Leaders of war: the evolution of collective decision-making in the face of intergroup conflict

自然界中充满了令人惊叹的集体动物行为的例子：鸟群在傍晚的阳光下起伏，成群的鱼紧紧地盘旋在一起以避免捕食，蚂蚁沿着错综复杂的小径成群结队地探索和利用它们的环境。在过去的几十年里，生物学家已经发现了简单的行为决策规则是如何产生这些复杂的群体级别行为的。类似的决策规则已经被发现可以预测人类在人群和交通拥堵中的集体行动，这让人们对以下观点产生了巨大的跨学科兴趣：非常广泛的自然现象可以用简单的普遍规则来解释--这些规则可以通过对动物行为的研究来发现。对于组成稳定的社会群体的动物来说，集体行为的核心问题是如何就去哪里和做什么达成一致，尽管群体成员之间存在利益和偏好的冲突。在许多动物中，这个问题是通过领导者的出现来解决的--领导者是对其他动物的行为和运动具有不成比例的影响的个体。例如，面对在哪里寻找食物或如何避免捕食的不确定性，单个动物可能会最好地跟随群体中经验最丰富或消息最灵通的成员，或者那些对它们自己的健康最有价值的成员。到目前为止，关于集体决策和领导力的研究几乎完全集中在单个群体上，而不是所有其他群体。但现在有广泛的证据表明，群体间的互动可以对群居动物的群体内行为产生强大的影响，从而影响决策的可能模式。例如，鉴于协作对于团队在战斗中的成功至关重要，我们可能会预期，与觅食的团队相比，在战斗的团队中会转向更快速、更专制的决策。另一方面，考虑到所涉及的风险，追随者应该极其挑剔地选择他们在战斗中追随的人，这可能会更有利于权力分享，而不是这样的关键决定。群体间的冲突可能会对领导力和追随者的模式以及群体就统一行动达成一致的速度产生深远的影响，但到目前为止，对于这些群体间对集体决策的影响的性质，我们几乎一无所知。我们将通过开发新的理论来预测群体间的竞争如何影响移动动物群体中领导力和追随者的演变，并通过使用无人机(无人机，UAV)在理想的野生哺乳动物系统-带状猫鼬上测试我们的预测。圈养的猫鼬生活在高度属地的群体中，经常与邻国群体发生暴力冲突。斑点猫鼬的群体间冲突比猫鼬、黑猩猩或(据我们所知)任何其他非人类哺乳动物的群体间冲突更严重。然而，在群体间和跨生殖周期的群体间冲突的强度以及男性和女性之间的群体内冲突的程度也有很大的差异。群体间和群体内冲突的这种变化提供了一个机会，利用实验和对群体间遭遇之前、期间和之后集体运动的自然观察来检验我们的理论预测。在这个过程中，我们将演示如何将无人机的视频与新的“深度学习”人工智能方法相结合，以自动识别单个动物并跟踪它们在野外的行动--为野生动物管理和畜牧业的一系列潜在应用打开大门。该项目的成果将是对集体行为和群体间冲突的更好理解，在进化和行为生态学之外具有广泛的跨学科影响，例如在经济学、心理学、政治学和计算机科学中。

Nature abounds with breathtaking examples of collective animal behaviour: bird flocks undulating in the evening light, schools of fish swirling tightly together to avoid predation, ants teeming along intricate trails to explore and exploit their environment. In the last few decades biologists have discovered how simple behavioural decision rules produce these complex group-level behaviours. Similar decision rules have been found to predict the collective movements of humans in crowds and traffic jams, generating huge interdisciplinary interest in the idea that a very wide range of natural phenomena can be explained by simple universal rules - and that these rules are discoverable through research on animal behaviour. For animals that form stable social groups, the central problem of collective behaviour is how to agree on where to go and what to do, despite conflicting interests and preferences among group members. In many animals this problem is solved through the emergence of leaders - individuals with disproportionate influence on the behaviour and movement of others. For example, in the face of uncertainty about where to find food or how to avoid predation, individual animals may do best to follow the most experienced or best informed members of a group, or those that are most valuable to their own fitness. Up to now, research on collective decision-making and leadership has focused almost exclusively on single groups in isolation of all others. But there is now widespread evidence that intergroup interactions can exert a powerful influence on within-group behaviour in social animals, and hence the likely patterns of decision-making. For example, given that coordination is vital to group success in combat, we might expect a shift to more rapid, dictatorial decision-making in groups that are fighting compared to groups that are foraging. On the other hand, followers should be extremely choosy about which individuals they follow into battle, given the risks involved, which could favour more even power sharing over such vital decisions. Intergroup conflict is likely to have profound impacts on patterns of leadership and followership and the speed with which groups are able to agree on unified action, but to date almost nothing is known about the nature of these intergroup impacts on collective decision-making.We will tackle this gap in knowledge by developing new theory to predict how intergroup competition shapes the evolution of leadership and followership in mobile animal groups; and by testing our predictions using drones (unmanned aerial vehicles, or UAVs) on an ideally suited wild mammal system, the banded mongoose. Banded mongooses live in highly territorial groups which engage in frequent violent conflicts with neighbouring groups. Intergroup conflict is more severe in banded mongooses than in meerkats, chimpanzees, or (to our knowledge) any other non-human mammal. Yet there is also great variation in the intensity of intergroup conflict between groups and across the reproductive cycle, and in levels of within-group conflict between the males and females. This variation in inter- and intragroup conflict provides an opportunity to test our theoretical predictions using experiments and natural observations of collective movement before, during, and after intergroup encounters. In the process we will demonstrate how video from UAVs combined with new 'deep learning' artificial intelligence methods can be used to automatically recognise individual animals and track their movements in the wild - opening the door to a host of potential applications in wildlife management and husbandry. The outcome of the project will be an improved understanding of collective behaviour and intergroup conflict, with broad interdisciplinary implications beyond evolutionary and behavioural ecology, for example, in economics psychology, political science, and computer science.