BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoring
Bird population monitoring is crucial for understanding biodiversity trends, with various methods differing in effectiveness. This study compared traditional observer-based methods, including point counts and manual analysis of passive acoustic monitoring (PAM) recordings, with automated species det...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25006776 |
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| author | Dominika Winiarska Grzegorz Neubauer Michał Budka Paweł Szymański Julia Barczyk Marta Cholewa Tomasz S. Osiejuk |
| author_facet | Dominika Winiarska Grzegorz Neubauer Michał Budka Paweł Szymański Julia Barczyk Marta Cholewa Tomasz S. Osiejuk |
| author_sort | Dominika Winiarska |
| collection | DOAJ |
| description | Bird population monitoring is crucial for understanding biodiversity trends, with various methods differing in effectiveness. This study compared traditional observer-based methods, including point counts and manual analysis of passive acoustic monitoring (PAM) recordings, with automated species detection using BirdNET during springtime in a European primaeval forest, Białowieża National Park in Poland. We evaluated 97 10-minute surveys from each method and input all-day recordings into BirdNET to explore the differences in alpha richness and beta diversity at both the survey level and cumulative over the entire breeding season.The results show that BirdNET detected the highest alpha richness (80 species) when processing all-day recordings. However, its performance during short-term surveys was lower (49 species) compared to that of human observers, with recording analysis yielding the highest species count among the observer-based methods (61 species). Beta diversity also differed according to the site and method used (p < 0.05). The number of species detected during all short-term surveys showed a weak correlation with the number of species obtained from all-day BirdNET analysis (Pearson’s correlation: 0.34–0.49), questioning the adequacy of short-term surveys in species-rich habitats such as primaeval forests.Our findings demonstrate that monitoring methods should be adjusted to improve coverage. Automated analysis offers scalability and efficiency but still requires significant time dedicated to processing, so manual review remains essential for validating automated classifications and ensuring data accuracy. On the other hand, point counts and recording analysis can provide valuable insights in situations where time is limited or when finer verification of species detection is necessary. |
| format | Article |
| id | doaj-art-5214f4eb558e4e84bc0307f3b4b7d7bf |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-5214f4eb558e4e84bc0307f3b4b7d7bf2025-08-20T03:29:35ZengElsevierEcological Indicators1470-160X2025-08-0117711374710.1016/j.ecolind.2025.113747BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoringDominika Winiarska0Grzegorz Neubauer1Michał Budka2Paweł Szymański3Julia Barczyk4Marta Cholewa5Tomasz S. Osiejuk6Department of Behavioural Ecology, Institute of Environmental Biology, Faculty of Biology, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 6, 61-614 Poznań, Poland; Division of Biodiversity and Behavioural Ecology, Faculty of Biology, University of Bialystok, Ciołkowskiego 1J, 15-245 Białystok, Poland; Corresponding author at: Division of Biodiversity and Behavioural Ecology, Faculty of Biology, University of Bialystok, Ciołkowskiego 1J, 15-245 Białystok, Poland.Laboratory of Forest Biology, Faculty of Biological Sciences, University of Wrocław, Sienkiewicza 21, 50–335 Wrocław, Poland; Ornithological Station, Museum and Institute of Zoology, Polish Academy of Sciences, Nadwiślańska 108, 80-680 Gdańsk, PolandDepartment of Behavioural Ecology, Institute of Environmental Biology, Faculty of Biology, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 6, 61-614 Poznań, PolandDepartment of Behavioural Ecology, Institute of Environmental Biology, Faculty of Biology, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 6, 61-614 Poznań, PolandLaboratory of Forest Biology, Faculty of Biological Sciences, University of Wrocław, Sienkiewicza 21, 50–335 Wrocław, PolandLaboratory of Forest Biology, Faculty of Biological Sciences, University of Wrocław, Sienkiewicza 21, 50–335 Wrocław, PolandDepartment of Behavioural Ecology, Institute of Environmental Biology, Faculty of Biology, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 6, 61-614 Poznań, PolandBird population monitoring is crucial for understanding biodiversity trends, with various methods differing in effectiveness. This study compared traditional observer-based methods, including point counts and manual analysis of passive acoustic monitoring (PAM) recordings, with automated species detection using BirdNET during springtime in a European primaeval forest, Białowieża National Park in Poland. We evaluated 97 10-minute surveys from each method and input all-day recordings into BirdNET to explore the differences in alpha richness and beta diversity at both the survey level and cumulative over the entire breeding season.The results show that BirdNET detected the highest alpha richness (80 species) when processing all-day recordings. However, its performance during short-term surveys was lower (49 species) compared to that of human observers, with recording analysis yielding the highest species count among the observer-based methods (61 species). Beta diversity also differed according to the site and method used (p < 0.05). The number of species detected during all short-term surveys showed a weak correlation with the number of species obtained from all-day BirdNET analysis (Pearson’s correlation: 0.34–0.49), questioning the adequacy of short-term surveys in species-rich habitats such as primaeval forests.Our findings demonstrate that monitoring methods should be adjusted to improve coverage. Automated analysis offers scalability and efficiency but still requires significant time dedicated to processing, so manual review remains essential for validating automated classifications and ensuring data accuracy. On the other hand, point counts and recording analysis can provide valuable insights in situations where time is limited or when finer verification of species detection is necessary.http://www.sciencedirect.com/science/article/pii/S1470160X25006776Passive acoustic monitoringDetection methodAcoustic surveysPoint countsAutomated analysisBirdNET |
| spellingShingle | Dominika Winiarska Grzegorz Neubauer Michał Budka Paweł Szymański Julia Barczyk Marta Cholewa Tomasz S. Osiejuk BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoring Ecological Indicators Passive acoustic monitoring Detection method Acoustic surveys Point counts Automated analysis BirdNET |
| title | BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoring |
| title_full | BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoring |
| title_fullStr | BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoring |
| title_full_unstemmed | BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoring |
| title_short | BirdNET provides superior diversity estimates compared to observer-based surveys in long-term monitoring |
| title_sort | birdnet provides superior diversity estimates compared to observer based surveys in long term monitoring |
| topic | Passive acoustic monitoring Detection method Acoustic surveys Point counts Automated analysis BirdNET |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25006776 |
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