Monitoring through many eyes: Integrating disparate datasets to improve monitoring of the Great Barrier Reef

TL;DR

This paper presents a Bayesian model that integrates diverse coral monitoring datasets from the Great Barrier Reef, improving spatial predictions and demonstrating the value of combining professional and citizen science data for ecosystem monitoring.

Contribution

A novel weighted spatiotemporal Bayesian model that effectively combines disparate coral datasets, enhancing monitoring accuracy and cost-efficiency.

Findings

Data integration increased predictive ability by 43%

Model predictions include uncertainty estimates

Effective for both professional and citizen-collected data

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 spatiotemporal 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 models predictive ability by 43 percent, 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 and stewardship.