Bayesian item response models for citizen science ecological data
share
So-called citizen science data elicited from crowds has become increasingly popular in many fields including ecology. However, the quality of this information is being fiercely debated by many within the scientific community. Therefore, modern citizen science implementations require measures of the users' proficiency that account for the difficulty of the tasks. We introduce a new methodological framework of item response and linear logistic test models with application to citizen science data used in ecology research. A specific feature of this approach is that spatial autocorrelation is accommodated within the item difficulties. The models produce relevant ecological measures of species and site-related difficulties, discriminatory power and guessing behavior. These, along with estimates of the subject abilities allow better management of these programs. We found that the suggested methods outperform the traditional item response models in terms of RMSE, accuracy, and WAIC based on leave-one-out cross-validation on simulated and empirical data. The fit of item response models to big data via divide-and-conquer is also discussed. We illustrate the implementation using a case study of species identification in the Serengeti, Tanzania. The main R and Stan codes are provided in the supplementary materials section, which allows the reproducibility and extrapolation to other settings.
READ FULL TEXT