Including trait-based early warning signals helps predict population collapse.
Including trait-based early warning signals helps predict population collapse.
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DOI：
10.1038/ncomms10984
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发表时间：
2016-03-24
影响因子：
16.6
通讯作者：
Ozgul A
中科院分区：
文献类型：
--
作者：
Clements CF;Ozgul A
预测人口崩溃是生态学的一个持续目标，这导致在分叉之前根据主要指标的预期变化制定预警信号。人们一直在寻找关于感兴趣的人口的大量时间序列数据，取得了不同程度的成功。在这里，我们超越了这些既定的方法，包括并行的时间序列数据的丰度和健身相关的性状动态。使用从微观实验的数据，我们表明，包括表型性状的动态信息，如身体大小到复合预警指数可以产生更准确的推论，人口是否接近一个关键的转变比单独使用丰度时间序列。通过将与健康相关的性状信息与传统的基于丰度的早期预警信号一起纳入单一风险指标，我们的可推广方法提供了一种强大的新方法来评估哪些种群可能处于崩溃的边缘。 通过监测时间序列数据中的关键预警信号来预测人口崩溃，可以突出何时需要采取干预措施。在这里，作者表明，包括身体大小等表型特征的信息可以比单独的丰度数据更准确地预测关键转变。
Foreseeing population collapse is an on-going target in ecology, and this has led to the development of early warning signals based on expected changes in leading indicators before a bifurcation. Such signals have been sought for in abundance time-series data on a population of interest, with varying degrees of success. Here we move beyond these established methods by including parallel time-series data of abundance and fitness-related trait dynamics. Using data from a microcosm experiment, we show that including information on the dynamics of phenotypic traits such as body size into composite early warning indices can produce more accurate inferences of whether a population is approaching a critical transition than using abundance time-series alone. By including fitness-related trait information alongside traditional abundance-based early warning signals in a single metric of risk, our generalizable approach provides a powerful new way to assess what populations may be on the verge of collapse. Predicting population collapse by monitoring key early warning signals in time-series data may highlight when interventions are needed. Here, the authors show that including information on phenotypic traits like body size can more accurately predict critical transitions than abundance data alone.