The Ecology and Evolution of Cooperative Breeding

我的研究小组应用现代计算方法来研究进化生态学的理论问题。我的团队的工作发表在顶级期刊上，如《自然》、《美国博物学家》、《进化论》和《学报B》。此外，我们的发现推动了我们的学科向前发展，无数次受邀参加会议和加入国际科学工作组就是明证。 我研究的长期目标是理解为什么像合作这样的社会行为是适应性的。乍一看，在一个狗咬狗的世界里合作是令人费解的，而解决这个谜题是进化生物学的一大挑战。 在短期内，我建议研究在哺乳动物、鸟类、昆虫甚至一些微生物中发现的合作繁殖系统。我的研究小组将使用数学和计算机模型来理解一个物种的生态如何促进(或抑制)合作繁殖的出现。与其他小组以前的工作不同，我们将应用仔细关注物种生态的动态过程的模型。我们的工作将解决合作繁殖的发生与(I)扩散、(Ii)交配系统、(Iii)父母对后代的投资模式以及(Iv)社会群体内繁殖成功的划分之间的联系。对于任何希望准确研究野外合作(或一般社会行为)的人来说，我们提出的问题的答案是至关重要的。 拟议的工作将涉及至少12名受训人员。每个人都将学习如何使用学术界、政府和工业界重视的计算方法进行进化生态学的研究。因此，学员将非常有资格为这三个部门中的任何一个做出贡献。

My research group applies modern computational methods to study theoretical problems in evolutionary ecology. Work from my group appears in top journals like Nature, the American Naturalist, Evolution, and Proceedings B. Furthermore, our discoveries have driven our discipline forward, as evidenced by numerous invitations to attend conferences and join international scientific working groups. The long-term objective of my research is to understand why social behaviours like cooperation are adaptive. At first glance, cooperation in a "dog-eat-dog world" is puzzling, and solving this puzzle is a major challenge in evolutionary biology. In the short-term, I am proposing to study the cooperative breeding systems found in mammals, birds, insects, and even some micro-organisms. My research group will use mathematical and computer models to understand how the ecology of a species can promote (or inhibit) the emergence of cooperative breeding. Unlike previous work in other groups, we will apply models that pay careful attention to the dynamic processes that feed into species' ecology. Our work will address connections between the occurrence of cooperative breeding, and (i) dispersal, (ii) mating systems, (iii) patterns of parental investment in offspring, and (iv) the division of reproductive success within social groups. Answers to the questions we ask are crucial to anyone who wishes to accurately study cooperation (or social behaviour in general) in the wild. The proposed work will involve at least twelve trainees. Each will learn how to conduct research in evolutionary ecology using computational approaches valued by academia, government, and industry. Trainees, therefore, will be highly qualified to make contributions to any one of these three sectors.