Acoustic Indices #

    Marina D. A. Scarpelli - PhD candidate at QUT
    @ScarpelliNina

    FCS

    Two approaches: Bioacoustics vs Ecoacoustics #

    • Species-specific approach: consider the communication system exclusively between sender and receiver
      • Usually called bioacoustics

    Communication1to1

    • Communication structure is more similar to a network (Sueur, 2014)
      • Ecoacoustics or soundscape approach
        • Soundscape: all the sounds in the landscape
        • Biophony + Geophony + Technophony
      • Enables the detection not only of species calls, but also social, behavioural and ecological cues

    Communicationnetwork

    Passive Acoustic Monitoring (PAM) #

    • Passive acoustic recorders facilitated the investigation of more comprehensive questions, enabling research on:
      • Soundscapes
      • Multiple species
      • Upscaling bioacoustics questions: temporal and spatially
    • However analytical tools still needed to be improved

    sensor2

    Approaches to analyse the data #

    • Manually listening/annotating
    • Species recognisers (automatic/semi-automatic)
    • Acoustic indices (AI): summarising information
      • More than 69 existing indices (Alcocer et al., 2022)

    sugai-et-al-2019

    Sugai LSM, Silva TSF, Ribeiro JW, Llusia D. 2019. Terrestrial Passive Acoustic Monitoring: Review and Perspectives. BioScience 69:5–11.

    Acoustic indices #

    • Linked to the “Soundscape” or Ecoacoustics approach (Alcocer et al., 2022)
    • Mathematical function applied to waveform or spectrogram
      • Can be derived from traditional ecological indices (i.e.: diversity, richness, etc)
    • Summarise the acoustic information with a community perspective
      • Moving away from species-specific questions

    Types of Indices #

    Sueur et al., 2014 #

    • Intensity indices: measure sound intensity (in dB) with different frequency weighting and time averaging
    • They usually miss information on frequency and temporal patterns of soundscapes

    towsey-et-al-2014

    Types of Indices #

    Sueur et al., 2014 #

    • Complexity indices: more species and individuals communicating will increase the complexity of the soundscape
    • Heterogeneity would be a proxy of animal acoustic activity

    pieretti-et-al-2011

    Types of Indices #

    Sueur et al., 2014 #

    • Soundscape derived indices: estimates the relative contribution of biophony in relation to technophony and geophony
    • Splits the spectrum into frequency bands:
      • 0.2 to 2 kHz = technophony
      • 2 to 8kHz = biophony
      • geophony: occupies the entire spectrum

    gage-and-axel-2014

    Soundscape indices #

    Splitting frequency bands #

    Sometimes it works…

    Technophony technophony-example

    Soundscape indices #

    Splitting frequency bands #

    Sometimes it works…

    Biophony biophony-example

    Soundscape indices #

    Splitting frequency bands #

    Sometimes it works…

    Geophony geophony-example

    Soundscape indices #

    Splitting frequency bands #

    Sometimes it does not work…

    Geophony rustling-example

    Soundscape indices #

    Splitting frequency bands #

    Sometimes it does not work…

    Biophony boobook-example

    Soundscape indices #

    Splitting frequency bands #

    Sometimes it does not work…

    Biophony birds-example

    Acoustic indices disadvantages #

    • Sounds are multidimensional: frequency, time and amplitude
    • Environmental recordings have an additional constraint:
      • It is hard to determine the distance between sound source (e.g.: animal) to microphone
        • Amplitude metrics depend on distance because of attenuation effects
    • Other factors might influence values:
      • E.g. background noise, intensity and repetition of calls by one individual, overlaps between calls,etc
      • Therefore the relationship between one index/one ecological metric is still unclear

    Acoustic indices measuring biodiversity #

    Pasted image 20220915145342.png

    Summary of meta-analysis #

    Pasted image 20220920125409.png

    • AI had an overall moderate positive correlation with biodiversity
    • Best biodiversity metric correlated with AI was abundance of sounds
    • There was no single best AI

    Summary of meta-analysis #

    Pasted image 20220920125409.png

    • Majority of studies investigated terrestrial habitats and birds are the main group studied;
    • Species richness and abundance of sounds are the most explored metric to relate acoustic indices values to biodiversity measures

    Now, let’s do some practical exercises!

    Go to the practical .