School of the Environment, University of Queensland, St Lucia 4072, QLD, Australia.
School of the Environment, University of Queensland, St Lucia 4072, QLD, Australia.
Sci Total Environ. 2024 Nov 10;950:175282. doi: 10.1016/j.scitotenv.2024.175282. Epub 2024 Aug 5.
Outbreaks of corallivorous Crown of Thorns Starfish (Acanthaster spp.; CoTS) cause substantial coral mortality throughout the Indo-Pacific, particularly on the Great Barrier Reef (GBR). Refining CoTS population density modelling and understanding the disparities between real-world observations and model predictions is crucial for developing effective control strategies. Using a spatially explicit ecosystem model of the GBR, we compared CoTS density model predictions to observations and incorporated a new zone-specific mortality rate to account for differences in predation of CoTS between fished and protected reefs. We found high congruence between predictions and observations: ∼81 % of categorical reef level CoTS densities matched or only differed by one category. However, underpredictions increased with higher observed densities. Zone-specific CoTS mortality reduced severe underpredictions from 7.1 % to 5.6 %, which is critical for managers as underpredictions indicate missing outbreaks where targeted culling is necessary, but also lead to underestimated coral loss attributed to CoTS outbreaks. Reef protection status affected prediction accuracy, highlighting the importance of further research on in situ CoTS mortality rates. The location of a reef inside or outside the "initiation box", a speculative area of primary outbreaks (i.e., initial abrupt population increases) on the GBR, also influenced accuracy, with exact predictions more likely outside. Accurately modelling initiation box dynamics is challenging due to limited empirical data on CoTS outbreaks, highlighting the need for focussed research on outbreak dynamics to enhance predictive accuracy. Spatial factors, such as region and shelf position, contributed to the variance between observations and predictions, underscoring the importance of the spatial-temporal context of each observation. Observations of CoTS can help refine model predictions, guide targeted control measures, and contribute to effective ecosystem management for the long-term resilience of the GBR and other reefs targeted by CoTS throughout the Indo-Pacific.
棘冠海星(Acanthaster spp.;CoTS)爆发会导致整个印度洋-太平洋地区的珊瑚大量死亡,尤其是大堡礁(GBR)。改进 CoTS 种群密度模型并了解现实观测与模型预测之间的差异对于制定有效的控制策略至关重要。我们使用 GBR 的空间显式生态系统模型,将 CoTS 密度模型预测与观测结果进行了比较,并纳入了一个新的特定区域死亡率,以解释在捕鱼和受保护的珊瑚礁中 CoTS 捕食的差异。我们发现预测结果与观测结果高度一致:约 81%的珊瑚礁级别的 CoTS 密度匹配或仅相差一个类别。然而,随着观测密度的增加,预测结果的偏差也随之增加。特定区域的 CoTS 死亡率将严重低估的比例从 7.1%降低到 5.6%,这对于管理者来说至关重要,因为低估表明有漏报的疫情爆发,需要进行有针对性的扑杀,但也会导致 CoTS 爆发导致的珊瑚损失被低估。珊瑚礁保护状况会影响预测精度,这突出了进一步研究原位 CoTS 死亡率的重要性。珊瑚礁位于 GBR 的“起始箱”内还是外,这对预测精度也有影响,“起始箱”是主要疫情(即初始急剧种群增加)的推测区域,在起始箱外的预测结果更有可能准确。由于 CoTS 爆发的经验数据有限,准确模拟起始箱动态具有挑战性,这突显了需要对疫情动态进行集中研究以提高预测精度。空间因素,如区域和大陆架位置,也是观测值与预测值之间存在差异的原因之一,这突显了每个观测值的时空背景的重要性。棘冠海星的观测结果可以帮助改进模型预测,指导有针对性的控制措施,并有助于实现大堡礁和整个印度洋-太平洋地区受 CoTS 影响的其他珊瑚礁的长期生态系统管理,以提高其长期恢复力。