Detection of social group instability among captive rhesus macaques using joint network modeling.
Detection of social group instability among captive rhesus macaques using joint network modeling.
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DOI：
10.1093/czoolo/61.1.70
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发表时间：
2015-02
期刊：
影响因子：
2.2
通讯作者：
Mccowan B
中科院分区：
文献类型：
--
作者：
Beisner BA;Jin J;Fushing H;Mccowan B
群居动物的社会稳定性是多个行为网络相互作用的结果。然而，准确确定一个社会群体何时面临崩溃的风险是困难的。我们使用联合网络建模的方法来检验四个稳定的猕猴和三个不稳定的猕猴两个行为网络--攻击和状态信号之间的相互依赖关系，以确定稳定的群体中网络相互依赖的特征模式，这些特征模式很容易从不稳定的群体中区分出来。我们的研究结果表明，在稳定的社会群体中，攻击-状态网络相互依赖的最显著来源来自于比预期更频繁的具有相反方向的状态-攻击(即A威胁B，B信号接受从属地位)的二元。相比之下，不稳定组表现出相反方向的攻击状态二元组减少(但仍高于预期)，以及比预期更频繁的双向攻击性二元组。这些结果表明，不仅攻击性和地位网络之间稳定的联合关系很容易与不稳定的时间点区分开来，社会不稳定至少以两种不同的方式表现出来。总之，我们的联合建模方法在量化和监测任何野生或圈养社会系统的复杂社会动态方面可能被证明是有用的，因为所有社会系统都是由多个相互连接的网络组成的
Social stability in group-living animals is an emergent property which arises from the interaction amongst multiple behavioral networks. However, pinpointing when a social group is at risk of collapse is difficult. We used a joint network modeling approach to examine the interdependencies between two behavioral networks, aggression and status signaling, from four stable and three unstable groups of rhesus macaques in order to identify characteristic patterns of network interdependence in stable groups that are readily distinguishable from unstable groups. Our results showed that the most prominent source of aggression-status network interdependence in stable social groups came from more frequent dyads than expected with opposite direction status-aggression (i.e. A threatens B and B signals acceptance of subordinate status). In contrast, unstable groups showed a decrease in opposite direction aggression-status dyads (but remained higher than expected) as well as more frequent than expected dyads with bidirectional aggression. These results demonstrate that not only was the stable joint relationship between aggression and status networks readily distinguishable from unstable time points, social instability manifested in at least two different ways. In sum, our joint modeling approach may prove useful in quantifying and monitoring the complex social dynamics of any wild or captive social system, as all social systems are composed of multiple interconnected networks
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影响因子：
2.4
作者：
Beisner, B. A.;Jackson, M. E.;Cameron, A.;McCowan, B.
通讯作者：
McCowan, B.
影响因子：
3.7
作者：
Beisner BA;Jackson ME;Cameron AN;McCowan B
通讯作者：
McCowan B
影响因子：
2.5
作者：
DITTUS, WPJ
通讯作者：
DITTUS, WPJ
影响因子：
2.8
作者：
DAZEY, J;KUYK, K;ERWIN, J
通讯作者：
ERWIN, J
影响因子：
2.4
作者：
Beisner, Brianne A.;McCowan, Brenda
通讯作者：
McCowan, Brenda