Protecting Great Barrier Reef resilience through effective management of crown-of-thorns starfish outbreaks

Abstract

Resilience-based management is essential to protect ecosystems in the Anthropocene. Unlike large-scale climate threats to Great Barrier Reef (GBR) corals, outbreaks of coral-eating crown-of-thorns starfish (COTS; Acanthaster cf. solaris) can be directly managed through targeted culling. Here, we evaluate the outcomes of a decade of strategic COTS management in suppressing outbreaks and protecting corals during the 4th COTS outbreak wave at reef and regional scales (sectors). We compare COTS density and coral cover dynamics during the 3rd and 4th outbreak waves. During the 4th outbreak wave, sectors that received limited to no culling had sustained COTS outbreaks causing significant coral losses. In contrast, in sectors that received timely and sufficient cull effort, coral cover increased substantially, and outbreaks were suppressed with COTS densities up to six-fold lower than in the 3rd outbreak wave. In the Townsville sector for example, despite exposure to comparable disturbance regimes during the 4th outbreak wave, effective outbreak suppression coincided with relative increases in sector-wide coral cover (44%), versus significant coral cover declines (37%) during the 3rd outbreak wave. Importantly, these estimated increases span entire sectors, not just reefs with active COTS control. Outbreaking reefs with higher levels of culling had net increases in coral cover, while the rate of coral loss was more than halved on reefs with lower levels of cull effort. Our results also indicate that outbreak wave progression to adjoining sectors has been delayed, probably via suppression of COTS larval supply. Our findings provide compelling evidence that proactive, targeted, and sustained COTS management can effectively suppress COTS outbreaks and deliver coral growth and recovery benefits at reef and sector-wide scales. The clear coral protection outcomes demonstrate the value of targeted manual culling as both a scalable intervention to mitigate COTS outbreaks, and a potent resilience-based management tool to "buy time" for coral reefs, protecting reef ecosystem functions and biodiversity as the climate changes.

Fig 1. COTS outbreaks, spatial extent and management action.

Maximum COTS density and the corresponding outbreak category at each reef during the 3^rd outbreak wave (A) (1992–2009) and the 4^th outbreak wave (B) (2011-Present) at LTMP reefs. Only reefs from sectors and outbreaks included in the following analysis are shown. Sector-scale colour coding depicts the management action taken during the 4^th outbreak wave with the number of culling hours (total of all cull divers bottom time) listed below the sector labels. ⁺ Site-Scale Action refers to COTS culling conducted at high-value tourism sites during the 3^rd outbreak wave, which had no discernible impact on sectoral level outbreak dynamics. * No Action refers to sectors where no control was implemented. ^ Insufficient Data reflects sectors where time series data was insufficient to determine Outbreak periods or no distinct outbreak wave was observed. These sectors have thus been excluded from these analyses. Sector outlines republished from AIMS: https://apps.aims.gov.au/reef-monitoring/sector/list under a CC BY license, with permission from Mike Emslie, AIMS under a CC BY license, original copyright 2023.

Fig 2. COTS and Coral cover trajectories by sector.

Temporal trends in modelled median hard coral cover (black symbols) and raw mean COTS densities (white symbols) from seven focal latitudinal sectors, categorised by the four management Actions (Limited, Reactive, Timely, Proactive) applied during the 4th COTS outbreak. Grey bands are the 95% credible intervals for coral cover and +/- Standard error for COTS densities. Horizontal dashed line is the threshold for a severe outbreak (>1 COTS per tow), while the grey dotted line represents the threshold of an established outbreak. Symbols and arrows along the top of each panel represents the timing of major cyclone and coral bleaching disturbances that resulted in sector-wide mortality. Importantly the plot shows when the mortality was observed which can lag the actual event by up to 1 year. Bar plots below each temporal plot is the total amount of culling effort invested per year. Culling effort represents the remobilised Program from 2012, and site-scale culling during the 3^rd Outbreak wave is not displayed. NB the Townsville and Swain Sector plots COTS axes are on a different scale due to higher observed COTS densities. Sector outlines obtained with permission from AIMS under a CC BY license, original copyright 2023.

Fig 3. Model outputs comparing outcomes from the 3^rd and 4^th COTS outbreaks.

Posterior probability distributions from Bayesian generalised linear mixed models of: A) Mean COTS densities per manta tow and, B) mean relative change in % coral cover during the previous 3^rd (1993; grey) and current 4^th (2010; coloured) COTS outbreaks. Data points below probability distributions are the mean responses ±66% (thick bars) and 90% (thin bars) credible intervals. Sectors are colour coded according to the management action: Limited Action (orange; CL = Cooktown-Lizard; SW = Swain), Reactive Action (yellow; CA = Cairns; IN = Innisfail), Timely Action (green; TO = Townsville; CB = Capricorn-Bunkers), and Proactive Action (blue; CU = Cape Upstart). Note that only one posterior probability distribution is shown for the Capricorn Bunker sector as there was no population outbreak in 3^rd Outbreak.

Fig 4. Relative coral cover change during the 4^th outbreak.

Relative change in coral cover (%) according to the type of management action implemented (A): ‘Limited Action’ (orange), ‘Reactive’ (yellow), and ‘Timely’ (green) and by sector (B). ‘Proactive’ is not shown due to insufficient data for this analysis. Each point represents the relative change in coral cover at a given reef, up to 6 years following the start of the sector-specific 4^th outbreak wave (see Table 1). The trendline is fitted using a generalized additive model (GAM) and the transparent ribbon represents the 95% confidence intervals. Disturbance markers indicate timing of events that resulted in sector-wide coral mortality (i.e. not all disturbance events).

Fig 5. Annual coral cover change at varying intensities of COTS control.

Comparison of posterior probability distributions from Bayesian generalised linear mixed models of annual change in coral cover at increasing levels of culling effort observed during the 4^th outbreak wave. Data points below probability distributions are the mean responses ±66% (thick bars) and 90% (thin bars) credible intervals. Culling effort is categorised based on the number of culling hours divided by the maximum COTS density observed during the 4^th outbreak wave (see Table 1 for time period), as (1) reefs with no recorded COTS outbreak (blue); (2) reefs with recorded COTS outbreaks and above median culling effort relative to maximum COTS density (green); (3) reefs with recorded COTS outbreaks and below median culling effort relative to maximum COTS density (orange) and (4) reefs with recorded COTS outbreaks and no culling effort (red).