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Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.

التفاصيل البيبلوغرافية
العنوان: Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.
المؤلفون: Müller J; Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Glashüttenstr. 5, 96181, Rauhenebrach, Germany. Joerg.Mueller@npv-bw.bayern.de.; Bavarian Forest National Park, Freyungerstr. 2, 94481, Grafenau, Germany. Joerg.Mueller@npv-bw.bayern.de., Mitesser O; Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Glashüttenstr. 5, 96181, Rauhenebrach, Germany., Schaefer HM; Fundación Jocotoco, Valladolid N24-414 y Luis Cordero, Quito, Ecuador., Seibold S; Technical University of Munich, School of Life Sciences, Ecosystem Dynamics and Forest Management Research Group, Hans-Carl-von-Carlowitz-Platz 2, 85354, Freising, Germany.; Berchtesgaden National Park, Doktorberg 6, Berchtesgaden, 83471, Germany., Busse A; Saxon-Switzerland National Park, An der Elbe 4, 01814, Bad Schandau, Germany., Kriegel P; Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Glashüttenstr. 5, 96181, Rauhenebrach, Germany., Rabl D; Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Glashüttenstr. 5, 96181, Rauhenebrach, Germany., Gelis R; Yanayacu Research Center, Cosanga, Ecuador., Arteaga A; Biodiversity Field Lab (BioFL), Khamai Foundation, Quito, Ecuador., Freile J; Pasaje El Moro E4-216 y Norberto Salazar, EC 170902, Tumbaco, DMQ, Ecuador., Leite GA; Rainforest Connection, Science Department, 440 Cobia Drive, Suite 1902, Katy, TX, 77494, USA., de Melo TN; Rainforest Connection, Science Department, 440 Cobia Drive, Suite 1902, Katy, TX, 77494, USA., LeBien J; Rainforest Connection, Science Department, 440 Cobia Drive, Suite 1902, Katy, TX, 77494, USA., Campos-Cerqueira M; Rainforest Connection, Science Department, 440 Cobia Drive, Suite 1902, Katy, TX, 77494, USA., Blüthgen N; Ecological Networks Lab, Department of Biology, Technische Universität Darmstadt, Schnittspahnstr. 3, 64287, Darmstadt, Germany., Tremlett CJ; Ecological Networks Lab, Department of Biology, Technische Universität Darmstadt, Schnittspahnstr. 3, 64287, Darmstadt, Germany., Böttger D; Phyletisches Museum, Institute for Zoology and Evolutionary Research, Friedrich-Schiller-University Jena, Jena, Germany., Feldhaar H; Animal Population Ecology, Bayreuth Center for Ecology and Environmental Research (BayCEER), University of Bayreuth, 95440, Bayreuth, Germany., Grella N; Animal Population Ecology, Bayreuth Center for Ecology and Environmental Research (BayCEER), University of Bayreuth, 95440, Bayreuth, Germany., Falconí-López A; Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Glashüttenstr. 5, 96181, Rauhenebrach, Germany.; Grupo de Investigación en Biodiversidad, Medio Ambiente y Salud-BIOMAS-Universidad de las Américas, Quito, Ecuador., Donoso DA; Grupo de Investigación en Biodiversidad, Medio Ambiente y Salud-BIOMAS-Universidad de las Américas, Quito, Ecuador.; Departamento de Biología, Facultad de Ciencias, Escuela Politécnica Nacional, Av. Ladrón de Guevara E11-253, CP 17-01-2759, Quito, Ecuador., Moriniere J; AIM - Advanced Identification Methods GmbH, Niemeyerstr. 1, 04179, Leipzig, Germany., Buřivalová Z; University of Wisconsin-Madison, Department of Forest and Wildlife Ecology and The Nelson Institute for Environmental Studies, 1630 Linden Drive, Madison, WI, 53706, USA.
المصدر: Nature communications [Nat Commun] 2023 Oct 17; Vol. 14 (1), pp. 6191. Date of Electronic Publication: 2023 Oct 17.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مواضيع طبية MeSH: Deep Learning*, Animals ; Tropical Climate ; Forests ; Biodiversity ; Trees ; Ecosystem ; Conservation of Natural Resources
مستخلص: 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.
(© 2023. Springer Nature Limited.)
التعليقات: Erratum in: Nat Commun. 2023 Nov 2;14(1):7014. (PMID: 37919264)
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سلسلة جزيئية: figshare 10.6084/m9.figshare.23620323
Dryad 10.5061/dryad.59zw3r2dm
تواريخ الأحداث: Date Created: 20231017 Date Completed: 20231023 Latest Revision: 20231119
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC10582010
DOI: 10.1038/s41467-023-41693-w
PMID: 37848442
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-023-41693-w