Dynamic networks of fighting and mating in a wild cricket population
Dynamic networks of fighting and mating in a wild cricket population
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DOI：
10.1016/j.anbehav.2019.05.026
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发表时间：
2019-09-01
期刊：
影响因子：
2.5
通讯作者：
Tregenza, Tom
中科院分区：
文献类型：
--
作者：
Fisher, David N.;Rodriguez-Munoz, Rolando;Tregenza, Tom
动物种群的生殖成功往往高度倾斜。然而，导致这种情况的过程并不总是很清楚。同样，动物社交网络中的联系通常也是非随机分布的，有些个体有很多联系，而另一些个体则很少，但这种模式是否有简单的解释还没有确定。许多社会互动都涉及到嵌入在更广泛网络中的二元体。因此，通过对社交网络结构的更一般的理解，以及这种结构如何随时间变化，可以对哪些个体积累社交互动进行建模。我们分析了在摄像机网络监控下的野生田野蟋蟀种群在整个繁殖季节的打斗和交配互动。我们拟合了随机行为者导向模型，以确定蟋蟀战斗和交配互动网络形成的动态过程，以及它们如何相互影响。我们发现，蟋蟀倾向于与那些与它们空间接近的人以及那些在战斗网络中拥有相互联系的人战斗，而较重的蟋蟀更经常战斗。我们还发现，与许多其他蟋蟀交配的蟋蟀在接下来的时间里往往会减少战斗。这表明，空间限制、个人特征和直接社会环境特征的混合是决定社会互动的关键。交配相互作用网络需要很少的参数来了解它的增长，因此它的结构;只有交配成功的同质性需要模拟在这个群体中看到的交配相互作用的偏斜。这表明，相对简单，但动态的过程可以给交配成功的高度倾斜的分布。(C)2019年，任作家。由Elsevier Ltd代表动物行为研究协会出版。
Reproductive success is often highly skewed in animal populations. Yet the processes leading to this are not always clear. Similarly, connections in animal social networks are often nonrandomly distributed, with some individuals with many connections and others with few, yet whether there are simple explanations for this pattern has not been determined. Numerous social interactions involve dyads embedded within a wider network. As a result, it may be possible to model which individuals accumulate social interactions through a more general understanding of the social network's structure, and how this structure changes over time. We analysed fighting and mating interactions across the breeding season in a population of wild field crickets under surveillance from a network of video cameras. We fitted stochastic actor-oriented models to determine the dynamic process by which networks of cricket fighting and mating interactions form, and how they co-influence each other. We found crickets tended to fight those in close spatial proximity to them and those possessing a mutual connection in the fighting network, and heavier crickets fought more often. We also found that crickets that mated with many others tended to fight less in the following time period. This demonstrates that a mixture of spatial constraints, characteristics of individuals and characteristics of the immediate social environment are key for determining social interactions. The mating interaction network required very few parameters to understand its growth and thus its structure; only homophily by mating success was required to simulate the skew of mating interactions seen in this population. This demonstrates that relatively simple, but dynamic, processes can give highly skewed distributions of mating success. (C) 2019 The Authors. Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour.