Shark movement strategies influence poaching risk and can guide enforcement decisions in a large, remote marine protected area. Issue 9 (11th June 2020)

• Record Type: Journal Article
• Authors: Jacoby, David M. P.; Ferretti, Francesco; Freeman, Robin; Carlisle, Aaron B.; Chapple, Taylor K.; Curnick, David J.; Dale, Jonathan J.; Schallert, Robert J.; Tickler, David; Block, Barbara A.
• Editors: Russell, Debbie
• Is Part Of: Journal of applied ecology. Volume 57:Issue 9(2020)
• Page Start: 1782
• Page End: 1792
• Publication Date: 2020-06-11
• Subjects: elasmobranch -- illegal fishing -- marine protected area -- mobility patterns -- network analysis -- patrolling -- reef shark -- telemetry; Agriculture -- Periodicals; Biology, Economic -- Periodicals; Agricultural ecology -- Periodicals; Applied ecology -- Periodicals; 577
• DOI: 10.1111/1365-2664.13654
• Languages: English
• ISSNs: 0021-8901

Abstract

Abstract: Large, remote marine protected areas (MPAs) containing both reef and pelagic habitats, have been shown to offer considerable refuge to populations of reef‐associated sharks. Many large MPAs are, however, impacted by illegal fishing activity conducted by unlicensed vessels. While enforcement of these reserves is often expensive, it would likely benefit from the integration of ecological data on the mobile animals they are designed to protect. Consequently, shark populations in some protected areas continue to decline, as they remain a prime target for illegal fishers. To understand shark movements and their vulnerability to illegal fishing, 3 years of acoustic tracking data, from 101 reef‐associated sharks, were analysed as movement networks to explore the predictability of movement patterns and identify key movement corridors within the British Indian Ocean Territory (BIOT) MPA. We examined how space use and connectivity overlap with spatially explicit risk of illegal fishing, through data obtained from the management consultancy enforcing the MPA. Using individual‐based models, the movement networks of two sympatric shark species were efficiently predicted with distance‐decay functions (>95% movements accurately predicted). Model outliers were used to highlight the locations with unexpectedly high movement rates where MPA enforcement patrols might most efficiently mitigate predator removal. Activity space estimates and network metrics illustrate that silvertip sharks were more dynamic, less resident and link larger components of the MPA than grey reef sharks. However, we show that this behaviour potentially enhances their exposure to illegal fishing activity. Synthesis and applications . Marine protected area (MPA) enforcement strategies are often limited by resources. The British Indian Ocean Territory MPA, one of the world's largest 'no take' MPAs, has a single patrol vessel to enforce 640, 000 km 2 of open ocean, atoll and reef ecosystems. We argue that to optimize the patrol vessel search strategy and thus enhance their protective capacity, ecological data on the space use and movements of desirable species, such as large‐bodied reef predators, must be incorporated into management plans. Here, we use electronic tracking data to evaluate how shark movement dynamics influence species mortality trajectories in exploited reef ecosystems. In doing so we discuss how network analyses of such data might be applied for protected area enforcement. Abstract : Marine protected area (MPA) enforcement strategies are often limited by resources. The British Indian Ocean Territory MPA, one of the world's largest 'no take' MPAs, has a single patrol vessel to enforce 640, 000 km 2 of open ocean, atoll and reef ecosystems. We argue that to optimize the patrol vessel search strategy and thus enhance their protective capacity, ecological data on the space use and movements of desirable species, such as large‐bodied reef predators, must be incorporated into management plans. Here, we use electronic tracking data to evaluate how shark movement dynamics influence species mortality trajectories in exploited reef ecosystems. In doing so we discuss how network analyses of such data might be applied for protected area enforcement.