Species Identification Guides – Helpful or Harmful to Data Quality?

One way to encourage active public participation in citizen science—by individuals without professional knowledge or experience in the field—is by developing resources and educational materials. Species identification guides are a common and simple tool for recognizing and deepening familiarity with local biodiversity. These guides help citizen science participants identify unfamiliar species and report their presence in the field, thereby contributing to research. However, beyond their role in facilitating data collection, the species featured in these guides may also shape the data gathered, introducing sampling biases. This study examines the impact of identification guides on bird reporting during the Great Bird Count. The research hypothesis suggests that overreliance on such guides may lead to overreporting of species included in the guide and underreporting of those omitted from it, thereby biasing the dataset.

Preliminary Findings

The study employed an experimental approach to compare the reports of participants who used different versions of an identification guide, with each guide containing a different list of bird species. It was found that the version of the identification guide participants used had a significant impact on bird identification. When a species appeared in the guide, participants were more likely to report having seen it—and less likely when it was absent. These differences were especially pronounced between pairs of similar-looking species, such as the common myna and the blackbird, the rose-ringed parakeet and the monk parakeet, and others, indicating a potential for misidentification.

These findings highlight the need for a critical evaluation of the role and design of species identification guides in citizen science. On one hand, they enhance participants’ identification skills, but on the other, they may skew the data by emphasizing certain species over others. These results underscore the importance of thoughtful design in citizen science learning tools to ensure that the data collected through their use remains reliable.