Collective Self-Assessment in Banded Mongoose Intergroup Contests

50 Contests over resources are widespread in nature. To optimize outcomes, animals assess fighting 51 abilities, deciding to escalate conflicts based on th eir own strength (self-assessment) or comparing 52 their own strength with that of their rival (mutual assessment). While most research focuses on 53 one-on-one (dyadic) contests, the assessment strategies employed by groups remain poorly 54 understood. Mutual assessment is frequently assumed, as more information is thought to improve 55 decision-making; however, this assumption has rarely been tested. Here we used a dataset 56 spanning 23 years and 641 intergroup contests in a banded mongoose ( Mungos mungo) 57 population in Queen Elizabeth National Park, Uganda. Our results support a model of self-58 assessment: groups with many males tend to es calate conflicts regardl ess of the rival group's 59 strength, thus contrasting the commonly held assu mption that decisions during intergroup contests 60 are made by mutual assessment. We suggest that assessing rival group strength during conflict 61 could be disproportionately costly, compared with assessing own group strength, which can be 62 done over longer time periods and is easier to obtain. Greater understanding of these dynamics 63 can shed light on the drivers and escalation patterns of intergroup conflict across social species, 64 including humans. 65 66

Contest Theory, Intergroup Conflict, Animal Contests, Self-assessment, Banded 67 mongoose 68 69 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 5

70 Natural resources can be limited and unevenly distributed in both in time and space. This creates 71 competition for those resources when they can be monopolized (Bornstein 2003; De Dreu et al. 72 2020; De Dreu & Triki 2022; Grover 1997; Mullon & Lehmann 2022; Nagel 1995; Rusch & Gavrilets 73 2020; Wrangham 1999). Contests are one way to settl e disputes over such resources and are 74 widespread across taxa (Briffa et al. 2013). Although the benefits from winning contests can lead to 75 increases in fitness (Le Boeuf 1974; De Dreu & Triki 2022), the costs of escalation can be as 76 impactful; for example, through the loss of energy (Briffa & Sneddon 2007; Hack 1997; Payne & 77 Pagel 1996, 1997), and risk of injury (Lane & Briffa 2017; Payne 1998) or death (Enquist & Leimar 78 1990; Harbom et al. 2008; Thompson et al. 2017; Wrangham et al. 2006). As such, evolutionary 79 theory predicts that contestants should minimi ze the costs and maximize the benefits when 80 deciding whether to fight, and for how long (Birch 2016; Budaev et al. 2019; Parker & Smith 1990). 81 82 To respond optimally and reduce uncertainty animals can assess fighting ability (also termed 83 resource holding potential (RHP)) (Arnott & Elwood 2008, 2009; Parker 1974; Parker & Rubenstein 84 1981; Smith & Parker 1976). Studies of assessment in dyadic contests have shown that individuals 85 can assess many different RHP-contributing features of themselves and their opponents (e.g., 86 (Arnott & Elwood 2009; Green et al. 2021a; Green & Patek 2018; Morrell et al. 2005; Pinto et al. 87 2019)), including morphology (Bohórquez-Alonso et al. 2014; Palaoro & Briffa 2017), physiology 88 (Briffa & Sneddon 2007; Copeland et al. 2011), and behaviour (Camerlink et al. 2015; Wilson et al. 89 2011). This is well studied in contests between i ndividuals (i.e., dyadic contests), but much less is 90 known about assessment in contests between groups (but see Briffa et al. 2014). 91 92 When groups gather information to make conflict decisions, do they assess own strength (self-93 assessment) or do they assess both their own st rength and the strength of the other group (mutual 94 assessment)? In dyadic contests, the “assessment strategy framework” that contrasts self- and 95 mutual assessment has been a widely utilized fram ework to differentiate between strategies used 96 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 6 to assess RHP (Arnott & Elwood 2009; Fig. 1). It has been suggested that this framework can be 97 fruitfully extended to intergroup contests (Green et al. 2021a). Yet, most intergroup contest studies 98 do not properly test between assessment m odels. Some experimental studies that use 99 presentations (playbacks or scent marks) have been fundamental in probing information-gathering 100 during intergroup contests, but as no actual contest occurs during presentations it is not possible to 101 relate them directly to how this information- gathering influences the decisions made in contests 102 (Herbinger et al. 2009; Spezie et al. 2023; Benson-Amram et al. 2011;, Müller & Manser 2007; 103 Furrer et al. 2011. To the best of our knowledge the assessment strategy framework has still not 104 been appropriately implemented in an intergroup context outside of humans (Briffa, 2014). 105 Adapting the assessment strategy framework (Arnott and Elwood, 2009) to intergroup contests can 106 reveal potentially shared principles across levels of biological organization (individuals to groups), 107 while suggesting how groups come to collaborative decisions during contests. 108 109 The assessment strategy framework makes assu mptions and predictions about the drivers of 110 contest behaviors, which can be tested by comparing relationships between competitor RHP and 111 contest costs (e.g., duration, likelihood of escalation; summarized in Figure 1). If animals use a 112 pure self-assessment strategy, contests are won by those with the greater cost threshold, 113 determined by their RHP , and no information about the opponent is assessed (Maynard Smith 114 1974; Patrick TREE paper). Contest costs are driven mo stly by the fighting ability of losers, since 115 winners only need to withstand a higher cost than their opponent (Fig. 1A-B) (Mesterton-Gibbons 116 et al. 1996; Payne & Pagel 1996, 1997). In mutual assessment, a loser forfeits not when costs 117 reach a certain limit, but as they discern that they have a lower RHP than their rival (Enquist et al. 118 1990; Enquist & Leimar 1983, 1987). As in self-assessment, high RHP losers can incur higher 119 costs (Fig. 1C), yet, as opponents are assessed, high RHP winners are more quickly identified by 120 the loser, resulting in lower overall costs of conflict for winners and losers (Fig. 1D). It is worth 121 noting that some species can fit more than one model, depending on context, or can even change 122 strategies during a contest (Lobregat et al. 2019; Chen et al. 2022; Dinh et al. 2020). 123 124 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 125 Figure 1. Assessment strategy framework (Arnott & Elwood 2009). Pure self- assessment 126 predicts A) a positive relationship between loser RHP and costs (e.g., duration, likelihood of 127 escalation), and B) a weaker positive relationship between winner RHP and costs. Mutual 128 assessment predicts C) a positive relationship between loser RHP and costs , and D) a negative 129 relationship between winner RHP and costs. 130 When applying the assessment strategy framework to the group context, we assume that group 131 members each choose the conflict costs they are willing to incur based only on an assessment of 132 their own group’s RHP (collective self- assessment), or the RHP of their own and the rival group’s 133 RHP (collective mutual assessment), and that these individual choices combine to determine the 134 decision of the group. Note as a shorthand we describe groups as taking decisions or using 135 assessment strategies, without assuming that groups are agents in themselves (Okasha 2018). 136 137 Here, we use the assessment strategy framework to test which assessment strategy is used in138 escalation during intergroup contests in banded mongooses ( Mungos mungo) – a cooperatively 139 breeding species which hold territories and fight fiercely over resources (Cant et al. 2013, 2016)140 such as oestrus females (Green et al 2024) and food (Thompson et al 2017).. Banded mongooses 141 are an ideal species to test for intergroup c ontest assessment strategy for several reasons. Firstly,142 banded mongoose intergroup contests are highl y consequential. Intergroup conflict in this species 143 is responsible for 10% or more of adult deaths with identifiable causes, which in mammals is 144 matched only by chimpanzees, wolves, lions, and some human societies (Johnstone et al. 2020; 145 Wrangham et al. 2006; Cubaynes et al. 2014). These high stakes likely impose strong selection 146 pressures on assessment strategies. Sec ond, contests have a range of escalating intensities from147 nt of al ve p of ’s e g in ly 6) es ly, es is 0; n m .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 8 non-physical (e.g., war crying, forming battle lines, chasing) to physical (e.g., biting, scratching, 148 wrestling) including injurious lethal violence (Cant et al. 2016) (Fig. 2). This variance in intensity (a 149 proxy for cost) is essential for testing assessment strategy (Arnott & Elwood 2009), and similar 150 metrics of escalation have been used in studies of dyadic assessment strategies ( Green & Patek 151 2018; McGinley et al 2015; Yasuda et al. 2012). Finally, we have known proxies for banded 152 mongoose group RHP , which is a crucial prerequisite for testing assessment of RHP . We know 153 that contest success is most strongly determi ned by the number of adult males in the group and 154 age of the group’s oldest male (Green et al. 2022). Overall, the intensity and variability of conflict, 155 and our solid baseline understanding of RHP proxies set the stage for banded mongooses to be an 156 ideal model for testing assessment strategies in intergroup contests. 157 158 Figure 2. Categories of agonistic behavior observed during intergroup contests in banded 159 mongooses. A) Agonistic behaviors such as war crying, forming battle lines, and chasing were 160 considered as a “non-physica l” level of intensity; B) Agonistic behaviors such as biting, scratching, 161 wrestling, or where aggressive physical contact was observed was considered as a “physical” level 162 of intensity. Injurious and lethal violence, where aggressive physical contact resulted in severe or 163 fatal injury or death was also included as a “physical” level of intensity. (Image credit from left to 164 right: Harry Marshall, Harry Marshall, Harry Marshall, Dave Seager, and Harry Marshall) 165 166 167 Here we tested collective self-assessment and collective mutual-assessment as two alternative 168 hypotheses that describe patterns of escalati on in banded mongoose contests. Following common 169 assumptions in intergroup conflict, we predicted that groups would use mutual assessment. 170 Although more cognitively demanding, the use of more information from mutual assessments can 171 allow groups to give up earlier when clearly outmatched in fights, and is often assumed the 172 superior or default strategy for intergroup assessment (Arnott and Elwood, 2012;Van Belle & 173 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 9 Scarry 2015; Bennett & Stam 1996; Chan 2003; Langlois & Langlois 2009; Radford 2003; Stulp et 174 al. 2012). For our measure of costs, we used the degree of escalation, from non-physical to 175 physical (Fig. 2). If banded mongooses used collect ive self-assessment, we expect the previously 176 identified RHP proxies—(i) number of adult males or (ii) the age of the oldest male (Green et al. 177 2022)—would be significantly positively correlated with the probability of escalation to physical 178 violence for losing groups and would show a weaker positive relationship in winning groups (Fig. 179 1A-B). Conversely, if, as predicted, banded mongooses do employ collective mutual assessment, 180 we expect these same variables to exhibit a signi ficant positive correlation in losing groups and a 181 significant negative correlation in winning groups (Fig. 1C-D). 182 183

184 Study Population and Data Collection 185 All data was collected as part of the Banded Mongoose Research Project, a long-term study on a 186 population of banded mongooses in and around Mweya Peninsula, Queen Elizabeth National Park, 187 Uganda (0°12 ′ S, 29°54 ′ E). This study includes data collected from 2 nd February 2000 to 7 th 188 February 2023 encompassing 641 intergroup contests from 43 different groups and 81 unique 189 pairings of groups. In general, at any given time, there are approximately 250 individuals present 190 within the population making up 10-12 groups cons isting of around 10-30 adults each (Cant 2000; 191 Cant et al. 2013). Every 1-3 days researchers recorded data on life-history (e.g., births, deaths), 192 composition of each group, and details about their intergroup interactions (below), among other 193 records not relevant to the present work. 194 195 Scoring Intergroup Contest Intensity and Escalation 196 Intergroup contests were recorded opportunistica lly as they occurred. Intergroup contests were 197 defined as any time when at least two groups dire cted agonistic behavior towards each other (Fig. 198 2). Intensity and escalation of intergroup contests were scored using comments recorded by the 199 Banded Mongoose Research Project team. As an inte rgroup contest often involves a series of 200 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 10 behaviors in which intensity escalates until one or both groups retreat (at which point the intergroup 201 contest ends) the highest point of escalation was used as the level of intensity for each contest. 202 For ease of analysis, the range of intensities possible during an intergroup contest was divided into 203 two categories (Fig. 2). The lowest level of inte nsity was “non-physical” which was defined as an 204 instance where two groups directed non-physical agonistic behavior (e.g., vocal and visual 205 displays; (Cant et al. 2016)) towards each other and/or fled upon sighting. If this then escalated to 206 fighting between the two groups in which there was aggressive physical contact (e.g., biting, 207 scratching, wrestling; (Cant et al. 2016)), this was defined as “physical”. Intergroup contests which 208 escalate to physical combat are expected to have higher contest costs (such as energy used and 209 injury risk) compared to non-physical contests (Green & Patek 2018; Lane & Briffa 2017; McGinley 210 et al. 2015). Our scoring approach was then translated into a binomial escalation metric with 0 211 representing non-physical and 1 representing physical. Injurious and lethal violence in which 212 aggressive physical contact results in severe injury or death of one or more individuals was 213 included in the “physical” category, rather than a ca tegory of its own. This is because, firstly, injury 214 and mortality were data poor (9.4% of all intergroup interactions; N = 60), but more importantly, 215 whether individuals suffer an injury is not so much a decision as it is a (potentially random) 216 outcome of such physical combat. By contrast, whether violence escalates from non-physical to 217 physical combat is a decision the group may make . Therefore, our use of the escalation metric 218 likely reflects the cost each group was willing to pay. 219 220 For each intergroup contest, a qualitative comment (a description of the events of the contest) was 221 recorded by observers in the field. These comments were then assessed by three researchers 222 (CR, FM, DS) to evaluate whether the contest escalated into physical violence (with 0 representing 223 non-physical and 1 representing physical), or whet her the comments did not allow us to determine 224 whether or not the contest escalated (termed: “undeterminable”). Second, all researchers assigned 225 a confidence score (1–3) to each of their categorizat ions of intensity score, reflecting their certainty 226 in the intensity score. The confidence score was bas ed on criteria such as the clarity and detail of 227 the recorded comment, as well as contextual fa ctors that might influence interpretation (e.g., 228 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 11 visibility conditions or proximity to the event). A score of 1 indicated high confidence, 2 indicated 229 moderate confidence, and 3 indicated low confidence. We ended up removing contests that scored 230 with low confidence (3) from the dataset because they were missing data on other variables 231 included as a fixed effect in our analysis. The three researchers discussed any ambiguous 232 comments in detail to assign an escalation and confidence score if possible. The discussion was 233 guided by FM, who has over 27 years of experience observing and collecting field data on banded 234 mongooses, and managing the Banded Mongoose Research Project . 235 236 We report models using both highly confident and moderately confident scores (1,2), and models 237 including only highly confident scores (1). 238 239 Statistical Analysis 240 All statistical analyses were carried out using R 4.01 (Team 2021). Binomial escalation response 241 data were analyzed using generalized linear mixed effe ct models (GLMMs) with a binomial error 242 structure and a logit link function (Bolker et al. 2009) with the ‘lme4’ package (Bates et al. 2015). 243 For both losing groups and winning groups, we tested the effect of two RHP predictor variables—244 number of adult males (>6 months old) and age of the oldest male (days) (Green et al. 2022). Both 245 variables were scaled using the scale function (Becker et al. 1988). 246 247 In total, we ran two GLMM models, one model using data from high and moderate confidence 248 scores, and another using data from only high c onfidence scores. The model for each confidence 249 category is as follows (“~” repr esents “as predicted by”): 1) escalation ~ loser number of adult 250 males+ loser age of oldest male+ winner number of adult males+ winner age of oldest male. 251 Models without interactions between these variables were simpler and a better fit than models 252 including interactions. Winner group ID, loser group ID, and unique pairings of groups (winner 253 group ID + loser group ID) were included as random intercepts in every model to account for 254 repeated measures of intergroup contests between the same groups and group dyads. Test 255 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 12 statistics were obtained using the Anova function and confidence intervals using bootstrapping. We 256 present the p-values, chi-squared values , parameter estimates ( β ) on the logit-scale, standard 257 errors, and confidence intervals of each GLMM model, and compare p-values and direction of 258 parameter estimates to the assumptions of pure self-assessment, and mutual assessment (Fig. 1). 259 The collective self-assessment hypothesis would be supported if there was a positive and 260 statistically significant relationship between RHP predictor variables and escalation for losing 261 groups (β >0; p<0.05) and a weaker relationship for winning groups (no significance threshold) (Fig. 262 1A-B). The collective mutual assessment hypothesis would be supported if these same tests 263 indicated a significant positive relationship for losing groups ( β >0; p<0.05) and a significant 264 negative relationship for winning groups (β <0; p<0.05) (Fig. 1C-D). 265 266

267 Overall, of the 641 intergroup contests we obs erved, 280 were ‘non-physical’ (43.7%), 230 were 268 ‘physical’ (35.9%), and 131 were undeterminable (20.4%). 269 Under the analysis where high and moderate confi dence scores of our escalation metrics were 270 combined (N = 253), our results supported self -assessment: there was a significant positive 271 relationship between the probability of escalation and number of adult males for groups that lost 272 fights (losers; β = 0.47, SE = 0.18, χ 2 = 7.13, 95% CI [0.14, 0.86], p=0.008), and a non-significant, 273 positive relationship between these variables for groups that won fights (winners; β = 0.21, SE = 274 0.17, χ 2 = 1.61, 95% CI [-0.13, 0.57], p=0.20) (Fig . 3A-B; Table 1), supporting the hypothesis. 275 Support remained for the self-assessment hypothes is when using only high confidence score 276 escalation metrics (N = 229). Here, loser groups still showed a greater probability of escalation 277 with larger numbers of adult males (losers; β = 0.52, SE = 0.19, χ 2 = 7.15, 95% CI [0.14, 0.95], 278 p=0.008), with a non-significant, positive relationship between escalation and number of adult 279 males for winners (winners; β = 0.31, SE = 0.18, χ 2 = 2.82, 95% CI [-0.05, 0.71], p=0.09; Table 1). 280 This is as expected under self-assessment. 281 282 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint There was no significant relationship between oldest male age and the probability of escalation for 283 winners or losers in either confidence categor y, providing no statistical support for either model of 284 assessment (Table 1). 285 286 287 288 289 Figure 3. Support for collective self-assessment of number of adult males in banded 290 mongooses. Probability of escalation plotted against A) loser number of adult males (p=0.008); B)291 winner number of adult males (p=0.20). (Escalation was a binary metric where contests were either292 0 = non-physical, and 1 = physical). The grey shaded area shows the standard error around the 293 fitted line. Data (circles) represent individual contests and is randomly jittered on the y-axis. Data in 294 plot includes both high and moderate confidence scores of our escalation metrics (N = 253)) 295 296 297 298 299 or of d B) er e in .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 14 Table 1. Models predicting the probability of escalation for loser or winner RHP in intergroup contests. The estimate (β ), standard error (SE), chi-squared value (χ 2), p-value (P), and confidence interval (CI) of a model predicting the escalation of loser or winner RHP in intergroup contests. Model escalation ~ winner or loser RHP + (1 | winner ID) + (1 | loser ID) + (1 | unique pairings) Dataset RHP proxy β SE χ 2 P CI (95%) High and moderate confidence scores (n=253) Number of adult males Winner Loser 0.21 0.47 0.17 0.18 1.61 7.13 0.20 0.008 * -0.13, 0.57 0.14, 0.86 Oldest male age (days) Winner Loser 0.15 0.10 0.15 0.17 1.04 0.36 0.31 0.55 -0.11, 0.48 -0.22, 0.46 High confidence scores only (n=229) Number of adult males Winner Loser 0.31 0.52 0.18 0.19 2.82 7.15 0.09 0.008 * -0.05, 0.71 0.14, 0.95 Oldest male age (days) Winner Loser 0.21 0.02 0.16 0.19 1.72 0.01 0.19 0.92 -0.09, 0.53 -0.36, 0.38 Generalized linear mixed effects models with a binomial error structure and logit link function. Winner group ID, loser group ID, and unique pairings (winner group ID + loser group ID) were used as random intercepts (high and moderate confidence scores GLMM, N = 253 intergroup contests in 12 winner group IDs, 20 loser group IDs, and 35 unique pairings) (high confidence scores only GLMM, N = 229 intergroup contests in 11 winner group IDs, 19 loser group IDs, and 32 unique pairings). Significant terms are presented in bold and denoted by an asterisk (*). Note that parameter estimates are on the logit-scale. 300 301

302 We found support for the hypothesis that mongoose groups used collective self-assessment when 303 deciding to escalate a contest into physical viol ence. Specifically, our results suggest that losing 304 banded mongoose groups were more likely to escalate in contests when they had many adult 305 males (a known proxy of RHP; Green et al. 2022), ir respective of the number of adult males in the 306 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 15 winning group. By contrast, we found no support fo r any assessment model based on the oldest 307 male age within the group. Despite assumptions that intergroup contests are determined through 308 mutual assessment (e.g., (Van Belle & Scarry 2015; Bennett & Stam 1996; Chan 2003; Langlois & 309 Langlois 2009; Radford 2003; Stulp et al. 2012)) we found no support for this in our study. 310 311 At first sight our results are surprising. Why should banded mongooses assess only their own 312 fighting force, and ignore potential valuable info rmation about the outgroup? Research in dyadic 313 contests has suggested that, while mutual asse ssment can minimize the costs of conflict by 314 allowing the weaker party to back down earlier, acquiring information about an opponent may be 315 constrained or entail prohibitively high costs (Guillermo-Ferreira et al. 2015; Mesterton-Gibbons & 316 Heap 2014). For instance, assessing the strength of outgroups in the short time-window before 317 conflict could be disproportionately costly, compared with own-group assessment which can be 318 attained at relatively low cost over longer time periods such as through play-fighting and intragroup 319 contests (Nolfo et al. 2021; Turner et al. 2020; Weller et al. 2020). 320 321 Our finding that the number of adult males in a banded mongoose group is associated with the 322 probability of escalation accords with previous evidence of the importance of adult males for 323 intergroup fighting success (Green et al. 2022). Males suffer greater mortality from conflict than 324 females (Johnstone et al. 2020) and there is experimental evidence that in simulated encounters 325 males were more likely to approach caged intruders (Cant 2002). This suggests that males are the 326 main participants in conflicts. It has also been suggested that males have evolved adaptations as a 327 result, such as greater body mass and head size (Green et al. 2022), that may be important for 328 fighting behavior (Briffa et al. 2013; Chelliah & Sukumar 2013; Gvoždík & Van Damme 2003; 329 Jennings & Gammell 2013; Vieira & Peixoto 2013; Wroe et al. 2005) and determining contest 330 success (Husak et al. 2006; Huyghe et al. 2005; McLean & Stuart-Fox 2015). More closely-related 331 to intergroup conflict, our findings are consistent with the male warrior hypothesis, which proposes 332 that male mammals possess specific adaptations for intergroup fighting and participate more in 333 intergroup conflict than females, and thus have a pronounced effect on group RHP (Muñoz-Reyes 334 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 16 et al. 2020; Smith et al. 2022). Therefore, the number of adult males is likely an important and 335 informative RHP proxy which can be assessed w hen making decisions during intergroup contests 336 in banded mongooses. 337 338 Additionally, numerical superiority appears to play such an important role in intergroup fighting 339 because several attackers can completely overwhelm a lone opponent. In banded mongooses 340 most fatalities seem to occur when individual s become isolated and overwhelmed by groups of 341 attackers ( personal observations ; e.g., Video S1). Outnumbering one’s opponent presents many 342 advantages such as the ability to attack simultaneously from multiple angles, to pin down an 343 opponent while others deliver bites or blows, or to take turns in energetically intense fighting 344 (Adams & Mesterton-Gibbons 2003; Baglione et al. 2010; Nunn & Deaner 2004; Rusch & Gavrilets 345 2020; Wilson et al. 2002). The importance of numerical super iority and concentration of force is 346 also emphasized in models of warfare between human groups (Lanchester 1916; Rapoport & von 347 Clausewitz 1968). As discussed above, assessing own-group number of adult males may be a 348 low-cost, more accurate way to use this information in competitive decision-making. 349 350 Despite the importance of oldest male age for banded mongoose group RHP (Green et al. 2022), 351 the age of the oldest male in the group was not associated with escalation or show support for any 352 assessment strategy. While age may play a role during intergroup contests and assessment in 353 general (Briffa & Lane 2017; Fawcett & Johnstone 2010; Green et al. 2022), number of adult males 354 may be a faster, simpler, or more reliable cue on which to base escalation decisions in intergroup 355 contests. Assessing several attributes for more than one individual may be a cognitively 356 demanding process (Budaev et al. 2019; Johnson & Fowler 2013; Tecwyn et al. 2017). Age in 357 general may also be less conspicuous (Elwood & Arnott 2012; Fawcett & Mowles 2013; Nieder 358 2020), scalable or countable (Akre & Johnsen 2014; Bonanni et al. 2011; Nieder 2020) making 359 estimates of age more prone to error. Additionally, in banded mongooses, the age of the oldest 360 male was a weaker predictor of contest outcomes compared to number of adult males (Green et al. 361 2022), which is consistent with its weakness in association with contest escalation here. 362 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 17 363 Despite finding support for self-assessment in this analysis, intergroup conflict in banded 364 mongooses does not fit classical assumptions of pure self-assessment models. The original self-365 assessment models assume no physical contact and that the costs of conflict are only gradual 366 escalation of display intensity over ti me (Bishop & Cannings 1978). In banded mongooses, 367 frequent physical aggression and severe costs (e.g., injuries, deaths) violate these assumptions 368 (Cant et al. 2013; Thompson et al. 2017). This divergence mirrors findings in the dyadic contest 369 literature, where violations of classical assumptions are widespread. For instance, Pinto et al. 370 (2019) report that in 34 of 36 species in which self-assessment was supported, assumptions such 371 as no physical contact and single- phase (display ritual) contests were violated. Many dyadic 372 contest studies use a range of different proxies for cost, such as latency to approach and distance 373 travelled (e.g., (Beeching 1992; Wilson et al. 2011)), action rate (e.g., (Hack 1997; Jennings et al. 374 2012; Pratt et al. 2003)), and escalation (Green & Patek 2018; McGinley et al. 2015; Yasuda et al. 375 2012). Subsequently, and mirroring calls in dyadic research (Pinto et al 2019), we advocate for 376 further theoretical development that builds add itional realism into the study of assessment 377 strategies (Kokko 2013; Mesterton-Gibbons & Heap 2013). 378 379 Moving forward, key differences between individual and group contests may demand a more 380 dedicated intergroup-specific theoretical framew ork. Not only do intergroup conflicts involve 381 collective decision-making (Sankey et al., 2022), and variation in leadership dynamics (Hunt et al., 382 2024), but it is also possible that contests between groups could escalate into violence through 383 mechanisms other than assessment-based decisions. For example, collective decisions to 384 escalate into conflict may be dependent on the presence of a key individual (with an inherently risk-385 prone personality) already committed to escalation (Glowacki & McDermott, 2022). No collective 386 assessment is necessary, just a propensity for the group to follow those individuals which commit 387 themselves to conflict initiation. (The incentive fo r followers to join is thought to increase because 388 key individuals pay a larger share of the startup costs – De Dreu et al. 2016; Gavrilets and 389 Fortunato, 2014). We modelled the dynamics of this alternative hypothesis but found continued 390 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint 18 support for collective self-assessment in banded mongooses (See Supplemental Material). In 391 future work, expanding and formalizing intergroup-spec ific contest models will help determine the 392 generality of various intergroup assessment (or non-assessment-based) strategies across taxa. 393 394

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