bei Google ScholarTitelangaben
Müller, Jörg ; Mitesser, Oliver ; Schaefer, H. Martin ; Seibold, Sebastian ; Busse, Annika ; Kriegel, Peter ; Rabl, Dominik ; Gelis, Rudy ; Arteaga, Alejandro ; Freile, Juan ; Leite, Gabriel Augusto ; de Melo, Tomaz Nascimento ; LeBien, Jack ; Campos-Cerqueira, Marconi ; Blüthgen, Nico ; Tremlett, Constance J. ; Böttger, Dennis ; Feldhaar, Heike ; Grella, Nina ; Falconí-López, Ana ; Donoso, David A. ; Moriniere, Jerome ; Buřivalová, Zuzana:
Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.
In: Nature Communications.
Bd. 14
(2023)
.
- 6191.
ISSN 2041-1723
DOI: https://doi.org/10.1038/s41467-023-41693-w
Angaben zu Projekten
| Projektfinanzierung: |
Deutsche Forschungsgemeinschaft Deutsche Forschungsgemeinschaft (DFG) funding via the DFG Research Unit REASSEMBLY (FOR 5207) the Prince Albert II of Monaco Foundation, grant 3386 |
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Abstract
Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics and metabarcoding to measure forest recovery post-agriculture in a global biodiversity hotspot in Ecuador. We show that the community composition, and not species richness, of vocalizing vertebrates identified by experts reflects the restoration gradient. Two automated measures – an acoustic index model and a bird community composition derived from an independently developed Convolutional Neural Network - correlated well with restoration (adj-R² = 0.62 and 0.69, respectively). Importantly, both measures reflected composition of non-vocalizing nocturnal insects identified via metabarcoding. We show that such automated monitoring tools, based on new technologies, can effectively monitor the success of forest recovery, using robust and reproducible data.