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Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes

Abstract

Context

There is a long-standing quest in landscape ecology for holistic biodiversity metrics accounting for multi-taxa diversity in heterogeneous habitat mosaics. Passive acoustic monitoring of biodiversity may provide integrative indices allowing to investigate how soundscapes are shaped by compositional and configurational heterogeneity of mosaic landscapes.

Objectives

We tested the effects of dominant habitat and landscape heterogeneity on acoustic diversity indices across a large range of mosaic landscapes from two long-term socio-ecological research areas in Occitanie, France and Arizona, USA.

Methods

We assessed acoustic diversity by automated recording for 44 landscapes distributed along gradients of compositional and configurational heterogeneity. We analyzed the responses of six acoustic indices and a composite multiacoustic index to habitat type and multi-scale landscape metrics for three time periods: 24 h-diel cycles, dawns and nights.

Results

Landscape mosaics dominated by permanent grasslands in Occitanie and woodlands in Arizona produced the highest values of acoustic diversity. Moreover, several indices including H, ADI, NDSI, NP and the multiacoustic index consistently responded to edge density in both study regions, but with contrasting patterns, increasing in Occitanie and decreasing in Arizona. Landscape configuration was a key driver of acoustic diversity for diel and nocturnal soundscapes, while dawn soundscapes depended more on landscape composition.

Conclusions

Acoustic diversity was correlated more with configurational than compositional heterogeneity in both regions, with contrasting effects explained by the interplay between biogeography and land use history. We suggest that multiple acoustic indices are needed to properly account for complex responses of soundscapes to large-scale habitat heterogeneity in mosaic landscapes.

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Data availability

Datasets on acoustic indices per site and recording periods are available upon request at https://nextcloud.inrae.fr/apps/files/?dir=/dataacoustic&fileid=20260044

Code availability

R codes for computing acoustic indices can be found in AG’s GitHub site at https://github.com/agasc/Soundscape-analysis-with-R

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Acknowledgements

We thank Bastien Castagneyrol, Fabien Laroche, Sylvie Ladet, Pierre Gaüzère, Daphné Durant and Jean-François Julien, and all our colleagues from the Sonates and Sonatas research projects for the fruitful exchanges. Field work in Arizona was made possible thanks to Ruth Gosset, François-Michel Le Tourneau, Régis Ferrière, Lisa Vincent and all at the OHMi Pima county and UMI iGlobes in Tucson. We thank all the people from our study areas that kindly allowed us to access their land: in Arizona, Luis Calvo at Chuparosa Inn, Ben Wilder at Tumamoc Hill, Jonathan Horst, Jonathan Lutz, Luke Safford and all at the Tucson Audubon Society and Paton center for hummingbirds in Patagonia, Debbie Colodner at the Desert Museum, Kevin Bonine at Biosphere2, and in Occitanie: Margot and Alexander, Jane and Jean-Louis, Marie, Clare and Alastair, Maryse and all the inhabitants of the Aurignac county. We thank three anonymous reviewers for their useful comments that helped to improve the former version.

Funding

Project fundings were obtained from INRAE ACT (PARUS 2020), LTSER ZA PyGar (SOULCIE 2019) and LabExs DRIIHM (Sonatas 2018–2021) and Dynamite (Sonates 2019–2021).

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Authors

Contributions

LB and AS designed the study, LB, AS, JSPF, MC and FC conducted the field work, FC computed the GIS data, AG provided the R code for acoustic diversity indices, LB conducted the analysis with inputs by JSP, MC and AG, and all authors contribute to writing and editing the final manuscript.

Corresponding author

Correspondence to Luc Barbaro.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 430 kb)

10980_2021_1391_MOESM2_ESM.pdf

Supplementary file2 Appendix S2. Matrix correlation plots between the six median AI values for 24hr-diel cycles and the two best landscape predictors (edge density and woodland cover) measured at five increasing buffer scales (250, 500, 1000, 2500 and 5000m around recorders; see Methods) (PDF 275 kb)

Supplementary file3 (PDF 259 kb)

10980_2021_1391_MOESM4_ESM.pdf

Supplementary file4 Appendix S4. Results of post-hoc tests on Linear Mixed Models of AIs’ responses to the dominant habitat type in each study area. Codes and definitions of acoustic indices are listed in Table 1. Significance levels from post-hoc Tukey tests adjusted for multiple comparisons as follows: ***P < 0.001; **P < 0.01; *P < 0.05 (PDF 586 kb)

10980_2021_1391_MOESM5_ESM.pdf

Supplementary file5 Appendix S5. Estimates ± SE, z and P values of best LMMs obtained after stepwise backward elimination of non-significant terms from the full model. AICc of best, full and null models are indicated (PDF 525 kb)

Supplementary file6 (PDF 1418 kb)

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Barbaro, L., Sourdril, A., Froidevaux, J.S.P. et al. Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes. Landsc Ecol 37, 1125–1143 (2022). https://doi.org/10.1007/s10980-021-01391-8

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Keywords

  • Acoustic diversity
  • Edge density
  • Landscape heterogeneity
  • Multiacoustic index
  • Soundscapes