Predicting occurrence of juvenile shark habitat to improve conservation planning. Issue 3 (24th January 2017)

• Record Type: Journal Article
• Authors: Oh, Beverly Z.L.; Sequeira, Ana M.M.; Meekan, Mark G.; Ruppert, Jonathan L.W.; Meeuwig, Jessica J.
• Is Part Of: Conservation biology. Volume 31:Issue 3(2017:Jun.)
• Page Start: 635
• Page End: 645
• Publication Date: 2017-01-24
• Subjects: baited remote underwater video -- Carcharhinus amblyrhnchos -- Carcharhinus plumbeus -- species distribution models -- Triaenodon obesus -- Carcharhinus amblyrhnchos -- Carcharhinus plumbeus -- modelos de distribución de especies -- Triaenodon obesus -- video remoto subacuático con carnada; Conservation biology -- Periodicals; 333.9516
• DOI: 10.1111/cobi.12868
• Languages: English
• ISSNs: 0888-8892

Abstract

Abstract: 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 habitat suitability for juvenile sharks. 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 sharks [Triaenodon obesus]). We predicted the occurrence of juvenile sharks across 490, 515 km 2 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 (≥48%) than our order‐level model ( ĸ = 0.36 and deviance explained of 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 for method for testing the effectiveness of MPAs.