Sexual selection in humans: theoretical models and human behavioral studies

动物通常通过他们的身体特征或表型来评估潜在的配偶。在许多物种中，个体学会了更喜欢与他们年轻时遇到的表型相似的配偶。例如，个体可能会学会更喜欢长得像父母的伴侣。这种现象被称为性印记。性印记在鸟类、鱼类和哺乳动物（包括人类）中很常见，可以深刻地表征种群的进化、物种形成和社会结构。然而，研究自然界的性印记是困难的，因为配偶偏好很难评估，甚至更难复制。因此，通过性印记评估的性状表型尚不清楚，性印记对生态的影响也知之甚少。在这个项目中，学生将运用先进的数学和计算方法来i)测量人类的性印记，这比其他物种更容易调查；ii)建立性印记如何影响进化的模型，从而使种群对环境的适应程度或好或坏。学生将使用几何形态计量学，一种统计形状分析的形式，来问“人类在性印记中使用了哪些特定的表型？”然后，学生将结合适应性动力学和基于主体的模拟来问：“在什么条件下，我们应该期望性印记导致种群的局部适应或不适应，而种群的选择在空间上是不同的？”人类数据和新模型的结合将为研究这一重要的社会行为开辟新的途径。

Animals often assess potential mates by their physical characteristics, or phenotypes. In many species, individuals learn to prefer mates with phenotypes similar to those they encountered when they were young. For example, individuals may learn to prefer mates that look like their parents. This phenomenon is called sexual imprinting. Sexual imprinting is common in birds, fish and mammals (including humans), and can profoundly trait evolution, speciation, and the social structure of populations. However, it is difficult to study sexual imprinting in nature, because mate preferences are difficult to assess and even more difficult to replicate. Thus, the traits phenotypes that are evaluated by sexual imprinting are not known, and how sexual imprinting impacts on ecology is poorly understood. In this project, the student will apply advanced mathematical and computational approaches to i) measure sexual imprinting in humans, which are more easily surveyed than other species; and ii) model how sexual imprinting influences evolution so that populations become either well adapted or poorly adapted to their environments. The student will use geometric morphometrics, a form of statistical shape analysis, to ask "What specific phenotypes do humans use in sexual imprinting?" Then, the student will use a combination of adaptive dynamics and agent-based simulations to ask "Under what conditions should we expect sexual imprinting to lead to local adaptation or maladaptation in populations where selection varies in space?" The combination of human data and novel models will open new avenues for the study of this important social behaviour.