Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD, Australia; Australian Institute of Marine Science, Townsville, QLD, Australia.
The Environment Institute and School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia.
Adv Mar Biol. 2020;87(1):223-258. doi: 10.1016/bs.amb.2020.08.007. Epub 2020 Oct 8.
Outbreaks of the coral eating crown-of-thorns starfish (COTS; Acanthasts cf. solaris) occur in cyclical waves along the Great Barrier Reef (GBR), contributing significantly to the decline in hard coral cover over the past 30 years. One main difficulty faced by scientists and managers alike, is understanding the relative importance of contributing factors to COTS outbreaks such as increased nutrients and water quality, larval connectivity, fishing pressure, and abiotic conditions. We analysed COTS abundances from the most recent outbreak (2010-2018) using both boosted regression trees and generalised additive models to identify key predictors of COTS outbreaks. We used this approach to predict the suitability of each reef on the GBR for COTS outbreaks at three different levels: (1) reefs with COTS present intermittently (Presence); (2) reefs with COTS widespread and present in most samples and (Prevalence) (3) reefs experiencing outbreak levels of COTS (Outbreak). We also compared the utility of two auto-covariates accounting for spatial autocorrelation among observations, built using weighted inverse distance and weighted larval connectivity to reefs supporting COTS populations, respectively. Boosted regression trees (BRT) and generalised additive mixed models (GAMM) were combined in an ensemble model to reduce the effect of model uncertainty on predictions of COTS presence, prevalence and outbreaks. Our results from best performing models indicate that temperature (Degree Heating Week exposure: relative importance=13.1%) and flood plume exposure (13.0%) are the best predictors of COTS presence, variability in chlorophyll concentration (12.6%) and flood plume exposure (8.2%) best predicted COTS prevalence and larval connectivity potential (22.7%) and minimum sea surface temperature (8.0%) are the best predictors of COTS outbreaks. Whether the reef was open or closed to fishing, however, had no significant effect on either COTS presence, prevalence or outbreaks in BRT results (<0.5%). We identified major hotspots of COTS activity primarily on the mid shelf central GBR and on the southern Swains reefs. This study provides the first empirical comparison of the major hypotheses of COTS outbreaks and the first validated predictions of COTS outbreak potential at the GBR scale incorporating connectivity, nutrients, biophysical and spatial variables, providing a useful aid to management of this pest species on the GBR.
棘冠海星(COTS;Acanthasts cf. solaris)的爆发呈周期性波浪状沿大堡礁(GBR)发生,是过去 30 年来硬珊瑚覆盖率下降的主要原因之一。科学家和管理者面临的主要困难之一是,要了解导致棘冠海星爆发的各种因素的相对重要性,如营养物质和水质增加、幼虫连通性、捕捞压力和非生物条件等。我们使用增强回归树和广义加性模型分析了最近一次爆发(2010-2018 年)中的棘冠海星丰度,以确定棘冠海星爆发的关键预测因子。我们使用这种方法预测了大堡礁上每个珊瑚礁爆发棘冠海星的适宜性,分为三个不同水平:(1)间歇性存在棘冠海星的珊瑚礁(存在);(2)广泛存在棘冠海星且在大多数样本中存在的珊瑚礁(流行);(3)爆发水平的棘冠海星(爆发)。我们还比较了两种自动协变量的效用,这两种协变量分别使用加权倒数和加权幼虫连通性来描述与支持棘冠海星种群的珊瑚礁之间的空间自相关。增强回归树(BRT)和广义加性混合模型(GAMM)组合在一个集成模型中,以减少模型不确定性对棘冠海星存在、流行和爆发预测的影响。来自表现最佳模型的结果表明,温度(受热周暴露:相对重要性=13.1%)和洪水羽流暴露(13.0%)是预测棘冠海星存在的最佳预测因子,叶绿素浓度的可变性(12.6%)和洪水羽流暴露(8.2%)是预测棘冠海星流行的最佳预测因子,幼虫连通性潜力(22.7%)和最小海面温度(8.0%)是预测棘冠海星爆发的最佳预测因子。然而,珊瑚礁是否对捕捞开放或关闭,在 BRT 结果中对棘冠海星的存在、流行或爆发没有显著影响(<0.5%)。我们确定了棘冠海星活动的主要热点,主要位于中架中部大堡礁和南部斯温斯礁。这项研究首次对棘冠海星爆发的主要假设进行了实证比较,并首次在大堡礁范围内对棘冠海星爆发潜力进行了验证预测,其中包括连通性、营养物质、生物物理和空间变量,为管理大堡礁上这种有害物种提供了有用的辅助。