Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research

2021/02/08 by Cristian Pérez‐Granados, Juan Traba, Juán Traba · 35 citations
Biochemistry, Genetics and Molecular Biology · Environmental Science · #Animal Vocal Communication and Behavior #Marine animal studies overview #Wildlife Ecology and Conservation

paper · doi:10.1111/ibi.12944

Abstract

Passive acoustic monitoring is a non‐invasive tool for automated wildlife monitoring. This technique has several advantages and addresses many of the biases related to traditional field surveys. However, locating animal sounds using autonomous recording units (ARUs) can be technically challenging and therefore ARUs have traditionally been little employed to estimate animal density. Nonetheless, several approaches have been proposed in recent years to carry out acoustic‐based bird density estimations. We conducted a literature review of studies that used ARUs for estimating bird densities or bird abundances in order to describe the applications and improve future monitoring programmes. We detected a growing interest in the use of ARUs for estimating bird density in the last 6 years (2014–19), with a total of 31 articles assessing the topic. The most common approach was to estimate the relationship between the number of vocalizations per recording time with bird density or bird abundance estimated in the field (61%). In 26 studies (79%), bird estimates obtained by human surveyors agreed with those obtained using ARUs. Some approaches have proven able to reduce biases in acoustic surveys, such as considering imperfect detection (spatially explicit capture–recapture, using microphone arrays), applying paired acoustic sampling to control for different sampling radius between humans and ARUs, or including relative sound level measurements that allow researchers to estimate bird distance to recorder. However, several studies did not include any covariates to reduce existing biases and some did not estimate the sampling radius of the recorder, which may hamper future comparisons between human and ARU surveys. Future studies should include a measurement of the sampling radius of the recorder employed to be able to obtain density estimations using ARUs. Finally, we provide some guidelines to improve the applicability of ARUs to infer bird population estimates in future studies.

Citations

• Terrestrial Passive Acoustic Monitoring: Review and Perspectives

Cited by

• BirdNET: applications, performance, pitfalls and future opportunities
• A global assessment of BirdNET performance: Differences among continents, biomes, and species
• Sensors versus surveyors: Comparing passive acoustic monitoring, camera trapping and observer‐based monitoring for terrestrial mammals
• Bioacoustics as a Measure of Population Size and Breeding Success of European Storm Petrels Hydrobates pelagicus
• Quantifying nocturnal bird migration using acoustics: opportunities and challenges
• Passive acoustic monitoring paired with machine learning outperforms playback surveys for a rare and cryptic species, the Black‐billed Cuckoo ( Coccyzus erythropthalmus )
• Data-driven sampling strategies for fine-tuning bird detection modelsa).
• Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists
• Animal-borne acoustic biologgers to broaden the applicability of passive acoustic monitoring to terrestrial mammals: a feasibility study for deer
• Leveraging passive acoustic monitoring for result-based agri-environmental schemes: Opportunities, challenges and next steps
• Relating forest structural characteristics to bat and bird diversity in the Italian Alps
• Acoustic analysis shows Clark’s Nutcracker seasonal and spatial habitat use during post-fire recovery of two endangered pine species
• Integrating AI models into ecological research workflows: The case of terrestrial bioacoustics
• Strategic management of a widespread native interference competitor
• Passive acoustic monitoring provides a fresh perspective on fundamental ecological questions
• Evaluating the Effectiveness of Passive Acoustic Monitoring at Detecting Anurans in Caribbean Tropical Forests
• Application of passive acoustic monitoring to compare avian populations in perennial grasslands and croplands in Nebraska
• One-step progressive representation transfer learning for bird sound classification
• Habitat preferences, estimated abundance and behavior of tree hyrax (Dendrohyrax sp.) in fragmented montane forests of Taita Hills, Kenya. [europepmc]
• Refining manual annotation effort of acoustic data to estimate bird species richness and composition: The role of duration, intensity, and time. [europepmc]
• Self-Consistent Soundscape Ranking Index: The Case of an Urban Park. [europepmc]
• A Quantitative Evaluation of the Performance of the Low-Cost AudioMoth Acoustic Recording Unit. [europepmc]
• Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques. [europepmc]
• Effect of Environmental Variables on African Penguin Vocal Activity: Implications for Acoustic Censusing. [europepmc]
• Social information modifies the associations between forest fragmentation and the abundance of a passerine bird. [europepmc]
• An acoustic detection dataset of birds (Aves) in montane forests using a deep learning approach. [europepmc]
• Species and habitat specific changes in bird activity in an urban environment during Covid 19 lockdown. [europepmc]
• Acoustic monitoring reveals a diel rhythm of an arctic seabird colony (little auk, Alle alle). [europepmc]
• Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests. [europepmc]
• Towards acoustic monitoring of bees: wingbeat sounds are related to species and individual traits. [europepmc]
• A comparison of the sampling effectiveness of acoustic recorder, camera trap and point count methods in sampling nocturnal birds in Afrotropical landscapes. [europepmc]
• Monitoring soil fauna with ecoacoustics. [europepmc]
• Semiautomated generation of species-specific training data from large, unlabeled acoustic datasets for deep supervised birdsong isolation. [europepmc]
• Bioacoustics as a Measure of Population Size and Breeding Success of European Storm Petrels Hydrobates pelagicus . [europepmc]
• Patterns of Vocal Activity of the Chinese Bamboo Partridge Using BirdNET Analyzer. [europepmc]

Related

• Automated birdsong recognition in complex acoustic environments: a review
• Humans outperform Merlin Sound ID in field-based point-count surveys
• Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
• WABAD: A World Annotated Bird Acoustic Dataset for Passive Acoustic Monitoring
• Passive acoustic monitoring paired with machine learning outperforms playback surveys for a rare and cryptic species, the Black‐billed Cuckoo ( Coccyzus erythropthalmus )