On quantifying the apparent temperature sensitivity of plant phenology
On quantifying the apparent temperature sensitivity of plant phenology
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DOI：
10.1111/nph.16114
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发表时间：
2019-09-17
期刊：
影响因子：
9.4
通讯作者：
Hufkens, Koen
中科院分区：
文献类型：
--
作者：
Keenan, Trevor F.;Richardson, Andrew D.;Hufkens, Koen
许多植物物候事件对温度敏感，导致气候变暖时生态系统功能的季节循环发生变化。为了评估温度变化对植物物候的当前和未来影响，研究人员通常使用温度敏感性指标，该指标量化了温度每度变化的物候变化。在这里，我们研究了物候学的温度敏感性，并强调了广泛使用的每度敏感性方法的天数受到方法学问题的影响，这些问题可能会产生误导性的结果。我们确定了几个因素，特别是温度积分的时间长度，以及积分温度的统计特性的变化，这可能会影响估计的表观温度敏感性。我们将展示由此产生的文物如何导致虚假的差异，明显的温度敏感性和人工空间梯度。这些问题在物候学的温度敏感性分析中很少被考虑。考虑到所发现的问题，我们提倡以过程为导向的建模方法，通过观察和充分表征的不确定性来提供信息，作为简单的每度天数温度敏感性度量的更稳健的替代方案。我们还建议的方法，以尽量减少和评估虚假的影响，在每度度量的天。
Many plant phenological events are sensitive to temperature, leading to changes in the seasonal cycle of ecosystem function as the climate warms. To evaluate the current and future implications of temperature changes for plant phenology, researchers commonly use a metric of temperature sensitivity, which quantifies the change in phenology per degree change in temperature. Here, we examine the temperature sensitivity of phenology, and highlight conditions under which the widely used days-per-degree sensitivity approach is subject to methodological issues that can generate misleading results. We identify several factors, in particular the length of the period over which temperature is integrated, and changes in the statistical characteristics of the integrated temperature, that can affect the estimated apparent sensitivity to temperature. We show how the resulting artifacts can lead to spurious differences in apparent temperature sensitivity and artificial spatial gradients. Such issues are rarely considered in analyses of the temperature sensitivity of phenology. Given the issues identified, we advocate for process-oriented modelling approaches, informed by observations and with fully characterised uncertainties, as a more robust alternative to the simple days-per-degree temperature sensitivity metric. We also suggest approaches to minimise and assess spurious influences in the days-per-degree metric.