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Predicting occurrence of juvenile shark habitat to improve conservation planning

Beverly Oh, Ana Sequeira, Jessica Meeuwig | Nov 30, 2016

Beverly Oh, Ana Sequeira, Jessica Meeuwig

Nov 30, 2016

  Cover image

Juvenile sharks circling a baited remote underwater video station (BRUVS).

Photo: Centre for Marine Futures.

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Beverly Oh
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Oh BZL, Sequeira AMM, Meekan MG, Ruppert JLW, Meeuwig JJ. 2017. Predicting occurrence of juvenile shark habitat to improve conservation planning. Conservation Biology, 31(3): 635-645.


  • BRUVS offer a useful platform to model distributions and assess the effectiveness of marine protected areas.
  • Climate change may modify the future distributions of juvenile sharks, as salinity, mid-water temperature and turbidity were important drivers of distribution.
  • Species-specific data acquisition is needed to provide accurate baselines and better-defined maps.
  • No-take MPAs could achieve better representation of high value habitats juvenile sharks.


Fishing and habitat degradation have increased the extinction risk of sharks, and conservation strategies recognize that survival of juveniles is critical for the effective management of shark populations. Despite the rapid expansion of marine protected areas (MPAs) globally, the paucity of shark-monitoring data on large scales (100s–1000s km) means that the effectiveness of MPAs in halting shark declines remains unclear. Using data collected by baited remote underwater video systems (BRUVS) in northwestern Australia, we developed generalized linear models to elucidate the ecological drivers of the occurrence of juvenile shark habitat. We assessed occurrence patterns at the order and species levels. We included all juvenile sharks sampled and the 3 most abundant species sampled separately (grey reef [Carcharhinus amblyrhynchos], sandbar [Carcharhinus plumbeus], and whitetip reef [Triaenodon obesus]. We predicted the occurrence of juvenile sharks across 490,515 km2 of coastal waters and quantified the representation of highly suitable habitats within MPAs. Our species-level models had higher accuracy (ĸ ≥ 0.69) and deviance explained (DE ≥ 48 %) than our order-level model (ĸ = 0.36 and DE = 10 %). Maps of predicted occurrence revealed different species-specific patterns of highly suitable habitat. These differences likely reflect different physiological or resource requirements between individual species and validate concerns over the utility of conservation targets based on aggregate species groups as opposed to a species-focused approach. Highly suitable -habitats were poorly represented in MPAs with the most restrictions on extractive activities. This spatial mismatch possibly indicates a lack of explicit conservation targets and information on species distribution during the planning process. Non-extractive BRUVS provided a useful platform for building the suitability models across large scales to assist conservation planning across multiple maritime jurisdictions, and our approach provides a simple method for testing the effectiveness of MPAs.


Caught on candid camera

Screen captures of sharks observed on BRUVS (from top left to bottom right), Carcharhinus amblyrhynchos (grey reef shark), Galeocerdo cuvier (tiger shark), Triaenodon obesus (whitetip reef shark) and Carcharhinus plumbeus (sandbar shark). Figure: Beverly Oh.


We thank all those who contributed BRUVS data for this study, in particular the The University of Western Australia, Australian Institute of Marine Science, and PTTEP Australasia Ltd. We thank Geoscience Australia for provision of environmental data for our study. This work was conducted as part of a PhD thesis (BZLO) funded by a UWA scholarship and the Holsworth Wildlife Research Endowment (RA/1/411/59). AMMS was supported by a Collaborative Post-doctoral Fellowship (AIMS, CSIRO, and UWA) from the Indian Ocean Marine Research Centre.