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Birding 2.0: Citizen Science and Effective Monitoring in the Web 2.0 World

Author(s): Yolanda F. Wiersma

Journal: Avian Conservation and Ecology
ISSN 1712-6568

Volume: 5;
Issue: 2;
Start page: 13;
Date: 2010;
Original page

Keywords: citizen science | citizen sensors | experimental design | monitoring | volunteered geographical information (VGI) | Web 2.0

The amateur birding community has a long and proud tradition of contributing to bird surveys and bird atlases. Coordinated activities such as Breeding Bird Atlases and the Christmas Bird Count are examples of "citizen science" projects. With the advent of technology, Web 2.0 sites such as eBird have been developed to facilitate online sharing of data and thus increase the potential for real-time monitoring. However, as recently articulated in an editorial in this journal and elsewhere, monitoring is best served when based on a priori hypotheses. Harnessing citizen scientists to collect data following a hypothetico-deductive approach carries challenges. Moreover, the use of citizen science in scientific and monitoring studies has raised issues of data accuracy and quality. These issues are compounded when data collection moves into the Web 2.0 world. An examination of the literature from social geography on the concept of "citizen sensors" and volunteered geographic information (VGI) yields thoughtful reflections on the challenges of data quality/data accuracy when applying information from citizen sensors to research and management questions. VGI has been harnessed in a number of contexts, including for environmental and ecological monitoring activities. Here, I argue that conceptualizing a monitoring project as an experiment following the scientific method can further contribute to the use of VGI. I show how principles of experimental design can be applied to monitoring projects to better control for data quality of VGI. This includes suggestions for how citizen sensors can be harnessed to address issues of experimental controls and how to design monitoring projects to increase randomization and replication of sampled data, hence increasing scientific reliability and statistical power.

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