Bonnell Tyler R, Vilette Chloé, Young Christopher, Henzi Stephanus Peter, Barrett Louise
Department of Psychology, University of Lethbridge, 4401 University Drive, Lethbridge, T1K 3M4, Canada.
Applied Behavioural Ecology and Ecosystems Research Unit, University of South Africa, Florida, Gauteng, South Africa.
Curr Zool. 2021 Feb;67(1):49-57. doi: 10.1093/cz/zoaa041. Epub 2020 Jul 29.
The development of multilayer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other, and how these in turn might influence group dynamics. Here, we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys . Our previous analyses of this phenomenon used a monolayer approach, and our aim here is to extend these analyses using a dynamic multilayer approach. To do so, we constructed a temporal series of male and female interaction layers. We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male's centrality in the female grooming layer and changes in male Elo ratings. Our results confirmed our original findings: changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo ratings. However, the multilayer network approach offered additional insights into this social process, identifying how changes in a male's centrality cascade through the other network layers. This dynamic view indicates that the changes in Elo ratings are likely to be short-lived, but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole, especially on reducing intermale aggression (i.e., aggression directed by males toward other males). We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days, using a variety of methods. Such data are inherently multilevel and multilayered, and thus offer the ability to quantify more precisely the dynamics of animal social behaviors.
多层网络技术的发展对于那些希望了解不同交互层如何相互影响,以及这些交互层又如何反过来影响群体动态的研究人员来说是一大福音。在此,我们研究了雄性和雌性梳理毛发及攻击行为交互网络之间的整合如何影响绿猴中雄性的权力轨迹。我们之前对这一现象的分析采用的是单层方法,而我们在此的目的是使用动态多层方法来扩展这些分析。为此,我们构建了一系列雄性和雌性交互层的时间序列。然后,我们使用多元多层次自回归模型来比较雄性在雌性梳理毛发层中的中心性与雄性Elo评分变化之间的交叉滞后关联。我们的结果证实了我们最初的发现:雄性在雌性梳理毛发网络中的中心性变化与它们的Elo评分变化之间存在微弱但正向的关联。然而,多层网络方法为这一社会过程提供了更多见解,确定了雄性中心性的变化如何在其他网络层中层层递进。这种动态视角表明,Elo评分的变化可能是短暂的,但雄性在雌性网络中的中心性对整个多层网络的影响要强烈得多,尤其是在减少雄性间的攻击行为(即雄性对其他雄性的攻击)方面。我们认为,多层社会网络方法可以利用如今更常收集的大量社会数据,运用多种方法。此类数据本质上是多层次和多层的,因此能够更精确地量化动物社会行为的动态变化。