Connectivity and systemic resilience of the Great Barrier Reef.
Connectivity and systemic resilience of the Great Barrier Reef.
复制标题
DOI：
10.1371/journal.pbio.2003355
复制
发表时间：
2017-11
期刊：
影响因子：
9.8
通讯作者：
Mumby PJ
中科院分区：
文献类型：
--
作者：
Hock K;Wolff NH;Ortiz JC;Condie SA;Anthony KRN;Blackwell PG;Mumby PJ
澳大利亚标志性的大堡礁（GBR）继续遭受飓风、珊瑚白化和食珊瑚棘冠海星（COTS）爆发的反复影响，在此过程中失去了大部分珊瑚覆盖。这就提出了生态系统的系统复原力及其在大规模人口流失后恢复的能力的问题。在这里，我们发现，大约100个珊瑚礁的GBR，或约3%，具有理想的属性，以促进恢复受干扰的地区，从而赋予系统的弹性水平，并帮助其持续恢复。这些珊瑚礁（1）通过洋流与更广泛的珊瑚礁网络高度相连，（2）受到干扰的风险相对较低，因此当其他珊瑚礁枯竭时，它们可能会提供补充，（3）有能力促进理想物种的恢复，但不太可能经历或传播COTS爆发。这些“强大的源礁”，这可能会提供47%的生态系统在一个单一的分散事件，从海洋条件和地理位置之间的相互作用，这是可能会重复在其他珊瑚礁系统的过程中出现的巨大补充潜力。随着气候变化导致的扰动频率加快，珊瑚礁系统的这种自然复原力将变得越来越重要。澳大利亚的大堡礁是一个由3,800多个珊瑚礁组成的大型珊瑚生态系统。栖息在这些珊瑚礁上的珊瑚种群由被洋流驱散的幼虫连接在一起。模拟区域连接模式显示，珊瑚礁可以作为突出的幼虫来源，并为该地区的其他珊瑚种群提供幼虫。珊瑚礁上的珊瑚种群也受到各种干扰，如漂白和食用珊瑚的棘冠海星的爆发。这些干扰往往有明确的空间模式，导致不同程度的影响珊瑚礁。在这项研究中，我们首先使用高分辨率的传播模拟的幼虫，以确定珊瑚礁最有可能支持区域恢复过程中，由于其高连通性。然后，我们使用海洋学和气候模型来显示哪些珊瑚礁可能暴露于珊瑚漂白和海星爆发的风险较低。最后，我们结合联合收割机这些结果，找到珊瑚礁，不仅可能是良好的来源，被良好的连接，但也更有可能有成年繁殖种群需要提供必要的幼虫供应。这些信息可以支持旨在分配管理资源的决策，并优先考虑对整个珊瑚礁系统的恢复力至关重要的地点。
Australia’s iconic Great Barrier Reef (GBR) continues to suffer from repeated impacts of cyclones, coral bleaching, and outbreaks of the coral-eating crown-of-thorns starfish (COTS), losing much of its coral cover in the process. This raises the question of the ecosystem’s systemic resilience and its ability to rebound after large-scale population loss. Here, we reveal that around 100 reefs of the GBR, or around 3%, have the ideal properties to facilitate recovery of disturbed areas, thereby imparting a level of systemic resilience and aiding its continued recovery. These reefs (1) are highly connected by ocean currents to the wider reef network, (2) have a relatively low risk of exposure to disturbances so that they are likely to provide replenishment when other reefs are depleted, and (3) have an ability to promote recovery of desirable species but are unlikely to either experience or spread COTS outbreaks. The great replenishment potential of these ‘robust source reefs’, which may supply 47% of the ecosystem in a single dispersal event, emerges from the interaction between oceanographic conditions and geographic location, a process that is likely to be repeated in other reef systems. Such natural resilience of reef systems will become increasingly important as the frequency of disturbances accelerates under climate change. Australia’s Great Barrier Reef is a large coral ecosystem consisting of more than 3,800 reefs. Coral populations inhabiting these reefs are connected by larvae that are dispersed by ocean currents. Modelling regional connectivity patterns reveals reefs that can act as prominent larval sources and supply larvae to other coral populations in the area. Coral populations on reefs are also subject to various disturbances, such as bleaching and outbreaks of the coral-eating crown-of-thorns starfish. These disturbances tend to have spatially explicit patterns, resulting in different levels of impact among reefs. In this study, we first use high-resolution dispersal simulations of larvae to identify the reefs most likely to support regional recovery processes due to their high connectivity. We then use oceanographic and climate models to show which reefs are likely to have a lower risk of exposure to coral bleaching and starfish outbreaks. Finally, we combine these results to find reefs that are not only likely to be good sources by being well connected but also more likely to have adult breeding stocks needed to provide the necessary larval supply. This information can support decision-making that aims to allocate management resources and prioritise sites important for the resilience of the entire reef system.
登录
查看更多内容
影响因子：
3.7
作者：
Eakin CM;Morgan JA;Heron SF;Smith TB;Liu G;Alvarez-Filip L;Baca B;Bartels E;Bastidas C;Bouchon C;Brandt M;Bruckner AW;Bunkley-Williams L;Cameron A;Causey BD;Chiappone M;Christensen TR;Crabbe MJ;Day O;de la Guardia E;Díaz-Pulido G;DiResta D;Gil-Agudelo DL;Gilliam DS;Ginsburg RN;Gore S;Guzmán HM;Hendee JC;Hernández-Delgado EA;Husain E;Jeffrey CF;Jones RJ;Jordán-Dahlgren E;Kaufman LS;Kline DI;Kramer PA;Lang JC;Lirman D;Mallela J;Manfrino C;Maréchal JP;Marks K;Mihaly J;Miller WJ;Mueller EM;Muller EM;Orozco Toro CA;Oxenford HA;Ponce-Taylor D;Quinn N;Ritchie KB;Rodríguez S;Ramírez AR;Romano S;Samhouri JF;Sánchez JA;Schmahl GP;Shank BV;Skirving WJ;Steiner SC;Villamizar E;Walsh SM;Walter C;Weil E;Williams EH;Roberson KW;Yusuf Y
通讯作者：
Yusuf Y
影响因子：
5.8
作者：
Fabricius, K;De'ath, G;Williams, DM
通讯作者：
Williams, DM
影响因子：
6.3
作者：
Fox, HE;Mous, PJ;Caldwell, RL
通讯作者：
Caldwell, RL
影响因子：
5.8
作者：
Devlin, M. J.;McKinna, L. W.;Brodie, J.
通讯作者：
Brodie, J.
影响因子：
5.8
作者：
Brodie, J. E.;Kroon, F. J.;Davis, A. M.
通讯作者：
Davis, A. M.