Fisher David N, Rodríguez-Muñoz Rolando, Tregenza Tom
Centre for Ecology and Conservation, Penryn Campus, University of Exeter, Penryn, TR109FE, Cornwall, UK.
Department for Integrative Biology, Summerlee Science Complex, University of Guelph, Guelph, N1G 2W1, ON, Canada.
BMC Evol Biol. 2016 Jul 27;16:151. doi: 10.1186/s12862-016-0726-9.
A central part of an animal's environment is its interactions with conspecifics. There has been growing interest in the potential to capture these interactions in the form of a social network. Such networks can then be used to examine how relationships among individuals affect ecological and evolutionary processes. However, in the context of selection and evolution, the utility of this approach relies on social network structures persisting across generations. This is an assumption that has been difficult to test because networks spanning multiple generations have not been available. We constructed social networks for six annual generations over a period of eight years for a wild population of the cricket Gryllus campestris.
Through the use of exponential random graph models (ERGMs), we found that the networks in any given year were able to predict the structure of networks in other years for some network characteristics. The capacity of a network model of any given year to predict the networks of other years did not depend on how far apart those other years were in time. Instead, the capacity of a network model to predict the structure of a network in another year depended on the similarity in population size between those years.
Our results indicate that cricket social network structure resists the turnover of individuals and is stable across generations. This would allow evolutionary processes that rely on network structure to take place. The influence of network size may indicate that scaling up findings on social behaviour from small populations to larger ones will be difficult. Our study also illustrates the utility of ERGMs for comparing networks, a task for which an effective approach has been elusive.
动物生存环境的一个核心部分是其与同种个体的互动。人们越来越关注以社交网络的形式捕捉这些互动的潜力。这样的网络随后可用于研究个体间的关系如何影响生态和进化过程。然而,在选择和进化的背景下,这种方法的实用性依赖于社交网络结构在多代间持续存在。这是一个难以检验的假设,因为跨越多代的网络并不存在。我们在八年时间里为野生蟋蟀Gryllus campestris构建了六个年度世代的社交网络。
通过使用指数随机图模型(ERGMs),我们发现对于某些网络特征,任何给定年份的网络能够预测其他年份网络的结构。任何给定年份的网络模型预测其他年份网络的能力并不取决于这些其他年份在时间上的间隔有多远。相反,一个网络模型预测另一年网络结构的能力取决于那些年份之间种群规模的相似性。
我们的结果表明蟋蟀社交网络结构能够抵御个体更替,并且在多代间保持稳定。这将使得依赖网络结构的进化过程得以发生。网络规模的影响可能表明将关于小种群社会行为的研究结果推广到更大种群将是困难的。我们的研究还说明了ERGMs在比较网络方面的实用性,而对于这一任务,一种有效的方法一直难以捉摸。