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A novel method for estimating the strength of positive mating preference by similarity in the wild

机译:一种通过野外相似性估计阳性交配偏好强度的新方法

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Abstract Mating preference can be a driver of sexual selection and assortative mating and is, therefore, a key element in evolutionary dynamics. Positive mating preference by similarity is the tendency for the choosy individual to select a mate which possesses a similar variant of a trait. Such preference can be modelled using Gaussian-like mathematical functions that describe the strength of preference, but such functions cannot be applied to empirical data collected from the field. As a result, traditionally, mating preference is indirectly estimated by the degree of assortative mating (using Pearson's correlation coefficient, r ) in wild captured mating pairs. Unfortunately, r and similar coefficients are often biased due to the fact that different variants of a given trait are nonrandomly distributed in the wild, and pooling of mating pairs from such heterogeneous samples may lead to ?¢????false?¢????positive?¢???? results, termed ?¢????the scale-of-choice effect?¢???? (SCE). Here we provide two new estimators of mating preference ( C rough and C scaled ) derived from Gaussian-like functions which can be applied to empirical data. Computer simulations demonstrated that r coefficient showed robust estimations properties of mating preference but it was severely affected by SCE, C rough showed reasonable estimation properties and it was little affected by SCE, while C scaled showed the best properties at infinite sample sizes and it was not affected by SCE but failed at biological sample sizes. We recommend using C rough combined with the r coefficient to infer mating preference in future empirical studies.
机译:摘要交配偏好可能是性选择和交配的驱动力,因此是进化动力学的关键因素。通过相似性进行积极的交配偏好是有选择的个体倾向于选择具有相似性状变异的伴侣。可以使用描述偏好强度的高斯式数学函数对此类偏好进行建模,但是此类函数无法应用于从现场收集的经验数据。结果,传统上,交配偏好是通过野生捕获的交配对中的交配程度(使用Pearson相关系数r)间接估算的。不幸的是,由于给定性状的不同变体在野外非随机分布的事实,r和相似系数经常是有偏差的,并且来自这种异质样本的交配对的合并可能导致“错误”。 posit积极¢结果,称为“选择规模效应”。 (SCE)。在这里,我们提供了两个新的对交配偏好的估计(C粗糙和C缩放),该估计是从类似高斯函数的函数得出的,可以应用于经验数据。计算机仿真表明,r系数显示出对交配偏好的鲁棒估计性质,但受SCE严重影响,C粗略显示出合理的估计性质,而不受SCE影响,而C标度显示了在无限样本量下的最佳性质,而没有受SCE的影响,但在生物样本量方面失败。我们建议在未来的经验研究中结合使用C粗糙与r系数来推断交配偏好。

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