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评估自然水生生态系统关键转型的预警指标
【发布时间:2017-01-05 】 【 】【打印】【关闭

  水生生态系统状态在外界驱动下除了发生渐变,还会发生突发性持续性变化。然而,要预测这些关键状态的转化非常困难,因为系统状态在发生这些变化前往往只显示出细微的变化。早期预警指标(EWIs)被用于捕捉生态系统弹性丧失发出的信号,并且在理论模型中可对生态系统的关键转型前进行预警,目前已经广泛运用到了古气候时间序列,实验室以及全湖试验的监测中。

  目前EWIs监测自然水生生态系统的关键转型的研究还是普遍基于经验数据时间序列的,且大量研究结果尚未得到有效验证。本研究评估了4项常用的 EWI 指标,将它们运用到了5个淡水湖泊长期生态系统变化中,这5个湖泊的生态系统都经历了突发性持续性变化,通过该项研究可以更清晰地理解相关的生态机制及驱动力。这些案例研究按触发生态系统突变的3种机制——竞争、营养级联以及共位群内捕食被划分为3类。尽管在绝大多数的案例中都能监测到EWIs,但这4项指标间的一致性很低。在一些案例中,生态系统关键转型发生前会大量采用EWIs进行监测。然而,该项研究结果显示,尽管目前采用了许多最好的EWIs进行淡水生态系统状态的常规监测,但仍然不能提供在即将发生的关键转型前可靠和一致的信号。最后研究结果强烈建议必需具有驱动生态系统关键转型潜在机制的先验知识,才能通过识别相关状态变量成功监控 EWIs。

  (来源:http://www.pnas.org/content/early/2016/11/21/1608242113.full.pdf )

  Ecosystems can show sudden and persistent changes in state despite only incremental changes in drivers. Such critical transitions are difficult to predict, because the state of the system often shows little change before the transition. Early-warning indicators (EWIs) are hypothesized to signal the loss of system resilience and have been shown to precede critical transitions in theoretical models,paleo-climate time series, and in laboratory as well as whole lake experiments. The generalizability of EWIs for detecting critical transitions in empirical time series of natural aquatic ecosystems remains largely untested, however. Here we assessed four commonly used EWIs on long-term datasets of five freshwater ecosystems that have experienced sudden, persistent transitions and for which the relevant ecological mechanisms and drivers are well understood. These case studies were categorized by three mechanisms that can generate critical transitions between alternative states: competition, trophic cascade, and intraguild predation. Although EWIs could be detected in most of the case studies, agreement among the four indicators was low. In some cases, EWIs were detected considerably ahead of the transition. Nonetheless, our results show that at present, EWIs do not provide reliable and consistent signals of impending critical transitions despite using some of the best routinely monitored freshwater ecosystems. Our analysis strongly suggests that a priori knowledge of the underlying mechanisms driving ecosystem transitions is necessary to identify relevant state variables for successfully monitoring EWIs.

  (来源:PNAS, 2016, 113(50) : E8089-E8095)

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