressed file named "mobile_device_recs.zip".
For those seeking a quicker review, we recommend downloading the "Appendix_S6_recs.zip" file, which contains selected examples of echolocation or social calls from the nine bat species recorded during our research. Spectrograms of these example recordings are displayed in Appendix S6 of the published study.
README: Using mobile device built-in microphones to monitor bats: a new opportunity for large-scale participatory science initiatives
Here we present audio data collected in the study titled "Using mobile device built-in microphones to monitor bats: a new opportunity for large-scale participatory science initiatives", now accepted for publication in Biodiversity and Conservation (DOI: 10.1007/s10531-024-02818-9).
To facilitate a comprehensive evaluation of the acoustic data quality achievable through the described method, we are providing access to all bat recordings collected via mobile devices during the study. These have been compiled into the compressed file named "mobile_device_recs.zip".
For those seeking a quicker review, we recommend downloading the "Appendix_S6_recs.zip" file, which contains selected examples of echolocation or social calls from the nine bat species recorded during our research. Spectrograms of these example recordings are displayed in Appendix S6 of the published study.
All mobile device recordings: Description of the data and file structure
In this dataset, audio files are organised into subfolders based on the sampling session and the bat species they refer to.
There are a total of 80 sampling sessions (main folders). The name of each main folder includes, separated by underscores:
- The operating system of the mobile device used (“and” for Android and “ios” for iOS);
- The mobile device model (e.g., “SM-G920F”);
- The code of the sampling site (e.g., “LEB01”);
- The date of the sampling session in the format “YYYYMMDD”.
Subfolders refer to species names:
- hypsav: Hypsugo savii
- nyclas: Nyctalus lasiopterus
- nyclei: Nyctalus leisleri
- nycnoc: Nyctalus noctula
- pipkuh: Pipistrellus kuhlii
- pipnat: Pipistrellus nathusii
- pippip: Pipistrellus pipistrellus
- pippyg: Pipistrellus pygmaeus
- tadten: Tadarida teniotis
Two additional subfolders may be found:
- aliasing: presence of aliasing artifacts in the audio file;
- undet: one or more bat calls in the audio file were not identified.
The following table provides information on the municipality where each sampling site was located.
sampling site | municipality |
---|---|
LEB01 | Villastellone (IT) |
LEB02 | Villastellone (IT) |
LEB03 | Villastellone (IT) |
LEB04 | Villastellone (IT) |
LEB05 | Gravere (IT) |
LEB07 | Buttigliera Alta (IT) |
LEB08 | Bagnasco (IT) |
LEB09 | Rodello (IT) |
LEB10 | Turin (IT) |
LEB11 | Turin (IT) |
LEB12 | Turin (IT) |
LEB13 | Turin (IT) |
LEB14 | Trento (IT) |
LEB16 | Turin (IT) |
LEB17 | Pragelato (IT) |
LEB18 | Sommariva Perno (IT) |
LEB19 | Leini (IT) |
LEB20 | Turin (IT) |
LEB21 | Castelfranco Emilia (IT) |
LEB22 | San Lazzaro di Savena (IT) |
LEB23 | Turin (IT) |
LEB24 | Turin (IT) |
LEB25 | Turin (IT) |
LEB27 | Turin (IT) |
LEB28 | Turin (IT) |
LEB29 | Seville (ES) |
LEB30 | Turin (IT) |
LEB31 | Turin (IT) |
LEB32 | Bra (IT) |
Appendix S6 recordings: Description of the data and file structure
In this dataset, audio files are organised in two groups: Group 1 is composed by recordings collected by bat detector and one or more mobile device models, therefore a visual comparison can be done on the same sequence recorded by different devices; Group 2 is composed by additional example recordings collected only by mobile devices.
Each of the two groups is in turn organised into subfolders directly referring to the Appendix S6 figure numbers and contents. The name of each subfolder consists of (separated by underscores):
- The reference figure number in Appendix S6 (e.g., “S1”);
- The species abbreviation;
- The call type abbreviation.
Species abbreviations are as follows:
- hypsav: Hypsugo savii
- nyclas: Nyctalus lasiopterus
- nyclei: Nyctalus leisleri
- nycnoc: Nyctalus noctula
- pipkuh: Pipistrellus kuhlii
- pipnat: Pipistrellus nathusii
- pippip: Pipistrellus pipistrellus
- pippyg: Pipistrellus pygmaeus
- tadten: Tadarida teniotis
Call type abbreviations are as follows:
- echo: echolocation call sequence
- feed: presence of feeding buzz
- socialC: type C social call(s)
- socialD: type D social call(s)
- socialD2: type D2 social call(s)
- at_upper_limit: call sequence partially exceeding the upper limit of mobile device detectability range
- aliasing: additional abbreviation indicating the presence of aliasing artifacts
In each subfolder, the name of each audio file consists of (separated by underscores):
- The sampling site code (e.g., “LEB01”);
- The date of the sampling session in the format “YYYYMMDD”.
- The type or operating system of the device used (“bd” for bat detector, “and” for Android and “ios” for iOS);
- The device model (e.g., “SM-G920F”; for bat detector recordings, “SMMB” is the abbreviation for Song Meter Mini Bat);
- The original recording name (variable among sessions).
The following table provides information on the municipality where each sampling site was located.
sampling site | municipality |
---|---|
LEB01 | Villastellone (IT) |
LEB02 | Villastellone (IT) |
LEB05 | Gravere (IT) |
LEB08 | Bagnasco (IT) |
LEB14 | Trento (IT) |
LEB17 | Pragelato (IT) |
LEB27 | Turin (IT) |
LEB29 | Seville (ES) |
LEB30 | Turin (IT) |
LEB31 | Turin (IT) |
LEB32 | Bra (IT) |