Overview
Participatory Monitoring refers to the process where non-expert ordinary citizens (volunteers, local residents, enthusiasts, etc.) observe and record the state of the environment and organisms to collect data.
It is one of the most representative practical forms of "Citizen Science," and is rapidly increasing in importance in biodiversity conservation as the only way to collect broad-ranging and long-term data at low cost.
Theoretical Background
The Need to Fill "Gaps"
There are limits to the range that scientists alone can survey (budget, personnel, time constraints). In particular, biodiversity data is highly localized and seasonal, so expert surveys once every few years are insufficient.
This developed from the idea of filling these "temporal and spatial data gaps" by borrowing the power of people living in the area.
Classification
It is classified as follows according to the degree of citizen involvement (Bonney et al., 2009).
- Contributory: Experts design it, and citizens only collect data (IKIMON is mainly this).
- Collaborative: Citizens participate in design improvements and analysis in addition to data collection.
- Co-created: Citizens and experts work equally from the setting of research themes.
Detailed Explanation
Three Elements of Success
For citizen monitoring to become scientifically usable data, the following three things are necessary.
- Standardized Protocol: Rules are unified for "when, where, what, and how" to record.
- Validation: Mechanisms to prevent misidentification or fabrication (photo attachment, double-checking by experts or AI).
- Feedback: Returning results to citizens who provided data, showing "your data was useful in this way" (maintaining motivation).
Major Global Platforms
- eBird: Operated by the Cornell Lab of Ornithology. The world's largest bird observation database. Overwhelmingly cited in scientific papers.
- iNaturalist: Operated by the California Academy of Sciences and others. Covers all organisms. Broadened the base by implementing image recognition AI.
Critical Examination
Data Bias
Citizens tend to survey in biased patterns: "near home," "good weather weekends," "conspicuous organisms (beautiful birds and butterflies)."
- Spatial Bias: Data concentrates in cities and along roads, with little data from remote mountains.
- Taxonomic Bias: Inconspicuous insects and algae are rarely recorded.
When conducting statistical analysis, advanced modeling techniques are needed to correct for such biases.
IKIMON's Contribution
IKIMON contributes as a high-quality citizen monitoring platform from Japan in the following ways:
- AI × Expert Hybrid: In addition to AI identification like iNaturalist, we provide reliable data through precise validation by Japan's expert network.
- Continue While Having Fun: By incorporating gamification, we transform monitoring—which tends to be abandoned—into a "fun habit," enabling long-term data collection.
- Eliminating Blank Areas: By showing on the map "there is no data for this area yet" and guiding users to unsurveyed regions, we help fill spatial data bias.
References
- Bonney, R., et al. (2009). Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy. BioScience.
- Pocock, M. J. O., et al. (2017). The Diversity of Citizen Science.
