Estimating the variation, autocorrelation, and environmental sensitivity of phenotypic selection
Estimating the variation, autocorrelation, and environmental sensitivity of phenotypic selection
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DOI：
10.1111/evo.12741
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发表时间：
2015-09-01
期刊：
影响因子：
3.3
通讯作者：
Tufto, Jarle
中科院分区：
文献类型：
--
作者：
Chevin, Luis-Miguel;Visser, Marcel E.;Tufto, Jarle
尽管在时间和空间变异的表型选择相当大的兴趣，很少有方法允许量化这种变化，同时正确地占每个个体估计的误差方差。此外，现有的方法不估计表型选择的自相关性，这是一个主要的决定因素，在不断变化的环境中的生态进化动态。我们介绍了一种新的方法来衡量变量表型选择使用随机回归。我们依靠模型选择来评估稳定选择的支持，以及可能包括趋势加（可能自相关）波动的移动最优值。选择的环境敏感性也可以通过包括环境协变量来估计。在广泛的模拟测试我们的方法后，我们将其应用于在荷兰的一个大山雀种群的繁殖时间。我们的分析发现，春季温度可以很好地预测最佳状态，并且在食物生物量达到峰值之前约33天出现，这与该物种的生物学特征一致。我们还检测到自相关波动的最佳，超出了温度和食物峰值所造成的。由于我们的方法直接估计出现在理论模型中的参数，它应该是特别有用的预测生态进化对环境变化的反应。
Despite considerable interest in temporal and spatial variation of phenotypic selection, very few methods allow quantifying this variation while correctly accounting for the error variance of each individual estimate. Furthermore, the available methods do not estimate the autocorrelation of phenotypic selection, which is a major determinant of eco-evolutionary dynamics in changing environments. We introduce a new method for measuring variable phenotypic selection using random regression. We rely on model selection to assess the support for stabilizing selection, and for a moving optimum that may include a trend plus (possibly autocorrelated) fluctuations. The environmental sensitivity of selection also can be estimated by including an environmental covariate. After testing our method on extensive simulations, we apply it to breeding time in a great tit population in the Netherlands. Our analysis finds support for an optimum that is well predicted by spring temperature, and occurs about 33 days before a peak in food biomass, consistent with what is known from the biology of this species. We also detect autocorrelated fluctuations in the optimum, beyond those caused by temperature and the food peak. Because our approach directly estimates parameters that appear in theoretical models, it should be particularly useful for predicting eco-evolutionary responses to environmental change.