{"paper_id":"1db04358-2797-4aba-9812-cdce6ebb0f46","body_text":"1 \nLay Summary 1 \nWhen two rival groups come together, what determines whether or not they fight? We found 2 \nsupport for the hypothesis that banded mongoose groups  escalate into physical fights based upon 3 \nan estimation of their own group’s strength, which we term collective self-assessment. We did not 4 \nfind evidence for a commonly held notion that groups compare their own group’s strength with their 5 \nrival’s. Distinguishing between assessment strat egies is intensively researched in contests 6 \nbetween individuals but has rarely been applied to intergroup conflict. Here we applied the 7 \nsuccessful “assessment strategy framework” to group conflict, analyzing over 20 years of data from 8 \nover 600 intergroup interactions. We suggest that collective self-assessment is employed because 9 \nit is faster than mutual assessment, which offers an advantage in conflicts where time is of the 10 \nessence.  11 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         2 \nTitle: Collective Self-Assessment in Banded Mongoose Intergroup Contests 12 \nRunning Title: Self-Assessment in Banded Mongooses 13 \nAuthor names and affiliations:  14 \nRayner, CW 1; Green, PA 2; Hunt, KL 1; Thompson, FJ 1; Mwanguhya, F 3; Cant, MA* 1; Sankey, 15 \nDWE*1,4  16 \n1Centre for Ecology and Conservation, Faculty of Environment, Science and Economy, University 17 \nof Exeter, Penryn Campus, Cornwall, TR10 9FE, UK 18 \n2Brown University Department of Ecology, Evol ution, and Organismal Biology, Providence, RI, 19 \nUSA 02912 20 \n3Banded Mongoose Research Project, Queen Elizabeth National Park, Uganda 21 \n4School of Natural and Environmental Science, Newcastle University, Newcastle upon Tyne, NE1 22 \n7RU, UK 23 \n* Correspondence to M.A.Cant@exeter.ac.uk; Dan.Sankey@newcastle.ac.uk  24 \nEmail Addresses:  25 \ncwr202@exeter.ac.uk; patrick_green@brown.edu; klh238@exeter.ac.uk; 26 \nf.j.thompson@exeter.ac.uk; fmwanguhyatwooki@gmail.com;  M.A.Cant@exeter.ac.uk; 27 \nDan.Sankey@newcastle.ac.uk  28 \nFunding: 29 \nThe long-term project was supported by Natural Environment Research Council Grant 30 \nNE/S009914/1. Thompson, FJ was supported by a Natural Environment Research Council 29 31 \nIndependent Research Fellowship NE/V014471/1. 32 \n  33 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         3 \nAcknowledgments 34 \nWe would like to thank all of Michael Cant’s Socialis lab group for helpful meetings and discussion. 35 \nWe thank the whole Banded Mongoose Research Project team for all their support, data collection, 36 \nguidance, and insight which has been invaluable throughout. In particular, we thank the Uganda 37 \nWildlife Authority and the Uganda National Council for Science and Technology for permission to 38 \ncarry out our research; the Wardens of Queen Elizabeth National Park for logistical support; and 39 \nSolomon Kyabulima, Kenneth Mwesige, Robert Businge, and Solomon Ahabyona for helping 40 \ncollect data in the field. We are grateful to Harry Marshall and Emma Vitikainen for curation and 41 \nmaintenance of the long-term data, and Jason Gilchrist, Sarah Hodge, Matthew Bell, Corsin Müller, 42 \nNeil Jordan, Bonnie Metherell, Roman Furrer, David Jansen, Jenni Sanderson, and Beth Preston 43 \nfor valuable contributions to the project. 44 \n 45 \nData Availability Statement 46 \nThe data and code supporting the findings of this study are available on Dryad at 47 \nhttps://datadryad.org/stash/share/NhPuYo8S3lHDlkhN5_bi-zZgxRp9zj9YpNjajgucyus. 48 \n49 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         4 \nAbstract  50 \nContests over resources are widespread in nature.  To optimize outcomes, animals assess fighting 51 \nabilities, deciding to escalate conflicts based on th eir own strength (self-assessment) or comparing 52 \ntheir own strength with that of their rival (mutual  assessment). While most research focuses on 53 \none-on-one (dyadic) contests, the assessment strategies employed by groups remain poorly 54 \nunderstood. Mutual assessment is frequently assumed, as more information is thought to improve 55 \ndecision-making; however, this assumption has rarely been tested. Here we used a dataset 56 \nspanning 23 years and 641 intergroup contests in a banded mongoose ( Mungos mungo) 57 \npopulation in Queen Elizabeth National Park, Uganda. Our results support a model of self-58 \nassessment: groups with many males tend to es calate conflicts regardl ess of the rival group's 59 \nstrength, thus contrasting the commonly held assu mption that decisions during intergroup contests 60 \nare made by mutual assessment. We suggest that  assessing rival group strength during conflict 61 \ncould be disproportionately costly, compared with  assessing own group strength, which can be 62 \ndone over longer time periods and is easier to obtain. Greater understanding of these dynamics 63 \ncan shed light on the drivers and escalation patterns of intergroup conflict across social species, 64 \nincluding humans. 65 \n 66 \nKeywords: Contest Theory, Intergroup Conflict, Animal Contests, Self-assessment, Banded 67 \nmongoose 68 \n69 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         5 \nIntroduction 70 \nNatural resources can be limited and unevenly distributed in both in time and space. This creates 71 \ncompetition for those resources when they can be monopolized (Bornstein 2003; De Dreu et al. 72 \n2020; De Dreu & Triki 2022; Grover 1997; Mullon & Lehmann 2022; Nagel 1995; Rusch & Gavrilets 73 \n2020; Wrangham 1999). Contests are one way to settl e disputes over such resources and are 74 \nwidespread across taxa (Briffa et al. 2013). Although the benefits from winning contests can lead to 75 \nincreases in fitness (Le Boeuf 1974; De Dreu & Triki 2022), the costs of escalation can be as 76 \nimpactful; for example, through the loss of energy (Briffa & Sneddon 2007; Hack 1997; Payne & 77 \nPagel 1996, 1997), and risk of injury (Lane & Briffa 2017; Payne 1998) or death (Enquist & Leimar 78 \n1990; Harbom et al. 2008; Thompson et al. 2017; Wrangham et al. 2006). As such, evolutionary 79 \ntheory predicts that contestants should minimi ze the costs and maximize the benefits when 80 \ndeciding whether to fight, and for how long (Birch 2016; Budaev et al. 2019; Parker & Smith 1990).     81 \n 82 \nTo respond optimally and reduce uncertainty animals can assess fighting ability (also termed 83 \nresource holding potential (RHP)) (Arnott & Elwood 2008, 2009; Parker 1974; Parker & Rubenstein 84 \n1981; Smith & Parker 1976). Studies of assessment in dyadic contests have shown that individuals 85 \ncan assess many different RHP-contributing features of themselves and their opponents (e.g., 86 \n(Arnott & Elwood 2009; Green et al. 2021a; Green & Patek 2018; Morrell et al. 2005; Pinto et al. 87 \n2019)), including morphology (Bohórquez-Alonso et al.  2014; Palaoro & Briffa 2017), physiology 88 \n(Briffa & Sneddon 2007; Copeland et al. 2011), and behaviour (Camerlink et al. 2015; Wilson et al. 89 \n2011). This is well studied in contests between i ndividuals (i.e., dyadic contests), but much less is 90 \nknown about assessment in contests between groups (but see Briffa et al. 2014).  91 \n 92 \nWhen groups gather information to make conflict decisions, do they assess own strength (self-93 \nassessment) or do they assess both their own st rength and the strength of the other group (mutual 94 \nassessment)? In dyadic contests, the “assessment strategy framework” that contrasts self- and 95 \nmutual assessment has been a widely utilized fram ework to differentiate between strategies used 96 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         6 \nto assess RHP (Arnott & Elwood 2009; Fig. 1). It has been suggested that this framework can be 97 \nfruitfully extended to intergroup contests (Green et al. 2021a). Yet, most intergroup contest studies 98 \ndo not properly test between assessment m odels. Some experimental studies that use 99 \npresentations (playbacks or scent marks) have been fundamental in probing information-gathering 100 \nduring intergroup contests, but as no actual contest occurs during presentations it is not possible to 101 \nrelate them directly to how this information- gathering influences the decisions made in contests 102 \n(Herbinger et al.  2009; Spezie et al.  2023; Benson-Amram et al.  2011;, Müller & Manser 2007; 103 \nFurrer et al. 2011. To the best of our knowledge the assessment strategy framework has still not 104 \nbeen appropriately implemented in an intergroup context outside of humans (Briffa, 2014). 105 \nAdapting the assessment strategy framework (Arnott and Elwood, 2009) to intergroup contests can 106 \nreveal potentially shared principles across levels of biological organization (individuals to groups), 107 \nwhile suggesting how groups come to collaborative decisions during contests. 108 \n 109 \nThe assessment strategy framework makes assu mptions and predictions about the drivers of 110 \ncontest behaviors, which can be tested by comparing relationships between competitor RHP and 111 \ncontest costs (e.g., duration, likelihood of escalation; summarized in Figure 1). If animals use a 112 \npure self-assessment strategy, contests are won by those with the greater cost threshold, 113 \ndetermined by their RHP , and no information about the opponent is assessed (Maynard Smith 114 \n1974; Patrick TREE paper). Contest costs are driven mo stly by the fighting ability of losers, since 115 \nwinners only need to withstand a higher cost than their opponent (Fig. 1A-B) (Mesterton-Gibbons 116 \net al.  1996; Payne & Pagel 1996, 1997). In mutual assessment, a loser forfeits not when costs 117 \nreach a certain limit, but as they discern that they have a lower RHP than their rival (Enquist et al. 118 \n1990; Enquist & Leimar 1983, 1987). As in self-assessment, high RHP losers can incur higher 119 \ncosts (Fig. 1C), yet, as opponents are assessed, high RHP winners are more quickly identified by 120 \nthe loser, resulting in lower overall costs of conflict for winners and losers (Fig. 1D). It is worth 121 \nnoting that some species can fit more than one model, depending on context, or can even change 122 \nstrategies during a contest (Lobregat et al. 2019; Chen et al. 2022; Dinh et al. 2020).  123 \n 124 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n125 \nFigure 1. Assessment strategy framework  (Arnott & Elwood 2009). Pure self- assessment 126 \npredicts A) a positive relationship between loser RHP and costs (e.g., duration, likelihood of 127 \nescalation), and B) a weaker positive relationship between winner RHP and costs. Mutual 128 \nassessment predicts C) a positive relationship between loser RHP and costs , and D) a negative 129 \nrelationship between winner RHP and costs.  130 \nWhen applying the assessment strategy framework to the group context, we assume that group 131 \nmembers each choose the conflict costs they are willing to incur based only on an assessment of 132 \ntheir own group’s RHP (collective self- assessment), or the RHP of their own and the rival group’s 133 \nRHP (collective mutual assessment), and that these individual choices combine  to determine the 134 \ndecision of the group. Note as a shorthand we describe groups as taking decisions or using 135 \nassessment strategies, without assuming that groups are agents in themselves (Okasha 2018). 136 \n 137 \nHere, we use the assessment strategy framework to test which assessment strategy is used in138 \nescalation during intergroup contests in banded mongooses ( Mungos mungo) – a cooperatively 139 \nbreeding species which hold territories and fight fiercely over resources (Cant et al. 2013, 2016)140 \nsuch as oestrus females (Green et al 2024) and food (Thompson et al 2017).. Banded mongooses 141 \nare an ideal species to test for intergroup c ontest assessment strategy for several reasons. Firstly,142 \nbanded mongoose intergroup contests are highl y consequential. Intergroup conflict in this species 143 \nis responsible for 10% or more of adult deaths  with identifiable causes, which in mammals is 144 \nmatched only by chimpanzees, wolves, lions, and some human societies (Johnstone et al. 2020; 145 \nWrangham et al. 2006; Cubaynes et al.  2014). These high stakes likely impose strong selection 146 \npressures on assessment strategies. Sec ond, contests have a range of escalating intensities from147 \n \nnt \nof \nal \nve \np \nof \n’s \ne \ng \nin \nly \n6) \nes \nly, \nes \nis \n0; \nn \nm \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         8 \nnon-physical (e.g., war crying, forming battle lines, chasing) to physical (e.g., biting, scratching, 148 \nwrestling) including injurious lethal violence (Cant et al. 2016) (Fig. 2). This variance in intensity (a 149 \nproxy for cost) is essential for testing assessment strategy (Arnott & Elwood 2009), and similar 150 \nmetrics of escalation have been used in studies of dyadic assessment strategies (  Green & Patek 151 \n2018; McGinley et al 2015; Yasuda et al.  2012). Finally, we have known proxies for banded 152 \nmongoose group RHP , which is a crucial prerequisite for testing assessment of RHP .  We know 153 \nthat contest success is most strongly determi ned by the number of adult males in the group and 154 \nage of the group’s oldest male (Green et al. 2022). Overall, the intensity and variability of conflict, 155 \nand our solid baseline understanding of RHP proxies set the stage for banded mongooses to be an 156 \nideal model for testing assessment strategies in intergroup contests. 157 \n 158 \nFigure 2. Categories of agonistic behavior observed during intergroup contests in banded 159 \nmongooses. A) Agonistic behaviors such as war crying,  forming battle lines, and chasing were 160 \nconsidered as a “non-physica l” level of intensity; B) Agonistic behaviors such as biting, scratching, 161 \nwrestling, or where aggressive physical contact was observed was considered as a “physical” level 162 \nof intensity. Injurious and lethal violence, where aggressive physical contact resulted in severe or 163 \nfatal injury or death was also included as a “physical” level of intensity. (Image credit from left to 164 \nright: Harry Marshall, Harry Marshall, Harry Marshall, Dave Seager, and Harry Marshall) 165 \n 166 \n 167 \nHere we tested collective self-assessment and collective mutual-assessment as two alternative 168 \nhypotheses that describe patterns of escalati on in banded mongoose contests. Following common 169 \nassumptions in intergroup conflict, we predicted that groups would use mutual assessment. 170 \nAlthough more cognitively demanding, the use of more information from mutual assessments can 171 \nallow groups to give up earlier when clearly outmatched in fights, and is often assumed the 172 \nsuperior or default strategy for intergroup assessment (Arnott and Elwood, 2012;Van Belle & 173 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         9 \nScarry 2015; Bennett & Stam 1996; Chan 2003; Langlois & Langlois 2009; Radford 2003; Stulp et 174 \nal. 2012). For our measure of costs, we used the degree of escalation, from non-physical to 175 \nphysical (Fig. 2).  If banded mongooses used collect ive self-assessment, we expect the previously 176 \nidentified RHP proxies—(i) number of adult males or (ii) the age of the oldest male (Green et al. 177 \n2022)—would be significantly positively correlated with the probability of escalation to physical 178 \nviolence for losing groups and would show a weaker positive relationship in winning groups (Fig. 179 \n1A-B). Conversely, if, as predicted, banded mongooses do employ collective mutual assessment, 180 \nwe expect these same variables to exhibit a signi ficant positive correlation in losing groups and a 181 \nsignificant negative correlation in winning groups (Fig. 1C-D). 182 \n 183 \nMethods 184 \nStudy Population and Data Collection 185 \nAll data was collected as part of the Banded Mongoose Research Project, a long-term study on a 186 \npopulation of banded mongooses in and around Mweya Peninsula, Queen Elizabeth National Park, 187 \nUganda (0°12 ′ S, 29°54 ′ E). This study includes data collected from 2 nd February 2000 to 7 th 188 \nFebruary 2023 encompassing 641 intergroup contests  from 43 different groups and 81 unique 189 \npairings of groups. In general, at any given time, there are approximately 250 individuals present 190 \nwithin the population making up 10-12 groups cons isting of around 10-30 adults each (Cant 2000; 191 \nCant et al. 2013). Every 1-3 days researchers recorded data on life-history (e.g., births, deaths), 192 \ncomposition of each group, and details about their intergroup interactions (below), among other 193 \nrecords not relevant to the present work. 194 \n 195 \nScoring Intergroup Contest Intensity and Escalation 196 \nIntergroup contests were recorded opportunistica lly as they occurred. Intergroup contests were 197 \ndefined as any time when at least two groups dire cted agonistic behavior towards each other (Fig. 198 \n2). Intensity and escalation of intergroup contests were scored using comments recorded by the 199 \nBanded Mongoose Research Project team. As an inte rgroup contest often involves a series of 200 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         10 \nbehaviors in which intensity escalates until one or both groups retreat (at which point the intergroup 201 \ncontest ends) the highest point of escalation was used as the level of intensity for each contest. 202 \nFor ease of analysis, the range of intensities possible during an intergroup contest was divided into 203 \ntwo categories (Fig. 2). The lowest level of inte nsity was “non-physical” which was defined as an 204 \ninstance where two groups directed non-physical agonistic behavior (e.g., vocal and visual 205 \ndisplays; (Cant et al. 2016)) towards each other and/or fled upon sighting. If this then escalated to 206 \nfighting between the two groups in which there was aggressive physical contact (e.g., biting, 207 \nscratching, wrestling; (Cant et al. 2016)), this was defined as “physical”. Intergroup contests which 208 \nescalate to physical combat are expected to have higher contest costs (such as energy used and 209 \ninjury risk) compared to non-physical contests  (Green & Patek 2018; Lane & Briffa 2017; McGinley 210 \net al.  2015).  Our scoring approach was then translated into a binomial escalation metric with 0 211 \nrepresenting non-physical and 1 representing physical. Injurious and lethal violence in which 212 \naggressive physical contact results in severe injury or death of one or more individuals was 213 \nincluded in the “physical” category, rather than a ca tegory of its own. This is because, firstly, injury 214 \nand mortality were data poor (9.4% of all intergroup interactions; N = 60), but more importantly, 215 \nwhether individuals suffer an injury is not so much a decision as it is a (potentially random) 216 \noutcome of such physical combat. By contrast, whether violence escalates from non-physical to 217 \nphysical combat is a decision the group may make . Therefore, our use of the escalation metric 218 \nlikely reflects the cost each group was willing to pay.  219 \n 220 \nFor each intergroup contest, a qualitative comment (a  description of the events of the contest) was 221 \nrecorded by observers in the field. These comments were then assessed by three researchers 222 \n(CR, FM, DS) to evaluate whether the contest escalated into physical violence (with 0 representing 223 \nnon-physical and 1 representing physical), or whet her the comments did not allow us to determine 224 \nwhether or not the contest escalated (termed: “undeterminable”). Second, all researchers assigned 225 \na confidence score (1–3) to each of their categorizat ions of intensity score, reflecting their certainty 226 \nin the intensity score. The confidence score was bas ed on criteria such as the clarity and detail of 227 \nthe recorded comment, as well as contextual fa ctors that might influence interpretation (e.g., 228 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         11 \nvisibility conditions or proximity to the event). A score of 1 indicated high confidence, 2 indicated 229 \nmoderate confidence, and 3 indicated low confidence. We ended up removing contests that scored 230 \nwith low confidence (3) from the dataset because they were missing data on other variables 231 \nincluded as a fixed effect in our analysis.  The three researchers discussed any ambiguous 232 \ncomments in detail to assign an escalation and confidence score if possible. The discussion was 233 \nguided by FM, who has over 27 years of experience observing and collecting field data on banded 234 \nmongooses, and managing the Banded Mongoose Research Project . 235 \n 236 \nWe report models using both highly confident and moderately confident scores (1,2), and models 237 \nincluding only highly confident scores (1).  238 \n 239 \nStatistical Analysis 240 \nAll statistical analyses were carried out using R 4.01 (Team 2021). Binomial escalation response 241 \ndata were analyzed using generalized linear mixed effe ct models (GLMMs) with a binomial error 242 \nstructure and a logit link function (Bolker et al.  2009) with the ‘lme4’ package (Bates et al. 2015). 243 \nFor both losing groups and winning groups, we tested the effect of two RHP predictor variables—244 \nnumber of adult males (>6 months old) and age of the oldest male (days) (Green et al. 2022). Both 245 \nvariables were scaled using the scale function (Becker et al. 1988).  246 \n  247 \nIn total, we ran two GLMM models, one model using data from high and moderate confidence 248 \nscores, and another using data from only high c onfidence scores. The model for each confidence 249 \ncategory is as follows (“~” repr esents “as predicted by”): 1) escalation ~ loser number of adult 250 \nmales+ loser age of oldest male+ winner number of adult males+ winner age of oldest male. 251 \nModels without interactions between these variables were simpler and a better fit than models 252 \nincluding interactions. Winner group ID, loser group ID, and unique pairings of groups (winner 253 \ngroup ID + loser group ID) were included as random intercepts in every model to account for 254 \nrepeated measures of intergroup contests between the same groups and group dyads. Test 255 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         12 \nstatistics were obtained using the Anova function and confidence intervals using bootstrapping. We 256 \npresent the p-values, chi-squared values , parameter estimates ( β ) on the logit-scale, standard 257 \nerrors, and confidence intervals of each GLMM model, and compare p-values and direction of 258 \nparameter estimates to the assumptions of pure self-assessment, and mutual assessment (Fig. 1). 259 \nThe collective self-assessment hypothesis would be supported if there was a positive and 260 \nstatistically significant relationship between RHP predictor variables and escalation for losing 261 \ngroups (β >0; p<0.05) and a weaker relationship for winning groups (no significance threshold) (Fig. 262 \n1A-B). The collective mutual assessment hypothesis would be supported if these same tests 263 \nindicated a significant positive  relationship for losing groups ( β >0; p<0.05) and a significant 264 \nnegative relationship for winning groups (β <0; p<0.05) (Fig. 1C-D).  265 \n 266 \nResults 267 \nOverall, of the 641 intergroup contests we obs erved, 280 were ‘non-physical’ (43.7%), 230 were 268 \n‘physical’ (35.9%), and 131 were undeterminable (20.4%).  269 \nUnder the analysis where high and moderate confi dence scores of our escalation metrics were 270 \ncombined (N = 253), our results supported self -assessment: there was a significant positive 271 \nrelationship between the probability of escalation and number of adult males for groups that lost 272 \nfights (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 \npositive relationship between these variables  for groups that won fights (winners; β  = 0.21, SE = 274 \n0.17, χ 2 = 1.61, 95% CI [-0.13, 0.57], p=0.20) (Fig . 3A-B; Table 1), supporting the  hypothesis. 275 \nSupport remained for the self-assessment hypothes is when using only high confidence score 276 \nescalation metrics (N = 229). Here,  loser groups still showed a greater probability of escalation 277 \nwith larger numbers of adult males (losers; β  = 0.52, SE = 0.19, χ 2 = 7.15, 95% CI [0.14, 0.95], 278 \np=0.008), with a non-significant, positive relationship between escalation and number of adult 279 \nmales 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 \nThis is as expected under self-assessment.  281 \n 282 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \nThere was no significant relationship between oldest male age and the probability of escalation for 283 \nwinners or losers in either confidence categor y, providing no statistical support for either model of 284 \nassessment (Table 1).  285 \n 286 \n 287 \n288 \n 289 \nFigure 3. Support for collective self-assessment of number of adult males in banded 290 \nmongooses. Probability of escalation plotted against A) loser number of adult males (p=0.008); B)291 \nwinner number of adult males (p=0.20). (Escalation was a binary metric where contests were either292 \n0 = non-physical, and 1 = physical). The grey shaded area shows the standard error around the 293 \nfitted line. Data (circles) represent individual contests and is randomly jittered on the y-axis. Data in 294 \nplot includes both high and moderate confidence scores of our escalation metrics (N = 253)) 295 \n 296 \n 297 \n 298 \n  299 \nor \nof \n \nd \nB) \ner \ne \nin \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         14 \nTable 1. Models predicting the probability of escalation for loser or winner RHP in \nintergroup contests. The estimate (β ), standard error (SE), chi-squared value (χ 2), p-value (P), \nand confidence interval (CI) of a model predicting the escalation of loser or winner RHP in \nintergroup contests. \nModel  \nescalation ~ winner or loser RHP + (1 | winner ID) + (1 | loser ID) + (1 | unique pairings) \nDataset RHP proxy   β   SE  χ 2 P  CI (95%)  \nHigh and \nmoderate \nconfidence \nscores \n(n=253) \nNumber of adult males  \nWinner \nLoser \n0.21 \n0.47 \n0.17 \n0.18 \n1.61 \n7.13 \n0.20 \n 0.008 * \n-0.13, 0.57 \n0.14, 0.86 \nOldest male age (days)  \nWinner \nLoser \n0.15 \n0.10 \n0.15 \n0.17 \n1.04 \n0.36 \n0.31 \n0.55 \n \n-0.11, 0.48 \n-0.22, 0.46 \nHigh \nconfidence \nscores only \n(n=229) \nNumber of adult males  \nWinner \nLoser \n0.31 \n0.52 \n0.18 \n0.19 \n2.82 \n7.15 \n0.09 \n0.008 * \n \n-0.05, 0.71 \n0.14, 0.95 \nOldest male age (days)  \nWinner \nLoser \n0.21 \n0.02 \n0.16 \n0.19 \n1.72 \n0.01 \n0.19 \n0.92 \n \n-0.09, 0.53 \n-0.36, 0.38 \nGeneralized linear mixed effects models with a binomial error structure and logit link function. \nWinner group ID, loser group ID, and unique pairings (winner group ID + loser group ID) were \nused as random intercepts (high and moderate confidence scores GLMM, N = 253 intergroup \ncontests in 12 winner group IDs, 20 loser group IDs, and 35 unique pairings) (high confidence \nscores only GLMM, N = 229 intergroup contests in 11 winner group IDs, 19 loser group IDs, and \n32 unique pairings). Significant terms are presented in bold and denoted by an asterisk (*). Note \nthat parameter estimates are on the logit-scale.  \n 300 \n 301 \nDiscussion 302 \nWe found support for the hypothesis that mongoose groups used collective self-assessment when 303 \ndeciding to escalate a contest into physical viol ence. Specifically, our results suggest that losing 304 \nbanded mongoose groups were more likely to escalate in contests when they had many adult 305 \nmales (a known proxy of RHP; Green et al. 2022), ir respective of the number of adult males in the  306 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         15 \nwinning group. By contrast, we found no support fo r any assessment model based on the oldest 307 \nmale age within the group. Despite assumptions that intergroup contests are determined through 308 \nmutual assessment (e.g., (Van Belle & Scarry 2015; Bennett & Stam 1996; Chan 2003; Langlois & 309 \nLanglois 2009; Radford 2003; Stulp et al. 2012)) we found no support for this in our study.  310 \n 311 \nAt first sight our results are surprising.  Why should banded mongooses assess only their own 312 \nfighting force, and ignore potential valuable info rmation about the outgroup? Research in dyadic 313 \ncontests has suggested that, while mutual asse ssment can minimize the costs of conflict by 314 \nallowing the weaker party to back down earlier, acquiring information about an opponent may be 315 \nconstrained or entail prohibitively  high costs (Guillermo-Ferreira et al. 2015; Mesterton-Gibbons & 316 \nHeap 2014). For instance, assessing the strength of outgroups in the short time-window before 317 \nconflict could be disproportionately costly, compared with own-group assessment which can be 318 \nattained at relatively low cost over longer time periods such as through play-fighting and intragroup 319 \ncontests (Nolfo et al. 2021; Turner et al. 2020; Weller et al. 2020).  320 \n 321 \nOur finding that the number of adult males in a banded mongoose group is associated with the 322 \nprobability of escalation accords with previous evidence of the importance of adult males for 323 \nintergroup fighting success (Green et al.  2022). Males suffer greater mortality from conflict than 324 \nfemales (Johnstone et al. 2020) and there is experimental evidence that in simulated encounters 325 \nmales were more likely to approach caged intruders (Cant 2002). This suggests that males are the 326 \nmain participants in conflicts. It has also been suggested that males have evolved adaptations as a 327 \nresult, such as greater body mass and head size (Green et al. 2022), that may be important for 328 \nfighting behavior (Briffa et al.  2013; Chelliah & Sukumar 2013; Gvoždík & Van Damme 2003; 329 \nJennings & Gammell 2013; Vieira & Peixoto 2013; Wroe et al.  2005) and determining contest 330 \nsuccess (Husak et al. 2006; Huyghe et al. 2005; McLean & Stuart-Fox 2015). More closely-related 331 \nto intergroup conflict, our findings  are consistent with the male warrior hypothesis, which proposes 332 \nthat male mammals possess specific adaptations for intergroup fighting and participate more in 333 \nintergroup conflict than females, and thus have a pronounced effect on group RHP (Muñoz-Reyes 334 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         16 \net al.  2020; Smith et al.  2022). Therefore, the number of adult  males is likely an important and 335 \ninformative RHP proxy which can be assessed w hen making decisions during intergroup contests 336 \nin banded mongooses. 337 \n 338 \nAdditionally, numerical superiority appears to play  such an important role in intergroup fighting 339 \nbecause several attackers can completely  overwhelm a lone opponent. In banded mongooses 340 \nmost fatalities seem to occur when individual s become isolated and overwhelmed by groups of 341 \nattackers ( personal observations ; e.g., Video S1). Outnumbering one’s opponent presents many 342 \nadvantages such as the ability to attack simultaneously from multiple angles, to pin down an 343 \nopponent while others deliver bites or blows, or to take turns in energetically intense fighting 344 \n(Adams & Mesterton-Gibbons 2003; Baglione et al. 2010; Nunn & Deaner 2004; Rusch & Gavrilets 345 \n2020; Wilson et al. 2002). The importance of numerical super iority and concentration of force is 346 \nalso emphasized in models of warfare between human groups (Lanchester 1916; Rapoport & von 347 \nClausewitz 1968).  As discussed above, assessing own-group number of adult males may be a 348 \nlow-cost, more accurate way to use this information in competitive decision-making. 349 \n 350 \nDespite the importance of oldest male age for banded mongoose group RHP (Green et al. 2022), 351 \nthe age of the oldest male in the group was not associated with escalation or show support for any 352 \nassessment strategy. While age may play a role during intergroup contests and assessment in 353 \ngeneral (Briffa & Lane 2017; Fawcett & Johnstone 2010; Green et al. 2022), number of adult males 354 \nmay be a faster, simpler, or more reliable cue on which to base escalation decisions in intergroup 355 \ncontests. Assessing several attributes for more than one individual may be a cognitively 356 \ndemanding process (Budaev et al.  2019; Johnson & Fowler 2013; Tecwyn et al.  2017). Age in 357 \ngeneral may also be less conspicuous (Elwood & Arnott 2012; Fawcett & Mowles 2013; Nieder 358 \n2020), scalable or countable (Akre & Johnsen 2014; Bonanni et al.  2011; Nieder 2020) making 359 \nestimates of age more prone to error. Additionally, in banded mongooses, the age of the oldest 360 \nmale was a weaker predictor of contest outcomes compared to number of adult males (Green et al. 361 \n2022), which is consistent with its weakness in association with contest escalation here. 362 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         17 \n 363 \nDespite finding support for self-assessment in this analysis, intergroup conflict in banded 364 \nmongooses does not fit classical assumptions of pure self-assessment models. The original self-365 \nassessment models assume no physical contact and that the costs of conflict are only gradual 366 \nescalation of display intensity over ti me (Bishop & Cannings 1978). In banded mongooses, 367 \nfrequent physical aggression and severe costs (e.g., injuries, deaths) violate these assumptions 368 \n(Cant et al. 2013; Thompson et al.  2017). This divergence mirrors findings in the dyadic contest 369 \nliterature, where violations of classical assumptions are widespread. For instance, Pinto et al. 370 \n(2019) report that in 34 of 36 species in which self-assessment was supported, assumptions such 371 \nas no physical contact and single- phase (display ritual) contests were violated. Many dyadic 372 \ncontest studies use a range of different proxies for cost, such as latency to approach and distance 373 \ntravelled (e.g., (Beeching 1992; Wilson et al. 2011)), action rate (e.g., (Hack 1997; Jennings et al. 374 \n2012; Pratt et al. 2003)), and escalation (Green & Patek 2018; McGinley et al. 2015; Yasuda et al. 375 \n2012). Subsequently, and mirroring calls in dyadic research (Pinto et al 2019), we advocate for 376 \nfurther theoretical development that builds add itional realism into the study of assessment 377 \nstrategies (Kokko 2013; Mesterton-Gibbons & Heap 2013). 378 \n 379 \nMoving forward, key differences between individual and group contests may demand a more 380 \ndedicated intergroup-specific theoretical framew ork. Not only do intergroup conflicts involve 381 \ncollective decision-making (Sankey et al., 2022), and variation in leadership dynamics (Hunt et al., 382 \n2024), but it is also possible that contests between groups could escalate into violence through 383 \nmechanisms other than assessment-based decisions. For example, collective decisions to 384 \nescalate into conflict may be dependent on the presence of a key individual (with an inherently risk-385 \nprone personality) already committed to escalation (Glowacki & McDermott, 2022). No collective 386 \nassessment is necessary, just a propensity for the group to follow those individuals which commit 387 \nthemselves to conflict initiation. (The incentive fo r followers to join is thought to increase because 388 \nkey individuals pay a larger share of the startup costs – De Dreu et al.  2016; Gavrilets and 389 \nFortunato, 2014). We modelled the dynamics of this alternative hypothesis but found continued 390 \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.678018doi: bioRxiv preprint \n\n \n         18 \nsupport for collective self-assessment in banded mongooses (See Supplemental Material). 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