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Improved Coral Population Estimation Reveals Trends at Multiple Scales on Australia’s Great Barrier Reef

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Abstract

The global erosion of biodiversity presents unique challenges for identifying major changes in population dynamics, establishing their causes, and managing and conserving affected ecosystems at broad spatial scales. Adaptive learning approaches connecting different spatial scales through the transfer of hierarchical information are powerful tools to address such challenges. Here, we use a Semi-Parametric Bayesian Hierarchical (SPa-BaH) model to estimate coral cover trajectories using 16 years of a broad-scale survey on Australia’s Great Barrier Reef (GBR). The spatiotemporal variability of coral populations has been considered by separating three-tiered spatial scales and allowing for alternating phases of increasing and decreasing in the estimation of their trajectories. Model estimates revealed coral cover trajectories that were highly variable according to location but that fairly consistently declined at a regional spatial scale. Notwithstanding this general trend, individual reefs within subregions in the central part of the GBR often displayed different trajectory types between sites separated by only a few hundred meters. These coral dynamics were also associated with reduced recovery rates in the Cairns and Swain subregions. Our study highlights the importance of accounting for local variation in coral cover when estimating the spatiotemporal trends in coral cover trajectories, in this case, at the GBR scale. By retaining information at different hierarchical spatial scales, our SPa-BaH model supports better estimation of large-scale coral cover trajectories. The quantitative approaches developed here can also be applied to other species with complex dynamics thereby enhancing estimations of their trajectories at local- and larger-scales and options for their management.

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Acknowledgments

We thank Samantha Low-Choy, Mohsen Kayal and Alan Pearse for their contributions to the paper. We also thank all staff from the Australian Institute of Marine Science Long-term Monitoring Program who contributed to the collection of the data used here.

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Authors and Affiliations

  1. School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia

    Julie Vercelloni, Kerrie Mengersen & M. Julian Caley

  2. Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Queensland, Australia

    Julie Vercelloni, Kerrie Mengersen & M. Julian Caley

  3. CNR IMATI, Milan, Italy

    Fabrizio Ruggeri

  4. Institute for Future Environments, Queensland University of Technology, Brisbane, Queensland, Australia

    Fabrizio Ruggeri

Authors
  1. Julie Vercelloni
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  2. Kerrie Mengersen
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  3. Fabrizio Ruggeri
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  4. M. Julian Caley
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Corresponding author

Correspondence to Julie Vercelloni.

Additional information

The data underlying the findings are fully and freely available under creative commons licence from the Australian Institute of Marine Science Data Centre. Data are from the Long Term Monitoring Program whose authors may be contacted at adc@aims.gov.au.

Authors’ contributions

JV, KM and MJC designed the study. JV, KM and MJC performed the research and analyzed the data. JV, KM and FR contributed new methods and the model. JV wrote the first draft, and all other authors edited and helped revise the manuscript.

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Vercelloni, J., Mengersen, K., Ruggeri, F. et al. Improved Coral Population Estimation Reveals Trends at Multiple Scales on Australia’s Great Barrier Reef. Ecosystems 20, 1337–1350 (2017). https://doi.org/10.1007/s10021-017-0115-2

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  • DOI: https://doi.org/10.1007/s10021-017-0115-2

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