Defining the Degree of Seasonality and its Significance for Future Research
Defining the Degree of Seasonality and its Significance for Future Research
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DOI：
10.1093/icb/icx040
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发表时间：
2017-11-01
影响因子：
2.6
通讯作者：
Wingfield, John C.
中科院分区：
文献类型：
--
作者：
Lisovski, Simeon;Ramenofsky, Marilyn;Wingfield, John C.
季节性描述了环境中的周期性和基本上可预测的波动。日照长度、温度、降雨量和资源可用性的这种变化是普遍存在的，可以对生物体施加强大的选择压力，以适应季节性环境。然而，季节变化表现出大规模的地理差异，这是由一整套因素造成的，如太阳辐射，洋流，大陆的范围和地形。认识到这些在驱动整体季节性模式中的贡献，可能有助于我们进一步理解我们应该在全球范围内期待的进化适应。在这里，我们引入了一个新的概念，并提供数据描述的整体程度的季节性，根据其两个主要组成部分的振幅和可预测性。使用全球陆地数据集的温度，降水和初级生产力，我们表明，这些重要的季节性因素表现出强烈的差异，其空间格局与显着的不对称性之间的南半球和北方半球。此外，我们的分析表明，季节性是高度不同的纬度以及纵向梯度。这表明，使用季节性及其组成部分，幅度和可预测性的直接措施，可以产生更好的了解生物体是如何适应季节性环境，并提供支持的快速环境变化的后果预测。
Seasonality describes cyclic and largely predictable fluctuations in the environment. Such variations in day length, temperature, rainfall, and resource availability are ubiquitous and can exert strong selection pressure on organisms to adapt to seasonal environments. However, seasonal variations exhibit large scale geographical divergences caused by a whole suite of factors such as solar radiation, ocean currents, extent of continents, and topography. Realizing these contributions in driving patterns of overall seasonality may help advance our understanding of the kinds of evolutionary adaptations we should expect at a global scale. Here, we introduce a new concept and provide the data describing the overall degree of seasonality, based on its two major components-amplitude and predictability. Using global terrestrial datasets on temperature, precipitation and primary productivity, we show that these important seasonal factors exhibit strong differences in their spatial patterns with notable asymmetries between the southern and the northern hemisphere. Furthermore, our analysis reveals that seasonality is highly diverse across latitudes as well as longitudinal gradients. This indicates that using a direct measure of seasonality and its components, amplitude and predictability, may yield a better understanding of how organisms are adapted to seasonal environments and provide support for predictions on the consequences of rapid environmental change.