DOI: 10.5061/dryad.rv15dv4kr
title: Dominance rank, facial morphology, and testes size in male white-faced capuchins: evidence for pre- and post-mating competition

description: Male reproductive success is determined by the interplay of female mate
 choice and male-male competition, often linked to dominance rank in social
 animals. Across taxa, elaborate ornaments, such as bright coloration or
 large antlers, often function as badges of status, signaling male
 competitive ability to rivals. In species where females mate with multiple
 males, post-mating sperm competition also plays an important role in male
 reproductive success and is associated with larger relative testes size.
 We investigate the relationship between morphological features and
 dominance rank in wild male white-faced capuchins. Using parallel-laser
 photogrammetry, we measured aspects of facial morphology, including facial
 width-to-height ratio, and testes size. We found that alpha males had
 significantly larger facial width-to-height ratios, wider faces, and wider
 scrota than subordinate males. These results suggest that facial traits
 potentially function as badges of status in male white-faced capuchins and
 may play a role in pre-mating competition and/or mate choice, while
 differences in scrotal size reflect adaptations for post-mating
 competition. This study highlights the underrecognized role of facial
 trait evolution in sexual selection among relatively gracile yet highly
 visually oriented mammals and highlights the potential variability of
 sexual traits in species characterized by strong reproductive skew among
 males.
description: We collected data on all males (6+ years old) residing in five
 habituated groups of white-faced capuchins (<em>Cebus
 imitator</em>) between January 2021 and May 2024 in the Santa Rosa
 Sector of the Área de Conservación Guanacaste, Costa Rica (SSR). We used
 parallel-laser photogrammetry (108,109) to noninvasively measure facial
 features, scrotal width, and body length, of male white-faced capuchins
 during periods of group stability. For each male, we collected multiple
 photographs (3 – 5) of their face, body length, and scrotum at least twice
 per month every three months over both dry and wet seasons. Selected
 photographs were uploaded in jpeg format to the GIMP GNU image
 manipulation program (version 2.10.34), a free and open-source image
 editing program (https://www.gimp.org).
 We applied GIMP’s standard high contrast filter to each photograph, which
 enhanced the visibility of the morphological features of interest due to
 the black and white pelage of the study species. Following Richardson et
 al. (2022), we used the calibrated inter-laser distance (30.00 or 45.00 mm
 depending on the laser used for the photograph) divided by the inter-laser
 distance from the image in pixels (calculated using the ‘measure tool’ in
 GIMP), to create a millimeter per pixel scale (112). We measured four
 facial features (Figure 1): a) brow-width – i.e., the lateral-medial width
 of the supraorbital torus, b) facial height – i.e., the height of the face
 from between the eyes to the middle of the upper lip, c) muzzle width –
 i.e., the lateral width of the upper snout, measured across the maxilla
 just above the upper lip and d) facial width – i.e., the width from the
 most lateral points of each side of the mandible, which is the widest
 visible portion of a capuchin male face. From these measurements, we
 calculated the fWHR by dividing facial width by facial height.
description: # Dominance rank, facial morphology, and testes size in male white-faced
 capuchins: evidence for pre- and post-mating competition Dataset DOI:
 [10.5061/dryad.rv15dv4kr](10.5061/dryad.rv15dv4kr) ## Description of the
 data and file structure ### Files and variables #### File:
 Capuchin-Morphology.R **Description:** We first performed a Principal
 Component Analysis (PCA) using the principal() function from the ‘psych’
 package (115) in R (v4.4.2; 116) and RStudio (v2024.9.1.394; 117) to
 assess the relationships among facial measurements (facial width, brow
 width, muzzle width, and facial height) and to check for
 multicollinearity. Prior to the PCA, we checked for sampling adequacy
 (Kaiser-Meyer-Olkin test: MSA = 0.66) and sphericity (Bartlett’s test:
 𝜒2(6) = 49.75, p &lt; 0.001) to ensure the appropriateness of our data for
 factor analysis. The data were standardized prior to the PCA using the
 scale() function, and the PCA analysis was conducted with a varimax
 rotation to enhance the interpretability of the principal components. The
 PCA identified four principal components, each accounting for
 approximately 25% of the total variance (Table S2). Each facial
 measurement loaded strongly onto a separate principal component,
 indicating that each measurement captured distinct aspects of
 morphological variation. Based on these findings, we analyzed each facial
 measurement independently. Six separate linear mixed models (LMMs) were
 fit individually for scrotum width, facial width, muzzle width, brow
 width, facial height, and fWHR using the ‘lmer’ function from the ‘lme4’
 package (118). Dominance rank (alpha/subordinate), body length (in mm),
 and age (in years) were included as fixed effects, while individual
 identity was included as a random effect to account for repeated measures
 from the same individuals. Each response variable followed a normal
 distribution (Shapiro-Wilks tests, p&gt; 0.05). To further evaluate the
 significance of the fixed effects, we used the drop1() function to assess
 the contribution of each predictor to the model by comparing nested
 models. Conditional R2 values were estimated using the r.squaredGLMM()
 function from the ‘MuMIn’ package (119). Assumptions for each model were
 checked using the 'DHARMa’ package (120). Homogeneity of variance was
 assessed by examining residual vs. fitted plots, ensuring consistent
 variance across predictor levels. Residuals were tested for normality
 using visual inspection of Q-Q plots and confirmed by the
 Kolmogorov-Smirnov test, which indicated no deviations from normality.
 Additionally, no models exhibited signs of overdispersion (range across
 all models: 0.90 - 0.97), confirming the adequacy of the model fit. ####
 File: photogrammetry\_dataset\_stable\_(4).xlsx
 **Description:** Measurements of individual photos (in mm) for each of the
 body parts/facial features of interest ##### Variables All missing values
 in the dataset are represented as "na". These indicate instances
 where a measurement could not be taken — for example, due to poor
 visibility of the anatomical region in photogrammetry images or the
 position of the monkey. In some cases, such as facial photographs,
 measurements of the testicles were not possible. "na" may
 therefore mean "not available" or "not applicable,"
 depending on context. * obs: unique row index for each observation *
 individual: Individual monkey ID code * group: Social group identifier *
 dominance_rank: Dominance status at time of photo (a = alpha, s =
 subordinate) * date: Date the photograph was taken in YYYY.MM.DD format *
 laser_type Laser: apparatus used (1 or 2) — differentiates between
 photogrammetry apparatuses  * Temp: Average ambient temperature in degrees
 Celsius * age: Age in years (as a decimal) at time of photo *
 known/estimate: Indicates whether birthdate was known or estimated *
 brow_width: Width of the brow * muzzle_width: Width of the muzzle *
 facial_width: Overall facial width * facial_length: Vertical length of the
 face * scrotum_width: Width of the scrotum * left_test_length: Length of
 the left testicle * right_test_length: Length of the right testicle *
 body_length: Length from the base of the neck to base of the tail all
 measurements are in mm ## Code/software R software
 ([https://www.r-project.org/](https://www.r-project.org/)) Gimp GNU Image
 Manipulation Program ([https://www.gimp.org)](https://www.gimp.org)