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Safari Science: assessing the reliability of citizen science data for wildlife surveys

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Protected areas are the cornerstone of global conservation, yet financial support for basic monitoring infrastructure is lacking in 60% of them. Citizen science holds potential to address these shortcomings in wildlife monitoring, particularly for resource‐limited conservation initiatives in developing countries – if we can account for the reliability of data produced by volunteer citizen scientists (VCS). This study tests the reliability of VCS data vs. data produced by trained ecologists, presenting a hierarchical framework for integrating diverse datasets to assess extra variability from VCS data. Our results show that while VCS data are likely to be overdispersed for our system, the overdispersion varies widely by species. We contend that citizen science methods, within the context of East African drylands, may be more appropriate for species with large body sizes, which are relatively rare, or those that form small herds. VCS perceptions of the charisma of a species may also influence their enthusiasm for recording it. Tailored programme design (such as incentives for VCS) may mitigate the biases in citizen science data and improve overall participation. However, the cost of designing and implementing high‐quality citizen science programmes may be prohibitive for the small protected areas that would most benefit from these approaches. Synthesis and applications. As citizen science methods continue to gain momentum, it is critical that managers remain cautious in their implementation of these programmes while working to ensure methods match data purpose. Context‐specific tests of citizen science data quality can improve programme implementation, and separate data models should be used when volunteer citizen scientists' variability differs from trained ecologists' data. Partnerships across protected areas and between protected areas and other conservation institutions could help to cover the costs of citizen science programme design and implementation.
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Keywords: Bayesian; East Africa; citizen science; data reconciliation; hierarchical modelling; monitoring; protected areas; uncertainty; validation; wildlife surveys

Document Type: Research Article

Publication date: December 1, 2017

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