Rapid Acoustic Survey for Biodiversity Appraisal

By Jérôme Sueur, Sandrine Pavoine, Olivier Hamerlynck, Stéphanie Duvail. PLoS ONE, 2008

645 citations recorded by the source corpus.

Biodiversity assessment remains one of the most difficult challenges encountered by ecologists and conservation biologists. This task is becoming even more urgent with the current increase of habitat loss. Many methods–from rapid biodiversity assessments (RBA) to all-taxa biodiversity inventories (ATBI)–have been developed for decades to estimate local species richness. However, these methods are costly and invasive. Several animals–birds, mammals, amphibians, fishes and arthropods–produce sounds when moving, communicating or sensing their environment. Here we propose a new concept and method to describe biodiversity. We suggest to forego species or morphospecies identification used by ATBI and RBA respectively but rather to tackle the problem at another evolutionary unit, the community level. We also propose that a part of diversity can be estimated and compared through a rapid acoustic analysis of the sound produced by animal communities. We produced α and β diversity indexes that we first tested with 540 simulated acoustic communities. The α index, which measures acoustic entropy, shows a logarithmic correlation with the number of species within the acoustic community. The β index, which estimates both temporal and spectral dissimilarities, is linearly linked to the number of unshared species between acoustic communities. We then applied both indexes to two closely spaced Tanzanian dry lowland coastal forests. Indexes reveal for this small sample a lower acoustic diversity for the most disturbed forest and acoustic dissimilarities between the two forests suggest that degradation could have significantly decreased and modified community composition. Our results demonstrate for the first time that an indicator of biological diversity can be reliably obtained in a non-invasive way and with a limited sampling effort. This new approach may facilitate the appraisal of animal diversity at large spatial and temporal scales.

Subfields: Developmental Biology, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/8fd14ef0bbdf2c1e9637451977058fd27e5a5533

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Citations in the corpus

Acoustic indices for biodiversity assessments: Analyses of bias based on simulated bird assemblages and recommendations for field surveys (2015, positive): “We considered seven acoustic diversity indices (Table A1): (1) spectral entropy, Hf (Sueur et al., 2008a); (2) temporal entropy, Ht (Sueur et al., 2008a); (3) acoustic entropy, H, which is composed of Hf and Ht (Sueur et al., 2008a); (4) the acoustic complexity index, ACI (Pieretti et al., 2011);…”
Underwater Soundscape Monitoring and Fish Bioacoustics: A Review (2018, methodology): “The acoustic dissimilarity index (D) was also used to compare the two Tanzanian forests [23].”
Celestial patterns in marine soundscapes (2014, background): “While passive acoustic monitoring holds great promise as an ecological assessment tool (Luczkovich et al. 2008, Sueur et al. 2008, 2012, Pieretti et al. 2011, Gasc et al. 2013), more work is required in order to carefully link ecological and acoustic measures.”
The use of acoustic indices to determine avian species richness in audio-recordings of the environment (2014, neutral): “More recently, temporal entropy (H[t]) was introduced to characterize the temporal dispersal of acoustic energy within a recording (Sueur et al., 2008).”
The soundscape of the Anthropocene ocean (2021, background): “Despite this progress, the paradigm that soundscapes can be used as proxies for ecosystem health, developed for terrestrial ecosystems ( 41 ), has been applied to the marine environment with only limited success [e.g., ( 42 , 43 )].”
Acoustic classification of Australian anurans based on hybrid spectral-entropy approach (2011, methodology): “[12] proposed the use of Shannon entropy on a time series sequence, where the categories are represented by time units and their frequencies are referred to the probability mass function of the amplitude envelope.”
Acoustic Indices for Biodiversity Assessment and Landscape Investigation (2014, positive): “This index returned expected results on recordings made in a Tanzanian forest where animal acoustic activity was high and background noise due to wind, rain and human activity very low leading to a high signal-to-noise ratio [44].”
A primer of acoustic analysis for landscape ecologists (2011, methodology): “Seewave can also save the data of the spectrogram as a numeric matrix with amplitude values by setting the value of plot in the function spectro() to false—saving the values as a matrix.”
Avian soundscapes and cognitive landscapes: theory, application and ecological perspectives (2011, methodology): “This technology, coupled with dedicated software (e.g., Seewave; Sueur et al. 2008b) and a robust metric like the ACI (Pieretti et al. 2011) and other derivative indexes like the Niche Acoustic Breadth and Overlap and the Shannon entropy, is the essential premise for a longterm plan of…”
Acoustic fish communities: sound diversity of rocky habitats reflects fish species diversity (2019, background): “In the first case, abundant species dominating the acoustic space could reduce the local acoustic diversity because they lack competitors that use the same communication window (Sueur et al. 2008).”