Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
17 10 2023
17 10 2023
Historique:
received:
28
04
2023
accepted:
07
09
2023
medline:
23
10
2023
pubmed:
18
10
2023
entrez:
17
10
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37848442
doi: 10.1038/s41467-023-41693-w
pii: 10.1038/s41467-023-41693-w
pmc: PMC10582010
doi:
Banques de données
figshare
['10.6084/m9.figshare.23620323']
Dryad
['10.5061/dryad.59zw3r2dm']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6191Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023. Springer Nature Limited.
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