Müller, Jörg ; Mitesser, Oliver ; Schaefer, H. Martin ; Seibold, Sebastian ; Busse, Annika ; Kriegel, Peter ; Rabl, Dominik ; Gelis, Rudy ; Arteaga, Alejandro ; Freile, Juan ; Leite, Gabriel Augusto ; Melo, Tomaz Nascimento de ; 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 (2023)
Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests.
In: Nature Communications, 14 (1)
doi: 10.1038/s41467-023-41693-w
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Müller, Jörg ; Mitesser, Oliver ; Schaefer, H. Martin ; Seibold, Sebastian ; Busse, Annika ; Kriegel, Peter ; Rabl, Dominik ; Gelis, Rudy ; Arteaga, Alejandro ; Freile, Juan ; Leite, Gabriel Augusto ; Melo, Tomaz Nascimento de ; 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 |
| Art des Eintrags: | Bibliographie |
| Titel: | Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests |
| Sprache: | Englisch |
| Publikationsjahr: | 17 Oktober 2023 |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Nature Communications |
| Jahrgang/Volume einer Zeitschrift: | 14 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1038/s41467-023-41693-w |
| Kurzbeschreibung (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. |
| ID-Nummer: | pmid:37848442 |
| Fachbereich(e)/-gebiet(e): | 10 Fachbereich Biologie 10 Fachbereich Biologie > Ecological Networks |
| Hinterlegungsdatum: | 24 Okt 2023 05:38 |
| Letzte Änderung: | 07 Nov 2023 08:09 |
| PPN: | 512652910 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum