Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests

Jörg Müller (), Oliver Mitesser, H. Martin Schaefer, Sebastian Seibold, Annika Busse, Peter Kriegel, Dominik Rabl, Rudy Gelis, Alejandro Arteaga, Juan Freile, Gabriel Augusto Leite, Tomaz Nascimento Melo, Jack LeBien, Marconi Campos-Cerqueira, Nico Blüthgen, Constance J. Tremlett, Dennis Böttger, Heike Feldhaar, Nina Grella, Ana Falconí-López, David A. Donoso, Jerome Moriniere and Zuzana Buřivalová
Additional contact information
Jörg Müller: Biocenter, University of Würzburg
Oliver Mitesser: Biocenter, University of Würzburg
H. Martin Schaefer: Fundación Jocotoco, Valladolid N24-414 y Luis Cordero
Sebastian Seibold: Ecosystem Dynamics and Forest Management Research Group
Annika Busse: Saxon-Switzerland National Park
Peter Kriegel: Biocenter, University of Würzburg
Dominik Rabl: Biocenter, University of Würzburg
Rudy Gelis: Yanayacu Research Center
Alejandro Arteaga: Biodiversity Field Lab (BioFL), Khamai Foundation
Juan Freile: Pasaje El Moro E4-216 y Norberto Salazar
Gabriel Augusto Leite: Rainforest Connection, Science Department
Tomaz Nascimento Melo: Rainforest Connection, Science Department
Jack LeBien: Rainforest Connection, Science Department
Marconi Campos-Cerqueira: Rainforest Connection, Science Department
Nico Blüthgen: Technische Universität Darmstadt
Constance J. Tremlett: Technische Universität Darmstadt
Dennis Böttger: Friedrich-Schiller-University Jena
Heike Feldhaar: University of Bayreuth
Nina Grella: University of Bayreuth
Ana Falconí-López: Biocenter, University of Würzburg
David A. Donoso: Medio Ambiente y Salud-BIOMAS-Universidad de las Américas
Jerome Moriniere: AIM - Advanced Identification Methods GmbH
Zuzana Buřivalová: University of Wisconsin-Madison, Department of Forest and Wildlife Ecology and The Nelson Institute for Environmental Studies

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-41693-w

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