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Avian Research 2023, 14(3): 100117 https://doi.org/10.1016/j.avrs.2023.100117
Research Article | Open Access | Issue | Published: 16 July 2023

Data reliability of the emerging citizen science in the Greater Bay Area of China

Show Author's Information Xilin Huang,  Yihong Wang,  Yang Liu,  Lyu Bing Zhang( )
School of Ecology, Sun Yat-sen University, Shenzhen, 528406, China
Keywords:
Data quality, Citizen science, Species richness, Bird identification skill, Sampling bias, The Greater Bay Area of China
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Huang X, Wang Y, Liu Y, et al. Data reliability of the emerging citizen science in the Greater Bay Area of China. Avian Research, 2023, 14(3): 100117. https://doi.org/10.1016/j.avrs.2023.100117
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Abstract Full text About this article

The potential of citizen science projects in research has been increasingly acknowledged, but the substantial engagement of these projects is restricted by the quality of citizen science data. Based on the largest emerging citizen science project in the country—Birdreport Online Database (BOD), we examined the biases of birdwatching data from the Greater Bay Area of China. The results show that the sampling effort is disparate among land cover types due to contributors' preference towards urban and suburban areas, indicating the environment suitable for species existence could be underrepresented in the BOD data. We tested the contributors' skill of species identification via a questionnaire targeting the citizen birders in the Greater Bay Area. The questionnaire show that most citizen birdwatchers could correctly identify the common species widely distributed in Southern China and the less common species with conspicuous morphological characteristics, while failed to identify the species from Alaudidae, Caprimulgidae, Emberizidae, Phylloscopidae, Scolopacidae and Scotocercidae. With a study example, we demonstrate that spatially clustered birdwatching visits can cause underestimation of species richness in insufficiently sampled areas; and the result of species richness mapping is sensitive to the contributors' skill of identifying bird species. Our results address how avian research can be influenced by the reliability of citizen science data in a region of generally high accessibility, and highlight the necessity of pre-analysis scrutiny on data reliability regarding to research aims at all spatial and temporal scales. To improve the data quality, we suggest to equip the data collection frame of BOD with a flexible filter for bird abundance, and questionnaires that collect information related to contributors' bird identification skill. Statistic modelling approaches are encouraged to apply for correcting the bias of sampling effort.


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About this article

Data reliability of the emerging citizen science in the Greater Bay Area of China

Show Author's information Xilin Huang, Yihong Wang, Yang Liu, Lyu Bing Zhang( )
School of Ecology, Sun Yat-sen University, Shenzhen, 528406, China

Abstract

The potential of citizen science projects in research has been increasingly acknowledged, but the substantial engagement of these projects is restricted by the quality of citizen science data. Based on the largest emerging citizen science project in the country—Birdreport Online Database (BOD), we examined the biases of birdwatching data from the Greater Bay Area of China. The results show that the sampling effort is disparate among land cover types due to contributors' preference towards urban and suburban areas, indicating the environment suitable for species existence could be underrepresented in the BOD data. We tested the contributors' skill of species identification via a questionnaire targeting the citizen birders in the Greater Bay Area. The questionnaire show that most citizen birdwatchers could correctly identify the common species widely distributed in Southern China and the less common species with conspicuous morphological characteristics, while failed to identify the species from Alaudidae, Caprimulgidae, Emberizidae, Phylloscopidae, Scolopacidae and Scotocercidae. With a study example, we demonstrate that spatially clustered birdwatching visits can cause underestimation of species richness in insufficiently sampled areas; and the result of species richness mapping is sensitive to the contributors' skill of identifying bird species. Our results address how avian research can be influenced by the reliability of citizen science data in a region of generally high accessibility, and highlight the necessity of pre-analysis scrutiny on data reliability regarding to research aims at all spatial and temporal scales. To improve the data quality, we suggest to equip the data collection frame of BOD with a flexible filter for bird abundance, and questionnaires that collect information related to contributors' bird identification skill. Statistic modelling approaches are encouraged to apply for correcting the bias of sampling effort.

Keywords: Data quality, Citizen science, Species richness, Bird identification skill, Sampling bias, The Greater Bay Area of China

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Publication history
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Publication history

Received: 28 February 2023
Revised: 07 June 2023
Accepted: 27 June 2023
Published: 16 July 2023
Issue date: September 2023

Copyright

© 2023 The Authors.

Acknowledgements

Acknowledgements

We appreciate China Birdwatching Association for providing the birdwatching data of the Greater Bay Area, China. We would also thank all the skillful birders who contributed to the construction of the bird identification test.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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