However, even if citizen observations are accurate, there is an additional risk for sampling bias because most are opportunistic samples ( Bird et al.

Most of this development has focused on the accuracy and trustworthiness of their observations (e.g., Crall et al. In response to those challenges, many best practices in citizen science now feature mechanisms to assess the quality of data. As the citizen science approach became more popular, some scientists voiced concerns about the potential for volunteers to produce data sets without large amounts of error ( Dickinson, Zuckerberg, and Bonter 2010) that would be able to detect changes in the population status of wild species ( Danielsen et al. These analyses show that opportunistic sampling can be representative of large areas if sample size is high enough and that a priori sampling goals can help improve coverage by encouraging volunteers to sample in certain places or through supplemental data collection by staff.ĭata quality is central to citizen science projects producing trustworthy scientific outcomes.

Compared with results from the full data set, the relative bias and error with subsets of the data dropped below 10% relatively quickly with increasing sample size for estimates of occupancy, suggesting that results estimated with the full sample are robust, although the precision of particular ecological relationships were more variable. At the ecoregion level we sampled 99.2% (☐.01) of the variation of potential habitat “adequately” and 96.4% (☐.03) “very adequately.” Supplemental sampling by staff helped meet sampling adequacy for 6.8% of ecoregion-habitat classes, especially in less populated parts of the state. We assessed potential biases by comparing seven dimensions of habitat (i.e., land cover, elevation, road density, etc.) sampled by camera traps with those available in the state, using a minimum sample threshold approach, and found that the variation of habitat across the state was sufficiently sampled. Although most sampling was opportunistic, we used a “Plan, Encourage, Supplement” approach to improve our spatial coverage. Here we evaluate how well the sampling locations of North Carolina’s Candid Critters citizen science camera trapping project represented available land cover types in the state and whether the sample size (4,295 sites) was sufficient to estimate ecological parameters (i.e., species occupancy) with low bias and error.

Although quality control for accuracy is increasingly common in citizen science projects, there is still a risk that spatial biases of opportunistic data could affect results, especially if sample size is low.

This One Problem by M.C. Pease