ORIGINAL RESEARCH article
Front. Bird Sci.
Sec. Science of Birding
Volume 4 - 2025 | doi: 10.3389/fbirs.2025.1686148
This article is part of the Research TopicThe Science of Birding in AfricaView all articles
Avifauna richness and detection reliability between structured scientific surveys and citizen science data
Provisionally accepted
- 1Department of Religious Studies, College of Humanities and Social Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- 2Ghana Wildlife Society, Accra, Ghana
- 3Advocates for Biodiversity Conservation, Accra, Ghana
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Citizen science has emerged as a cost-effective complement to structured biodiversity surveys, yet its reliability for small-scale avian monitoring in Africa remains underexplored. This study compared avifaunal species richness, detection accuracy, and the influence of species traits on detectability between full-protocol African BirdMap data (citizen science) and structured surveys conducted within the Cape Coast Metropolitan area, Ghana. Structured surveys recorded 208 species, while citizen science reported 215, with 176 species (71.3%) shared. A total of 32 and 39 species were unique to the structured survey and citizen science data, respectively. Structured surveys showed higher data accuracy and narrower confidence interval (99.5%, CI: 0.97–0.99) than citizen science (96.4%, CI: 0.93–0.98). Generalised linear modelling also showed species detectability was influenced primarily by traits rather than by the survey method. High vocalisation and moderate plumage conspicuousness increased detection likelihood, whereas rarely vocal species were significantly under-detected. Once traits were accounted for, method type was not a significant predictor of detectability (p = 0.85). Our findings indicate that well-standardised citizen science protocols can yield avian richness and detectability estimates comparable to structured surveys, though expert-led verification remains vital to minimise misidentification and maintain data quality. These results support the integration of citizen science into local avifaunal monitoring, especially in resource-limited contexts.
Keywords: Bird monitoring, Detection probability, species traits, Data accuracy, AfricanBird Atlas
Received: 14 Aug 2025; Accepted: 15 Oct 2025.
Copyright: © 2025 Afrifa, Segbefia and Sulemana. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Joseph Kwasi Afrifa, joseph.k.afrifa@gmail.com
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