Condie SA et al. (2021), Large-scale interventions may delay decline of ...

ECB-ART-49171

R Soc Open Sci 2021 Apr 28;84:201296. doi: 10.1098/rsos.201296.

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Large-scale interventions may delay decline of the Great Barrier Reef.

Condie SA , Anthony KRN , Babcock RC , Baird ME , Beeden R , Fletcher CS , Gorton R , Harrison D , Hobday AJ , Plagányi ÉE , Westcott DA .

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On the iconic Great Barrier Reef (GBR), the cumulative impacts of tropical cyclones, marine heatwaves and regular outbreaks of coral-eating crown-of-thorns starfish (CoTS) have severely depleted coral cover. Climate change will further exacerbate this situation over the coming decades unless effective interventions are implemented. Evaluating the efficacy of alternative interventions in a complex system experiencing major cumulative impacts can only be achieved through a systems modelling approach. We have evaluated combinations of interventions using a coral reef meta-community model. The model consisted of a dynamic network of 3753 reefs supporting communities of corals and CoTS connected through ocean larval dispersal, and exposed to changing regimes of tropical cyclones, flood plumes, marine heatwaves and ocean acidification. Interventions included reducing flood plume impacts, expanding control of CoTS populations, stabilizing coral rubble, managing solar radiation and introducing heat-tolerant coral strains. Without intervention, all climate scenarios resulted in precipitous declines in GBR coral cover over the next 50 years. The most effective strategies in delaying decline were combinations that protected coral from both predation (CoTS control) and thermal stress (solar radiation management) deployed at large scale. Successful implementation could expand opportunities for climate action, natural adaptation and socioeconomic adjustment by at least one to two decades.

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	Figure 1. . Components of the Coral Community Network (CoCoNet) model showing within-reef interactions on the left (interventions shown as line drawings) and between-reef interactions on the lower right.
	Figure 2. . Key components and workflow for the CoCoNet model, including model initialization, population dynamics for corals and CoTS, spawning and reef connectivity, environmental influences, natural adaptation of corals and six types of intervention applied either individually or in combination.
	Figure 3. . (a) Factors controlling connections to a downstream reef. (b) In-degree centrality of reefs (averaged over three coral spawning periods: 2016–2018) mapped onto a 0.2 × 0.2° grid. In-degree centrality values ranged from 0 (dark purple) to 2638 (yellow) with an average of 676. High values in the southeast reflect high densities of small interconnected reefs.
	Figure 4. . (a) Ranges of mortality experienced by corals within the impact zone for each cyclone category [17,73,77]. (b) Maximum annual DHW used under the three RCP scenarios [71]. Each year, DHW were set at a level randomly selected from below the maximum annual DHW curve. (c) Average proportion of locations bleached per annum under the three RCP scenarios and corresponding estimates from empirical data for 1980–2016 [41]. The long-term values are consistent with the frequency of bleaching (greater than 2° heating months) estimated from climate model projections for RCP 2.6 (0.35–0.45) and RCP 4.5 (0.55–0.75) [4,78], as well as forecasts of annual bleaching across nearly all of the GBR by 2070 under RCP 8.5 [79]. (d) Maximum bleaching mortality as a function of DHW for each of the coral groups (equation (2.13)), including the thermally tolerant strain of staghorn Acropora. Also shown are observed bleaching mortality rates on individual reefs following the 2016 bleaching event on the GBR [40]. (e) Modelled decline in coral growth rate for fast-growing staghorn Acropora and slow-growing Poritidae due to ocean acidification (equation (2.16)). These trends exclude any effects of natural adaptation. (f) Increase in thermal tolerance of coral surviving a bleaching event as a function of bleaching mortality for a range of adaptability levels (equation (2.15)). Initial thermal tolerance values were: 1.0 DHW for staghorn Acropora; 1.5 DHW for tabular Acropora; 2.0 DHW for Montipora; 3.0 DHW for Poritidae and favids and 6.0 DHW for thermally tolerant corals (equation (2.14)).
	Figure 5. . (a) Comparison of observed and modelled coral cover averaged over northern, central and southern reefs for the period 1986–2019. Observations are from the AIMS LTMP [55] covering 6–8% of GBR reefs in any year and represented here by the mean (red line) and 95% credible intervals (red shading). The model results are represented by the 100-member ensemble mean (blue dashed line) and ±2 s.d. spanning approximately 95% of the data in any year (blue shading). (b) Modelled annual coral cover averaged over all GBR reefs for the period 1985–2020 from all 100 ensemble runs. (c) As in (b) for modelled coral diversity (evenness index). (d) Comparison of observed latitudes of CoTS active outbreaks (greater than 1.0 CoTS per manta tow, equivalent to 67 CoTS ha−1) [1] and model outbreak latitudes from the first model ensemble member. A histogram of average modelled CoTS density both outside of the outbreak zone and inside of the outbreak zone across the 100-member ensemble is shown in the right-hand panel.
	Figure 6. . Model coral cover averaged across all reefs and 100 ensemble members: (a) three climate projections with no intervention, with and without plausible levels of natural adaptation of corals to thermal stress; (b) current interventions (including data from individual ensemble runs) compared with no intervention; (c) interventions applied individually under RCP 4.5 (excluding those that had only a small effect on coral cover prior to 2070); (d) combination of interventions under RCP 4.5 including one combination with a plausible level of natural adaptation of corals to thermal stress and (e) effects of interventions on coral cover and CoTS density under RCP 4.5 for years 2030, 2040, 2050, 2060 and 2070. Cohen's d is a measure of effect size (small when \|d\| < 0.2; small to medium when 0.2 < \|d\| < 0.5; medium to large when 0.5 < \|d\| < 0.8 and large when \|d\| > 0.8).

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Albright, Reversal of ocean acidification enhances net coral reef calcification. 2016, Pubmed