Community engagement and data quality: best practices and lessons learned from a citizen science project on birdsong.

Citizen Science (CS) is a research approach that has become popular in recent years and offers innovative potential for dialect research in ornithology. As the scepticism about CS data is still widespread, we analysed the development of a 3-year CS project based on the song of the Common Nightingale (Luscinia megarhynchos) to share best practices and lessons learned. We focused on the data scope, individual engagement, spatial distribution and species misidentifications from recordings generated before (2018, 2019) and during the COVID-19 outbreak (2020) with a smartphone using the 'Naturblick' app. The number of nightingale song recordings and individual engagement increased steadily and peaked in the season during the pandemic. 13,991 nightingale song recordings were generated by anonymous (64%) and non-anonymous participants (36%). As the project developed, the spatial distribution of recordings expanded (from Berlin based to nationwide). The rates of species misidentifications were low, decreased in the course of the project (10-1%) and were mainly affected by vocal similarities with other bird species. This study further showed that community engagement and data quality were not directly affected by dissemination activities, but that the former was influenced by external factors and the latter benefited from the app. We conclude that CS projects using smartphone apps with an integrated pattern recognition algorithm are well suited to support bioacoustic research in ornithology. Based on our findings, we recommend setting up CS projects over the long term to build an engaged community which generates high data quality for robust scientific conclusions. Supplementary Information: The online version contains supplementary material available at 10.1007/s10336-022-02018-8.

The Animal Audiogram Database: A community-based resource for consolidated audiogram data and metadata.

Knowledge of hearing ability, as represented in audiograms, is essential for understanding how animals acoustically perceive their environment, predicting and counteracting the effects of anthropogenic noise, and managing wildlife. Audiogram data and relevant background information are currently only available embedded in the text of individual scientific publications in various unstandardized formats. This heterogeneity makes it hard to access, compare, and integrate audiograms. The Animal Audiogram Database (https://animalaudiograms.org) assembles published audiogram data, metadata about the corresponding experiments, and links to the original publications in a consistent format. The database content is the result of an extensive survey of the scientific literature and manual curation of the audiometric data found therein. As of November 1, 2021, the database contains 306 audiogram datasets from 34 animal species. The scope and format of the provided metadata and design of the database interface were established by active research community involvement. Options to compare audiograms and download datasets in structured formats are provided. With the focus currently on vertebrates and hearing in underwater environments, the database is drafted as a free and open resource for facilitating the review and correction of the contained data and collaborative extension with audiogram data from any taxonomic group and habitat.

Opportunities and limitations: A comparative analysis of citizen science and expert recordings for bioacoustic research.

Citizen science is an approach that has become increasingly popular in recent years. Despite this growing popularity, there still is widespread scepticism in the academic world about the validity and quality of data from citizen science projects. And although there might be great potential, citizen science is a rarely used approach in the field of bioacoustics. To better understand the possibilities, but also the limitations, we here evaluated data generated in a citizen science project on nightingale song as a case study. We analysed the quantity and quality of song recordings made in a non-standardized way with a smartphone app by citizen scientists and the standardized recordings made with professional equipment by academic researchers. We made comparisons between the recordings of the two approaches and among the user types of the app to gain insights into the temporal recording patterns, the quantity and quality of the data. To compare the deviation of the acoustic parameters in the recordings with smartphones and professional devices from the original song recordings, we conducted a playback test. Our results showed that depending on the user group, citizen scientists produced many to a lot of recordings of valid quality for further bioacoustic research. Differences between the recordings provided by the citizen and the expert group were mainly caused by the technical quality of the devices used-and to a lesser extent by the citizen scientists themselves. Especially when differences in spectral parameters are to be investigated, our results demonstrate that the use of the same high-quality recording devices and calibrated external microphones would most likely improve data quality. We conclude that many bioacoustic research questions may be carried out with the recordings of citizen scientists. We want to encourage academic researchers to get more involved in participatory projects to harness the potential of citizen science-and to share scientific curiosity and discoveries more directly with society.