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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]

• River plumes are a source of pollutants in the Great Barrier Reef, Australia. • Plume dispersion is simulated using a coupled Lagrangian-Eulerian-ocean model. • Lagrangian particle density is computed from application of Voronoi diagrams. • Lagrangian particle densities and Eulerian tracer concentrations are comparable. • Lagrangian model is validated using satellite imagery and satellite Chlorophyll-a. In this paper, we describe methods to verify the adequacy and accuracy of Lagrangian particles from a Lagrangian model to reproduce the concentrations of a passive tracer from an Eulerian-model in river plumes. The modelling simulates plumes from two major rivers discharging in the Great Barrier Reef (GBR), Australia, under real-world scenarios. The study has been a part of a major project to aid in the protection of the GBR system from the impacts of extreme events and climate change. We employ the Regional Ocean Modelling System (ROMS) activated with its built-in Lagrangian model, and forced with wind fields from global models and recorded river volume discharges. The ROMS-Lagrangian model tracks the Lagrangian particles using the spatially interpolated velocities computed on the Eulerian ROMS three-dimensional (3D) grid. The Lagrangian particles are released in the river in proportion to the measured river volume flux. We apply a novel technique that exploits Voronoi polygon areas to convert Lagrangian particle separation into a concentration field. This facilitates comparison with the passive tracer concentrations driven by the Eulerian velocities computed on the ROMS 3D grid. We evaluate the fate of Lagrangian particles activated on a 4-km grid resolution with those of equivalent Eulerian tracer concentrations. For validation, we compare the Lagrangian particles with the Eulerian passive tracer simulated using a higher model grid resolution of 500-m, and found that the computed Lagrangian particle concentrations showed similar overall patterns compared to the passive tracer concentrations from both the coarse 4-km, and high-resolution 500-m Eulerian models. The spatial extent of the particles was in better agreement with the coarse 4-km model than with the higher resolution 500-m model. The clustering of particles resulted in structures at finer scales than the Eulerian model, over the high particle density areas that compared well against observations. The Lagrangian particles are able to capture the general river plume patterns seen in the satellite MODIS images and the satellite derived Chlorophyll-a. We further compare the online ROMS model with an offline particle tracker, OceanPARCELS and determined the similarity of results from both online and offline methods. Overall, our study demonstrates the viability to use Lagrangian particles to reproduce the Eulerian tracer properties, which can be combined with particle properties include tracer age providing to enhance the guidance on the distribution and concentration of effluent from localised flooding river events. [ABSTRACT FROM AUTHOR]