DOI: 10.5061/dryad.2280gb5z4
title: Using mobile device built-in microphones to monitor bats: A new opportunity for large-scale participatory science initiatives

description: 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.
description: # 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) |