Ecological monitoring is crucial for understanding the causal drivers and assessing solutions of the current biodiversity crisis. More so than ever, volunteers are being recruited to gather ecological observations, and technical advances such as the Internet, social media, and mobile/handheld computers have allowed the engagement of hundreds of thousands of participants in citizen science projects. The result is that hundreds of millions of ecological observations are being gathered annually. However, these projects vary greatly in the types of information that they collect, with important consequences in their ability to meet intended outcomes for science and society.
Proponents of citizen science often emphasize that poor data quality can be mitigated by data volume, which can be substantial in some cases. However, regardless of the quantity of data collected, the paucity of information on how the data were gathered can further degrade data quality. Specifically, more information needs to be collected regarding the observational process in order to account for potential biases in the data. By doing this, the scope and quality of the resulting inferences can be substantially enhanced.
Results/Conclusions
This presentation argues that citizen science projects aimed at robustly monitoring species distributions should follow several basic data-collection principles to provide a solid foundation for data analysis. We build on existing recommendations for biological monitoring programs that collect sufficient information on observation processes such that the resulting data can be analyzed in rigorous fashion. eBird is an example of a citizen project that gathers hundreds of millions of observations from a global network of participants. Because eBird also compiles information on the ecological and the observational processes, potential biases in the data can be accounted for. The resulting estimates of abundance and occurrence can then be used to model population trends in a reliable fashion across space and time.