Table 3.

GLMMs examining the effects of an interaction between females’ dominance rank and male introduction success on the outcome of their normalized David’s scores (i.e., hierarchy steepness), allogrooming social network degree (i.e., degree rank-related skew), and allogrooming social network eigenvector (i.e., eigenvector rank-related skew).

	β	SE	t	p r(>|t|)	p perm
Outcome: Normalized David’s scores
(Intercept)	13.82	5.14	2.69	0.17
Introductions (Unsuccessful vs Successful)	1.72	3.80	0.45	0.68
Rank (Successful introductions)	−0.50	0.03	−17.07	<0.01**
Rank (Unsuccessful introductions)	−0.22	0.01	−28.98	<0.01**
Introductions: Rank	0.28	0.03	9.32	<0.01**
Outcome: Allogrooming degree
(Intercept)	0.31	0.12	2.53	0.03*
Introductions (Unsuccessful vs Successful)	0.00	0.14	0.03	0.98
Rank (Successful introductions)	0.53	0.12	4.43	<0.01**	<0.01**
Rank (Unsuccessful introductions)	0.16	0.07	2.18	0.03*	0.04*
Age	0.01	0.00	2.03	0.04*
Relatedness coefficient	0.04	0.17	0.22	0.83
Introductions: Rank	−0.37	0.14	−2.59	0.01*	0.01*
Outcome: Allogrooming eigenvector
(Intercept)	0.33	0.11	3.03	0.01*
Introductions (Unsuccessful vs Successful)	0.02	0.12	0.18	0.86
Rank (Successful introductions)	0.51	0.12	4.30	<0.01**	<0.01**
Rank (Unsuccessful introductions)	0.16	0.07	2.11	0.04*	0.04*
Age	0.01	0.00	1.80	0.07(*)
Relatedness coefficient	0.07	0.16	0.43	0.67
Introductions: Rank	−0.36	0.14	−2.52	0.01*	0.01

In each model, we included a 3-level nested random effect of categorical ‘group size’ (level-3), ‘group ID’ (level-2), and ‘year of observation’ (level-1). For models on degree and eigenvector, p_perm represents p values calculated based on comparing observed model coefficients with coefficients generated based on network measures calculated from 1000 permuted networks generated from post-network ‘node-swapping’ randomizations.

^*p < 0.05;

^**p < 0.01;

^(*)0.05 < p < 0.10.