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Summary: Crowdsourcing methods facilitate the production of scientific information by non‐experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data‐driven decisions and study challenging problems. However, concerns about the validity of these data often constrain their utility. In this paper, we focus on the use of citizen science data in addressing complex challenges in environmental conservation. We consider this issue from three perspectives. First, we present a literature scan of papers that have employed Bayesian models with citizen science in ecology. Second, we compare several popular majority vote algorithms and introduce a Bayesian item response model that estimates and accounts for participants' abilities after adjusting for the difficulty of the images they have classified. The model also enables participants to be clustered into groups based on ability. Third, we apply the model in a case study involving the classification of corals from underwater images from the Great Barrier Reef, Australia. We show that the model achieved superior results in general and, for difficult tasks, a weighted consensus method that uses only groups of experts and experienced participants produced better performance measures. Moreover, we found that participants learn as they have more classification opportunities, which substantially increases their abilities over time. Overall, the paper demonstrates the feasibility of CS for answering complex and challenging ecological questions when these data are appropriately analysed. This serves as motivation for future work to increase the efficacy and trustworthiness of this emerging source of data. [ABSTRACT FROM AUTHOR]

In this study, Bayesian estimation is performed for a class of random coefficient self-exciting integer-valued threshold autoregressive processes with explanatory variables. A new model with a linear structure is obtained through model reconstruction, which makes Markov Chain Monte Carlo method easy to perform. By introducing the latent variables series, a complete data likelihood is obtained. Based on this likelihood, the full conditional distributions are easily obtained for all the parameters and latent variables. By maximizing the posterior probability function, the threshold parameter is accurately estimated. Finally, some numerical results of the estimates and a real data example of crime counts in Ballina, New South Wales, Australia are presented. [ABSTRACT FROM AUTHOR]