DOI: 10.5061/dryad.fxpnvx16w
title: Territoriality, sociality and male weaponry shape horn investment in female bovids

description: Male weaponry is well understood and extensively studied across taxa,
 while female weaponry remains understudied, and the reasons why females
 bear similar traits are still unclear. In the horned family Bovidae, males
 possess horns for sexual contests, but the presence and size of female
 horns vary considerably. Previous research has suggested that female
 weaponry may assist in intraspecific competition for territory and/or
 serve as an anti-predator defense in more exposed species. However, these
 studies did not fully explore how socio-ecological factors might influence
 variation in female horn length. In this study, we revisited the impact of
 socio-ecological factors on horn presence and conducted more robust tests
 of their effects on relative weapon investment (WQ). Our family-level
 comparative analyses (N = 115) reaffirmed that female territorial behavior
 and body size are the strongest predictors of female horn presence. For
 relative weapon investment, we found that among species where females bear
 horns, horn investment was positively correlated with larger body size and
 group size. In contrast, smaller-bodied, territorial, and monogamous
 species—such as klipspringers and dik-diks—tend to invest in relatively
 shorter horns. Although male horn investment did not significantly predict
 female horn presence, we observed that when females did bear horns, their
 size scaled positively with conspecific males. Our findings continue to
 support the idea that female horn presence is primarily driven by
 intraspecific competition for territories and/or anti-predator defense in
 larger-bodied species, while providing new evidence that social structure
 (monogamy vs. polygyny) influences female investment in mammalian
 weaponry.
description: <em>Horn Investment</em>  Maximum
 female and male horn lengths (HL: in cm), shoulder heights (SH: in cm),
 and body masses (BM: in kg) were collected from the Bovids of the World
 field book (Castelló 2016). We chose to only use morphological data from
 this source to control for measurement consistency, and this book provided
 the most updated and detailed information on female horns. Our final
 dataset included 80 female horned species and 35 hornless species (Fig.
 1). We adapted a traditional method used to calculate
 relative brain size –  ‘encephalization quotient’ [EQ] (Boddy et al. 2012)
 to calculate investment in horn size or ‘Weapon Quotient’ [WQ] for each
 sex. This method has recently been adapted to estimate relative weapon
 investment for tusks, horns, and antlers (Lopez <em>et
 al.</em>, 2024). We first ran phylogenetically corrected linear
 regressions across all species separately for each sex:
 log<sub>10</sub> HL vs log<sub>10</sub> BM. We
 pruned the Upham et al. (2019) DNA-based consensus mammal-wide tree to
 include our 115 species (Fig. 1) and performed phylogenetic generalized
 least squares tests using the ‘pgls’ function in the R packages ‘caper’
 and ‘ape’ (Orme 2013; Paradis 2019).  To account for phylogenetic
 uncertainty and ensure statistical robustness, we repeated our analyses
 across 300 phylogenetic trees (Upham et al. 2019). The coefficient for
 log₁₀FHL ~ log₁₀FBM averaged 0.697 (SD = 0.097) and coefficient for
 log₁₀MHL ~ log₁₀MBM averaged 0.480 (SD = 0.0220), with all trees (100%)
 yielding significant results at both α = 0.05 and α = 0.01.
 We used the average corrected ẞ (slope) and b (intercept)
 estimates to calculate the predicted HLs for each sex for each species
 (<em>i</em>) based on BM (Table 2):
 HL<em><sub>i(predicted)</sub></em> =
 10<sup>b(HLvsBM)</sup> ×
 BM<em><sub>i</sub></em><sup>ẞ(HLvsBM)</sup>. We then calculated WQ<em><sub>i</sub></em> for each sex and species as HL<em><sub>i(measured)</sub></em>/HL<em><sub>i(predicted)</sub></em>, where a WQ above 1.0 would represent a relatively larger horn, a WQ below 1.0 would be a relatively smaller horn, and a WQ of 0 would represent hornless species. <em>Habitat Openness & Exposure</em> A species’ habitat openness was scored based upon the relative cover it provides and how far away the prey can be seen by a potential predator. Following many previous studies (Stankowich and Caro 2009; Stankowich and Campbell 2016; Stankowich and Stensrud 2019; Caro et al. 2021) , using the IUCN Red List database (IUCN, 2020), each species’ presence and suitability in each of the 30 possible IUCN habitat types was scored as one of the following: 3= suitable and of major importance, 2= suitable but not of importance, 1.5= unknown, 1=marginal, and 0=not found.  Marine, Artificial Marine, and Caves/Subterranean biomes were not included in the data analysis. Because some species had more than five possible habitats reported, to emphasize the habitats each species primarily lives in rather than where they can potentially be found we used the <em>Handbook of Mammals of the World</em> (Wilson and Mittermeier 2011)** and <em>Bovids of the World</em> (Castelló 2016) to extract the three primary habitat types listed for each species and pruned the IUCN habitat list accordingly. Open habitat types (e.g., tundra, desert) were scored as near 1, while closed habitat types (e.g., tropical rainforest) were given scores near 0 (e.g., Temperate Grassland (0.8), Rocky areas (0.5), Temperate Forest (0.25)). We then calculated a weighted species-average habitat openness score by summing the products of the presence/suitability score (0-3) by habitat openness (0-1) for each habitat the species lives in, and divided that by the sum of weight presence/suitability scores (see equation below; Caro et al., 2021): \text{Species Weighted Avg Habitat Openness} = \frac{ \sum_{i=1}^3 |\text{HabitatSuitabilityScore} \times \text{HabitatOpennessScore}| }{ \sum_{i=1}^3 \text{HabitatSuitabilityScore} } We estimated visual <em>exposure</em> to predators for each species and sex as the product of the sex-specific log<sub>10</sub> transformed shoulder height and habitat openness score.  <em>Body Size, Sociality & Territoriality Behavior</em> General social/group structure and intraspecific behavior (territoriality) were primarily collected from <em>Bovids of the World</em> (Castelló, 2016). Missing or incomplete sociality data were supplemented and/or verified using <em>Handbook of Mammals of the World Vol. 2</em> (Wilson & Mittermeier, 2011) and other published literature  (Roberts and Dunbar 2000; Vervaecke et al. 2005).  For sociality, we converted numeric group size to an ordinal <em>sociality</em> score: ‘1’ (socially bonded pair), ‘2’ small (&lt;10 individuals), ‘3’ intermediate (11-39 individuals), and ‘4’ large (40+ individuals). In few accounts, females are described to perform a range of territorial behaviors from simple scent marking to aggressive fighting with or running off intruders with their mated male (Roberts 1994; Roberts and Dunbar 2000; Wilson and Mittermeier 2011; Castelló 2016). There were insufficient details on these behaviors, and it rarely described the use of horns in territorial defense, thus <em>female territoriality</em> was generally scored as present “1” or absent “0”. We found 24 species accounts that explicitly described females to be territorial – mostly within the subfamily, Cephalophinae (duikers, klipspringers). Additionally, there was a lack of detail on female dominance hierarchy behavior (besides stating it has been seen in female herds) to divide them into ordinal categories. A few reports suggest horn size can be a determining factor of hierarchy status (e.g., Vervaecke et al., 2005), but hornless females are also described to established hierarchies (Castelló, 2016; Wilson & Mittermeier, 2011), so we did not include explicit female hierarchal behavior in our study. <em>Bayesian Hurdle Models</em> We log<sub>10</sub> transformed all body metrics and scaled all continuous variables (shoulder height, body mass, exposure score, habitat openness score). Categorical variables (territoriality score and sociality score) were left unscaled. Because some species do not possess horns, we added 1 to all species’ average horn lengths prior to log transformation to account for hornless species with 0 cm horn lengths. We then performed hurdle models to test for the strongest predictor of female horn presence and horn size using the Bayesian function ‘brm’ in the package ‘brms’(Bürkner 2017). Hurdle models are used for zero-inflated datasets (Feng 2021) and for our study females of 34 species were hornless (i.e., WQ = 0). The first component of the hurdle model estimates the binary probability (hu). If the outcome is non-zero, it passes the hurdle, and the model then fits the non-zero data separately. In our study, this first stage identifies which factors predict whether a female has horns (0 = absent, 1 = present). Importantly, a negative estimate (β) indicates a lower probability of being in a ‘zero’ state, meaning the trait is more likely to be present (non-zero). The second part tells us which factors are strongly predictive of horn size when horns are present (i.e., WQ is non-zero). We specified the ‘family’ as ‘hurdle_gamma’ to model distribution for positive continuous values and all models were performed across 4 chains with 2000 iterations per chain (Bürkner 2017). <em>Model Reduction</em> We performed model reduction by generating all possible subset models and compared LOO (leave-one-out cross-validation) scores (Vehtari et al. 2017). The importance values represent how often each variable appears across the averaged models, weighted by model probabilities (Galipaud et al. 2014). We tested if female horn presence and size are most explained by (1) predation risk, (2) intrasexual competition, or (3) conspecific male weaponry by including the following variables: (1a) body mass, b) shoulder height, (1c) habitat openness, and (1d) exposure (shoulder height x habitat openness), (2a) territoriality score, (2b) sociality score, and (3) male horn investment (MWQ). After testing all model combinations of our 7 variables, we used LOO cross-validation for model comparison (Vehtari et al. 2017). Our model selection method calculated variable importance as the sum of model weights for models containing each variable and provides model rankings (Galipaud et al. 2014). We then pruned models that included “BM” and “SH” together as they both represent a body size metric, and we removed models that included “Expo” and “SH” or “HS” because “Expo” is a product of SH and HS. Our final pruned model combinations included 64 models. Using LOO scores and model weight (k), our best fit and biologically relevant model (LOO = 211.8) is: <em>FWQ ~ log10(Body Mass) + Territoriality</em> <em>+ Sociality + MWQ</em> (Table 2). We ran additional ANOVAs and Tukey tests to test differences in horn investment among categorical group sizes (Table S1) among female horned species (N=80 species). We used the function ‘aov’ and ‘TukeyHSD  in the package ‘stats’ (R Core Team 2021). Female horns appear to have evolved independently multiple times across the Bovidae (Stankowich & Caro 2009; Fig 1a); thus, we suspect different factors may influence female horn presence and investment across different lineages. We conducted tribe-level hurdle analyses on groupings with the most species: Group 1 (N=40; Caprini, Alcelaphini, Hippotragini), Group 2 (N=18; Cephalophini, Oreotragini), Group 3 (N=28 species; Antilopini, Neotragini), and Group 4 (N=18; Bovini, Tragelaphini). Due to smaller sample sizes, Reduncini (N=9), Aepycerotini (N=1), and Nesotragini (N=1) were excluded from tribe-level comparisons. For Group 2, sociality score and territoriality score were not analyzed because all species are territorial and form socially bonded pairs. In our supplement, we performed multivariate (Tables S3-S6) and single-variable subfamily hurdle models (Table 7). We ran additional ANOVAs and Tukey tests to test differences in horn investment among tribe groupings among female horned species (Table S2; N=80 species). We used the function ‘aov’ and ‘TukeyHSD  in the package ‘stats’ (R Core Team 2021).
description: # Territoriality, sociality and male weaponry shape horn investment in
 female bovids Dataset DOI:
 [10.5061/dryad.fxpnvx16w](https://doi.org/10.5061/dryad.fxpnvx16w) ##
 Description of the data and file structure ### Files and variables ####
 File: Alloutput.nex **Description:** 300 tree sample from Upham et al.
 2019 including all 115 species analyzed in this study. #### File:
 FemHoroutput.nex **Description:** 300 tree sample from Upham et al. 2019
 including 80 female horned species analyzed in this study. #### File:
 BEHECO-2025-0139_Dyrad_Code.R **Description:** Final R code including
 pgls, Bayesian hurdles, ANOVAs, LOO model reductions, and
 visualizations/plots #### File: BEHECO-2025-0139.R2_Data.csv
 **Description:** Final dataset (.csv) with all variables for all 115
 species analyzed in this study ##### Variables * Binomial: Genus_species *
 Species: Genus_species * Tribe: Bovid Tribe ID * Tribe ID: Catergorical ID
 for Anovas/Plots * Female Horn Length : Maximum published female horn
 length (cm) - Bovids of the World * Female Horn Presence Score: Binary
 Score for Horned (1) or Hornless (0) * Horn Type: Categorical Score Horned
 or Hornless (females only) * Female Shoulder Height (cm): Maximum
 published female shoulder height (cm) - Bovids of the World * Female Body
 Mass (kg): Maximum published female body mass (kg) - Bovids of the World *
 HS: Habitat Openness Score (0-1); See methods for calculation * log10FHL:
 log10 transformed Female Horn Length * log10FSH: log10 transformed Female
 Shoulder Height * log10FBM: log10 transformed Female Body Mass * ExpoF: HS
 * log10FSH * TerrF: Female Territoriality Binary Score (0 or 1) *
 TerrType: Female Territoriality Category Score (NO or YES) * Group Size:
 Published Group Size (# of individuals) - Bovids of the World * GroupCat:
 Categorical Group Scale (A) Pair, B) Small, C) Intermediate, D) Large *
 GroupOrd: Categorical Group Scale (1, 2, 3, 4) * Male Horn Length: Maximum
 published male horn length (cm) - Bovids of the World * log10MHL: log10
 transformed Male Horn Length * Male Body Mass (kg): Maximum published male
 body mass (kg) - Bovids of the World * log10MBM: log10 transformed Male
 Horn male body mass * predicted MHL: The predicted MHL based on pgls
 regression outputs of log10MHL ~ log10MBM - See Methods for more detail. *
 MWQ_1: Measured MHL/predicted MHL - See Methods for more detail. *
 predicted FHL: The predicted FHL based on pgls regression outputs of
 log10FHL ~ log10FBM - See Methods for more detail. * FWQ_1: Measured
 FHL/predicted FHL - See Methods for more detail. ## Access information
 Other publicly accessible locations of the data: * NA Data was derived
 from the following sources: * Maximum female and male horn lengths (HL: in
 cm), shoulder heights (SH: in cm), and body masses (BM: in kg) were
 collected from the Bovids of the World field book (Castelló 2016). * We
 adapted a traditional method used to calculate relative brain size – 
 ‘encephalization quotient’ [EQ] (Boddy et al. 2012) to calculate
 investment in horn size or ‘Weapon Quotient’ [WQ] for each sex. This
 method has recently been adapted to estimate relative weapon investment
 for tusks, horns, and antlers (Lopez *et al.*, 2024). * We first ran
 phylogenetically corrected linear regressions across all species
 separately for each sex: log~10~ HL vs log~10~ BM. We pruned the Upham et
 al. (2019) DNA-based consensus mammal-wide tree to include our 115 species
 (Fig. 1) and performed phylogenetic generalized least squares tests using
 the ‘pgls’ function in the R packages ‘caper’ and ‘ape’ (Orme 2013;
 Paradis 2019).  To account for phylogenetic uncertainty and ensure
 statistical robustness, we repeated our analyses across 300 phylogenetic
 trees (Upham et al. 2019). * A species’ habitat openness was scored based
 upon the relative cover it provides and how far away the prey can be seen
 by a potential predator. Following many previous studies (Stankowich and
 Caro 2009; Stankowich and Campbell 2016; Stankowich and Stensrud 2019;
 Caro et al. 2021) , using the IUCN Red List database (IUCN, 2020), each
 species’ presence and suitability in each of the 30 possible IUCN habitat
 types was scored as one of the following: 3= suitable and of major
 importance, 2= suitable but not of importance, 1.5= unknown, 1=marginal,
 and 0=not found.  * General social/group structure and intraspecific
 behavior (territoriality) were primarily collected from *Bovids of the
 World* (Castelló, 2016). Missing or incomplete sociality data were
 supplemented and/or verified using *Handbook of Mammals of the World Vol.
 2* (Wilson & Mittermeier, 2011) and other published literature 
 (Roberts and Dunbar 2000; Vervaecke et al. 2005) * We conducted
 tribe-level hurdle analyses on groupings with the most species: Group 1
 (N=40; Caprini, Alcelaphini, Hippotragini), Group 2 (N=18; Cephalophini,
 Oreotragini), Group 3 (N=28 species; Antilopini, Neotragini), and Group 4
 (N=18; Bovini, Tragelaphini).