Rogers JGD and Plagányi ÉE (2022), Culling corallivores improves short-term coral ...

ECB-ART-50341

Nat Commun 2022 Oct 14;131:2520. doi: 10.1038/s41467-022-30213-x.

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Culling corallivores improves short-term coral recovery under bleaching scenarios.

Rogers JGD , Plagányi ÉE .

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Management of coral predators, corallivores, is recommended to improve coral cover on tropical coral reefs under projected increasing levels of accumulated thermal stress, but whether corallivore management can improve coral cover, which is necessary for large-scale operationalisation, remains equivocal. Here, using a multispecies ecosystem model, we investigate intensive management of an invertebrate corallivore, the Crown-of-Thorns Starfish (Acanthaster cf. solaris), and show that culling could improve coral cover at sub-reef spatial scales, but efficacy varied substantially within and among reefs. Simulated thermal stress events attenuated management-derived coral cover improvements and was dependent on the level of accumulated thermal stress, the thermal sensitivity of coral communities and the rate of corallivore recruitment at fine spatial scales. Corallivore management was most effective when accumulated thermal stress was low, coral communities were less sensitive to heat stress and in areas of high corallivore recruitment success. Our analysis informs how to manage a pest species to promote coral cover under future thermal stress events.

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	Fig. 1. Overview of crown-of-thorns (CoTS) biology, threats and management on the Great Barrier Reef (GBR).1a Management of CoTS on the GBR is structured around the abundance of different size classes of CoTS. 1b Reproductively mature age-2 and age-3+ CoTS spawn their gametes into the environment and rely upon prevailing conditions to achieve fertilisation and distribute larvae. 2–3 Pacific CoTS preferentially target faster-growing corals (e.g. Acropora, Pocillopora and Montipora) over coral taxa characterised by slower growth rates and massive morphologies (e.g. Porites) which are generally consumed less than expected based on their abundance. 4–5 Coral are subject to environmental perturbations with preferred taxa generally more susceptible to cyclone events and thermally induced bleaching. 6 On the GBR, CoTS outbreaks may span large geographical regions encompassing many reefs across which a limited number of control vessels and divers must be distributed. 7–8a Current management of the species primarily entails the deployment of divers at prioritised locations where they manually cull individual CoTS to reduce coral consumption by the species. 8b Principal known drivers of heterogenous manual removal impacts are CoTS size and age as well as population density. Detectability of smaller individuals is a key constraint of contemporary management intervention. Cumulatively, corallivore intervention success at the regional scale first demands success at the finer scales at which they operate. Strategising intervention requires resolving analogous processes 1–5 and 7–8 to achieve broader scale results through 6.
	Fig. 2. Study sites on Australia’s Great Barrier Reef (GBR).Map delineating reefs on which management control sites used in this study were located and key geographical points referenced in this study. Prefix “R” denotes reef, for example, R1 is reef 1. Developed in QGIS with GBR features dataset113.
	Fig. 3. Management site 2 model fits and total coral cover (%) and catch-per-unit-effort (CPUE; CoTS.min−1) of crown-of-thorns starfish (CoTS) trajectories under no manual control vs monthly manual control given no adaptive capacity (A = 0).In all panels, black solid lines define the no control scenario and the blue dashed line defines the controlled scenario. a The model fit to total coral cover for the management site (data points plotted with error bars depicting ± 90% CI; green points and error bars). Each data point constitutes a single observation. Variance for the site was calculated at the Maximum Likelihood Estimate (MLE) which was simultaneously fitted to n = 539 observations (both coral cover and CoTS CPUE, see Supplementary Table 7). The 90% CIs were calculated based on the variance obtained for Management site 2’s coral cover series and the number of observations in the series n = 21. b, c Total coral cover trajectories under different thermal stress levels expressed as Degree Heating Weeks (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}\in \left\{4,7\right\}$$\end{document}DHW∈4,7) simulated in year 2022. Mean trajectories are presented ± 90% CI depicted as errors bands (n = 80 simulations; grey shading is a 90% CI error bands for the no control scenario, blue shading is 90% CI for the controlled scenario). If coral cover recovery to pre-perturbation levels is not observed by year 2029 the management scenario is denoted, ‘Unrecovered’. A lack of perturbation-induced mortality is denoted by, ‘Recovery not applicable’. d, e CPUE trajectories under \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}=4$$\end{document}DHW=4 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}=7$$\end{document}DHW=7 events. CPUE is a management-based measure of CoTS abundance conditional on demographics and detectability. Error bars are not displayed in b–e to simplify display. Variability in each management scenario’s trajectory due to stochastic tropical cyclones impacts over years 2018–2029 is indicated by shaded uncertainty bands and was limited in CPUE trajectories. The ecological threshold of 0.05 CPUE above which CoTS consumption exceeds coral growth based on a coral cover of 35% is plotted63 (orange dash-dot line). CPUE sawtooth curve patterns are due to model population dynamics as individuals annually become detectable to the management program. Source data (model outputs) provided.
	Fig. 4. Management site 7 model fits and total coral cover (%) and catch-per-unit-effort (CPUE; CoTS.min−1) of crown-of-thorns starfish (CoTS) trajectories under no manual control vs monthly manual control given no adaptive capacity (A = 0).In all panels, black solid lines define the no control scenario and the blue dashed line defines the controlled scenario. a The model fit to total coral cover for the management site (data points plotted with error bars depicting ±90% CI; green points and error bars). Each data point constitutes a single observation. Variance for the site was calculated at the Maximum Likelihood Estimate (MLE) which was simultaneously fitted to n = 539 observations (both coral cover and CoTS CPUE, see Supplementary Table 7). The 90% CIs were calculated based on the variance obtained for Management site 7’s coral cover series and the number of observations in the series n = 17. b, c Total coral cover trajectories under different thermal stress levels expressed as Degree Heating Weeks (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}\in \left\{4,7\right\}$$\end{document}DHW∈4,7) simulated in year 2022. Mean trajectories are presented ±90% CI depicted as errors bands (n = 80 simulations; grey shading is a 90% CI error bands for the no control scenario, blue shading is 90% CI for the controlled scenario). If coral cover recovery to pre-perturbation levels is not observed by year 2029 the management scenario is denoted, ‘Unrecovered’. A lack of perturbation-induced mortality is denoted by, ‘Recovery not applicable’. d, e CPUE trajectories under \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}=4$$\end{document}DHW=4 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}=7$$\end{document}DHW=7 events. CPUE is a management-based measure of CoTS abundance conditional on demographics and detectability. Error bars are not displayed in b–e to simplify display. Variability in each management scenario’s trajectory due to stochastic tropical cyclones impacts over years 2018–2029 is indicated by shaded uncertainty bands and was limited in CPUE trajectories. The ecological threshold of 0.05 CPUE above which CoTS consumption exceeds coral growth based on a coral cover of 35% is plotted63 (orange dash-dot line). CPUE sawtooth curve patterns are due to model population dynamics as individuals annually become detectable to the management program. Source data (model outputs) provided.
	Fig. 5. Adaptive capacity vs median improvement in coral cover due to manual control.The median impact that monthly manual control makes across all sub-reef management sites expressed in terms of the difference in coral cover (%) between control and no control over a 10-year projected period. Different line colours correspond to different modelled levels of accumulated thermal stress experienced by corals. The green curve corresponded to no stress (Degree Heating Weeks (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}$$\end{document}DHW) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=0$$\end{document}=0), the blue curve corresponded to mild stress (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}$$\end{document}DHW=4), the orange curve corresponded to moderate stress, (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}$$\end{document}DHW=7) and the grey curve corresponded to high accumulated thermal stress (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{DHW}}}}}}$$\end{document}DHW=10). Figure panels vary in terms of modelled coral adaptive and acclimation capacity. a no adaptive and acclimation capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=0$$\end{document}A=0). b low adaptive and acclimation capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=2.5$$\end{document}A=2.5). c medium adaptive and acclimation capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=5$$\end{document}A=5). Increasing adaptive and acclimation capacity improved management-derived improvements in coral cover across higher levels of thermal stress. Mean was of similar dynamics, but attained higher values. Source data (model outputs) provided.
	Fig. 6. Adaptive capacity vs median reduction in crown-of-thorns starfish (CoTS) catch-per-unit-effort rates (CPUE, CoTS.min−1).The median impact monthly manual control makes across all sub-reef management sites expressed in terms of the difference in catch-per-unit-effort rates (CPUE) between control and no control over a 10-year projected period. Different line colours correspond to different modelled levels of accumulated thermal stress experienced by corals. The green curve corresponded to no stress (DHWs = 0), the blue curve corresponded to mild stress (DHW = 4), the orange curve corresponded to moderate stress, (DHWs = 7), and the grey curve corresponded to high accumulated thermal stress (DHWs = 10). Figure panels vary in terms of modelled coral adaptive and acclimation capacity. a no adaptive and acclimation capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=0$$\end{document}A=0). b Low adaptive and acclimation capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=2.5$$\end{document}A=2.5). c Medium adaptive and acclimation capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=5$$\end{document}A=5). Increasing levels of accumulated thermal stress reduced CoTS catch rates across different adaptive and acclimation capacities. Mean was of similar dynamics but attained higher difference values. Source data (model outputs) provided.
	Fig. 7. Sensitivity for model outputs over the data period of 2013–2018.Figure is based on model outputs over 2013–2018 (June used as observed data ranged from mid-2013 to mid-2018 for most sites) which used undertaken control efforts by the Crown-of-Thorns Starfish (CoTS) Management Program as input (e.g. site visits and dive time) to inform potential coral trajectories in its absence. Sensitivity considered no adaptive and acclimation capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=0$$\end{document}A=0). Abbreviation ‘C’ denotes ‘Control’ (CoTS culling) and ‘NC’ denotes ‘No Control’. Boxplots are based on model outcomes for the modelled management sites (n = 13). a Summary of differences in coral cover between management scenarios of no manual control and inputted observed manual control over years 2013–2018. Difference represents the (absolute) difference in coral cover expressed as a percentage as opposed to a proportional difference. Both mean (green line) and median (blue line) differences are plotted alongside the management-induced difference at each Management Site considered (grey lines). b Box and whisker plot indicating median coral cover model output (middle line), the 25th and 75th percentiles (the box) and any outliers (crosses) over years 2013–2018 with and without control. c Box and whisker plot indicating the model-suggested relative change in coral cover for 2018 relative to 2013 with and without control, positive values indicate an increase, negative values indicate a decrease and 0 indicates no change. NC: lower adjacent (lower whisker) −75.9%, 25th percentile (lower box) −56.4%, median (centre line) −23.1%, 75th percentile (upper box) −12.4%, upper adjacent (upper whisker) 19.3%. C: lower adjacent (lower whisker) −75.1%, 25th percentile (lower box) −51.3%, median (centre line) −17.9%, 75th percentile (upper box) −0.1%, upper adjacent (upper whisker) 33.5%. d Box and whisker plot indicating model-suggested relative change over 2013–2018 due to operation on the CoTS control program. Lower adjacent (lower whisker) 0.6%, 25th percentile (lower box) 1.2%, median (centre line) 2.8%, 75th percentile (upper box) 5.4%, upper adjacent (upper whisker) 6.6%. Source data (model outputs) provided, includes boxplot details.

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Anthony, Operationalizing resilience for adaptive coral reef management under global environmental change. 2015, Pubmed, Echinobase