Peterson, Erin E., Santos-Fernández, Edgar, Chen, Carla, Clifford, Sam, Vercelloni, Julie, Pearse, Alan, Brown, Ross, Christensen, Bryce, James, Allan, Anthony, Ken, Loder, Jennifer, González-Rivero, Manuel, Roelfsema, Chris, Caley, M. Julian, Mellin, Camille, Bednarz, Tomasz, and Mengersen, Kerrie (2020) Monitoring through many eyes: Integrating disparate datasets to improve monitoring of the Great Barrier Reef. Environmental Modelling and Software, 124. 104557.

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Abstract

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.

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