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Editorial Material
Biochemistry & Molecular Biology
Connor M. Wood et al.
Summary: The BirdNET App is a free bird sound identification app that allows users to identify bird species based on their sounds. It includes over 3,000 bird species and generates millions of bird observations worldwide, which can be utilized for studying avian ecology.
Article
Ecology
Robert Manzano-Rubio et al.
Summary: Passive acoustic monitoring is a powerful tool for monitoring vocally active taxa. In this study, the use of AudioMoth, BirdNET, and Kaleidoscope Pro was evaluated for monitoring the Eurasian bittern. The results show that AudioMoth can effectively detect the species at large distances, and the combination of BirdNET or Kaleidoscope Pro with passive acoustic monitoring is an accurate and cost-efficient method for monitoring the Eurasian bittern at large spatial and temporal scales.
ECOLOGICAL INFORMATICS
(2022)
Article
Zoology
Richard W. Hedley et al.
Summary: The researchers proposed a method to restrict detections within a pre-specified survey radius by using sound level thresholds, aiming to address uncertainty regarding species-habitat associations in patchy habitats. By applying logistic regression to select sound level thresholds corresponding to desired distance thresholds, the method efficiently increased the percentage of detections within the desired survey areas.
BIOACOUSTICS-THE INTERNATIONAL JOURNAL OF ANIMAL SOUND AND ITS RECORDING
(2021)
Article
Ecology
Stefan Kahl et al.
Summary: Recent advances in deep artificial neural networks have revolutionized the field of bird sound recognition, with models like BirdNET able to accurately identify 984 North American and European bird species. Task-specific model designs and training regimes play a crucial role in audio event recognition, while high temporal resolution of input spectrograms improves classification performance for bird sounds.
ECOLOGICAL INFORMATICS
(2021)
Review
Ornithology
Cristian Perez-Granados et al.
Summary: Passive acoustic monitoring is a non-invasive tool for automated wildlife monitoring, with potential benefits including reducing biases related to traditional field surveys. While using autonomous recording units (ARUs) to estimate animal density has been traditionally challenging, recent studies have proposed approaches to estimate bird density using ARUs. Many studies have shown that bird estimates obtained from ARUs are consistent with those from human surveyors, and some methods have been successful in reducing biases in acoustic surveys.
Article
Ecology
Connor M. Wood et al.
Summary: The study indicates that observed species richness is influenced by survey coverage and recording duration, while species detection probabilities are affected by reducing the number of days of recording and daily recording duration. The impact of survey coverage decreases as recording duration decreases, and rare species are more likely to be underestimated as survey coverage decreases. Increasing recording duration lowers the cost per species observed.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Biology
Erik Verreycken et al.
Summary: Verreycken and colleagues have developed a large, flexible microphone array system for bio-acoustic monitoring of animal species, which allows for detailed study of animal vocalizations and behavior without intrusive methods. The technology has been successfully used in experiments monitoring bat echolocation beams and localizing songbirds within their habitat. The flexibility of the microphone array system allows for simultaneous tracking of multiple species, making it a valuable tool for studying complex behaviors of vocalizing animals.
COMMUNICATIONS BIOLOGY
(2021)
Article
Biology
Jose J. Lahoz-Monfort et al.
Summary: The article provides a comprehensive overview of the technologies currently utilized in biodiversity conservation, including sensors, wildlife tracking, and artificial intelligence applications. It covers both established and novel technologies being trialed in the field.
Article
Fisheries
Matthew Toenies et al.
Summary: Recent advances in acoustic recorder technology and automated species identification offer great potential for bird monitoring. Field trials in Monterey County, California, compared four acoustic recorder models and point counts and evaluated the BirdNET neural network for bird species identification. Results showed that low-cost AudioMoth performed comparably to higher-cost units and BirdNET achieved a high rate of correct species identification, showcasing the potential for low-cost and automated tools to greatly enhance bird survey efforts.
CALIFORNIA FISH AND WILDLIFE JOURNAL
(2021)
Article
Ornithology
Cristian Perez-Granados et al.
JOURNAL OF ORNITHOLOGY
(2020)
Article
Ornithology
Cristian Perez-Granados et al.
ARDEOLA-INTERNATIONAL JOURNAL OF ORNITHOLOGY
(2019)
Review
Biology
Larissa Sayuri Moreira Sugai et al.
Article
Biodiversity Conservation
Kevin Darras et al.
JOURNAL OF APPLIED ECOLOGY
(2018)
Article
Biodiversity Conservation
Julia Shonfield et al.
AVIAN CONSERVATION AND ECOLOGY
(2017)
Article
Ornithology
Robert S. Rempel et al.
JOURNAL OF FIELD ORNITHOLOGY
(2013)
