Monitoring through many eyes: integrating disparate datasets to improve monitoring of the Great Barrier Reef
Date
2020
Authors
Peterson, E.E.
Santos-Fernández, E.
Chen, C.
Clifford, S.
Vercelloni, J.
Pearse, A.
Brown, R.
Christensen, B.
James, A.
Anthony, K.
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Advisors
Journal Title
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Journal article
Citation
Environmental Modelling and Software, 2020; 124:104557-1-104557-20
Statement of Responsibility
Erin E. Peterson, Edgar Santos-Fernández , Carla Chen, Sam Clifford, Julie Vercelloni, Alan Pearse, Ross Brown, Bryce Christensen, Allan James, Ken Anthony, Jennifer Loder, Manuel González-Rivero, Chris Roelfsema, M. Julian Caley, Camille Mellin, Tomasz Bednarz, Kerrie Mengersen
Conference Name
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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Crown Copyright © 2019 Published by Elsevier Ltd. All rights reserved.