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https://hdl.handle.net/2440/129304
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Type: | Journal article |
Title: | Monitoring through many eyes: integrating disparate datasets to improve monitoring of the Great Barrier Reef |
Author: | Peterson, E.E. Santos-Fernández, E. Chen, C. Clifford, S. Vercelloni, J. Pearse, A. Brown, R. Christensen, B. James, A. Anthony, K. Loder, J. González-Rivero, M. Roelfsema, C. Caley, M.J. Mellin, C. Bednarz, T. Mengersen, K. |
Citation: | Environmental Modelling and Software, 2020; 124:104557-1-104557-20 |
Publisher: | Elsevier |
Issue Date: | 2020 |
ISSN: | 1364-8152 1873-6726 |
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 |
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. |
Keywords: | Great Barrier Reef; coral cover; citizen science; spatio-temporal modelling; data integration; weighted regression |
Rights: | Crown Copyright © 2019 Published by Elsevier Ltd. All rights reserved. |
DOI: | 10.1016/j.envsoft.2019.104557 |
Grant ID: | ARC |
Published version: | http://dx.doi.org/10.1016/j.envsoft.2019.104557 |
Appears in Collections: | Aurora harvest 4 Ecology, Evolution and Landscape Science publications |
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