Inferring longitudinal hierarchies: Framework and methods for studying the dynamics of dominance
Inferring longitudinal hierarchies: Framework and methods for studying the dynamics of dominance
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推断纵向层次结构：研究主导动态的框架和方法
DOI：
10.1111/1365-2656.12951
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发表时间：
2019
影响因子：
4.8
通讯作者：
Jackson, ed., Andrew
中科院分区：
文献类型：
--
作者：
Strauss, Eli D.;Holekamp, Kay E.;Jackson, ed., Andrew
社会不平等是动物社会的一贯特征，通常表现为支配等级，其中每个个体的特征是支配等级，表明其在群体成员之间竞争关系网络中的位置。大多数研究把支配等级看作是静态的实体，尽管它们具有真正的纵向，有时甚至是高度动态的性质。为了指导支配等级动态的研究，我们提出了纵向等级的概念：描述一个单一的、潜在的等级及其随时间的动态。纵向层次描述了与每个个体相关联的层次位置（r）和动态（r），作为其交互数据的属性，这些数据基于周期描绘规则（p）和选择用于推断层次的方法被划分成的周期。层级动力学是由主动过程（英语：Active processes）和被动过程（英语：Passive processes）两种过程产生的。推断纵向层次结构的方法应该优化的等级动态以及等级顺序本身的准确性，但还没有研究评估的准确性，不同的方法推断层次dynamics.We修改三个流行的排名方法，使它们更适合于推断纵向层次结构。我们的三个“知情”的方法分配的排名，从以前的时期，而不是计算ranksde novoin每个观察期的数据，并使用先前的知识优势相关通知新的个人在层次结构中的位置。这些方法在R软件包中提供。我们使用模拟数据集和来自具有两种不同性别优势结构的物种的长期经验数据集，比较这些方法及其未修改的对应方法的性能。我们发现，方法的选择有显着的影响，推理的层次动态通过不同的估计的degrea。方法，计算ranksde novoin每个时期高估了层次结构的动态，但纳入先验信息导致更准确地推断出的ranksde novoin。其中，Informed MatReorder和Informed Elo分别给出了最保守的层级动态估计和最动态的层级动态估计，为研究社会支配及其动态提供了必要的概念框架和方法验证。
Social inequality is a consistent feature of animal societies, often manifesting as dominance hierarchies, in which each individual is characterized by a dominance rank denoting its place in the network of competitive relationships among group members. Most studies treat dominance hierarchies as static entities despite their true longitudinal, and sometimes highly dynamic, nature.To guide study of the dynamics of dominance, we propose the concept of a longitudinal hierarchy: the characterization of a single, latent hierarchy and its dynamics over time. Longitudinal hierarchies describe the hierarchy position (r) and dynamics (∆) associated with each individual as a property of its interaction data, the periods into which these data are divided based on a period delineation rule (p) and the method chosen to infer the hierarchy. Hierarchy dynamics result from both active (∆a) and passive (∆p) processes. Methods that infer longitudinal hierarchies should optimize accuracy of rank dynamics as well as of the rank orders themselves, but no studies have yet evaluated the accuracy with which different methods infer hierarchy dynamics.We modify three popular ranking approaches to make them better suited for inferring longitudinal hierarchies. Our three “informed” methods assign ranks that are informed by data from the prior period rather than calculating ranksde novoin each observation period and use prior knowledge of dominance correlates to inform placement of new individuals in the hierarchy. These methods are provided in an R package.Using both a simulated dataset and a long‐term empirical dataset from a species with two distinct sex‐based dominance structures, we compare the performance of these methods and their unmodified counterparts. We show that choice of method has dramatic impacts on inference of hierarchy dynamics via differences in estimates of∆a. Methods that calculate ranksde novoin each period overestimate hierarchy dynamics, but incorporation of prior information leads to more accurately inferred∆a. Of the modified methods, Informed MatReorder infers the most conservative estimates of hierarchy dynamics and Informed Elo infers the most dynamic hierarchies.This work provides crucially needed conceptual framing and methodological validation for studying social dominance and its dynamics.
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影响因子：
2.5
作者：
Newton-Fisher NE
通讯作者：
Newton-Fisher NE
野生白脸卷尾猴（Cebus capucinus）群体中雄性等级逆转的病例报告
影响因子：
1.7
作者：
S. Perry
通讯作者：
S. Perry
DOI：
10.1098/rspb.2010.2512
发表时间：
2011
期刊：
Proceedings of the Royal Society B: Biological Sciences
影响因子：
--
作者：
Eli M. Swanson;I. Dworkin;K. Holekamp
通讯作者：
K. Holekamp
影响因子：
2.5
作者：
Murray, Carson M.;Mane, Sandeep V.;Pusey, Anne E.
通讯作者：
Pusey, Anne E.
影响因子：
2.5
作者：
Bro-Jorgensen, Jakob;Beeston, Joshua
通讯作者：
Beeston, Joshua