Numerous organisations collect data in the Great Barrier Reef (GBR), but they are rarely analysed together due to different program objectives, methods, and data quality. We developed a weighted spatio-temporal Bayesian model and used it to integrate image-based hard-coral data collected by professional and citizen scientists, who captured and/or classified underwater images. We used the model to predict coral cover across the GBR with estimates of uncertainty; thus filling gaps in space and time where no data exist. Additional data increased the model's predictive ability by 43%, but did not affect model inferences about pressures (e.g. bleaching and cyclone damage). Thus, effective integration of professional and high-volume citizen data could enhance the capacity and cost-efficiency of monitoring programs. This general approach is equally viable for other variables collected in the marine environment or other ecosystems; opening up new opportunities to integrate data and provide pathways for community engagement/stewardship.

许多组织都在大堡礁（GBR）中收集数据，但是由于程序目标，方法和数据质量不同，因此很少将它们一起分析。我们开发了加权时空贝叶斯模型，并将其用于整合由专业和公民科学家收集的基于图像的硬珊瑚数据，这些科学家捕获和/或分类了水下图像。我们使用该模型来预测整个GBR的珊瑚覆盖率，并进行不确定性评估。从而填补了没有数据的时空空白。其他数据使模型的预测能力提高了43％，但并未影响模型有关压力的推断（例如，漂白和旋风破坏）。因此，专业和大量市民数据的有效整合可以提高监测方案的能力和成本效益。对于在海洋环境或其他生态系统中收集到的其他变量，这种通用方法同样可行；开辟新的机会来整合数据并提供社区参与/管理的途径。